search for: gm1

Hi list, I'm working with gamm models of this sort, using Simon Wood's mgcv library: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group in order to test whether tensor product smooths vary across factor levels. I was wondering if comparing those two models would be enough to conclude? I saw a preceding p...

...of group1, Factor2 is a binary variable for individuals of group 2 I use gam models of this sort with random effects coded using a s( ..., bs="re") term: gm<-gam(Y~Factor+te(x1,x2,by=Factor) )+s(Individual,bs="re"),dat=Data,family=binomial(link=logit),method="REML") gm1<-gam(Y~Factor+te(x1,x2)+ te(x1,x2,by=Factor1)+ te(x1,x2,by=Factor2)+s(Individual,bs="re"),dat=Data,family=binomial(link=logit),method="REML") 1)First question: is it OK to use gam() to model a binary variable with random effects coded as a "bs="re" term"?...

...The package passes the routine parts of R CMD check. The following test shows that, with lme4 loaded, coef(summary([object of class "mer"])) works in the global environment, but not in a function defined inside the namespace of the package. The output ends with: > coeftab.default(gm1) [1] "summaryDefault" "table" Error in object$coefficients : $ operator is invalid for atomic vectors Calls: coeftab.default -> coef -> coef -> coef.default which indicates that inside the function, summary() is calling summary.default instead of seeing the summary m...

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp)) install.packages("aod") library(aod) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd...

Dear list, I am fitting a logistic multi-level regression model and need to test the difference between the ordinary logistic regression from a glm() fit and the mixed effects fit from glmer(), basically I want to do a likelihood ratio test between the two fits. The data are like this: My outcome is a (1,0) for health status, I have several (1,0) dummy variables RURAL, SMOKE, DRINK, EMPLOYED,

...lt;- logLik(obj1) L1 <- logLik(obj2) L01 <- as.vector(- 2 * (L0 - L1)) df <- attr(L1, "df") - attr(L0, "df") list(L01 = L01, df = df, "p-value" = pchisq(L01, df, lower.tail = FALSE)) } gm0 <- glm(cbind(female,male)~date,family = binomial, data = liz3) gm1 <- glmer(cbind(female,male)~date+(1|dam),family=binomial,data= liz3) lrt(gm0, gm1) and I have compared the deviances as follows: (d0 <- deviance(gm0)) (d1 <- deviance(gm1)) (LR <- d0 - d1) pchisq(LR, 1, lower.tail = FALSE) As in some of the examples posted, although the varianc...

Hi all, I try to ask helps again about chan_h323 extensions. I define this in h323.conf: [general] port = 1720 bindaddr = 0.0.0.0 allow=gsm allow=ulaw gatekeeper = DISABLE context=default [gm1] type=friend host=192.168.1.20 context=default [gm2] type=friend host=192.168.1.25 context=default and I have in extensions.conf : [demo] ... [default] include => demo exten => 1020,1,Dial(H323/gm1@192.168.1.20) exten => 1025,1,Dial(...

...sed to the function no longer calls the S4 method ... Any explanations/workarounds for this ... ? cheers Ben Bolker ## before loading the lme4 package: > summary function (object, ...) UseMethod("summary") <environment: namespace:base> ## now load lme4: library(lme4) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) ## examine "summary": > summary standardGeneric for "summary" defined from package "base" function (object, ...) standardGeneric("summary"...

Dear All Sorry for what appears a trivial matter - I'm new to R and am stumbling ahead. I have a table of numerical data (36 rows by 12 columns) such as below: GM1 GM2 GM3 GM4 GM5 ...etc GM12 Run1 1 2 1 2 3 ... Run2 2 1 3 2 1 ... ... Run36 2 1 1 1 1 I would like to plot simple line graphs of some of the runs or all 36 to compare together but I can't even get a plot to have the GM1-...

Hello, everyone I have a piece of code that looks like this: mrets <- merge(mrets, BMM.SR=apply(mrets, 1, MyFunc, ret="BMM.AV120", stdev="BMM.SD120")) mrets <- merge(mrets, GM1.SR=apply(mrets, 1, MyFunc, ret="GM1.AV120", stdev="GM1.SD120")) mrets <- merge(mrets, IYC.SR=apply(mrets, 1, MyFunc, ret="IYC.AV120", stdev="IYC.SD120")) mrets <- merge(mrets, FCA.SR=apply(mrets, 1, MyFunc, ret="FCA.AV120", stdev="FCA.SD1...

