R: Extract Linear Predictor Values and Standard Errors For A...

lpred {gamlss} | R Documentation

## 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`.

### Author(s)

Mikis Stasinopoulos

### 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

`predict.gamlss`

### Examples

    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)

[Package gamlss version 5.0-2 Index]