gamlss.cens-package: tools:::Rd_package_title("gamlss.cens")

Description

tools:::Rd_package_description("gamlss.cens")

Arguments

Author

tools:::Rd_package_author("gamlss.cens")

Maintainer: tools:::Rd_package_maintainer("gamlss.cens")

Details

The DESCRIPTION file: tools:::Rd_package_DESCRIPTION("gamlss.cens") tools:::Rd_package_indices("gamlss.cens")

References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.

Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.

(see also https://www.gamlss.com/).

Examples

Run this code

library(survival)
library(gamlss)
library(gamlss.dist)
# comparing results with package survival
# fitting the exponential distribution
ms1<-survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, 
             dist='exponential')
mg1<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
             family=cens(EXP),c.crit=0.00001)
if(abs(-2*ms1$loglik[2]-deviance(mg1))>0.001) stop(paste("descrepancies in exp")) 
if(sum(coef(ms1)-coef(mg1))>0.001) warning(paste("descrepancies in coef in exp")) 
summary(ms1)
summary(mg1)
# fitting the Weibull distribution
ms2 <-survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull')
mg2 <-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
           family=cens(WEI, delta=c(0.001,0.001)), c.crit=0.00001)
if(abs(-2*ms2$loglik[2]-deviance(mg2))>0.005) 
     stop(paste("descrepancies in deviance in WEI")) 
summary(ms2);summary(mg2)
# compare the scale parameter
 1/exp(coef(mg2,"sigma"))
# now fit the Weibull in different parameterrazions  
mg21<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
             family=cens(WEI2), method=mixed(2,30)) 
mg21<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
             family=cens(WEI3))