Re: [R] Scatterplot Showing All Points
Wayne Aldo Gavioli wrote: > > Hello all, > > > I'm trying to graph a scatterplot of a large (5,000 x,y coordinates) of data > with the caveat that many of the data points overlap with each other (share > the > same x AND y coordinates). In using the usual "plot" command, > > > >>plot(education, xlab="etc", ylab="etc") > > > > it seems that the overlap of points is not shown in the graph. Namely, there > are 5,000 points that should be plotted, as I mentioned above, but because so > many of the points overlap with each other exactly, only about 50-60 points > are > actually plotted on the graph. Thus, there's no indication that Point A > shares > its coordinates with 200 other pieces of data and thus is very common while > Point B doesn't share its coordinates with any other pieces of data and thus > isn't common at all. Is there anyway to indicate the frequency of such points > on such a graph? Should I be using a different command than "plot"? > Hi Wayne, While this is not a really pretty picture, you can get a viewable plot with count.overplot if the first two elements of "education" are named "x" and "y" and they are the coordinates you want to plot. Otherwise, pass the x and y coordinates separately. library(plotrix) count.overplot(education, tol=c(diff(range(education$x))/10, diff(range(education$y))/10)) Jim __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
Wayne Aldo Gavioli fas.harvard.edu> writes: > > > Hello all, > > I'm trying to graph a scatterplot of a large (5,000 x,y coordinates) of data > with the caveat that many of the data points overlap with each other (share > the > same x AND y coordinates). In using the usual "plot" command, > > > plot(education, xlab="etc", ylab="etc") > > it seems that the overlap of points is not shown in the graph. Namely, there > are 5,000 points that should be plotted, as I mentioned above, but because so > many of the points overlap with each other exactly, only about 50-60 points > are > actually plotted on the graph. Thus, there's no indication that Point A > shares > its coordinates with 200 other pieces of data and thus is very common while > Point B doesn't share its coordinates with any other pieces of data and thus > isn't common at all. Is there anyway to indicate the frequency of such points > on such a graph? Should I be using a different command than "plot"? > > One suggestion seems to be still missing: 'sunflowerplot' of base R. May look taggy, though, if you have 200 "petals". Actually the documentation of sunflowerplot is wrong in botanical sense. Sunflowers have composite flowers in capitula, and the things called 'petals' in documentation are ligulate, sterile ray-florets (each with vestigial petals which are not easily visible in sunflower, but in some other species you may see three (occasionally two) teeth). cheers, jari oksanen __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] bar plot colors
Winkel, David wrote: > All, > > > > I have a question regarding colors in bar plots. I want to stack a > total of 18 cost values in each bar. Basically, it is six cost types and > each cost type has three components- direct, indirect, and induced > costs. I would like to use both solid color bars and bars with the > slanted lines (using the density parameter). The colors would > distinguish cost types and the lines would distinguish > direct/indirect/induced. I want the cost types (i.e. colors) to be > stacked together for each cost type. In other words, I don't want all > of the solid bars at the bottom and all of the slanted lines at the top. > > > So far, I have made a bar plot with all solid colors and then tried to > overwrite that bar plot by calling barplot() again and putting the white > slanted lines across the bars. However, I can't get this method to work > while still grouping the cost types together. > > Hi David, This is a real challenge: heights<-matrix(sample(10:70,54),ncol=3) bar.colors<-rep(rep(2:7,each=3),3) bar.densities<-rep(10,54) bar.angles<-matrix(rep(rep(c(45,90,135),6),3),ncol=3) barplot(heights,col=bar.colors) barplot(heights,angle=bar.angles,add=TRUE,density=bar.densities) Jim __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] ggplot-How to define fill colours?
Dear R's (most likely Hadley),
I want to build a stacked bar plot where I would like to define which
colours will be used for each of the groups. However, I do not seem
to find a way to do this, even if I've been looking over many places.
I have tried several variations, and my final try was this code, but
I still do not manage to get the colours as I pre-define. Any hints
about how to get this?
Thanks in advance,
Pedro
my code:
>plotdata1<-data.frame(x=rep(factor(1:4),4), y=rep(0.1*(1:4),4),
+group=as.character(rep(c('white', 'red', 'blue', 'green'),rep(4,4
>plot0<-ggplot()
>plot3<-plot0+layer(data=plotdata1, mapping=aes_string(x='x',y='y',
+fill='group'),geom='bar', stat='identity', position='stack')
>print(plot3)
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[R] integration
Dear All, I need to perform a numerical integration of one dimensional fucntions. The extrems of integration are both finite and the functions I'm working on are quite complicated. I have already tried both area() and integrate(), but they do not perform well: area() is very slow and integrate() does not converge. Are in R other functions for numerical integration of one dimentional functions? Thanks in advance Davide Tiscali.Fax: il tuo fax online in promo fino al 31 dicembre, paghi 15€ e ricarichi 20€ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] hazard ratio of interaction Cox model
Dear Forum, I have a question about interaction estimate in the Cox model: why the hazard ratio of the interaction is not produced in the summary of the model? (Instead, the estimate of the coefficient is given in the print of the model.) # Example: modINT <-cph( Surv(T_BASE, T_FIN,STATUS)~ NYHA + ASINI + RFP + FE_REC + XX_PR*XX_DISF) print(modINT) coef se(coef) zp NYHA=2 1.2540.584 2.15 0.031767 ASINI 0.6650.409 1.62 0.104247 RFP=20.7250.704 1.03 0.302578 FE_REC=2-1.6370.810 -2.02 0.043331 XX_PR2.1890.649 3.37 0.000748 XX_DISF 3.2331.000 3.23 0.001222 XX_PR * XX_DISF -2.8521.280 -2.23 0.025852 summary(modINT) Effects Response : Surv(T_BASE, T_FIN, STATUS) Factor LowHigh Diff. Effect S.E. Lower 0.95 Upper 0.95 ASINI 2.0725 2.85 0.7775 0.52 0.32 -0.111.14 Hazard Ratio 2.0725 2.85 0.7775 1.68NA 0.903.13 XX_PR 0. 1.00 1. 2.19 0.65 0.923.46 Hazard Ratio 0. 1.00 1. 8.92NA 2.50 31.86 XX_DISF0. 1.00 1. 3.23 1.00 1.275.19 Hazard Ratio 0. 1.00 1. 25.35NA 3.57 179.88 NYHA - 2:1 1. 2.00 NA 1.25 0.58 0.112.40 Hazard Ratio 1. 2.00 NA 3.50NA 1.12 11.00 RFP - 2:1 1. 2.00 NA 0.73 0.70 -0.652.10 Hazard Ratio 1. 2.00 NA 2.07NA 0.528.20 FE_REC - 2:1 1. 2.00 NA -1.64 0.81 -3.23 -0.05 Hazard Ratio 1. 2.00 NA 0.19NA 0.040.95 Adjusted to: XX_PR=0 XX_DISF=0 Be a better friend, newshound, and [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] R-users
Kunio takezawa wrote:
> R-users
> E-mail: r-help@r-project.org
>
>I have a quenstion on "gam()" in "gam" package.
> The help of gam() says:
> 'gam' uses the _backfitting
> algorithm_ to combine different smoothing or fitting methods.
>
> On the other hand, lm.wfit(), which is a routine of gam.fit() contains:
>
> z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *
> wts, ny = ny, tol = as.double(tol), coefficients = mat.or.vec(p,
> ny), residuals = y, effects = mat.or.vec(n, ny), rank = integer(1),
> pivot = 1:p, qraux = double(p), work = double(2 * p),
> PACKAGE = "base")
> It may indicate that QR decomposition is used to derive an additive model
> instead of backfitting.
>I am wondering if my guess is correct, or this "the _backfitting
> algorithm"
> has another meaning.
>
Please don't ask the same question multiple times!
And no, backfitting and QR are unrelated concepts. You need to read up
on the theory, there are two fundamental books: Hastie & Tibshirani (gam
package) and Simon Wood (mgcv package). Both are a bit much to ask to
have summarized in email.
--
O__ Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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[R] R command "leap"
After applying the "step" command to a long list of predictors I came up with the following showing there still are some non significant coefficients. I'd like to try the command "leap". However, I don't quite understand how it returns the spared predicting variables. Can please, someone help ? Tank you in advance. Mara EM >summary(stepmod) Call: lm(formula = yy ~ cosmat[, 1] + cosmat[, 2] + cosmat[, 3] + cosmat[, 4] + cosmat[, 5] + cosmat[, 6] + cosmat[, 7] + cosmat[, 8] + cosmat[, 9] + cosmat[, 11] + cosmat[, 12] + cosmat[, 15] + cosmat[, 16] + cosmat[, 17] + cosmat[, 22] + cosmat[, 25] + cosmat[, 29] + cosmat[, 30] + cosmat[, 31] + cosmat[, 38] + cosmat[, 42] + cosmat[, 44] + cosmat[, 45] + cosmat[, 46] + cosmat[, 47] + cosmat[, 48] + cosmat[, 49] + cosmat[, 50] + cosmat[, 51] + cosmat[, 52] + cosmat[, 53] + cosmat[, 54] + cosmat[, 56] + sinmat[, 1] + sinmat[, 3] + sinmat[, 4] + sinmat[, 5] + sinmat[, 6] + sinmat[, 8] + sinmat[, 9] + sinmat[, 10] + sinmat[, 13] + sinmat[, 14] + sinmat[, 17] + sinmat[, 19] + sinmat[, 22] + sinmat[, 23] + sinmat[, 26] + sinmat[, 35] + sinmat[, 36] + sinmat[, 39] + sinmat[, 43] + sinmat[, 45] + sinmat[, 46] + sinmat[, 47] + sinmat[, 48] + sinmat[, 49] + sinmat[, 50] + sinmat[, 51] + sinmat[, 52] + sinmat[, 53]) Residuals: Min1QMedian3Q Max -0.175619 -0.009864 0.001284 0.008596 0.160115 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.474379 0.003426 -138.484 < 2e-16 *** cosmat[, 1] -0.767037 0.004832 -158.726 < 2e-16 *** cosmat[, 2] -0.214150 0.004833 -44.306 < 2e-16 *** cosmat[, 3] -0.022436 0.004836 -4.639 2.15e-05 *** cosmat[, 4] -0.022201 0.004840 -4.587 2.58e-05 *** cosmat[, 5] -0.052807 0.004845 -10.899 1.84e-15 *** cosmat[, 6] 0.012219 0.0048472.521 0.014573 * cosmat[, 7] 0.033468 0.0048546.895 5.16e-09 *** cosmat[, 8] 0.007026 0.0048571.447 0.153564 cosmat[, 9] -0.009502 0.004858 -1.956 0.055481 . cosmat[, 11] 0.015284 0.0048583.146 0.002647 ** cosmat[, 12] 0.006231 0.0048561.283 0.204778 cosmat[, 15] 0.005461 0.0048411.128 0.264160 cosmat[, 16] 0.010987 0.0048412.269 0.027110 * cosmat[, 17] 0.005367 0.0048361.110 0.271857 cosmat[, 22] -0.005541 0.004834 -1.146 0.256570 cosmat[, 25] -0.010025 0.004849 -2.068 0.043310 * cosmat[, 29] -0.006114 0.004861 -1.258 0.213668 cosmat[, 30] -0.005734 0.004860 -1.180 0.243061 cosmat[, 31] -0.005932 0.004860 -1.221 0.227281 cosmat[, 38] 0.006269 0.0048311.298 0.199735 cosmat[, 42] 0.006167 0.0048321.276 0.207140 cosmat[, 44] -0.006002 0.004854 -1.237 0.221395 cosmat[, 45] 0.006484 0.0048491.337 0.186496 cosmat[, 46] 0.010074 0.0048512.077 0.042425 * cosmat[, 47] -0.004843 0.004859 -0.997 0.323225 cosmat[, 48] -0.009098 0.004852 -1.875 0.065982 . cosmat[, 49] 0.008740 0.0048651.797 0.077797 . cosmat[, 50] 0.009175 0.0048461.893 0.063481 . cosmat[, 51] -0.005664 0.004865 -1.164 0.249310 cosmat[, 52] -0.007255 0.004853 -1.495 0.140536 cosmat[, 53] 0.004950 0.0048451.022 0.311249 cosmat[, 54] 0.006561 0.0048531.352 0.181764 cosmat[, 56] -0.005954 0.004847 -1.228 0.224436 sinmat[, 1] -0.086812 0.004858 -17.871 < 2e-16 *** sinmat[, 3] -0.041194 0.004853 -8.488 1.22e-11 *** sinmat[, 4] -0.040007 0.004849 -8.250 3.00e-11 *** sinmat[, 5] 0.012811 0.0048442.645 0.010593 * sinmat[, 6] 0.010845 0.0048422.240 0.029096 * sinmat[, 8] -0.018422 0.004833 -3.812 0.000346 *** sinmat[, 9] 0.008006 0.0048311.657 0.103063 sinmat[, 10] 0.023215 0.0048314.805 1.20e-05 *** sinmat[, 13] 0.013663 0.0048372.824 0.006550 ** sinmat[, 14] 0.015827 0.0048453.267 0.001860 ** sinmat[, 17] 0.006820 0.0048531.405 0.165442 sinmat[, 19] 0.007181 0.0048611.477 0.145211 sinmat[, 22] 0.010290 0.0048552.120 0.038492 * sinmat[, 23] 0.005533 0.0048541.140 0.259254 sinmat[, 26] 0.014857 0.0048393.070 0.003298 ** sinmat[, 35] -0.006008 0.004847 -1.240 0.220321 sinmat[, 36] -0.004984 0.004845 -1.029 0.308021 sinmat[, 39] -0.006897 0.004862 -1.419 0.161574 sinmat[, 43] -0.004804 0.004828 -0.995 0.324064 sinmat[, 45] 0.005079 0.0048421.049 0.298680 sinmat[, 46] -0.008775 0.004839 -1.813 0.075144 . sinmat[, 47] -0.008346 0.004831 -1.728 0.089575 . sinmat[, 48] 0.007352 0.0048391.519 0.134335 sinmat[, 49] 0.007382 0.0048271.529 0.131870 sinmat[, 50] -0.005618 0.004844 -1.160 0.251083 sinmat[, 51] -0.007934 0.004826 -1.644 0.105786 sinmat[, 52] 0.006890 0.0048391.424 0.160050 sinmat[, 53] 0.009242 0.0048471.907 0.061708 . --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Residual standard error: 0.03716 on 56 degrees of freedom Multiple R-Squared: 0.998, Adjusted R-square
[R] axis names in triangle.plot
Hi folks, I am using a data.frame with sediment grain sizes: > grain sand silt clay OAT 10.03 56.77 18.25 OAT 10.40 57.40 17.94 WG1 50.03 20.68 12.57 WG1 43.20 25.69 13.41 WG1 33.89 31.10 14.48 WG2 2.84 62.81 20.79 WG2 2.79 60.46 19.16 WG2 16.27 33.04 6.48 WG2 1.39 57.90 9.13 WG3 4.54 52.91 17.20 WG3 5.20 50.55 15.65 WG3 7.71 49.13 10.80 WG3 4.43 50.03 11.83 WG3 1.72 57.53 14.20 WG3 1.51 58.99 13.96 I would like to do a trinagle.plot with labeled axis-names "sand" "silt" and "clay". However using the command: tringle.plot(grain) from ade4-apckage) does not plot the axis names and there is no paramter to set the labels like "xlab" in the plot command. Does anybody has a good advice? Thanks in advance Thomas __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] read.table() and precision?
On Mon, 17 Dec 2007, Moshe Olshansky wrote: > Dear List, > > Following the below question I have a question of my > own: > Suppose that I have large matrices which are produced > sequentially and must be used sequentially in the > reverse order. I do not have enough memory to store > them and so I would like to write them to disk and > then read them. This raises two questions: > 1) what is the fastest (and the most economic > space-wise) way to do this? Using save/load is the simplest. Don't worry about finding better solutions until you know those are not good enough. (serialize / unserialize is another interface to the same underlying idea.) > 2) functions like write, write.table, etc. write the > data the way it is printed and this may result in a > loss of accuracy. Is there any way to prevent this, > except for setting the "digits" option to a higher > value or using format prior to writing the data? Do please read the help before making false claims. ?write.table says Real and complex numbers are written to the maximal possible precision. OTOH, ?write says it is a wrapper for cat, whose help says 'cat' converts numeric/complex elements in the same way as 'print' (and not in the same way as 'as.character' which is used by the S equivalent), so 'options' '"digits"' and '"scipen"' are relevant. However, it uses the minimum field width necessary for each element, rather than the same field width for all elements. so this hints as.character() might be a useful preprocessor. > Is it possible to write binary files (similar to Fortran)? See ?writeBin. save/load by default write binary files, but use of writeBin can be faster (and less flexible). > Any suggestion will be greatly appreciated. Somehow you have missed a great deal of information about R I/O. Try help.start() and reading the sections the search engine shows you that look relevant. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Res: Scatterplot Showing All Points
?jitter is simpler: x<-rep(1:10,10) y<-x plot(x,y) #100 points, only 10 show plot(jitter(x),jitter(y)) #overlap removed. >>> Milton Cezar Ribeiro <[EMAIL PROTECTED]> 18/12/2007 04:36 >>> Hi Wayne, I have two suggestion to you. 1. You add some random noise on both x and y data or 2. You graph bubble points, where the size is proportional to the frequence of the xy combination. x<-sample(1:10,1,replace=T) y<-sample(1:10,1,replace=T) xy<-cbind(x,y) x11(1400,800) par(mfrow=c(1,3)) plot(xy) xy.random<-xy+rnorm(2,0.1,0.1) plot(xy.random,cex=0.1) xy.tab<-data.frame(table(x,y)) xy.tab$x<-as.numeric(as.character(xy.tab$x)) xy.tab$y<-as.numeric(as.character(xy.tab$y)) min.freq<-min(xy.tab$Freq) max.freq<-max(xy.tab$Freq) plot(xy.tab$x,xy.tab$y,cex=(xy.tab$Freq-min.freq)/(max.freq-min.freq)*5) Kind regards, Miltinho Brazil - Mensagem original De: Wayne Aldo Gavioli <[EMAIL PROTECTED]> Para: r-help@r-project.org Enviadas: Segunda-feira, 17 de Dezembro de 2007 22:14:23 Assunto: [R] Scatterplot Showing All Points Hello all, I'm trying to graph a scatterplot of a large (5,000 x,y coordinates) of data with the caveat that many of the data points overlap with each other (share the same x AND y coordinates). In using the usual "plot" command, > plot(education, xlab="etc", ylab="etc") it seems that the overlap of points is not shown in the graph. Namely, there are 5,000 points that should be plotted, as I mentioned above, but because so many of the points overlap with each other exactly, only about 50-60 points are actually plotted on the graph. Thus, there's no indication that Point A shares its coordinates with 200 other pieces of data and thus is very common while Point B doesn't share its coordinates with any other pieces of data and thus isn't common at all. Is there anyway to indicate the frequency of such points on such a graph? Should I be using a different command than "plot"? Thanks, Wayne __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. para armazenamento! [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] ggplot-How to define fill colours?
