diag: Marginal and model-conditional summaries of Bayesian interval...

In fboehm/qtlbim: QTL Bayesian Interval Mapping

Description

A density histogram is drawn for model-averaged summary diagnostics such as LOD, variance, or heritability.

Usage

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, ... )

Arguments

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.

Details

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.

Author(s)

Brian S. Yandell, [e-mail]

References

http://www.qtlbim.org

See Also

plot.qb, density, boxplot, qb.BayesFactor

Examples

data(qbExample)

temp <- qb.diag(qbExample)
summary(temp)
plot(temp)

fboehm/qtlbim documentation built on Feb. 16, 2021, 12:04 a.m.