Re: [R] combination which limited
Dear All, Many thanks to Marc Schwartz and Gabor Grothendieck who have explained me about using expand.grid function and clearly explain how to use JGR. dd - expand.grid(interface = interface, screen = screen, computer = computer, available = available) There are several possibilities now: 1. you could list out dd on the console and note the number of the rows you want to keep: idx - c(1,5,7) dd2 - dd[,idx] I like a possible no. 1, because I can use and explore with my hand, idx - c(1:5,9,17,25) dd2 - dd[idx,] dd2 interface screen computer available 1usblcd pc yes 2 firewarelcd pc yes 3 infralcd pc yes 4 bluetoothlcd pc yes 5usb cube pc yes 9usblcd server yes 17 usblcd laptop yes 25 usblcd pcno Regards, Muhammad Subianto Notepad, Copy and Paste are my best friend to use R.2.1.0 on windows 2000 On 6/11/05, Gabor Grothendieck [EMAIL PROTECTED] wrote: On 6/11/05, Marc Schwartz [EMAIL PROTECTED] wrote: On Sat, 2005-06-11 at 20:44 +0200, Muhammad Subianto wrote: Dear R-helpers, I am learning about combination in R. I want to combination all of possible variable but it limited. I am sorry I could not explain exactly. For usefull I give an example interface - c(usb,fireware,infra,bluetooth) screen- c(lcd,cube) computer - c(pc,server,laptop) available - c(yes,no) What the result I need, something like this below, usb lcd pc yes fireware lcd pc yes infralcd pc yes bluetoothlcd pc yes usb cubepc yes usb lcd server yes usb lcd laptop yes usb lcd pc no How can I do that? I was wondering if someone can help me. Thanks you for your time and best regards, Muhammad Subianto Use: expand.grid(interface, screen, computer, available) Var1 Var2 Var3 Var4 1usb lcd pc yes 2 fireware lcd pc yes 3 infra lcd pc yes 4 bluetooth lcd pc yes 5usb cube pc yes 6 fireware cube pc yes 7 infra cube pc yes 8 bluetooth cube pc yes 9usb lcd server yes 10 fireware lcd server yes 11 infra lcd server yes 12 bluetooth lcd server yes 13 usb cube server yes 14 fireware cube server yes 15 infra cube server yes 16 bluetooth cube server yes 17 usb lcd laptop yes 18 fireware lcd laptop yes 19 infra lcd laptop yes 20 bluetooth lcd laptop yes 21 usb cube laptop yes 22 fireware cube laptop yes 23 infra cube laptop yes 24 bluetooth cube laptop yes 25 usb lcd pc no 26 fireware lcd pc no 27 infra lcd pc no 28 bluetooth lcd pc no 29 usb cube pc no 30 fireware cube pc no 31 infra cube pc no 32 bluetooth cube pc no 33 usb lcd server no 34 fireware lcd server no 35 infra lcd server no 36 bluetooth lcd server no 37 usb cube server no 38 fireware cube server no 39 infra cube server no 40 bluetooth cube server no 41 usb lcd laptop no 42 fireware lcd laptop no 43 infra lcd laptop no 44 bluetooth lcd laptop no 45 usb cube laptop no 46 fireware cube laptop no 47 infra cube laptop no 48 bluetooth cube laptop no See ?expand.grid for more information. After you do the above you will still want to cut it down to just the rows you need. As expained, use expand.grid. Let's assume you used this statement: dd - expand.grid(interface = interface, screen = screen, computer = computer, available = available) There are several possibilities now: 1. you could list out dd on the console and note the number of the rows you want to keep: idx - c(1,5,7) dd2 - dd[,idx] or if you want most of them it may be easier to record which ones you do not want: ndix - c(2,4,7) dd2 - dd[,-ndix] 2. Another possibility is to export it to a spreadsheet and visually delete the rows you don't want. 3. A third possibility is to install JGR (which is a free Java GUI front end to R). First download and install JGR from:http://stats.math.uni-augsburg.de/JGR/ In JGR (I am using Windows and its possible that the instructions vary slightly on other platforms): 1. create dd as explained 2. bring up the object browser using the menu Tools | Object Browser or just ctrl-B 3. Select dd from the object browser 4. This will put you into a spreadsheet in which you can select the rows you want to delete (hold down ctrl for the 2nd and subsequent selection to have a non-contiguous multi-row selection). 5.
[R] y-axis and resizing window
hi using plot(..., las=1), i.e. horizontal axis labels, the labels on the y-axis jams if the heigth of the graphics windov becomes too low while both x-axis and y-axis kind of removes superflus lables with las=0 (default) is there a way to make plot behave alike with horizontal lables? regards søren __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] y-axis and resizing window
On Sun, 12 Jun 2005, Søren Merser wrote:
using plot(..., las=1), i.e. horizontal axis labels, the labels on the
y-axis jams if the heigth of the graphics windov becomes too low
while both x-axis and y-axis kind of removes superflus lables with las=0
(default)
is there a way to make plot behave alike with horizontal lables?
It I understand you correctly (what does `jams' mean?), this is nothing to
do with resizing. The axis labelling code checks for enough width-wise
space for labels, but not for enough height-wise space. Specifically,
do_axis for the y axis contains
/* Check room for perpendicular labels. */
if (Rf_gpptr(dd)-las == 1 ||
Rf_gpptr(dd)-las == 2 ||
tnew - tlast = gap) {
so y-axis labels are always plotted for las %in% c(1,2) and hence may
overlap. (Similar code exists for an x-axis.)
--
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@stat.math.ethz.ch mailing list
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[R] delete -character from strings in matrix
Hi! I have strings where occasionally some -chars occur. How can I delete these chars? I tried it with gsub but using as replace does not work. Thanks a lot for any hint! Regards, Werner __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] glm with variance = mu+theta*mu^2?
