Re: [R] IF LOOOP
Hi Matthias.
The first thing I notice is that the vector r:
r<-c(1.1717118,1.1605215,1.1522907,1.1422830,1.1065277,1.1165451,
1.1163768,1.1048872,1.0848836,1.0627211,1.0300964,1.0296879,
1.0308194,1.0518188,1.0657229,1.0685514,1.0914881,1.1042577,
1.1039351,1.0880163)
doesn't contain the value 0.990956. If it ain't there, you can't
remove it. It has already been suggested that the failure of any
explicit value like 0.990956 to equal its displayed representation is
due to machine precision. I'll guess that you really want to remove a
range of values, something like those less than 1.00. If so, try
this:
r[r>=1]
Jim
On Wed, Jun 14, 2017 at 6:46 AM, matthias worni wrote:
> Hey
>
> This should be a rather simple quesiton for some of you. I want to make
> some progress in looping...
> I have the vector r, which contains single values --> see below:
>
> r
> [1] 1.1717118 1.1605215 1.1522907 1.1422830 1.1065277 1.1165451 1.1163768
> 1.1048872 1.0848836 1.0627211
> [11] 1.0300964 1.0296879 1.0308194 1.0518188 1.0657229 1.0685514 1.0914881
> 1.1042577 1.1039351 1.0880163
>
>
> I would like to take out simply the value "0.990956" from the vector,
> printing out the rest of it. The code is from the internet but does not
> seem to work for my vector. Can't figure out why... Thanks for the help
>
> r <- as.vector(lw)
> count=0
> for (i in r) {
> if(i == 0.990956) {
> break
> }
> print(i)
> }
>
>
> Best
> Matthias
>
> [[alternative HTML version deleted]]
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
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> and provide commented, minimal, self-contained, reproducible code.
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Re: [R] reading data
You need to provide reproducible data. What does the file contain? Why
are you using 'sep=' when reading fixed format. You might be able to
attach the '.txt' to your email to help with the problem. Also you did not
state what the differences that you are seeing. So help us out here.
Jim Holtman
Data Munger Guru
What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.
On Tue, Jun 13, 2017 at 5:09 PM, Ashta wrote:
> Hi all,
>
> I am using R to extract data on a regular basis.
> However, sometimes using the same script and the same data I am
> getting different observation.
> The library I am using and how I am reading it is as follows.
>
> library(stringr)
> namelist <- file("Adress1.txt",encoding="ISO-8859-1")
> Name <- read.fwf(namelist,
> colClasses="character", skip=2,sep="\t",fill=T,
> width =c(2,8,1,1,1,1,1,1,9,5)+1,col.names=ccol)
>
> Can some one suggest me how track the issue?
> Is it the library issue or Java issue?
> May I read as free format instead of fixed format?
>
> Thank you in advance
>
> __
> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
> 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.
>
[[alternative HTML version deleted]]
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Re: [R] [FORGED] IF LOOOP
> On Jun 13, 2017, at 4:10 PM, Rolf Turner wrote:
>
> On 14/06/17 08:46, matthias worni wrote:
>> Hey
>> This should be a rather simple quesiton for some of you. I want to make
>> some progress in looping...
>> I have the vector r, which contains single values --> see below:
>> r
>> [1] 1.1717118 1.1605215 1.1522907 1.1422830 1.1065277 1.1165451 1.1163768
>> 1.1048872 1.0848836 1.0627211
>> [11] 1.0300964 1.0296879 1.0308194 1.0518188 1.0657229 1.0685514 1.0914881
>> 1.1042577 1.1039351 1.0880163
>> I would like to take out simply the value "0.990956" from the vector,
>> printing out the rest of it. The code is from the internet but does not
>> seem to work for my vector.
I'm not sure that the source of this code should be considered a trusted
foundation for learning R. You should not be using for-loops to modify vectors
in this manner.
>> Can't figure out why... Thanks for the help
>> r <- as.vector(lw)
That's probably not needed.
>> count=0
>> for (i in r) {
>> if(i == 0.990956) {
>> break
I suspect you meant to use:
?'next' # since `break` completely terminates a for-loop.
The ?'help' page has all the "control structures": `for`, `repeat`, `while` and
associated boundaries and terminators
Both `break` and `next` are reserved words (names of functions, control
tokens), so using the `?` operator requires quoting.
