Check results for 'coxphf'

using R version 2.10.0 Patched (2009-11-18 r50482)
using session charset: ASCII
checking for file 'coxphf/DESCRIPTION' ... OK
checking extension type ... Package
this is package 'coxphf' version '1.0-2'
checking package name space information ... OK
checking package dependencies ... OK
checking if this is a source package ... OK
checking for executable files ... OK
checking whether package 'coxphf' can be installed ... OK
checking package directory ... OK
checking for portable file names ... OK
checking for sufficient/correct file permissions ... OK
checking DESCRIPTION meta-information ... OK
checking top-level files ... OK
checking index information ... OK
checking package subdirectories ... OK
checking R files for non-ASCII characters ... OK
checking R files for syntax errors ... OK
checking whether the package can be loaded ... OK
checking whether the package can be loaded with stated dependencies ... OK
checking whether the name space can be loaded with stated dependencies ... OK
checking for unstated dependencies in R code ... OK
checking S3 generic/method consistency ... OK
checking replacement functions ... OK
checking foreign function calls ... OK
checking R code for possible problems ... NOTE
decomposeSurv: no visible binding for global variable 'response'
checking Rd files ... NOTE
prepare_Rd: coxphfplot.Rd:70-71: Dropping empty section \note
prepare_Rd: coxphftest.Rd:61-62: Dropping empty section \note
checking Rd metadata ... OK
checking Rd cross-references ... OK
checking for missing documentation entries ... OK
checking for code/documentation mismatches ... OK
checking Rd \usage sections ... OK
checking line endings in C/C++/Fortran sources/headers ... OK
checking line endings in Makefiles ... OK
checking for portable use of $BLAS_LIBS ... OK
checking examples ... ERROR
Running examples in 'coxphf-Ex.R' failed.
The error most likely occurred in:

> ### * coxphf
>
> flush(stderr()); flush(stdout())
>
> ### Name: coxphf
> ### Title: Cox regression with Firth's penalized likelihood
> ### Aliases: coxphf
> ### Keywords: survival
>
> ### ** Examples
>
> # fixed covariate and monotone likelihood
> time<-c(1,2,3)
> cens<-c(1,1,1)
> x<-c(1,1,0)
> sim<-cbind(time,cens,x)
> sim<-data.frame(sim)
> coxphf(sim, formula=Surv(time,cens)~x) #convergence attained!
coxphf(formula = Surv(time, cens) ~ x, data = sim)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood

coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
x 1.329104 1.929072 3.777658 0.2914006 528.7306 0.9151565 0.3387499

Likelihood ratio test=0.9151565 on 1 df, p=0.3387499, n=3

> coxph(sim, formula=Surv(time,cens)~x) #no convergence!
Warning in fitter(X, Y, strats, offset, init, control, weights = weights, :
Ran out of iterations and did not converge
Call:
coxph(formula = Surv(time, cens) ~ x, data = sim)

coef exp(coef) se(coef) z p
x 21.0 1.29e+09 29282 0.000716 1

Likelihood ratio test=2.2 on 1 df, p=0.138 n= 3
>
> # time-dependent covariate
> test2 <- data.frame(list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8),
+ stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17),
+ event=c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0),
+ x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) ))
>
> summary( coxphf( formula=Surv(start, stop, event) ~ x, data=test2))
coxphf(formula = Surv(start, stop, event) ~ x, data = test2)

Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood

coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
x -0.1213111 0.7920055 0.8857583 0.2031077 4.21294 0.02621190 0.8713839

Likelihood ratio test=0.02621190 on 1 df, p=0.8713839, n=10
Wald test = 0.02346091 on 1 df, p = 0.8782646

Covariance-Matrix:
x
x 0.6272728
>
>
> # time-dependent effect
> # the coxphf function can handle interactions of a (fixed or time-dependent)
> # covariate with time
> # such that the hazard ratio can be expressed as a function of time
>
> summary(coxphf(formula=Surv(start, stop, event)~x+x:log(stop), data=test2, firth=FALSE))
coxphf(formula = Surv(start, stop, event) ~ x + x:log(stop),
data = test2, firth = FALSE)

Model fitted by Standard ML
Confidence intervals and p-values by Profile Likelihood

coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
x 1.854398 27.038116 6.387854e+00 NaN NaN 0 1
log(stop) -11.976909 9.618655 6.287737e-06 NaN NaN 0 1
x:log(stop) -1.548477 13.168942 2.125715e-01 NaN NaN 0 1

Likelihood ratio test=-27.09733 on 3 df, p=1, n=10
Wald test = 2.551232 on 3 df, p = 0.4661035

Covariance-Matrix:
x log(stop) x:log(stop)
x 731.0597 127.75834 -355.4551
log(stop) 127.7583 92.51852 -61.1405
x:log(stop) -355.4551 -61.14050 173.4210
>
> # note that coxph would treat x:log(stop) as a fixed covariate
> # (computed before the iteration process)
> # coxphf treats x:log(stop) as a time-dependent covariate which
> # changes (for the same individual!) over time
>
>
> # time-dependent effect with monotone likelihood
>
> test3 <- data.frame(list(start=c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8),
+ stop =c(2, 3, 6, 7, 8, 9, 9, 9,14,17),
+ event=c(1, 0, 0, 1, 0, 1, 1, 0, 0, 0),
+ x =c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) ))
>
> summary( coxphf( formula=Surv(start, stop, event) ~ x+x:log(stop), data=test3))
elapsed time (check, wall clock): 60:14