You can fit negative binomial using the 'zicounts' package library(zicounts) data(teeth) names(teeth) ## c) fit negative binomial regression model nb.zc - zicounts(resp = dmft~.,x =~gender + age,data=teeth, distr = NB) nb.zc Even, library(zicounts) library(Fahrmeir) # use cells data data(cells) nb.cells - zicounts(parm=c(2,0,0,0,1),resp = y~.,x =~TNF+IFN+TNF:IFN,data=cells, distr = NB) nb.cells Samuel. Kjetil Brinchmann Halvorsen [EMAIL PROTECTED] wrote: Spencer Graves wrote: How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare log-linear model fits to cellular differentiation data based on quasi-likelihoods between variance = phi*mu (quasi-Poisson), variance = phi*mu^2 (quasi-exponential), and variance = mu+theta*mu^2. The quasi function accepted for the family argument in glm generates functions variance, validmu, and dev.resids. I can probably write functions to mimic the quasi function. However, I have two questions in regard to this: (1) I don't know what to use for dev.resids. This may not matter for fitting. I can try a couple of different things to see if it matters. (2) Might someone else suggest something different, e.g., using something like optim to solve an appropriate quasi-score function? Thanks, spencer graves Since nobody has answerd this I will try. The variance function mu+theta*mu^2 is the variance function of the negative binomial family. If this variance function is used to construct a quasi-likelihood, the resulting quasi- likelihood is identical to the negative binomial likelihood, so for fitting we can simly use glm.nb from MASS, which will give the correct estimated values. However, in a quasi-likelihood setting the (co)varince estimation from glm.nb is not appropriate, and from the book (fahrmeir ..) it seems that the estimation method used is a sandwich estimator, so we can try the sandwich package. This works but the numerical results are somewhat different from the book. Any comments on this? my code follows: library(Fahrmeir) library(help=Fahrmeir) library(MASS) cells.negbin - glm(y~TNF+IFN+TNF:IFN, data=cells, family=negative.binomial(1/0.215)) summary(cells.negbin) Call: glm(formula = y ~ TNF + IFN + TNF:IFN, family = negative.binomial(1/0.215), data = cells) Deviance Residuals: Min 1Q Median 3Q Max -1.6714 -0.8301 -0.2153 0.4802 1.4282 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) 3.39874495 0.18791125 18.087 4.5e-10 *** TNF 0.01616136 0.00360569 4.482 0.00075 *** IFN 0.00935690 0.00359010 2.606 0.02296 * TNF:IFN -0.5910 0.7002 -0.844 0.41515 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for Negative Binomial(4.6512) family taken to be 1.012271) Null deviance: 46.156 on 15 degrees of freedom Residual deviance: 12.661 on 12 degrees of freedom AIC: 155.49 Number of Fisher Scoring iterations: 5 confint(cells.negbin) Waiting for profiling to be done... 2.5 % 97.5 % (Intercept) 3.0383197319 3.7890206510 TNF 0.0091335087 0.0238915483 IFN 0.0023292566 0.0170195707 TNF:IFN -0.0001996824 0.960427 library(sandwich) Loading required package: zoo vcovHC( cells.negbin ) (Intercept) TNF IFN TNF:IFN (Intercept) 0.01176249372 -0.0001279740135 -0.0001488223001 0.0212541999 TNF -0.00012797401 0.039017282 0.021242875 -0.0019793137 IFN -0.00014882230 0.021242875 0.054314079 -0.0013277626 TNF:IFN 0.0212542 -0.001979314 -0.001327763 0.0002370104 cov2cor(vcovHC( cells.negbin )) (Intercept) TNF IFN TNF:IFN (Intercept) 1.000 -0.5973702 -0.5887923 0.1272950 TNF -0.5973702 1.000 0.4614542 -0.6508822 IFN -0.5887923 0.4614542 1.000 -0.3700671 TNF:IFN 0.1272950 -0.6508822 -0.3700671 1.000 cells.negbin2 - glm.nb( y~TNF+IFN+TNF:IFN, data=cells) summary(cells.negbin) Call: glm(formula = y ~ TNF + IFN + TNF:IFN, family = negative.binomial(1/0.215), data = cells) Deviance Residuals: Min 1Q Median 3Q Max -1.6714 -0.8301 -0.2153 0.4802 1.4282 Coefficients: Estimate Std. Error t value Pr(|t|) (Intercept) 3.39874495 0.18791125 18.087 4.5e-10 *** TNF 0.01616136 0.00360569 4.482 0.00075 *** IFN 0.00935690 0.00359010 2.606 0.02296 * TNF:IFN -0.5910 0.7002 -0.844 0.41515 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Dispersion parameter for Negative Binomial(4.6512) family taken to be 1.012271) Null deviance: 46.156 on 15 degrees of freedom Residual deviance: 12.661 on 12 degrees of freedom AIC: 155.49 Number of Fisher Scoring iterations: 5 confint( cells.negbin2 ) Waiting for profiling to be done... 2.5 % 97.5 % (Intercept) 3.0864669072
Re: [R] y-axis and resizing window
thanks
with 'jams' i meant messes up, but your term overlap is exactly what i
actually had in mind
though a minor problem, do you think that the code will change to enable
checking for enough height-wise space?
regards søren
- Original Message -
From: Prof Brian Ripley [EMAIL PROTECTED]
To: Søren Merser [EMAIL PROTECTED]
Cc: R - help r-help@stat.math.ethz.ch
Sent: Sunday, June 12, 2005 12:39 PM
Subject: Re: [R] y-axis and resizing window
On Sun, 12 Jun 2005, Søren Merser wrote:
using plot(..., las=1), i.e. horizontal axis labels, the labels on the
y-axis jams if the heigth of the graphics windov becomes too low
while both x-axis and y-axis kind of removes superflus lables with las=0
(default)
is there a way to make plot behave alike with horizontal lables?
