plot.qb.diag {qtlbim} | R Documentation

## Marginal and model-conditional summaries of Bayesian interval mapping diagnostics

### 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, yandell@stat.wisc.edu

### References

http://www.qtlbim.org

### See Also

`plot.qb`, `density`, `boxplot`, `qb.BayesFactor`

### Examples

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

[Package qtlbim version 1.6.5 Index]