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()
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