search for: pql

Dear R users I encounter a problem when i perform a generalized linear mixed model (binary data) with the lmer function (package lme4) with R 2.1.0 on windows XP and the latest version of package "lme4" (0.96-1) and "matrix" (0.96-2) both options "PQL" and "Laplace" for the method argument in lmer function gave me the same results (random and fixed effects estimates, standard error and p.values). However, Loglikelihood and deviance are different. here is an example reproduced with the bacteria data set available in the MASS packa...

...ndom effect for code_tripnr. The offset (‘offsetter’) accounts for the haul duration and sub sampling factor. There are no extreme outliers in lat/lon. The model we try to fit is: > gamm3<-gamm(count~offset(offsetter)+s(lon,lat),random=list(code_tripnr=~1),family="poisson", niterPQL=200) Maximum number of PQL iterations: 200 iteration 1 iteration 2 Error in MEestimate(lmeSt, grps) : NA/NaN/Inf in foreign function call (arg 1) We tried several things. We added some noise to lon and lat, modelled the density instead of using a count with model offset, and we normali...

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of ?Laplace?, ?AGQ?

I am analysing binomial data using a generalised mixed effects model. I understand that if I use glmmPQL it is not appropriate to compare AIC values to obtain a minimum adequate model. I am assuming that this means it is also inappropriate to use AIC values from lmer since, when analysing binomial data, lmer also uses PQL methods. However, I wasn't sure so please could somebody clarify this f...

Hello all, I'm attempting to fit a generalized linear mixed-effects model using lmer (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call: vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the

Dear R community, I have two questions regarding fitting GLMM using maximum likelihood method. The first one arises from trying repeat an analysis in the Breslow and Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random effects, one subject specific, and one observation specific. Thus if we count random effects, there are more parameters than observations. When I try to run the

...nd 52) named siteid. I'm estimating a logistic regression model with random intercept and another version with random intercept and random slope for one of the independent variables. fit.1 <- lmer(glaucoma~(1|siteid)+x1 +x2,family=binomial,data=set1,method="ML", control=list(usePQL=FALSE,msVerbose=TRUE)) Generalized linear mixed model fit using PQL Formula: glaucoma ~ (1 | siteid) + x1 + x2 Data: set1 Family: binomial(logit link) AIC BIC logLik deviance 236.7448 249.4944 -114.3724 228.7448 Random effects: Groups Name Variance Std.Dev. sitei...

...as well as Pinheiro and Bates and Venables and Ripley, - nlme does not have the facility to do what amounts to a mixed-effects logistic regression. - The canonical alternative is GLMMgibbs, but there are concerns about this as well. - There has been some talk of wrappers around nlme that would add PQL (a technique about which I know nothing) as a way of estimating such equations. Does this accurately summarize the state of software availability? If not, what updates should I know about? If so, what would be the costs and benefits of the following courses of action: 1.) Use nlme, violating the...

Greetings- a reviewer for a paper of mine noted an anomaly in some models I ran using glmmPQL (from the MASS package). Specifically, the models are three-level hierarchical probit models estimated using PQL under R. The anomaly is that the log-likelihoods decrease (or, alternatively -2logLik increases) as variables are added to the null model. This is unusual, and I'm trying to figure...

Dear R users: I am using lmer fo fit binomial data with a probit link function: > fer_lmer_PQL<-lmer(fer ~ gae + ctipo + (1|perm) -1, + family = binomial(link="probit"), + method = 'PQL', + data = FERTILIDAD, + msVerbose= True) The output look like this: > fer_lmer_PQL Generalized linear mixed model fit u...

Dear R Users, I want to fit GLMM with lmer with binomial data and a one-way random effects model with an overall mean and random effects. From R help, Laplace is slower than PQL, but more accurate. When I fit my model with Laplace method with control = list (usePQL = FALSE)), for most data sets it works well, but for some I get an error message (Error in if (any(sd < 0)) return("'sd' slot has negative entries") : missing value where TRUE...

Dear friends, I have asked last few days about cross-random effects using PQL, but I have not receive any answer because might my question was not clear. My question was about analysing the salamander mating data using PQL. This data contain cross-random effects for (male) and for (female). By opining MASS and lme library. I wrote this code sala.glmm <- glmmPQL(fixed=y~...