It I understand you correctly (what does `jams' mean?), this is nothing to
do with resizing. The axis labelling code checks for enough width-wise
space for labels, but not for enough height-wise space. Specifically,
do_axis for the y axis contains
/* Check room for perpendicular labels. */
if (Rf_gpptr(dd)-las == 1 ||
Rf_gpptr(dd)-las == 2 ||
tnew - tlast = gap) {
so y-axis labels are always plotted for las %in% c(1,2) and hence may
overlap. (Similar code exists for an x-axis.)
--
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@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
__
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PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] delete -character from strings in matrix
Please define does not work. Here's what I get: m - matrix(paste(letters[1:4], does not work.), 2, 2) m [,1] [,2] [1,] a does not work. c does not work. [2,] b does not work. d does not work. gsub(does not work., , m) [1] a b c d structure(gsub(does not work., , m), dim=dim(m)) [,1] [,2] [1,] a c [2,] b d R-2.1.0 on WinXPPro. Andy From: Werner Wernersen Hi! I have strings where occasionally some -chars occur. How can I delete these chars? I tried it with gsub but using as replace does not work. Thanks a lot for any hint! Regards, Werner __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] delete -character from strings in matrix
Thanks for the reply, Andy! My problem was that I could not get rid of a double quote character within the string. I don't know what I have done before, but now it works...?!?! Sorry for bothering you. Best, Werner Liaw, Andy wrote: Please define does not work. Here's what I get: m - matrix(paste(letters[1:4], does not work.), 2, 2) m [,1] [,2] [1,] a does not work. c does not work. [2,] b does not work. d does not work. gsub(does not work., , m) [1] a b c d structure(gsub(does not work., , m), dim=dim(m)) [,1] [,2] [1,] a c [2,] b d R-2.1.0 on WinXPPro. Andy From: Werner Wernersen Hi! I have strings where occasionally some -chars occur. How can I delete these chars? I tried it with gsub but using as replace does not work. Thanks a lot for any hint! Regards, Werner __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Notice: This e-mail message, together with any attachments, contains information of Merck Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates (which may be known outside the United States as Merck Frosst, Merck Sharp Dohme or MSD and in Japan, as Banyu) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system. -- __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] memory allocation problem under linux
I have some compiled code that works under winXp but not under linux (kernel 2.6.10-5). I'm also using R 2.1.0 After debugging, I've discovered that this code: #define NMAX 256 long **box; ... box = (long **)R_alloc(NMAX, sizeof(long *)); gives a null pointer, so subsequent line: for (i=0; iNMAX; i++) box[i] = (long *) R_alloc(NMAX, sizeof(long)); gives a SIGSEGV signal. In the same shared library, I have a function with this code: partitions=16; ... h2=(long **)R_alloc(partitions,sizeof(long *)); for (i=0;ipartitions;i++) h2[i]=(long *)R_alloc(partitions,sizeof(long)); that works! Naturally, I've tried to change NMAX from 256 to 16, without any success. Any idea on where the problem can reside? (Note that this not happens under WinXp). And just another question. When R_alloc fails, should-it terminate the function with an error, without returning control to the function? __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Replacing for loop with tapply!?
Dear Adaikalavan,
Your solution (the second function) is definitely the most elegant and
generic solution of all replies in this discussion. Robust for missing
values and flexible to allow as many calculations as desired! It is so
clear, I even managed to hack it (of course also thanks to the new
insight from all the other posts)!
As the data consists of weather stations in rows and days in columns, I
have adapted the function to work on rows instead of columns. Did not
manage to get the results directly into the right rows/cols layout, so a
transpose (t) is still required. However this seems instant, so does not
mean a reduction in speed! Calculating proportions is now a snip!!
Thanks for you help,
Sander.
### simulate data
set.seed(1)# for reproducibility
mat - matrix(sample(-15:50, 15 * 10, TRUE), 15, 10)
mat[ mat 45 ] - NA # create some missing values
mat[ 9, ] - NA # station 9's data is completely missing
mat
find.stats - function( data, threshold ){
n - length(threshold)
excess - numeric( n )
out- matrix( ncol=nrow(data), nrow=(n + 2) ) # initialise
good - which( apply( data, 1, function(x) !all(is.na(x)) ) )
# rows that are not completely missing
out[ ,good ] - apply( data[ good, ], 1, function(x){
m - max( x, na.rm=T )
# determine maximum value per row
c - length(x[!is.na(x)])
# determine number of non-missing values
for(i in 1:n){ excess[i] - sum( x threshold[i], na.rm=TRUE
)/length(x[!is.na(x)]) }
# calc proportion of non-missing values over multiple thresholds
return( c(m, c, excess) )
} )
rownames(out) - c( TmpMax, Count, paste(Over, threshold, sep=) )
colnames(out) - rownames(data) # name of the stations
return( t(out) )
}
lstTemps=c(37,39,41,43)
tmp - find.stats( mat, lstTemps )
tmp
Adaikalavan Ramasamy wrote:
OK, so you want to find some summary statistics for each column, where
some columns could be completely missing.