Pedro,
I've had a similar problem. See this post for the solution:
http://thread.gmane.org/gmane.comp.lang.r.general/100649/focus=100673
Cheers,
Thierry
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
[EMAIL PROTECTED]
www.inbo.be
Do not put your faith in what statistics say until you have carefully
considered what they do not say. ~William W. Watt
A statistical analysis, properly conducted, is a delicate dissection of
uncertainties, a surgery of suppositions. ~M.J.Moroney
-Oorspronkelijk bericht-
Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
Namens Pedro de Barros
Verzonden: maandag 17 december 2007 23:55
Aan: [EMAIL PROTECTED]
Onderwerp: [R] ggplot-How to define fill colours?
Urgentie: Hoog
Dear R's (most likely Hadley),
I want to build a stacked bar plot where I would like to define which
colours will be used for each of the groups. However, I do not seem
to find a way to do this, even if I've been looking over many places.
I have tried several variations, and my final try was this code, but
I still do not manage to get the colours as I pre-define. Any hints
about how to get this?
Thanks in advance,
Pedro
my code:
>plotdata1<-data.frame(x=rep(factor(1:4),4), y=rep(0.1*(1:4),4),
+group=as.character(rep(c('white', 'red', 'blue', 'green'),rep(4,4
>plot0<-ggplot()
>plot3<-plot0+layer(data=plotdata1, mapping=aes_string(x='x',y='y',
+fill='group'),geom='bar', stat='identity', position='stack')
>print(plot3)
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] axis names in triangle.plot
Hi Thomas, This looks quite strange. By default, the function use the column names as labels. What is grain ? A data.frame ? Why have you duplicated row.names? Please return the results of class(grain) and names(grain) Cheers, PS: If you have questions related to ade4, you can use the adelist (http://listes.univ-lyon1.fr/wws/info/adelist) Thomas Hoffmann wrote: > Hi folks, > > I am using a data.frame with sediment grain sizes: > > > grain > sand silt clay > OAT 10.03 56.77 18.25 > OAT 10.40 57.40 17.94 > WG1 50.03 20.68 12.57 > WG1 43.20 25.69 13.41 > WG1 33.89 31.10 14.48 > WG2 2.84 62.81 20.79 > WG2 2.79 60.46 19.16 > WG2 16.27 33.04 6.48 > WG2 1.39 57.90 9.13 > WG3 4.54 52.91 17.20 > WG3 5.20 50.55 15.65 > WG3 7.71 49.13 10.80 > WG3 4.43 50.03 11.83 > WG3 1.72 57.53 14.20 > WG3 1.51 58.99 13.96 > > I would like to do a trinagle.plot with labeled axis-names "sand" "silt" > and "clay". However using the command: > > tringle.plot(grain) > > from ade4-apckage) does not plot the axis names and there is no paramter > to set the labels like "xlab" in the plot command. Does anybody has a > good advice? > > Thanks in advance > Thomas > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > > -- Stéphane DRAY ([EMAIL PROTECTED] ) Laboratoire BBE-CNRS-UMR-5558, Univ. C. Bernard - Lyon I 43, Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France Tel: 33 4 72 43 27 57 Fax: 33 4 72 43 13 88 http://biomserv.univ-lyon1.fr/~dray/ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Dual Core vs Quad Core
Hiding in the windows faq is the observation that "R's computation is single-threaded, and so it cannot use more than one CPU". So multi-core should make no difference other than allowing R to run with less interruption from other tasks. That is often a significant advantage, though. >>> Andrew Perrin <[EMAIL PROTECTED]> 18/12/2007 01:13 >>> On Mon, 17 Dec 2007, Kitty Lee wrote: > Dear R-users, > > I use R to run spatial stuff and it takes up a lot of ram. Runs can take hours or days. I am thinking of getting a new desktop. Can R take advantage of the dual-core system? > > I have a dual-core computer at work. But it seems that right now R is using only one processor. > > The new computers feature quad core with 3GB of RAM. Can R take advantage of the 4 chips? Or am I better off getting a dual core with faster processing speed per chip? > > Thanks! Any advice would be really appreciated! > > K. If I have my information right, R will use dual- or quad-cores if it's doing two (or four) things at once. The second core will help a little bit insofar as whatever else your machine is doing won't interfere with the one core on which it's running, but generally things that take a single thread will remain on a single core. As for RAM, if you're doing memory-bound work you should certainly be using a 64-bit machine and OS so you can utilize the larger memory space. -- Andrew J Perrin - andrew_perrin (at) unc.edu - http://perrin.socsci.unc.edu Associate Professor of Sociology; Book Review Editor, _Social Forces_ University of North Carolina - CB#3210, Chapel Hill, NC 27599-3210 USA __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] gene shaving method
Gene shaving is implemented in the GeneClust package for R which you can download from http://odin.mdacc.tmc.edu/~kim/geneclust/ For more details see "The Analysis of Gene Expression Data: Methods and Software" edited by Giovanni Parmiagiani, Elizabeth S. Garett, Rafael A. Irizarry and Scott L. Zeger (you can get a preview on Google Book Search http://books.google.com/books?id=r9gROQvdelcC&pg=PA352&lpg=PA352&dq=gene clust&source=web&ots=3FO3jQlfrp&sig=BeOgUK2cgfuv7d12vWsvpRXOkBU#PPA353,M 1) Best wishes, Heather -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Aimin Yan Sent: 17 December 2007 19:51 To: r-help@r-project.org Subject: [R] gene shaving method Does anyone know if Hastie's gene shaving method is implemented in R Thanks, Aimin __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] accessing dimension names
I have a matrix y:
> dimnames(y)
$x93
[1] "1" "2"
$x94
[1] "0" "1" "2"
.. so on (there are other dimensions as well)
I need to access a particular dimension, but a random mechanism tells me
which dimension it would. So, sometimes I might need to access
dimnames(y)$x93, some other time it would be dimnames(y)$x94.. and so on.
Now let that random dimension be idx, then dimnames(y)$paste('x',idx,sep='')
doesn't work.
Can anyone help?
Thanks!
[[alternative HTML version deleted]]
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Re: [R] Two repeated warnings when running gam(mgcv) to analyse my dataset?
The model here is just a penalised GLM, and the warnings relate to the GLM
fitting process. Fitted probabilities of 0 or 1 can be perfectly appropriate,
but do indicate that the linear predictor is not really uniquely defined, and
that some care may be needed in interpreting results (for example, if the
fitted probabilities are zero or one, then a CI for the corresponding linear
predictor will depend more on the prior assumptions about smoothness than
anything else). This problem is not really GAM specific, it relates to any
`logistic regression' model.
Similarly, the GLM fitting IRLS iterations are not guaranteed to converge, and
can fail, especially for overly flexible logistic regression models. Try
this, for example
x <- 1:10
y <- c(0,0,0,0,0,1,1,1,1,1)
glm(y~x,family=binomial)
I get...
...
Warning messages:
1: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart =
etastart, :
algorithm did not converge
2: In glm.fit(x = X, y = Y, weights = weights, start = start, etastart =
etastart, :
fitted probabilities numerically 0 or 1 occurred
...as models become more complex the scope for this sort of thing to happen
increases, and some simplification may be appropriate.
That said, mgcv::gam fitting with all smoothing parameters fixed, is slightly
more likely to fail in this way than `glm' or `mgcv::gam' with some smoothing
parameters estimated, because of the steps taken to stabilise divergent fit
iterations. When all smoothing parameters are fixed, mgcv uses older fitting
routines that don't try as hard to stabilise a divergent fit as the newer
fitting routines. This is a bit of an anomaly and I'll try and fix it for a
future release.
best,
Simon
On Monday 17 December 2007 11:53, zhijie zhang wrote:
> Dear Simon,
> Sorry for an incomplete listing of the question.
> #mgcv version is 1.3-29, R 2.6.1, windows XP
> #m.gam<-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+
>disbinary,family=binomial(logit),data=point) The above program's the core
> codes in my following loop programs.
> It works well if i run the above codes only one time for my dataset, but
> warnings will occur if i run many times for the following loop.
>
> > while (j<1001) {
>
> + index=sample(ID, replace=F)
> + m.data$x=coords[index,]$x
> + m.data$y=coords[index,]$y
> + # For each permutation, we run the GAM using the optimal span for the
> above model m.gam
> + s.gam
> <-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disbin
>ary,,sp=c( 5.582647e-07,4.016504e-02,2.300424e-04,1.274065e+03,9.558236e-09,
> 1.868827e-08),family=binomial(logit),data=m.data)
> + permresults[,i]=predict.gam(s.gam)
> + i=i+1
> + if (j%%100==0) print(i)
> + j=j+1
> + }
> [1] 101
> [1] 201
> [1] 301
> [1] 401
> [1] 501
> [1] 601
> [1] 701
> [1] 801
> [1] 901
> [1] 1001
> warnings() over 50
>
> > warnings()
>
> 1: In gam.fit(G, family = G$family, control = control, gamma = gamma, ...
> : fitted probabilities numerically 0 or 1 occurred
> ..
> 14: In gam.fit(G, family = G$family, control = control, gamma = gamma, ...
>
> Algorithm did not converge
> ..
>
> On Dec 17, 2007 4:54 PM, Simon Wood <[EMAIL PROTECTED]> wrote:
> > What mgcv version are you running (and on what platform)?
> >
> > n Thursday 13 December 2007 17:46, zhijie zhang wrote:
> > > Dear all,
> > > I run the GAMs (generalized additive models) in gam(mgcv) using the
> > > following codes.
> > >
> > > m.gam
> >
> > <-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disb
> >in
> >
> > >ary,family=binomial(logit),data=point)
> > >
> > > And two repeated warnings appeared.
> > > Warnings：
> > > 1: In gam.fit(G, family = G$family, control = control, gamma = gamma,
> >
> > ...
> >
> > > : Algorithm did not converge
> > >
> > > 2: In gam.fit(G, family = G$family, control = control, gamma = gamma,
> >
> > ...
> >
> > > : fitted probabilities numerically 0 or 1 occurred
> > >
> > > Q1: For warning1, could it be solved by changing the value of
> > > mgcv.toloptions for
> > > gam.control(mgcv.tol=1e-7)?
> > >
> > > Q1: For warning2, is there any impact for the results if the "fitted
> > > probabilities numerically 0 or 1 occurred" ? How can i solve it?
> > >
> > > I didn't try the possible solutions for them, because it took such a
> > > longer time to run the whole programs.
> > > Could anybody suggest their solutions?
> > > Any help or suggestions are greatly appreciated.
> > > Thanks.
> >
> > --
> >
> > > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> > > +44 1225 386603 www.maths.bath.ac.uk/~sw283
--
> Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK
> +44 1225 386603 www.maths.bath.ac.uk/~sw283
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Re: [R] clean programming
Gabor Grothendieck <[EMAIL PROTECTED]> a écrit :
> Its a FAQ
Oups... Sorry for that.
Just to close the topic :
cleanProg <- function(name,tolerance){
if(length(findGlobals(get(name),FALSE)$variables) > tolerance){
cat("More than",tolerance,"global variable(s) in ",name,"\a\n")
}
}
cleanProg(fun,0)
Ce message a ete envoye par IMP, grace a l'Universite Paris 10 Nanterre
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Re: [R] Dual Core vs Quad Core
On Tue, 18 Dec 2007, S Ellison wrote: > Hiding in the windows faq is the observation that "R's computation is > single-threaded, and so it cannot use more than one CPU". So multi-core > should make no difference other than allowing R to run with less > interruption from other tasks. That is often a significant advantage, > though. Yes, but that is Windows-specific. On most other platforms you can benefit from using a multi-threaded BLAS, such as ATLAS, ACML or Dr Goto's. The speedup for linear algebra can be substantial (although sometimes it will slow things down). Luke Tierney has an experimental package to make use of parallel threads for some basic R computations which may appear in R 2.7.0. It should be possible to use a multi-threaded BLAS under Windows, but I know no one who has done it. There is a viable pthreads implementation for Windows, and I've tested Luke's experimental package using it. Some compilers' runtimes will be able to use parallel threads for other tasks. Since all the examples I am aware of are expensive commercial compilers, I suspect R will make limited use of them. (In particular, base R does not use the Fortran 9x vector operations at which many of these features are targeted: we probably would if we routinely used such compilers.) I've had dual-CPU desktops for more than ten years. Given how little speedup you are likely to get via parallel processing (only under ideal conditions do the optimized BLASes run >1.5x faster using two CPUs), the most effective way to make use of multiple CPUs has been to run multiple jobs: I typically run 3-4 at once to keep the CPUs fully used. One way to run multiple R processes to cooperate on a single task is to use a package such as snow to distribute the load. Andrew Perrin <[EMAIL PROTECTED]> 18/12/2007 01:13 >>> > On Mon, 17 Dec 2007, Kitty Lee wrote: > >> Dear R-users, >> >> I use R to run spatial stuff and it takes up a lot of ram. Runs can > take hours or days. I am thinking of getting a new desktop. Can R take > advantage of the dual-core system? >> >> I have a dual-core computer at work. But it seems that right now R is > using only one processor. >> >> The new computers feature quad core with 3GB of RAM. Can R take > advantage of the 4 chips? Or am I better off getting a dual core with > faster processing speed per chip? >> >> Thanks! Any advice would be really appreciated! >> >> K. > > If I have my information right, R will use dual- or quad-cores if it's > doing two (or four) things at once. The second core will help a little > bit > insofar as whatever else your machine is doing won't interfere with the > one core on which it's running, but generally things that take a single > thread will remain on a single core. > > As for RAM, if you're doing memory-bound work you should certainly be > using a 64-bit machine and OS so you can utilize the larger memory > space. They only have 3GB of RAM, which 32-bit OSes can address. The benefits really come with more than that. -- Brian D. Ripley, [EMAIL PROTECTED] Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UKFax: +44 1865 272595 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] axis names in triangle.plot
Thanks for this advice. grain was not a data.frame but a matrix. Now it works:-) Cheers Thomas Stéphane Dray schrieb: > Hi Thomas, > This looks quite strange. By default, the function use the column > names as labels. > What is grain ? A data.frame ? Why have you duplicated row.names? > Please return the results of class(grain) and names(grain) > > Cheers, > PS: If you have questions related to ade4, you can use the adelist > (http://listes.univ-lyon1.fr/wws/info/adelist) > > > Thomas Hoffmann wrote: >> Hi folks, >> >> I am using a data.frame with sediment grain sizes: >> >> > grain >> sand silt clay >> OAT 10.03 56.77 18.25 >> OAT 10.40 57.40 17.94 >> WG1 50.03 20.68 12.57 >> WG1 43.20 25.69 13.41 >> WG1 33.89 31.10 14.48 >> WG2 2.84 62.81 20.79 >> WG2 2.79 60.46 19.16 >> WG2 16.27 33.04 6.48 >> WG2 1.39 57.90 9.13 >> WG3 4.54 52.91 17.20 >> WG3 5.20 50.55 15.65 >> WG3 7.71 49.13 10.80 >> WG3 4.43 50.03 11.83 >> WG3 1.72 57.53 14.20 >> WG3 1.51 58.99 13.96 >> >> I would like to do a trinagle.plot with labeled axis-names "sand" >> "silt" and "clay". However using the command: >> >> tringle.plot(grain) >> >> from ade4-apckage) does not plot the axis names and there is no >> paramter to set the labels like "xlab" in the plot command. Does >> anybody has a good advice? >> >> Thanks in advance >> Thomas >> >> __ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> >> >> > > __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] ggplot-How to define fill colours?
Thanks Thierry,
Rigth on target.
Cheers,
Pedro
At 10:35 2007/12/18, you wrote:
>Pedro,
>
>I've had a similar problem. See this post for the solution:
>http://thread.gmane.org/gmane.comp.lang.r.general/100649/focus=100673
>
>Cheers,
>
>Thierry
>
>
>
>
>ir. Thierry Onkelinx
>Instituut voor natuur- en bosonderzoek / Research Institute for Nature
>and Forest
>Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
>methodology and quality assurance
>Gaverstraat 4
>9500 Geraardsbergen
>Belgium
>tel. + 32 54/436 185
>[EMAIL PROTECTED]
>www.inbo.be
>
>Do not put your faith in what statistics say until you have carefully
>considered what they do not say. ~William W. Watt
>A statistical analysis, properly conducted, is a delicate dissection of
>uncertainties, a surgery of suppositions. ~M.J.Moroney
>
>-Oorspronkelijk bericht-
>Van: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
>Namens Pedro de Barros
>Verzonden: maandag 17 december 2007 23:55
>Aan: [EMAIL PROTECTED]
>Onderwerp: [R] ggplot-How to define fill colours?
