This function can used when the fitted model centiles do not coincide with the sample centiles.
calibration(object, xvar, cent = c(0.4, 2, 10, 25, 50, 75, 90, 98, 99.6),
legend = FALSE, fan = FALSE, ...)a gamlss fitted object
The explanatory variable
a vector with elements the % centile values for which the centile curves have to be evaluated
whether legend is required
whether to use the fan version of centiles
other argument pass on to centiles() function
A centile plot is produced and the sample centiles below each centile curve are printed (or saved)
The function finds the sample quantiles of the residuals of the fitted model (the z-scores) and use them as sample quantile in the argument cent of the centiles() function. This procedure is appropriate if the fitted model centiles do not coincide with the sample centiles and when this failure is the same in all values of the explanatory variable xvar.
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)
m1<-gamlss(y~pb(x), sigma.fo=~pb(x), family=LO, data=abdom)
calibration(m1, xvar=abdom$x, fan=TRUE)
# }
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