classpi {nempi} | R Documentation |
Builds and uses different classifiers to infer perturbation profiles
classpi( D, unknown = "", full = TRUE, method = "svm", size = NULL, MaxNWts = 10000, ... )
D |
either a binary effects matrix or log odds matrix as for Nested Effects Models (see package 'nem') |
unknown |
colname of samples without mutation data, E.g. "" |
full |
if FALSE, does not change the known profiles |
method |
either one of svm, nn, rf |
size |
parameter for neural network (see package 'nnet') |
MaxNWts |
parameters for neural network (see package 'nnet') |
... |
additional parameters for mnem::nem |
plot
Martin Pirkl
D <- matrix(rnorm(1000*100), 1000, 100) colnames(D) <- sample(seq_len(5), 100, replace = TRUE) Gamma <- matrix(sample(c(0,1), 5*100, replace = TRUE, p = c(0.9, 0.1)), 5, 100) Gamma <- apply(Gamma, 2, function(x) return(x/sum(x))) Gamma[is.na(Gamma)] <- 0 rownames(Gamma) <- seq_len(5) result <- classpi(D)