plot.bim.diag {bim} | R Documentation |
A density histogram is drawn for model-averaged summary diagnostics such as LOD, variance, or heritability.
plot.bim.diag(x, nqtl=1, pattern=NULL, exact=FALSE, items=<<see below>>, mains=items, mfrow=<<see below>>, ... )
x |
object of class bim |
nqtl |
subset on number of QTL |
pattern |
subset on chromosome pattern of QTL |
exact |
subset on exact pattern or number of QTL if true |
items |
diagnostics to be summarized; must be column of data |
mains |
titles for items |
mfrow |
plot arrangement parameter for par() (default is
rows = number of items by cols = 2) |
... |
graphical parameters can be given as arguments to plot |
Model-averaged density is smooth kernel estimate similar to ordinary
histogram. A boxplot
(without outliers) is
overlaid for comparison with conditional boxplots. Conditional
boxplots by number of QTL may show indication of model bias for
small number of QTL. This and bim.nqtl
can help
suggest the minimal model. Diagnostic items that make sense to plot
are "LOD"
, "envvar"
(environmental variance),
"herit"
(heritability), "mean"
(grand mean),
"addvar"
(variance of add
), "domvar"
(variance of
add
). Marginal and conditional medians are printed.
Brian S. Yandell, yandell@stat.wisc.edu
http://www.stat.wisc.edu/~yandell/qtl/software/Bmapqtl
data( verngeo.bim ) plot.bim.diag( verngeo.bim, 2, items = c("LOD","herit") )