Writing a small wrapper should help. When you use apply(), you are
actually applying a function to every column (or row). First, let us
simulate a dataset with 15 days/rows and 10 stations/columns
### simulate data
set.seed(1)# for reproducibility
mat - matrix(sample(-15:50, 15 * 10, TRUE), 15, 10)
mat[ mat 45 ] - NA # create some missing values
mat[ ,9 ] - NA # station 9's data is completely missing
Here are two example of such wrappers :
find.stats1 - function( data, threshold=c(37,39,41) ){
n - length(threshold)
out - matrix( nrow=(n + 1), ncol=ncol(data) ) # initialise
out[1, ] - apply(data, 2, function(x)
ifelse( all(is.na(x)), NA, max(x, na.rm=T) ))
for(i in 1:n) out[ i+1, ] - colSums( data threshold[i], na.rm=T )
rownames(out) - c( daily_max, paste(above, threshold, sep=_) )
colnames(out) - rownames(data) # name of the stations
return( out )
}
find.stats2 - function( data, threshold=c(37,39,41) ){
n - length(threshold)
excess - numeric( n )
out- matrix( nrow=(n + 1), ncol=ncol(data) ) # initialise
good - which( apply( data, 2, function(x) !all(is.na(x)) ) )
# colums that are not completely missing
out[ , good] - apply( data[ , good], 2, function(x){
m - max( x, na.rm=T )
for(i in 1:n){ excess[i] - sum( x threshold[i], na.rm=TRUE ) }
return( c(m, excess) )
} )
rownames(out) - c( daily_max, paste(above, threshold, sep=_) )
colnames(out) - rownames(data) # name of the stations
return( out )
}
find.stats1( mat )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
daily_max 44 42 39 41 45 43 42 45 NA42
above_37 212132210 1
above_39 210132110 1
above_41 210022110 1
find.stats2( mat )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
daily_max 44 42 39 41 45 43 42 45 NA42
above_37 21213221 NA 1
above_39 21013211 NA 1
above_41 21002211 NA 1
On my laptop 'find.stats1' and 'find.stats2' (which is more flexible)
takes 7 and 6 seconds respectively to execute on a dataset with 1
stations and 365 days.
Regards, Adai
On Fri, 2005-06-10 at 20:05 +0200, Sander Oom wrote:
Dear all,
Dimitris and Andy, thanks for your great help. I have progressed to the
following code which runs very fast and effective:
mat - matrix(sample(-15:50, 15 * 10, TRUE), 15, 10)
mat[mat45] - NA
mat-NA
mat
temps - c(35, 37, 39)
ind - rbind(
t(sapply(temps, function(temp)
rowSums(mat temp, na.rm=TRUE) )),
rowSums(!is.na(mat), na.rm=FALSE),
apply(mat, 1, max, na.rm=TRUE))
ind - t(ind)
ind
However, some weather stations have missing values for the whole
[R] linking R to goto blas
Dear all, I am currently trying to link R 2.1.0 to the GOTO BLAS 0.99.3 library on a box running Fedora Core 3 , basically following the steps indicated in the R-Admin document: 1: I downloaded the current libgoto.xxx.so from http://www.cs.utexas.edu/users/kgoto/libraries/libgoto_prescott-32-r0.99-3.so.gz, a version suitable for our XEON machine (Nocona core), unpacked it to /usr/lib and created a symlink libgoto.so pointing to the library. 2: Then, I got ready to re-configure and re-compile R (2.1.0) using the following configure flags: ./configure --prefix=/usr --enable-R-shlib --enable-shared --with-tcltk --with-blas=-lgoto -lpthread -lm I did read the R-Admin doc and therefore I am aware of the fact that passing -lgoto is supposed to be sufficient, but as a matter of fact configuring with --with-blas=-lgoto only ends up in a libR.so being linked to the standard libblas.so. config.log reports in this settings that libgoto.xxx.so is missing links to libpthread etc. Therefore, I added the two flags -lpthread -lm as indicated at GOTO's website and I got a clean configure run. (Am I concluding correctly that I am using a threaded version of goto blas?) 3: Running make, however, freezed when trying to build grDevices, without throwing any warning or error messages: [...] ../../../../library/grDevices/libs/grDevices.so is unchanged make[5]: Leaving directory `/home/ssoberni/R-2.1.0/src/library/grDevices/src' make[4]: Leaving directory `/home/ssoberni/R-2.1.0/src/library/grDevices/src' [freeze] 4: I then rummaged the R mailing list archives and stumbled over a thread dating from May this year pointing to a similar issue, concerning gcc-3.4 and broken lapack libraries provided by FC3 (see https://stat.ethz.ch/pipermail/r-devel/2005-May/033117.html). Following these opinions/ findings, I did the following (though I knew that -- in principle -- R is supposed to handle this issue by passing a --ffloat-store flag to the fortran compiler, doesn't it?): * I wanted to remove the FC3 native lapack libraries, and to my surprise, they were not installed at all (no liblapack.so.xxx in /usr/lib). * I set up an older gcc environment, i.e. the last release from the 3.3.x family (3.3.6) and tried to recompile R ending up with the same hang-up. As a last step, I tried to exclude R's internal package explicitly by setting --wihtout-lapack, which did not hava a visible effect on the building process and did not provide a workaround for the hang-up. Please, I highly appreciate any thoughts or hints as my colleagues and I are eager to get into GOTO's universe. //stefan -- Stefan Sobernig Department of Information Systems and New Media Vienna University of Economics Augasse 2-6 A - 1090 Vienna Phone: +43 - 1 - 31336 - 4878 Fax: +43 - 1 - 31336 - 746 Email: [EMAIL PROTECTED] mailto:[EMAIL PROTECTED] PubKey: http://julia.wu-wien.ac.at/~ssoberni/0x5FC2D3FA.asc http://julia.wu-wien.ac.at/%7Essoberni/0x5FC2D3FA.asc [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] linking R to goto blas
