R: Plot Residual Diagnostics for an GAMLSS Object

plot.gamlss {gamlss} | R Documentation

## Plot Residual Diagnostics for an GAMLSS Object

### Description

This function provides four plots for checking the normalized (randomized for a discrete response distribution) quantile residuals of a fitted GAMLSS object, referred to as residuals below : a plot of residuals against fitted values, a plot of the residuals against an index or a specific explanatory variable, a density plot of the residuals and a normal Q-Q plot of the residuals. If argument `ts=TRUE` then the first two plots are replaced by the autocorrelation function (ACF) and partial autocorrelation function (PACF) of the residuals

### Usage

    ## S3 method for class 'gamlss'
    plot(x, xvar = NULL, parameters = NULL, ts = FALSE,
               summaries = TRUE, ...)

### Arguments

`x` | a GAMLSS fitted object
`xvar` | an explanatory variable to plot the residuals against
`parameters` | plotting parameters can be specified here
`ts` | set this to TRUE if ACF and PACF plots of the residuals are required
`summaries` | set this to FALSE if no summary statistics of the residuals are required
`...` | further arguments passed to or from other methods.

### Details

This function provides four plots for checking the normalized (randomized) quantile residuals (called `residuals`) of a fitted GAMLSS object. Randomization is only performed for discrete response variables. The four plots are

- residuals against the fitted values (or ACF of the residuals if `ts=TRUE`)

- residuals against an index or specified x-variable (or PACF of the residuals if `ts=TRUE`)

- kernel density estimate of the residuals

- QQ-normal plot of the residuals

For time series response variables option `ts=TRUE` can be used to plot the ACF and PACF functions of the residuals.

### Value

Returns four plots related to the residuals of the fitted GAMLSS model and prints summary statistics for the residuals if the `summary=T`

### Author(s)

Mikis Stasinopoulos mikis.stasinopoulos@gamlss.org, Bob Rigby and Kalliope Akantziliotou

### 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

`gamlss`

### Examples

    data(aids)
    a<-gamlss(y~pb(x)+qrt,family=PO,data=aids)
    plot(a)
    rm(a)

[Package gamlss version 5.0-2 Index]