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...Then, I want to compare these 2 models with Likelihood Ratio Test. Here are my lmer codes that I don't feel comfortable about their correctness. model1 <- try(lmer(cbind( yvect, nvect-yvect) ~ 1 + (1 | center), family = binomial, niter = 25, method = "Laplace", control = list(usePQL = FALSE) )) model2 <- try(lmer(cbind( yvect, nvect-yvect) ~ trt*center + ( 1 | center) , family = binomial, niter = 25, method = "Laplace", control = list(usePQL = FALSE) )) (I have attached outputs below) What I don't understand is; I thought in model2 I have defined cent...

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/FALSE needed) In these cases I get an estimate for the fixed effect but do not get an e...

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) in addtion, I try these two models, it seems they are same. what is the difference between these two model. Is this a cell means model? m00 <- glm(sc ~aa-1,data = p5) m000 <- glm...

...it did. We want to fit GLMM with lmer with binomial data and a one-way random effects model (overall mean is a fixed effect and there are random effects for each binomial). We are using the Laplace method. We are simulating multiple data sets and use the Laplace method with control = list (usePQL = FALSE)). For most data sets it works well, but for some we get an error message (Error in if (any(sd < 0)) return("'sd' slot has negative entries") : missing value where TRUE/FALSE needed) In these cases we get an estimate for the fixed effect but do not get an estima...

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) thanks, Aimin Yan

I have a dataset like this: p aa index x y z sdx sdy sdz delta as ms cur sc 1 821p MET 1 -5.09688 32.8830 -5.857620 1.478200 1.73998 0.825778 13.7883 126.91 92.37 -0.1320180 111.0990 2 821p THR 2 -4.07357 28.6881 -4.838430 0.597674 1.37860 1.165780 13.7207 64.09 50.72 -0.0977129 98.5319 3 821p GLU 3 -5.86733 30.4759

..., and 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...

Hi, I am using lmer to analyze habitat selection in wolverines using the following model: (me.fit.of <- lmer(USED~1+STEP+ALT+ALT2+relM+relM:ALT+(1|ID)+(1|ID:TRKPT2),data=vdata, control=list(usePQL=TRUE),family=poisson,method="Laplace")) Here, the habitat selection is calaculated using a so-called discrete choice model where each used location has a certain number of alternatives which the animal could have chosen. These sets of locations are captured using the TRKPT2 random group...

...ing the R-help. I am imaging that in theory it should be possible with some call to attr(), but i have so far had no success. An example model output would be: > modeltest<-lmer(Coleodactylus_amazonicus_N~USD + (1|site),data=SFArray,family=poisson,method="Laplace",control=list(usePQL=FALSE, msVerbose=TRUE)) > summary(modeltest) ------------ Generalized linear mixed model fit using Laplace Formula: Coleodactylus_amazonicus_N ~ USD + (1 | site) Data: SFArray Family: poisson(log link) AIC BIC logLik deviance 75.94996 81.68603 -34.97498 69.94996 Random ef...

...lems: model2<-lmer(int.length~mean.sst+(1|female)) Warning message: Estimated variance for factor 'female' is effectively zero in: `LMEoptimize<-`(`*tmp*`, value = list(maxIter = 200L, tolerance = 1.49011611938477e-08, I have tried disabling PQL iterations using control = list(usePQL = FALSE, msVerbose=TRUE), following Douglas Bates' recommendation on the mailing list archives but I still get a similar message. Does this mean that the variance among subjects is too close to zero for estimation of the random effects? I compared the random effects model to a linear model with...

Dear R-users, I have a frustrating problem that I am hoping has a simple fix. I am running a series of lmer models from the lme4 package of the general form: model<-lmer(y~x1 + x2 ..... + xn + (1|site),data=dataframe,family=poisson,method="Laplace",control=list(usePQL=FALSE,msVerbose=TRUE)) where the same model is executed multiple times on a bootstrapped dataframe. For each bootstrapped model run the resulting model object is used to return the AIC (and models are then compared using a bootstrapped weight - frequency of runs a given model had the lowest AIC)....

Dear R users, I am estimating Poisson mixed models using glmmPQL (MASS) and lmer (lme4). We know that glmmPQL do not provide the correct loglikelihood for such models (it gives the loglike of a 'pseudo' or working linear mixed model). I would like to know how the loglike is calculated by lmer. A minor question is: why do glmmPQL and lmer give different degrees-of-freedom for the same

...l = mc)) } if (method %in% c("ML", "REML")) method <- "Laplace" if (method == "AGQ") stop("method = \"AGQ\" not yet implemented for supernodal representation") if (method == "PQL") cv$usePQL <- TRUE glmFit <- glm.fit(X, Y, weights = weights, offset = offset, family = family, intercept = attr(mt, "intercept") > 0) Y <- as.double(glmFit$y) mer <- .Call(mer_create, fl, Zt, X, Y, 0, nc, cnames) if (!is.null(start)) mer...

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of

...umber (here 6) varying) In R-archives I came across some threads that treated this problem, nevertheless they refer to lmer when using it with family = "binomial", so the solutions that were offered did not solve my problem (family="quasipoisson") (e.g. adding control=list(usePQL=FALSE,msVerbose=TRUE) does not make sense nor changes anything - error remains). A sample data (d.rba) set is pasted at the end. attach (d.rba) y <- d.rba$ms cut2 <- d.rba$cut^2 boot <- 10 index <- seq(1, length(y), 1) cut.pred <- seq(0,20,2) cut2.pred<-cut.pred^2 pred <- m...

Dear all, I get an error message when I run my model and I am not sure what to do about it. I try to determine what factors influence the survival of voles. I use a mixed-model because I have several voles per site (varying from 2 to 19 voles). Here is the model: ### fm5 <-lmer(data=cdrgsaou2, alive~factor(pacut)+factor(agecamp)+factor(sex)+ResCondCorp+(1|factor(cdrgsa ou2$ids)),

Dear list, I am having some problems with extracting Variance Components from a random-effects model: I am running a simple random-effects model using lme: model<-lme(y~1,random=~1|groupA/groupB) which returns the output for the StdDev of the Random effects, and model AIC etc as expected. Until yesterday I was using R v. 2.0, and had no problem in calling the variance components of the