similar to: glmmPQL in R and S-PLUS 6 - differing results

I have a problem with function 'inout()' in package 'splancs' on CRAN-R Version 1.2.3 under FreeBSD4.3-STABLE. The following script produces and draws points and a polygon-surrounded area. Repeating the same script many times shows, that points on the polygon-line often, but not always, are outside of the polygon-area. library(splancs) # dataset with polygon (convex

1. Table dealings has the field: id(int(11)), inout(varchar(11)) If I use this select: @deals = Dealing.find(:all,:condition=>["id = ? and inout = ?","1","in"]) then rails will return error with the tips of SQL syntax error if I change it to : @deals = Dealing.find(:all,:condition=>["dealings.id = ? and dealings.inout =

I've written out codes for one particular file, and now I want to generate the same kind of graphs and files for the rest of similar data files. For example, a file "8.csv" would look like such: enc_callee inout o_duration type A out 342 de B in 234 de C out 132 de E in 111 de A in 13 cf H in 15.7 cf G out 32 de A out 32 cf I in 14 de K in 189 de J out 34.1 cf B in 98.7 de H out

These macros will be used by multiple VMWare modules for handling host communication. Signed-off-by: Sinclair Yeh <syeh at vmware.com> Reviewed-by: Thomas Hellstrom <thellstrom at vmware.com> Reviewed-by: Alok N Kataria <akataria at vmware.com> Cc: Thomas Gleixner <tglx at linutronix.de> Cc: Ingo Molnar <mingo at redhat.com> Cc: "H. Peter Anvin" <hpa at

These macros will be used by multiple VMWare modules for handling host communication. Signed-off-by: Sinclair Yeh <syeh at vmware.com> Reviewed-by: Thomas Hellstrom <thellstrom at vmware.com> Reviewed-by: Alok N Kataria <akataria at vmware.com> Cc: Thomas Gleixner <tglx at linutronix.de> Cc: Ingo Molnar <mingo at redhat.com> Cc: "H. Peter Anvin" <hpa at

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

Hello, I am using the R function glmmPQL to fit a logistic GLMM, with weights. I am finding that I get fairly different parameter estimates in glmmPQL from fitting the full dataset (with no "weight" statement) and an equivalent, shorter dataset with the weights statement. I am using the weights statement in the 'glmmPQL' function exactly as in the 'glm' function. I

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, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

I have come across the previous communication on this list in September (copied below) because I had received the same error message. I understand from Brian Ripley's reply that anova should not be used with glmmPQL because it is not an adequate method, and that this is now shown with an error message. My question is, what method *should* be used? Using summary does not give me the result

Dear R-help, I recently tried implementing glmmPQL in 2.3.1, and I discovered a few differences as compared to 2.2.1. I am fitting a regression with fixed and random effects with Gamma error structure. First, 2.3.1 gives different estimates than 2.2.1, and 2.3.1, takes more iterations to converge. Second, when I try using the anova function it says, "'anova' is not available

Hi, In glmmPQL in the MASS library, the function uses repeated calls to the function lme(), using ML. Does anyone know how you can change this to REML? I know that in lme(), the default is actually set to REML and you can also specify this as 'method=REML' or 'method'ML' but this isn't applicable to glmmPQL(). I'd appreciate any help or advice! Thanks, Emma

hello all, i'm an R newbie struggling a bit with the glmmPQL function (from the nlme pack). i think i've managed to run the model successfully, but can't seem to plot the resulting function. plot(glmmPQL(...)) plots the residuals rather than the model... i know this should be basic, but i can't seem to figure it out. many thanks in advance. j -- View this message in context:

Hello, currently I am trying to fit a generalized linear mixed model using the glmmPQL function in the MASS package. I am working with the data provided by the book from Heck, Thomas and Tabata (2012) - https://www.routledge.com/Multilevel-Modeling-of-Categorical-Outcomes-Using-IBM-SPSS/Heck-Thomas-Tabata/p/book/9781848729568 I was wondering, which variance-covariance structure the glmmPQL

Hola! I am encountering the following problem, in a multilevel analysis, using glmmPQL from MASS. This occurs with bothj rw1062 and r-devel, respectively with nlme versions 3.1-38 and 3.1-39 (windows XP). > S817.mod1 <- glmmPQL( S817 ~ MIEMBROScat+S901+S902A+S923+URBRUR+REGION+ + S102+S103+S106A+S108+S110A+S109A+S202+S401+S557A+S557B+ + YHOGFcat,

Dear R users, I have been using the pdMat class "pdIdnot" (from the mgcv package)instead of "pdIdent" to avoid overflow in GLMM fits with the MASS package function glmmPQL, of the following form: fit1 <- glmmPQL(fixed=y0~-1+xx0, random=list(gp=pdIdent(~-1+zz0)), family=binomial) # vulnerable to overflow fit2 <- glmmPQL(fixed=y0~-1+xx0,

Dear all, I have two questions concerning model simplification in GlmmPQL, for for random and fixed effects: 1. Fixed effects: I don't know if I can simply specify anova(model) and trust the table that comes up with the p value for each variable in the fixed effects formula. I have read that the only way to test for fixed effects is to do approximate wald tests based on the standard errors

I fit the following model using glmmPQL from MASS: fit.glmmPQL <- glmmPQL(ifelse(class=="Disease",1,0)~age+x1+x2,random=~1|subject,family=binomial) summary(fit.glmmPQL) The response is paired (pairing denoted by subject), although some subjects only have one response. Also, there is a perfect positive correlation between the paired responses. x1 and x2 can and do differ within each

Dear listers, glmmPQL (package MASS) is given to work by repeated call to lme. In the classical outputs glmmPQL the Variance Structure is given as " fixed weights, Formula: ~invwt". The script shows that the function varFixed() is used, though the place where 'invwt' is defined remains unclear to me. I wonder if there is an easy way to specify another variance

Hello R users, I am trying to run a model with a binary response variable (nesting success: 0 failure, 1 success) and 8 fixed terms. Nesting success was examined in 72 cases in 34 territories (TER) during a 6 study years. Territories are nested within 14 patches (PATCH). I want to run a model taking into account these nested factors and repeated observation. To do this, I assume that the best