This function plots centiles curves for separate ranges of the unique explanatory variable x.
It is similar to the `centiles`

function but the range of x is split at a user defined values `xcut.point`

into r separate ranges.
The functions also tabulates the sample percentages below each centile curve for each of the r ranges of x
(for comparison with the model percentage given by cent)
The model should have only one explanatory variable.

```
centiles.split(obj, xvar, xcut.points = NULL, n.inter = 4,
cent = c(0.4, 2, 10, 25, 50, 75, 90, 98, 99.6),
legend = FALSE, main = NULL, main.gsub = "@",
ylab = "y", xlab = "x", ylim = NULL, overlap = 0,
save = TRUE, plot = TRUE, ...)
```

Centile plots are produced and the sample centiles below each centile curve for each of the r ranges of x can be saved into a matrix.

- obj
a fitted gamlss object from fitting a gamlss continuous distribution

- xvar
the unique explanatory variable

- xcut.points
the x-axis cut off points e.g.

`c(20,30)`

. If`xcut.points=NULL`

then the`n.inter`

argument is activated- n.inter
if

`xcut.points=NULL`

this argument gives the number of intervals in which the x-variable will be split, with default 4- cent
a vector with elements the % centile values for which the centile curves are to be evaluated

- legend
whether a legend is required in the plots or not, the default is

`legent=FALSE`

- main
the main title as character. If NULL the default title (shown the intervals) is shown

- main.gsub
if the

`main.gsub`

(with default "@") appears in the`main`

title then it is substituted with the default title.- ylab
the y-variable label

- xlab
the x-variable label

- ylim
the range of the y-variable axis

- overlap
how much overlapping in the

`xvar`

intervals. Default value is`overlap=0`

for non overlapping intervals- save
whether to save the sample percentages or not with default equal to

`TRUE`

. In this case the functions produce a matrix giving the sample percentages for each interval- plot
whether to plot the centles. This option is useful if the sample statistics only are to be used

- ...
for extra arguments

Mikis Stasinopoulos, d.stasinopoulos@londonmet.ac.uk, Bob Rigby r.rigby@londonmet.ac.uk, with contributions from Elaine Borghie

This function is appropriate when only one continuous explanatory variable is fitted in the model

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/).

`gamlss`

`centiles`

, `centiles.com`

```
data(abdom)
h<-gamlss(y~pb(x), sigma.formula=~pb(x), family=BCT, data=abdom)
mout <- centiles.split(h,xvar=abdom$x)
mout
rm(h,mout)
```

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