lpred: Extract Linear Predictor Values and Standard Errors For A GAMLSS Model

Description

lpred is the GAMLSS specific method which extracts the linear predictor and its (approximate) standard errors for a specified parameter from a GAMLSS objects. The lpred can be also used to extract the fitted values (with its approximate standard errors) or specific terms in the model (with its approximate standard errors) in the same way that the predict.lm() and predict.glm() functions can be used for lm or glm objects. The function lp extract only the linear predictor. If prediction is required for new data values then use the function predict.gamlss().

Usage

lpred(obj, what = c("mu", "sigma", "nu", "tau"), parameter= NULL,
           type = c("link", "response", "terms"), 
           terms = NULL, se.fit = FALSE, ...)
lp(obj, what = c("mu", "sigma", "nu", "tau"), parameter= NULL, ... )

Arguments

obj

a GAMLSS fitted model

what

which distribution parameter is required, default what="mu"

parameter

equivalent to what

type

type="link" (the default) gets the linear predictor for the specified distribution parameter. type="response" gets the fitted values for the parameter while type="terms" gets the fitted terms contribution

terms

if type="terms", which terms to be selected (default is all terms)

se.fit

if TRUE the approximate standard errors of the appropriate type are extracted

…

for extra arguments

Value

If se.fit=FALSE a vector (or a matrix) of the appropriate type is extracted from the GAMLSS object for the given parameter in what. If se.fit=TRUE a list containing the appropriate type, fit, and its (approximate) standard errors, se.fit.

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.

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.

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 http://www.gamlss.org/).

Examples

Run this code

# NOT RUN {
data(aids)
mod<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids) # 
mod.t <- lpred(mod, type = "terms", terms= "qrt")
mod.t
mod.lp <- lp(mod)
mod.lp 
rm(mod, mod.t,mod.lp)
# }