[R] Tinn-R keyboard problem
The right hand side of my keyboard (Enter, shift, arrows, etc.) just stopped working only when I am using Tinn-R. It works perfectly fine with any other application. To check if there was a problem with my keyboard I connected an external keyboard and the same keys did not work with that either. Is there anyone who had the same problem before and know the solution to this problem? Thanks, Sermin [[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] how to call a C-library in R
Hi Cindy, Take a look at the following. This should help get you started. http://www.stat.umn.edu/~charlie/rc/ rcoder cindy Guo wrote: > > Hi, everyone, > > I need to use a C library. But since I have little experience in C, I want > to call this C library in R and program the rest in R. Does anyone know > how > to do this? > > Thanks, > > Cindy > > [[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. > > -- View this message in context: http://www.nabble.com/how-to-call-a-C-library-in-R-tp19125202p19126668.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.
Re: [R] [BioC] similarity between two gene lists with varied length
Actually, making this question more nontrivial is, 1. the lengths for the two gene lists are very different; 2. I could add another list as gene weight to any gene, for example, of union of two gene lists. On Sun, Aug 24, 2008 at 9:15 AM, Shannon, William <[EMAIL PROTECTED]>wrote: > First thought is a similarity can be based on the ratio of the number of > genes in the intersection of the two lists divided by the number of genes in > the union of the two lists. If the two lists are identical the similarity > is 1 and if they have no genes in common they have a similarity of 0. Of > course this won't take into account the length of the gene lists. > > You would have to think through what would happen to the similarity for > cases where some genes are in both lists. > > > Bill Shannon > Associate Professor of Biostatistics in Medicine > Washington University School of Medicine > > President-Elect, Classification Society > > > From: [EMAIL PROTECTED] [ > [EMAIL PROTECTED] On Behalf Of Weiwei Shi [ > [EMAIL PROTECTED] > Sent: Saturday, August 23, 2008 7:55 PM > To: [EMAIL PROTECTED] > Cc: Bioconductor > Subject: [BioC] similarity between two gene lists with varied length > > Dear listers, > > a little off-topic: > > I am looking for and compare algorithms which can calculate "distance" or > "similarity" between two gene lists with different lengths. > > Any paper, any implementation in R and any suggestion is welcome! > > Thanks, > > -- > Weiwei Shi, Ph.D > Research Scientist > GeneGO, Inc. > > "Did you always know?" > "No, I did not. But I believed..." > ---Matrix III > > [[alternative HTML version deleted]] > > ___ > Bioconductor mailing list > [EMAIL PROTECTED] > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III [[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] similarity between two gene lists with varied length
Dear listers, a little off-topic: I am looking for and compare algorithms which can calculate "distance" or "similarity" between two gene lists with different lengths. Any paper, any implementation in R and any suggestion is welcome! Thanks, -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III [[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] Adding a common legend to an arrangement of plots
Dear Users, I would like to know if there is a way to add a common
legend to an arrangement of plots. In the example below, I get four
plots in my device. each one has a density for 1995 and one for 2006.
I have found that using legend or smartlegend I can add a legend to
each plot, but I am looking for something in the spirit of mtext. That
is, putting the legend anywhere I want on the device. In my situation
I have:
par(mfrow=c(2,2), ann = FALSE)
p.names <- c("1","2","3","4")
lapply(p.names, function(x) {
d1 <- density(rnorm(100,0,1))
d2 <- density(rnorm(100,.3,1.2))
plot(range(d1$x, d2$x), range(d1$y, d2$y), type = "n", xlab = NULL,
ylab = NULL)
lines(d1, col = "black", lty = 2)
lines(d2, col = "black", lty = 1)
})
mtext("Learning R the hard way", side = 1, line=-1.5, outer=TRUE)
smartlegend(x= "center", y="top", c("1995","2006"), lty=2:1)
the last line puts the legend on the lower-right plot. I'd like to put
it in the middle - or top of the device. Any suggestion?
Thanks!
Nelson
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Re: [R] how to call a C-library in R
On Sat, Aug 23, 2008 at 3:56 PM, cindy Guo <[EMAIL PROTECTED]> wrote: > Hi, everyone, > > I need to use a C library. But since I have little experience in C, I want > to call this C library in R and program the rest in R. Does anyone know how > to do this? Yes, I do. Assuming that your next question will be "How do you do this?" I will answer that one too. You begin by reading the manual "Writing R Extensions". If, as you indicate, you don't know much about programming in C that manual will be a tough read, but that is the nature of the beast. You want to do something complicated and you will need to learn quite a bit to be able to do it. __ 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] open source data mining on tap anyone ?
