plot.qb.diag {qtlbim} | R Documentation |
A density histogram is drawn for model-averaged summary diagnostics such as LOD, variance, or heritability.
qb.diag(qbObject, items= c("mean","envvar","var","herit"), ...) ## S3 method for class 'qb.diag': plot(x, ... ) ## S3 method for class 'qb.diag': print(x, ... ) ## S3 method for class 'qb.diag': summary(object, digits = 5, ... )
qbObject |
Object of class qb . |
object |
Object of class qb.diag . |
x |
Object of class qb.diag . |
items |
Diagnostics to be summarized; must be name of a column in
element . |
digits |
Number of significant digits. |
... |
Parameters to methods. Not used for qb.diag . |
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 qb.BayesFactor
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
plot.qb
, density
,
boxplot
, qb.BayesFactor
data(qbExample) temp <- qb.diag(qbExample) summary(temp) plot(temp)