Description Usage Arguments Details Value Examples

Computes k-fold cross-validation for elastic net.

1 | ```
ElasticNetCV(x, y)
``` |

`x` |
A data.frame or matrix of predictors |

`y` |
A vector of response variables |

This function reads data into its environment and returns a list of three outcomes. To perform elastic net or cross-validation of elastic net, use the corresponding element of the returned list. See examples below. The penalty of L1-norm and L2-norm is denoted by lambda1 and lambda2 respectively.

`cv.choosemodel` |
Given the parameter k folds and lambda2 (optional), cv.choosemodel performs cross-validation to select the opti- mal lambda1 and computes the corresponding coefficient of each variable. If lambda2 is NULL, cv.choosemodel selects the optimal lambda2 from a sequence going from 0 to 1 in steps of 0.1 and the corresponding optimal lambda1, then it returns the coefficient of each variable. |

A list of three outcomes will be returned:

`Elasticnet` |
Given lambda1 (optional) and lambda2, Elasticnet_ calculates an elastic net-regularized regression and returns the coefficients of each variable. If lambda1 is NULL, Elasticnet_ prints out the trace of lambda1 and the corresponding coefficient of each variable. |

`output` |
Prints the cross-validation outputs, including the minimum MSE, the coefficient of each variable, lambda1 and lambda2. |

`predict` |
Reads a data.frame of the testing data set and returns predictions using the trained model. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
#Use R built-in datasets mtcars for a model fitting
x <- mtcars[,-1]
y <- mtcars[, 1]
#fit model
model <- ElasticNetCV(x,y)
#fit a elastic net with lambda2 = 1
model$Elasticnet_(lambda2 = 1)
#choose model using cv
model$cv.choosemodel(k = 31) #Leave-one-out cross validation
model$output() #See the output
#predict
pre <- mtcars[1:3,-1]
model$predict(pre)
``` |

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