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Last data update: 2014.03.03

R: Fits a logistic regression model using the linear scores

compute.logistic.score

R Documentation

Fits a logistic regression model using the linear scores

Description

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.

Usage

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

Arguments

`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.

Details

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

Value

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.

Note

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

Author(s)

Habil Zare

References

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

Examples

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(FeaLect)
Loading required package: lars
Loaded lars 1.2

Loading required package: rms
Loading required package: Hmisc
Loading required package: lattice
Loading required package: survival
Loading required package: Formula
Loading required package: ggplot2

Attaching package: 'Hmisc'

The following objects are masked from 'package:base':

    format.pval, round.POSIXt, trunc.POSIXt, units

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FeaLect/compute.logistic.score.Rd_%03d_medium.png", width=480, height=480)
> ### Name: compute.logistic.score
> ### Title: Fits a logistic regression model using the linear scores
> ### Aliases: compute.logistic.score
> ### Keywords: regression multivariate classif models
> 
> ### ** Examples
> 
> 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
PAT20762 PAT14569 PAT20839 PAT10301 PAT10384 PAT10863 PAT10105  PAT8893 
       1        1        1        0        0        0        1        0 
> 
> 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)
$logistic.scores
[1] 9.999999e-01 9.999985e-01 9.999676e-01 3.047586e-04 1.412099e-05
[6] 1.412099e-05 9.993009e-01 3.047586e-04

$logistic.cofs
    Intercept linear.scores 
    -14.23998      30.72148 

> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>