Changes from FRESA.CAD 2.2.0 to FRESA.CAD version 2.2.1
Enhancemetns:
meatMaps(...)
Class color bar next to categories
UnivariateRankVariables()
Now has the option to include only tail analysis
Now it store the beta coefficient
rankInverseNormalDataFrame(...,strata=NA)
Now you can specify a conditional ranking by specifying the strata
Now we can predict results from LASSO by retuning the filtered features
LASSO formulas now reported.
Buggs:
Univariate analysis of improved residuals fixed
several minos bug fixed.
Changes from FRESA.CAD 2.1.3 to FRESA.CAD version 2.2.0
FRESA.CAD expanded its capabilities.
Now it provided Bagged models and ensemble analysis from the list of formulas created by:
+ ForwardSelections.Models.Bin
+ ForwardSelections.Models.Res
+ crossValidationFeatureSelection.Bin
+ crossValidationFeatureSelection.Res
The baggedModel function bag the formula coefficients and creates a single model from the list of formulas.
The plotModels.ROC ensemble the model predictions and creates an unique test evaluation of the ensembled models.
-Added function:
baggedModel.R function for coefficient bagging, and variable frequency analysis
-Enhanced function:
plotModels.ROC.R: This function provides ensemble predictions and confusion analysis table
heatMaps.R can accept a list of five colors for its display
-c++ code revised and minor bugs corrected.
-r code revised and bugs corrected.
Changes from FRESA.CAD 2.0.1 to FRESA.CAD version 2.1.3
FRESA.CAD suffered mayor changes from the previous version.
The new version is more effective in handling memory, some functions and outputs
were renamed.
-Function Name Changes
+backVarElimination to backVarElimination_Bin
+backVarNeRiElimination to backVarElimination_Res
+bootstrapValidation to bootstrapValidation_Bin
+bootstrapNeRiValidation to bootstrapValidation_Res
+bootstrapVarElimination to bootstrapVarEliminiation_Bin
+bootstrapVarNeRiElimination to bootstrapVarEliminiation_Res
+crossValidationFeatureSelection to +crossValidationFeatureSelection_Bin
+crossValidationNeRiFeatureSelection to +crossValidationFeatureSelection_Res
+ReclassificationFRESA.Model to ForwardModel_Res
+NeRIBasedFRESA.Model to ForwardModel_Res
+getVarReclassification to getVar_Bin
+getVarNeRI to getVar_Res
+plot.bootstrapValidation to plot.bootstrapValidation_Bin
+plot.bootstrapValidationNeRI to plot.bootstrapValidation_Res
+updateModel to updateModel_Bin
+updateNeRImodel to updateModel_Res
-Renamed Outputs
Model created form forward models followed by back elimination renamed BSWiMS models
enet renamed LASSO
-Enhancements
+cross-validation now stores ID of sampled subject as well as training fits
+cross-validation now reports the ensemble estimations
+Update model added model-size-based Benjaminiâ€“Hochberg procedure (BH)
+Timeseriesanalysis chaged the presentation of p values to t values
+beforeFSC formulas produced before the BH correction
+Minor bugs:
+report equivalent variables for regression models
+removed first term of formula list of cross-validation process
+removed exact wilcoxon test
+other minor bugs
-Code reviews
+NAN were replaced by nan("") c++ function
+cpp code revision to remove abs and sign warnings
+median predict revised to work with the new structure of the formula list provided by cross-validation
+speedglm removed from dependencies
Changes from FRESA.CAD 2.0.1 to FRESA.CAD version 2.0.2
+variable _X from code was renamed _xmat
Changes from FRESA.CAD 2.0 to FRESA.CAD version 2.0.1
C++ code was reviewed to met section 1.6.4 "Portable C and C++ code" of "Writing R Extensions" manual.
Dependencies to c-standard libraries removed and round(x) changed to R::fprec(x,0).
Changes from FRESA.CAD 1.0 to FRESA.CAD version 2.0
+ Added c++ libraries to speed-up feature selection.
The c++ libraries functions were written using ARMADILLO and openMP.
+ FRESAcommons.cpp : Auxiliary functions with ARMADILLO implementations of COX, logit and linear fitting
+ binaryFeatureSelectionCpp.cpp: Main functions for bootstrapping, selection and estimation of
features confidence intervals for binary classification models.
+ regresionFeatureSelectionCpp.cpp: Main functions for bootstrapping, selection and estimation of
linear models coefficients.
+ rankInverseNormalCpp.cpp: Function to standardize features based on their ranking
+ Improvements and bug-fix across the FRESA.CAD package to deal with exceptions and zero size models.
+ Interface changes:
*in bootstrapValidation_Bin(...,dataframe,...)
"dataframe" argument renamed "data"
*in bootstrapValidation_Res(...,dataframe,...)
"dataframe" argument renamed "data"
*in bootVarNeRIElimination(...,bootLoops=250,bootFraction=1.0,...)
"bootLoops" and "bootFraction" arguments renamed "loops" and "fraction" respectively.
*in crossValidationFeatureSelection_Bin(...,dataframe,...,backBootLoops,...,bootEstimations,...)
"dataframe", "backBootLoops" and "bootEstimations" arguments renamed
"data", "elimination.bootstrap.steps" and "bootstrap.steps" respectively
*in crossValidationFeatureSelection_Res(...,dataframe,...,backBootLoops,...)
"dataframe" and "backBootLoops" arguments renamed
"data" and "elimination.bootstrap.steps" respectively
*in featureAdjustment(...,dataframe,...)
"dataframe" argument renamed "data"
*FRESA.Model(...,k,...)
"k" argument renamed "nk"
*getKNNpredictionFromFormula(modelFormula,...,k,...)
"modelFormula" and "k" arguments renamed "model.formula" and "nk" respectively
*getVar.Res(...,dataframe,...)
"dataframe" argument renamed "data"
*getVar.Bin(...,dataframe,...)
"dataframe" argument renamed "data"
*heatMaps(...,dataframe,...)
"dataframe" argument renamed "data"
*listTopCorrelatedVariables(...,dataframe,...)
"dataframe" argument renamed "data"
*medianPredict(...,newdata,...,k,...)
"newdata" and "k" arguments renamed "testdata" and "nk" respectively
*modelFitting(model,dataframe,...)
"model" and "dataframe" arguments renamed "mode.formula" and "data" respectively
*ForwardSelection.Model.Res(...,dataframe,...)
"dataframe" argument renamed "data"
*predictForFresa(...,newdata,type,...) ->
"newdata" and "type" arguments renamed "testdata" and "predictType" respectively
*rankInverseNormalDataFrame(varList, dataframe,..)
"varList" and "dataframe" arguments renamed "variableList" and "data" respectively
*ForwardSelection.Model.Bin(...,dataframe,...)
"dataframe" argument renamed "data"
*reportEquivalentVariables(...,dataframe,...)
"dataframe" argument renamed "data"
*residualForFRESA(...,newdata,...)
"newdata" argument renamed "testData"
*timeSerieAnalysis(...,dataframe,...)
"dataframe" argument renamed "data"
*uniRankVar(...,dataframe,...,FitType,..)
"dataframe" and "FitType" arguments renamed "data" and "type" respectively
*univariateRankVariables(...,dataframe,...,FitType,..)
"dataframe" and "FitType" arguments renamed "data" and "type" respectively
*updateModel.Bin(...,dataframe,...)
"dataframe" argument renamed "data"
*updateModel.Res(...,dataframe,...)
"dataframe" argument renamed "data"