http://decisionstats.com/2008/ohri/ Some time back, I had created a framework for data mining through on demand cloud computing. This is the next version- it is free to use for all, with only authorship credit back to meâ¦â¦â¦â¦.. NOTE - i DO retain authorship credit rights (even though it is a GPL framework) for free to anyone to use commercially It tries to do away with fixed server ,desktop costs AND fixed software costs in softwares which are used for data mining ,stats and analytics and have huge huge per CPU count annual license fees The modified Ohri Framework tries to mash the following 0) HTTPS rather than HTTP 1) Encryption and Compression Software for data transfer (like PGP) 2) Open source stats package like R in cloud computer (like Amazon EC2 or Rightscale with hadoop) 3) GUI to make it easy to use (like Rattle GUI and PMML Package) 4) A Data Mining Open Source Package (like Rapid Miner or Splunk) 5) RIA Graphics (like Silverlight ) 6) Secure Output to cloud computing devices (like Google Docs) 7) Billing or Priced at simple cost plus X % (where simple cost can be like 0.85 cent /per instance hour or more depending on usage and X should not be more than 15 %) 8) Open source sharing of all code to ensure community sandboxing Intention is to remove fixed computing costs of servers and desktops to normal PC's (Ubuntu Linux ) with (Firefox or IE Explorer ) access to secure data mining on demand . On tap demand mining to anyone in the world without going for the big license purchases/renewals (software expenses) or big hardware purchases (which become obsolete in 2-3 years). i now read sas -l posts only on web, so any reply not specifically marked to me , would remain unanswered till ...;) -- Regards, Ajay Ohri http://tinyurl.com/liajayohri [[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] how to call a C-library in R
Hi, everyone, I need to use a C library. But since I have little experience in C, I want to call this C library in R and program the rest in R. Does anyone know how to do this? Thanks, Cindy [[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] graphs for pretest data
On Sat, Aug 23, 2008 at 1:10 PM, Michael Kubovy <[EMAIL PROTECTED]> wrote:
> Dear Juliet,
>
> Perhaps start here:
>
> require(lattice)
> mwpp <- data.frame(y = c(43,42,26,39,60,60,46,40,41,36,42,54,
>58,43,46,56,81,56,70,70,44,52,81,59,69,68),
>sex = rep(c(rep('men', 14), rep('women', 12))),
>pp = c(rep(c('pre', 'post'), each = 7), rep(c('pre', 'post'), each =
> 6)),
>sub = c(1:7, 1:7, 8:13, 8:13))
Or in ggplot2:
library(ggplot2)
qplot(pp, y, data=mwpp, geom=c("point","line"), group = sub, colour=sex)
qplot(pp, y, data=mwpp, geom=c("point","line"), group = sub, facets = . ~ sex)
The key is to get your data into a data frame with variables that
explicitly label the experimental units, as Michael did for you.
Hadley
--
http://had.co.nz/
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Re: [R] graphs for pretest data
Dear Juliet,
Perhaps start here:
require(lattice)
mwpp <- data.frame(y = c(43,42,26,39,60,60,46,40,41,36,42,54,
58,43,46,56,81,56,70,70,44,52,81,59,69,68),
sex = rep(c(rep('men', 14), rep('women', 12))),
pp = c(rep(c('pre', 'post'), each = 7), rep(c('pre', 'post'), each =
6)),
sub = c(1:7, 1:7, 8:13, 8:13))
xyplot(y ~ pp | sex, groups = sub, type = 'b', mwpp)
_
Professor Michael Kubovy
University of Virginia
Department of Psychology
USPS: P.O.Box 400400Charlottesville, VA 22904-4400
Parcels:Room 102Gilmer Hall
McCormick RoadCharlottesville, VA 22903
Office:B011+1-434-982-4729
Lab:B019+1-434-982-4751
Fax:+1-434-982-4766
WWW:http://www.people.virginia.edu/~mk9y/
On Aug 23, 2008, at 12:04 PM, Juliet Hannah wrote:
> Is there an easy way to make graphs for the following data. I have
> pretest and posttest scores for men and
> women. I would like to form a 'titlted segment' plot for the data.
> That is, make segments joining the scores,
> with different types of segments for men and women.
>
> Example data:
>
> menpre <- c(43,42,26,39,60,60,46)
> menpost <- c(40,41,36,42,54,58,43)
>
> womenpre <- c(46,56,81,56,70,70)
> womenpost <- c(44,52,81,59,69,68)
>
> Thanks!
>
> Juliet
[[alternative HTML version deleted]]
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[R] Forthcoming R Conferences
Dear useRs and developeRs, I hope all attending useR! in Dortmund last week had as much a good time as I had and a safe trip home. This email is to announce our plans for forthcoming conferences. In 2009 there will be a useR! in Rennes, France (July 8-10), directly followed by a DSC in Copenhagen, Denmark (July 13-14). We would like to have a useR! 2010 in North America, and 2011 in Europe. Locations for 2010 and 2011 have not been fixed yet, but there are already some plans. Proposals to host a conference are of course more than welcome. There have also been questions why 2009 is again in Europe. The main reasons were: a) we had a very good offer from Rennes, and (and at least almost one year ago, when planning started) none from outside Europe b) Having European useR!s in odd years will make it easier to set a date: in even years there are the biannual Compstat conferences, which together with all their satellite meetings block mid-August to the beginning of September. In odd years on the other hand, there is no regular big conference on computational statistics in Europe. So the basic plan is to be in Europe in odd years, and North America in even years from now on. Either continent could of course be replaced by one of the other 5 continents if we get a good offer. Antarctica may be hard to get to, though ;-) On behalf of the R Foundation, Fritz Leisch -- --- Prof. Dr. Friedrich Leisch Institut für Statistik Tel: (+49 89) 2180 3165 Ludwig-Maximilians-Universität Fax: (+49 89) 2180 5308 Ludwigstraße 33 D-80539 München http://www.statistik.lmu.de/~leisch --- Journal Computational Statistics --- http://www.springer.com/180 Münchner R Kurse --- http://www.statistik.lmu.de/R ___ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-announce __ 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] graphs for pretest data
?plot ?lines Something like this perhaps plot( menpre, type="l", col="red") lines(menpost, col="blue") lines(womenpre,col="green" lines(womenpost, col= "orange") also have a look at ?par for various options --- On Sat, 8/23/08, Juliet Hannah <[EMAIL PROTECTED]> wrote: > From: Juliet Hannah <[EMAIL PROTECTED]> > Subject: [R] graphs for pretest data > To: r-help@r-project.org > Received: Saturday, August 23, 2008, 12:04 PM > Is there an easy way to make graphs for the following data. > I have > pretest and posttest scores for men and > women. I would like to form a 'titlted segment' > plot for the data. > That is, make segments joining the scores, > with different types of segments for men and women. > > Example data: > > menpre <- c(43,42,26,39,60,60,46) > menpost <- c(40,41,36,42,54,58,43) > > womenpre <- c(46,56,81,56,70,70) > womenpost <- c(44,52,81,59,69,68) > > Thanks! > > Juliet > > __ > 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. __ [[elided Yahoo spam]] __ 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] Survey Design / Rake questions
On Fri, 22 Aug 2008, Farley, Robert wrote:
I *think* I'm making progress, but I'm still failing at the same step. My rake
call fails with:
Error in postStratify.survey.design(design, strata[[i]],
population.margins[[i]], :
Stratifying variables don't match
To my naïve eyes, it seems that my factors are "in the wrong order". If so,
how do I "assert" an ordering in my survey dataframe, or copy an "image" from
the survey dataframe to my marginals dataframes? I'd prefer to "pull" the
original marginals dataframe(s) from the survey dataframe so that I can
automate that in production.