Also that for-next loop would do _nothing_ to the value of r. Printing would
not modify the value of `r`.
You should read:
?Reserved
?'for' # since `for` is also reserved
?print
>> }
>> print(i)
>> }
>
> FAQ 7.31
After following Rolf's advice ... Try:
r[ all.equal(r, 0.990956) ]
>
> cheers,
>
> Rolf Turner
Further note to matthias:
All caps in Subject is considered poor form, as is posting in HTML.
--
David Winsemius
Alameda, CA, USA
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Re: [R] [FORGED] IF LOOOP
On 14/06/17 08:46, matthias worni wrote:
Hey
This should be a rather simple quesiton for some of you. I want to make
some progress in looping...
I have the vector r, which contains single values --> see below:
r
[1] 1.1717118 1.1605215 1.1522907 1.1422830 1.1065277 1.1165451 1.1163768
1.1048872 1.0848836 1.0627211
[11] 1.0300964 1.0296879 1.0308194 1.0518188 1.0657229 1.0685514 1.0914881
1.1042577 1.1039351 1.0880163
I would like to take out simply the value "0.990956" from the vector,
printing out the rest of it. The code is from the internet but does not
seem to work for my vector. Can't figure out why... Thanks for the help
r <- as.vector(lw)
count=0
for (i in r) {
if(i == 0.990956) {
break
}
print(i)
}
FAQ 7.31
cheers,
Rolf Turner
--
Technical Editor ANZJS
Department of Statistics
University of Auckland
Phone: +64-9-373-7599 ext. 88276
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[R] IF LOOOP
Hey
This should be a rather simple quesiton for some of you. I want to make
some progress in looping...
I have the vector r, which contains single values --> see below:
r
[1] 1.1717118 1.1605215 1.1522907 1.1422830 1.1065277 1.1165451 1.1163768
1.1048872 1.0848836 1.0627211
[11] 1.0300964 1.0296879 1.0308194 1.0518188 1.0657229 1.0685514 1.0914881
1.1042577 1.1039351 1.0880163
I would like to take out simply the value "0.990956" from the vector,
printing out the rest of it. The code is from the internet but does not
seem to work for my vector. Can't figure out why... Thanks for the help
r <- as.vector(lw)
count=0
for (i in r) {
if(i == 0.990956) {
break
}
print(i)
}
Best
Matthias
[[alternative HTML version deleted]]
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[R] S-mode PCAs
Hi all, I have a file of average SWE observations for 40 years at over 4,000 points and am attempting to do a spatiotemporal analysis of the data using PCA, much like this paper did using snow depth: http://journals.ametsoc.org/doi/pdf/10.1175/1520-0442%281998%29011%3C0856%3ATCIRWS%3E2.0.CO%3B2 I have followed the code in the link below by my loadings are far too small (For example, the paper listed above had loading above 0.5 and below -0.5 while my loadings for PC1 go between 0.02 and -0.03). https://stackoverflow.com/questions/41022927/principal-component-analysis-pca-of-time-series-data-spatial-and-temporal-pat I have standardized the data in the prcomp function so that a correlation matrix is used in my study, just like the paper above. Does anyone have any idea why my loadings might be so off and any suggestions regarding the PCA functions in R to help solve this problem? Thanks in advance, Dan -- *Daniel J. Vecellio* *Ph.D. Student - Climate Science Lab, **Department of Geography, **Texas A&M University* [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] reading data
Hi all,
I am using R to extract data on a regular basis.
However, sometimes using the same script and the same data I am
getting different observation.
The library I am using and how I am reading it is as follows.
library(stringr)
namelist <- file("Adress1.txt",encoding="ISO-8859-1")
Name <- read.fwf(namelist,
colClasses="character", skip=2,sep="\t",fill=T,
width =c(2,8,1,1,1,1,1,1,9,5)+1,col.names=ccol)
Can some one suggest me how track the issue?
Is it the library issue or Java issue?
May I read as free format instead of fixed format?
