similar to: prediction from a logistic mixed effects model

Hi, I am wondering if we can make prediction on a linear mixed model by lmer() from lme4 package? Specifically I am fitting a very simple glmer() with binomial family distribution, and want to see if I can get the predicted probability like that in regular logistic regression?   fit<-glmer(y~x+(1|id),dat,family=binomial)   where y is the response variable (0, 1), and x is a continuous variable.

Hello everyone, I'm quite new to R and am trying to run a logistic model to look at how various measures of boldness in individual animals influences probability of capture, however I also want to include random effects and I'm not sure how to construct a model that incorporates both of these things. Data was collected from 6 different groups of 6 individuals with 10 replicates for

Hi, I have to analyse the number of provisioning trips to nestlings according to a number of biological and environmental factors. I was thinking of building a mixed-effects model with species and nestid as random effects, using a Poisson distribution, but the data are overdispersed (variance/mean = 5). I then thought of using a mixed-effects model with negative binomial distribution, but I have

I have a linear mixed-effects model (from the package nlme) with a random effect; Is there something like an "r squared" for the whole model which I can state? I´d like to kown: How would I do anova for a linear mixed-effects model? Lic. Florencia BonattoUniversidad Nacional de Rio Cuarto, Cordoba. [[alternative HTML version deleted]]

I want to analyze binary, multinomial, and count outcomes (as well as the occasional continuous one) for clustered data. The more I search the less I know, and so I'm hoping the list can provide me some guidance about which of the many alternatives to choose. The nlme package seemed the obvious place to start. However, it seems to be using specifications from nls, which does non-linear

HI, I am using lmer() for a simple mixed effects model. The model is of the form logit(y)~ x + (1|z), where x is an indicator variable and z a multi-level factor. I would like an estimate of the response variable (either y or logit y) with an associated confidence interval for a given value of x. There does not appear to be a predict function written for lmer(). The output for the fixed

Hi all! I am working with GLMM using the binomial family I use the following codes I dropped no significant terms, refitting the model and comparing the changes with likelihood: G.1<-lmer(data$Ymat~stu+spi+stu*sp1+(1|ber),data=data,family="binomial") G.1b<-lmer(data$Ymat~stu+spi+(1|ber),data=data,family="binomial") anova (G.1,G.2) But, when I want to evaluate the

https://bugzilla.samba.org/show_bug.cgi?id=9560 Summary: drop-cache option Product: rsync Version: 3.0.9 Platform: All OS/Version: All Status: NEW Severity: normal Priority: P5 Component: core AssignedTo: wayned at samba.org ReportedBy: colundrum at gmail.com QAContact: rsync-qa at

Hi All, I'm working with some SAS code to analyze an experiment set up as follows: 66 subjects (colonies) treated with a random treatment (1-8) and measured at three time points. The data structure looks like: input colony tmt y1 y2 y3; y=y1; date=*1*; output; y=y2; date=*2*; output; y=y3; date=*3*; output; datalines; 1

Hi I'm getting the intercepts of the Random effects as 0. Please help me to understand why this is coming Zero This is my R code Data<- read.csv("C:/FE and RE.csv") Formula="Y~X2+X3+X4 + (1|State) + (0+X5|State)" fit=lmer(formula=Formula,data=Data) ranef(fit). My sample Data State Year Y X2 X3 X4 X5 X6 S2 1960 27.8 397.5 42.2 50.7 78.3 65.8 S1 1960 29.9 413.3 38.1

Hello I have a data set with fixed and random effects, therefore I am using the lme function: lm(y ~ xfixed, random=~1|xrandom, data) After this I want to get the F-values for both the fixed and random predictors. I can easily get the F-value and df for the xfixed predictors (anova()), but how to get the F-value for the xrandom predictors? Thanks in advance. /R

Hi all, is there a way to calculate standard error of random effect from the estimated model in lme? Best Marcus [[alternative HTML version deleted]]

Hi everyone! I have a simple model that i would like to plot with 95% CIs. It is like follows: m1<-lmer(Richness~Grazing+I(Grazing^2)+(1|Plot),family=poisson) By using the effects package I get two plots, one for the linear term and one for the squared term. Q1: Can I get all in one? I.e. with one line for the whole model? Q2: Can I also visualize the random effects? I would be very happy for

Hi Everyone,  Sorry to ask what I think is a basic question but I really haven't found my answer yet in the archives.  I am trying to run a mixed effects model in R using the lme package. My experiment is such that I am interested in the effects of Temperature (2 levels) and Species (3 levels) on Growth. I collected individuals from three populations within each species. Because individuals

Hi, I’ve tried to find a general approach to my problem without any success, although this might very well be due to my inexperience with R help resources (and statistics in general). My general problem is a straightforward 2 by 2 table (“Belonging to the upper quartile” vs “not-belonging to the upper quartile”, intervention vs non-intervention), but with a random effect addressing the

I am using lme4 to fit a mixed effects model to my data. I have a significant interaction between two variables. My question is what is the correct way to get p-values for single terms involved in that interaction. I have been using stepwise backwards deletion and model comparisons to get p-values,and refitting the model using a REML approach to get estimates.However, presumably to get the p

I'm using FC4 and R 2.3.1 to fit a mixed effects logistic regression. The response is 0/1 and both the response and the age are the same for each pair of observations for each subject (some observations are not paired). For example: id response age 1 0 30 1 0 30 2 1 55 2 1 55 3 0 37 4 1 52 5 0 39 5 0 39 etc. I get the

How can I get prediction intervals from a mixed-effects model? Consider the following example: library(nlme) fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1) df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5), Subject=rep(Subject[1], 4), Sex=rep(Sex[1], 4))) predict(fm3, df3.1, interval='prediction') # M01 M01

Dear all, I've been reading for many days trying to predict with lmer but I haven't managed to do it. I've fitted an allometric model for trees where I have included climatic variables and diameter in the fixed part and in the random part I've included the experimental sites where trees are and also their provenance region. The model is like this :

Hello all, I would like to fit a mixed effects model, but my response is of the negative binomial (or overdispersed poisson) family. The only (?) package that looks like it can do this is glmm.ADMB (but it cannot run on Mac OS X - please correct me if I am wrong!) [1] I think that glmmML {glmmML}, lmer {Matrix}, and glmmPQL {MASS} do not provide this "family" (i.e. nbinom, or