search for: fitd

...Time+factor(Group), random=~1|SS,data=blah$alldata) followed by anova(fita,fitb). How do I get the omnibus test for the main effects i.e. for Time and factor(Group)? I could drop each from the model, i.e. fitc<-lme(Post~ factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) fitd<-lme(Post~Time+ factor(Group)*Time, random=~1|SS,data=blah$alldata) and then run anova(fita,fitc) anova(fita,fitd) but I don't like this option as it will have in interaction that contains a factor that is not included in the model as a main effect. How then do I get...

Hello everyone, I tried to use coxph.detail() to get the hazard function. But a warning messge always returns to me, even in the example provided by its help document: > ?coxph.detail > fit <- coxph(Surv(futime,fustat) ~ age + rx + ecog.ps, ovarian, x=TRUE) > fitd <- coxph.detail(fit) Warning message: data length [37] is not a sub-multiple or multiple of the number of rows [12] in matrix Can anyone suggest why it does not work? I use R 2.1.1 with Windows XP Thanks a lot. Zhen Zhang

...Time+factor(Group), random=~1|SS,data=blah$alldata) followed by anova(fita,fitb). How do I get the omnibus test for the main effects i.e. for Time and factor(Group)? I could drop each from the model, i.e. fitc<-lme(Post~ factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) fitd<-lme(Post~Time+ factor(Group)*Time, random=~1|SS,data=blah$alldata) and then run anova(fita,fitc) anova(fita,fitd) but I don't like this option as it will have in interaction that contains a factor that is not included in the model as a main effect. How then do I get...

Hello all, I have a data set where the response variable is the percent cover of a specific plant (represented in cover classes 0,1,2,3,4,5, or 6). This data set has a lot of zeros (plots where the plant was not present). I am trying to model cover class of the plant as a function of both total nitrogen and shrub cover. After quite a bit of research I have come across a conditional approach

Dear All, I've been?searching for?appropriate codes to compute the rate of change and the curvature?of ?nonparametric regression model whish was denoted by a smooth function?but?unfortunately?don't manage to?do?it. I presume that such characteristics from a smooth curve can be determined by the first and second derivative operators. The following are the example of fitting a

...pre is an indicator variable (and the covariate of interest) I want to investigate if pre has a significant impact on time to event for patients who died and for those who suffered disease progression (as well as it's impact on the overall time to event). The code I have used is as follows: fitd <- crr(dat1$ccr,dat1$Crcens,dat1$pre,failcode=1,cencode=0) fitp <- crr(dat1$ccr,dat1$Crcens,dat1$pre,failcode=2,cencode=0) In these cases I get p-values of 0 and 0.66 respectively. What I would now like to do, is to plot two cumulative incidence curves - one for the 'pre' variable s...