search for: fitb

...e: lattice > fitAB <- lmer(flowRate~Nozzle+(Nozzle|Operator),data=Nozzle, method="ML") Warning messages: 1: In .local(x, ..., value) : Estimated variance-covariance for factor ?Operator? is singular 2: In .local(x, ..., value) : nlminb returned message false convergence (8) > fitB <- lmer(flowRate~1+(1|Operator), data=Nozzle, method="ML") > anova(fitAB, fitB) Data: Nozzle Models: fitB: flowRate ~ 1 + (1 | Operator) fitAB: flowRate ~ Nozzle + (Nozzle | Operator) Df AIC BIC logLik Chisq Chi Df Pr(>Chisq) fitB 2 359.36 362.98 -177.68 fitAB 9 362.13 378.39 -1...

...as two levels and Time which has three levels: fita<-lme(Post~Time+factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) I want to get the omnibus significance tests for each factor and the interaction. I believe I can get the omnibus test for the interaction by running the model: fitb<-lme(Post~Time+factor(Group), random=~1|SS,data=blah$alldata) followed by anova(fita,fitb). How do I get the omnibus test for the main effects i.e. for Time and factor(Group)? I could drop each from the model, i.e. fitc<-lme(Post~ factor(Group)+factor(Group)*Time, random=~1|SS,data=...

...:r-help-bounces at r-project.org] Im Auftrag von John Sorkin Gesendet: Monday, September 07, 2009 4:00 PM An: r-help at r-project.org Betreff: [R] Omnibus test for main effects in the face of aninteraction containing the main effects. R 2.9.1 Windows XP UPDATE, Even my first suggestion anova(fita,fitb) is probably not appropriate as the fixed effects are different in the two model, so I don't even know how to perform the ombnibus test for the interaction! I am fitting a random effects ANOVA with two factors Group which has two levels and Time which has three levels: fita<-lme(Post~Tim...

...a,pgu2a,log(agea),firstinta/1000,totsezbasea) > fita <- crr(snearma$with.Withtime,csaea,covaea,failcode=2,cencode=0) and in case b: > covaeb <- cbind(sexb,fshb,fdrb,nsignb,eegb,th1b,th2b,stype1b,stype2b,stype3b,stype4b,stype5b,pgu1b,pgu2b,(ageb/10)^(-1),firstintb,log(totsezbaseb)) > fitb <- crr(snearmb$with.Withtime,csaeb,covaeb,failcode=2,cencode=0) csaea and csaeb are the censoring indicators for a and b respectively which equal 1 for the event of interest, 2 for the competing risks event and 0 otherwise. Can anyone suggest a reason for the error message? I've tried run...

...ictions. If `se = TRUE', a list with components fit: Predictions se.fit: Estimated standard errors So, I imagined I'd get the same result for predictions arrived at these two ways: things <- list() things$fitA <- predict(foo.glm, type = "response") things$fitB <- predict(foo.glm, type = "response", se.fit = TRUE)$fit however, that is not the case Warning messages: 1: longer object length is not a multiple of shorter object length in: predictor + offset 2: longer object length is not a multiple of shorter object length in: se.fit * abs(...

...my difficulties with getting a legend to print on a survplot from the rms package. I am plotting the following: survplot(survest(fita), n.risk=T, conf='none', cex.n.risk=.85, dots=T, col='gray10', lty=2) survplot(survest(fit), n.risk=F, conf='none', add=T) survplot(survest(fitb), n.risk=F, conf='none', col='gray20', add=T, lty=4) I have tried to place 'label.curves=list(method="arrow", cex=.8)' and others as suggested in ?survplot in any and all of the above commands, but I do not get a legend regardless of whether I try to plot only one...

I'm attempting to do model selection with AIC, using a glm and a lognormal distribution, but: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian(link="log")) ## gives the same result as either of the following: fit1<-glm(BA~Year,data=pdat.sp1.65.04, family=gaussian) fit1<-lm(BA~Year,data=pdat.sp1.65.04) fit1 #Coefficients: #(Intercept) Year2004 # -1.6341