It looks like a problem with the NumStn factor. One copy has been converted to
character and then factor, giving levels in alphabetical order; the other copy
has been converted directly to factor, giving levels in numerical order.
If you use as.factor(1:12) rather than as.character(1:12) it should work.
-thomas
If that's not my problem, where might I look for enlightenment? Neither "?why"
nor ?whatamimissing return citations. :-)
**
How I fail Now **
SurveyData <- read.spss("C:/Data/R/orange_delivery.sav", use.value.labels=TRUE,
max.value.labels=Inf, to.data.frame=TRUE)
#===
temp <- sub(' +$', '', SurveyData$direction_)
SurveyData$direction_ <- temp
#===
SurveyData$NumStn=abs(as.numeric(SurveyData$lineon)-as.numeric(SurveyData$lineoff))
mean(SurveyData$NumStn)
[1] 6.785276
### Kludge
SurveyData$NumStn <- pmax(1,SurveyData$NumStn)
mean(SurveyData$NumStn)
[1] 6.789877
SurveyData$NumStn <- as.factor(SurveyData$NumStn)
###
EBSurvey <- subset(SurveyData, direction_ == "EASTBOUND" )
XTTable <- xtabs(~direction_ , EBSurvey)
XTTable
direction_
EASTBOUND
345
WBSurvey <- subset(SurveyData, direction_ == "WESTBOUND" )
XTTable <- xtabs(~direction_ , WBSurvey)
XTTable
direction_
WESTBOUND
307
#
EBDesign <- svydesign(id=~sampn, weights=~expwgt, data=EBSurvey)
# svytable(~lineon+lineoff, EBDesign)
StnName <- c( "Warner Center", "De Soto", "Pierce College", "Tampa", "Reseda", "Balboa", "Woodley", "Sepulveda", "Van
Nuys", "Woodman", "Valley College", "Laurel Canyon", "North Hollywood")
EBOnNewTots <- c(1000, 600, 1200, 500,
1000, 500, 200, 250, 1000, 300, 100,
123.65,0 )
StnTraveld <- c(as.character(1:12))
EBNumStn<- c(673.65, 800, 1000, 1000, 800, 700, 600, 500, 400, 200,
50, 50 )
ByEBOn <- data.frame(StnName, Freq=EBOnNewTots)
ByEBNum <- data.frame(StnTraveld, Freq=EBNumStn)
RakedEBSurvey <- rake(EBDesign, list(~lineon, ~NumStn), list(ByEBOn, ByEBNum) )
Error in postStratify.survey.design(design, strata[[i]],
population.margins[[i]], :
Stratifying variables don't match
str(EBSurvey$lineon)
Factor w/ 13 levels "Warner Center",..: 3 1 1 1 2 13 1 5 1 5 ...
EBSurvey$lineon[1:5]
[1] Pierce College Warner Center Warner Center Warner Center De Soto
Levels: Warner Center De Soto Pierce College Tampa Reseda Balboa Woodley
Sepulveda Van Nuys Woodman Valley College Laurel Canyon North Hollywood
str(ByEBOn$StnName)
Factor w/ 13 levels "Balboa","De Soto",..: 11 2 5 8 6 1 12 7 10 13 ...
ByEBOn$StnName[1:5]
[1] Warner Center De SotoPierce College Tampa Reseda
Levels: Balboa De Soto Laurel Canyon North Hollywood Pierce College Reseda
Sepulveda Tampa Valley College Van Nuys Warner Center Woodley Woodman
str(EBSurvey$NumStn)
Factor w/ 12 levels "1","2","3","4",..: 10 12 4 12 8 1 8 8 12 4 ...
EBSurvey$NumStn[1:5]
[1] 10 12 4 12 8
Levels: 1 2 3 4 5 6 7 8 9 10 11 12
str(ByEBNum$StnTraveld)
Factor w/ 12 levels "1","10","11",..: 1 5 6 7 8 9 10 11 12 2 ...
ByEBNum$StnTraveld[1:5]
[1] 1 2 3 4 5
Levels: 1 10 11 12 2 3 4 5 6 7 8 9
**
**
Robert Farley
Metro
www.Metro.net
-Original Message-
From: Thomas Lumley [mailto:[EMAIL PROTECTED]
Sent: Thursday, August 21, 2008 13:55
To: Farley, Robert
Cc: r-help@r-project.org
Subject: Re: [R] Survey Design / Rake questions
On Tue, 19 Aug 2008, Farley, Robert wrote:
While I'm trying to catch up on the statistical basis of my task, could
someone point me to how I should fix my R error?
The variables in the formula in rake() need to be the raw variables in the
design object, not summary tables.
-thomas
Thanks
library(survey)
SurveyData <- read.spss("C:/Data/R/orange_delivery.sav",
use.value.labels=TRUE, max.value.labels=Inf, to.data.frame=TRUE)
#
Re: [R] lost in the SNOW at 4 AM; parallelization confusion...
Hi Eric --
Eric Rupley <[EMAIL PROTECTED]> writes:
> Apologies at what must be a very basic question, but I have not found
> any clear examples on how to design the following
>
> I would like to run iterative analysis over several processors. A toy
> example of the analysis is attached; for a resampling function run 1k
> times, with two different sets of conditioning variables i,j on some
> data vec...
