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galgo (version 1.4)

Genetic Algorithms for Multivariate Statistical Models from Large-Scale Functional Genomics Data

Description

Build multivariate predictive models from large datasets having far larger number of features than samples such as in functional genomics datasets. Trevino and Falciani (2006) .

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Install

install.packages('galgo')

Monthly Downloads

6

Version

1.4

License

GPL (>= 2)

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Repository

https://github.com/vtrevino/GALGO

Maintainer

Victor Trevino

Last Published

October 14th, 2018

Functions in galgo (1.4)

The representation of a set of niches with migration for genetic algorithms

Acute Lymphoblastic Leukemia data (Yeoh et. al., 2002) for GALGO package

The representation of a set of genes for genetic algorithms

assignParallelFile.BigBang

Assigns a different saveFile value for parallelization

Acute Lymphoblastic Leukemia data (Yeoh et. al., 2002) for GALGO package

The representation of a Genetic Algorithm

buildCount.BigBang

Builds the rank and frequency stability counting

classPrediction

Function used to predict class evaluating a fitness function in many train-test sets

Clones itself and its niches

Returns the best chromosome

addCount.BigBang

Add a chromosome to rank and frequency stability counting

configBB.VarSel

Creates and configure all objects needed for a ``variable selection for classificacion'' problem

addRandomSolutions.BigBang

Adds random pre-existed solutions

Converts the gene parameters (shape1, shape2) to matrix

computeCount.BigBang

Compute the counts for every gene from a set of chromosomes.

bestFitness.Galgo

Returns the fitness of the best chromosome

as.matrix.Niche

Converts the chromosome values (genes) to a matrix

A list-like Object

Represents the ensemble of the results of evolving several Galgo objects

configBB.VarSelMisc

Creates and configure all objects needed for a ``variable selection'' problem

addSolutions.BigBang

Adds user-built or external chromosomes as solutions

bestFitness.Niche

Returns the fitness of the best chromosome in the niche

generateRandom.Chromosome

Generates random values for all genes in the chromosome

filterSolution.BigBang

Filters solutions

Returns the best chromosome of the niche

generateRandom.Galgo

Generates random values for all populations in the Galgo object

bestFitness.World

Returns the fitness of the best chromosome

Returns the chromosome in the niche whose current fitness is maximum

as.double.Chromosome

Converts the chromosome values (genes) to its numerical representation

Function used to evaluate a chromosome

Convert a variable of class Object to a list

Computes the distance in GALGO for KNN based methods

decode.Chromosome

Converts the gene values to user-readable values

distanceImportanceNetwork.BigBang

Converts geneImportanceNetwork matrix to distance matrix

as.matrix.BigBang

Prints the representation of the BigBang object

forwardSelectionModels.BigBang

Gets the ``best'' models using top-ranked genes and a forward-selection strategy

geneBackwardElimination

Searches for shorter or better models using backward elimination strategy

classPredictionMatrix.BigBang

Predicts class for samples from chromosomes

Returns the best chromosome

Returns the chromosome whose current fitness is maximum

clone.Chromosome

Clones itself and its genes

generateRandom.Gene

Generates a random value from the defined function

Evaluate all niches with a fitness function

Evolves the chromosomes populations of a Galgo (Genetic Algorithm)

Gets the length of the object as its list version

generateRandom.Niche

Generates random values for all genes contained in all chromosomes in the niche

confusionMatrix.BigBang

Computes the class confusion matrix from a class prediction matrix

maxFitness.World

Returns the fitness of the maximum chromosome

Evolves Galgo objects saving the results for further analysis

geneImportanceNetwork.BigBang

Computes the number of times a couple of top-ranked-genes are present in models

meanFitness.BigBang

Computes the ``mean'' fitness from several solutions

Clones itself and all its objects

geneRankStability.BigBang

Computes the rank history for top-ranked genes

meanGeneration.BigBang

Computes the mean number of generations requiered to reach a given fitness value

Galgo perform feature selection from large scale data.

