This function compares centiles curves for more than one GAMLSS objects.It is based on the centiles
function.
The function also tabulates the sample percentages below each centile curve (for comparison with the model percentages
given by the argument cent
.) A restriction of the function is that it applies to models with one
explanatory variable only
centiles.com(obj, ..., xvar, cent = c(0.4, 10, 50, 90, 99.6),
legend = TRUE, ylab = "y", xlab = "x", xleg = min(xvar),
yleg = max(obj$y), xlim = range(xvar), ylim = NULL,
no.data = FALSE, color = TRUE, main = NULL, plot = TRUE)
a fitted gamlss object from fitting a gamlss continuous distribution
optionally more fitted GAMLSS model objects
the unique explanatory variable
a vector with elements the % centile values for which the centile curves have to be evaluated
whether a legend is required in the plot or not, the default is legent=TRUE
the y-variable label
the x-variable label
position of the legend in the x-axis
position of the legend in the y-axis
the limits of the x-axis
the limits of the y-axis
whether the data should plotted, default no.data=FALSE
or not no.data=TRUE
whether the fitted centiles are shown in colour, color=TRUE
(the default) or not
color=FALSE
the main title
whether to plot the centiles
Centile plots are produced for the different fitted models and the sample centiles below each centile curve are printed
This function is appropriate only when one continuous explanatory variable is fitted in the model
Rigby, R. A. and Stasinopoulos D. M.(2005). Generalized additive models for location, scale and shape, (with discussion),Appl. Statist., 54, part 3, pp 507-554.
Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC. An older version can be found in https://www.gamlss.com/.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, https://www.jstatsoft.org/v23/i07/.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
(see also https://www.gamlss.com/).
# NOT RUN {
data(abdom)
h1<-gamlss(y~cs(x,df=3), sigma.formula=~cs(x,1),family=BCT, data=abdom)
h2<-gamlss(y~pb(x), sigma.formula=~pb(x), family=BCT, data=abdom )
centiles.com(h1,h2,xvar=abdom$x)
rm(h1,h2)
# }
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