Description Usage Arguments Details Value Note See Also Examples

Generates bootstrap samples of the data vector.

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`formula` |
a formula expression. The constrained effect(s) must come before any unconstrained covariates on the right-hand side of the expression. The first |

`data` |
data frame containing the variables in the model. |

`gfix` |
optional vector of group levels for residual variances. Data should be sorted by this value. |

`eps` |
estimates of residuals. |

`xi` |
estimates of random effects. |

`null.resids` |
logical indicating if residuals should be computed under the null hypothesis. |

`theta` |
estimates of fixed effects coefficients. Estimated if not submitted. |

`ssq` |
estimates of residual variance components. Estimated if not submitted. |

`tsq` |
estimates of random effects variance components. Estimated if not submitted. |

`cov.theta` |
covariance matrix of fixed effects coefficients. Estimated if not submitted. |

`seed` |
set the seed for the RNG. |

`nsim` |
number of bootstrap samples to use for significance testing. |

`mySolver` |
solver to use, passed to |

`...` |
space for additional arguments. |

If any of the parameters `theta`

, `ssq`

, `tsq`

, `eps`

, or `xi`

are provided, the function will use those values in generating the bootstrap samples. They will be estimated if not submitted. If`null.resids=TRUE`

, then `theta`

will be projected onto the space of the null hypothesis ( *Ho: theta_1 = theta_2 = ... = theta_p1*) regardless of whether it is provided or estimated. To generate bootstraps with a specific `theta`

, set `null.residuals=FALSE`

.

Output is *N x nsim* matrix, where each column is a bootstrap sample of the response data `Y`

.

This function is primarily designed to be called by `clme`

.

By default, homogeneous variances are assumed for the residuals and (if included) random effects. Heterogeneity can be induced using the arguments `Nks`

and `Qs`

, which refer to the vectors *(n1, n2 ,... , nk)* and *(c1, c2 ,... , cq)*, respectively. See `clme_em`

for further explanation of these values.

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