similar to: Problem with ci.lmer() in package:gmodels

Hi, I am trying to calculate ci from lmer. However, I have the error message below: library(gmodels) library(lme4) fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) ci(fm2) Error in as.vector(x, mode) : cannot coerce type 'S4' to vector of type 'any' In addition: Warning message: In mean.default(x, na.rm = na.rm) : argument is not

Full_Name: Thomas R Famula Version: 2.2.1 OS: Linux - fedora core 4 Submission from: (NULL) (169.237.28.28) Sorry to bother - I hope this is a simple fix. Here is the set of error messages I recieved in trying to install the "gmodels" package. I typed in "install.packages(c("gmodels"))" as the root and recieved the following series of replies:

Dear All, I'm having a problem using functions in the gmodels library on an object of class mer from the lmer package. Code for a reproducible example is below. # Load lme4 library and sample data library(lme4) library(faraway) library(gmodels) data(penicillin) # Fit a linear mixed effects model fit4 <- lmer(yield ~ treat + (1|blend), penicillin) # Extract confidence intervals

Library gmodels include a function CrossTable that is useful for crosstabulation. In the help, it is indicated that one can call this function as CrossTable(data), were data is a matrix. However, when I try to use this option, it doesn't help. Any idea? Is there a bug? Thanks for your help. Prof. Albert Sorribas Grup de Biomatem?tica i Bioestad?stica Departament de Ci?ncies M?diques B?siques

Dear r-helpers, I sent a cc of a recent message about a problem with ci.lmer() in the gmodels package to the author (Gregory R Warnes), and the message bounced. If the author or someone else is maintaining this package or this function, would you kindly supplement the author's name and/or address with a current maintainer and/or provide a current email address?

Is the code below showing a bug in Crosstable()? My expectation was that the values produced by xtabs were rounded instead of truncated: library(gmodels) abc <- c("a", "a", "b", "b", "c", "c") def <- c("d", "e", "f", "f", "d", "e") wgt <- c(0.8, 0.6, 0.4, 0.5, 1.4, 1.3)

Hi R-users, I have the following problem with CrossTable function within ?gmodels? package: the output of the function (format ?spss? and asresid=T) can not be stored within another object. For example: >library(gmodels) >data(infert, package = "datasets") > CrossTable(infert$education, infert$induced)->aa # the function prints everything ok on the screen > aa # works

Hello, I have a question as to how the syntax for glht(package multcomp) and estimable (gmodels) works, since I'm not getting everything from the documents I've googled so far, especially with models with 2nd order terms. A modestly complex model: 2-way anova with one continuous covariate, no random effects(and no repeated measures) to keep it modestly complex: Y = treatmentgroup + sex

Hello, We have submitted the updated version of gdata, gmodels, gplots and gtools to CRAN. Summary of the changes is attached at the end. Best, Nitin ______________________ Nitin Jain, PhD <nitin.jain at pfizer.com> Non Clinical Statistics Pfizer, Inc. (Groton, CT) Bldg: 260, # 1451 Ph: (860) 686-2526 (Office) Fax: (860) 686-6170 Brief description of changes: CHANGES IN GDATA 2.1.2

Hello, We have submitted the updated version of gdata, gmodels, gplots and gtools to CRAN. Summary of the changes is attached at the end. Best, Nitin ______________________ Nitin Jain, PhD <nitin.jain at pfizer.com> Non Clinical Statistics Pfizer, Inc. (Groton, CT) Bldg: 260, # 1451 Ph: (860) 686-2526 (Office) Fax: (860) 686-6170 Brief description of changes: CHANGES IN GDATA 2.1.2

Hi, I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following. X <- matrix(rnorm(50),10,5) Y <- matrix(rnorm(50),10,5) lm(Y~X) Call: lm(formula = Y ~ X) Coefficients: [,1] [,2] [,3] [,4] [,5] (Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727 X1 0.2007 -0.8505 0.0520

> On Oct 22, 2017, at 3:56 PM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > David, > Thank you for responding to my post. > > Please consider the following output (typeregional is a factor having two levels, "regional" vs. "general"): > Call: > glm(formula = events ~ type, family = poisson(link = log), data = data, > offset =

> On Oct 22, 2017, at 5:01 PM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > David, > Again you have my thanks!. > You are correct. What I want is not technically a contrast. What I want is the estimate for "regional" and its SE. There needs to be a reference value for the contrast. Contrasts are differences. I gave you the choice of two references

David, predict.glm and se.fit were exactly what I was looking for. Many thanks! John John David Sorkin M.D., Ph.D. Professor of Medicine Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone) 410-605-7119 (Fax)

David, Thank you for responding to my post. Please consider the following output (typeregional is a factor having two levels, "regional" vs. "general"): Call: glm(formula = events ~ type, family = poisson(link = log), data = data, offset = log(SS)) Deviance Residuals: Min 1Q Median 3Q Max -43.606 -17.295 -4.651 4.204 38.421 Coefficients:

David, Again you have my thanks!. You are correct. What I want is not technically a contrast. What I want is the estimate for "regional" and its SE. I don't mind if I get these on the log scale; I can get the anti-log. Can you suggest how I can get the point estimate and its SE for "regional"? The predict function will give the point estimate, but not (to my knowledge)

Hello and sorry to disturb. I'm trying to plot the confidence intervals for the fixed effects of a lme. I want to obtain graphically, if it is possible, a bar with Estimate, upper and lower CI for each level of the factors. I know how to do for a lm model but for a lme one, I tried with plot(intervals(...)) and plot(ci(...)) from the gmodels package but it doesn't work well. Thanks

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

After I save an object that contains reference class objects in some of its slots to a file and then re-load it, all.equal() seems to break for me. Is this a bug in all.equal, or is it likely caused by bad implementation of methods on my side? (I see that "'all.equal()' gains methods for 'environment's and 'refClass'es" for R 3.2.0, but that was a little while

If you are indeed using lme and not lmer then the needed function is VarCorr(). However, 2 recommendations. First, this is a busy list and better emails subject headers get better attention. Second, I would recommend using lmer as it is much faster. However, VarCorr seems to be incompatible with lmer and I do not know of another function to work with lmer. Hence, a better email subject header