GARS_GA: The wrapper fuction to use GARS

In GARS: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Description

This function allows the users to run all GARS funtion at once. This is the easier and recommended way to use GARS.

Usage

GARS_GA(data, classes, chr.num = 1000, chr.len, generation = 500,
  co.rate = 0.8, mut.rate = 0.01, n.elit = 10, type.sel = c("RW",
  "TS"), type.co = c("one.p", "two.p"), type.one.p.co = c("I.quart",
  "II.quart", "III.quart"), n.gen.conv = 80, plots = c("yes", "no"),
  n.Feat_plot = 10, verbose = c("yes", "no"))

Arguments

data	A SummarizedExperiment object or a matrix or a data.frame. In case of matrix or data.frame: Rows and Cols have to be, respectively, observations and features. The variables are tipically genes; GARS also accept other -omic features as well as any continuous or factorial variables (e.g. sex, age, cholesterol level,...); Usually the number of observation is << than the number of features '
classes	The class vector
chr.num	The number of chromosomes to generate. Default is 1000
chr.len	The length of chromosomes. This value corresponds to the desired length of the feature set
generation	The maximum number of generations. Default is 1000
co.rate	The probability of each random couple of chromosomes to swap some parts. It must be between 0 and 1. Default is 0.8
mut.rate	The probability to apply a random mutation to each element. It must be between 0 and 1. Default is 0.01
n.elit	The number of best chromosomes to be selected by elitism. This number must be even. Default is 10
type.sel	The type of selection method; Roulette Wheel ("RW") and Tournament Selection ("TS") are allowed. Default is "RW"
type.co	The type of crossover method; one-point ("one.p") and two-point ("two.p") are allowed. Default is "one.p"
type.one.p.co	The position of the cromosome where performing the crossover, if "one.p" is selected. The first quartile ("I.quart"), the second quartile ("II.quart", i.e. the median) and the third quartile ("III.quart") are allowed. Default is "I.quart"
n.gen.conv	The number of consecutive generations with the same maximum fitness score.
plots	If graphs have to be plotted; "yes" or "no" are allowed. Default is "yes"
n.Feat_plot	The number of features to be plotted
verbose	If statistics have to be printed; "yes" or "no" are allowed. Default is "yes"

Value

A GarsSelectedFeatures object, containg:

data_red

a matrix of selected features

last_pop

a matrix containg the last chromosome population

pop_list

a list containing all the populations produced over the generations

fit_list

a numeric vector containing the maximum fitness scores, computed in each generation

Author(s)

Mattia Chiesa, Luca Piacentini

Examples

# use example data:
data(GARS_data_norm)
data(GARS_classes)

res_ex <- GARS_GA(GARS_data_norm,
   GARS_classes,
   chr.num = 100,
   chr.len=10,
   generation = 5,
   co.rate = 0.8,
   mut.rate = 0.1,
   n.elit = 10,
   type.sel = "RW",
   type.co ="one.p",
   type.one.p.co = "II.quart",
   n.gen.conv = 80,
   plots = "no",
   verbose = "no")

GARS documentation built on Nov. 8, 2020, 7:23 p.m.