I have a bunch of microarray data. After doing PCA, I want to calculate the probability of each sample belong to its own class. Say I have 20 samples with 1000 genes projected to the first 3 PCs. So my matrix is 20 (samples) by 3 (components). I played around with the package mvtnorm, but seems that the function dmvnorm only gives the pdf (I guess). In the function pmvnorm, the input variables do not contain the unknown point information. There is only upper,lower, mean, sigma, etc. these are the only information about a class. Could somebody tell me how to calculate the probability of any point X belonging to class Y is assuming Y follows multivariate normal distribution? Suppose X is a row vector with dimension 1 by n, Y is a matrix with dimension m by n.

Many thanks,
James


 		
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