Thank you in advance
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Classification and Regression Tree for Survival Analysis
On Tue, 13 Jun 2017, Dimitrie Siriopol via R-help wrote: I am trying to use the CART in a survival analysis. I have three variables of interest (all 3 ordinal - x, y and z, each of them with 5 categories) from which I want to make smaller groups (just an example 1st category from X variable with the 2nd and 3rd categories from the Y category and 2, 3 and 4 categories from the Z category etc) based on their, let's say, association with mortality. Now I would also want that this analysis to be adjusted for a number of variables (that I don't want to incorporate in the decision tree, just to take them into consideration in the relationship between the 3 variables and the outcome; I would also want to mention that for this confounders I have missing values - how should this be deal with?), this survival analysis to be stratified and also to use clusters. I have tried party and rpart packages, but I don't seem to get how to properly do what I want. I don't think that such an analysis is available "out of the box". In principle, you can iterate between (a) estimating a survival regression with the confounders - given the groups from the tree, and (b) estimating the tree - given an offset in the survival regression for the confounders. Such a strategy is implemented in the palmtree() function from the "partykit" package - however only for lm() and glm() models, not for survreg(). But the same idea could be applied in that case as well, e.g., using a Weibull distribution. For incorporating stratification/clustering one could either use clustered inference in the variable selection or add some random effect. For lm/glm this is provided in the package "glmertree" but I don't think there are readily available code blocks to do the same for a survival response. And as for the missing values in the confounders: I can't think of a good strategy for this. One could try generic imputation strategies but it's rather unlikely that this does not affect the subsequent regression plus tree selection process. References for palmtree and glmertree: http://arxiv.org/abs/1612.07498 http://EconPapers.RePEc.org/RePEc:inn:wpaper:2015-10 Thank you [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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 -- To UNSUBSCRIBE and more, see 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] fedback from foreach
>
> On 13 Jun 2017, at 23:05, Bond, Stephen wrote:
>
> Hi useRs,
>
> I am running a foreach loop and hoped to get a small message when it hits a
> multiple of 1000, but it does not work.
>
>p <- foreach(i=1:1, .combine='c') %dopar% {
>if(i%%1000==0) print(i)
>sqrt(i)
>}
>
> What is the proper way to do it.
> Thanks everybody.
>
> Stephen B
https://stackoverflow.com/questions/10903787/how-can-i-print-when-using-dopar
https://www.r-bloggers.com/monitoring-progress-inside-a-foreach-loop/
https://sumidiot.wordpress.com/2011/11/05/printing-in-foreachs-dopar/
[R] fedback from foreach
Hi useRs,
I am running a foreach loop and hoped to get a small message when it hits a
multiple of 1000, but it does not work.
p <- foreach(i=1:1, .combine='c') %dopar% {
if(i%%1000==0) print(i)
sqrt(i)
}
What is the proper way to do it.
Thanks everybody.
Stephen B
[[alternative HTML version deleted]]
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Re: [R] understanding I() in lmer formula
If you don't get a prompt reply here, you might do better posting this on the r-sig-mixed-models list (for obvious reasons). Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Jun 13, 2017 at 11:25 AM, Don Cohen wrote: > Is there a difference between I(x*y) and I(y*x) ? > I have a call to lmer that results in this complaint: > Error in is.alpha2.subordinate * ~z.min.co.res : > non-numeric argument to binary operator > when I change this line: > I(is.alpha2.subordinate*z.min.co.res)+ > to this: > I(z.min.co.res*is.alpha2.subordinate)+ > the complaint goes away. > I'd like to understand why. > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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 -- To UNSUBSCRIBE and more, see 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] understanding I() in lmer formula
Is there a difference between I(x*y) and I(y*x) ? I have a call to lmer that results in this complaint: Error in is.alpha2.subordinate * ~z.min.co.res : non-numeric argument to binary operator when I change this line: I(is.alpha2.subordinate*z.min.co.res)+ to this: I(z.min.co.res*is.alpha2.subordinate)+ the complaint goes away. I'd like to understand why. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Classification and Regression Tree for Survival Analysis