On Sun, 12 Jun 2005, Stefan Sobernig wrote: I am currently trying to link R 2.1.0 to the GOTO BLAS 0.99.3 library on a box running Fedora Core 3 , basically following the steps indicated in the R-Admin document: 1: I downloaded the current libgoto.xxx.so from http://www.cs.utexas.edu/users/kgoto/libraries/libgoto_prescott-32-r0.99-3.so.gz, a version suitable for our XEON machine (Nocona core), unpacked it to /usr/lib and created a symlink libgoto.so pointing to the library. 2: Then, I got ready to re-configure and re-compile R (2.1.0) using the following configure flags: ./configure --prefix=/usr --enable-R-shlib --enable-shared --with-tcltk --with-blas=-lgoto -lpthread -lm I did read the R-Admin doc and therefore I am aware of the fact that passing -lgoto is supposed to be sufficient, but as a matter of fact Only for single-threaded versions. For others you need --with-blas=-lgoto -lpthread. configuring with --with-blas=-lgoto only ends up in a libR.so being linked to the standard libblas.so. config.log reports in this settings that libgoto.xxx.so is missing links to libpthread etc. Therefore, I added the two flags -lpthread -lm as indicated at GOTO's website and I got a clean configure run. (Am I concluding correctly that I am using a threaded version of goto blas?) Dunno: the organization of the Goto site has changed since that section was written. Looks like only multi-threaded (2 threads) versions are currently available. 3: Running make, however, freezed when trying to build grDevices, without throwing any warning or error messages: [...] ../../../../library/grDevices/libs/grDevices.so is unchanged make[5]: Leaving directory `/home/ssoberni/R-2.1.0/src/library/grDevices/src' make[4]: Leaving directory `/home/ssoberni/R-2.1.0/src/library/grDevices/src' [freeze] 4: I then rummaged the R mailing list archives and stumbled over a thread dating from May this year pointing to a similar issue, concerning gcc-3.4 and broken lapack libraries provided by FC3 (see https://stat.ethz.ch/pipermail/r-devel/2005-May/033117.html). Following these opinions/ findings, I did the following (though I knew that -- in principle -- R is supposed to handle this issue by passing a --ffloat-store flag to the fortran compiler, doesn't it?): It does. For me this works with the internal BLAS and with Goto's blas versions 0.96-2 and 0.99-3, on an Opteron. (It also works on i686 with several other BLASes.) * I wanted to remove the FC3 native lapack libraries, and to my surprise, they were not installed at all (no liblapack.so.xxx in /usr/lib). * I set up an older gcc environment, i.e. the last release from the 3.3.x family (3.3.6) and tried to recompile R ending up with the same hang-up. They are not used unless you explicitly asked for them. As a last step, I tried to exclude R's internal package explicitly by setting --wihtout-lapack, which did not hava a visible effect on the building process and did not provide a workaround for the hang-up. Assuming that is a typo for --without-lapack, it does nothing (it is the default and excludes an external LAPACK). Please, I highly appreciate any thoughts or hints as my colleagues and I are eager to get into GOTO's universe. First get a version with the internal BLAS working. That will rule out any issues about LAPACK. Then change the BLAS: it looks as if this might be a problem with the particular Goto BLAS. Please note: the R-devel list would be a much better choice for such issues -- see the posting guide. -- 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@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] linking R to goto blas
Stefan Sobernig [EMAIL PROTECTED] writes: Dear all, I am currently trying to link R 2.1.0 to the GOTO BLAS 0.99.3 library on a box running Fedora Core 3 , basically following the steps indicated in the R-Admin document: 1: I downloaded the current libgoto.xxx.so from http://www.cs.utexas.edu/users/kgoto/libraries/libgoto_prescott-32-r0.99-3.so.gz, a version suitable for our XEON machine (Nocona core), unpacked it to /usr/lib and created a symlink libgoto.so pointing to the library. 2: Then, I got ready to re-configure and re-compile R (2.1.0) using the following configure flags: ./configure --prefix=/usr --enable-R-shlib --enable-shared --with-tcltk --with-blas=-lgoto -lpthread -lm ... Please, I highly appreciate any thoughts or hints as my colleagues and I are eager to get into GOTO's universe. Hmm. Looks over-complicated to me. What works for me on AMD64 is to have a config.site file in my BUILD-GOTO directory, containing cat config.site BLAS_LIBS=-L/home/pd/GOTO -lgoto_opt64p-r0.96 -lpthread CFLAGS=-O3 -g #CFLAGS=-g FFLAGS=$CFLAGS CXXFLAGS=$CFLAGS (the .*FLAGS business is optional, of course). With this in place, a simple ../R/configure followed by make seems to do the trick. I'll give it a try on my FC3 system, but it's a 500 MHz PIII, so it takes a while... -- 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 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] 0 * NA
Hi list! Debuging one of my R programs I found: 0 * NA [1] NA It this a bug, or intentional? I would expect 0 or 0.0 depending on the type of the NA. Gabor __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] 0 * NA
I believe that's intentional. NA means we don't know what the value is, so just about any operation with NA will result in NA. You might think anything times 0 is 0, but: 0*Inf [1] NaN and there's no guarantee that the true value not observed is not Inf... Andy From: BORGULYA Gábor Hi list! Debuging one of my R programs I found: 0 * NA [1] NA It this a bug, or intentional? I would expect 0 or 0.0 depending on the type of the NA. Gabor __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] linking R to goto blas