>
> What is the usual way to attack such a problem using snow? My
> understanding up to this point is that one should:
>
> (1) set the random seed to uncorrelate the processors' actions in
> select()
>
> (2) make a function myfunc(vec,i,j) which returns the item of interest
>
> (3) set up a wrapper which iterates through i,j, and makes the call to
> the cluster
>
> (4) call the cluster using clusterApply(cl,vec, myfunc)
I think you're on the right track. You say:
for (i in c(2,4)) { # a series of nested iterations...
for (j in c(5:6)) {
clusterApply(cl, vec, analysis.func, i, j)
The clusterApply says, for each element of vec, invoke
analysis.func. vec is of length 1000, so you invoke analysis.func 1000
times, and with the outer loops you're calling analysis func 2 * 2 *
1000 times.
In your single processor code you have
for (i in c(2,4)) { # a series of nested iterations...
for (j in c(5:6)) {
res <- analysis.func(vec,i,j)
which invokes analysis.func 2 * 2 times. A strategy is to convert your
'for' loops into an appropriate *apply function, which I might do as
(approximately)
> its <- expand.grid(i=c(2, 4), j=c(5, 6))
> mapply(analysis.func, its$i, its$j,
+MoreArgs=list(vec=vec))
[1] 120719.09 60403.20 144993.44 72468.66
(maybe you mean i=2:4, j=5:6 ?) and then to use the appropriate
cluster* function, e.g.,
> clusterMap(cl, analysis.func, its$i, its$j,
+MoreArgs=list(vec=vec))
Maybe it is now early enough (though not too early?) for that drink?
Martin
> I must be terribly confused based on the results attached belowany
> advice will be appreciated...
>
>
> Many thanks,
> Best,
> Eric
>
> --
> Eric Rupley
> University of Michigan, Museum of Anthropology
> 1109 Geddes Ave, Rm. 4013
> Ann Arbor, MI 48109-1079
>
> [EMAIL PROTECTED]
> +1.734.276.8572
>
>
>
> # set up
> #
> # cl <- makeCluster(7)
> # 8 slaves are spawned successfully. 0 failed.
> #clusterSetupRNG(cl)
> #[1] "RNGstream"
>
>
> vec <- runif(1000,1,100)
> d <- NULL; c.j <- NULL;c.i <- NULL
>
> # the toy function
>
> analysis.func <- function (vec,i,j) {
> b <- NULL
> for (k in c(1:1000)) {
> a <- sample(vec,1000,replace=T) #requires randoms...
> b <- append(b, mean(a))
> }
> c <- (sum(b)*j)/i
> return(c)
> }
>
>
> # the "analysis"
>
> system.time(for (i in c(2,4)) { # a series of nested iterations...
>
> for (j in c(5:6)) {
>
> d <-
> append( mean( as.numeric( clusterApply(cl,vec,analysis.func,i,j) ) ) ,
> d)
> # this is ugly and contorted; there has to be a better way?
> c.j <- append(j, c.j)
> c.i <- append(i, c.i)
> }
> })
>
> # user system elapsed
> # 9.758 0.291 48.771
> #>
>
> # but the old way is faster...
>
> d <- NULL; c.j <- NULL; c.i <- NULL # set up again
>
> system.time(for (i in c(2,4)) { # a series of nested iterations...
>
> for (j in c(5:6)) {
>
> d <-append( mean( as.numeric( analysis.func(vec,i,j) )) ,d)
> # keeping it ugly for timing comparision...
> c.j <- append(j, c.j)
> c.i <- append(i, c.i)
> }
> })
>
>
> # user system elapsed
> # 0.299 0.002 0.299
> #> # arrgrgrgrgrg!!!
>
> stopCluster(cl)
> #[1] 1
> sessionInfo()
> #R version 2.7.1 (2008-06-23)
> #i386-apple-darwin8.10.1
> #
> #locale:
> #en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
> #
> #attached base packages:
> #[1] stats graphics grDevices utils datasets methods base
> #
> #other attached packages:
> #[1] rlecuyer_0.1 boot_1.2-33 snow_0.3-3 Rmpi_0.5-5
> #
> #loaded via a namespace (and not attached):
> #[1] tools_2.7.1
> date()
> #[1] "Sat Aug 23 04:25:50 2008"
> #>
> #Too late for a drink. Pity.
>
> __
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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.
--
Martin Morgan
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109
Location: Arnold Building M2 B169
Phone: (206) 667-2793
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and provide commented, minimal, self-contained, reproducible code.
[R] graphs for pretest data
Is there an easy way to make graphs for the following data. I have pretest and posttest scores for men and women. I would like to form a 'titlted segment' plot for the data. That is, make segments joining the scores, with different types of segments for men and women. Example data: menpre <- c(43,42,26,39,60,60,46) menpost <- c(40,41,36,42,54,58,43) womenpre <- c(46,56,81,56,70,70) womenpost <- c(44,52,81,59,69,68) Thanks! Juliet __ 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] Sending "..." to a C external
On Fri, Aug 22, 2008 at 2:16 PM, John Tillinghast <[EMAIL PROTECTED]> wrote:
> I'm trying to figure this out with "Writing R Extensions" but there's not a
> lot of detail on this issue.
> I want to write a (very simple really) C external that will be able to take
> "..." as an argument.
> (It's for optimizing a function that may have several parameters besides the
> ones being optimized.)
> I got the "showArgs" code (from R-exts) to compile and install, but it only
> gives me error messages when I run it. I think I'm supposed to pass it
> different arguments from what I'm doing, but I have no idea which ones.
> What exactly are CAR, CDR, and CADR anyway? Why did the R development team
> choose this very un-C-like set of commands? They are not explained much in
> R-exts.
Emmanuel has answered that - very engagingly too. On the train from
Dortmund to Dusseldorf last week I was describing exactly that
etymology of the names CAR, CDR, CDDR, ... to my companions but I got
it wrong. I had remembered CDR as "contents of the data register" but
Emmanuel is correct that it was "contents of the decrement register".
