checkfit {gaga} | R Documentation |
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.
checkfit(gg.fit, x, groups, type='data', logexpr=FALSE, xlab, ylab, main, lty, lwd, ...)
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 |
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'
).
Produces a plot.
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
.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
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.