newCollection.Chromosome

Generates a list of chromosomes cloning the original chromosome object

Class prediction using KNN method calling the C code

crossover.Niche

Performs crossover between chromosomes of the niche

newCollection.Gene

Generates a list of cloned objects

fitnessSplits.BigBang

Computes the fitness function from chromosomes for different splits

mergeBangs.BigBang

Merges the information from other BigBang objects

nearcent_C_predict

Class prediction using the nearest centroid method calling the C code

getFrequencies.BigBang

Computes gene freqencies

Plots information about the Galgo object

heatmapModels.BigBang

Plots models using heatmap plot

nearcent_R_predict

Class prediction using the nearest centroid method calling the R code

Class prediction using KNN method calling the R code

Clones itself and its chromosomes

pcaModels.BigBang

Plots models in principal components space

Plots information about niche object

Prints the representation of a world object

formatChromosome.BigBang

Converts chromosome for storage in BigBang object

geneCoverage.BigBang

Computes the fraction of genes present in the top-rank from the total genes present in chromosomes

geneFrequency.BigBang

Computes the frequency of genes based on chromosomes

Plots about the collected information in a BigBang object

Evaluates all chromosomes with a fitness function

genes.Chromosome

Converts the genes values to a numeric vector

getFitness.Niche

Returns the fitness vector related to chromosomes

Gets the number of niches defined in the world

Evaluates the chromosome using a fitness function

maxFitness.Galgo

Returns the fitness of the maximum chromosome

Performs offspring, crossover, mutation, and elitism mechanism to generate the ``evolved'' niche

maxFitness.Niche

Returns the fitness of the maximum chromosome in the niche

generateRandom.World

Generates random values for all niches in the world

generateRandomModels

Generates Random shorter models

refreshStats.World

Updates the internal statistics from the current population

loadParallelFiles.BigBang

Load all files saved during the parallelization

Gets the number of populations defined in the Galgo object

Mutates a niche calling mutate method for all chromosomes

Load saved data of class Object and use reObject as necessary

robustGeneBackwardElimination

Searches for shorter or better models using backward elimination strategy

newRandomCollection.Niche

Creates a list of cloned niches with its internal values generated by random

newRandomCollection.World

Creates a list of cloned object with its internal values generated by random

print.Chromosome

Prints the representation of the chromosome object

Returns the chromosome whose current fitness is maximum

Gets the number of chromosomes defined in the niche

Prints the representation of a Galgo object

saveObject.BigBang

Saves the BigBang object into a file in a suitable format

Calls progeny method to all niches in the world object

newRandomCollection.Chromosome

Creates a list of cloned chromosomes object with its internal values generated by random

Function used to evaluate a fitness function in many train-test sets

Assigns a weight for every chromosome to be selected for the next generation

mutate.Chromosome

Mutates a chromosome in specific positions

Class prediction using Maximum Likelihood Discriminant Functions method calling the C code

newRandomCollection.Gene

Generates a list of cloned objects and random values

newCollection.Niche

Generates a list of cloned niches

randomforest_R_predict

Class prediction using RandomForest method calling the R code

Class prediction using the neural networks method calling the R code

Erases all internal values in order to re-use the world object

offspring.Niche

Overwrites the new niche selecting a new population from the best chromosomes

newCollection.World

Generates a list cloning an object

Class prediction using Maximum Likelihood Discriminant Functions method calling the R code

Erases all internal values in order to re-use the object

Erases all internal values in order to re-use the object

Prints the representation of the Bag object

rpart_R_predict

Class prediction using the recursive tree partitions method calling the R code

Generation of random uniform integer values

Prints the representation of a BigBang object

Class prediction using support vector machines method calling the R code

Creates proper extended Object from a list obtained by unObject

sensitivityClass.BigBang

Computes the sensitivity of class prediction

specificityClass.BigBang

Computes the specificity of class prediction

Converts variables from class Object (and derived classes) to list

refreshStats.Galgo

Updates the internal values from the current populations

Plots information about world object

predict.BigBang

Predicts the class or fitting of new set of samples

refreshStats.Niche

Updates the internal values from the current population

Prints the representation of the Bag object

Prints the representation of a gene object

Prints the representation of a niche object

reInit.Chromosome

Erases all internal values in order to re-use the object

Erases all internal values in order to re-use the object

summary.BigBang

Prints the representation of the BigBang object

summary.Chromosome

Prints the representation of the chromosome object and all its genes

Prints the representation and statistics of the galgo object

Prints the representation of a gene object

Prints the representation and statistics of the world object

Class prediction using support vector machines method calling the C/R code

Prints the representation and statistics of the niche object

length.Chromosome

Gets the number of genes defined in the chromosome

The representation of a gene in a chromosome for genetic algorithms

The representation of a set of chromosomes for genetic algorithms

activeChromosomeSet.BigBang

Focus the analysis to different sets of chromosomes

Converts the gene parameters (shape1, shape2) to its numerical representation

as.double.Niche

Converts the chromosome values (genes) to a vector