>Urgentie: Hoog
>
>Dear R's (most likely Hadley),
>
>I want to build a stacked bar plot where I would like to define which
>colours will be used for each of the groups. However, I do not seem
>to find a way to do this, even if I've been looking over many places.
>
>I have tried several variations, and my final try was this code, but
>I still do not manage to get the colours as I pre-define. Any hints
>about how to get this?
>
>Thanks in advance,
>Pedro
>
>my code:
> >plotdata1<-data.frame(x=rep(factor(1:4),4), y=rep(0.1*(1:4),4),
>+group=as.character(rep(c('white', 'red', 'blue', 'green'),rep(4,4
> >plot0<-ggplot()
> >plot3<-plot0+layer(data=plotdata1, mapping=aes_string(x='x',y='y',
>+fill='group'),geom='bar', stat='identity', position='stack')
> >print(plot3)
>
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>http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
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Re: [R] Scatterplot Showing All Points
Jari Oksanen wrote: > Wayne Aldo Gavioli fas.harvard.edu> writes: > > >> Hello all, >> >> I'm trying to graph a scatterplot of a large (5,000 x,y coordinates) of data >> with the caveat that many of the data points overlap with each other (share >> the >> same x AND y coordinates). In using the usual "plot" command, >> >> >>> plot(education, xlab="etc", ylab="etc") >>> >> it seems that the overlap of points is not shown in the graph. Namely, there >> are 5,000 points that should be plotted, as I mentioned above, but because so >> many of the points overlap with each other exactly, only about 50-60 points >> are >> actually plotted on the graph. Thus, there's no indication that Point A >> shares >> its coordinates with 200 other pieces of data and thus is very common while >> Point B doesn't share its coordinates with any other pieces of data and thus >> isn't common at all. Is there anyway to indicate the frequency of such >> points >> on such a graph? Should I be using a different command than "plot"? >> >> >> > One suggestion seems to be still missing: 'sunflowerplot' of base R. May look > taggy, though, if you have 200 "petals". > > Actually the documentation of sunflowerplot is wrong in botanical sense. > Sunflowers have composite flowers in capitula, and the things called 'petals' > in > documentation are ligulate, sterile ray-florets (each with vestigial petals > which are not easily visible in sunflower, but in some other species you may > see > three (occasionally two) teeth). > Could you please put together a patch that replaces "petals" with "ligulate, sterile ray-florets" in appropriate places? ;-) Duncan Murdoch > cheers, jari oksanen > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Odp: accessing dimension names
Hi
[EMAIL PROTECTED] napsal dne 18.12.2007 12:01:41:
> I have a matrix y:
>
> > dimnames(y)
> $x93
> [1] "1" "2"
>
> $x94
> [1] "0" "1" "2"
> .. so on (there are other dimensions as well)
>
>
>
> I need to access a particular dimension, but a random mechanism tells me
> which dimension it would. So, sometimes I might need to access
> dimnames(y)$x93, some other time it would be dimnames(y)$x94.. and so
on.
> Now let that random dimension be idx, then
dimnames(y)$paste('x',idx,sep='')
> doesn't work.
Why not
dimnames(y)[idx]
Regards
Petr
>
> Can anyone help?
>
> Thanks!
>
>[[alternative HTML version deleted]]
>
> __
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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Re: [R] Scatterplot Showing All Points
Wayne, Try the iplot command in iPlots. You can then vary both the pointsize and the transparency of your scatterplot interactively and decide which scatterplot conveys the information best. Sometimes it's helpful to use more than one scatterplot when presenting your results. (I must admit to being very surprised that jittering and sunflower plots have been suggested for a dataset of 5000 points. Do those who mentioned these methods have examples on that scale where they are effective?) Antony Unwin Professor of Computer-Oriented Statistics and Data Analysis, University of Augsburg, Germany [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] hazard ratio of interaction Cox model
Giulia Barbati wrote: > Dear Forum, > I have a question about interaction estimate in the Cox model: > why the hazard ratio of the interaction is not produced in the summary of the > model? > (Instead, the estimate of the coefficient is given in the print of the > model.) The 'hazard ratio of the interaction' is not well defined. Decide what hazard ratio you want to estimate, then ask summary to compute that, e.g. summary(modINT, XX_PR=c(1,3), XX_DISF=2) will estimate the 3:1 XX_PR hazard ratio at XX_DISF=2 Frank Harrell > > > # Example: > > modINT <-cph( Surv(T_BASE, T_FIN,STATUS)~ NYHA + ASINI + RFP + FE_REC + > XX_PR*XX_DISF) > > print(modINT) > > > coef se(coef) zp > NYHA=2 1.2540.584 2.15 0.031767 > ASINI 0.6650.409 1.62 0.104247 > RFP=20.7250.704 1.03 0.302578 > FE_REC=2-1.6370.810 -2.02 0.043331 > XX_PR2.1890.649 3.37 0.000748 > XX_DISF 3.2331.000 3.23 0.001222 > XX_PR * XX_DISF -2.8521.280 -2.23 0.025852 > > summary(modINT) > > Effects Response : Surv(T_BASE, T_FIN, STATUS) > > Factor LowHigh Diff. Effect S.E. Lower 0.95 Upper 0.95 > ASINI 2.0725 2.85 0.7775 0.52 0.32 -0.111.14 > Hazard Ratio 2.0725 2.85 0.7775 1.68NA 0.903.13 > XX_PR 0. 1.00 1. 2.19 0.65 0.923.46 > Hazard Ratio 0. 1.00 1. 8.92NA 2.50 31.86 > XX_DISF0. 1.00 1. 3.23 1.00 1.275.19 > Hazard Ratio 0. 1.00 1. 25.35NA 3.57 179.88 > NYHA - 2:1 1. 2.00 NA 1.25 0.58 0.112.40 > Hazard Ratio 1. 2.00 NA 3.50NA 1.12 11.00 > RFP - 2:1 1. 2.00 NA 0.73 0.70 -0.652.10 > Hazard Ratio 1. 2.00 NA 2.07NA 0.528.20 > FE_REC - 2:1 1. 2.00 NA -1.64 0.81 -3.23 -0.05 > Hazard Ratio 1. 2.00 NA 0.19NA 0.040.95 > > Adjusted to: XX_PR=0 XX_DISF=0 > > > > > > > Be a better friend, newshound, and > > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
>> Antony Unwin <[EMAIL PROTECTED]> >> >I must admit to being very surprised that jittering and sunflower >plots have been suggested for a dataset of 5000 points. Do those who >mentioned these methods have examples on that scale where they are >effective?) You have a point. haha. But check the microarray literature; scatterplots have been used - often - to display microarray data with 1 observations at a time. And in their defence, even on screen, a 600x600 pixel plot window holds 36 pixels - 5000 is not a large fraction of that. Jittering has visible effects on data at that resolution. Compare the two plots in library(MASS) Sigma <- matrix(c(10,4,4,2),2,2) xy<- round(mvrnorm(n=5000, rep(0, 2), Sigma), 1) plot(xy,pch=".") plot(jitter(xy, factor=2),pch=".") But you're of course right to question how sensible this is. The best you can get is a visual impression of the 'shape' of the data with a greater perceived density at multiple observations which otherwise overlapped. S. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Capture warning messages from coxph()
Xinyi Li asked how to keep track of which coxph models, called from within a
loop, were responsible for warning messges. One solution is to modify the
coxph code so that those models are marked in the return coxph object. Below
is a set of changes to the final 40 lines of coxph.fit, that will cause the
component "infinite.warn" to be added to the result whenever a warning was
generated; it will be a vector of T/F showing which component(s) of the
coefficient vector generated the warning.
Change the code for coxph.fit.s, then do
> source('coxph.fit.revised.s')#or whatever you called it
> coxph <- source('coxph.s')
> coxph.wtest <- survival:::coxph.wtest
Line 2 causes you to have a local version of coxph, otherwise, due
to name spaces, the original version of coxph.fit will still be used. Line 3
is another consequence of name spaces. Then code such as
for(i in 1:ncol(x)){
fit=coxph(TIME~x[,i])
if (!is.null(fit$infinite.warn)) cat("Waring in fit", i, "\n")
sfit <- summary(fit)
results[i,1]=sfit$coef[1]
results[i,2]=sfit$coef[3]
results[i,3]=sfit$coef[5]
}
Will report out the models with a warning.
Notes
1. The warning message is not completely reliable
2. Name spaces protect a package from accidental overrides, when a user or
some other package reuses a function name. With its hundreds of packages, they
are a necessity for R. But sometimes you really do want to override a
function; then they are a bit of a pain. Others with a better grasp of R
internals may be able to suggest a better override than I have done here.
Terry Therneau
-
infs <- abs(coxfit$u %*% var)
keep.infs <- F# new line
if (maxiter >1) {
if (coxfit$flag == 1000)
warning("Ran out of iterations and did not converge")
else {
infs <- ((infs > control$eps) &
infs > control$toler.inf*abs(coef))
if (any(infs))
warning(paste("Loglik converged before variable ",
paste((1:nvar)[infs],collapse=","),
"; beta may be infinite. "))
keep.infs <- T #new line
}
}
names(coef) <- dimnames(x)[[2]]
lp <- c(x %*% coef) + offset - sum(coef*coxfit$means)
score <- exp(lp[sorted])
coxres <- .C("coxmart", as.integer(n),
as.integer(method=='efron'),
stime,
sstat,
newstrat,
as.double(score),
as.double(weights),
resid=double(n))
resid <- double(n)
resid[sorted] <- coxres$resid
names(resid) <- rownames
coef[which.sing] <- NA
temp <- list(coefficients = coef, #modified line
var= var,
loglik = coxfit$loglik,
score = coxfit$sctest,
iter = coxfit$iter,
linear.predictors = as.vector(lp),
residuals = resid,
means = coxfit$means,
method='coxph')
if (keep.infs) temp$infinite.warn <- infs #new line
temp#new line
}
}
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Re: [R] Scatterplot Showing All Points
On 18/12/2007 7:31 AM, Antony Unwin wrote: > Wayne, > > Try the iplot command in iPlots. You can then vary both the > pointsize and the transparency of your scatterplot interactively and > decide which scatterplot conveys the information best. Sometimes > it's helpful to use more than one scatterplot when presenting your > results. > > (I must admit to being very surprised that jittering and sunflower > plots have been suggested for a dataset of 5000 points. Do those who > mentioned these methods have examples on that scale where they are > effective?) Sure. The original post said there were about 50-60 unique locations. This plot: x <- rbinom(5000, 20, 0.15) y <- rbinom(5000, 20, 0.15) plot(x,y) has a few more unique locations; tune those probabilities if you want it closer. Due to the overlap, the distribution is very unclear. But this plot plot(jitter(x), jitter(y)) makes the distribution quite clear. I wouldn't use the default pch if I had 5 points, but with pch=".", it's not so bad even in that case. Duncan Murdoch __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] GLM and factor in forular
I used a GLM with a factor variable and wondered about that the first factor is missing in the results. means there is no result for Y1 Is it wrong to use factors in GLM or is there a statistical reason that there is no Y1 result ? X<-rnorm(31:40) Y<-factor(c(1:10)) glm(X~Y) Knut __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] accessing dimension names
Hard to help as i do not have "y" and it definitelly is not a matrix as
you tried to pretend.
1. Try to look at structure of your y object by str(y)
2. Try to learn about how to extract parts of objects e.g. by reading
?"["
3. Try to use what you learned on your y object
4. If you still does not get what you want then make some example
which can be reproduced and ask again
> mat<-matrix(rnorm(12),3,4)
> dmat<-data.frame(mat)
> dimnames(dmat)
[[1]]
[1] "1" "2" "3"
[[2]]
[1] "X1" "X2" "X3" "X4"
> dimnames(dmat)[1]
[[1]]
[1] "1" "2" "3"
> dimnames(dmat)[1][1]
[[1]]
[1] "1" "2" "3"
> dimnames(dmat)[[1]][1]
[1] "1"
Regards
Petr
[EMAIL PROTECTED]
[EMAIL PROTECTED] napsal dne 18.12.2007 14:25:06:
> Thanks. Actually, I need something else as well.
>
> I need to get as.numeric(dimnames(y)$x93[1]), which in this case is 1. I
tried
> as.numeric(dimnames(y)$paste('x',idx,sep='')[1]), and it did not work.
>
> Please help.
>
>
>
> On Dec 18, 2007 6:26 AM, Petr PIKAL <[EMAIL PROTECTED]> wrote:
> Hi
>
> [EMAIL PROTECTED] napsal dne 18.12.2007 12:01:41:
>
> > I have a matrix y:
> >
> > > dimnames(y)
> > $x93
> > [1] "1" "2"
> >
> > $x94
> > [1] "0" "1" "2"
> > .. so on (there are other dimensions as well)
> >
> >
> >
> > I need to access a particular dimension, but a random mechanism tells
me
> > which dimension it would. So, sometimes I might need to access
> > dimnames(y)$x93, some other time it would be dimnames(y)$x94.. and so
> on.
> > Now let that random dimension be idx, then
> dimnames(y)$paste('x',idx,sep='')
> > doesn't work.
> Why not
>
> dimnames(y)[idx]
>
> Regards
> Petr
>
>
> >
> > Can anyone help?
> >
> > Thanks!
> >
> >[[alternative HTML version deleted]]
> >
> > __
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Reshape Dataframe
Hi, I'm having a bit of problems in creating a new dataframe. Below you'll find a description of the current dataframe and of the dataframe that needs to be created. Can someone help me out on this one? Thx in advance. Bert Current Dataframe Var1Var2Var3Var4 A Fa W1 1 A Si W1 2 A Fa W2 3 A Si W3 4 B Si W1 5 C La W2 6 C Do W4 7 New Dataframe Var1Var2W1 W2 W3 W4 A Fa 1 3 A Si 2 4 A La A Do B Fa B Si 5 B La B Do C Fa C Si C La 6 C Do 7 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
On 12/17/07, Jim Porzak <[EMAIL PROTECTED]> wrote: > Wayne, > > I am fond of the bagplot (think 2D box plot) to replace scatter plots > for large N. See > http://www.wiwi.uni-bielefeld.de/~wolf/software/aplpack/ and aplpack > in CRAN. The big drawback of the bagplot, like the boxplot, is that it's difficult to see multimodality. Hadley -- http://had.co.nz/ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Reshape Dataframe
On 12/18/07, Bert Jacobs <[EMAIL PROTECTED]> wrote: > > Hi, > > I'm having a bit of problems in creating a new dataframe. > Below you'll find a description of the current dataframe and of the > dataframe that needs to be created. > Can someone help me out on this one? library(reshape) dfm <- melt(df, id = 1:3) cast(dfm, ... ~ Var3) You can find out more about the reshape package at http://had.co.nz/reshape Hadley -- http://had.co.nz/ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] ggplot2 - getting at the grobs
Dear All, I continue trying to get several of my plotting functions to use ggplot, because I really do like the concept of the graphical objects, and working with them in the abstract. I am now trying to access the grobs to manipulate using grid. However, until now all I managed was to get the plot as a gTree object, and manipulate it as a gTree from there. The problem is that then it is no longer a ggplot, and thus I can no longer use the ggplot functions. How to get at the grobs, without converting the ggplot into a gTree? Thanks, Pedro __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] integration
Hi Davide, It is difficult to say what the problem is without knowing more about the nature of the integrand. So, you should do a couple of preliminary things before attempting compute the integral. First, is the integral is finite? You should establish this. Second, plot the integrand over the entire interval. Then you need to think about the following: Is the integrand unimodal, with the mass concentrated over a small region? Or is it multimodal? Does it have thick tail? Assuming that the integral is finite, you could try a few things: 1. Divide the interval of integration into several small intervals (say, 10 or 100), and then use integrate() on each and then add up the results. You can make this process more efficient if you know where the mass is concentrated. 2. Transform the integrand. 3. Try a simple trapezoidal rule quadrature. Ravi. --- Ravi Varadhan, Ph.D. Assistant Professor, The Center on Aging and Health Division of Geriatric Medicine and Gerontology Johns Hopkins University Ph: (410) 502-2619 Fax: (410) 614-9625 Email: [EMAIL PROTECTED] Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of [EMAIL PROTECTED] Sent: Monday, December 17, 2007 11:03 AM To: r-help@r-project.org Subject: [R] integration Dear All, I need to perform a numerical integration of one dimensional fucntions. The extrems of integration are both finite and the functions I'm working on are quite complicated. I have already tried both area() and integrate(), but they do not perform well: area() is very slow and integrate() does not converge. Are in R other functions for numerical integration of one dimentional functions? Thanks in advance Davide Tiscali.Fax: il tuo fax online in promo fino al 31 dicembre, paghi 15€ e ricarichi 20€ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Reshape Dataframe
On Dec 18, 2007 9:07 AM, Bert Jacobs <[EMAIL PROTECTED]> wrote:
>
> Hi,
>
> I'm having a bit of problems in creating a new dataframe.
> Below you'll find a description of the current dataframe and of the
> dataframe that needs to be created.
> Can someone help me out on this one?
> Thx in advance.
> Bert
>
> Current Dataframe
>
> Var1Var2Var3Var4
> A Fa W1 1
> A Si W1 2
> A Fa W2 3
> A Si W3 4
> B Si W1 5
> C La W2 6
> C Do W4 7
>
> New Dataframe
>
> Var1Var2W1 W2 W3 W4
> A Fa 1 3
> A Si 2 4
> A La
> A Do
> B Fa
> B Si 5
> B La
> B Do
> C Fa
> C Si
> C La 6
> C Do 7
Try this:
out <- ftable(xtabs(Var4 ~ Var1 + Var2 + Var3, DF))
out[out == 0] <- NA
Omit the last line is 0 fill is what you had wanted.