...have a standard error of 0.4354 from glmm and 0.5338 from glmmML. Any idea about this problem?? Thanks in advance Clemens Tilke library (MASS) data (bacteria) contrasts (bacteria$trt) <- structure (contr.sdif (3), dimnames = list (NULL, c ("drug", "enc"))) bacteria.pql <- glmmPQL (y ~ trt + I(week > 2), random = ~ 1 | ID, family = binomial, data = bacteria) summary (bacteria.pql) library (repeated) y1 <- 1 * (bacteria$y == "y") bacteria1 <- data.frame (bacteria, y1 = y1) bacteria.glmm <- glmm (y1 ~ trt + I(week > 2), nest = ID, d...

...el (if no other model selection criterias are suggested). the syntax looks like this: > mod <- lmer(sfox ~ roefoxratio + (1|fam), data=manu2, family=binomial) The output looks ok, except that the deviance is extremely high (1.798e+308). > mod Generalized linear mixed model fit using PQL Formula: sfox ~ roefoxratio + (1 | fam) Data: manu2 Family: binomial(logit link) AIC BIC logLik deviance 1.797693e+308 1.797693e+308 -8.988466e+307 1.797693e+308 Random effects: Groups Name Variance Std.Dev. fam (Intercept)...

...nsure a light effect bm.surv$death<-4-bm.surv$survival # fitting the model m1<-lmer(cbind(survival, death)~light+species+damage+(1|house), data=bm.surv, family="quasibinomial") summary(m1) # suggests that light is very significant Generalized linear mixed model fit using PQL Formula: cbind(survival, death) ~ light + species + damage + (1 | table) Data: bm.surv Family: quasibinomial(logit link) AIC BIC logLik deviance 227.0558 239.6218 -107.5279 215.0558 Random effects: Groups Name Variance Std.Dev. table (Intercept) 1.8158e-09 4.2613...

Hello, I have compared the potentials of R and Stata about GLMM, analysing the dataset 'ohio' in the package 'faraway' (the same dataset is analysed with GEE in the book 'Extending the linear model with R' by Julian Faraway). Basically, I've tried the 2 commands 'glmmPQL' and 'glmer' of R and the command 'xtmelogit' of Stata. If I'm not wrong, 'glmer' uses the Laplacian approximation as default, corresponding to adaptive Gauss-Hermite approximation with only 1 point, while 'xtmelogit' uses 7 points. In order to compare them,...

Dear list members, In re-running with GLMM() from the lme4 package a generalized-linear mixed model that I had previously fit with glmmPQL() from MASS, I'm getting a warning of a convergence failure, even when I set the method argument of GLMM() to "PQL": > bang.mod.1 <- glmmPQL(contraception ~ as.factor(children) + cage + urban, + random=~as.factor(children) + cage + urban|district, + family=binomial, dat...

...f at useR!2004. Using sparse matrix methods - in particular, Tim Davis' LDL package - we are able to fit models with crossed random effects quickly and effectively. This package also contains an implementation of GLMM for Generalized Linear Mixed Models using either Penalized Quasi-Likelihood (PQL) or ML estimation using the Laplacian approximation to the marginal likelihood. At present method = 'Laplace' is considerably slower than method = 'PQL'. We would recommend using PQL for model building and determining final parameter estimates using method = 'Laplace'. Fe...

Greetings- Using R 1.5.0 under linux and the latest MASS and nlme, I am trying to develop a three-level (two levels of nesting) model with a dichotomous oucome variable. The unconditional model is thus: > doubt1.pql<-glmmPQL(fixed = r.info.doubt ~ 1, random = ~1 | groupid/participantid, + family = binomial, data = fgdata.10statements.df) iteration 1 iteration 2 iteration 3 iteration 4 iteration 5 > summary(doubt1.pql) Linear mixed-effects model fit by maximum likelihood Data: fgdata.10statements.df...

Hi All, does anyone know if the package GLME by J. Pinheiro is available anywhere in any form? checking on the archive I got that it was at some point, as as a beta version (for S-Plus only, alas)... Cheers, Federico ========================= Federico C.F. Calboli Department of Biology University College London Room 327 Darwin Building Gower Street London WClE 6BT Tel: (+44) 020 7679 4395