Plots dyadic residuals as five separate plots showing histogram of residuals, qqnorm plot of residuals, fitted values against residuals, dyadic covariances against residuals, and dyadic covariances against fittes values. Multi trait case shows all trait pairs on each plot.
1 2 
x 
An object of class 
traitset 
Either a character vector specifying trait names to be plotted, or the default valuse which is 
gls 
A logical flag. Should the plot be of dyadic residuals given OLSb fixed effects, or of dyadic residuals given GLSb fixed effects. Default is 
... 
Other arguments passed to plotting functions. 
In plots with gls=FALSE
there will be N^{2} residuals, where N is the number of individuals with data. In plots with gls=TRUE
there will be N^{2} * L^{2} residuals, where L is the number of traits. This is because the GLSb fit is always multivariate, whereas the OLSb fit is multitrait, just like a multiple regression with multitrait response.
There is no return value. Function is used for its side effects.
Neville Jackson
Function print.dmm()
.
1 2 3 4 5 6 7 8 9 10 11 12  library(dmm)
data(sheep.df)
sheep.mdf < mdf(sheep.df,pedcols=c(1:3),factorcols=c(4:6),ycols=c(7:9),
sexcode=c("M","F"),relmat=c("E","A"))
# make a simple fit object  OLSb only
sheep.fit1 < dmm(sheep.mdf, Ymat ~ 1 + Year + Sex,dmekeep=TRUE,dmekeepfit=TRUE)
# plot dyadic model residuals for all traits
plot(sheep.fit1)
#cleanup
rm(sheep.fit1)
rm(sheep.mdf)
rm(sheep.df)

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.
Please suggest features or report bugs with the GitHub issue tracker.
All documentation is copyright its authors; we didn't write any of that.