similar to: Help in ranef Function

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

Hi, I want to remove the entire row from the dataset if any of the row contains blank or NA my dataset look like State Year Y X2 X3 X4 X5 X6 State1 1960 27.8 397.5 42.2 50.7 78.3 65.8 State2 1960 29.9 413.3 38.1 52 79.2 66.9 State1 1961 29.8 439.2 40.3 54 79.2 67.8 State2 1961 30.8 459.7 39.5 79.2 69.6 State1 1962 31.2 492.9 37.3 54.7 77.4 68.7 State2 1962 528.6 38.1 80.2 73.6 State1 1963

> To sum up, I can't figure out how MLWin calculates the > standardized residuals. But I understand that this is not a > question for the R list. > Nevertheless, it would help if someone could point me to some > arguments why not to use them and stick to the results > obtainable by ranef(). Hi Dirk: Well, it is interesting that mlWin and lmer generate the same exact

Hello. I'm running a generalized linear model and am interested in using the random effects that are output for further analysis. My random effect is interacting with two different fixed effects (which which are factors with two levels each). When I retrieve the random effects I get something like this: (Intercept) nutrient (Intercept) light (Intercept) Aa-0 0.59679192

I really hope sombody could help me with the following, I'm having problems accessing the random effect samples following the example on MCMCsamp: (fm1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) set.seed(101); samp0 <- mcmcsamp(fm1, n = 1000, saveb=TRUE) str(samp0) Formal class 'merMCMC' [package "lme4"] with 9 slots ..@ Gp :

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

Dear R list members; I have a 10 x 423 data frame which consisting of response, time, subject, site, plot and covariates (continueous and categorical) measured at the plot level. When the data frame was converted into a groupedData object, a warning appeared > A <- groupedData(ht ~ time | Subject, data = tt, outer = ~ site * plot, + labels=list(y = "Height", x =

I recently replaced a 8600 GT with the above card on openSUSE Tumbleweed x86_64. The screen now flickers and when I change desktops it takes a some 10 to 15 seconds to display the new windows. e.g if I change from desktop 1 (konsole) to desktop 8 (thunderbird) it switches but konsole is displayed for 10 to 15 seconds before thunderbird appears. Occasionally the screen flickers wildly before

Please provide your dmesg and xorg logs from the affected machine. Thanks, -ilia On Wed, Jun 29, 2016 at 8:17 AM, Sid Boyce <sboyce at blueyonder.co.uk> wrote: > I recently replaced a 8600 GT with the above card on openSUSE Tumbleweed > x86_64. > > The screen now flickers and when I change desktops it takes a some 10 to 15 > seconds to display the new windows. > e.g if

Hi I am following the model building strategy that is outlined in the Pinheiro and Bates book wrt including covariates but am having a problem with the plot. Basically I am using 4 covariates (1 of them is continuous) and 3 of them are fine but the 4th one is being shown as a scatterplot despite the fact that it is a factor. I have explicitly declared this to be a factor (pcat<-as.factor(pcat))

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Dear List, Can anyone please explain the difference between cluster() and frailty() in a coxph? I am a bit puzzled about it. Would appreciate any useful reference or direction. cheers, Ehsan > marginal.model <- coxph(Surv(time, status) ~ rx + cluster(litter), rats) > frailty.model <- coxph(Surv(time, status) ~ rx + frailty(litter), rats) > marginal.model Call: coxph(formula =

Hi All, We have a single Centos 5.11 server running a xen hypervisor that went down hard after an extended power outage this weekend. I'm mostly familiar for KVM as that is the majority of what our guests run under and have tried getting up to speed on xen bridging to no avail. The problem is that after the xen server spun back up, the previously defined xen bridges were lost. I've

Dear R guru, I am Saikat Sarkar working as a researcher of Economics in Tampere University, Finland. I am trying to estimate some Garch related tests with Bayesian analysis by R programme. I am not good in R but trying to survive. Anyway I have the coding but not working properly. I have tried to find the problem but failed. I am writing to all R gurus to help me out. Could you please look at

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

Hi Everyone, I have a question regarding the construction of 3D graphs in 'R', BUT these graphs also need to illustrate movement (with time) of the prostate gland (using radiological techniques). I am not sure how to do this in 'R' although I'm sure there is some way of doing it. Below, I have copied and pasted some of the data with which I'm working on. The data

Hi, I have a question about estimating IRT models by using nlme, not just rasch model, but also other models. Behavior Research Methods <http://www.springerlink.com/content/1554-351x/> Volume 37, Number 2 <http://www.springerlink.com/content/1554-351x/37/2/>, 202-218, DOI: 10.3758/BF03192688 Using SAS PROC NLMIXED to fit item response theory models (2005). Ching-Fan

Hi, I'd like to unsubscribe from the list. Thanks Neyra ________________________________ De: "r-help-request@r-project.org" <r-help-request@r-project.org> Para: r-help@r-project.org Enviado: sáb, octubre 30, 2010 5:30:07 AM Asunto: R-help Digest, Vol 92, Issue 31 Send R-help mailing list submissions to r-help@r-project.org To subscribe or unsubscribe via the