gamlss.cens (version 5.0-7)

gen.cens: A Function to Generate Appropriate Functions to Be Used to Fit a Censored Response variable in GAMLSS

Description

The gen.cens() function allows the user to generate a d, p, (dummy) q and fitting gamlss functions for censor and interval response variables. The function can take any gamlss.family distribution.

Usage

gen.cens(family = "NO", type = c("right", "left", "interval"), 
       name = "cens", ...)

Value

Returns the d, p, (dummy) q and the fitting used in the fitting gamlss algorithm (The one used in the fitting gamlss algorithm) of a gamlss.family distribution.

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 the first letter of type and c i.e WEIic for WEI (weibull) interval response variable

type

whether right, left or in interval censoring is required, (right is the default)

...

for extra arguments

Author

Mikis Stasinopoulos and Bob Rigby

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, cens

Examples

library(gamlss.dist)
data(lip)
gen.cens(WEI,type="interval") 
WEIic
gg1<- gamlss(y ~ poly(Tem,2)+poly(pH,2)+poly(aw,2), data=lip, 
     family=WEIic, c.crit=0.00001, n.cyc=200, trace=FALSE)