gamlss (version 5.2-0)

rqres.plot: Creating and Plotting Randomized Quantile Residuals

Description

This function plots worm plots, van Buuren and Fredriks M. (2001), or QQ-plots of the normalized randomized quantile residuals (Dunn and Smyth, 1996) for a model using a discrete GAMLSS family distribution.

Usage

rqres.plot(obj = NULL, howmany = 6, plot.type = c("few", "all"), 
           type = c("wp", "QQ"),  xlim = NULL, ylim = NULL, ...)
get.rqres(obj = NULL, howmany = 10, order = FALSE)

Arguments

obj

a fitted GAMLSS model object from a "discrete" type of family

howmany

The number randomise quantile residuals required i.e. howmany=6

plot.type

whether to plot few of the randomised quantile residual realisations, "few" in a separate plots (there must be less than 8) or all "all" in one plot (with their median)

type

whether to plot worm plots "wp"or QQ plots "QQ" with default worm plots

xlim

setting manually the xlim of the graph

ylim

setting manually the ylim of the graph

order

whether to order the ealization of randomised quantile residuals

…

for extra arguments to be passed to wp()

Value

If save it is TRUE then the vector of the median residuals is saved.

Details

For discrete family distributions, the gamlss() function saves on exit one realization of randomized quantile residuals which can be plotted using the generic function plot which calls the plot.gamlss. Looking at only one realization can be misleading, so the current function creates QQ-plots for several realizations. The function allows up to 10 QQ-plots to be plotted. Occasionally one wishes to create a lot of realizations and then take a median of them (separately for each ordered value) to create a single median realization. The option all in combinations with the option howmany creates a QQ-plot of the medians of the normalized randomized quantile residuals. These 'median' randomized quantile residuals can be saved using the option (save=TRUE).

References

Dunn, P. K. and Smyth, G. K. (1996) Randomised quantile residuals, J. Comput. Graph. Statist., 5, 236--244

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/).

van Buuren and Fredriks M. (2001) Worm plot: simple diagnostic device for modelling growth reference curves. Statistics in Medicine, 20, 1259--1277

See Also

plot.gamlss, gamlss

Examples

# NOT RUN {
data(aids) # fitting a model from a discrete distribution 
h<-gamlss(y~pb(x)+qrt, family=NBI, data=aids) # 
plot(h)
# plot qq- plots from 6 realization of the randomized quantile residuals
rqres.plot(h) 
# a worm-plot of the medians from 10 realizations
rqres.plot(h,howmany=40,plot="all") # 
# }