Peter Dalgaard [EMAIL PROTECTED] writes: Stefan Sobernig [EMAIL PROTECTED] writes: Dear all, I am currently trying to link R 2.1.0 to the GOTO BLAS 0.99.3 library on a box running Fedora Core 3 , basically following the steps indicated in the R-Admin document: 1: I downloaded the current libgoto.xxx.so from http://www.cs.utexas.edu/users/kgoto/libraries/libgoto_prescott-32-r0.99-3.so.gz, a version suitable for our XEON machine (Nocona core), unpacked it to /usr/lib and created a symlink libgoto.so pointing to the library. 2: Then, I got ready to re-configure and re-compile R (2.1.0) using the following configure flags: ./configure --prefix=/usr --enable-R-shlib --enable-shared --with-tcltk --with-blas=-lgoto -lpthread -lm ... Please, I highly appreciate any thoughts or hints as my colleagues and I are eager to get into GOTO's universe. Hmm. Looks over-complicated to me. What works for me on AMD64 is to have a config.site file in my BUILD-GOTO directory, containing cat config.site BLAS_LIBS=-L/home/pd/GOTO -lgoto_opt64p-r0.96 -lpthread CFLAGS=-O3 -g #CFLAGS=-g FFLAGS=$CFLAGS CXXFLAGS=$CFLAGS (the .*FLAGS business is optional, of course). With this in place, a simple ../R/configure followed by make seems to do the trick. I'll give it a try on my FC3 system, but it's a 500 MHz PIII, so it takes a while... Hmm... That gives me the grDevices issue, which boils down to an R that segfaults immediately upon startup, in #0 0x05c0aea7 in tilde_expand () from /usr/lib/libreadline.so.4 #1 0x08170254 in R_ExpandFileName_readline ( s=0x8bb4020 #/home/pd/r-patched/BUILD-GOTO/library/grDevices/R/sysdata.rdb, #buff=0x8295300 #/home/pd/r-patched/BUILD-GOTO/library/grDevices/R/grDevices) at ../../../R/src/unix/sys-std.c:406 #2 0x0816f5da in R_ExpandFileName ( s=0x8bb4020 #/home/pd/r-patched/BUILD-GOTO/library/grDevices/R/sysdata.rdb) at #../../../R/src/unix/sys-unix.c:129 #3 0x08167352 in R_FileExists ( path=0x8bb4020 #/home/pd/r-patched/BUILD-GOTO/library/grDevices/R/sysdata.rdb) at #stat.h:365 #4 0x08105da3 in do_fileexists (call=0x84fd544, op=0x82c5f78, #args=0x0, rho=0x8c514a4) at ../../../R/src/main/platform.c:857 #5 0x080edc85 in do_internal (call=0x0, op=0x82ba5d4, args=0x3920, env=0x8c514a4) at ../../../R/src/main/names.c:1078 #6 0x080c0daa in Rf_eval (e=0x84fd57c, rho=0x8c514a4) at ../../../R/src/main/eval.c:382 #7 0x080c3695 in Rf_applyClosure (call=0x8668c28, op=0x84fd5b4, arglist=0x8c50564, rho=0x8ad5cc4, suppliedenv=0x82aa5f0) running --no-readline gives me another crash (gdb) bt #0 0x003fb0da in strcmp () from /lib/ld-linux.so.2 #1 0x003f009a in _dl_map_object () from /lib/ld-linux.so.2 #2 0x004fdb58 in dl_open_worker () from /lib/tls/libc.so.6 #3 0x in ?? () ...which suggests that something is up with dynamic linking. I'll give it another spin... -- 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 __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] memory allocation problem under linux
I've written: #define NMAX 256 long **box; ... box = (long **)R_alloc(NMAX, sizeof(long *)); gives a null pointer, so subsequent line: for (i=0; iNMAX; i++) box[i] = (long *) R_alloc(NMAX, sizeof(long)); gives a SIGSEGV signal. Sorry, that's not exact: I have a segmentation fault just *inside* R_alloc! Substituting R_alloc with malloc and Calloc gives the same error. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] [R-pkgs] New versions of Matrix and lme4 packages
I have uploaded version 0.96-1 of both Matrix and lme4 to CRAN. The source package should migrate to CRAN over the weekend and binary packages should be available some time next week. As for previous releases, the versions of these two packages are interdependent. The lme4 package requires Matrix_0.96-1 or later but we cannot enforce the other dependency. Please remember that if you upgrade the Matrix package you should also upgrade the lme4 package. The method for fitting generalized linear mixed models using the Laplacian approximation is considerably faster in this version. Also, the packages have been reorganized so the interdependence will not be as strong in the future. ___ R-packages mailing list [EMAIL PROTECTED] https://stat.ethz.ch/mailman/listinfo/r-packages __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Essay identification
Hi R-help, I have a database of 10 students who have written an overall of 78 essays. The challenge? I would like to identify who wrote the 79th essay. Has anybody used R in this context? Even if not, would you suggest me which pattern recognition technique I might possibly apply? Thanks a lot and regards, Tom - [[alternative HTML version deleted]] __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Essay identification
I assume that you know the usual procedure is to 'score' each essay by a
vector that gives the frequency of occurrence of commonly used (sometimes
adding subject matter specific) words and phrases. This multivariate
response is then fed in as a training set into your favorite supervised
learning/classification procedure. R has many of these -- trees, logisic
regression, boosting, Random Forests,svm's,LDA,SOM's (whoops -- that's an
Unsupervised one), ... . Try
RSiteSearch('Classification',restrict=('functions').
The devil is in the details as to what works best, I believe. With only 78
exemplars in 10 groups, unless there is a lot of separation (disparate
styles that you could probably detect manually) it may be difficult. It also
depends on how large each group is (balance is generally better).
Cheers,
Bert
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Werner Bier
Sent: Sunday, June 12, 2005 12:30 PM
To: r-help@stat.math.ethz.ch
Subject: [R] Essay identification
Hi R-help,
I have a database of 10 students who have written an overall of 78 essays.
The challenge? I would like to identify who wrote the 79th essay.
Has anybody used R in this context?
Even if not, would you suggest me which pattern recognition technique I
might possibly apply?
