similar to: Question regarding lmer with binary response

I am having some trouble creating a hierarchy of virtual classes (akin to the class structure in the 'Matrix' package). I think they arise from my not understanding the best way to specify virtual subclasses of a virtual class. please see questions below code. setClass("mom") setClass("kid1", representation("mom", "VIRTUAL"))

Hi, I'm a total beginner in R and this question is probably very simple but I've spent hours reading about it and can't find the answer. I'm trying to reshape a data table from long to wide format. I've tried reshape() and cast() but I get error messages every time and I can't figure why. In my data, I have the length of two fish from each family. My data table (called

Dear all, all data I am talking about can be found at <http://www.metaanalyse.de/tmp/test.dat>. The R-code is located at <http://www.metaanalyse.de/tmp/test.R> I'd like to plot the frequencies for status against year. Typing table(tmp$year) it can be simple seen that some years are missing, e.g. 1975-1978, 1981 etc. Using xyplot to display the data I do not like the fact

Hello, After looking through the archive and documentation for quite some time, I'd be very happy about some help with metaplot and meta.summaries. metaplot: --------- Can I change the label size? I've got 126 values and the intersection of the labels makes it impossible to read them. Why do I have to give sumse (Standard Error) and sumnn (Precision) of the summary estimate? I can

I am simulating sickness among a group of families. Part of the task is to randomly draw who in the family will be sick, randomly drawing from family ID's where Dad =1, Mom = 2, Kid1 = 3, Kid2 = 4., etc. My census of Dads is of the form shown below. Dad_ID Spouse (Y=1;N=0) #Kids #People_Becoming_Sick 1 1 0 1 2

I have been looking into both of these approaches to conducting a GLMM, and want to make sure I understand model specification in each. In particular - after looking at Bates' Rnews article and searching through the help archives, I am unclear on the specification of nested factors in lmer. Do the following statements specify the same mode within each approach? m1 = glmmPQL(RICH ~ ZONE,

am trying to produce two dot plot figures in ggplot2. So far the first one (p) in the program below is working fine. However when I want to move to a faceted plot (p1) I seem to lose my ordering or, more likely, I'm just getting an ordering I am not expecting and I always have trouble understanding ordering in R! What I would like is the 2011 panel to be ordered in descending order as is

> On Nov 14, 2017, at 5:13 AM, Fix Ace via R-help <r-help at r-project.org> wrote: > > Dear R Community, > My data have 3 conditions and each condition has 6 replicates. I am trying to fit my data for a linear mixed model using the lmer function from lme4 package to find the random effects of the replicates; Better venue for this question might be SIG-mixed-models. See the link

---------- Forwarded message ---------- From: <r-help-bounces@r-project.org> Date: Mon, May 11, 2009 at 10:24 PM Subject: The results of your email commands To: das.moumita.online@gmail.com The results of your email command are provided below. Attached is your original message. - Results: Ignoring non-text/plain MIME parts - Unprocessed: What is the function for partial

> 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

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

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

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

Hi, David, Thank you very much for getting back to me! Sorry about the messy code example. I am re-posting here (including the error message): > example.3=data.frame(levels=as.numeric(XXX[,c(4)]),replicate=rep(c("0","1","2","3","4","5"),3),conditions=c(rep("11",6),rep("12",6),rep("13",6)))> example.3? ?

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

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

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))

Hello could you please tell me what is the function to get the absolute value of the real or complex number. most of other languages it is abs(x) , what is it in r? I did few searhces in the help docs for no avail. thanks F.J

I'm estimating two models for data with n = 179 with four clusters (21, 70, 36, 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",

Hi, I am making myself familiar with ggplot2 (I really like the examples at <http://had.co.nz/ggplot2/>). One thing that really annoys me is the default use of white grid lines and a gray background [1, 2]. I simply would like to have black grid lines and a white background. No problem, I thought, "This is R. There is no if. Only how." (fortune("Simon Blomberg")).