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.
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)
If save
it is TRUE then the vector of the median residuals is saved.
a fitted GAMLSS model object from a "discrete" type of family
The number randomise quantile residuals required i.e. howmany=6
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)
whether to plot worm plots "wp"
or QQ plots "QQ"
with default worm plots
setting manually the xlim
of the graph
setting manually the ylim
of the graph
whether to order the ealization of randomised quantile residuals
for extra arguments to be passed to wp()
Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk
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
).
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
plot.gamlss
, gamlss
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") #
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