# NOT RUN {
# Data with features in columns
data(rosenwald)
group <- rosenwald.cli$group
expr <- t(rosenwald.expr)
# NA imputation (feature's mean to minimize impact)
f <- function(x) { x[ is.na(x) ] <- round(mean(x, na.rm=TRUE), 3); x }
expr <- apply(expr, 2, f)
# Coefficients
coeff <- LPS.coeff(data=expr, response=group)
# 10 best features model
m <- LPS(data=expr, coeff=coeff, response=group, k=10)
# Distributions of scores in each group
plot(m, "density")
# Probability for each group along the score axis
plot(m, "probability", yaxt="s")
# }
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