qb.meancomp {qtlbim} | R Documentation |
Examine grand mean and covariate Monte Carlo samples to glean estimates of data center and importance of covariates.
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, ...)
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. |
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.
qb.meancomp
is a matrix with columns for the grand mean and for
each fixed covariate. Summaries show mean and upper and lower percentiles.
Brian S. Yandell
http://www.qtlbim.org
data(qbExample) temp <- qb.meancomp(qbExample) summary(temp) plot(temp)