similar to: intervals in lme() and ill-defined models

I've moved nlme from Depends to Imports in my coxme package. However, a few of the examples for lmekin use one of the data sets from nlme. This is on purpose, to show how the results are the same and how they differ. If I use data(nlme::ergoStool) the data is not found, data(nlme:::ergoStool) does no better. If I add importFrom(nlme, "ergoStool") the error message is that

Hi all, I have a questions regarding mixed effects models: I'm trying to plot e.g. fitted vs residuals for each level of the random effects, and i'm getting the same error. I guess this might be a problem of the graphic capabilities of R. Is there any way to obtain those plots? library(nlme) attach(ergoStool) names(ergoStool) [1] "effort" "Type"

Using the "ergoStool" data cited in Mixed-Effects Models in S and S-PLUS by Pinheiro and Bates as an example, we have ======== > library(nlme) > fm <- lme(effort~Type-1, data=ergoStool, random=~1|Subject) > summary(fm) Linear mixed-effects model fit by REML Data: ergoStool AIC BIC logLik 133.1308 141.9252 -60.5654 Random effects: Formula: ~1 | Subject

Dear R users, I would like to exactly reproduce a figure like the 1.5 or 1.9 or 4.13 from the book Mixed effects models in S and S-Plus. Not for the sake of it, but because I have my own data I would like to plot in that fashion (no colors) If I write plot(ergoStool) I can get a good informative plot with colors, but I would like to have a B&W one instead. I've played a little with

Greetings- I'm working through Pinheiro and Bates' _Mixed Effects Models in S and S-Plus_ using R (1.3.1 for linux). On page 13 (okay, so I haven't got that far :)) is: plot.design( ergoStool) which returns on my system: > plot.design(ergoStool) Error: couldn't find function "plot.design" any ideas? Thanks.

Hi all,  I was trying to use glht() from multcomp package to construct a contrast on interaction term in a linear model to do some comparisons. I am little uncertain on how to construct contrasts on a 3-way interaction containing a continuous variable, and hope someone can confirm what I did is correct or wrong: The linear model has a continuous dependent variable “y”, with treatment factor

When I try to mount a Windows XP share I get this error: getlmhostsent: Ill formed hosts line [<<<<<<<<<<<<<<<<<<<<] getlmhostsent: Ill formed hosts line [<<<<<<<<<<<<<<<<<<<<] getlmhostsent: Ill formed hosts line

Hi, everyone. Does anyone faced with the MSVC Debug build issues of clang-tblgen and llvm-tblgen? I'm using Windows10 Pro. MSVC and GCC. Here is my cmake setup: cmake -GNinja ../llvm -DLLVM_ENABLE_PROJECTS=clang -DLLVM_TARGETS_TO_BUILD=X86 -DCMAKE_BUILD_TYPE=Debug When building debug with GCC, cmd shows next: D:\llvm-project\buildn2\bin>clang-tblgen.exe --version LLVM

Kia ora list members: I'm having a little difficulty getting the correct standard errors from a glm.object (R 1.9.0 under Windows XP 5.1). predict() will gives standard errors of the predicted values, but I am wanting the standard errors of the mean. To clarify: Assume I have a 4x3x2 factorial with 2 complete replications (i.e. 48 observations, I've appended a dummy set of data at the

After fixing princomp(), recently, {tiny negative eigen-values are possible for non-negative definite matrices} Fritz Leisch drew my attention to the fact the not only eigen() can be funny, but also svd(). Adrian Trappleti found that the singular values returned can be "-0" instead of "0". This will be a problem in something like sd <- svd(Mat) $ d

tl;dr The Debian Xen packages have had a very bad patch which I propose to simply drop, with minor compatibility implications, unless someone can explain what it is for and why it is still needed, and/or has a better plan. I have been going through delta queue in the Debian Xen package. I found a commit (patch) describing itself only this way:

If possible I would like to combine two different character arrays in combinations Array1 <- c("height","weight","age","sex") Array2 <- c("trt0","trt1","trt2") I would like to combine these two character vectors to end up with such ... Array3 "height.trt0.trt1" "height.trt0.trt2"

The lm() function in R seems to handle the inversion of singular X'X matrices (where there is collinearity between regression inputs) in a way where one of the inputs is dropped and this also seems to be the default behavior in SAS (please let me know if i'm wrong about this). In some other packages (i.e. octave ols() function) the pseudo inverse is computed where singular values less

Hi, I am trying to test some contrasts, using glht() in multcomp package on fixed effects in a linear mixed model fitted with lme() in nlme package. The command I used is: ## a simple randomized block design, ## type is fixed effect ## batch is random effect ## model with interaction dat.lme<-lme(info.index~type, random=~1|batch/type, data=dat) glht(dat.lme, linfct = mcp(type

#Dear All, #I'm having a bit of a trouble here, please help me... #I have this data set.seed(4) mydata <- data.frame(var = rnorm(100), temp = rnorm(100), subj = as.factor(rep(c(1:10),5)), trt = rep(c("A","B"), 50)) #and this model that fits them lm <- lm(var ~ temp * subj, data = mydata) #i want to

I'm looking at Pinheiro & Bates "Mixed-Effects Models in S and S-PLUS" at the moment. Several datasets are used, one of which is called "PBIB" (a partially balanced incomplete block design). All the other datasets can be found somewhere or other in R. However, I cannot locate PBIB, and it does not seem to be mentioned in the latest edition of the R Full Reference

I have been trying for hours now to perform an orthogonal contrast through an ANOVA in R. I have done a two-factor factorial experiment, each factor having three levels. I converted this dataset to a dataframe with one factor with nine treatments, as I couldn't work out what else to do. I have set up a matrix with the eight orthogonal contrasts that I wish to perform, but despite

I can not understand why this fails > > faicoutput2 <- list(stuff21 = as.numeric(faicout$coefficients[2]), + stuff31=as.numeric(faicout$coefficients[3]), + stuff41=as.numeric(faicout$coefficients[4]), + stuff32=(stuff21-stuff31), + stuff42=(stuff21-stuff41), +

Dear R users; Ive got two questions concerning nlme library 3.1-65 (running on R 2.2.0 / Win XP Pro). The first one is related to augPred function. Ive been working with a nonlinear mixed model with no problems so far. However, when the parameters of the model are specified in terms of some other covariates, say treatment (i.e. phi1~trt1+trt2, etc) the augPred function give me the following

Hello, R experts, I have a list called dataHP which has 30 elements (m1, m2, ..., m30). Each element is a 7x6 matrix holding yield data from two factors experimental design, with treatment in column, position in row. For instance, the element 20 is: dataHP[[20]] col1 col2 col3 trt1 trt2 trt3 [1,] 22.0 20.3 29.7 63.3 78.5 76.4 [2,]