R: Plot regression terms for a specified parameter of a fitted...

term.plot {gamlss} | R Documentation

## Plot regression terms for a specified parameter of a fitted GAMLSS object

### Description

Plots regression terms against their predictors, optionally with standard errors and partial residuals added. It is based on the R function `termplot` but is suitably changed to apply to GAMLSS objects.

### Usage

    term.plot(object, what = c("mu", "sigma", "nu", "tau"),
             parameter= NULL, data = NULL,
             envir = environment(formula(object)), partial.resid = FALSE,
             rug = FALSE, terms = NULL, se = TRUE, ylim = c("common", "free"),
             scheme = c("shaded", "lines"), xlabs = NULL, ylabs = NULL,
             main = NULL, pages = 0, col.term = "darkred",
             col.se = "orange", col.shaded = "gray", col.res = "lightblue",
             col.rug = "gray", lwd.term = 1.5, lty.se = 2, lwd.se = 1,
             cex.res = 1, pch.res = par("pch"),
             ask = interactive() && nb.fig < n.tms && .Device != "postscript",
             use.factor.levels = TRUE, surface.gam = FALSE,
             polys = NULL, polys.scheme = "topo",...)

### Arguments

`object` | a fitted GAMLSS object
`what` | the required parameter of the GAMLSS distribution i.e. "mu"
`parameter` | equivalent to `what`
`data` | data frame in which variables in `object` can be found
`envir` | environment in which variables in `object` can be found
`partial.resid` | logical; should partial residuals be plotted or not
`rug` | add rug plots (jitter 1-d histograms) to the axes?
`terms` | which terms to be plotted (default 'NULL' means all terms)
`se` | plot point-wise standard errors?
`ylim` | there are two options here a) "common" and b) "free". The "common" option plots all figures with the same `ylim` range and therefore allows the viewer to check the relative contribution of each terms compate to the rest. In the‘free’ option the limits are computed for each plot seperatly.
`scheme` | whether the se's should appear shaded or as lines
`xlabs` | vector of labels for the x axes
`ylabs` | vector of labels for the y axes
`main` | logical, or vector of main titles; if 'TRUE', the model's call is taken as main title, 'NULL' or 'FALSE' mean no titles.
`pages` | in how many pages the plot should appear. The default is 0 which allows differnt page for each plot
`col.term` | the colour of the term line
`col.se` | the colour of the se's lines
`col.shaded` | the colour of the shaded area
`col.res` | the colour of the partial residuals
`col.rug` | the colour of the rug
`lwd.term` | line width of the fitted terms
`lty.se` | line ype for standard errors
`lwd.se` | line width for the stadard errors
`cex.res` | plotting character expansion for the parsial residuals
`pch.res` | characters for points in the parsial residuals
`ask` | logical; if 'TRUE', the user is asked before each plot, see 'par(ask=.)'.
`use.factor.levels` | Should x-axis ticks use factor levels or numbers for factor terms?
`surface.gam` | whether to use surface plot if a `ga()` term is fitted
`polys` | The polygone nformation filr for MRF models
`polys.scheme` | Color scheme for polygones for RMF models
`...` | other graphical parameters

### Details

The function uses the `lpred` function of GAMLSS. The 'data' argument should rarely be needed, but in some cases 'termplot' may be unable to reconstruct the original data frame. Using 'na.action=na.exclude' makes these problems less likely. Nothing sensible happens for interaction terms.

### Value

a plot of fitted terms.

### Author(s)

Mikis Stasinopoulos based on the existing termplot() function

### 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

`termplot`

### Examples

    data(aids)
    a<-gamlss(y~pb(x)+qrt,data=aids,family=NBI)
    term.plot(a, pages=1)
    rm(a)

[Package gamlss version 5.0-2 Index]