Displaying 4 results from an estimated 4 matches for "pbcseq".
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2010 Dec 10
1
survreg vs. aftreg (eha) - the relationship between fitted coefficients?
...ith left truncation I hope)
library(survival)
library(eha)
# COMPARE coefs between survreg ('survival' pkg) and aftreg ('eha' pkg)
#Fitting NULL models (no covariates) results in (approximately) the same coefs (which is good!)
m1_NULL=survreg(Surv(futime/365, status==1) ~ 1, data=pbcseq)
m2_NULL=aftreg(Surv(futime/365, status==1) ~ 1, data=pbcseq)
c(m1_NULL$coef, 1/m1_NULL$scale) #--> intercept= 3.878656 , shape = 1.478177
c(m2_NULL$coef[1], exp(m2_NULL$coef[2])) #--> intercept= 3.878859 , shape=1.478150
# NOW I adjust for covariates
m1=survreg(Surv(futime/365, status...
2003 Aug 04
1
coxph and frailty
...llowing model, using the Pbc data (with time-varying
covariates) from Therneau and Grambsch's book:
fitf <- coxph(Surv(start,stop,event==2) ~ age + log(bili) + log(protime) +
log(albumin) + edema + frailty(group),
na.action=na.exclude, data=Pbcseq)
Then I obtain:
> fitf[10]
$frail
[1] 0.06273372 0.16192093 0.10050877 0.37716999 -0.20853156 -0.71887977
[7] -0.10922275
And:
> fitf[11]
$fvar
[1] 0.03631634 0.03261972 0.03750465 0.04459689 0.05113802 0.08107876 0.11482236
My understanding is that fitf$frail are the group level fr...
2024 Jun 15
1
Hard crash of lme4 in R-devel
...-6
loaded via a namespace (and not attached):
[1] minqa_1.2.5 MASS_7.3-60.2 compiler_4.5.0 Rcpp_1.0.11 splines_4.5.0
[6] nlme_3.1-164 grid_4.5.0 nloptr_2.0.3 boot_1.3-30 lattice_0.22-6
>
> # Create a data set of all PBC subjects with 4 or more values
> count <- table(pbcseq$id)
> data4 <- subset(pbcseq, id %in% names(count)[count>3])
> data4$year <- data4$day/365.25
>
> fit <- lmer(log(bili) ~ year + (1+year | id), data4)
-- more lines than I can capture --
= NULL, verbose = 0L, control = list(optimizer = "nloptwrap", restart_ed...
2010 Dec 11
0
is there a packge or code to generate markov chains in R
...ith left truncation I hope)
library(survival)
library(eha)
# COMPARE coefs between survreg ('survival' pkg) and aftreg ('eha' pkg)
#Fitting NULL models (no covariates) results in (approximately) the same coefs (which is good!)
m1_NULL=survreg(Surv(futime/365, status==1) ~ 1, data=pbcseq)
m2_NULL=aftreg(Surv(futime/365, status==1) ~ 1, data=pbcseq)
c(m1_NULL$coef, 1/m1_NULL$scale) #--> intercept= 3.878656 , shape = 1.478177
c(m2_NULL$coef[1], exp(m2_NULL$coef[2])) #--> intercept= 3.878859 , shape=1.478150
# NOW I adjust for covariates
m1=survreg(Surv(futime/365, status...