Dear all, I am running glmer() in R. How can I retrieve the estimates of fixed effects and the variance of the random effects from the result? Thank you so much. Joe ___________________________________________________ ±zªº¥Í¬¡§Y®É³q ¡Ð ·¾³q¡B®T¼Ö¡B¥Í¬¡¡B¤u§@¤@¦¸·d©w¡I [[alternative HTML version deleted]]

Hi, I'm working with gamm models of this sort: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group if I use plot(gm1$gam), I obtain 3 different surface plots, one for each level of my factor but I would like to create more complex contour plots for those 3 graphics an...

...d from the timestamp seem to be just prior to the crash, but shouldn't lead to the crash of the nfs server. The lack of logout after the 15:22:54 one suggests that this was the point that the file server went offline. The only directory that was mounted at that time was in /export (/dev/mirror/gm1a): alson@damaged:~$ gmirror status Name Status Components mirror/gm0 DEGRADED ad4s1 mirror/gm1 COMPLETE ad8s1 ad10s1 The server had been up for about 250 days with similar (and usually significantly heavier) load with 6.2-STABLE before the upgrade. The evening a...

...s since all (or its interaction) have a p-value < 0.1, I get a model with 4 variables and 2 interactions and an AIC of 33.879 So my questions: Why didn't the stepAIC give me the model with AIC=33.879? And which model should I think of as the best? For my calculations I used these formulae: gm1<-glm(cpi~time+tank+...,data=d1) gm2<-stepAIC(gm1) summary(gm2) #to get the "best" model -> AIC=40.008 #afterwards I reduced manually using the formula: summary(glm(cpi~time+tank+...,data=d1)) giving me a model with AIC=33.879 Hope you understand what I did, and that you can help...

...ata.frame(cbind(id,group)) a id group 1 1 1 2 2 1 3 3 1 4 4 1 5 5 2 6 6 2 7 7 3 8 8 3 9 9 3 10 10 3 11 11 3 12 12 3 Result should be (gm = groupmember) id group gm1 gm2 gm3 gm4 gm5 gm6 gm7 etc. 1 1 2 3 4 NA NA NA NA 2 1 1 3 4 NA NA NA NA 3 1 1...

Dear list, I am trying to use MuMIn to compare all possible mixed models using the dredge function on binomial data but I am getting an error message that I cannot decode. This error only occurs when I use glmer. When I use an lmer analysis on a different response variable every works great. Example using a simplified glmer model global model: mod<- glmer(cbind(st$X2.REP.LIVE,

Hello, I am using glmer() from lmer(lme4) to run generalized linear mixed models. I can't figure out how to extract the z values for the fixed effects that are reported using the summary function . Any help would be appreciated. Thanks, Spencer [[alternative HTML version deleted]]

Hello, I am using glmer() from lmer(lme4) to run generalized linear mixed models. However, I am having a problem extracting the standard errors for the fixed effects. I have used: summary(model)$coef fixed.effects(model) coef(model) to get out the parameter estimates, but do not seem able to extract the se's. Anybody have a solution? Thanks, John

I've searched the help archives of both lists and apologize if I missed the answer to my question: Is there an update on developing mcmcsamp for glmer? I'm using R v. 2.7.2 (on our Unix server - will hopefully be updated soon) and 2.8.1 on my PC and get the message for both: gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),family = binomial, data = cbpp) temp<-mcmcsamp(gm1, 1000) Error in .local(object, n, verbose, ...) : Update not yet written Here is the session info if that's helpful: PC: > sessionInfo() R version 2.8.1 (2008-12-22) i...

...to find genes that are statistically significant across the four experiments. I carried out one-way anova as follows: sTest<-read.table("myData.dat",header = T, row.names = 1) group <- gl(4,4,16, label=c("Glucose","Citrate", "Tide","Dawn")) gm1 <- (x~group, data=sTest) I received error messages that "x" is unknown, and did not know how to go further from here. Please help!!! James [[alternative HTML version deleted]]