I don't think it is necessary to go through the agonies of dealing
with the argument list in a .External call., which is what I assume
you are doing. (You haven't given us very much information on how you
are trying to pass the ... argument and such information would be very
helpful. The readers of this list are quite intelligent but none, as
far as I know, have claimed to be telepathic.)
The way that I would go about this is using .Call in something like
.Call("myCfunction", arg1, arg2, dots = list(...), PACKAGE = "myPackage")
Then in your C code you check the length and the names of the dots
argument and take appropriate action.
An alternative, if you want to use the ... arguments in an R
expression to be evaluated by your optimizer, is to create an
environment, assign the elements of list(...) to the appropriate names
in that environment and pass the environment through .Call to be used
as the evaluation environment for your R expression. There is a
somewhat complicated example of this in the nlmer function in the lme4
package which you can find at http://lme4.r-forge.r-project.org/.
However, I don't feel embarrassed about the example being complicated.
This is complex stuff and it is not surprising that it isn't
completely straightforward to accomplish. If you feel that this is
opaque in R i can hardly wait to see what you think about writing the
SPSS version.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Re: [R] ggplot facet: change layout of panels
Hi Tom, Not yet, but I'm working on it for the next version. Regards, Hadley On Sat, Aug 23, 2008 at 10:08 AM, Tom Boonen <[EMAIL PROTECTED]> wrote: > Hi, > > is there anyway to adjust how ggplot(facet=) displays the layout of > panels? I have a dataset with many 25 groups and gplot(y,x,facet= > .~group) displays all 25 y~x plots next to each other so overall the > plot is too wide. if i do the same plot in lattice xyploy(y~x|group) > the y~x plots are arranged nicely 5 in each row to overall the plots > is a nice 5 by 5 rectangular grid. > > Is there any way to adjust this in gplot? > > Thank you very much. > > Best, > Tom > > __ > 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.
[R] ggplot facet: change layout of panels
Hi, is there anyway to adjust how ggplot(facet=) displays the layout of panels? I have a dataset with many 25 groups and gplot(y,x,facet= .~group) displays all 25 y~x plots next to each other so overall the plot is too wide. if i do the same plot in lattice xyploy(y~x|group) the y~x plots are arranged nicely 5 in each row to overall the plots is a nice 5 by 5 rectangular grid. Is there any way to adjust this in gplot? Thank you very much. Best, Tom __ 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] Error message in termplot
Hi I am trying to plot the following gam with termplot but keep getting the error message: Error in order(xx) : unimplemented type 'list' in 'orderVector1' Is there anyway I can rectify this to get my parametric coefficients plotted? Thanks Will Family: binomial Link function: logit Formula: fgha$pa ~ s(fgha$wspd) + fgha$depth + fgha$slha + fgha$hooks + wspd:hooks + wspd:hooks:depth Parametric coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -2.188e+00 4.331e-01 -5.051 4.39e-07 *** fgha$depth1.878e-04 5.275e-05 3.559 0.000372 *** fgha$slha-1.968e-02 7.899e-03 -2.492 0.012698 * fgha$hooks -3.155e-06 1.383e-06 -2.282 0.022500 * wspd:hooks1.351e-06 2.650e-07 5.099 3.41e-07 *** wspd:hooks:depth -1.959e-10 4.271e-11 -4.586 4.52e-06 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Approximate significance of smooth terms: edf Ref.df Chi.sq p-value s(fgha$wspd) 8.732 9.232 23.87 0.00518 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 R-sq.(adj) = 0.0991 Deviance explained = 8.85% UBRE score = 0.18451 Scale est. = 1 n = 1167 William Vincent 07979745433 [[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] simple generation of artificial data with defined features
Hello Mr. Greg Snow! Thank you very much for your prompt answer. > I don't think that the election data is the right data to demonstrate Kappa, > you need subjects that are classified by 2 or more different raters/methods. > The election data could be considered classifying the voters into which party > they voted for, but you only have 1 rater. I think, It should be possible to calculate kappa in case one has a little different point of view from the one you described above: Take the voters as raters who "judge" the category "election" with one party out of the six mentioned in my previous e-mail (which are simply the top six). This makes sense to me, because an election is somehow nothing else but a survey with the question "who should lead our country" - given six options in this example. As kappa is a measure of agreement, it should be able to illustrate the agreement of the voters answers to this question. For me this is - in priciple - no different from asking "Where is the stenosis in the video of this endoscopy" offering six options representing anatomic locations each. > Otherwise you may want to stick with the sample datasets. > The example data sets are of excellent quality and very interesting. I am sure there would be brilliant examples among them. But I have to admit that,t a I have no t a good overview of the available datasets at the moment (as a newbie). I just wanted to give an example out of every days life, everybody is familiar with. An election is something which came to my mind spontaneously. > There are other packages that compute Kappa values as well (I don't know if > others calculate this particular version), but some of those take the summary > data as input rather than the raw data, which may be easier if you just have > the summary tables. > > I chose Fleiss Kappa, because it is a more general form of Cohen's Kappa allowing m raters and n categories (instead of only two raters and to categories when using Cohen's kappa). Looking for another package calculating it from summary tables might be the simplest solution to my problem. Thank you very much for this hint! On the other hand it would be nice to use the very same method for the example as for the "real" data. The example will be part of the "methods" section. Thank you again very much for your tips and the quick reply. Have a nice weekend! Greetings from Munich, Felix Mueller-Sarnowski >> -Original Message- >> From: [EMAIL PROTECTED] >> [mailto:[EMAIL PROTECTED] On Behalf Of drflxms >> Sent: Friday, August 22, 2008 6:12 AM >> To: r-help@r-project.org >> Subject: [R] simple