1. Please read and follow the posting guide below. Your post does not meet the guidelines. 2. Search before posting! e.g. on rseek.org: "Regression trees survival analysis" in which you will find: https://cran.r-project.org/web/views/MachineLearning.html -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Jun 13, 2017 at 6:02 AM, Dimitrie Siriopol via R-help wrote: > I am trying to use the CART in a survival analysis. I have three variables of > interest (all 3 ordinal - x, y and z, each of them with 5 categories) from > which I want to make smaller groups (just an example 1st category from X > variable with the 2nd and 3rd categories from the Y category and 2, 3 and 4 > categories from the Z category etc) based on their, let's say, association > with mortality. > Now I would also want that this analysis to be adjusted for a number of > variables (that I don't want to incorporate in the decision tree, just to > take them into consideration in the relationship between the 3 variables and > the outcome; I would also want to mention that for this confounders I have > missing values - how should this be deal with?), this survival analysis to be > stratified and also to use clusters. > I have tried party and rpart packages, but I don't seem to get how to > properly do what I want. > Thank you > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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 -- To UNSUBSCRIBE and more, see 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] Classification and Regression Tree for Survival Analysis
I am trying to use the CART in a survival analysis. I have three variables of interest (all 3 ordinal - x, y and z, each of them with 5 categories) from which I want to make smaller groups (just an example 1st category from X variable with the 2nd and 3rd categories from the Y category and 2, 3 and 4 categories from the Z category etc) based on their, let's say, association with mortality. Now I would also want that this analysis to be adjusted for a number of variables (that I don't want to incorporate in the decision tree, just to take them into consideration in the relationship between the 3 variables and the outcome; I would also want to mention that for this confounders I have missing values - how should this be deal with?), this survival analysis to be stratified and also to use clusters. I have tried party and rpart packages, but I don't seem to get how to properly do what I want. Thank you [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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] Σχετ: plotting gamm results in lattice
Hi Duncan
Thank you very much for your help.
The function model$gam is correct; it applies to gamm4 as well.
I believe that it is the smooth term the reason that the plots from lattice are
not the same as the plots from the plot(model$gam) but at least I can get the
smooth line and the confidence intervals.
Kind regards,Maria
Στις 4:26 μ.μ. Δευτέρα, 12 Ιουνίου 2017, ο/η Duncan Mackay
έγραψε:
Hi Maria
If you have problems just start with a small model with predictions and then
plot with xyplot
the same applies to xyplot
Try
library(gamm4)
spring <- dget(file = "G:/1/example.txt")
str(spring)
'data.frame': 11744 obs. of 11 variables:
$ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2
3 3 3 4 4 ...
$ SITE_ID : int 157166 157166 1636 1636 1636 1635 1635 1635
134261 1631 ...
$ Year : int 2011 2014 2013 2006 2003 2002 2013 2005 2013
2006 ...
$ Q95 : num 100 100 80 80 80 98 98 98 105 105 ...
$ LIFE.OE_spring : num 1.02 1.03 1.02 1.06 1.06 1.07 1.12 1.05 1.14
1.05 ...
$ super.end.group : Factor w/ 6 levels "B","C","D","E",..: 1 1 3 3 3 2 2
2 4 4 ...
$ X.urban.suburban : num 0 0 0.07 0.07 0.07 0.53 0.53 0.53 8.07 8.07 ...
$ X.broadleaved_woodland: num 2.83 2.83 10.39 10.39 10.39 ...
$ X.CapWks : num 0 0 0 0 0 0 0 0 0 0 ...
$ Hms_Poaching : int 0 0 10 10 10 0 0 0 0 0 ...
$ Hms_Rsctned : int 0 0 0 0 0 0 0 0 0 0 ...
model<-
gamm4(LIFE.OE_spring~s(Q95,
by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland
+X.urban.suburban+X.CapWks, data=spring,
random=~(1|WATERBODY_ID/SITE_ID))
Warning message:
Some predictor variables are on very different scales: consider rescaling
# plot(model$gam, page=1, font.lab=2, xlab="Residual Q95")
M <-predict(model$gam,type="response",se.fit=T)
# joining the dataset and the predictions keep it "in house" and on path
springP = cbind(spring, M)
springP$upper = with(springP,fit+1.96*se.fit)
springP$lower = with(springP,fit-1.96*se.fit)
str(springP)
'data.frame': 11744 obs. of 15 variables:
$ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2
3 3 3 4 4 ...
$ SITE_ID : int 157166 157166 1636 1636 1636 1635 1635 1635
134261 1631 ...
$ Year : int 2011 2014 2013 2006 2003 2002 2013 2005 2013
2006 ...
