similar to: glmmPQL and anova

Dear all, I'm trying to access the Sums of Squares resulting from a summary(aov(....)) so I can use them in a function. Is there an easy way to access these? The structure of a summary.aov object is something like this: > str(summary(fhc.aov)) List of 5 $ Error: PPNR :List of 1 ..$ :Classes anova and `data.frame': 1 obs. of 5 variables: .. ..$ Df : num 70 .. ..$

Hello All, I have a question regarding how glmmPQL should be specified. Which of these two is correct? summary(fm.3 <- glmmPQL(cbind(response, 100 - response) ~ expt, data = data.1, random = ~ 1 | subject, family = binomial)) summary(fm.4 <- glmmPQL(response ~ expt, data = data.2, random = ~ 1 | subject, family =

Dear R-users, Suppose I have a function which takes three arguments. I would like to repeatedly apply the function, using a matrix N*3 in which each row supplies the three argements for the function. Is this possible? Thank you for your help in advance! Kind regards, Maarten --------------------------------------------------------------------- Maarten Speekenbrink Psychological Methodology

Greetings RoR Folks, I''ve seen lots of examples that show how to load images into MySQL databases using RoR with the file actually sitting on the filesystem and the file path stored in the database. What I would like to do is upload a file (.jpg, .png, .gif, .mp3, or .wav) into a MySQL BLOB column so that the file is not on the file system, but rather in the MySQL database. The

Hi, I'm new to R and GLMMs, and I've been unable to find the answers to my questions by trawling through the R help archives. I'm hoping someone here can help me. I'm running an analysis on Seedling survival (count data=Poisson distribution) on restoration sites, and my main interest is in determining whether the Nutrients (N) and water absorbing polymer Gel (G) additions to the

Hello, I've been enjoying using the "Mixture and Hidden Markov Models in R" by Visser & Speekenbrink to learn how to apply these analyses to my own data using depmixS4. I currently have a fitted 4-state mixture model with three emissions variables and one binomial covariate (HS). I am trying to compute confidence intervals using the following code, where fmms4s is the model:

On Tue, 30 May 2023 17:43:31 +0000 Heather Lucas <hlucas2 at lsu.edu> wrote: > Hello, > > I've been enjoying using the "Mixture and Hidden Markov Models in R" > by Visser & Speekenbrink to learn how to apply these analyses to my > own data using depmixS4. > > I currently have a fitted 4-state mixture model with three emissions > variables and one

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