generation of artificial data with >> defined features >> >> Dear R-colleagues, >> >> I am quite a newbie to R fighting my stupidity to solve a >> probably quite simple problem of generating artificial data >> with defined features. >> >> I am conducting a study of inter-observer-agreement in >> child-bronchoscopy. One of the most important measures is >> Kappa according to Fleiss, which is very comfortable >> available in R through the irr-package. >> Unfortunately medical doctors like me don't really understand >> much of statistics. Therefore I'd like to give the reader an >> easy understandable example of Fleiss-Kappa in the Methods >> part. To achieve this, I obtained a table with the results of >> the German election from 2005: >> >> partynumber of votespercent >> >> SPD1619466534,2 >> CDU1313674027,8 >> CSU34943097,4 >> Gruene38383268,1 >> FDP46481449,8 >> PDS41181948,7 >> >> I want to show the agreement of voters measured by >> Fleiss-Kappa. To calculate this with the >> kappam.fleiss-function of irr, I need a data.frame like this: >> >> (id of 1st voter) (id of 2nd voter) >> >> party spd cdu >> >> Of course I don't plan to calculate this with the million of >> cases mentioned in the table above (I am working on a small >> laptop). A division by 1000 would be more than perfect for >> this example. The exact format of the table is generally not >> so important, as I could reshape nearly every format with the >> help of the reshape-package. >> >> Unfortunately I could not figure out how to create such a >> fictive/artificial dataset as described above. Any data.frame >> would be nice, that keeps at least the percentage. String-IDs >> of parties could be substituted by numbers of course (would >> be even better for function kappam.fleiss in irr!). >> >> I would appreciate any kind of help very much indeed. >> Greetings from Munich, >> >> Felix Mueller-Sarnowski >> >> __ >> 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] simple generation of artificial data with defined features
Hi,
to add voter.id and election.year to your data frame you could try:
el.dt.exp$voter.id=seq(1:nrow(el.dt.exp))
el.dt.exp$election.year=2005
Cheers,
Christoph Meyer
***
Dr. Christoph Meyer
Institute of Experimental Ecology
University of Ulm
Albert-Einstein-Allee 11
D-89069 Ulm
Germany
Phone: ++49-(0)731-502-2675
Fax:++49-(0)731-502-2683
Mobile: ++49-(0)1577-156-7049
E-mail: [EMAIL PROTECTED]
http://www.uni-ulm.de/index.php?id=7885
***
Saturday, August 23, 2008, 1:25:05 PM, you wrote:
> Dear Mr. Christos Hatzis,
> thank you so much for your answer which is in my eyes just brilliant! I
> followed it step by step (great and detailed explanation) and nearly
> everything is fine. - Except a problem in the very end, I haven't found
> a solution for until now. (Despite playing arround quite a lot...)
> Please let me explain:
>> election.2005 <- c(16194,13136,3494,3838,4648,4118) #cut of last 3
> digits, cause my laptop can't handle millions of rows...
>> attr(election.2005, "class") <- "table"
>> attr(election.2005, "dim") <- c(1,6)
>> attr(election.2005, "dimnames") <- list(c("votes"), c("spd", "cdu",
> "csu", "gruene", "fdp", "pds"))
>> head(election.2005)
> spd cdu csu gruene fdp pds
> votes 16194 13136 3494 3838 4648 4118
>> el.dt <- as.data.frame(election.2005)
>> el.dt.exp <- el.dt[rep(1:nrow(el.dt), el.dt$Freq), -ncol(el.dt)]
>> dim(el.dt.exp)
> [1] 45428 2
>> head(el.dt.exp)
> Var1 Var2
> 1 votes spd
> 1.1 votes spd
> 1.2 votes spd
> 1.3 votes spd
> 1.4 votes spd
> 1.5 votes spd
> My problem now is, that I would need either an autoincrementing
> identifier instead of "votes" in Var1 or the possibility to access the
> numbering by a column name (i.e. Var0). In addition I need a 3rd
> Variable for the year oft the election (2005, which is the same for all,
> but needed later on). So this is what it should look like:
> voter.id party election.year
> 1 1spd2005
> 1.1 2 spd 2005
> 1.2 3spd 2005
> 1.3 4spd2005
> 1.4 5spd2005
> 1.5 6spd2005
...
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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] Sending "..." to a C external
2008/8/22 Emmanuel Charpentier <[EMAIL PROTECTED]>: > Le vendredi 22 août 2008 à 15:16 -0400, John Tillinghast a écrit : >> I'm trying to figure this out with "Writing R Extensions" but there's not a >> lot of detail on this issue. >> I want to write a (very simple really) C external that will be able to take >> "..." as an argument. >> (It's for optimizing a function that may have several parameters besides the >> ones being optimized.) > > !!! That's a hard one. I have never undertaken this kind of job, but I expect > that your "..." argument, if you can reach it from C (which I don't know) will > be bound to a Lisp-like structure, notoriously hard to decode in C. Basically, > you'll have to create very low level code (an duplicate a good chunk of the R > parser-interpreter...). > > I'd rather treat the "..." argument in a wrapper that could call the relevant > C function with all arguments interpreted and bound... This wrapper would > probably be an order of magnitude slower than C code, but two orders of > magnitude > easier to write (and maintain !). Since "..." argument parsing would be done > *once* > before the "grunt work" is accomplished by C code, the slowdown would > (probably) > be negligible... I think you're overstating the problem somewhat! Everything you need to process "..." in C is pretty much in the showArgs function in the R-ext help. The problem is that John's not told us what his error was! It works for me: > showArgs(x=2,y=3,z=4) [1] 'x' 2.00 [2] 'y' 3.00 [3] 'z' 4.00 NULL > showArgs(x=2,y=3,z="s") [1] 'x' 2.00 [2] 'y' 3.00 [3] 'z' s NULL But I can get an error if I don't name an argument: > showArgs(x=2,y=3,"s") [1] 'x' 2.00 [2] 'y' 3.00 Error in showArgs(x = 2, y = 3, "s") : CHAR() can only be applied to a 'CHARSXP', not a 'NULL' But that's just because the C doesn't check for this. Is that what you're getting? What errors are you getting? Barry [this was on an R 2.7.0 I had kicking around, so maybe changed for later versions...] __ 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] simple generation of artificial data with defined features
Dear Mr. Christos Hatzis,
thank you so much for your answer which is in my eyes just brilliant! I
followed it step by step (great and detailed explanation) and nearly
everything is fine. - Except a problem in the very end, I haven't found
a solution for until now. (Despite playing arround quite a lot...)
