Displaying 4 results from an estimated 4 matches for "fit6".
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2011 Sep 12
1
coxreg vs coxph: time-dependent treatment
...t with coxph having cluster option
fit4 <- coxph(Surv(start,stop,event)~transplant,
data=heart, weights = iptw)
fit4 # fit with coxph
# coxreg
fit5 <- coxreg(Surv(start,stop,event)~transplant + cluster(id),
data=heart, weights = iptw)
fit5 # fit with coxreg from eha having cluster option
fit6 <- coxreg(Surv(start,stop,event)~transplant,
data=heart, weights = iptw)
fit6 # fit with coxreg from eha
############################
> exp(coef(fit3)) # HR from coxph having cluster option
transplant1
0.3782417
> exp(coef(fit4)) # HR from coxph
transplant1
0.3782417
> exp(...
2008 Aug 30
1
Unable to send color palette through plot.Design to method="image"
...ply a col
argument. I even took a crack at hacking the plot.Design function,
adding a col=col parameter to be passed in the function call to
image(), but failed to get the desired effect:
# else image(xseqn, y, zmat, xlab = xlab, ylab = laby , col = col)
library(Hmisc); library(Design)
lr.fit6 <- lrm(death ~
rcs(BL_CHOLEST.A,c(180,220,280))*rcs(BL_HDL.A,c(40,55,70))*Sex, data =
pref900)
# str(pref900[,c("BL_HDL.A","BL_CHOLEST.A","death")])
$'data.frame': 910659 obs. of 3 variables:
$ BL_HDL.A : num 34 35 40 46 39 45 46 34 42 52 ...
$...
2008 Aug 25
1
Specifying random effects distribution in glmer()
...=1, family=poisson) #note: can't use nAGQ>1, not
yet implemented
summary(fit5)
Here 'seizures' is a count and 'id' is the subject number.
This fit works, but uses the Poisson distribution with the gamma heterogeneity.
Based on the example in the help for glmer(), I tried
fit6<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) +
(1|pgamma(id, shap, scal)), data=pdata, nAGQ=1, start=c(shap=1, scal=1),
family=poisson) #note: can't use nAGQ>1, not yet implemented
summary(fit6)
but this ends up with "Error in pgamma(id, shap, scal) : obj...
2005 Mar 14
1
calling objects in a foreloop
...fit1<-lm(dBA.spp16$sp2.dBA.ha~dBA.spp16$sp1.dBA.ha)
> fit2<-lm(dBA.spp16$sp3.dBA.ha~dBA.spp16$sp1.dBA.ha)
> fit3<-lm(dBA.spp16$sp3.dBA.ha~dBA.spp16$sp2.dBA.ha)
> fit4<-lm(dBA.spp16$sp5.dBA.ha~dBA.spp16$sp4.dBA.ha)
> fit5<-lm(dBA.spp16$sp6.dBA.ha~dBA.spp16$sp4.dBA.ha)
> fit6<-lm(dBA.spp16$sp5.dBA.ha~dBA.spp16$sp6.dBA.ha)
> fit7<-lm(dBA.spp16$sp1.dBA.ha~dBA.spp16$sp4.dBA.ha)
> fit8<-lm(dBA.spp16$sp1.dBA.ha~dBA.spp16$sp5.dBA.ha)
>
> dBA.spp16.fits<-matrix(NA, nrow=8, ncol=5)
> colnames(dBA.spp16.fits)<-c("formula","intercept&q...