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

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", direction='absolute'),
feature.type='normalized', verbose = 1)



object of class siamcat-class


string, specifies the type of model to be trained, may be one of these: c('lasso', 'enet', 'ridge', 'lasso_ll', 'ridge_ll', 'randomForest')


boolean, should the folds in the internal cross-validation be stratified?, defaults to TRUE


list, specifies the model selection criterion during internal cross-validation, may contain these: c('auc', 'f1', 'acc', 'pr'), defaults to list('auc')


integer number of minimum nonzero coefficients that should be present in the model (only for 'lasso', 'ridge', and 'enet'), defaults to 1


list, set of extra parameters for mlr run, may contain:

  • cost and class.weights - for lasso_ll and ridge_ll

  • alpha - for enet

  • ntree and mtry - for RandomForrest.

See below for details. Defaults to NULL


boolean, should feature selection be performed? Defaults to FALSE


list, parameters for the feature selection, must contain:

  • thres.fs - threshold for the feature selection,

  • method.fs - method for the feature selection, may be AUC, gFC, or Wilcoxon

  • direction - for AUC and gFC, select either the top associated features (independent of the sign of enrichment), the top positively associated featured, or the top negatively associated features, may be absolute, positive, or negative. Will be ignored for Wilcoxon.

See Details for more information. Defaults to list(thres.fs=100, method.fs="AUC", direction='absolute')


string, on which type of features should the function work? Can be either "original", "filtered", or "normalized". Please only change this paramter if you know what you are doing!


integer, 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


object of class siamcat-class with added model_list


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 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 different machine learning methods are implemented as mlr-tasks:

  • 'lasso', 'enet', and 'ridge' use the 'classif.cvglmnet' Learner,

  • 'lasso_ll' and 'ridge_ll' use the 'classif.LiblineaRL1LogReg' and the 'classif.LiblineaRL2LogReg' Learners respectively

  • 'randomForest' is implemented via the 'classif.randomForest' Learner.

Hyperparameters You also have additional control over the machine learning procedure by supplying information through the param.set parameter within the function. We encourage you to check out the excellent mlr documentation for more in-depth information.

Here is a short overview which parameters you can supply in which form:

  • enet The alpha parameter describes the mixture between lasso and ridge penalty and is -per default- determined using internal cross-validation (the default would be equivalent to param.set=list('alpha'=c(0,1))). You can supply either the limits of the hyperparameter exploration (e.g. with limits 0.2 and 0.8: param.set=list('alpha'=c(0.2,0.8))) or you can supply a fixed alpha value as well (param.set=list('alpha'=0.5)).

  • lasso_ll/ridge_ll You can supply both class.weights and the cost parameter (cost of the constraints violation, see LiblineaR for more info). The default values would be equal to param.set=list('class.weights'=c(5, 1), 'cost'=c(10 ^ seq(-2, 3, length = 6 + 5 + 10)).

  • randomForest You can supply the two parameters ntree (Number of trees to grow) and mtry (Number of variables randomly sampled as candidates at each split). See also randomForest for more info. The default values correspond to param.set=list('ntree'=c(100, 1000), 'mtry'= c(round(sqrt.mdim / 2), round(sqrt.mdim), round(sqrt.mdim * 2))) with sqrt.mdim=sqrt(nrow(data)).

Feature selection 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

  • 'gFC' - 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

For AUC and gFC, feature selection can also be directed, that means that the features will be selected either based on the overall association (absolute - gFC will be converted to absolute values and AUC values below 0.5 will be converted by 1 - AUC), or on associations in a certain direction (positive - positive enrichment as measured by positive values of the gFC or AUC values higher than 0.5 - and reversely for negative).


data(siamcat_example) # simple working example siamcat_example <- train.model(siamcat_example, method='lasso')
#> Trained lasso models successfully.