wp.twin {AGD}  R Documentation 
Superposes two worm plots
Description
Superposes two worm plots from GAMLSS fitted objects. This is a diagnostic tool for comparing two solutions.
Usage
wp.twin(obj1, obj2 = NULL, xvar = NULL, xvar.column = 2, n.inter = 16,
show.given = FALSE, ylim.worm = 0.5, line = FALSE, cex = 1,
col1 = "black", col2 = "orange", warnings = FALSE, ...)
Arguments
obj1 
a GAMLSS fitted object 
obj2 
an optional second GAMLSS fitted object 
xvar 
the explanatory variable against which the worm plots will be plotted 
xvar.column 
the number referring to the column of 
n.inter 
the number of intervals in which the explanatory variable

show.given 
whether to show the xvariable intervals in the top of the
graph, default is 
ylim.worm 
for multiple plots, this values is the yvariable limit,
default value is 
line 
whether to plot the polynomial line in the worm plot, default
value is 
cex 
the cex plotting parameter with default 
col1 
the color for the points of 
col2 
the color for the points of 
warnings 
a logical indicating whether warnings should be produced. The
default 
... 
for extra arguments, 
Details
This function is a customized version of the wp()
function found in
the gamlss
package. Function wp.twin()
allows overplotting of
two worm plots, each in its own color. The points of obj1
are plotted
first, the points of obj2
are superposed. This twin worm plot provide
a visual assessment of the differences between the solutions. Extra
arguments can be specified (e.g. xvar
) that are passed down to the
wp()
function of gamlss
if specified. The worm plot is a
detrended normal QQplot that highlight departures from normality.
Argument xvar
takes priority over xvar.column
. The xvar
variable is cut into n.iter
intervals with an equal number
observations and detrended normal QQ (i.e. worm) plots for each interval are
plotted. This is a way of highlighting failures of the model within
different ranges of the explanatory variable.
If line=TRUE
and n.inter>1
, the fitted coefficients from
fitting cubic polynomials to the residuals (within each xvariable interval)
can be obtain by e.g. coeffs<wp.twin(model1,xvar=x,n.iner=9)
. van
Buuren et al. (2001) used these residuals to identify regions
(intervals) of the explanatory variable within which the model does not fit
adequately the data (called "model violation")
Value
For multiple plots the xvar
intervals and the coefficients of
the fitted cubic polynomials to the residuals (within each xvar
interval) are returned.
Author(s)
Stef van Buuren, using R code of Mikis Stasinopoulos and Bob Rigby
References
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.
van Buuren and Fredriks M. (2001) Worm plot: simple diagnostic device for modelling growth reference curves. Statistics in Medicine, 20, 1259–1277.
van Buuren and Fredriks M. (2007) Worm plot to diagnose fit in quantile regression. Statistical Modelling, 7, 4, 363–376.
See Also
Examples
library(gamlss)
data(abdom)
a < gamlss(y~cs(x,df=1),sigma.fo=~cs(x,0),family=LO,data=abdom)
b < gamlss(y~cs(x,df=3),sigma.fo=~cs(x,1),family=LO,data=abdom)
coeff1 < wp.twin(a,b,line=TRUE)
coeff1
rm(a,b,coeff1)