R: Fittes various models based on a combination on penalized...
train.doctor
R Documentation
Fittes various models based on a combination on penalized linear models and logistic regression.
Description
Various linear models are fitted to the training samples using lars method.
The models differ in the number of features and each is validated by validating samples.
A score is also assigned to each feature based on the tendency of LASSO in including that feature in the models.
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)
message(dim(F)[1], " samples and ",dim(F)[2], " features.")
all.samples <- rownames(F); ts <- all.samples[5:10]; vs <- all.samples[c(1,22)]
doctor <- train.doctor(F_=F, L_=L, training.samples=ts, validating.samples=vs,
considered.features=colnames(F), maximum.features.num=10)
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/train.doctor.Rd_%03d_medium.png", width=480, height=480)
> ### Name: train.doctor
> ### Title: Fittes various models based on a combination on penalized linear
> ### models and logistic regression.
> ### Aliases: train.doctor
> ### 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)
> message(dim(F)[1], " samples and ",dim(F)[2], " features.")
22 samples and 236 features.
>
> all.samples <- rownames(F); ts <- all.samples[5:10]; vs <- all.samples[c(1,22)]
>
> doctor <- train.doctor(F_=F, L_=L, training.samples=ts, validating.samples=vs,
+ considered.features=colnames(F), maximum.features.num=10)
>
>
>
>
>
>
> dev.off()
null device
1
>