predict.gamlssZadj: Extract Predictor Values and Standard Errors For New Data in...
In gamlss.inf: Fitting Mixed (Inflated and Adjusted) Distributions

Description Usage Arguments Details Value Author(s) References See Also Examples

predict.gamlssZadj is the specific method which produce predictors for a new data set for a specified parameter from a gamlssZadj objects. The predict.gamlssZadj can be used to extract the linear predictors, fitted values and specific terms in the model at new data values in the same way that the predict.lm() and predict.glm() functions can be used for lm or glm objects. Note that linear predictors, fitted values and specific terms in the model at the current data values can also be extracted using the function lpred() (which is called from predict if new data is NULL).

## S3 method for class 'gamlssZadj'
predict(object, parameter = c("mu", "sigma", "nu", "tau", "xi0"), 
 newdata = NULL, type = c("link", "response", "terms"),                           
   terms = NULL, se.fit = FALSE,data = NULL, ...)

`object`	a gamlssZadj fitted model
`parameter`	which distribution (or inflation) parameter is required, default `parameter="mu"`
`newdata`	a data frame containing new values for the explanatory variables used in the model
`type`	the default, gets the linear predictor for the specified distribution (or inflation) 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 if exist
`data`	the data frame used in the original fit if is not defined in the call
`...`	for extra arguments

The predict function assumes that the object given in newdata is a data frame containing the right x-variables used in the model. This could possible cause problems if transformed variables are used in the fitting of the original model. For example, let us assume that a transformation of age is needed in the model i.e. nage<-age^.5. This could be fitted as mod<-gamlss(y~cs(age^.5),data=mydata) or as nage<-age^.5; mod<-gamlss(y~cs(nage), data=mydata). The later could more efficient if the data are in thousands rather in hundreds. In the first case, the code predict(mod,newdata=data.frame(age=c(34,56))) would produce the right results. In the second case a new data frame has to be created containing the old data plus any new transform data. This data frame has to be declared in the data option. The option newdata should contain a data.frame with the new names and the transformed values in which prediction is required, (see the last example).

A vector or a matrix depending on the options.

Abu Hossain, Mikis Stasinopoulos mikis.stasinopoulos@gamlss.org, Bob Rigby and Marco Enea

Hossain, A., Stasinopoulos, M., Rigby, R. and Enea, M. (2015). Centile estimation for a proportion response variable. Statistics in Medicine, doi: 10.1002/sim.6748.

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. (2003) Instructions on how to use the GAMLSS package in R. Accompanying documentation in the current GAMLSS help files, (see also http://www.gamlss.org/).

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.

gamlssZadj

set.seed(3210)
x <- (runif(800)*4)-2
data(sda)
fmu <- splinefun(sda$x, sda$mu)
curve(fmu, -2,2)
fsigma <- splinefun(sda$x, sda$sigma)
curve(fsigma, -2,2)
fnu <- function(x)
  {f <- splinefun(sda$x, sda$nu)
f(x)/6
}
curve(fnu, -2,2)
set.seed(321)
y0 <- rZAGA(800, mu=fmu(x), sigma=fsigma(x), nu=fnu(x))
da <- data.frame(y0,x)
g0p <- gamlss(y0~pb(x), sigma.fo=~pb(x), nu.fo=~pb(x), data=da, family=ZAGA)
t0p <- gamlssZadj(y=y0, mu.fo=~pb(x), sigma.fo=~pb(x),data=da,
                  trace=TRUE, xi0.fo=~pb(x), family="GA")

plot(predict(g0p,"nu",type="link"),
     predict(t0p,"xi0",type="link"))