$ Q95 : num 100 100 80 80 80 98 98 98 105 105 ...
$ LIFE.OE_spring : num 1.02 1.03 1.02 1.06 1.06 1.07 1.12 1.05 1.14
1.05 ...
$ super.end.group : Factor w/ 6 levels "B","C","D","E",..: 1 1 3 3 3 2 2
2 4 4 ...
$ X.urban.suburban : num 0 0 0.07 0.07 0.07 0.53 0.53 0.53 8.07 8.07 ...
$ X.broadleaved_woodland: num 2.83 2.83 10.39 10.39 10.39 ...
$ X.CapWks : num 0 0 0 0 0 0 0 0 0 0 ...
$ Hms_Poaching : int 0 0 10 10 10 0 0 0 0 0 ...
$ Hms_Rsctned : int 0 0 0 0 0 0 0 0 0 0 ...
$ fit : num [1:11744(1d)] 1.03 1.04 1.04 1.02 1.01 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr "1" "2" "3" "4" ...
$ se.fit : num [1:11744(1d)] 0.00263 0.00266 0.00408 0.00408
0.00411 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr "1" "2" "3" "4" ...
$ upper : num [1:11744(1d)] 1.03 1.04 1.05 1.03 1.02 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr "1" "2" "3" "4" ...
$ lower : num [1:11744(1d)] 1.02 1.03 1.03 1.01 1 ...
..- attr(*, "dimnames")=List of 1
.. ..$ : chr "1" "2" "3" "4" ...
# this produces a mess of lines for the upper and lower
xyplot(fitted(model$gam) ~ Q95 |super.end.group, data = springP,
lower = springP$lower,
upper = springP$upper,
subscripts = TRUE,
panel = function(x,y, upper, lower, subscripts, ...){
panel.xyplot(x,y, type="smooth")
panel.xyplot(x[order(x)], upper[subscripts][order(x)], lty=2,
col="red")
panel.xyplot(x[order(x)], lower[subscripts][order(x)], lty=2,
col="red")
#panel.loess(x,y,...) # have not tried to fix these lines-
depends on what you want to do
#panel.rug(x = x[is.na(y)], y = y[is.na(x)])
}
)
# smoothing them produces reasonable lines
xyplot(fitted(model$gam) ~ Q95 |super.end.group, data = springP,
lower = springP$lower,
upper = springP$upper,
subscripts = TRUE,
panel = function(x,y, upper, lower, subscripts, ...){
panel.xyplot(x,y, type="smooth")
panel.xyplot(x[order(x)], upper[subscripts][order(x)], type =
"smooth", lty=2, col="red")
panel.xyplot(x[order(x)], lower[subscripts][order(x)], type =
"smooth", lty=2, col="red")
#panel.loess(x,y,...)
#panel.rug(x = x[is.na(y)], y = y[is.na(x)])
}
)
# by newdata = ...
# take a cross-section of data - reduces the amount of data to be plotte
[R] Mean correlation within cluster
Hello all, I'd like to calculate the mean correlation within a cluster and understand if it's significantly >0. I'm using packages 'geomorph' and 'paleomorph'. #Simulate an array A <- array ( rep ( 1 : 36 , by = 4 ), dim = c ( 12 , 3 , 4 )) #Load 'geomorph' package and superimpose coordinates test.gpa <- gpagen ( A , print.progress = FALSE ) #Load 'paleomorph' and generate covariance and correlation matrices cvmatrix <- dotcvm ( test.gpa $ coords ) corrmatrix <- dotcorr ( test.gpa $ coords ) Then I do a clustering with Ward method and euclidean distance, using the cvmatrix and I get a dendrogram. This part is not the problem, so I'll go directly to what I want. I would like to calculate the mean correlation between the elements of each cluster. Since clustering methods will mandatorily produce clusters, I'd like to know if the elements of my clusters are correlated (I mean, if the clusters are valid). I believe this might not be very complicated, given that I have all the elements. I just don't know how to do it on R. I tried to do the clustering with p-values for each cluster, with pvclust(), but I'm following a paper in which the authors test the significancy of the clusters by assessing the mean correlation of the elements within each cluster. I'd like to compare this method with the one from pvclust(). Thank you in advance. Best regards, Sérgio. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.
