Description Usage Arguments Value Examples

Simulate toy data with linear or logistic response.

1 2 |

`n` |
Number of samples for the training set. |

`p` |
Number of covariates. |

`n2` |
Number of independent samples for the test set. |

`muGrp` |
Prior mean for different groups. |

`varGrp` |
Prior variance for different groups. |

`indT` |
True group index of each covariate; p-dimensional vector. |

`sigma` |
Variance parameter for linear model. |

`model` |
Type of model. |

`flag` |
Should linear predictors and true response be plotted? |

A list with

`beta` |
Simulated regression coefficients |

`Xctd` |
Simulated observed data for training set |

`Y` |
Simulated response data for test set |

`X2ctd` |
Simulated observed data for test set |

`Y2` |
Simulated response data for test set |

1 2 3 4 5 6 7 8 9 10 11 12 | ```
n<-10
p<-30
#simulate beta from two normal distributions; beta_k ~ N(mu_k,tau^2_k)
muGrp <- c(0,0.1) #mean (mu_1,mu_2)
varGrp <- c(0.05,0.01) #variance (tau^2_1,tau^2_2)
#group number of each covariate; first half in group 1, second half in group 2
indT <- rep(c(1,2),each=15)
dataLin <- simDat(n, p, n2 = 20, muGrp, varGrp, indT, sigma = 1, model = "linear",
flag = TRUE)
dataLog <- simDat(n, p, n2 = 20, muGrp, varGrp, indT, model = "logistic",
flag = TRUE)
``` |

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