BioMMstage2pred {BioMM} | R Documentation

## Prediction performance for stage-2 data using supervised machine learning

### Description

Prediction performance for reconstructed stage-2 data using supervised machine learning with feature selection methods.

### Usage

    BioMMstage2pred(trainData, testData, resample = "CV", dataMode,
      repeatA = 1, repeatB = 1, nfolds, FSmethod, cutP, fdr,
      FScore = MulticoreParam(), classifier, predMode, paramlist,
      innerCore = MulticoreParam())

### Arguments

`trainData` | The input training dataset (stage-2 data). The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
`testData` | The input test dataset (stage-2 data). The first column is the label or the output. For binary classes, 0 and 1 are used to indicate the class member.
`resample` | The resampling methods. Valid options are 'CV' and 'BS'. 'CV' for cross validation and 'BS' for bootstrapping resampling. The default is 'CV'.
`dataMode` | The mode of data used. 'subTrain' or 'allTrain'.
`repeatA` | The number of repeats N is used during resampling prediction. The default is 1.
`repeatB` | The number of repeats N is used for test data prediction. The default is 1.
`nfolds` | The number of folds is defined for cross validation.
`FSmethod` | Feature selection methods. Available options are c(NULL, 'positive', 'wilcox.test', 'cor.test', 'chisq.test', 'posWilcox', or 'top10pCor').
`cutP` | The cutoff used for p value thresholding. Commonly used cutoffs are c(0.5, 0.1, 0.05, 0.01, etc). The default is 0.05.
`fdr` | Multiple testing correction method. Available options are c(NULL, 'fdr', 'BH', 'holm', etc). See also `p.adjust`. The default is NULL.
`FScore` | The number of cores used for feature selection if parallel computing needed.
`classifier` | Machine learning classifiers.
`predMode` | The prediction mode. Available options are c('probability', 'classification', 'regression').
`paramlist` | A set of model parameters defined in an R list object.
`innerCore` | The number of cores used for computation.

### Details

Stage-2 prediction is performed typically using positively correlated features. Since negative associations likely reflect random effects in the underlying data

### Value

The CV or BS prediction performance for stage-2 training data and test set prediction performance for stage-2 test data if the test set is given.

### Author(s)

Junfang Chen

### References

Perlich, C., & Swirszcz, G. (2011). On cross-validation and stacking: Building seemingly predictive models on random data. ACM SIGKDD Explorations Newsletter, 12(2), 11-15.

[Package BioMM version 1.4.0 Index]