Function reference • predomics

All functions

AnalyseStableModels_LOO(): analyse stability of models from digest

LPO_best_models(): Compute the cross-validation of leave one out for test stability

analyzeImportanceFeatures(): Prints as text the detail on a given experiment along with summarized results (if computed)

analyzeImportanceFeaturesFBM(): Visualize a summary of an experiment/set of experiments

analyzePopulationFeatures(): Prints as text the detail on a given experiment along with summarized results (if computed)

bestModelFeatureStability(): analyse stability of models from digest

bestModelStability(): analyse stability of models from digest

cir_test: Cirhosis stage 2 (frequencies)

cir_train: Cirhosis stage 1 (frequencies)

cleanPopulation(): cleanPopulation

computeCardEnrichment(): computeCardEnrichment

computeCoeffSVMLin(): Compute other prediction scores such as precision, recall and f-score

computeConfusionMatrix(): Evaluates the confusion Matrix of the predicted class and the class to predict

computeEffectSizes(): Compute effect sizes for features in binary classification/regression tasks

computeFeatureMetrics(): Computes different metrics for a given distributions

computeIntercept(): Computes the best intercept for the model while minimizing error

counter(): The counter for the experiment id (used in the clf builders)

crossing(): Creates new combinations of features based from a parents.

denseVecToModel(): denseVecToModel

digest(): Summarize the results from an experiment object

digestModelCollection(): digestModelCollection

disectModel(): Analyzes the score construction and model

estimateFeatureImportance(): Estimates the importance of each feature in the model object

evaluateAUC(): Computes the AUC of a model

evaluateAccuracy(): Evaluates the accuracy of a model

evaluateAdditionnalMetrics(): Compute other prediction scores such as precision, recall and f-score

evaluateFeatureImportanceInPopulation(): evaluates the feature importance in a population of models

evaluateFit(): Evaluates the fitting score of a model object

evaluateIntercept(): Evaluates the fitting score of a model object

evaluateModel(): Evaluates the fitting score of a model object

evaluateModelRegression(): Evaluates the fitting coefficents of a model object

evaluatePopulation(): evaluatePopulation

evaluatePrevalence(): Evaluate the prevalence of a given model

evaluateYhat(): Computes the predected classification using a given model

evolve(): Creates new combinations of features based from a parents.

evolve2m(): Second version of the evolve method

evolve3m(): Another version of the evolve method that distingishes the different lanugages

filterFeaturesByPrevalence(): Selects the most prevalent features in the dataset baset on the provided thresholds.

filterNoSignal(): filterNoSignal: Omits the variables with no information

filterfeaturesK(): Selects a the top k features that are significantly associated with the class to predict

findk(): Find the number of weights not yet integer.

fit(): fit: runs the classifier on a dataset

generateAllCombinations(): generateAllCombinations

generator_metal(): #' Computes best model of a metal clf #' #' @description Get best metal model #' @param X: dataset to classify #' @param y: variable to predict #' @param clf: an object containing the different parameters of the classifier #' @param clf_res: the result of metal #' @param k_penalty: penalty for k #' @return A list of result of best model for each k, their importance feature of each best model, individuels wrongly classified #' @export getTheBestMetalModel<- function(clf, clf_res, X, k_penalty=0.01, evalToOrder="accuracy_",selected=1) if(length(clf_res)==3) clf_res<-clf_res$classifier pop<-modelCollectionToPopulation(clf_res$models) acc <- populationGet_X(evalToOrder)(pop) k <- populationGet_X("eval.sparsity")(pop) acc.penalty <- acc-(k*k_penalty) best.acc <- max(acc.penalty) epsilon <- sqrt(best.acc*(1-best.acc)/ncol(X)) pop2 <- pop[acc.penalty>(best.acc - epsilon)] mod <- getMaxMinPrevalenceModel(pop2,X,selected=selected) return(mod) Generate a metal list of clfs containing information on the generators and unificators

getFeaturePrevalence(): Evaluates the prevalence of a list of features in the whole dataset and per each class

getFitIndividual(): Get the fitting score of an individual object

getFitModel(): Get the fitting score of a model object

getFitModels(): Get the fitting score of a list a models

getFitPopulation(): Get the fitting score of a list of individuals

getGraph(): getGraph

getImportanceFeaturesFBMobjects(): Get objects needed for a merged visualization task combining different experiments from different datasets (different X and y)

getIndicesIndividual(): Get the index of the features in a given individual

getIndicesPopulation(): Get the indices of the features used in a population of individuals

getMaxMinPrevalenceModel(): Get the model that has the highest minimal prevalence in its features

getModelScore(): Computes the ^y score of the model

getNBestModels(): Get the models from a classifier result for each k-sparsity

getSign(): Evaluates the sign for a given feature this is the old getMgsVsTraitSignDiscr function

get_IndividualToBeMutated(): Return list of individuals to mutate

get_Parents(): Return list of parents

glmnetRR(): Solve with GLMNET and create models

ibd: Inflammatory Bowel Disease (frequencies) from the MetaHIT study

index2names(): index2names

individual(): Creates an object individual

isClf(): Evaluates wether an object is a classifier

isExperiment(): Evaluates wether an object is an experiment

isLearnerSota(): Evaluates wether an object is a model SOTA SVM

isModel(): Evaluates wether an object is a model

isModelBTR(): Evaluates wether an object is a model BTR

isModelCollection(): Evaluates wether an object is a model collection objecct

isModelSota(): Evaluates wether an object is a model SOTA

isModelSotaGLMNET(): Evaluates wether an object is a model SOTA GLMNET

isModelSotaRF(): Evaluates wether an object is a model SOTA RF

isModelSotaSVM(): Evaluates wether an object is a model SOTA SVM

isModelTerda(): Evaluates wether an object is a model BTR Terda

isPopulation(): Evaluates wether an object is a population of models

isclose(): tests weather two values are close

listOfDenseVecToListOfModels(): Builds a model object from a list of vector coefficients

