* using log directory '/CRANPkg/check/gcmrec.Rcheck'
* using R version 2.1.1, 2005-06-20 
* checking for file 'gcmrec/DESCRIPTION' ... OK
* this is package 'gcmrec' version '0.9-1'
* checking if this is a source package ... OK
* checking whether package 'gcmrec' 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 package dependencies ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R files for syntax errors ... OK
* checking R files for library.dynam ... OK
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking Rd files ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking for CRLF line endings in C/C++/Fortran sources/headers ... OK
* creating gcmrec-Ex.R ... OK
* checking examples ... ERROR
Running examples in gcmrec-Ex.R failed.
The error most likely occurred in:

> ### * gcmrec
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: gcmrec
> ### Title: General Class of Models for recurrent event data
> ### Aliases: gcmrec
> ### Keywords: survival
> 
> ### ** Examples
> 
> 
> 
> ###################################
> ## Models using different data formats
> ###################################
> 
> #
> #    Data input as a data frame
> #
> 
> #   We use the well-known bladder cancer data set from survival package
> 
> library(survival)
Loading required package: splines

Attaching package: 'survival'


	The following object(s) are masked from package:boot :

	 aml 

> data(bladder2)
Warning in data(bladder2) : data set 'bladder2' not found
> 
> # we compute the interocurrence time
> bladder2$time<-bladder2$stop-bladder2$start
> 
> # If we execute:
> #     gcmrec(Survr(id,time,event)~rx+size+number,data=bladder2,s=2060)
> 
> # We will obtain the following error message:
> #   Error in Survr(id, time, event) : Data doesn't match...
> 
> # This means that we have some patients without right-censoring time. So,
> # we understand that the last event coincides with the end of study. 
> # Consequently,we need to add a line with time 0 and status value equal 
> # to 0, too. To do so, we can use the function "addCenTime" as follows:
> 
> bladderOK<-addCenTime(bladder2) 
> 
> # Now, we can fit the model using this new data set:
> 
> gcmrec(Survr(id,time,event)~rx+size+number,data=bladderOK,s=2060)
Call:
gcmrec(formula = Survr(id, time, event) ~ rx + size + number, 
    data = bladderOK, s = 2060)


          coef exp(coef) se(coef)      z      p
rx     -0.3189     0.727   0.2051 -1.555 0.1200
size   -0.0154     0.985   0.0695 -0.222 0.8200
number  0.1354     1.145   0.0511  2.649 0.0081

  General class model parameter estimates 
    rho function:  Alpha to k 
      alpha (s.e.): 0.983 (0.0737)
 
  log-likelihood= -521.42 
  n= 85 
  n times= 197 
  number of iterations:  5  Newton-Raphson 

> 
> 
> #
> #    Data as a list. See either GeneratedData or hydraulic data 
> #                    sets as an example.
> #
> 
> #
> # We can fit the model by transforming our data in a data frame 
> # using "List.to.Dataframe" function:
> #
> 
> data(hydraulic)
> hydraulicOK<-List.to.Dataframe(hydraulic)
> gcmrec(Survr(id,time,event)~covar.1+covar.2,data=hydraulicOK,s=4753)
Call:
gcmrec(formula = Survr(id, time, event) ~ covar.1 + covar.2, 
    data = hydraulicOK, s = 4753)


           coef exp(coef) se(coef)      z    p
covar.1 -0.0764     0.926    0.201 -0.381 0.70
covar.2 -0.0537     0.948    0.206 -0.261 0.79

  General class model parameter estimates 
    rho function:  Alpha to k 
      alpha (s.e.): 1.03 (0.0107)
 
  log-likelihood= -612.33 
  n= 6 
  n times= 158 
  number of iterations:  4  Newton-Raphson 

