Description Usage Arguments Value Note Author(s) References

The function does k-fold cross validation for selecting best value of regularization parameter.

1 | ```
cv.nnlasso.normal(x,y,k=5,nlambda=50,tau=1,plot=TRUE,errorbars=TRUE)
``` |

`x` |
x is matrix of order n x p where n is number of observations and p is number of predictor variables. Rows should represent observations and columns should represent predictor variables. |

`y` |
y is a vector of response variable of order n x 1. |

`k` |
Number of folds for cross validation. Default is k=5. |

`nlambda` |
Number of lambda values to be used for cross validation. Default is nlambda=50. |

`tau` |
Elastic net parameter, |

`plot` |
if TRUE, produces a plot of cross validated prediction mean squared errors against lambda. Default is TRUE. |

`errorbars` |
If TRUE, error bars are drawn in the plot. Default is TRUE. |

Produces a plot and returns a list with following components:

`lambda` |
Value of lambda for which average cross validation error is minimum |

`pmse` |
A vector of average cross validation errors for various lambda values |

`lambdas` |
A vector of lambda values used in cross validation |

`se` |
A vector containing standard errors of cross validation errors |

This function need not be called by user. The function is internally called by cv.nnlasso function.

Baidya Nath Mandal and Jun Ma

Mandal, B.N. and Ma, J. (2016). L1 regularized multiplicative iterative path algorithm for non-negative generalized linear models.

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