BioMMstage2pred: Prediction performance for stage-2 data using supervised...

In BioMM: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

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 predicted score for stage-2 training data and test set predicted score 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.

BioMM documentation built on Nov. 8, 2020, 11:04 p.m.