Creates and configure all objects needed for a ``variable selection'' problem. It configures Gene, Chromosome, Niche, World, Galgo and BigBang objects.
configBB.VarSelMisc(
file=NULL,
data=NULL,
strata=NULL,
train=rep(2/3,333),
test=1-train,
force.train=c(),
force.test=c(), main="project",
test.error=c(0,1),
train.error=c("kfolds","splits","loocv","resubstitution"),
train.Ksets=-1, # -1 : auto-detection ==> max(min(round(13-n/11),n),3) n=samples
train.splitFactor=2/3,
fitnessFunc=NULL,
scale=FALSE,
geneFunc=runifInt,
chromosomeSize=5,
populationSize=-1,
niches=1,
worlds=1,
immigration=c(rep(0,18),.5,1),
mutationsFunc=function(ni) length(ni),
crossoverFunc=function(ni) round(length(ni)/2,0),
crossoverPoints=round(chromosomeSize/2,0),
offspringScaleFactor=1,
offspringMeanFactor=0.85,
offspringPowerFactor=2,
elitism=c(rep(1,9),.5),
goalFitness=0.90,
galgoVerbose=20,
maxGenerations=200,
minGenerations=10,
galgoUserData=NULL, # additional user data for galgo
maxBigBangs=1000,
maxSolutions=1000,
onlySolutions=FALSE,
collectMode="bigbang",
bigbangVerbose=1,
saveFile="?.Rdata",
saveFrequency=50,
saveVariable="bigbang",
callBackFuncGALGO=function(...) 1,
callBackFuncBB=plot,
callEnhancerFunc=function(chr, parent) NULL,
saveGeneBreaks=NULL,
geneNames=NULL,
sampleNames=NULL,
bigbangUserData=NULL # additional user data for bigbang
)
The file containing the data. First row should be sample names. First column should be variable names (genes). Second row must be the class or strata for every sample if strata is not provided. The strata is used to balance the train-test sets relative to different strata. If there are only one strata, use the same value for all samples.
If a file is not provided, data is the a data matrix or data frame with samples in columns and genes in rows (with its respective colnames and rownames set). If data is provided, strata must be specified.
if a file is not provided, specifies the classes or strata of the data. If the file is provided and strata is specified, the second row of the file is considered as data. The strata is used to balance the train-test sets relative to different strata. If there are only one strata, use the same value for all samples.
A vector of the proportion of random samples to be used as training sets. The number of sets is determined by the length of train. The train+test should never be greather than 1. All sets are randomly chosen with the same proportion of samples per class than the original sample set.
A vector of the proportion of random samples to be used as testing sets. The number of sets is determined by the length of train. All sets are randomly chosen with the same proportion of samples per class than the original sample set.
A vector with sample indexes forced to be part of all training sets.
A vector with sample indexes forced to be part of all test sets.
A string or ID related to your project that will be used in all plots and would help you to distinguish results from different studies.
Vector of two weights specifing how the fitness function is evaluated to compute the test error. The first value is the weight of training and the second the weight of test. The default is c(0,1) which consider only test error. The sum of this values should be 1.
Specify how the training set is divided to compute the error in the training set (in evolve method for Galgo object). "splits" compute K (train.Ksets) random splits. "loocv" (leave-one-out-cross-validation) compute K=training samples. "resubstitution" no folding at all; it is faster and provided for quick overviews.
The number of training set folds/splits. Negative means automatic detection (n=samples, max(min(round(13-n/11),n),3)).
When train.error=="splits", specifies the proportion of samples used in spliting the training set.
Specify the function that would be used to compute the accuracy. The required prototype is function(chr, parent, tr, te, result) where chr is the chromosome to be evaluated. parent would be the BigBang object where all their variables are exposed. The fitness function commonly use parent$data$data, which has been trasposed. tr is the vector of samples (rows) that MUST be used as training and te the samples that must be used as test.
TRUE instruct to scale all rows for zero mean and unitary variance. By default, this value is FALSE.
Specify the function that mutate genes. The default is using an integer uniform distribution function (runifInt).
Specify the chromosome size (the number of variables/genes to be included in a model). Defaults to 5. See Gene and Chromosome objects.
Specify the number of chromosomes per niche. Defaults is min(20,20+(2000-nrow(data))/400). See Chromosome and Niche objects.
Specify the number of niches. Defaults to 2. See Niche, World and Galgo objects.
Specify the number of worlds. Defaults to 1. See World and Galgo objects.
Specify the migration criteria.
Specify the function that returns the number of mutations to perform in the population.
Specify the function that returns the number of crossover to perform. The default is the length of the niche divided by 2.
Specify the active positions for crossover operator. Defaults to a single point in the middle of the chromosome. See Niche object.
Scale factor for offspring generation. Defaults 1. See Niche object.
Mean factor for offspring generation. Defaults to 0.85. See Niche object.
Power factor for offspring generation. Defaults to 2. See Niche object.
Elitism probability/flag/vector. Defaults to c(1,1,1,1,1,1,1,1,1,0.5) (elitism present for 9 generations followed by a 50% chance, then repeated). See Niche object.
Specify the desired fitness value (fraction of correct classification). Defaults to 0.90. See Galgo object.
verbose parameter for Galgo object.
Maximum number of generations. Defaults to 200. See Galgo object.
Minimum number of generations. Defaults to 10. See Galgo object.
Additional user data for the Galgo object. See Galgo object.
Maximum number of bigbang cycles. Defaults to 1000. See BigBang object.
Maximum number of solutions collected. Defaults to 1000. See BigBang object.
Save only when a solution is reach. Defaults to FALSE (to use all the information, then a filter can be used afterwards). See BigBang object.
information to collect. Defaults to "bigbang". See BigBang object.
Verbose flag for BigBang object. Defaults to 1. See BigBang object.
File name where the data is saved. Defaults to NULL which implies the name is a concatenation of classification.method, method specific parameters, file and ".Rdata". See BigBang object.
How often the ``current'' solutions are saved. Defaults to 50. See BigBang object.
Internal R variable name of the saved file. Defaults to ``bigbang''. See BigBang object.
callBackFunc for Galgo object. See Galgo object.
callBackFunc for BigBang object. See BigBang object.
callEnhancerFunc for BigBang object. See BigBang object.
saveGeneBreaks vector for BigBang object. Defaults to NULL which means to be computed automatically (recommended). See BigBang object.
The gene (variable) names if they differ from the first column in file or rownames(data).
The sample names if they differ from first row in file or colnames(data).
Additional user data for BigBang object (stored in $data variable in BigBang object returned).
A ready to use bigbang object.
*** TO DO: EXPLAIN THE STRUCTURE OF "DATA" ***
Wrapper function. Configure all objects from parameters.
# NOT RUN {
bb <- configBB.VarSelMisc(...)
bb
blast(bb)
# }
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