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.

```
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",...)
```

a plot of fitted terms.

- 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 compare to the rest. In the`free' option the limits are computed for each plot separately.- 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 different 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 partial residuals

- pch.res
characters for points in the partial 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 polygon information file for MRF models

- polys.scheme
Color scheme for polygons for RMF models

- ...
other graphical parameters

Mikis Stasinopoulos based on the existing termplot() function

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.

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.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019)
*Distributions for modeling location, scale, and shape: Using GAMLSS in R*, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.

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, https://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 https://www.gamlss.com/).

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

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