Expected probability that a future sample is correctly classified.
Description
Estimates posterior expected probability that a future sample is correctly classified when performing class prediction. The estimate is obtained via Monte Carlo simulation from the posterior predictive.
Usage
1  powclasspred(gg.fit, x, groups, prgroups, v0thre=1, ngene=100, B=100)

Arguments
gg.fit 
GaGa or MiGaGa fit (object of type 
x 

groups 
If 
prgroups 
Vector specifying prior probabilities for each group. Defaults to equally probable groups. 
v0thre 
Only genes with posterior probability of being equally
expressed below 
ngene 
Number of genes to use to build the classifier. Genes with smaller probability of being equally expressed are selected first. 
B 
Number of Monte Carlo samples to be used. 
Details
The routine simulates future samples (microarrays) from the posterior
predictive distribution of a given group (e.g. control/cancer).
Then it computes the posterior probability
that the new sample belongs to each of the groups
and classifies the sample into the group with
highest probability. This process is repeated B
times, and the
proportion of correctly classified samples is reported for each
group. The standard
error is obtained via the usual normal approximation (i.e. SD/B).
The overall probability of correct classification is also provided
(i.e. for all groups together), but using a more efficient variant of
the algorithm. Instead of reporting the observed proportion of
correctly classified samples, it reports the expected proportion of
correctly classified samples (i.e. the average posterior probability
of the class that the sample is assigned to).
Value
List with components:
ccall 
Estimated expected probability of correctly classifying a future sample. 
seccall 
Estimated standard error of 
ccgroup 
Vector with the estimated probability of correctly classifying a sample from each group. 
segroup 
Estimated standard error of 
Author(s)
David Rossell
References
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
See Also
classpred
, fitGG
,
parest
. See powfindgenes
for differential
expression power calculations.