This will do it except that it will eliminate all rows
without data:
out2 <- reshape(DF, dir = "wide", timevar = "Var3", idvar = c("Var1", "Var2"))
out2[is.na(out2)] <- 0
Omit the last line if NA fill is what you wanted.
The reshape package melt/cast routines (see Hadley's solution in this
thread) can be used
to give a similar result to the reshape command above (i.e. all
missing rows are not
included) except that cast is a bit more flexible since it has a fill= argument.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Analyzing Publications from Pubmed via XML
On 12/18/07, David Winsemius <[EMAIL PROTECTED]> wrote:
> David Winsemius <[EMAIL PROTECTED]> wrote in
> news:[EMAIL PROTECTED]:
>
> > "Armin Goralczyk" <[EMAIL PROTECTED]> wrote in
> > news:[EMAIL PROTECTED]:
>
> >> I tried the above function with simple search terms and it worked
> >> fine for me (also more output thanks to Martin's post) but when I use
> >> search terms attributed to certain fields, i.e. with [au] or [ta], I
> >> get the following error message:
> >>> pm.srch()
> >> 1: "laryngeal neoplasms[mh]"
> >> 2:
>
> > I am wondering if you used spaces, rather than "+"'s? If so then you
> > may want your function to do more gsub-processing of the input string.
>
> I tried my theory that one would need "+"'s instead of spaces, but
> disproved it. Spaces in the input string seems to produce acceptable
> results on my WinXP/R.2.6.1/RGui system even with more complex search
> strings.
>
> --
>
It's not the spaces, the problem is the tag (sorry that I didn't
specify this), or maybe the string []. I am working on a Mac OS X 10.4
with R version 2.6. Is it maybe a string conversion problem? In the
following warning strings in the html adress seem to be different:
Fehler in .Call("RS_XML_ParseTree", as.character(file), handlers,
as.logical(ignoreBlanks), :
error in creating parser for
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&term=laryngeal
neoplasms[mh]
I/O warning : failed to load external entity
"http%3A//eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi%3Fdb=pubmed&term=laryngeal%20neoplasms%5Bmh%5D"
--
Armin Goralczyk, M.D.
--
Universitätsmedizin Göttingen
Abteilung Allgemein- und Viszeralchirurgie
Rudolf-Koch-Str. 40
39099 Göttingen
--
Dept. of General Surgery
University of Göttingen
Göttingen, Germany
--
http://www.chirurgie-goettingen.de
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
Duncan Murdoch wrote: > On 18/12/2007 7:31 AM, Antony Unwin wrote: >> Wayne, >> >> Try the iplot command in iPlots. You can then vary both the >> pointsize and the transparency of your scatterplot interactively and >> decide which scatterplot conveys the information best. Sometimes >> it's helpful to use more than one scatterplot when presenting your >> results. >> >> (I must admit to being very surprised that jittering and sunflower >> plots have been suggested for a dataset of 5000 points. Do those who >> mentioned these methods have examples on that scale where they are >> effective?) > > Sure. The original post said there were about 50-60 unique locations. > This plot: > > x <- rbinom(5000, 20, 0.15) > y <- rbinom(5000, 20, 0.15) > plot(x,y) > > has a few more unique locations; tune those probabilities if you want it > closer. Due to the overlap, the distribution is very unclear. But this > plot > > plot(jitter(x), jitter(y)) Another alternative is smoothscatter() in the geneplotter package from Bioconductor, which does a pretty reasonable job with these example data. Best, Jim > > makes the distribution quite clear. > > I wouldn't use the default pch if I had 5 points, but with pch=".", > it's not so bad even in that case. > > Duncan Murdoch > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- James W. MacDonald, M.S. Biostatistician Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Reshape Dataframe
On Dec 18, 2007 9:54 AM, Gabor Grothendieck <[EMAIL PROTECTED]> wrote:
>
> On Dec 18, 2007 9:07 AM, Bert Jacobs <[EMAIL PROTECTED]> wrote:
> >
> > Hi,
> >
> > I'm having a bit of problems in creating a new dataframe.
> > Below you'll find a description of the current dataframe and of the
> > dataframe that needs to be created.
> > Can someone help me out on this one?
> > Thx in advance.
> > Bert
> >
> > Current Dataframe
> >
> > Var1Var2Var3Var4
> > A Fa W1 1
> > A Si W1 2
> > A Fa W2 3
> > A Si W3 4
> > B Si W1 5
> > C La W2 6
> > C Do W4 7
> >
> > New Dataframe
> >
> > Var1Var2W1 W2 W3 W4
> > A Fa 1 3
> > A Si 2 4
> > A La
> > A Do
> > B Fa
> > B Si 5
> > B La
> > B Do
> > C Fa
> > C Si
> > C La 6
> > C Do 7
>
> Try this:
>
> out <- ftable(xtabs(Var4 ~ Var1 + Var2 + Var3, DF))
> out[out == 0] <- NA
>
> Omit the last line is 0 fill is what you had wanted.
>
> This will do it except that it will eliminate all rows
> without data:
>
> out2 <- reshape(DF, dir = "wide", timevar = "Var3", idvar = c("Var1", "Var2"))
> out2[is.na(out2)] <- 0
>
> Omit the last line if NA fill is what you wanted.
>
> The reshape package melt/cast routines (see Hadley's solution in this
> thread) can be used
> to give a similar result to the reshape command above (i.e. all
> missing rows are not
> included) except that cast is a bit more flexible since it has a fill=
> argument.
Just one correction. The cast function in reshape has an add.missing= argument
that can control this so actually any of the solutions could be
obtained with cast
using the fill= and add.missing= arguments to control which one you want.
>
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
On 18 Dec 2007, at 2:42 pm, Duncan Murdoch wrote: >> (I must admit to being very surprised that jittering and >> sunflower plots have been suggested for a dataset of 5000 >> points. Do those who mentioned these methods have examples on >> that scale where they are effective?) > > Sure. The original post said there were about 50-60 unique > locations. This plot: > > x <- rbinom(5000, 20, 0.15) > y <- rbinom(5000, 20, 0.15) > plot(x,y) > > has a few more unique locations; tune those probabilities if you > want it closer. Due to the overlap, the distribution is very > unclear. But this plot > > plot(jitter(x), jitter(y)) > > makes the distribution quite clear. No it doesn't! It makes it moderately clearer than the plot without jittering. One good alternative here is the fluctuation diagram variant of a mosaic plot: xx<-as.factor(x) yy<-as.factor(y) imosaic(xx,yy, type="f") Using jittering for categorical data is really not to be recommended and will certainly degrade in performance as the dataset gets bigger. Antony Unwin Professor of Computer-Oriented Statistics and Data Analysis, University of Augsburg, Germany [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Reshape Dataframe
Thx Hadley,
It works, but I need some finetuning.
If I use the following expression:
Newdf <-reshape(df, timevar="Var3", idvar=c("Var1","Var2"),direction="wide")
Newdf
Var1Var2Var3.W1 Var3.W2 Var3.W3 var3.W4
A Fa 1 3
A Si 2 4
B Si 5
C La 6
C Do 7
Is there an option so that for each Var1 all possible combinations of Var2
are listed (i.e. creation of blanco lines).
Is it possible to name the columns with the values of the original Var3
variable, so that the name Var3.W1 changes to W1?
Var1Var2W1 W2 W3 W4
A Fa 1 3
A Si 2 4
A La
A Do
B Fa
B Si 5
B La
B Do
C Fa
C Si
C La 6
C Do 7
Thx,
Bert
-Original Message-
From: hadley wickham [mailto:[EMAIL PROTECTED]
Sent: 18 December 2007 15:16
To: Bert Jacobs
Cc: [EMAIL PROTECTED]
Subject: Re: [R] Reshape Dataframe
On 12/18/07, Bert Jacobs <[EMAIL PROTECTED]> wrote:
>
> Hi,
>
> I'm having a bit of problems in creating a new dataframe.
> Below you'll find a description of the current dataframe and of the
> dataframe that needs to be created.
> Can someone help me out on this one?
library(reshape)
dfm <- melt(df, id = 1:3)
cast(dfm, ... ~ Var3)
You can find out more about the reshape package at http://had.co.nz/reshape
Hadley
--
http://had.co.nz/
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Re: [R] Scatterplot Showing All Points
On 18/12/2007 10:01 AM, Antony Unwin wrote: > On 18 Dec 2007, at 2:42 pm, Duncan Murdoch wrote: > >>> (I must admit to being very surprised that jittering and >>> sunflower plots have been suggested for a dataset of 5000 >>> points. Do those who mentioned these methods have examples on >>> that scale where they are effective?) >> Sure. The original post said there were about 50-60 unique >> locations. This plot: >> >> x <- rbinom(5000, 20, 0.15) >> y <- rbinom(5000, 20, 0.15) >> plot(x,y) >> >> has a few more unique locations; tune those probabilities if you >> want it closer. Due to the overlap, the distribution is very >> unclear. But this plot >> >> plot(jitter(x), jitter(y)) >> >> makes the distribution quite clear. > > No it doesn't! It makes it moderately clearer than the plot without > jittering. One good alternative here is the fluctuation diagram > variant of a mosaic plot: > > xx<-as.factor(x) > yy<-as.factor(y) > imosaic(xx,yy, type="f") That plot is better than jittering, but there's the problem in the mosaic plot of understanding the scale of the rectangles: is it area or diameter that encodes the count? With a jittered plot, you lose resolution when the number of points gets too high because you just see a mess of ink, but at least you only require the viewer to count in order to get a close numerical reading from the plot. I could also claim that while imperfect, at least jittering is widely applicable. For example, if the data were not on a regular grid, perhaps because they had been generated like this: xloc <- rnorm(50) yloc <- rnorm(50) index <- sample(1:50, 5000, rep=TRUE, prob = abs(xloc)) x <- xloc[index] y <- yloc[index] then jittering still works as well (or as poorly), but the imosaic would not work at all. There are better plots than jittering available, but jittering is easy. (Actually, with this dataset, plot(jitter(x), jitter(y)) is really poor, because jitter() chooses a bad amount of jittering. But with manual tuning (e.g. plot(jitter(x, a=0.1), jitter(y, a=0.1), pch=".")) it's not too bad. So I'd say jittering worked, but the R implementation of it may need improvement). > Using jittering for categorical data is really not to be recommended > and will certainly degrade in performance as the dataset gets bigger. Yes, I probably wouldn't recommend jittering if there were more than a few hundred replications at any point, or more than a few hundred unique points. Duncan Murdoch P.S. iplots 1.1-1 may have an init problem in Windows: in my first attempt, the plot made the boxes too large to fit in their cells, but it fixed itself when I resized the window, and the bug doesn't seem to be repeatable. > > Antony Unwin > Professor of Computer-Oriented Statistics and Data Analysis, > University of Augsburg, > Germany __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
On 18/12/2007 10:02 AM, James W. MacDonald wrote: > Duncan Murdoch wrote: >> On 18/12/2007 7:31 AM, Antony Unwin wrote: >>> Wayne, >>> >>> Try the iplot command in iPlots. You can then vary both the >>> pointsize and the transparency of your scatterplot interactively and >>> decide which scatterplot conveys the information best. Sometimes >>> it's helpful to use more than one scatterplot when presenting your >>> results. >>> >>> (I must admit to being very surprised that jittering and sunflower >>> plots have been suggested for a dataset of 5000 points. Do those who >>> mentioned these methods have examples on that scale where they are >>> effective?) >> Sure. The original post said there were about 50-60 unique locations. >> This plot: >> >> x <- rbinom(5000, 20, 0.15) >> y <- rbinom(5000, 20, 0.15) >> plot(x,y) >> >> has a few more unique locations; tune those probabilities if you want it >> closer. Due to the overlap, the distribution is very unclear. But this >> plot >> >> plot(jitter(x), jitter(y)) > > Another alternative is smoothscatter() in the geneplotter package from > Bioconductor, which does a pretty reasonable job with these example data. Yes, I agree. (As an aside, there's actually a capital S in smoothScatter(), and it's a bit of a pain to install, because geneplotter depends on something that depends on DBI, which is not so easily available these days.) Duncan Murdoch __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] accessing dimension names
dimnames(y)[[paste('x', idx, sep="")]]
On Dec 18, 2007 6:01 AM, <[EMAIL PROTECTED]> wrote:
> I have a matrix y:
>
> > dimnames(y)
> $x93
> [1] "1" "2"
>
> $x94
> [1] "0" "1" "2"
> .. so on (there are other dimensions as well)
>
>
>
> I need to access a particular dimension, but a random mechanism tells me
> which dimension it would. So, sometimes I might need to access
> dimnames(y)$x93, some other time it would be dimnames(y)$x94.. and so on.
> Now let that random dimension be idx, then dimnames(y)$paste('x',idx,sep='')
> doesn't work.
>
> Can anyone help?
>
> Thanks!
>
>[[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
Jim Holtman
Cincinnati, OH
+1 513 646 9390
What is the problem you are trying to solve?
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[R] [R-pkgs] New version of systemfit (not backward compatible)
Dear R users, the systemfit package contains functions for fitting systems of simultaneous equations by various estimation methods (e.g. OLS, SUR, 2SLS, 3SLS). Currently version 0.8 of systemfit is available on CRAN. However, shortly we will upload version 1.0, which is NOT BACKWARD COMPATIBLE. The changes that broke backward compatibility were necessary to make systemfit() more similar to standard regression tools in R such as lm(). We hope that the usage of systemfit() is more intuitive for R users now. We will continue to maintain the 0.8 branch so that users can still use the old version if they do not want to update their R scripts. Both versions are and will be available for download from systemfit's website: http://www.systemfit.org/ which is a shortcut to http://www.uni-kiel.de/agrarpol/ahenningsen/systemfit/ A paper that describes the (new version of the) systemfit package is forthcoming in the Journal of Statistical Software (JSS). A preprint of this paper is available on systemfit's website: http://www.systemfit.org/systemfit_paper_1.0.pdf The following list summarizes the most important changes from version 0.8 to 1.0: - some names of systemfit()'s arguments have changed to make it more similar to standard regression tools in R - the order of systemfit()'s arguments has changed to make it more similar to standard regression tools in R - the names of the elements in the object returned by systemfit() have changed to make it more similar to lm() - added several methods for systemfit objects that are generally available for standard regression tools in R - restrictions on the coefficients can be specified symbolically now - the functionality of systemfitClassic() has been integrated into systemfit() - replaced ftest.systemfit() and waldtest.systemfit() by the method linear.hypothesis() - systemfit now uses the "Matrix" package for matrix calculations (this makes the estimation of large models and large data sets much faster) - improved checking of the arguments so that error messages are more helpful now We thank two anonymous referees of the JSS, Achim Zeileis, John Fox, William H. Greene, Ott Toomet, Duncan Murdoch, Martin Maechler, Duglas Bates and several (other) systemfit users for their answers, comments, and/or suggestions that helped us to improve the systemfit package. Feedback is always welcome! Arne & Jeff -- Arne Henningsen Department of Agricultural Economics University of Kiel Olshausenstr. 40 D-24098 Kiel (Germany) Tel: +49-431-880 4445 or +49-4349-914871 Fax: +49-431-880 1397 [EMAIL PROTECTED] http://www.uni-kiel.de/agrarpol/ahenningsen/ ___ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
Duncan Murdoch wrote:
> Yes, I agree. (As an aside, there's actually a capital S in
> smoothScatter(), and it's a bit of a pain to install, because
> geneplotter depends on something that depends on DBI, which is not so
> easily available these days.)
Somehow I always forget the capital S and wonder if I have loaded the
correct package ;-D
As for installing the required dependencies, I believe this is actually
quite straightforward:
source("http://www.bioconductor.org/biocLite.R";)
biocLite("geneplotter")
Should install geneplotter and all required dependencies.
Best,
Jim
>
> Duncan Murdoch
--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
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Re: [R] bar plot colors
I think you're going to find that barchart with that
many values in a bar is going to be pretty well
uninterpretable.
Jim Lemon gives the desired barchart but it is very
difficult to read.
Stealing his code to create the same matrix I'd
suggest may be looking at a dotchart. I'm not sure if
this is even close to an optimal solution but I do
think it's a bit better than a barchart approach
==
heights<-matrix(sample(10:70,54),ncol=3)
bar.colors<-rep(rep(2:7,each=3),3)
cost.types <- c("Direct", "Indirec", "Induced")
colnames(heights) <- c("A", "B", "C")
rownames(heights) <- c(rep(cost.types, 6))
dotchart(heights, col=bar.colors, pch=16, cex=.6)
===
--- "Winkel, David" <[EMAIL PROTECTED]> wrote:
> All,
>
>
>
> I have a question regarding colors in bar plots. I
> want to stack a
> total of 18 cost values in each bar. Basically, it
> is six cost types and
> each cost type has three components- direct,
> indirect, and induced
> costs. I would like to use both solid color bars
> and bars with the
> slanted lines (using the density parameter). The
> colors would
> distinguish cost types and the lines would
> distinguish
> direct/indirect/induced. I want the cost types
> (i.e. colors) to be
> stacked together for each cost type. In other
> words, I don't want all
> of the solid bars at the bottom and all of the
> slanted lines at the top.
>
>
> So far, I have made a bar plot with all solid colors
> and then tried to
> overwrite that bar plot by calling barplot() again
> and putting the white
> slanted lines across the bars. However, I can't get
> this method to work
> while still grouping the cost types together.
>
>
>
> Thanks in advance for any help you can provide.
