train.model: Model training

In KonradZych/SIAMCAT: Statistical Inference of Associations between Microbial Communities And host phenoTypes

Description

This function trains the a machine learning model on the training data

Usage

train.model(siamcat,
method = c("lasso","enet","ridge","lasso_ll", "ridge_ll", "randomForest"),
stratify = TRUE, modsel.crit = list("auc"), min.nonzero.coeff = 1,
param.set = NULL, perform.fs = FALSE, 
param.fs = list(thres.fs = 100, method.fs = "AUC"), verbose = 1)

Arguments

siamcat	object of class siamcat-class
method	string, specifies the type of model to be trained, may be one of these: c('lasso', 'enet', 'ridge', 'lasso_ll', 'ridge_ll', 'randomForest')
stratify	boolean, should the folds in the internal cross-validation be stratified?, defaults to TRUE
modsel.crit	list, specifies the model selection criterion during internal cross-validation, may contain these: c('auc', 'f1', 'acc', 'pr'), defaults to list('auc')
min.nonzero.coeff	integer number of minimum nonzero coefficients that should be present in the model (only for 'lasso', 'ridge', and 'enet', defaults to 1
param.set	a list of extra parameters for mlr run, may contain: cost - for lasso_ll and ridge_ll alpha for enet ntree and mtry for RandomForrest. Defaults to NULL
perform.fs	boolean, should feature selection be performed? Defaults to FALSE
param.fs	a list of parameters for the feature selection, must contain: thres.fs - threshold for the feature selection, method.fs - method for the feature selection, may be AUC, FC, or Wilcoxon See Details for more information. Defaults to list(thres.fs=100, method.fs="AUC")
verbose	control output: 0 for no output at all, 1 for only information about progress and success, 2 for normal level of information and 3 for full debug information, defaults to 1

Details

This functions performs the training of the machine learning model and functions as an interface to the mlr-package.

The function expects a siamcat-class-object with a prepared cross-validation (see create.data.split) in the data_split-slot of the object. It then trains a model for each fold of the datasplit.

For the machine learning methods that require additional hyperparameters (e.g. lasso_ll), the optimal hyperparameters are tuned with the function tuneParams within the mlr-package.

The methods 'lasso', 'enet', and 'ridge' are implemented as mlr-taks using the 'classif.cvglmnet' Learner, 'lasso_ll' and 'ridge_ll' use the 'classif.LiblineaRL1LogReg' and the 'classif.LiblineaRL2LogReg' Learners respectively. The 'randomForest' method is implemented via the 'classif.randomForest' Learner.

The function can also perform feature selection on each individual fold. At the moment, three methods for feature selection are implemented:

• 'AUC' computes the Area Under the Receiver Operating Characteristics Curve for each single feature and selects the top param.fs$thres.fs, e.g. 100 features

• 'FC' computes the generalized Fold Change (see check.associations) for each feature and likewise selects the top param.fs$thres.fs, e.g. 100 features

• Wilcoxon computes the p-Value for each single feature with the Wilcoxon test and selects features with a p-Value smaller than param.fs$thres.fs

Value

object of class siamcat-class with added model_list

Examples

    data(siamcat_example)
    # simple working example
    siamcat_validated <- train.model(siamcat_example, method='lasso')

KonradZych/SIAMCAT documentation built on May 17, 2019, 6:20 p.m.