> 
> 
> #
> # Our model allows us to incorporate effective age information
> #
> # To illustrate this example, we will use a simulated data set generated 
> # under the minimal repair model with probability of perfect repair equal to 0.6
> #
> # As we have the data in a list, first we need to obtain a data frame containing
> # the time, event, and covariates information:
> #
> 
> data(GeneratedData)
> temp<-List.to.Dataframe(GeneratedData)
> 
> # then, we can fit the model incorporating the information about the effective 
> #        age in the effageData argument:
> 
> gcmrec(Survr(id,time,event)~covar.1+covar.2, data=temp, 
+                     effageData=GeneratedData, s=100)
Call:
gcmrec(formula = Survr(id, time, event) ~ covar.1 + covar.2, 
    data = temp, effageData = GeneratedData, s = 100)


         coef exp(coef) se(coef)    z       p
covar.1 0.498      1.65    0.227 2.20 2.8e-02
covar.2 1.018      2.77    0.155 6.57 5.0e-11

  General class model parameter estimates 
    rho function:  Alpha to k 
      alpha (s.e.): 0.995 (0.0163)
 
  log-likelihood= -393.61 
  n= 10 
  n times= 135 
  number of iterations:  7  Newton-Raphson 

> 
> 
> 
> #####################################################################
> ##  How to fit minimal or perfect repair models, with and without frailties
> #####################################################################
> 
> # Model with frailties 
> 
> mod.Fra<-gcmrec(Survr(id,time,event)~rx+size+number,data=bladderOK,s=2060,Frailty=TRUE)
> print(mod.Fra)
Call:
gcmrec(formula = Survr(id, time, event) ~ rx + size + number, 
    data = bladderOK, s = 2060, Frailty = TRUE)


          coef exp(coef) se(coef)  z  p
rx     -0.3189     0.727       NA NA NA
size   -0.0154     0.985       NA NA NA
number  0.1354     1.145       NA NA NA

  General class model parameter estimates 
    rho function:  Alpha to k 
      alpha (s.e.): 0.983 (NA)
    Frailty parameter, Xi (s.e. Jacknife):  3.17e+13  ( NA ) 
 
  Marginal log-likelihood= -521.42 
  n= 85 
  n times= 197 
  number of iterations:  13  EM steps 

> 
> # effective age function: perfect repair and minimal repair models 
> # (models without frailties)
> 
> data(readmission)
> 
> # perfect
> mod.per<-gcmrec(Survr(id,time,event)~as.factor(dukes),data=readmission,
+     s=3000,typeEffage="per")
> print(mod.per)
Call:
gcmrec(formula = Survr(id, time, event) ~ as.factor(dukes), data = readmission, 
    s = 3000, typeEffage = "per")


                   coef exp(coef) se(coef)    z       p
as.factor(dukes)2 0.365      1.44    0.112 3.26 1.1e-03
as.factor(dukes)3 0.965      2.63    0.133 7.24 4.4e-13

  General class model parameter estimates 
    rho function:  Alpha to k 
      alpha (s.e.): 1.13 (0.0143)
 
  log-likelihood= -2726.23 
  n= 403 
  n times= 861 
  number of iterations:  7  Newton-Raphson 

> 
> # minimal
> mod.min<-gcmrec(Survr(id,time,event)~as.factor(dukes),data=readmission,
+    s=3000,typeEffage="min")
> print(mod.min)
Call:
gcmrec(formula = Survr(id, time, event) ~ as.factor(dukes), data = readmission, 
    s = 3000, typeEffage = "min")


                   coef exp(coef) se(coef)    z       p
as.factor(dukes)2 0.402      1.50    0.112 3.60 0.00032
as.factor(dukes)3 1.214      3.37    0.134 9.03 0.00000

  General class model parameter estimates 
    rho function:  Alpha to k 
      alpha (s.e.): 1.21 (0.0150)
 
  log-likelihood= -2448.15 
  n= 403 
  n times= 861 
  number of iterations:  8  Newton-Raphson 

> 
> #####################################################################
> ##  How to fit models with \rho function equal to identity 
> #####################################################################
> 
> data(lymphoma)
> 
> gcmrec(Survr(id, time, event) ~ as.factor(distrib), 
+     data = lymphoma, s = 1000, Frailty = TRUE, rhoFunc = "Ident")
Error in gcmrec(Survr(id, time, event) ~ as.factor(distrib), data = lymphoma,  : 
	NA/NaN/Inf in foreign function call (arg 19)
Execution halted