similar to: anovalist.lm or anova.lmlist?

anova() on three or more objects behaves inconsistently in R. In R anovalist.lm does a sequential ANOVA using pairwise F tests, ignoring all the other objects, so the larger of the two models provides the denominator. In S anova.lmlist uses the denominator from the largest model (smallest residual df) in the set, as does anova.glmlist in both. I suggest that R's anovalist.lm is wrong (that

Hi, Did anyone sucessfully install SJava package to R and was able to call R function from java in redhat linux8.0? I tried several days it still give me error either in libR.so or libjvm.so. for example, I can compile JavaRCall.java(a example with the SJava package) without problem. When I run it, it can connect to R and accomplish part of results, but fail for other calls. The outputs are:

Full_Name: Vaidotas Zemlys Version: 2.9.0 OS: Ubuntu 8.10 Submission from: (NULL) (213.197.173.50) Steps to reproduce the bug: library(nlme) data(Oxboys) qm=lmList(height~age|Subject,data=Oxboys) grid=with(Oxboys,expand.grid(age=seq(min(age),max(age),length=50),Subject=levels(Subject))) res <- predict(qm,grid,se=TRUE) Erreur dans if (pool) { : l'argument est de longueur nulle res <-

Hello R-Helpers, I was trying to use the lmList function to get the lmList graphic similar to Pinheiro and Bates (pg 33). I did not have a problem creating the graphic when I used the Orthodont data frame or 2 other data sets when there are no missing values. My data has missing values. Do I need to remove the missing values before the lmList function will work? for a small example: > a

Dear list members, I'm interested in showing that within-group statistical dependence is negligible, so I can use ordinary linear models without including random effects. However, I can find no mention of testing a model with vs. without random effects in either Venable & Ripley (2002) or Pinheiro and Bates (2000). Our in-house statisticians are not familiar with this, either,

Hello- I understand that it's convention, when comparing two models using the anova function anova(model1, model2), to put the more "complicated" (for want of a better word) model as the second model. However, I'm using lme in the nlme package and I've found that the order of the models actually gives opposite results. I'm not sure if this is supposed to be the case

I stumbled across a mild glitch when trying to compare the result of gam() fitting with the result of lm() fitting. The following code demonstrates the problem: library(gam) x <- rep(1:10,10) set.seed(42) y <- rnorm(100) fit1 <- lm(y~x) fit2 <- gam(y~lo(x)) fit3 <- lm(y~factor(x)) print(anova(fit1,fit2)) # No worries. print(anova(fit1,fit3)) # Likewise. print(anova(fit2,fit3)) #

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

Dear all, I've got a problem in including square variables in lme and lmlist functions. I've tried to work on Oxboys data of Pinheiro and Bates'book, which consist of the heights of 26 boys, each mesured on nine different occasions : > Oxboys Grouped Data: height ~ age | Subject Subject age height Occasion 1 1 -1.0000 140.50 1 2 1 -0.7479 143.40

Dear r-helpers, Can anyone suggest why lm() doesn't complain here: summary(osss.lm1 <- lm(logOdds ~ c.setSize %in% task, data = osss)) whereas in package:nlme (and in package:lme4) osss.lmL <- lmList(logOdds ~ c.setSize %in% task | subj, data = osss) # Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : # contrasts can be applied only to factors with 2 or more

Dear Martin, Have you try to create a new variable for age squared, say agesq? If you fit the model using this new variable you should get the coefficients. So your new model is something like height~age+agesq I hope this helps, Saghir > -----Original Message----- > From: MARTIN Ludovic [SMTP:martinl@mathinfo.ens.univ-reims.fr] > Sent: Wednesday, 07 May, 2003 2:02 PM > To:

I am using rw0641. In my continuing quest to understand repeated measures analysis, I again return to lme. I exported the Potthoff and Roy data Orthodont.dat from S-PLUS 4.5 to avoid capture errors and ran the examples in the R help. I imported the data.frame with data <- read.table("Orthodont.dat",header=T) attach(data) and created the objects Orthodont.fit1 <-

Dear R user I have 2 problems with lmer. The statistical consultance service of my university has recomended to me to expose those problems here. Sorry for this quite long message. Your help will be greatly appreciated... Gilles San Martin 1) anova() I fit a first model : model1 <- lmer(eclw~1 + density + landsc + temp + landsc:temp + (1|region) + (1|region:pop) + (1|region:pop:family),

Dear All, recently, while setting me on the straight and narrow about linear contrasts for a linear mixed effect model, Prof Ripley pointed out that my interpertation of the call anova(mymodel.lme) was not correct, because I was meant to add type = "marginal", as in anova(mymodel.lme, type = "marginal") I tried to look deeper in the issue, asking people, checking on the

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Dear R list: I modelled changes in a variable (mconc) over time (d) for individuals (replicate) given one of three treatments (treatment) using: mconc.lme <- lme(mconc~treatment*poly(d,2), random=~poly(d,2)|replicate, data=my.data) summary(mconc.lme) shows that the linear coefficient of one of the treatments is significantly different to zero, viz. Value Std.Error

Dear r-helpers, After successfully running require(nlme) vfr.lmL <- lmList( estimate ~ (slant + respType + visField + hand)^2 | subject, vfr ) pairs(vfr.lmL, id = 0.01, adj = -0.5) # Pinheiro & Bates (p. 141) produces the following error: Error in sprintf(gettext(fmt, domain = domain), ...) : object "form" not found Any guesses as to what I may have done wrong?

Dea-R community. I'd like to draw your attention to an issue I have recently encountered while doing my current data analysis. I've got an unexpected (to me) result from the command: > augPred(lmList(my.object)), 'my.object' being a grouped data frame of class: > class(my.object) [1] "nfnGroupedData" "nfGroupedData" "groupedData"

Dear useRs, I am working on a series of field experiments (159 in total) carried out in different years in several locations. The cultivars in each experiment are not always the same, in fact they change over time. I would like to get the fitted values and MSE of the individual fits from the following lmList object, so I can use them to fit a mixed model using the fitted values and weight each

I am running an lme model with the main effects of four fixed variables (3 continuous and one categorical – see below) and one random variable. The data describe the densities of a mite species – awsm – in relation to four variables: adh31 (temperature related), apsm (another plant feeding mite) awpm (a predatory mite), and orien (sampling location within plant – north or south). I have read