similar to: Multiple tests on repeated measurements

Dear r-helpers, I have a question about multiple testing. Here an example that puzzles me: All matrixes and contrast vectors are presented in treatment contrasts. 1. example: library(multcomp) n<-60; sigma<-20 # n = sample size per group # sigma standard deviation of the residuals cov1 <- matrix(c(3/4,-1/2,-1/2,-1/2,1,0,-1/2,0,1), nrow = 3, ncol=3, byrow=TRUE, dimnames =

hi, I use the libvirt-python to manage my virtual machine. When I delete a volume use vol.delete(0), sometimes it note me that has occur the error: libvirtError: internal error '/sbin/lvremove -f /dev/vg.vmms/lvm-v097222.sqa.cm4' exited with non-zero status 5 and signal 0: Can't remove open logical volume

Hi, I am currently working with the sem package in R, to create pathway diagrams. Id like to use the standardized path coeffcients. To get these, I use std.coef. However, using this yields only the standardized coefficients, but does not give me the standard error. Does someone know how to get std.coef to show the standard error of the standardized path coefficients as well? Thanks, Bastiaan

Dear Martin, Since no one else has picked up on this, I?ll take a crack at it: The proposal is to define the S3 class of model-frame objects as c(?model.frame?, ?data.frame?) (not the formal class of these objects, even though this feature was coincidentally introduced in S4). That?s unlikely to do harm, since model frames would still ?inherit? data.frame methods. It's possible that some

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

Dear R-Developers, The traffic today on s-news where Terry Therneau, I, and others are reporting some of the problems we have had using or converting applications to SV4 reminded me of something Duncan Temple Lang had mentioned to me a year ago that I wanted to follow up on. I recall that Duncan said either that if SV4 were to be implemented in R that it would not be the default behavior, or

When formula() is applied to the output of model.frame() it ignores the formula in the model.frame's 'terms' attribute: > d <- data.frame(A=log(1:6), B=LETTERS[rep(1:2,c(2,4))], C=1/(1:6), D=rep(letters[25:26],c(4,2)), Y=1:6) > m0 <- model.frame(data=d, Y ~ A:B) > formula(m0) Y ~ A + B > `attributes<-`(terms(m0), value=NULL) Y ~ A:B This is in part

I would like to limit the significant figures of the calibrated parameters determined by genoud() in the Rgenoud package. Below is some example output, where column 1 is model run number, columns 2-7 are the parameter values, and columns 8-12 are model fit statistics. I would like genoud to internally limit parameters to 4 decimal places as shown in this output. It is clear that the function is

I don't have a copy of SV4 (or SV3, where model.frame was introduced), but S+ 8.3 (based on SV4) puts the class "model.frame" on model.frame()'s return value but has no methods (in the default packages) for class "model.frame". Perhaps that is why R omitted the class. However, S+ 8.3's (and problably S's) formula.data.frame did look for a "terms"

Full_Name: Kem Phillips Version: 2.7.1 (2008-06-23) OS: Windows Xp professional Submission from: (NULL) (98.221.200.108) The Hmisc function describe fails, giving the error message: Error in formatDateTime(dd, atx, !timeUsed) : could not find function "format.dates" Loading the chron package, where function dates apparently resides, does not fix the problem. Note

 Dear R-helpers, I'm a new R-user and I was trying to gain some experience with the GNLS function of the NLME package.  This is an extract from my dataset (it's a 432x6 data.frame) called "input", in the first column I have the values that I need to fit, while the remaining columns are input variables for the theoretical model, the function "mymodel" (which returns a

hi all, I have two questions about libvirt. At first, how can I write a XML file that libvirt could give me a command that include this: if=virtio. -drive file=/dev/vg.vmms/lvm-v097218.sqa.cm4,if=none,id=drive-ide0-0-0,format=raw -device ide-drive,bus=ide.0,unit=0,drive=drive-ide0-0-0,id=ide0-0-0 event I have let the <target dev=vda bus=virtio> the XML file also make the

Hi People ?,? I have this configuration in my samba?s server and when I use this command : wbinfo -u This message is showed : Error looking up domain users. I need find AD users to use samba. Thanks in advance,? Eugenio,? wbinfo --sequence SV0-SP : 1 BUILTIN : 1 LIVROS : DISCONNECTED REDE_SP : 4516361 smbclient -L localhost -U% Domain=[REDE_SP] OS=[Unix] Server=[Samba 3.0.10-1.4E.9] ? ? ?

Hi all, Was wondering if any body would know if the book "Programming with Data" a guide to S language could be used as a guide to working with the R language. Comments, suggestions, a book review is welcomed. Thanks Josef Bohart -------------- next part -------------- An HTML attachment was scrubbed... URL:

Hi, I use glht to make multcomp, using Tukey, from a glm model. It is possible to get a more simplified output of result? Somethink like ordering by letters. Thanks Ronaldo -- Human kind cannot bear very much reality. -- T. S. Eliot, "Four Quartets: Burnt Norton" -- > Prof. Ronaldo Reis J?nior | .''`. UNIMONTES/Depto. Biologia Geral/Lab. de Ecologia | : :' :

Hi, Alan Thanks for making it clear. But I was more confused now :( I tested on a simple program and used both gcov and lcov to get branch coverage. The code and build commands as below: *Example simple.cc* #include <string> // If not comment this line, the branch coverage won't reach to 100% // #include <iostream> int main(int argc, const char* argv[]) { std::string str =

Does R, or will R, be integrating the changes to the Chambers/Lucent S language under their version 5.0? If not already, then when? John Thaden Little Rock, Arkansas, USA -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe"

> From: fharrell@virginia.edu [mailto:fharrell@virginia.edu] > [snip] > I think your code is more complex that is really needed. > > The problem with defaulting to deparse(...) is that > multiple function pass-throughs return the wrong result: > [snip] > So I don't see a large role for the deparse(...) method. > Actually one of the reasons that I included the

Dear all, I'm facing a problem in estimation of glm model with weibull distribution. I run this : eqn0<-formula(fdh~cup1+cup2+cup3+cup4+fin1+vd1+cm2+cm4+milieu+cpro1+cpro2+cpro3a+cpro3b+schef+log(y)) regWeib0<-vglm(eqn0,family=weibull,subset(br, fdh<1)) I have en estimation but there is a message saying that regularity conditions are violated : Message d'avis : In eval(expr,

R-helpers: I have a dataset that has 168 subjects and 12 variables. Some of the variables have missing data and I want to use the multiple imputation capabilities of the "mice" package to address the missing data. Given that mice only supports linear models and generalized linear models (via the lm.mids and glm.mids functions) and that I need to fit Cox models, I followed the previous