Thanks a lot and regards,
Tom
-
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Re: [R] Essay identification
On 6/12/05, Werner Bier [EMAIL PROTECTED] wrote: Hi R-help, I have a database of 10 students who have written an overall of 78 essays. The challenge? I would like to identify who wrote the 79th essay. Has anybody used R in this context? Even if not, would you suggest me which pattern recognition technique I might possibly apply? Check out http://xxx.uni-augsburg.de/PS_cache/cond-mat/pdf/0108/0108530.pdf for a simple method. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] ANOVA vs REML approach to variance component estimation
Thank you for confirming this and introducing me to varcomp(). I have another question that I hope you or someone else can help me with. I was trying to generalise my codes for variable measurement levels and discovered that lme() was estimating the within group variance even with a single measure per subject for all subjects ! Here is an example where we have 12 animals but with single measurement. y - c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, 1.3, -0.3, 0.5, -1.4, -0.2, 1.8) ID - factor( 1:12 ) Analysis of variance method correctly says that there is no residual variance and it equals to total variance. summary(aov(y ~ ID)) Df Sum Sq Mean Sq ID 11 20.9692 1.9063 However the REML method is giving me a within animal variance when there is no replication at animal level. It seems like I can get components of variance for factors that are not replicated. library(ape) varcomp(lme(y ~ 1, random = ~ 1 | ID)) IDWithin 1.6712661 0.2350218 Am I reading this correct and can someone kindly explain this to me ? Thank you again. Regards, Adai On Fri, 2005-06-10 at 15:10 -0400, Chuck Cleland wrote: They look fine to me. Also, note varcomp() in the ape package and VarCorr() in the nlme package. I think in this case the ANOVA estimate of the intercept variance component is negative because the true value is close to zero. y - c( 2.2, -1.4, -0.5, # animal 1 +-0.3, -2.1, 1.5, # animal 2 + 1.3, -0.3, 0.5, # animal 3 +-1.4, -0.2, 1.8) # animal 4 ID - factor( rep(1:4, each=3) ) library(nlme) library(ape) summary(aov(y ~ ID)) Df Sum Sq Mean Sq F value Pr(F) ID 3 0.9625 0.3208 0.1283 0.9406 Residuals8 20.0067 2.5008 (0.3208 - 2.5008) / 3 [1] -0.727 varcomp(lme(y ~ 1, random = ~ 1 | ID)) ID Within 0.0002709644 1.9062505816 attr(,class) [1] varcomp VarCorr(lme(y ~ 1, random = ~ 1 | ID)) ID = pdLogChol(1) Variance StdDev (Intercept) 0.0002709644 0.01646100 Residual1.9062505816 1.38067034 Adaikalavan Ramasamy wrote: Can anyone verify my calculations below or explain why they are wrong ? I have several animals that were measured thrice. The only blocking variable is the animal itself. I am interested in calculating the between and within object variations in R. An artificial example : y - c( 2.2, -1.4, -0.5, # animal 1 -0.3 -2.1 1.5, # animal 2 1.3 -0.3 0.5, # animal 3 -1.4 -0.2 1.8) # animal 4 ID - factor( rep(1:4, each=3) ) 1) Using the ANOVA method summary(aov( y ~ ID )) Df Sum Sq Mean Sq F value Pr(F) ID 3 0.900 0.300 0.1207 0.9453 Residuals8 19.880 2.485 = within animal variation = 2.485 = between animal variation = (0.300 - 2.485)/3 = -0.7283 I am aware that ANOVA can give negative estimates for variances. Is this such a case or have I coded wrongly ? 2) Using the REML approach library(nlme) lme( y ~ 1, rand = ~ 1 | ID) Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 0.01629769 1.374438 = within animal variation = 1.374438^2 = 1.88908 = between animal variation = 0.01629769^2 = 0.0002656147 Is this the correct way of coding for this problem ? I do not have access to a copy of Pinheiro Bates at the moment. Thank you very much in advance. Regards, Adai __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] delete -character from strings in matrix
You will need to escape special characters. Here is an example : my.string - Here is a quote \ in a string my.string [1] Here is a quote \ in a string gsub(\, , my.string) [1] Here is a quote in a string See help(regexp) for more details. Regards, Adai On Sun, 2005-06-12 at 14:10 +0200, Werner Wernersen wrote: Thanks for the reply, Andy! My problem was that I could not get rid of a double quote character within the string. I don't know what I have done before, but now it works...?!?! Sorry for bothering you. Best, Werner Liaw, Andy wrote: Please define does not work. Here's what I get: m - matrix(paste(letters[1:4], does not work.), 2, 2) m [,1] [,2] [1,] a does not work. c does not work. [2,] b does not work. d does not work. gsub(does not work., , m) [1] a b c d structure(gsub(does not work., , m), dim=dim(m)) [,1] [,2] [1,] a c [2,] b d R-2.1.0 on WinXPPro. Andy From: Werner Wernersen Hi! I have strings where occasionally some -chars occur. How can I delete these chars? I tried it with gsub but using as replace does not work. Thanks a lot for any hint! Regards, Werner __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html -- Notice: This e-mail message, together with any attachments, contains information of Merck Co., Inc. (One Merck Drive, Whitehouse Station, New Jersey, USA 08889), and/or its affiliates (which may be known outside the United States as Merck Frosst, Merck Sharp Dohme or MSD and in Japan, as Banyu) that may be confidential, proprietary copyrighted and/or legally privileged. It is intended solely for the use of the individual or entity named on this message. If you are not the intended recipient, and have received this message in error, please notify us immediately by reply e-mail and then delete it from your system. -- __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] ANOVA vs REML approach to variance component estimation
On 6/12/05, Adaikalavan Ramasamy [EMAIL PROTECTED] wrote: Thank you for confirming this and introducing me to varcomp(). I have another question that I hope you or someone else can help me with. I was trying to generalise my codes for variable measurement levels and discovered that lme() was estimating the within group variance even with a single measure per subject for all subjects ! Here is an example where we have 12 animals but with single measurement. y - c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, 1.3, -0.3, 0.5, -1.4, -0.2, 1.8) ID - factor( 1:12 ) Analysis of variance method correctly says that there is no residual variance and it equals to total variance. summary(aov(y ~ ID)) Df Sum Sq Mean Sq ID 11 20.9692 1.9063 However the REML method is giving me a within animal variance when there is no replication at animal level. It seems like I can get components of variance for factors that are not replicated. library(ape) varcomp(lme(y ~ 1, random = ~ 1 | ID)) IDWithin 1.6712661 0.2350218 Am I reading this correct and can someone kindly explain this to me ? It's a spurious convergence in lme. There is no check in lme for the number of observations exceeding the number of groups. There should be. I'll add this to the bug reports list. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] Essay identification
On 12-Jun-05 Berton Gunter wrote:
I assume that you know the usual procedure is to 'score'
each essay by a vector that gives the frequency of occurrence
of commonly used (sometimes adding subject matter specific)
words and phrases. This multivariate response is then fed in
as a training set into your favorite supervised
learning/classification procedure. R has many of these -- trees,
logisic regression, boosting, Random Forests,svm's,LDA,SOM's
(whoops -- that's an Unsupervised one), ... . Try
RSiteSearch('Classification',restrict=('functions').