>
>
>
> David Winkel
>
> Applied Biology and Aerosol Technology
>
> Battelle Memorial Institute
>
> 505 King Ave.
>
> Columbus, Ohio 43201
>
> 614.424.3513
>
>
>
>
> [[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained,
> reproducible code.
>
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Re: [R] regression towards the mean, AS paper November 2007
On Dec 17, 2007 3:10 PM, hadley wickham <[EMAIL PROTECTED]> wrote:
> > This has nothing to do really with the question that Troels asked,
> > but the exposition quoted from the AA paper is unnecessarily
> > confusing.
> > The phrase ``Because X0 and X1 have identical marginal
> > distributions ...''
> > throws the reader off the track. The identical marginal
> > distributions
> > are irrelevant. All one needs is that the ***means*** of X0 and X1
> > be the same, and then the null hypothesis tested by a paired t-test
> > is true and so the p-values are (asymptotically) Uniform[0,1]. With
> > a sample size of 100, the ``asymptotically'' bit can be safely
> > ignored
> > for any ``decent'' joint distribution of X0 and X1. If one further
> > assumes that X0 - X1 is Gaussian (which has nothing to do with X0
> > and
> > X1 having identical marginal distributions) then ``asymptotically''
> > turns into ``exactly''.
>
> Another related issue is that uniform distributions don't look very uniform:
>
> hist(runif(100))
> hist(runif(1000))
> hist(runif(1))
>
> Be sure to calibrate your eyes (and your bin width) before rejecting
> the hypothesis that the distribution is uniform.
>
> Hadley
Thanks for the example, Hadley. To me, this suggests we should stop
teaching histograms in Stat 101 and instead use quantile plots, which
give excellent results for n=100 and even surprisingly good results
for n=10:
par(mfrow=c(2,2))
for(i in c(10, 100, 1000, 1)) {
qqplot(runif(i), qunif(seq(1/i, 1, length=i)), main=i,
xlim=c(0,1), ylim=c(0,1),
xlab="runif", ylab="Uniform distribution quantiles")
abline(0,1,col="lightgray")
}
Kevin (drifting even further off topic)
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Re: [R] Scatterplot Showing All Points
On 18 Dec 2007, at 4:49 pm, Duncan Murdoch wrote: >> One good alternative here is the fluctuation diagram variant of a >> mosaic plot: >> xx<-as.factor(x) >> yy<-as.factor(y) >> imosaic(xx,yy, type="f") > > That plot is better than jittering, but there's the problem in the > mosaic plot of understanding the scale of the rectangles: is it > area or diameter that encodes the count? Area is used. > With a jittered plot, you lose resolution when the number of points > gets too high because you just see a mess of ink, but at least you > only require the viewer to count in order to get a close numerical > reading from the plot. If someone needs a count, they should be given a table. Graphics are for qualitative conclusions not details. Anyway, counting will only work for really small datasets. > I could also claim that while imperfect, at least jittering is > widely applicable. For example, if the data were not on a regular > grid, perhaps because they had been generated like this: > > xloc <- rnorm(50) > yloc <- rnorm(50) > index <- sample(1:50, 5000, rep=TRUE, prob = abs(xloc)) > x <- xloc[index] > y <- yloc[index] > > then jittering still works as well (or as poorly), but the imosaic > would not work at all. That's right and that's (almost) the sort of example I was thinking of. For a limited number of locations like this a bubble plot would be best (which has already been suggested in this thread, I think). For many locations and few replications I would still go for varying pointsize and transparency. Incidentally, to check your suggestion I ran your code and discovered that the transparency in iplot does not seem to like replications. Very strange, we'll have to check why. I then looked closely at the numbers of replications generated and discovered that case 25 was picked 325 times and case 40 only once. Rather too extreme for my liking! Running it again gave very similar results, though not exactly the same: this time it was 325 times for case 25 and case 40 was not picked at all. Other numbers varied slightly. This is not what I expected, any ideas? > P.S. iplots 1.1-1 may have an init problem in Windows: in my first > attempt, the plot made the boxes too large to fit in their cells, > but it fixed itself when I resized the window, and the bug doesn't > seem to be repeatable. Thanks. This happens occasionally on the Mac too. Refreshing solves it in practice, but we need to find out why it can happen (and stop it happening!). Antony Unwin Professor of Computer-Oriented Statistics and Data Analysis, University of Augsburg, Germany [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] calculating the number of days from dates
> Sorry for using library instead package, but > library() is one command for using packages. ... which is why all efforts to make folks say "package" instead of >> "library" << are doomed to fail, IMHO. Besides, in English, "library" also means "a collection of software or data usually reflecting a specific theme or application" (#9 on the list from http://dictionary.reference.com/ ). Therefore: > "library" == "package" [1] TRUE! and just about the only way to clear up the "confusion" would be to rename library() to package(), and replace "library" with "folder" or "directory". > -Original Message- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Knut Krueger > Sent: Monday, December 17, 2007 2:11 AM > To: 'R R-help' > Subject: Re: [R] calculating the number of days from dates > > > > it's a >> package << , not a library, please! > > > > > Sorry for using library instead package, but > > library() is one command for using packages. > > Therefore I (and it seems that i am not the only one) used > library instead package. > > Knut > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Sweave and Scientific Workplace
Dear HelpeRs, a colleague of mine uses Scientific Workplace to write his LaTeX documents. I made his mouth water mentioning the advantages of using Sweave. Not using SW myself I wonder if anyone out there has gathered some experiences in using the combination of both. Thank you in advance Dietrich -- Dietrich Trenkler c/o Universitaet Osnabrueck Rolandstr. 8; D-49069 Osnabrueck, Germany email: [EMAIL PROTECTED] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] regression towards the mean, AS paper November 2007
> Thanks for the example, Hadley. To me, this suggests we should stop > teaching histograms in Stat 101 and instead use quantile plots, which > give excellent results for n=100 and even surprisingly good results > for n=10: It all depends on what you're trying to do - I don't think histograms are particularly good as density estimators, but that's not what you're using them for most of the time! You're using them as an exploratory tool to try and understand what's going on in your data - often you'll need to use very small bin widths which help find unexpected gaps and patterns in your data. It's helpful to have some feel for what common distributions look like. Hadley -- http://had.co.nz/ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] ggplot2 - getting at the grobs
Hi Pedro, Could you be a bit more explicit about what you're trying to do? Have you read the last chapter of the draft ggplot book? Hadley On Dec 18, 2007 8:41 AM, Pedro de Barros <[EMAIL PROTECTED]> wrote: > Dear All, > > I continue trying to get several of my plotting functions to use > ggplot, because I really do like the concept of the graphical > objects, and working with them in the abstract. > I am now trying to access the grobs to manipulate using grid. > However, until now all I managed was to get the plot as a gTree > object, and manipulate it as a gTree from there. The problem is that > then it is no longer a ggplot, and thus I can no longer use the > ggplot functions. > How to get at the grobs, without converting the ggplot into a gTree? > > Thanks, > Pedro > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- http://had.co.nz/ __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
On 12/18/2007 11:21 AM, James W. MacDonald wrote:
> Duncan Murdoch wrote:
>> Yes, I agree. (As an aside, there's actually a capital S in
>> smoothScatter(), and it's a bit of a pain to install, because
>> geneplotter depends on something that depends on DBI, which is not so
>> easily available these days.)
>
> Somehow I always forget the capital S and wonder if I have loaded the
> correct package ;-D
>
> As for installing the required dependencies, I believe this is actually
> quite straightforward:
>
> source("http://www.bioconductor.org/biocLite.R";)
> biocLite("geneplotter")
>
> Should install geneplotter and all required dependencies.
Yes, that works. Not sure why DBI was unavailable for a simple install
of geneplotter from the Windows Rgui; when I try it now (on a different
PC, maybe using a different mirror) it's there.
Duncan Murdoch
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
On 12/18/2007 12:44 PM, Antony Unwin wrote: > On 18 Dec 2007, at 4:49 pm, Duncan Murdoch wrote: > >>> One good alternative here is the fluctuation diagram variant of a >>> mosaic plot: >>> xx<-as.factor(x) >>> yy<-as.factor(y) >>> imosaic(xx,yy, type="f") >> >> That plot is better than jittering, but there's the problem in the >> mosaic plot of understanding the scale of the rectangles: is it >> area or diameter that encodes the count? > > Area is used. > >> With a jittered plot, you lose resolution when the number of points >> gets too high because you just see a mess of ink, but at least you >> only require the viewer to count in order to get a close numerical >> reading from the plot. > > If someone needs a count, they should be given a table. Graphics > are for qualitative conclusions not details. Anyway, counting will > only work for really small datasets. > >> I could also claim that while imperfect, at least jittering is >> widely applicable. For example, if the data were not on a regular >> grid, perhaps because they had been generated like this: >> >> xloc <- rnorm(50) >> yloc <- rnorm(50) >> index <- sample(1:50, 5000, rep=TRUE, prob = abs(xloc)) >> x <- xloc[index] >> y <- yloc[index] >> >> then jittering still works as well (or as poorly), but the imosaic >> would not work at all. > > That's right and that's (almost) the sort of example I was thinking > of. For a limited number of locations like this a bubble plot would > be best (which has already been suggested in this thread, I think). > For many locations and few replications I would still go for varying > pointsize and transparency. > > Incidentally, to check your suggestion I ran your code and discovered > that the transparency in iplot does not seem to like replications. > Very strange, we'll have to check why. I then looked closely at the > numbers of replications generated and discovered that case 25 was > picked 325 times and case 40 only once. Rather too extreme for my > liking! Running it again gave very similar results, though not > exactly the same: this time it was 325 times for case 25 and case 40 > was not picked at all. Other numbers varied slightly. This is not > what I expected, any ideas? abs(xloc) typically varies by a factor of about 100 from smallest to largest, but sometimes the small end is really small, and so the ratio is really big. Duncan Murdoch > >> P.S. iplots 1.1-1 may have an init problem in Windows: in my first >> attempt, the plot made the boxes too large to fit in their cells, >> but it fixed itself when I resized the window, and the bug doesn't >> seem to be repeatable. > > Thanks. This happens occasionally on the Mac too. Refreshing solves > it in practice, but we need to find out why it can happen (and stop > it happening!). > > Antony Unwin > Professor of Computer-Oriented Statistics and Data Analysis, > University of Augsburg, > Germany __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] R brakes when submitting a query to MySQL
Hello,
I would like to retrieve data stored in MySQL database, so I installed
RMySQL package.
I can successfully connect with the my database using the following code
> dvr<-dbDriver("MySQL")
> con2<-dbConnect(dvr,group="exbardiv")
> mysqlDescribeConnection(con2)
User: mmorag
Host: localhost
Dbname: exbardiv
Connection type: localhost via TCP/IP
No resultSet available
I can even see the tables in the database
> dbListTables(con2)
[1] "agoueb""high_ld" "rescue""sjlc_info" "sjlc_ld" "temp"
[7] "temp_snp1" "temp_snp2"
However, when I try to query the database, R breakes.
res<-dbSendQuery(con,'select * from sjlc_ld')
Can anyone help me tune up the connection between R and MySQL?
Thank you,
Marc.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
SCRI, Invergowrie, Dundee, DD2 5DA.
The Scottish Crop Research Institute is a charitable company limited by
guarantee.
Registered in Scotland No: SC 29367.
Recognised by the Inland Revenue as a Scottish Charity No: SC 006662.
DISCLAIMER:
This email is from the Scottish Crop Research Institute, but the views
expressed by the sender are not necessarily the views of SCRI and its
subsidiaries. This email and any files transmitted with it are confidential
to the intended recipient at the e-mail address to which it has been
addressed. It may not be disclosed or used by any other than that addressee.
If you are not the intended recipient you are requested to preserve this
confidentiality and you must not use, disclose, copy, print or rely on this
e-mail in any way. Please notify [EMAIL PROTECTED] quoting the
name of the sender and delete the email from your system.
Although SCRI has taken reasonable precautions to ensure no viruses are
present in this email, neither the Institute nor the sender accepts any
responsibility for any viruses, and it is your responsibility to scan the email
and the attachments (if any).
__
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and provide commented, minimal, self-contained, reproducible code.
[R] PCA - "cov.wt(z) : 'x' must contain finite values only"
I am trying to run PCA on a matrix (the first column and row are
headers). There are several cells with NA's. When I run PCA with the
following code:
__
setwd("I:/PCA")
AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1)
attach(AsianProp)
AsianProp
AsianProp.pca<-princomp(AsianProp, na.omit)
_
I get the error message:
cov.wt(z) : 'x' must contain finite values only
What am I doing wrong?
Thanks very much!
[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] R brakes when submitting a query to MySQL
Is it your use of 'con' rather than 'con2' in dbSendQuery? -Kevin
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Marc Moragues
Sent: Tuesday, December 18, 2007 1:14 PM
To: r-help@r-project.org
Subject: [R] R brakes when submitting a query to MySQL
Hello,
I would like to retrieve data stored in MySQL database, so I installed
RMySQL package.
I can successfully connect with the my database using the following code
> dvr<-dbDriver("MySQL")
> con2<-dbConnect(dvr,group="exbardiv")
> mysqlDescribeConnection(con2)
User: mmorag
Host: localhost
Dbname: exbardiv
Connection type: localhost via TCP/IP
No resultSet available
I can even see the tables in the database
> dbListTables(con2)
[1] "agoueb""high_ld" "rescue""sjlc_info" "sjlc_ld" "temp"
[7] "temp_snp1" "temp_snp2"
However, when I try to query the database, R breakes.
res<-dbSendQuery(con,'select * from sjlc_ld')
Can anyone help me tune up the connection between R and MySQL?
Thank you,
Marc.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _
SCRI, Invergowrie, Dundee, DD2 5DA.
The Scottish Crop Research Institute is a charitable company limited by
guarantee.
Registered in Scotland No: SC 29367.
Recognised by the Inland Revenue as a Scottish Charity No: SC 006662.
DISCLAIMER:\ \ This email is from the Scottish Crop Rese...{{dropped:30}}
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] R brakes when submitting a query to MySQL
Marc Moragues a écrit :
> Hello,
>
> I would like to retrieve data stored in MySQL database, so I installed
> RMySQL package.
> I can successfully connect with the my database using the following code
>
>> dvr<-dbDriver("MySQL")
>> con2<-dbConnect(dvr,group="exbardiv")
>> mysqlDescribeConnection(con2)
>
>
> User: mmorag
> Host: localhost
> Dbname: exbardiv
> Connection type: localhost via TCP/IP
> No resultSet available
>
> I can even see the tables in the database
>
>> dbListTables(con2)
> [1] "agoueb""high_ld" "rescue""sjlc_info" "sjlc_ld" "temp"
>
> [7] "temp_snp1" "temp_snp2"
>
> However, when I try to query the database, R breakes.
What does *that* means ? You should be a bit more descriptive...
> res<-dbSendQuery(con,'select * from sjlc_ld')
require(MindeReaderAlpha).
H ... Isn't the "breakage" just a lng wait with no answer ? Or
maybe a timeout ?
In which case I'd try to put a semicolon (";") at the end of the SQL
query, thus making it syntactically valid SQL...
HTH,
Emmanuel Charpentier
> Can anyone help me tune up the connection between R and MySQL?
>
> Thank you,
> Marc.
> _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
>
> SCRI, Invergowrie, Dundee, DD2 5DA.
> The Scottish Crop Research Institute is a charitable company limited by
> guarantee.
> Registered in Scotland No: SC 29367.
> Recognised by the Inland Revenue as a Scottish Charity No: SC 006662.
>
>
> DISCLAIMER:
>
> This email is from the Scottish Crop Research Institute, but the views
> expressed by the sender are not necessarily the views of SCRI and its
> subsidiaries. This email and any files transmitted with it are confidential
> to the intended recipient at the e-mail address to which it has been
> addressed. It may not be disclosed or used by any other than that addressee.
> If you are not the intended recipient you are requested to preserve this
> confidentiality and you must not use, disclose, copy, print or rely on this
> e-mail in any way. Please notify [EMAIL PROTECTED] quoting the
> name of the sender and delete the email from your system.
>
> Although SCRI has taken reasonable precautions to ensure no viruses are
> present in this email, neither the Institute nor the sender accepts any
> responsibility for any viruses, and it is your responsibility to scan the
> email
> and the attachments (if any).
>
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot Showing All Points
Another approach which I'm pleased with but was not suggested so far
is jitter + kde2d from MASS:
plot(jitter(x), jitter(y))
if (!exists("kde2d")) require(MASS)
kdesamp <- 2 #depending on your RAM
forkde <- if (kdesamp < length(x)) sample(1:length(x), kdesamp,
replace=FALSE) else 1:length(x)
d <- kde2d(x[forkde], y[forkde])
contour(d, add=TRUE)
> -Original Message-
> From: [EMAIL PROTECTED]
> Subject: Re: [R] Scatterplot Showing All Points
>
__
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Re: [R] R brakes when submitting a query to MySQL
You are right, it was a mistake copying and pasting the code. There is
no error message from R when I run con2. I get a Windows error message
saying "R for windows terminal front-end has encountered a problem and
need to close".
The error signature is:
AppName: rterm.exe AppVer: 2.60.43063.0ModName: msvcrt.dll
ModVer: 7.0.2600.2180Offset: 000378c0
Marc.
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Zembower, Kevin
Sent: 18 December 2007 18:18
To: r-help@r-project.org
Subject: Re: [R] R brakes when submitting a query to MySQL
Is it your use of 'con' rather than 'con2' in dbSendQuery? -Kevin
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of Marc Moragues
Sent: Tuesday, December 18, 2007 1:14 PM
To: r-help@r-project.org
Subject: [R] R brakes when submitting a query to MySQL
Hello,
I would like to retrieve data stored in MySQL database, so I installed
RMySQL package.