Please let me explain:
> election.2005 <- c(16194,13136,3494,3838,4648,4118) #cut of last 3
digits, cause my laptop can't handle millions of rows...
> attr(election.2005, "class") <- "table"
> attr(election.2005, "dim") <- c(1,6)
> attr(election.2005, "dimnames") <- list(c("votes"), c("spd", "cdu",
"csu", "gruene", "fdp", "pds"))
> head(election.2005)
spd cdu csu gruene fdp pds
votes 16194 13136 3494 3838 4648 4118
> el.dt <- as.data.frame(election.2005)
> el.dt.exp <- el.dt[rep(1:nrow(el.dt), el.dt$Freq), -ncol(el.dt)]
> dim(el.dt.exp)
[1] 45428 2
> head(el.dt.exp)
Var1 Var2
1 votes spd
1.1 votes spd
1.2 votes spd
1.3 votes spd
1.4 votes spd
1.5 votes spd
My problem now is, that I would need either an autoincrementing
identifier instead of "votes" in Var1 or the possibility to access the
numbering by a column name (i.e. Var0). In addition I need a 3rd
Variable for the year oft the election (2005, which is the same for all,
but needed later on). So this is what it should look like:
voter.id party election.year
1 1spd2005
1.1 2 spd 2005
1.2 3spd 2005
1.3 4spd2005
1.4 5spd2005
1.5 6spd2005
The reason for that is the input format of the kappam.fleiss function of
the irr package I use for calculation. It accepts a data.frame with the
categories as rows (here we would have only one catgory: the year of the
election) and the raters (here the voters) as columns. In the data.frame
there will be the chosen party for each combination of electionyear and
voter.
This format can be easily achieved using the reshape package. Assuming
voter.id would be an autoincrementing identifier, the command should be:
>library(reshape)
>el.dt.exp.molten<-melt(el.dt.exp, id=c("voter.id")) #which would
propably change not really anything in this case, because the data is
already in a "molten" form
>kappa.frame<-cast(el.dt.exp.molten, election.year ~ voter.id,
subset=variable=="party")
I'd be extremely happy in case you might help me out again!
Have a nice weekend and many thanks so far!
Greetings from Munich,
Felix Mueller-Sarnowski
Christos Hatzis wrote:
> On the general question on how to create a dataset that matches the
> frequencies in a table, function as.data.frame can be useful. It takes as
> argument an object of a class 'table' and returns a data frame of
> frequencies.
>
> Consider for example table 6.1 of Fleiss et al (3rd Ed):
>
>
>> birth.weight <- c(10,15,40,135)
>> attr(birth.weight, "class") <- "table"
>> attr(birth.weight, "dim") <- c(2,2)
>> attr(birth.weight, "dimnames") <- list(c("A", "Ab"), c("B", "Bb"))
>> birth.weight
>>
> B Bb
> A 10 40
> Ab 15 135
>
>> summary(birth.weight)
>>
> Number of cases in table: 200
> Number of factors: 2
> Test for independence of all factors:
> Chisq = 3.429, df = 1, p-value = 0.06408
>
>> bw.dt <- as.data.frame(birth.weight)
>>
>
> Observations (rows) in this table can then be replicated according to their
> corresponding frequencies to yield the expanded dataset that conforms with
> the original table.
>
>
>> bw.dt.exp <- bw.dt[rep(1:nrow(bw.dt), bw.dt$Freq), -ncol(bw.dt)]
>> dim(bw.dt.exp)
>>
> [1] 200 2
>
>> table(bw.dt.exp)
>>
> Var2
> Var1 B Bb
> A 10 40
> Ab 15 135
>
> The above approach is not restricted to 2x2 tables, and should be
> straightforward generate datasets that conform to arbitrary nxm frequency
> tables.
>
> -Christos Hatzis
>
>
>
>> -Original Message-
>> From: [EMAIL PROTECTED]
>> [mailto:[EMAIL PROTECTED] On Behalf Of Greg Snow
>> Sent: Friday, August 22, 2008 12:41 PM
>> To: drflxms; r-help@r-project.org
>> Subject: Re: [R] simple generation of artificial data with
>> defined features
>>
>> I don't think that the election data is the right data to
>> demonstrate Kappa, you need subjects that are classified by 2
>> or more different raters/methods. The election data could be
>> considered classifying the voters into which party they voted
>> for, but you only have 1 rater. Maybe if you had some survey
>> data that showed which party each voter voted for in 2 or
>> more elections, then that may be a good example dataset.
>> Otherwise you may want to stick with the sample datasets.
>>
>> There are other packages that compute Kappa values as well (I
>> don't know if others calculate this particular version), but
>> some of those take the summary data as input rather than the
>> raw data, which may be easier if you just have the summary tables.
>>
>>
>> --
>> Gregor
Re: [R] Using lme, how to specify: (1) repeated measures, and (2) Toeplitz covariance structure?
