Description Usage Arguments Details Value Author(s) References Examples

Simulated multiple linear regression data from a model used in simulation experiments reported in Shao's famous paper on cross-validation for model selection.

1 |

`n` |
sample size, length of output |

`beta` |
regression coefficients |

`rho` |
cross-covariance, must be less than in magnitude 1 |

`sig` |
residual standard deviation |

In general the regression equation used for simulation is:

* y = X β + ε*

where
*β* is a vector of the regression coefficients of length p,
X is the design matrix with n rows and p columns and
*ε* is a vector of n independent normal random variables
with mean zero and standard deviation sig.
The rows of X are p-variate normal with mean vector zero and p-by-p covariance
matrix (i,j)-entry *rho^|i-j|*.

Shao (1993) used the default settings in the arguments and n = 20, 60, 100 in simulation experiments with delete-d cross-validation.

Data frame with n rows and p+1 columns. The first p columns are labelled x1, ..., xp and the last column is y.

A. I. McLeod

Jun Shao (1993), Linear Model Selection by Cross-validation, Journal of the American Statistical Association, 88/422.

1 | ```
ShaoReg()
``` |

gencve documentation built on May 29, 2017, 7:12 p.m.

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