compute.logistic.score: Fits a logistic regression model using the linear scores
In FeaLect: Scores Features for Feature Selection

Description Usage Arguments Details Value Note Author(s) References See Also Examples

A logistic regression model is fitted to the linear scores using lrm() function and the logistic scores are computed using the formula: 1/(1+exp(-(a+bX))) where a and b are the logistic coefficients.

1 2	compute.logistic.score(F_, L_, considered.features, training.samples, validating.samples, linear.scores, report.fitting.failure = TRUE)

`F_`	The feature matrix, each column is a feature.
`L_`	The vector of labels named according to the rows of F.
`training.samples`	The names of rows of F that should be considered as training samples.
`validating.samples`	The names of rows of F that should be considered as validating samples.
`considered.features`	The names of columns of F that determine the features of interest.
`linear.scores`	A vector that contains for each training or validating sample, a linear score predicted by the linear method.
`report.fitting.failure`	If TRUE, any failure in fitting the linear of logistic models will be printed.

The logistic regression will be fitted to all training and validating samples.

Returns a list of:

`logistic.scores`	A vector of predicted logistic values for all samples.
`logistic.cofs`	The coefficients that are computed by logistic regression.

Logistic regression is also done on top of fitting the linear models.

Habil Zare

"Statistical Analysis of Overfitting Features", manuscript in preparation.

FeaLect, train.doctor, doctor.validate, random.subset, compute.balanced,compute.logistic.score, ignore.redundant, input.check.FeaLect

library(FeaLect)
data(mcl_sll)
F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
L <- as.numeric(mcl_sll[ ,1])	# The labels
names(L) <- rownames(F)
all.samples <- rownames(F); ts <- all.samples[5:10]; vs <- all.samples[c(1,22)]
L <- L[c(ts,vs)]
L

asymptotic.scores <- c(1,0.9,0.8,0.2,0.1,0.1,0.7,0.2)

compute.logistic.score(F_=F, L_=L, training.samples=ts, validating.samples=vs, 
			     considered.features=colnames(F),linear.scores= asymptotic.scores)