gamlss (version 5.1-4)

predict.gamlss: Extract Predictor Values and Standard Errors For New Data In a GAMLSS Model

Description

predict.gamlss is the GAMLSS specific method which produce predictors for a new data set for a specified parameter from a GAMLSS objects. The predict.gamlss 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).

Usage

# S3 method for gamlss
predict(object, what = c("mu", "sigma", "nu", "tau"), 
                parameter= NULL,
                newdata = NULL, type = c("link", "response", "terms"), 
                terms = NULL, se.fit = FALSE, data = NULL, ...)
predictAll(object, newdata = NULL, type = c("response", "link", "terms"), 
                terms = NULL, se.fit = FALSE, use.weights = FALSE, 
                data = NULL, y.value = "median", 
                set.to = .Machine$double.xmin,
                output = c("list", "matrix"), ...)

Arguments

object

a GAMLSS fitted model

what

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

parameter

equivalent to what

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 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

use.weights

if use.weights=TRUE the old data and the newdata are merged and the model is refitted with weights equal to the prior weights for the old data observationa and equal to a very small value (see option set.to) for the .newdata values. This trick allows to obtain standard errors for all parameters

data

the data frame used in the original fit if is not defined in the call

y.value

how to get the response values for the newdata if they do not exist. The default is taking the median, y.value="median". Other function like "max", "min" are alloed. Also numerical values.

set.to

what values the weights for the newdata should take

output

whether the output to be a `list' (default) or a 'matrix'

…

for extra arguments

Value

A vector or a matrix depending on the options.

Details

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).

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/).

See Also

lp, lpred

Examples

# NOT RUN {
data(aids)
a<-gamlss(y~poly(x,3)+qrt, family=PO, data=aids) # 
newaids<-data.frame(x=c(45,46,47), qrt=c(2,3,4))
ap <- predict(a, newdata=newaids, type = "response")
ap
# now getting all the parameters
predictAll(a, newdata=newaids)
rm(a, ap)
data(abdom)
# transform x 
aa<-gamlss(y~cs(x^.5),data=abdom)
# predict at old values
predict(aa)[610]
# predict at new values 
predict(aa,newdata=data.frame(x=42.43))
# now transform x first 
nx<-abdom$x^.5
aaa<-gamlss(y~cs(nx),data=abdom)
# create a new data frame 
newd<-data.frame( abdom, nx=abdom$x^0.5)
# predict at old values
predict(aaa)[610]
# predict at new values 
predict(aaa,newdata=data.frame(nx=42.43^.5), data=newd)
# }