qb.meancomp {qtlbim} | R Documentation

## Examine grand mean and covariate MCMC samples.

### 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

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

[Package qtlbim version 1.6.5 Index]