FRESA.CAD-package | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD) |
backVarElimination_Bin | IDI/NRI-based backwards variable elimination |
backVarElimination_Res | NeRI-based backwards variable elimination |
baggedModel | Get the bagged model from a list of models |
barPlotCiError | Bar plot with error bars |
BinaryBenchmark | Compare performance of different model fitting/filtering algorithms |
bootstrapValidation_Bin | Bootstrap validation of binary classification models |
bootstrapValidation_Res | Bootstrap validation of regression models |
bootstrapVarElimination_Bin | IDI/NRI-based backwards variable elimination with bootstrapping |
bootstrapVarElimination_Res | NeRI-based backwards variable elimination with bootstrapping |
BSWiMS.model | BSWiMS model selection |
cancerVarNames | Data frame used in several examples of this package |
correlated_Remove | Univariate Filters |
crossValidationFeatureSelection_Bin | IDI/NRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variables |
crossValidationFeatureSelection_Res | NeRI-based selection of a linear, logistic, or Cox proportional hazards regression model from a set of candidate variables |
CVsignature | Cross-validated Signature |
EmpiricalSurvDiff | Estimate the LR value and its associated p-values |
ensemblePredict | The median prediction from a list of models |
featureAdjustment | Adjust each listed variable to the provided set of covariates |
FilterUnivariate | Univariate Filters |
ForwardSelection.Model.Bin | IDI/NRI-based feature selection procedure for linear, logistic, and Cox proportional hazards regression models |
ForwardSelection.Model.Res | NeRI-based feature selection procedure for linear, logistic, or Cox proportional hazards regression models |
FRESA.CAD | FeatuRE Selection Algorithms for Computer-Aided Diagnosis (FRESA.CAD) |
FRESA.Model | Automated model selection |
FRESAScale | Data frame normalization |
getKNNpredictionFromFormula | Predict classification using KNN |
getSignature | Returns a CV signature template |
getVar.Bin | Analysis of the effect of each term of a binary classification model by analysing its reclassification performance |
getVar.Res | Analysis of the effect of each term of a linear regression model by analysing its residuals |
heatMaps | Plot a heat map of selected variables |
improvedResiduals | Estimate the significance of the reduction of predicted residuals |
KNN_method | KNN Setup for KNN prediction |
LASSO | CV LASSO fit with s="lambda.min" or s="lambda.1se" |
LASSO_1SE | CV LASSO fit with s="lambda.min" or s="lambda.1se" |
LASSO_MIN | CV LASSO fit with s="lambda.min" or s="lambda.1se" |
listTopCorrelatedVariables | List the variables that are highly correlated with each other |
LM_RIDGE_MIN | Ridge Linear Models |
modelFitting | Fit a model to the data |
mRMR.classic_FRESA | FRESA.CAD wrapper of mRMRe::mRMR.classic |
NAIVE_BAYES | Naive Bayes Modeling |
nearestNeighborImpute | nearest neighbor NA imputation |
OrdinalBenchmark | Compare performance of different model fitting/filtering algorithms |
plot | Plot ROC curves of bootstrap results |
plot.bootstrapValidation_Bin | Plot ROC curves of bootstrap results |
plot.bootstrapValidation_Res | Plot ROC curves of bootstrap results |
plot.FRESA_benchmark | Plot the results of the model selection benchmark |
plotModels.ROC | Plot test ROC curves of each cross-validation model |
predict | Linear or probabilistic prediction |
predict.fitFRESA | Linear or probabilistic prediction |
predict.FRESAKNN | Predicts 'class::knn' models |
predict.FRESAsignature | Predicts 'CVsignature' models |
predict.FRESA_LASSO | Predicts LASSO fitted objects |
predict.FRESA_NAIVEBAYES | Predicts 'NAIVE_BAYES' models |
predict.FRESA_RIDGE | Predicts 'LM_RIDGE_MIN' models |
predictionStats_binary | Prediction Evaluation |
predictionStats_ordinal | Prediction Evaluation |
predictionStats_regression | Prediction Evaluation |
randomCV | Cross Validation of Prediction Models |
rankInverseNormalDataFrame | rank-based inverse normal transformation of the data |
RegresionBenchmark | Compare performance of different model fitting/filtering algorithms |
reportEquivalentVariables | Report the set of variables that will perform an equivalent IDI discriminant function |
residualForFRESA | Return residuals from prediction |
signatureDistance | Distance to the signature template |
summary | Returns the summary of the fit |
summary.bootstrapValidation_Bin | Generate a report of the results obtained using the bootstrapValidation_Bin function |
summary.fitFRESA | Returns the summary of the fit |
summaryReport | Report the univariate analysis, the cross-validation analysis and the correlation analysis |
timeSerieAnalysis | Fit the listed time series variables to a given model |
uniRankVar | Univariate analysis of features (additional values returned) |
univariateRankVariables | Univariate analysis of features |
univariate_correlation | Univariate Filters |
univariate_Logit | Univariate Filters |
univariate_residual | Univariate Filters |
univariate_tstudent | Univariate Filters |
univariate_Wilcoxon | Univariate Filters |
update | Update the univariate analysis using new data |
update.uniRankVar | Update the univariate analysis using new data |
updateModel.Bin | Update the IDI/NRI-based model using new data or new threshold values |
updateModel.Res | Update the NeRI-based model using new data or new threshold values |