gamlss.cens (version 5.0-7)

cens: Function to Fit Censored Data Using a gamlss.family Distribution

Description

This function can be used to fit censored or interval response variables. It takes as an argument an existing gamlss.family distribution and generates a new gamlss.family object which then can be used to fit right, left or interval censored data.

Usage

cens(family = "NO", type = c("right", "left", "interval"), name = "cens", 
       local = TRUE, delta = NULL, ...)

Value

It returns a gamlss.family object which has all the components needed for fitting a distribution in gamlss.

Arguments

family

a gamlss.family object, which is used to define the distribution and the link functions of the various parameters. The distribution families supported by gamlss() can be found in gamlss.family and in the package gamlss.dist.

name

the characters you want to add to the name of new functions, by default is cens

type

what type of censoring is required, right, left or interval.

local

if TRUE the function will try to find the environment of gamlss to generate the d and p functions required for the fitting, if FALSE the functions will be generated in the global environment

delta

the delta increment used in the numerical derivatives

...

for extra arguments

Author

Mikis Stasinopoulos and Bob Rigby

Details

This function is created to help users to fit censored data using an existing gamlss.family distribution. It does this by taking an existing gamlss.family and changing some of the components of the distribution to help the fitting process. It particular it (i) creates a (d) function (for calculating the censored likelihood) and a (p) function (for generating the quantile residuals) within gamlss, (ii) changes the global deviance function G.dev.incr, the first derivative functions (see note below) and other quantities from the original distribution.

References

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, 1-38.

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/).

See Also

cens.d, cens.p, gen.cens

Examples

# comparing output with the survreg() of package survival
library(gamlss.dist)
library(survival)
#--------------------------------------------------------------------
# right censoring example 
# example from survreg() 
# fitting the exponential distribution
mexp<-survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='exponential')
gexp<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
             family=cens(EXP), c.crit=0.00001)
if(abs(-2*mexp$loglik[2]-deviance(gexp))>0.001) 
         stop(paste("descrepancies in exponential models")) 
if(sum(coef(mexp)-coef(gexp))>0.001) 
        warning(paste("descrepancies in coef in exponential models")) 
summary(mexp)
gen.cens(EXP)
summary(gexp)
# fitting different distributions
# weibull 
mwei <-survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='weibull')
gwei<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
             family=cens(WEI, delta=c(0.0001,0.0001)), c.crit=0.00001)
if(abs(-2*mwei$loglik[2]-deviance(gwei))>0.005) 
        stop(paste("descrepancies in deviance in WEI")) 
scoef <- sum(coef(mwei)-coef(gwei))
if(abs(scoef)>0.005) 
         warning(cat("descrepancies in coef in WEI of ", scoef, "\n")) 
# WEI3 is weibull parametrised with mu as the mean
gwei3 <- gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
                 family=cens(WEI3)) 
# log normal
mlogno <-survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, 
                  dist='lognormal')
glogno<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, 
                family=cens(LOGNO, delta=c(0.001,0.001)), c.cyc=0.00001)
if(abs(-2*mlogno$loglik[2]-deviance(glogno))>0.005) 
          stop(paste("descrepancies in deviance in LOGNO")) 
coef(mlogno);coef(glogno) 
#-------------------------------------------------------------------- 
# now interval response variable 
data(lip)
with(lip, y)
mg1<-survreg(y ~ poly(Tem,2)+poly(pH,2)+poly(aw,2), data=lip, dist="weibull")
gg1<- gamlss(y ~ poly(Tem,2)+poly(pH,2)+poly(aw,2), data=lip, 
      family=cens(WEI,type="interval"), c.crit=0.00001, n.cyc=200, trace=FALSE)
summary(mg1)
gen.cens(WEI,type="interval")
summary(gg1)
#--------------------------------------------------------------------
# now fitting discretised continuous distribution to count data
# fitting discretised Gamma
data(species)
# first generate the distributions
gen.cens(GA, type="interval")
gen.cens(IG, type="interval")
 mGA<-gamlss(Surv(fish,fish+1,type= "interval2")~log(lake)+I(log(lake)^2), 
       sigma.fo=~log(lake), data=species, family=GAic)
# fitting discretised inverse Gaussian
 mIG<-gamlss(Surv(fish,fish+1,type= "interval2")~log(lake)+I(log(lake)^2), 
      sigma.fo=~log(lake), data=species, family=IGic)
AIC(mGA,mIG)
plot(fish~log(lake), data=species)
with(species, lines(log(lake)[order(lake)], fitted(mIG)[order(lake)]))             
#--------------------------------------------------------------------