I can successfully connect with the my database using the following code
> dvr<-dbDriver("MySQL")
> con2<-dbConnect(dvr,group="exbardiv")
> mysqlDescribeConnection(con2)
User: mmorag
Host: localhost
Dbname: exbardiv
Connection type: localhost via TCP/IP
No resultSet available
I can even see the tables in the database
> dbListTables(con2)
[1] "agoueb""high_ld" "rescue""sjlc_info" "sjlc_ld" "temp"
[7] "temp_snp1" "temp_snp2"
However, when I try to query the database, R breakes.
res<-dbSendQuery(con,'select * from sjlc_ld')
Can anyone help me tune up the connection between R and MySQL?
Thank you,
Marc.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
_ _
SCRI, Invergowrie, Dundee, DD2 5DA.
The Scottish Crop Research Institute is a charitable company limited by
guarantee.
Registered in Scotland No: SC 29367.
Recognised by the Inland Revenue as a Scottish Charity No: SC 006662.
DISCLAIMER:\ \ This email is from the Scottish Crop
Rese...{{dropped:30}}
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
SCRI, Invergowrie, Dundee, DD2 5DA.
The Scottish Crop Research Institute is a charitable company limited by
guarantee.
Registered in Scotland No: SC 29367.
Recognised by the Inland Revenue as a Scottish Charity No: SC 006662.
DISCLAIMER:
This email is from the Scottish Crop Research Institute, but the views
expressed by the sender are not necessarily the views of SCRI and its
subsidiaries. This email and any files transmitted with it are confidential
to the intended recipient at the e-mail address to which it has been
addressed. It may not be disclosed or used by any other than that addressee.
If you are not the intended recipient you are requested to preserve this
confidentiality and you must not use, disclose, copy, print or rely on this
e-mail in any way. Please notify [EMAIL PROTECTED] quoting the
name of the sender and delete the email from your system.
Although SCRI has taken reasonable precautions to ensure no viruses are
present in this email, neither the Institute nor the sender accepts any
responsibility for any viruses, and it is your responsibility to scan the email
and the attachments (if any).
__
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] PCA - "cov.wt(z) : 'x' must contain finite values only"
The problem is the missing values. The argument "na.action" is not active
in princomp(), which I think is a bug, even though the help page claims that
"factory fresh" default is na.omit.
So, you need to either get rid of the rows with any missing values in them,
or use a PCA code that can deal with missing values by somehow imputing
them.
Ravi.
---
Ravi Varadhan, Ph.D.
Assistant Professor, The Center on Aging and Health
Division of Geriatric Medicine and Gerontology
Johns Hopkins University
Ph: (410) 502-2619
Fax: (410) 614-9625
Email: [EMAIL PROTECTED]
Webpage: http://www.jhsph.edu/agingandhealth/People/Faculty/Varadhan.html
-Original Message-
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On
Behalf Of Johnson, Bethany
Sent: Tuesday, December 18, 2007 1:14 PM
To: r-help@r-project.org
Subject: [R] PCA - "cov.wt(z) : 'x' must contain finite values only"
I am trying to run PCA on a matrix (the first column and row are
headers). There are several cells with NA's. When I run PCA with the
following code:
__
setwd("I:/PCA")
AsianProp<-read.csv("Matrix.csv", sep=",", header=T, row.names=1)
attach(AsianProp)
AsianProp
AsianProp.pca<-princomp(AsianProp, na.omit)
_
I get the error message:
cov.wt(z) : 'x' must contain finite values only
What am I doing wrong?
Thanks very much!
[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Scatterplot3d model reporting question
I've used the scatterplot3d function to graph some data and had it graph a "smooth" fit. Is there a way to actualy find out the function of the surface? I've looked through the help and figured out how to get it to report the following: Family: gaussian Link function: identity Formula: y ~ s(x, z) Parametric coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.207500.01223 16.97 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Approximate significance of smooth terms: edf Est.rank F p-value s(x,z) 8.403 17 9.729 1.76e-14 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 R-sq.(adj) = 0.684 Deviance explained = 70.9% GCV score = 0.017692 Scale est. = 0.016151 n = 108 But I'm still not really sure what I'm looking at, either that or "smooth" means something different than I thought. Any help would be great! thanks, -Max __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] comparing poisson distributions
Hello all, I would like to compare two sets of count data which form Poisson distributions. I'd like to generate some sort of p-value of the likely-hood that the distributions are the same. Thanks in advance for your advice. Cheers, Mark Mark Gosink, Ph.D. Head of Computational Biology Scripps Florida 5353 Parkside Drive - RFA Jupiter, FL 33458 tel: 561-799-8921 fax: 561-799-8952 [EMAIL PROTECTED] [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] 3d plotting
I am trying to dp a 3d plot.
I tried persp but my data is not a matrix.
the 3dplot function returns this error, (list) object cannot be coerced to
'double'
heres my code,
td<-read.csv("td.csv", header=TRUE)
price<-read.csv("price.csv", header=TRUE)
contractdate<-read.csv("contractdate.csv", header=TRUE)
library(rgl)
plot3d(td,contractdate,price)
the 3 csv data files have the following format,
1
2
3
4
5
6
...
60,000
basically I have 3 columns, x y and z that have 6 rows (data points) I
want to plot.
-
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and provide commented, minimal, self-contained, reproducible code.
[R] Forestplot
I know there is a function forestplot from rmeta package and also the plot.meta from the meta package and maybe others, but they are rather complicated with extra plot parameters that I do not need and also they process only objects created with other package functions. But I wonder if anyone has a much simpler function using the basic plot to make a forestplot with only a median (or mean) and just the confidence intervals, something like the data below in graphics. thanks Events 2.50% 50% 97.50% A 0.332.4924.96 B 0.251.9 19.56 C 0.341.285.35 D 1.582.945.54 E 0.821.944.71 F 1.043.1810.32 G 0.581.443.72 H 0.040.483.79 I 0.170.672.52 -- View this message in context: http://www.nabble.com/Forestplot-tp14404133p14404133.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Import GAUSS .FMT files
Dear All, Is it possible to import GAUSS .FMT files into R? Thanks for your time. Kind Regards, Pedro N. Rodriguez [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] 3d plotting
> 60,000
I hope that you actually haven't got any comma to separate the
thousands... it separates fields in a csv files (as the "Comma
Separated Values" name may suggest). If so, get rid of the commas.
> the 3dplot function returns this error,
> (list) object cannot be coerced to 'double'
> td<-read.csv("td.csv", header=TRUE)
> price<-read.csv("price.csv", header=TRUE)
> contractdate<-read.csv("contractdate.csv", header=TRUE)
You have to coerce the 1-column dataframes created by read.csv to
numeric vectors or to a 6x3 dataframe.
solution 1:
myData <- cbind(td,contractdate,price)
> library(rgl)
plot3d(mydata)
solution 2:
td <- as.numeric(td)
...
price <- as.numeric(price)
plot3d(td,contractdate,price)
Bye,
ScionForbai
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[R] Specifying starting values in lme (nlme package) using msScale
I am using package nlme and would like to specify initial values for a linear mixed-effects model to help with convergence. I am trying to specify those initial values using the msScale option under ‘control’ in the lme() function: lme(Y ~ X1, random= ~ X1|X2, control=list(msScale=lmeScale)) where, (as far as I understand), lmeScale is a function that can take initial values for parameters. However, I am unsure about how to input those starting values (e.g., what names lme will recognize for fixed and random effects, in what format, and if a partial list of initial values would be acceptable?). Any advice or examples of code inputting starting values would be extremely helpful. I have been unable to find examples myself online. Note, although it may be easier to do this in the lme4 package, I would prefer to use nlme. Thank you for your attention. Sincerely, Carrie Holt, Ph.D., M.Sc., B.Sc.(Honours) University of Washington School of Aquatic & Fishery Sciences Box 355020 Seattle, WA 98195 __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Scatterplot3d model reporting question
Dear Max, I'm guessing that you're actually using scatter3d() in the Rcmdr package rather than scatterplot3d(), since the latter, I believe, doesn't fit regression surfaces. If I'm right, then as it says in ?scatter3d, the smooth surface is fit by the gam() function in the mgcv package using a smoothing spline and there is no explicit equation to examine. See ?gam for more information. I hope this helps, John John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox > -Original Message- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > project.org] On Behalf Of Max > Sent: December-18-07 2:02 PM > To: [EMAIL PROTECTED] > Subject: [R] Scatterplot3d model reporting question > > I've used the scatterplot3d function to graph some data and had it > graph a "smooth" fit. Is there a way to actualy find out the function > of the surface? I've looked through the help and figured out how to get > it to report the following: > > Family: gaussian > Link function: identity > > Formula: > y ~ s(x, z) > > Parametric coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 0.207500.01223 16.97 <2e-16 *** > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Approximate significance of smooth terms: > edf Est.rank F p-value > s(x,z) 8.403 17 9.729 1.76e-14 *** > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > R-sq.(adj) = 0.684 Deviance explained = 70.9% > GCV score = 0.017692 Scale est. = 0.016151 n = 108 > > But I'm still not really sure what I'm looking at, either that or > "smooth" means something different than I thought. Any help would be > great! > > thanks, > > -Max > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] plotting magnitude
I am plotting fishing vessel positions and want these points to be
relative in size to the catch at that point. Is this possible? I am just
begining to use R and my search of the help section didnt help in this
area. Heres what Im using so far
xyplot(data$latdeg~data$londeg |vessek , groups=vessek,
xlim=rev(range(69:77)),ylim=(range(35:42)), data=data,
main=list ("Mackerel catches", cex=1.0),
ylab="latitude", notch=T, varwidth=T,
xlab="longitude", cex.axis=0.5,)
any info would be appreciated
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Re: [R] 3d plotting
that worked.
however Im trying to get a surface countour like persp() would show.
Since I dont have a matrix data set, I assumed that the wireframe function
would do.
since I get an error using wireframe,
no applicable method for "wireframe"
I am using this plot3d. I was under the impression that plot3d would do
similar as wireframe or persp but its not.
any other advice would be great.
Scionforbai <[EMAIL PROTECTED]> wrote:
> 60,000
I hope that you actually haven't got any comma to separate the
thousands... it separates fields in a csv files (as the "Comma
Separated Values" name may suggest). If so, get rid of the commas.
> the 3dplot function returns this error,
> (list) object cannot be coerced to 'double'
> td<-read.csv("td.csv", header=TRUE)
> price<-read.csv("price.csv", header=TRUE)
> contractdate<-read.csv("contractdate.csv", header=TRUE)
You have to coerce the 1-column dataframes created by read.csv to
numeric vectors or to a 6x3 dataframe.
solution 1:
myData <- cbind(td,contractdate,price)
> library(rgl)
plot3d(mydata)
solution 2:
td <- as.numeric(td)
...
price <- as.numeric(price)
plot3d(td,contractdate,price)
Bye,
ScionForbai
-
[[alternative HTML version deleted]]
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Re: [R] Scatterplot3d model reporting question
Dear Max, I'm guessing that you're actually using scatter3d() in the Rcmdr package rather than scatterplot3d(), since the latter, I believe, doesn't fit regression surfaces. If I'm right, then as it says in ?scatter3d, the smooth surface is fit by the gam() function in the mgcv package using a regression spline and there is no explicit equation to examine. I hope this helps, John John Fox, Professor Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 905-525-9140x23604 http://socserv.mcmaster.ca/jfox > -Original Message- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] > project.org] On Behalf Of Max > Sent: December-18-07 2:02 PM > To: [EMAIL PROTECTED] > Subject: [R] Scatterplot3d model reporting question > > I've used the scatterplot3d function to graph some data and had it > graph a "smooth" fit. Is there a way to actualy find out the function > of the surface? I've looked through the help and figured out how to get > it to report the following: > > Family: gaussian > Link function: identity > > Formula: > y ~ s(x, z) > > Parametric coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 0.207500.01223 16.97 <2e-16 *** > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > Approximate significance of smooth terms: > edf Est.rank F p-value > s(x,z) 8.403 17 9.729 1.76e-14 *** > --- > Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > R-sq.(adj) = 0.684 Deviance explained = 70.9% > GCV score = 0.017692 Scale est. = 0.016151 n = 108 > > But I'm still not really sure what I'm looking at, either that or > "smooth" means something different than I thought. Any help would be > great! > > thanks, > > -Max > > __ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting- > guide.html > and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Analyzing Publications from Pubmed via XML
"Armin Goralczyk" <[EMAIL PROTECTED]> wrote in
news:[EMAIL PROTECTED]:
> On 12/18/07, David Winsemius <[EMAIL PROTECTED]> wrote:
>> David Winsemius <[EMAIL PROTECTED]> wrote in
>> news:[EMAIL PROTECTED]:
>>
>> > "Armin Goralczyk" <[EMAIL PROTECTED]> wrote in
>> > news:[EMAIL PROTECTED]:
>>
>> >> I tried the above function with simple search terms and it worked
>> >> fine for me (also more output thanks to Martin's post) but when I
>> >> use search terms attributed to certain fields, i.e. with [au] or
>> >> [ta], I get the following error message:
>> >>> pm.srch()
>> >> 1: "laryngeal neoplasms[mh]"
>> >> 2:
>>
>> > I am wondering if you used spaces, rather than "+"'s? If so then
>> > you may want your function to do more gsub-processing of the input
>> > string.
>>
>> I tried my theory that one would need "+"'s instead of spaces, but
>> disproved it. Spaces in the input string seems to produce acceptable
>> results on my WinXP/R.2.6.1/RGui system even with more complex search
>> strings.
>>
>> --
>>
> It's not the spaces, the problem is the tag (sorry that I didn't
> specify this), or maybe the string []. I am working on a Mac OS X 10.4
> with R version 2.6. Is it maybe a string conversion problem? In the
> following warning strings in the html adress seem to be different:
> Fehler in .Call("RS_XML_ParseTree", as.character(file), handlers,
> as.logical(ignoreBlanks), :
> error in creating parser for
> http://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&ter
> m=laryngeal neoplasms[mh]
> I/O warning : failed to load external entity
> "http%3A//eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi%3Fdb=pubme
> d&term=laryngeal%20neoplasms%5Bmh%5D"
I do not have an up-to-date version of R on my Mac, since I have not yet
upgraded to OSX10.4. I can try with my older version of R, but failure
(or even success) with versions OSX-10.2/R-2.0 is not likely to be very
informative. If you will post an example of the input that is resulting
in the error, I can try it on my WinXP machine. If we cannot reproduce it
there, then it may be more appropriate to take further questions to the
Mac-R mailing list. The error message suggests to me that the fault lies
in the connection phase of the task.
--
David Winsemius
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[R] All anchored series from a vector?
Hi all,
What may be a smart, efficient way to get the following result:
myvector <- c("A","B","C","D","E")
myseries <- miracle(myvector)
myseries
[1]
[[1]] "A"
[2]
[[1]] "A" "B"
[3]
[[1]] "A" "B"
[4]
[[1]] "A" "B" "C"
[5]
[[1]] "A" "B" "C" "D"
[6]
[[1]] "A" "B" "C" "D" "E"
Thanks for any hints,
Joh
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Re: [R] All anchored series from a vector?
Should have been:
> myvector <- c("A","B","C","D","E")
> myseries <- miracle(myvector)
> myseries
> [1]
> [[1]] "A"
> [2]
> [[1]] "A" "B"
> [3]
> [[1]] "A" "B" "C"
> [4]
> [[1]] "A" "B" "C" "D"
> [5]
> [[1]] "A" "B" "C" "D" "E"
Sorry, Joh
Johannes Graumann wrote:
> Hi all,
>
> What may be a smart, efficient way to get the following result:
>
> myvector <- c("A","B","C","D","E")
> myseries <- miracle(myvector)
> myseries
> [1]
> [[1]] "A"
> [2]
> [[1]] "A" "B"
> [3]
> [[1]] "A" "B"
> [4]
> [[1]] "A" "B" "C"
> [5]
> [[1]] "A" "B" "C" "D"
> [6]
> [[1]] "A" "B" "C" "D" "E"
>
> Thanks for any hints,
>
> Joh
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html and provide commented,
> minimal, self-contained, reproducible code.
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Re: [R] All anchored series from a vector?
>From: Johannes Graumann <[EMAIL PROTECTED]>
>Date: 2007/12/18 Tue PM 04:40:37 CST
>To: [EMAIL PROTECTED]
>Subject: [R] All anchored series from a vector?
lapply(1:length(myvector) function(.length) {
c(myvector[1}:myvector[.length])
})
but test it because i didn't.
>Hi all,
>
>What may be a smart, efficient way to get the following result:
>
>myvector <- c("A","B","C","D","E")
>myseries <- miracle(myvector)
>myseries
>[1]
>[[1]] "A"
>[2]
>[[1]] "A" "B"
>[3]
>[[1]] "A" "B"
>[4]
>[[1]] "A" "B" "C"
>[5]
>[[1]] "A" "B" "C" "D"
>[6]
>[[1]] "A" "B" "C" "D" "E"
>
>Thanks for any hints,
>
>Joh
>
>__
>R-help@r-project.org mailing list
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: [R] All anchored series from a vector?
>From: [EMAIL PROTECTED]
>Date: 2007/12/18 Tue PM 02:50:52 CST
>To: Johannes Graumann <[EMAIL PROTECTED]>
>Cc: r-help@r-project.org
>Subject: Re: [R] All anchored series from a vector?
i'm sorry. i tested it afterwards and of course
it had some problems. below is the working version.
myvector<-c("A","B","C","D","E")
result<- lapply(1:length(myvector), function(.length) {
myvector[1:.length]
})
print(result)
>>From: Johannes Graumann <[EMAIL PROTECTED]>
>>Date: 2007/12/18 Tue PM 04:40:37 CST
>>To: [EMAIL PROTECTED]
>>Subject: [R] All anchored series from a vector?
