similar to: effects packages for mixed model?

Hello I am trying to compare two models using anova(), however I get a message error (see below). In the net I only found some information on certain library(car) for which one should use anova with A capital letter (Anova instead of anova), but I could not find car library as it says it does not exist. > Model <- lm(interceptG ~ SW + TSC + FSC + PF + SlopeG + K, data=AllTrait) >

Dear Sirs, i just downloaded the R programm on my Macbook, but I can´t open Rcmdr, although I installed the needed Rcmdr-packages. I would be very happy, if you could help me. Telephone: +49 151 10868600 (Germany) or e-mail Yous sincerely, Selda Korkmaz sk@seldakorkmaz.com www.seldakorkmaz.com [[alternative HTML version deleted]]

Hello, I have a MANOVA model and I want to test the following hypothesis: LBM = 0 where B is the parameter estimates. Is there any function to do this in R? Cheers, Philippe -- Philippe Hup? Institut Curie, CNRS UMR 144, INSERM U900 26 rue d'Ulm 75005 Paris - France Email : Philippe.Hupe at curie.fr T?l : +33 (0)1 56 24 69 91 Fax: +33 (0)1 56 24 69 11 website :

Hello! I have run a simple regression - lm and created a regression object "myreg". I can see all the coefficients when I print(myreg). Then I tried to run vif(myreg) from the package "car". However, it's giving me an error: in vif.lm(regr.f) : there are aliased coefficients in the model Very sorry for my question: Is there any way to get the vif's for all predictors?

Dear List, Several times I use this package I get the error message shown below. When I work out simple examples, it turns out to be fine, but when working with real and moderate size data sets I always get the same error. Do you know what could be the cause of the problem? Error in apply(mod.matrix[, components], 1, prod) : subscript out of bounds Error in

Dear all, How does the effect() function in the effects package calculate effects and standard errors for glm quasipoisson models? I was using effect() to calculate the impact of increasing x to e + epsilon, and then finding the expected percent change. I thought that this effect (as a percentage) should be exp(beta*epsilon), where beta is the appropriate coefficient from the model, but

Is there a way to use full information maximum likelihood (FIML) to estimate missing data in the sem package? For example, suppose I have a dataset with complete information on X1-X3, but missing data (MAR) on X4. Is there a way to use FIML in this case? I know lavaan and openmx allow you to do it, but I couldn't find anything in the documentation for the sem package. Thanks! -- Dustin Fife

Hi everyone, Running the vif() function from the car package like ---------------------------------------------------- > reg2 <- lm(CARsPur~Delay_max10+LawChange+MarketTrend_20d+MultiTrade, data=data.frame(VarVecPur)) > vif(reg2) Delay_max10 LawChange MarketTrend_20d MultiTrade 1.010572 1.009874 1.004278 1.003351

Hi everyone! I have a simple model that i would like to plot with 95% CIs. It is like follows: m1<-lmer(Richness~Grazing+I(Grazing^2)+(1|Plot),family=poisson) By using the effects package I get two plots, one for the linear term and one for the squared term. Q1: Can I get all in one? I.e. with one line for the whole model? Q2: Can I also visualize the random effects? I would be very happy for

Hi all, I am attempting to learn my way through the sem package by constructing a simple structural model for some of my data on bird diversity, abundance, and primary productivity. I have constructed a covariance matrix between these variables as per the following: >S_matrix = matrix(c( >+ 0.003083259, 0, 0, >+ 0.143870284, 89.7648490, 0, >+ 0.276950919,

Dear R fans, I have got a difficult sounding problem. For fitting a linear model using continuous response and then for re-fitting the model after excluding every single variable, the following functions can be used. library(MASS) model = lm(perf ~ syct + mmin + mmax + cach + chmin + chmax, data = cpus) dropterm(model, test = "F") But I am not sure whether any similar functions is

Hi, I would like to use the effect() function (actually a slightly modified version of it) on the output of the lmer() function in the lme4 package. But the effects package requires the nlme pacvkage, which is incompatible with lme4. Workaround? ______________________________________________ Professor Michael Kubovy University of Virginia Department of Psychology for mail add: for FedEx or

Hi, I am wondering if anyone knows of an easy way to fit a saturated model using the sem package on raw data? Say the data were: mtcars[, c("mpg", "hp", "wt")] The model would estimate the three means (intercepts) of c("mpg", "hp", "wt"). The variances of c("mpg", "hp", "wt"). The covariance of mpg with

Dear useRs, I very much like the effect display of the proportional odds model on page 29 (Figure 8) of the following paper by John Fox: http://socserv.mcmaster.ca/jfox/Papers/logit-effect-displays.pdf It really gives a very concise overview of the model. I would like to use it to illustrate the proportional odds mixed models we fit here for a project on Diabetes but I can't seem to reproduce

The model matrix for the code at the end the email is shown below. Since the model matrix doesn't have -1, I think that it is made of dummy regressors rather than deviation regressors. I'm wondering how to make a model matrix using deviation regressors. Could somebody let me know? > model.matrix(aaov) (Intercept) A2 B2 B3 A2:B2 A2:B3 1 1 0 0 0 0 0 2

Dear friends, Sorry for this somewhat generically titled posting but I had a question with using contrasts in a manova context. So here is my question: Suppose I am interested in doing inference on \beta in the case of the model given by: Y = X %*% \beta + e where Y is a n x p matrix of observations, X is a n x m design matrix, \beta is m x p matrix of parameters, and e is a

Dear r-devel list members, On a couple of occasions I've encountered the issue illustrated by the following examples: --------- snip ----------- > mod.1 <- lm(Employed ~ GNP.deflator + GNP + Unemployed + + Armed.Forces + Population + Year, data=longley) > mod.2 <- update(mod.1, . ~ . - Year + Year) > all.equal(mod.1, mod.2) [1] TRUE > > f <-

Dear John Fox, I am using Snowleopard with a Intel Core 2 Duo processor. I have installed the R console and followed your steps at: http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/installation-notes.html but every time I try "library(Rcmdr)" it loads Tcl/tk infinitely. I have tried loading "library(tcltk)" by itself and it does the same thing. I have tried with X11 running to

Hi, I have recently installed R on my mac and am trying to install R commander. When I type: install.packages("Rcmdr",dependencies=TRUE) the following message appears I have also tried installing commander via the package installer window. When I do this a large number of error messages (over 50) appear. They are mainly saying that certain dependencies are not available, or that they

Hi Folks, Please, when I ask the option reg.line at the scatterplot in package car, the OLS models includes a constant? If not how can I do it sing the following code: scatterplot(lfirms ~ lscale, data=dataset, reg.line=lm, smooth=FALSE, labels=FALSE, span=0.5, xlab="Relative Plant Fixed Cost", ylab="Relative Number of Firms", pch=c(18),