listOfDenseVecToModelCollection(): Builds a list of dense vector coefficients from a list of models

listOfModels2ModelCollection(): listOfModels2ModelCollection

listOfModelsToDenseCoefMatrix(): listOfModelsToDenseCoefMatrix

listOfModelsToListOfDenseVec(): Builds a list of dense vector coefficients from a list of models

listOfModelsToListOfSparseVec(): Builds a list of sparse vector coefficients from a list of models

listOfSparseVecToListOfModels(): listOfSparseVecToListOfModels

loadPopulation(): Load a population from a file

loadResults(): Load the results of a fit

make.counter(): Function used to create the counter for building clf$experiment$id

makeFeatureAnnot(): Prints as text the detail on a given experiment along with summarized results (if computed)

makeFeatureModelPrevalenceNetworkCooccur(): Prints as text the detail on a given experiment along with summarized results (if computed)

makeFeatureModelPrevalenceNetworkMiic(): Prints as text the detail on a given experiment along with summarized results (if computed)

mergeMeltBestScoreCV(): mergeMeltBestScoreCV

mergeMeltImportanceCV(): mergeMeltImportanceCV

mergeMeltScoreCV(): mergeMeltScoreCV

mergeMeltScoreEmpirical(): mergeMeltScoreEmpirical

mergeResults(): mergeResults

metal(): metal: metal searching algorithm

modelCollectionToPopulation(): Transform a model collection to a population (or list of model objects)

modelToDenseVec(): Transform the model object onto dense format (long) one

multipleRR(): multipleRR

multipleRR_par(): multipleRR_par

mutate(): Changes feature indexes in a given percentage of models.

myAssert(): Asserts a condition and prints a message or stops the block

myAssertNotNullNorNa(): Asserts the existance of an object and prints a message or stops the block

names2index(): names2index

normModelCoeffs(): Normalize the model coefficients needed for the plot

obesity: Obesity (frequencies) from the MetaHIT study

plotAUC(): Analyze the results from a given classifier

plotAUCg(): Plot the AUC of a given classifier

plotAbundanceByClass(): Plots the prevalence of a list of features in the whole dataset and per each class

plotComparativeBestCV(): Plots a graph for a given score

plotComparativeCV(): Plots a graph for a given score

plotComparativeEmpiricalScore(): Plots a graph for a given score

plotComparativeResults(): Plot performance scores for multiple learners

plotComparativeResultsBest(): Plot performance scores for multiple learners

plotFeatureModelCoeffs(): Plots the prevalence of a list of features in the whole dataset and per each class

plotImportanceFeaturesFBMobjects(): Visualize a list containing outouts of getImportanceFeaturesFBMobjects

plotModel(): Plots a model or a population of model objectsas barplots of scaled coefficients.

plotModelScore(): Plots a model or a population of model objectsas barplots of scaled coefficients.

plotPopulation(): Plots a population of models (or a single model) objects as barplots of scaled coefficients.

plotPrevalence(): Plots the prevalence of a list of features in the whole dataset and per each class

plotScoreBarcode(): Plots the barcode of the total score as well as positive and negative components

population(): Creates a population of index models.

populationGet_X(): Get the best model from a classifier result

populationSet_X(): Set models with a given liist of objects

populationToDataFrame(): populationToDataFrame

printClassifier(): Prints as text the detail on a given Classifier object

printExperiment(): Prints as text the detail on a given Experiment object

printModel(): # plot a horizontal barplot #' @export plotBarplot <- function(v, rev=TRUE, xlim=range(v), main="") if(rev) v <- rev(v) barplot(v, las=2, horiz=TRUE, col="black", main=main, xlim=xlim) Prints a model object as text.

printModelCollection(): Prints as text the detail on a given ModelCollection object

printPopulation(): Prints a population of model objects as text.

printy(): Prints as text the detail on a given object from the predomics package.

resetTags(): Resets selection, mutation and mate tags to inactive

runClassifier(): Runs the learning on a dataset

runCrossval(): Compute the cross-validation emprirical and generalization scores

savePopulation(): Save a population to a file

saveResults(): Save the results of the fit function

scoreRatio(): Computes the ^y score of the model as a ratio

selectBestPopulation(): Select the top significant best part of the population

selector_v1(): Does an elite selection on a population

sim_inter(): compare stability of different modeles (inter k)

sim_intra(): compare stability of different modeles (intra k)

sortPopulation(): sortPopulation

sota.glmnet(): sota.glmnet

sota.rf(): sota.rf

sota.svm(): sota.svm

sparseVecToModel(): sparseVecToModel

summarySE(): Plot performance scores for multiple learners.

t2d: Type 2 diabetes (frequencies) BGI

t2dw: Type 2 diabetes (frequencies) Women Sweden

tag_Couples(): Tag the couples

tag_SelectElite(): Tag individuals for parenting

tag_SelectRandom(): Randomly tag selected individuals parenting

tag_ToBeMutated(): Tag individuals for mutation

tag_select(): Add `selected` tag using elite and random selection

terBeam(): terbeam

terda(): terda

terga1(): terga1

terga2(): Model search algorithm based on genetic algorithms (GA).

updateModelIndex(): updateModelIndex

updateObjectIndex(): updateObjectIndex