>
>lapply(1:length(myvector) function(.length) {
>c(myvector[1}:myvector[.length])
>})
>
>but test it because i didn't.
>
>
>
>>Hi all,
>>
>>What may be a smart, efficient way to get the following result:
>>
>>myvector <- c("A","B","C","D","E")
>>myseries <- miracle(myvector)
>>myseries
>>[1]
>>[[1]] "A"
>>[2]
>>[[1]] "A" "B"
>>[3]
>>[[1]] "A" "B"
>>[4]
>>[[1]] "A" "B" "C"
>>[5]
>>[[1]] "A" "B" "C" "D"
>>[6]
>>[[1]] "A" "B" "C" "D" "E"
>>
>>Thanks for any hints,
>>
>>Joh
>>
>>__
>>R-help@r-project.org mailing list
>>https://stat.ethz.ch/mailman/listinfo/r-help
>>PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>and provide commented, minimal, self-contained, reproducible code.
>
>__
>R-help@r-project.org mailing list
>https://stat.ethz.ch/mailman/listinfo/r-help
>PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>and provide commented, minimal, self-contained, reproducible code.
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Re: [R] All anchored series from a vector?
miracle <- function(x) { lapply(seq(along=x), function(y) x[1:y]) }
Gabor
On Tue, Dec 18, 2007 at 11:40:37PM +0100, Johannes Graumann wrote:
> Hi all,
>
> What may be a smart, efficient way to get the following result:
>
> myvector <- c("A","B","C","D","E")
> myseries <- miracle(myvector)
> myseries
> [1]
> [[1]] "A"
> [2]
> [[1]] "A" "B"
> [3]
> [[1]] "A" "B"
> [4]
> [[1]] "A" "B" "C"
> [5]
> [[1]] "A" "B" "C" "D"
> [6]
> [[1]] "A" "B" "C" "D" "E"
>
> Thanks for any hints,
>
> Joh
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Csardi Gabor <[EMAIL PROTECTED]>MTA RMKI, ELTE TTK
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Re: [R] plotting magnitude
On Tuesday 18 December 2007, [EMAIL PROTECTED] wrote:
> I am plotting fishing vessel positions and want these points to be
> relative in size to the catch at that point. Is this possible? I am just
> begining to use R and my search of the help section didnt help in this
> area. Heres what Im using so far
>
> xyplot(data$latdeg~data$londeg |vessek , groups=vessek,
> xlim=rev(range(69:77)),ylim=(range(35:42)), data=data,
> main=list ("Mackerel catches", cex=1.0),
> ylab="latitude", notch=T, varwidth=T,
> xlab="longitude", cex.axis=0.5,)
> any info would be appreciated
>
how about scaling your plotting symbols by the sqrt() of their value. or
see ?bubble in the gstat package.
cheers,
Dylan
--
Dylan Beaudette
Soil Resource Laboratory
http://casoilresource.lawr.ucdavis.edu/
University of California at Davis
530.754.7341
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: [R] All anchored series from a vector?
Debugged version:
lapply(1:length(myvector), function(.length) {
myvector[1:.length]
})
Thanks for showing the direction!
Joh
[EMAIL PROTECTED] wrote:
>>From: Johannes Graumann <[EMAIL PROTECTED]>
>>Date: 2007/12/18 Tue PM 04:40:37 CST
>>To: [EMAIL PROTECTED]
>>Subject: [R] All anchored series from a vector?
>
> lapply(1:length(myvector) function(.length) {
> c(myvector[1}:myvector[.length])
> })
>
> but test it because i didn't.
>
>
>
>>Hi all,
>>
>>What may be a smart, efficient way to get the following result:
>>
>>myvector <- c("A","B","C","D","E")
>>myseries <- miracle(myvector)
>>myseries
>>[1]
>>[[1]] "A"
>>[2]
>>[[1]] "A" "B"
>>[3]
>>[[1]] "A" "B"
>>[4]
>>[[1]] "A" "B" "C"
>>[5]
>>[[1]] "A" "B" "C" "D"
>>[6]
>>[[1]] "A" "B" "C" "D" "E"
>>
>>Thanks for any hints,
>>
>>Joh
>>
>>__
>>R-help@r-project.org mailing list
>>https://stat.ethz.ch/mailman/listinfo/r-help
>>PLEASE do read the posting guide
>>http://www.R-project.org/posting-guide.html and provide commented,
>>minimal, self-contained, reproducible code.
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html and provide commented,
> minimal, self-contained, reproducible code.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: [R] All anchored series from a vector?
Elegant. Thanks to you too.
Joh
Gabor Csardi wrote:
> miracle <- function(x) { lapply(seq(along=x), function(y) x[1:y]) }
>
> Gabor
>
> On Tue, Dec 18, 2007 at 11:40:37PM +0100, Johannes Graumann wrote:
>> Hi all,
>>
>> What may be a smart, efficient way to get the following result:
>>
>> myvector <- c("A","B","C","D","E")
>> myseries <- miracle(myvector)
>> myseries
>> [1]
>> [[1]] "A"
>> [2]
>> [[1]] "A" "B"
>> [3]
>> [[1]] "A" "B"
>> [4]
>> [[1]] "A" "B" "C"
>> [5]
>> [[1]] "A" "B" "C" "D"
>> [6]
>> [[1]] "A" "B" "C" "D" "E"
>>
>> Thanks for any hints,
>>
>> Joh
>>
>> __
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html and provide commented,
>> minimal, self-contained, reproducible code.
>
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] All anchored series from a vector?
On Wed, Dec 19, 2007 at 12:01:25AM +0100, Johannes Graumann wrote:
> Debugged version:
> lapply(1:length(myvector), function(.length) {
> myvector[1:.length]
> })
>
> Thanks for showing the direction!
>
> Joh
Note that this fails if length(myvector)==0.
Good to know the corner cases.
Gabor
>
> [EMAIL PROTECTED] wrote:
>
> >>From: Johannes Graumann <[EMAIL PROTECTED]>
> >>Date: 2007/12/18 Tue PM 04:40:37 CST
> >>To: [EMAIL PROTECTED]
> >>Subject: [R] All anchored series from a vector?
> >
> > lapply(1:length(myvector) function(.length) {
> > c(myvector[1}:myvector[.length])
> > })
> >
> > but test it because i didn't.
> >
> >
> >
> >>Hi all,
> >>
> >>What may be a smart, efficient way to get the following result:
> >>
> >>myvector <- c("A","B","C","D","E")
> >>myseries <- miracle(myvector)
> >>myseries
> >>[1]
> >>[[1]] "A"
> >>[2]
> >>[[1]] "A" "B"
> >>[3]
> >>[[1]] "A" "B"
> >>[4]
> >>[[1]] "A" "B" "C"
> >>[5]
> >>[[1]] "A" "B" "C" "D"
> >>[6]
> >>[[1]] "A" "B" "C" "D" "E"
> >>
> >>Thanks for any hints,
> >>
> >>Joh
> >>
> >>__
> >>R-help@r-project.org mailing list
> >>https://stat.ethz.ch/mailman/listinfo/r-help
> >>PLEASE do read the posting guide
> >>http://www.R-project.org/posting-guide.html and provide commented,
> >>minimal, self-contained, reproducible code.
> >
> > __
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide
> > http://www.R-project.org/posting-guide.html and provide commented,
> > minimal, self-contained, reproducible code.
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Csardi Gabor <[EMAIL PROTECTED]>MTA RMKI, ELTE TTK
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] All anchored series from a vector?
Nothing to be sorry about. You suggested a viable solution untested ... my
job to figure it out ;0)
Joh
[EMAIL PROTECTED] wrote:
>>From: [EMAIL PROTECTED]
>>Date: 2007/12/18 Tue PM 02:50:52 CST
>>To: Johannes Graumann <[EMAIL PROTECTED]>
>>Cc: r-help@r-project.org
>>Subject: Re: [R] All anchored series from a vector?
>
> i'm sorry. i tested it afterwards and of course
> it had some problems. below is the working version.
>
>
> myvector<-c("A","B","C","D","E")
>
> result<- lapply(1:length(myvector), function(.length) {
> myvector[1:.length]
> })
>
>
> print(result)
>
>
>
>
>>>From: Johannes Graumann <[EMAIL PROTECTED]>
>>>Date: 2007/12/18 Tue PM 04:40:37 CST
>>>To: [EMAIL PROTECTED]
>>>Subject: [R] All anchored series from a vector?
>>
>>lapply(1:length(myvector) function(.length) {
>>c(myvector[1}:myvector[.length])
>>})
>>
>>but test it because i didn't.
>>
>>
>>
>>>Hi all,
>>>
>>>What may be a smart, efficient way to get the following result:
>>>
>>>myvector <- c("A","B","C","D","E")
>>>myseries <- miracle(myvector)
>>>myseries
>>>[1]
>>>[[1]] "A"
>>>[2]
>>>[[1]] "A" "B"
>>>[3]
>>>[[1]] "A" "B"
>>>[4]
>>>[[1]] "A" "B" "C"
>>>[5]
>>>[[1]] "A" "B" "C" "D"
>>>[6]
>>>[[1]] "A" "B" "C" "D" "E"
>>>
>>>Thanks for any hints,
>>>
>>>Joh
>>>
>>>__
>>>R-help@r-project.org mailing list
>>>https://stat.ethz.ch/mailman/listinfo/r-help
>>>PLEASE do read the posting guide
>>>http://www.R-project.org/posting-guide.html and provide commented,
>>>minimal, self-contained, reproducible code.
>>
>>__
>>R-help@r-project.org mailing list
>>https://stat.ethz.ch/mailman/listinfo/r-help
>>PLEASE do read the posting guide
>>http://www.R-project.org/posting-guide.html and provide commented,
>>minimal, self-contained, reproducible code.
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html and provide commented,
> minimal, self-contained, reproducible code.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Dual Core vs Quad Core
On Tue, 18 Dec 2007, Prof Brian Ripley wrote: > On Tue, 18 Dec 2007, S Ellison wrote: > >> Hiding in the windows faq is the observation that "R's computation is >> single-threaded, and so it cannot use more than one CPU". So multi-core >> should make no difference other than allowing R to run with less >> interruption from other tasks. That is often a significant advantage, >> though. > > Yes, but that is Windows-specific. > > On most other platforms you can benefit from using a multi-threaded BLAS, > such as ATLAS, ACML or Dr Goto's. The speedup for linear algebra can be > substantial (although sometimes it will slow things down). Luke Tierney > has an experimental package to make use of parallel threads for some basic > R computations which may appear in R 2.7.0. There are two experimental packages available in http://www.stat.uiowa.edu/~luke/R/experimental: pnmath, based on OpenMP, and pnmath0, based on basic pthreads. These packages provide parallelized versions of many of the R vectorized math functions. The README files in these packages give more details. OpenMP is I think the way we want to go in the longer term; there are a few configu issues that need sorting out and so in the interim a non OpenMP version might be useful. Best, luke > > It should be possible to use a multi-threaded BLAS under Windows, but I > know no one who has done it. There is a viable pthreads implementation > for Windows, and I've tested Luke's experimental package using it. > > Some compilers' runtimes will be able to use parallel threads for other > tasks. Since all the examples I am aware of are expensive commercial > compilers, I suspect R will make limited use of them. (In particular, > base R does not use the Fortran 9x vector operations at which many of > these features are targeted: we probably would if we routinely used such > compilers.) > > I've had dual-CPU desktops for more than ten years. Given how little > speedup you are likely to get via parallel processing (only under ideal > conditions do the optimized BLASes run >1.5x faster using two CPUs), the > most effective way to make use of multiple CPUs has been to run multiple > jobs: I typically run 3-4 at once to keep the CPUs fully used. > > One way to run multiple R processes to cooperate on a single task is to > use a package such as snow to distribute the load. > > > > Andrew Perrin <[EMAIL PROTECTED]> 18/12/2007 01:13 >>> >> On Mon, 17 Dec 2007, Kitty Lee wrote: >> >>> Dear R-users, >>> >>> I use R to run spatial stuff and it takes up a lot of ram. Runs can >> take hours or days. I am thinking of getting a new desktop. Can R take >> advantage of the dual-core system? >>> >>> I have a dual-core computer at work. But it seems that right now R is >> using only one processor. >>> >>> The new computers feature quad core with 3GB of RAM. Can R take >> advantage of the 4 chips? Or am I better off getting a dual core with >> faster processing speed per chip? >>> >>> Thanks! Any advice would be really appreciated! >>> >>> K. >> >> If I have my information right, R will use dual- or quad-cores if it's >> doing two (or four) things at once. The second core will help a little >> bit >> insofar as whatever else your machine is doing won't interfere with the >> one core on which it's running, but generally things that take a single >> thread will remain on a single core. >> >> As for RAM, if you're doing memory-bound work you should certainly be >> using a 64-bit machine and OS so you can utilize the larger memory >> space. > > They only have 3GB of RAM, which 32-bit OSes can address. The benefits > really come with more than that. > > -- Luke Tierney Chair, Statistics and Actuarial Science Ralph E. Wareham Professor of Mathematical Sciences University of Iowa Phone: 319-335-3386 Department of Statistics andFax: 319-335-3017 Actuarial Science 241 Schaeffer Hall email: [EMAIL PROTECTED] Iowa City, IA 52242 WWW: http://www.stat.uiowa.edu __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] [R-pkgs] Update of the np package (version 0.14-1)
Dear R users, An updated version of the np package has recently been uploaded to CRAN (version 0.14-1). The package is briefly described in a recent issue of Rnews (October, 2007, http://cran.r-project.org/doc/Rnews/Rnews_2007-2.pdf) for those who might be interested. A somewhat more detailed paper that describes the np package is forthcoming in the Journal of Statistical Software (http://www.jstatsoft.org) for those might be interested. A much more thorough treatment of the subject matter can be found in Li, Q. and J. S. Racine (2007), Nonparametric Econometrics: Theory and Practice, Princeton University Press, ISBN: 0691121613 (768 Pages) for those who might be interested (http://press.princeton.edu/titles/8355.html) Information on the np package: This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor datatypes. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca). Changes from version 0.13-1 to 0.14-1: * now use optim rather than nlm for minimisation in single index and smooth coefficient models * fixed bug in klein-spady objective function * regression standard errors are now available in the case of no continuous variables * summary should look prettier, print additional information * tidied up lingering issues with out-of-sample data and conditional modes * fixed error when plotting asymptotic errors with conditional densities * fixed a bug in npplot with partially linear regressions and plot.behavior='data' or 'plot-data' * maximum default number of multistarts is now set to 5 * least-squares cross-validation of conditional densities uses a new, faster algorithm * new, faster algorithm for least-squares cross-validation for both local-constant and local linear regressions. The estimator has changed somewhat: both cross-validation and the estimator use a method of shrinking towards the local constant estimator rather than the standard ridge approach that shrinks towards zero * optimised smooth coefficient code, added ridging * fixed bug in uniform CDF kernel * fixed bug where npindexbw would ignore bandwidth.compute = FALSE and compute bandwidths when supplied with a preexisting bw object * now can handle estimation out of discrete support. * summary would misreport the values of discrete scale factors which were computed with bwscaling = TRUE We are grateful to John Fox, Achim Zeilies, Roger Koenker, and numerous users for their valuable feedback which resulted in an improved version of the package. -- Jeffrey Racine & Tristen Hayfield. -- Professor J. S. Racine Phone: (905) 525 9140 x 23825 Department of EconomicsFAX:(905) 521-8232 McMaster Universitye-mail: [EMAIL PROTECTED] 1280 Main St. W.,Hamilton, URL: http://www.economics.mcmaster.ca/racine/ Ontario, Canada. L8S 4M4 `The generation of random numbers is too important to be left to chance' ___ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] How can I extract the AIC score from a mixed model object produced using lmer?
I am running a series of candidate mixed models using lmer (package lme4)
and I'd like to be able to compile a list of the AIC scores for those
models so that I can quickly summarize and rank the models by AIC. When I
do logistic regression, I can easily generate this kind of list by creating
the model objects using glm, and doing:
> md <- c("md1.lr", "md2.lr", "md3.lr")
> aic <- c(md1.lr$aic, md2.lr$aic, md3.lr$aic)
> aic2 <- cbind(md, aic)
but when I try to extract the AIC score from the model object produced by
lmer I get:
> md1.lme$aic
NULL
Warning message:
In md1.lme$aic : $ operator not defined for this S4 class, returning NULL
So... How do I query the AIC value out of a mixed model object created by
lmer?
<<->><<->><<->><<->><<->><<->><<->>
Peter Singleton
USFS Pacific Northwest Research Station
1133 N. Western Ave.
Wenatchee WA 98801
Phone: (509)664-1732
Fax: (509)665-8362
E-mail: [EMAIL PROTECTED]
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[R] Random forests
Dear all, I would like to use a tree regression method to analyze my dataset. I am interested in the fact that random forests creates in-bag and out-of-bag datasets, but I also need an estimate of support for each split. That seems hard to do in random forests since each tree is grown using a subset of the predictor variables. I was thinking of setting mtry = number of predictor variables, growing several trees, and computing the support for each node as the number of times that a certain predictor variable was chosen for that node. Can this be implemented using random forests? Thanks! Naiara. -- Naiara Pinto PhD Candidate Ecology, Evolution and Behavior University of Texas Austin __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] How can I extract the AIC score from a mixed model object produced using lmer?
You can calculate the AIC as follows:
(fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy))
aic1 <- AIC(logLik(fm1))
Hope this helps.