1) I think that the repeated measure is not "years", but, as you said, the "count of birds". If you are interested with the effect of the time variable (years), perhaps you need to introduce it as a fixed effect ? 2) See ?corARMA. 2008/8/22 mtb954 <[EMAIL PROTECTED]> > We are attempting to use nlme to fit a linear mixed model to explain bird > abundance as a function of habitat: > > lme(abundance~habitat-1,data=data,method="ML",random=~1|sampleunit) > > The data consist of repeated counts of birds in sample units across > multiple > years, and we have two questions: > > 1) Is it necessary (and, if so, how) to specify the repeated measure > (years)? > > 2) How can we specify a Toeplitz heterogeneous covariance structure for > this > model? We have searched the help file for lme, and the R-help archives, but > cannot find any pertinent information. > > Any help would be appreciated. > > Thanks, Mark > >[[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. > [[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] Coordinate systems for geostatistics in R (imicola)
If you use the spatial objects provided by the sp-package (http://cran.r-project.org/web/packages/sp/vignettes/sp.pdf) you transform your data to other projections using the spTransform package. Thus you will need the rgdal package in complement (it actually includes spTransform). This function is extremely convenient: you can manage coordinate transformations extremely easily for common systems (WGS84, UTM) within the R environment. __ 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] lost in the SNOW at 4 AM; parallelization confusion...
Apologies at what must be a very basic question, but I have not found
any clear examples on how to design the following
I would like to run iterative analysis over several processors. A toy
example of the analysis is attached; for a resampling function run 1k
times, with two different sets of conditioning variables i,j on some
data vec...
What is the usual way to attack such a problem using snow? My
understanding up to this point is that one should:
(1) set the random seed to uncorrelate the processors' actions in
select()
(2) make a function myfunc(vec,i,j) which returns the item of interest
(3) set up a wrapper which iterates through i,j, and makes the call to
the cluster
(4) call the cluster using clusterApply(cl,vec, myfunc)
I must be terribly confused based on the results attached belowany
advice will be appreciated...
Many thanks,
Best,
Eric
--
Eric Rupley
University of Michigan, Museum of Anthropology
1109 Geddes Ave, Rm. 4013
Ann Arbor, MI 48109-1079
[EMAIL PROTECTED]
+1.734.276.8572
# set up
#
# cl <- makeCluster(7)
# 8 slaves are spawned successfully. 0 failed.
#clusterSetupRNG(cl)
#[1] "RNGstream"
vec <- runif(1000,1,100)
d <- NULL; c.j <- NULL;c.i <- NULL
# the toy function
analysis.func <- function (vec,i,j) {
b <- NULL
for (k in c(1:1000)) {
a <- sample(vec,1000,replace=T) #requires randoms...
b <- append(b, mean(a))
}
c <- (sum(b)*j)/i
return(c)
}
# the "analysis"
system.time(for (i in c(2,4)) { # a series of nested iterations...
for (j in c(5:6)) {
d <-
append( mean( as.numeric( clusterApply(cl,vec,analysis.func,i,j) ) ) ,
d)
# this is ugly and contorted; there has to be a better way?
c.j <- append(j, c.j)
c.i <- append(i, c.i)
}
})
# user system elapsed
# 9.758 0.291 48.771
#>
# but the old way is faster...
d <- NULL; c.j <- NULL; c.i <- NULL # set up again
system.time(for (i in c(2,4)) { # a series of nested iterations...
for (j in c(5:6)) {
d <-append( mean( as.numeric( analysis.func(vec,i,j) )) ,d)
# keeping it ugly for timing comparision...
c.j <- append(j, c.j)
c.i <- append(i, c.i)
}
})
# user system elapsed
# 0.299 0.002 0.299
#> # arrgrgrgrgrg!!!
stopCluster(cl)
#[1] 1
sessionInfo()
#R version 2.7.1 (2008-06-23)
#i386-apple-darwin8.10.1
#
#locale:
#en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
#
#attached base packages:
#[1] stats graphics grDevices utils datasets methods base
#
#other attached packages:
#[1] rlecuyer_0.1 boot_1.2-33 snow_0.3-3 Rmpi_0.5-5
#
#loaded via a namespace (and not attached):
#[1] tools_2.7.1
date()
#[1] "Sat Aug 23 04:25:50 2008"
#>
#Too late for a drink. Pity.
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Re: [R] Interpreting Logistic Regression
Em Qua, 2008-08-20 às 23:54 -0700, Madhavi Bhave escreveu: Hi Madhavi, > Hi ! > > This is Madhavi from Mumbai, India. Incidently this is my first post. You are wellcome! > > I am working on Credit Scoring Model and using R, I have run the logistic > regression. I have received following Output. > > I have two questions > > (a) What is the significance of "family = binomial(link = logit)". Why do I > have to mention Binomial? Is it because my dependent variable assumes only > two values 0 and 1? Can I write name of some other Statistical distribution > (say Poisson or Negative Binomial) in place of Binomial? How will it affect > my results? Well the logistitc regression is a generalized linear model. Your specification is just "binomial" function with "logit" link function so "summary" mention this especification ... The binomial model is choose because your data have a thorical binomial distribution (two type of outcome wtih fix probabilty of outcome and independent observations) For ohert distribuitions existing other links functions see more details in: ?family > > (b) How do I interpret the "R" result as given below? I know all the > variables are significant. How do I get Log Likelihood ratio, Odds ratio etc.? well odds ratio: model<- glm (formula,family=binomial) exp(coef(model)) Log Likehood model$deviance -- Bernardo Rangel Tura, M.D,MPH,Ph.D National Institute of Cardiology Brazil __ 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] Using interactive plots to get information about data points
jcarmichael gmail.com> writes: > > > I have been experimenting with interactive packages such iplots and playwith. > Consider the following sample dataset: > > A B C D > 1 5 5 9 > 3 2 8 4 > 1 7 3 0 > 7 2 2 6 > > Let's say I make a plot of variable A. I would like to be able to click on > a data point (e.g. 3) and have a pop-up window tell me the corresponding > value for variable D (e.g. 4). ?identify with labels argument. Another approach you might like to consider is to use GGobi (www.ggobi.org) with the rggobi package linking directly to it from R. GGobi is built specifically for this kind of interactive purpose. > I am also trying to produce multiple small > plots. For example, four side-by-side boxplots for each of the four > variables A, B, C, D. ?par... eg par(mfrow=c(1,4)) (for base graphics). Hope this helps. Michael Bibo Queensland Health __ 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.
