gencve: General Cross Validation Engine
Version 0.3

Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.

AuthorA. I McLeod
Date of publication2016-04-12 00:56:56
MaintainerA. I. McLeod <aimcleod@uwo.ca>
LicenseGPL (>= 2)
Version0.3
URL http://www.stats.uwo.ca/faculty/aim
Package repositoryView on CRAN
InstallationInstall the latest version of this package by entering the following in R:
install.packages("gencve")

Getting started

Package overview

Popular man pages

churn: Customer Churn Data
dShao: Shao Holdout Sample Size for Linear Regression Variable...
fires: Forest Fires in Montesinho Natural Park
mape: Mean Absolute Percentage Error
misclassificationrate: Misclassification Rate for Class Prediction
mse: Mean Square Error Loss
smape: Mean Absolute Percentage Error
See all...

All man pages Function index File listing

Man pages

cgcv: Estimate Misclassification Rate Using d-fold Cross-Validation...
churn: Customer Churn Data
Detroit: Detroit Homicide Data for 1961-73
dShao: Shao Holdout Sample Size for Linear Regression Variable...
featureSelect: Feature Select For Wide Data
fires: Forest Fires in Montesinho Natural Park
gcv: Estimate EPE Using Delete-d Cross-Validation
gencve-package: General Cross Validation Engine General Cross Validation...
kNN_LOOCV: Select k with Leave-one-out CV
kNN_MLE: MLE k in kNN
kyphosis: Data on Children who have had Corrective Spinal Surgery
logloss: log-loss function for multiclass prediction
mae: Mean Absolute Error
mape: Mean Absolute Percentage Error
meatspec: Meat Spectrometry to Determine Fat Content
misclassificationrate: Misclassification Rate for Class Prediction
mse: Mean Square Error Loss
pollution: Pollution Data from McDonald and Schwing
prostate: Prostate Cancer Data
rdigitsBFOS: BFOS Digit Recognition Problem
regal: Regression EPE for All Implemented Methods
rmix: Random Mixture Classification Example
rxor: Random XOR Samples
ShaoReg: Synthetic Regression Data
SinghTest: Singh Prostate Microarray Test Data
SinghTrain: Singh Prostate Microarray Training Data
smape: Mean Absolute Percentage Error
vifx: Variance Inflation Factor
yhat_CART: CART regression prediction
yhat_gel: Elastic Net Regression Prediction
yhat_lars: Fit LASSO Regression using Mallows Cp and Predict
yhat_lm: Linear Predictor using Least-Squares Regression
yhat_nn: Nearest Neighbour Prediction
yhat_plus: SCAD or MCP Regression Prediction
yhat_RF: Fit Random Forest Regression Predictor
yhat_step: Backward Stagewise Regression with AIC or BIC
yhat_SVM: Support Vector Machine Regression Prediction
yh_C50: C50 Prediction
yh_CART: CART Prediction
yh_kNN: kNN or NN prediction
yh_lda: LDA predictions
yh_logistic: Logistic Regression and Regularized Logistic Regression...
yh_NB: Naive Bayes Prediction
yh_qda: QDA Prediction
yh_RF: Random Forest Prediction
yh_svm: Support Vector Machine Prediction

Functions

Detroit Man page
ShaoReg Man page Source code
SinghTest Man page
SinghTrain Man page
cgcv Man page Source code
churnTest Man page
churnTrain Man page
dShao Man page Source code
featureSelect Man page
fires Man page
gcv Man page Source code
gencve Man page
gencve-package Man page
kNN_LOOCV Man page
kNN_MLE Man page Source code
kyphosis Man page
logloss Man page Source code
mae Man page Source code
mape Man page Source code
meatspec Man page
misclassificationrate Man page
mse Man page Source code
nnc Source code
pollution Man page
prostate Man page
rdigitsBFOS Man page Source code
regal Man page Source code
rmix Man page Source code
rxor Man page Source code
smape Man page Source code
vifx Man page Source code
yh_C50 Man page Source code
yh_CART Man page Source code
yh_NB Man page Source code
yh_NN Man page Source code
yh_RF Man page Source code
yh_kNN Man page Source code
yh_lda Man page Source code
yh_logistic Man page Source code
yh_qda Man page Source code
yh_svm Man page Source code
yhat_CART Man page Source code
yhat_RF Man page Source code
yhat_SVM Man page Source code
yhat_gel Man page Source code
yhat_lars Man page Source code
yhat_lm Man page Source code
yhat_nn Man page Source code
yhat_plus Man page Source code
yhat_step Man page Source code

Files

inst
inst/doc
inst/doc/01-Overview.pdf
inst/doc/04-rmix.pdf
inst/doc/02-ShaoRegression.pdf
inst/doc/03-DigitRecognitionProblem.pdf
NAMESPACE
data
data/churn.RData
data/meatspec.rda
data/Detroit.rda
data/kyphosis.tab.gz
data/SinghTest.rda
data/SinghTrain.rda
data/prostate.rda
data/fires.rda
data/pollution.rda
R
R/yhat_step.R
R/yh_svm.R
R/vifx.R
R/yh_qda.R
R/cgcv.R
R/kNN_MLE.R
R/mape.R
R/gcv.R
R/dShao.R
R/featureSelect.R
R/mse.R
R/ShaoReg.R
R/yhat_lars.R
R/regal.R
R/smape.R
R/mae.R
R/yhat_gel.R
R/yhat_RF.R
R/yh_kNN.R
R/misclassificationrate.R
R/yh_logistic.R
R/yh_CART.R
R/rdigitsBFOS.R
R/yhat_plus.R
R/yhat_SVM.R
R/yh_lda.R
R/yhat_nn.R
R/rmix.R
R/rxor.R
R/yhat_lm.R
MD5
build
build/partial.rdb
DESCRIPTION
man
man/yh_logistic.Rd
man/rdigitsBFOS.Rd
man/yhat_plus.Rd
man/kyphosis.Rd
man/mape.Rd
man/yh_NB.Rd
man/yh_RF.Rd
man/yhat_lars.Rd
man/dShao.Rd
man/yh_lda.Rd
man/yhat_gel.Rd
man/rxor.Rd
man/yhat_lm.Rd
man/churn.Rd
man/mae.Rd
man/fires.Rd
man/Detroit.Rd
man/yh_C50.Rd
man/yh_CART.Rd
man/yhat_SVM.Rd
man/yhat_RF.Rd
man/kNN_LOOCV.Rd
man/prostate.Rd
man/meatspec.Rd
man/rmix.Rd
man/yhat_nn.Rd
man/kNN_MLE.Rd
man/SinghTest.Rd
man/yhat_step.Rd
man/gencve-package.Rd
man/yh_svm.Rd
man/yh_qda.Rd
man/yhat_CART.Rd
man/yh_kNN.Rd
man/ShaoReg.Rd
man/mse.Rd
man/regal.Rd
man/smape.Rd
man/pollution.Rd
man/SinghTrain.Rd
man/cgcv.Rd
man/gcv.Rd
man/featureSelect.Rd
man/misclassificationrate.Rd
man/logloss.Rd
man/vifx.Rd
gencve documentation built on May 19, 2017, 5:35 p.m.

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.