| centiles.split {gamlss} | R Documentation |
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 = NULL, 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, ...)
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. |
n.inter |
if |
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 |
main |
the main title as character. If NULL the default title (shown the intervals) is shown |
main.gsub |
if the |
ylab |
the y-variable label |
xlab |
the x-variable label |
ylim |
the range of the y-variable axis |
overlap |
how much overlapping in the |
save |
whether to save the sample percentages or not with default equal to |
plot |
whether to plot the centles. This option is usefull if the sample statistics only are to be used |
... |
for extra arguments |
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.
This function is appropriate when only one continuous explanatory variable is fitted in the model
Mikis Stasinopoulos, mikis.stasinopoulos@gamlss.org, Bob Rigby r.rigby@londonmet.ac.uk, with contributions from Elaine Borghie
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/).
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)