checkfit {gaga}R Documentation

Check goodness-of-fit of GaGa and MiGaGa models

Description

Produces plots to check fit of GaGa and MiGaGa model. Compares observed data with posterior predictive distribution of the model. Can also compare posterior distribution of parameters with method of moments estimates.

Usage

checkfit(gg.fit, x, groups, type='data', logexpr=FALSE, xlab, ylab, main, lty, lwd, ...)

Arguments

gg.fit GaGa or MiGaGa fit (object of type gagafit, as returned by fitGG).
x ExpressionSet, exprSet, data frame or matrix containing the gene expression measurements used to fit the model.
groups If x is of type ExpressionSet or exprSet, groups should be the name of the column in pData(x) with the groups that one wishes to compare. If x is a matrix or a data frame, groups should be a vector indicating to which group each column in x corresponds to.
type data checks marginal density of the data; shape checks shape parameter; mean checks mean parameter; shapemean checks the joint of shape and mean parameters
logexpr If set to TRUE, the expression values are in log2 scale.
xlab Passed on to plot
ylab Passed on to plot
main Passed on to plot
lty Ignored.
lwd Ignored.
... Other arguments to be passed to plot

Details

The routine generates random draws from the posterior and posterior predictive distributions, fixing the hyper-parameters at their estimated value (posterior mean if model was fit with method=='Bayes' or maximum likelihood estimate is model was fit with method=='EBayes').

Value

Produces a plot.

Note

Posterior and posterior predictive checks can lack sensitivity to detect model misfit, since they are susceptible to over-fitting. An alternative is to perform prior predictive checks by generating parameters and data with simGG.

Author(s)

David Rossell

References

Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.

See Also

simGG to simulate samples from the prior-predictive distribution, simnewsamples to generate parameters and observations from the posterior predictive, which is useful to check goodness-of-fit individually a desired gene.


[Package gaga version 1.2.0 Index]