Dave
On 12/18/07, Peter H Singleton <[EMAIL PROTECTED]> wrote:
>
> I am running a series of candidate mixed models using lmer (package lme4)
> and I'd like to be able to compile a list of the AIC scores for those
> models so that I can quickly summarize and rank the models by AIC. When I
> do logistic regression, I can easily generate this kind of list by creating
> the model objects using glm, and doing:
>
> > md <- c("md1.lr", "md2.lr", "md3.lr")
> > aic <- c(md1.lr$aic, md2.lr$aic, md3.lr$aic)
> > aic2 <- cbind(md, aic)
>
> but when I try to extract the AIC score from the model object produced by
> lmer I get:
>
> > md1.lme$aic
> NULL
> Warning message:
> In md1.lme$aic : $ operator not defined for this S4 class, returning NULL
>
> So... How do I query the AIC value out of a mixed model object created by
> lmer?
>
> <<->><<->><<->><<->><<->><<->><<->>
> Peter Singleton
> USFS Pacific Northwest Research Station
> 1133 N. Western Ave.
> Wenatchee WA 98801
> Phone: (509)664-1732
> Fax: (509)665-8362
> E-mail: [EMAIL PROTECTED]
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
--
David Barron
Said Business School Jesus College
Park End Street Oxford
Oxford OX1 1HP OX1 3DW
01865 288906 01865 279684
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Re: [R] calculating the number of days from dates
On 19/12/2007, at 6:46 AM, bogdan romocea wrote:
>> Sorry for using library instead package, but
>> library() is one command for using packages.
>
> ... which is why all efforts to make folks say "package" instead of >>
> "library" << are doomed to fail, IMHO.
Yes, but it gives so much pleasure to those who appreciate
the distinction to rail at those who don't! :-)
cheers,
Rolf Turner
##
Attention:\ This e-mail message is privileged and confid...{{dropped:9}}
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Re: [R] plotting magnitude
On Dec 18, 2007 2:06 PM, <[EMAIL PROTECTED]> wrote:
> I am plotting fishing vessel positions and want these points to be
> relative in size to the catch at that point. Is this possible? I am just
> begining to use R and my search of the help section didnt help in this
> area. Heres what Im using so far
>
> xyplot(data$latdeg~data$londeg |vessek , groups=vessek,
> xlim=rev(range(69:77)),ylim=(range(35:42)), data=data,
> main=list ("Mackerel catches", cex=1.0),
> ylab="latitude", notch=T, varwidth=T,
> xlab="longitude", cex.axis=0.5,)
> any info would be appreciated
This is pretty easy to do with the ggplot2 package:
library(ggplot2)
qplot(longdeg, latdeg, data = data, facets = . ~ vessek, size = catch)
or maybe
qplot(longdeg, latdeg, data = data, facets = . ~ vessek, size = catch)
+ scale_area()
if you want the area of the points proportional to the catch, rather
than their radius
Hadley
--
http://had.co.nz/
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[R] Clustering Question (Support Vector Clustering)
I am currently designing a clustering algorithm in collaboration with one of my colleagues. For comparison purposes we would like to contrast it with the Support Vector Clustering algorithm of (A. Ben-Hur, D. Horn, H.T. Siegelmann, and V. Vapnik. Support vector clustering. Journal of Machine Learning Research, 2:125-137, 2001). This is supposedly the most powerful unsupervised clustering algorithm available. Unfortunately, we cannot find code for it anywhere. We have tried emailing the authors and they cannot find the code either (one of them has not answer yet). I was wondering, perhaps someone in the R community has implemented it or knows about some implementation for it. We really do not want to implement it ourselves because this will probably delay our paper considerably. One more thing, I am talking about Support vector clustering not classification, algorithms for that we could find in abundance. Thank you for any help. Ionut Florescu [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Multiple plots with single box
Hello,
I am trying to display some harmonic functions in a plot. The kind of
display I have in mind is like the one that cn be obtained by a call
to plot.ts with plot.type = "multiple". The only difference is that I
want a single box containing all the plots instead of one box per
plot. I thought box(which = "outer") would have done the job, but it
didn't.
Below is the code I have used so far. (R 2.5.1, I know, I know...)
Any help is greatly appreciated.
Thank you in advance,
Giovanni
=
### Plot harmonic functions
n <- 6 # even
omega <- 2 * pi / n
par(mfrow = c(n - 1, 1), mar = c(0, 5.1, 0, 5.1), oma = c(3, 1, 2, 1))
for (i in 1:(n/2 - 1)) {
curve(cos(x * i * omega), 0, n, ylim = c(-1.1, 1.1), ylab = "", axes =
FALSE)
points(1:n, cos(i * omega * 1:n))
axis(2); abline(h = 0, col = "lightgrey")
curve(sin(x * i * omega), 0, n, ylim = c(-1.1, 1.1), ylab = "", axes =
FALSE)
points(1:n, sin(i * omega * 1:n))
axis(4); abline(h = 0, col = "lightgrey")
}
curve(cos(x * (n/2) * omega), 0, n, ylim = c(-1.1, 1.1), ylab = "", axes =
FALSE)
points(1:n, rep(c(-1,1), n/2))
axis(1); axis(2); abline(h = 0, col = "lightgrey")
--
Giovanni Petris <[EMAIL PROTECTED]>
Department of Mathematical Sciences
University of Arkansas - Fayetteville, AR 72701
Ph: (479) 575-6324, 575-8630 (fax)
http://definetti.uark.edu/~gpetris/
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Re: [R] Factor Madness
From ?cbind:
Data frame methods
The cbind data frame method is just a wrapper for data.frame(...,
check.names = FALSE). This means that it will split matrix columns in data
frame arguments, and convert character columns to factors unless
stringsAsFactors = TRUE is passed.
(I'm guessing 'spectrum' is a data.frame before the code fragment you've shown)
hope this helps,
Tony Plate
Johannes Graumann wrote:
> Why is class(spectrum[["Ion"]]) after this "factor"?
>
> spectrum <- cbind(spectrum,Ion=rep("",
> nrow(spectrum)),Deviation.AMU=rep(0.0, nrow(spectrum)))
>
> slowly going crazy ...
>
> Joh
>
> __
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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[R] Factor Madness
Why is class(spectrum[["Ion"]]) after this "factor"?
spectrum <- cbind(spectrum,Ion=rep("",
nrow(spectrum)),Deviation.AMU=rep(0.0, nrow(spectrum)))
slowly going crazy ...
Joh
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Re: [R] "gam()" in "gam" package
R-users E-mail: r-help@r-project.org >Please don't ask the same question multiple times! I am really sorry about it. I thought that my first mail did not work. >And no, backfitting and QR are unrelated concepts. You need to read up >on the theory, To derive an additive model, we have two methods: (1) backfitting, (2) solving multiple linear equation using QR decomposition. >If i read the code correctly, lm.wfit is called iteratively in gam.fit, >via the line > fit <- eval(bf.call) >The iteration is necessary to the backfitting algorithm. This iteration seems to be for "iteratively reweighted least squares" not for backfitting. And lm.wfit may solve multiple linear equation using QR decomposition; but I am not sure. -- *[EMAIL PROTECTED]* http://cse.naro.affrc.go.jp/takezawa/intro.html [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] leaps
Thank you very much for the example. I think interactively I could get
something.
But my obstacle is to write an R script that processes my set of data
automatically.
My difficulty is to extract the information that appears on the screen, when
R is operated interactively, from a scripts.
Let me go over some steps to make sure I am doing things right.
Assume my data have been read into the matrix xx. Such a matrix contains a
number of curve samples. I call each curve a cycle.
I am processing one cycle at a time and using regression analysis to find
the frequencies in the single cycle. Since I have many, I have to come up
with an automatic way to do that. The main phases are:
1) run a regression analysis including all possible frequencies
2) use "step" to cut off the non significant frequencies
3) input remaining frequencies from phase (2) to "regsubsets"
In the following I have cut and pasted some code and the information I get
from phase (2) and phase (3). To start with I would like to make sure that I
got the output from (3) right. The ouput of (3) tells me that the highest
R^2 value was reached after 8 iterations and there are only 8 significant
predictors in this model ??? In addition the only significant frequencies
(predictors) left are:
cos1, cos2, cos4, cos7, sin1,sin2, sin3, sin5
I got this information interactively. But I'm in troubles at extracting it
automatically.
Any suggestion ?
Question: Do I have to run "step" in advance of "regsubsets" for a
first-pass model pruning or may I run "regsubsets" on the original model
bringing in all possible frequencies (88 in all as sums of sinuoids) ?
Thank you so much.
Kind regards,
Maura
T <- xx$timestamp[end] - xx$timestamp[start]
nsamples <- end +1 - start
nfr <- ceiling(nsamples/2)
yy <- xx[start:end,"amplitude"]
tt <- xx[start:end,"timestamp"]
cosmat <- matrix(nrow=nsamples,ncol=nfr)
coscol <- NULL
sinmat <- matrix(nrow=nsamples,ncol=nfr)
sincol <- NULL
for(i in 1:nfr){
cosmat[,i] <- cos(tt*2*pi*i/T)
coscol <- c(coscol,paste("cos",i,sep=""))
sinmat[,i] <- sin(tt*2*pi*i/T)
sincol <- c(sincol,paste("sin",i,sep=""))
}
colnames(cosmat) <- coscol
colnames(sinmat) <- sincol
xnam1 <- NULL
xnam1 <- paste(sep="","cosmat[,",1:nfr,"]",collapse="+")
xnam2 <- NULL
xnam2 <- paste(sep="","sinmat[,",1:nfr,"]",collapse="+")
fmla <- as.formula(paste("yy ~ ", paste(xnam1,"+",xnam2,sep="")))
FTmod <- lm(fmla)
stepmod <- step(FTmod, direction="both")
summary(stepmod)
*Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.237808 0.008728 27.248 < 2e-16 ***
cosmat[, 1] -1.011932 0.012390 -81.675 < 2e-16 ***
cosmat[, 2] -0.417265 0.012329 -33.844 < 2e-16 ***
cosmat[, 3] 0.020143 0.012318 1.635 0.106604
cosmat[, 4] 0.081425 0.012397 6.568 8.43e-09 ***
cosmat[, 6] -0.042804 0.012388 -3.455 0.000951 ***
cosmat[, 7] -0.044927 0.012340 -3.641 0.000526 ***
cosmat[, 9] 0.039322 0.012411 3.168 0.002298 **
cosmat[, 11] -0.020710 0.012375 -1.673 0.098831 .
cosmat[, 14] 0.016393 0.012390 1.323 0.190245
cosmat[, 17] -0.016482 0.012374 -1.332 0.187319
cosmat[, 19] 0.016537 0.012387 1.335 0.186306
sinmat[, 1] -0.460705 0.012296 -37.469 < 2e-16 ***
sinmat[, 2] -0.289316 0.012356 -23.416 < 2e-16 ***
sinmat[, 3] -0.049610 0.012367 -4.011 0.000153 ***
sinmat[, 4] 0.023937 0.012289 1.948 0.01 .
sinmat[, 5] 0.045542 0.012406 3.671 0.000476 ***
sinmat[, 6] -0.017782 0.012298 -1.446 0.152790
sinmat[, 12] -0.016093 0.012325 -1.306 0.196038
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08135 on 68 degrees of freedom
Multiple R-Squared: 0.9932, Adjusted R-squared: 0.9914
F-statistic: 549.8 on 18 and 68 DF, p-value: < 2.2e-16*
newmat <- cosmat[,-c(5,8,10,12,13,15,16,18,20:44)]
newmat <- cbind(newmat,sinmat[,-c(7:11,13:44)])
Regmod <- regsubsets(newmat, yy)
rs <- summary(Regmod)
which.max(rs$adjr)
*[1] 8
*rs$which[which.max(rs$adjr), ]
*(Intercept)cos1cos2cos3cos4cos6
TRUETRUETRUE FALSETRUE FALSE
cos7cos9 cos11 cos14 cos17 cos19
TRUE FALSE FALSE FALSE FALSE FALSE
sin1sin2sin3sin4sin5sin6
TRUETRUETRUE FALSETRUE FALSE
sin12
FALSE*
[[alternative HTML version deleted]]
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Re: [R] Factor Madness
Whoops, it looks like there's a typo in ?cbind (R version 2.6.0 Patched
(2007-10-11 r43143)), and I blindly copied it into my message.
That should read (emphasis added):
"and convert character columns to factors unless stringsAsFactors =
***FALSE***"
Here's an example:
> x <- data.frame(X=1:3)
> sapply(cbind(x, letters[1:3]), class)
X letters[1:3]
"integer" "factor"
> sapply(cbind(x, letters[1:3], stringsAsFactors=FALSE), class)
X letters[1:3]
"integer" "character"
>
Thanks to Mark Leeds for pointing that out to me in a private message!
(I see this still in the source at
https://svn.r-project.org/R/trunk/src/library/base/man/cbind.Rd -- is that
the right place to look for the latest source to make sure it hasn't been
fixed already?)
-- Tony Plate
Tony Plate wrote:
> From ?cbind:
>
> Data frame methods
> The cbind data frame method is just a wrapper for data.frame(...,
> check.names = FALSE). This means that it will split matrix columns in data
> frame arguments, and convert character columns to factors unless
> stringsAsFactors = TRUE is passed.
>
> (I'm guessing 'spectrum' is a data.frame before the code fragment you've
> shown)
>
> hope this helps,
>
> Tony Plate
>
> Johannes Graumann wrote:
>> Why is class(spectrum[["Ion"]]) after this "factor"?
>>
>> spectrum <- cbind(spectrum,Ion=rep("",
>> nrow(spectrum)),Deviation.AMU=rep(0.0, nrow(spectrum)))
>>
>> slowly going crazy ...
>>
>> Joh
>>
>> __
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> __
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
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[R] strange timings in convolve(x,y,type="open")
Dear R-ophiles,
I've found something very odd when I apply convolve
to ever larger vectors. Here is an example below
with vectors ranging from 2^11 to 2^17. There is
a funny bump up at 2^12. Then it gets very slow at 2^16.
> for( i in 11:20 )print( system.time(convolve(1:2^i,1:2^i,type="o")))
user system elapsed
0.002 0.000 0.002
user system elapsed
0.373 0.002 0.375
user system elapsed
0.014 0.001 0.016
user system elapsed
0.031 0.002 0.034
user system elapsed
0.126 0.004 0.130
user system elapsed
194.095 0.013 194.185
user system elapsed
0.345 0.011 0.356
This example is run on a fedora machine with 64 bits. I hit the same
wall at 2^16 on a Macbook (Intel processor I think). The fedora machine
is running R 2.5.0. That's a bit old (April 07) but I saw no mention of
this speed
problem in some web searching, and it's not mentioned in the 2.6
what's new notes.
I've rerun it and found the same bump at 12 and wall at 16.
The timing at 2^16 can change appreciably. In one
other case it was about 270 user, 271 elapsed.
The 2^18 case ran for hours without ever finishing.
At first I thought that this was a memory latency issue. Maybe it
is. But that makes it hard to explain why 2^17 works better than
2^16. I've seen that three times now, so I'm almost ready to call it
reproducible.
Also, one of the machines I'm using has lots of memory. Maybe it's
a cache issue ... but that still does not explain why 2^12 is slower
than 2^13 or 2^16 is slower than 2^17.
I've checked by running convolve without type="o" and I don't
see the wall. Similarly fft does not have that problem.
Here's an example without type="open"
> for( k in 11:20)print(system.time( convolve( 1:2^k,1:2^k)))
user system elapsed
0.001 0.000 0.000
user system elapsed
0.001 0.000 0.001
user system elapsed
0.002 0.000 0.002
user system elapsed
0.004 0.000 0.004
user system elapsed
0.009 0.001 0.010
user system elapsed
0.017 0.001 0.018
user system elapsed
0.138 0.005 0.143
user system elapsed
0.368 0.012 0.389
user system elapsed
1.010 0.032 1.051
user system elapsed
1.945 0.069 2.015
This is more what I expected. Something like N or N log(N) , with
the difference hard to discern in granularity and noise.
The convolve function is not very big (see below). When type is
not specified, it defaults to "circular". So my guess is that something
mysterious might be happening inside the first else clause below,
at least on some architectures.
-Art Owen
> convolve
function (x, y, conj = TRUE, type = c("circular", "open", "filter"))
{
type <- match.arg(type)
n <- length(x)
ny <- length(y)
Real <- is.numeric(x) && is.numeric(y)
if (type == "circular") {
if (ny != n)
stop("length mismatch in convolution")
}
else {
n1 <- ny - 1
x <- c(rep.int(0, n1), x)
n <- length(y <- c(y, rep.int(0, n - 1)))
}
x <- fft(fft(x) * (if (conj)
Conj(fft(y))
else fft(y)), inv = TRUE)
if (type == "filter")
(if (Real)
Re(x)
else x)[-c(1:n1, (n - n1 + 1):n)]/n
else (if (Real)
Re(x)
else x)/n
}
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Re: [R] "gam()" in "gam" package
R-users
E-mail: r-help@r-project.org
> This iteration seems to be for "iteratively reweighted least squares" not
>for backfitting. And lm.wfit may solve multiple linear equation using
>QR decomposition; but I am not sure.
Let me tell you something about my guess above.
The iteration below is from 1 to maxit.
for (iter in 1:maxit) {
--
fit <- eval(bf.call)
--
}
The help of "gam.control()" says:
maxit: maximum number of local scoring iterations
bf.maxit: maximum number of backfitting iterations
Therefore the iteration above is local scoring iteration not
backfitting iteration.
The first line of "lm.wfit()" is:
function (x, y, w, offset = NULL, method = "qr", tol = 1e-07,
singular.ok = TRUE, ...)
There is no "bf.maxit" or its equivalent in these arguments. And it
contains 'method = "qr"'; it may mean that this routine solves
a multiple linear equation using QR decomposition.
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