cens {gamlss.cens} | R Documentation |
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.
cens(family = "NO", type = c("right", "left", "interval"), name = "cens", local = TRUE, delta = NULL, ...)
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 |
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.
It returns a gamlss.family
object which has all the components needed for fitting a distribution in gamlss
.
This function is experimental and could be changed in the future.
The function cens
changes the first derivatives of the original gamlss family
d
function to numerical derivatives for the new censored d
function.
The default increment delta
, for this numerical derivatives function,
is eps * pmax(abs(x), 1)
where eps<-sqrt(.Machine$double.eps)
.
The default delta
could be inappropriate for
specific applications and can be overwritten by using the argument delta
.
Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk and Bob Rigby r.rigby@londonmet.ac.uk
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.com/).
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.
# comparing output with the survreg() of package survival library(gamlss.dist) library(survival) #-------------------------------------------------------------------- # right censoring example # example from survreg() # fitting the exponential distribution mexp<-survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist='exponential') gexp<-gamlss(Surv(futime, fustat) ~ ecog.ps + rx, data=ovarian, family=cens(EXP), c.crit=0.00001) if(abs(-2*mexp$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) 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) summary(gg1) #-------------------------------------------------------------------- # now fitting discretised continuous distribution to count data # fitting discretised Gamma data(species) mGA<-gamlss(Surv(fish,fish+1,type= "interval2")~log(lake)+I(log(lake)^2), sigma.fo=~log(lake), data=species, family=cens(GA, type="interval")) # 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=cens(IG, type="interval")) AIC(mGA,mIG) plot(fish~log(lake), data=species) with(species, lines(log(lake)[order(lake)], fitted(mIG)[order(lake)])) #--------------------------------------------------------------------