The devil is in the details as to what works best, I believe.
With only 78 exemplars in 10 groups, unless there is a lot of
separation (disparate styles that you could probably detect
manually) it may be difficult. It also depends on how large
each group is (balance is generally better).
Cheers,
Bert
I would add to Berton's list such scores as numbers of different
words used, sentence lengths, relative frequencies of verbs,
nouns, adjectives, adverbs, and so on, perhaps scaled by overall
length. Length of Essay might even be a discriminant!
You could also look at more subtle characteristics such as
Zipf bins[*] -- the relative numbers of different
words which occur once only, twice, three times, ... (though
I'm not sure how you would score such a thing for classification
purposes).
[*] A term I've just invented inspired by the original instance
of this by the linguist Zipf, later giving rise to the
logarithmic distribution in the historic paper by Fisher,
Corbett Williams in the Numbers of Species and Numbers
of Individuals in butterfly traps.
If you really want to go to town you can try things related to
grammatical complexity, e.g. numbers of subordinate clauses
per sentence, relative clauses, the reach of relative pronouns
(how far from the referring pronoun is the thing referred to)
and so on.
There's quite an extensive literature on this sort of thing.
though it's not as fashionable as it used to be.
Th real problem is that you can get carried away by good
ideas of things to try!
The other factor to bear in mind is that if the Essays
can be grouped by subject this is likely to influence many
of the scores (such as the above).
Hoping this helps and does not distract!
Ted.
E-Mail: (Ted Harding) [EMAIL PROTECTED]
Fax-to-email: +44 (0)870 094 0861
Date: 13-Jun-05 Time: 00:43:10
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Re: [R] ANOVA vs REML approach to variance component estimation
Thank you. On Sun, 2005-06-12 at 18:54 -0500, Douglas Bates wrote: On 6/12/05, Adaikalavan Ramasamy [EMAIL PROTECTED] wrote: Thank you for confirming this and introducing me to varcomp(). I have another question that I hope you or someone else can help me with. I was trying to generalise my codes for variable measurement levels and discovered that lme() was estimating the within group variance even with a single measure per subject for all subjects ! Here is an example where we have 12 animals but with single measurement. y - c(2.2, -1.4, -0.5, -0.3, -2.1, 1.5, 1.3, -0.3, 0.5, -1.4, -0.2, 1.8) ID - factor( 1:12 ) Analysis of variance method correctly says that there is no residual variance and it equals to total variance. summary(aov(y ~ ID)) Df Sum Sq Mean Sq ID 11 20.9692 1.9063 However the REML method is giving me a within animal variance when there is no replication at animal level. It seems like I can get components of variance for factors that are not replicated. library(ape) varcomp(lme(y ~ 1, random = ~ 1 | ID)) IDWithin 1.6712661 0.2350218 Am I reading this correct and can someone kindly explain this to me ? It's a spurious convergence in lme. There is no check in lme for the number of observations exceeding the number of groups. There should be. I'll add this to the bug reports list. __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] slow loading with lme4
it takes a long time to load the lme4 package.anyone else encounter this problem? system.time(library(lme4)) Matrix lattice [1] 19.90 0.30 25.56NANA version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status Patched major2 minor1.0 year 2005 month05 day 29 language R OS:windows 2000 2005-06-13 -- Deparment of Sociology Fudan University Blog:www.sociology.yculblog.com __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] us zipcode data map
Not that I am aware of. Try library(help=maps) for a list of all the functions in the library. Anyhow, I am not sure that a US map with zipcodes will look very good/readable, unless you focus on a very small area (i.e. county). Cheers Francisco From: Mike R [EMAIL PROTECTED] Reply-To: r-help@stat.math.ethz.ch To: r-help@stat.math.ethz.ch Subject: Re: [R] us zipcode data map Date: Fri, 10 Jun 2005 18:06:39 -0700 On 6/10/05, Francisco J. Zagmutt [EMAIL PROTECTED] wrote: library(maps) example(match.map) #for coloring If you want to annotate the map look at ?map.text thanks Francisco, correct me if i am wrong, but maps_2.0-27.tar.gz does many many maps, but not any zipcode maps ? __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
