similar to: Multilevel Modeling using glmmPQL

Dear R-list I have a question concerning the argument "adjustSigma" in the function "lme" of the package "nlme". The help page says: "the residual standard error is multiplied by sqrt(nobs/(nobs - npar)), converting it to a REML-like estimate." Having a look into the code I found: stdFixed <- sqrt(diag(as.matrix(object$varFix))) if (object$method

>On Wed, 3 Aug 2005, Bernd Weiss wrote: > >> I am trying to replicate some multilevel models with binary outcomes >> using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. > >That's not going to happen as they are not using the same criteria. the glmmPQL and lmer both use the PQL method to do it ,so can we get the same result by

Dear all, I am trying to replicate some multilevel models with binary outcomes using R's "lmer" and "glmmPQL" and Stata's gllmm, respectively. The data can be found at <http://www.uni-koeln.de/~ahf34/xerop.dta>. The relevant Stata output can be found at <http://www.uni- koeln.de/~ahf34/stataoutput.txt>. First, you will find the unconditional model,

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

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

Dear All, I would like to ask you to help me with my memeory. I remember using some function that would list all the possible methods I could apply to an object. Say, if I had an object of class=lme, it would tell me that that I could do stuff like qqnorm(myobjct), or VarCorr(myobject). In general, a very complete list. I though this list of all possible methods would pop out by typing

Hello: Will someone PLEASE help me with this problem. This is the third time I've posted it. When I appply anova() to two equations estimated using glmmPQL, I get a complaint, > anova(fm1, fm2) Error in anova.lme(fm1, fm2) : Objects must inherit from classes "gls", "gnls" "lm","lmList", "lme","nlme","nlsList", or

Hello, Is there an easy way to format the output of a result table that R generates from a regression? I like the table, but would like to limit the number of decimal points in the entries if possible. For instance I would like only 3 digits of precision for the Value, Std.Error. (And if it would be easy to get rid of scientific notation, that would be good to know too). So ideally keep the

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Hello, Any advice or pointers for implementing Sobel's test for mediation in 2-level model setting? For fitting the hierarchical models, I am using "lme4" but could also revert to "nlme" since it is a relatively simple varying intercept model and they yield identical estimates. I apologize for this is an R question with an embedded statistical question. I noticed that a

Dear WizaRds, This is mostly a statistics question, but I'm figuring that R is the right solution (even before I start!) I have some bio data of heart rate versus time (rats taken from resting to maximal heart rate). I want to regress heart rate on time. The data have been normalized such that resting heart rate is zero at time=0, so that all curves intersect at the origin (and at the origin

Dear R-Helpers, I want to compare the results of outputs from glmmPQL and lmer analyses. I could do this if I could extract the coefficients and standard errors from the summaries of the lmer models. This is easy to do for the glmmPQL summaries, using > glmm.fit <- try(glmmPQL(score ~ x*type, random = ~ 1 | subject, data = df, family = binomial), TRUE) > summary(glmmPQL.fit)$tTable

Hello, I have difficulties to deal with multilevel model. My dataset is composed of 10910 observations, 1237 plants nested within 17 stations. The data set is not balanced. Response variable is binary and repeated. I tried to fit this model model<- glmmPQL( y ~ z1.lon*lun + z2.lat*lun + z1.lon*lar + z2.lat*lar + z1.lon*sca + z2.lat*sca +z1.lon*eta + z2.lat*eta, random = ~ lun + lar + sca

Hello, I'm running a simulation study of a multilevel model with binary response using the binomial probit link. It is a random intercept and random slope model. GLMMPQL and lmer fail to converge on a *significant* portion of the *generated* datasets, while MlWin gives reasonable estimates on those datasets. This is unacceptable. Does anyone has similar experiences? Regards, Roel de

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,

I have fit a model with glmmPQL function in MASS library. I fit a binomial longitudinal response variable nested in 17 stations. I would like to know how I can obtain elements of diagnostic checks about these models in order to choose best model. I use summary(), but can I use other functions like in lme, for example anova? I would be thankfull for all the insights. Fabrizio Consentino.

Greetings- I'm working with data that are multilevel in nature and have a dichotomous outcome variable (presence or absence of an attribute). As far as I can tell from reading archives of the R and S lists, 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

Dear all I use the gls function but in contrast to the lm function in which when I type summary(lm(...))$coef I receive all the coefficients (estimate, Std. Error, t-value and pvalue), with gls when I type summary(gls(...))$coef I only receive the estimate of the reg. coefficient without std. error and t- and p-values. Dou you have any suggestion how to solve my problem? With kind regards