meancomp: Examine grand mean and covariate MCMC samples.

In fboehm/qtlbim: QTL Bayesian Interval Mapping

Description

Examine grand mean and covariate Monte Carlo samples to glean estimates of data center and importance of covariates.

Usage

qb.meancomp(qbObject, adjust.covar, ...)
## S3 method for class 'qb.meancomp'
summary(object, percent = 5, ...)
## S3 method for class 'qb.meancomp'
print(x, ...)
## S3 method for class 'qb.meancomp'
plot(x, covar, percent = 5, cex, ...)

Arguments

qbObject	Object of class qb.
adjust.covar	Adjustments to covariates. Default is NA, which adjusts by covariate mean values. Values are assumed to be in order of fixed covariates.
object	Object of class qb.meancomp.
x	Object of class qb.meancomp.
percent	Percentile between 0 and 100 for summaries.
covar	Sequence of covariate identifiers for plot.
cex	Character expansion for plot symbols. Default shrinks with number of MCMC iterations.
...	Extra parameters passed along.

Details

Grand mean is adjusted to mean level of covariates. Diagonal of scatterplot matrix includes density plot. Setting covar = 0 yields a density plot for the grand mean alone.

Value

qb.meancomp is a matrix with columns for the grand mean and for each fixed covariate. Summaries show mean and upper and lower percentiles.

Author(s)

Brian S. Yandell

References

http://www.qtlbim.org

See Also

qb.mcmc

Examples

data(qbExample)

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

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