Description Usage Arguments Details Value Warning Author(s) References See Also Examples

Given a specific total number of observations and variance-covariance structure for random effect, the function simulates different association of number of group and replicates, giving the specified sample size, and assess p-values and power of random intercept and random slope

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`numsim` |
number of simulation for each step |

`tss` |
total sample size, nb group * nb replicates |

`nbstep` |
number of group*replicates associations to simulate |

`randompart` |
vector of lenght 4 or 5 with 1: variance component
of intercept, VI; 2: variance component of slope, VS; 3: residual
variance, VR; 4: relation between random intercept and random
slope; 5: "cor" or "cov" determine id the relation between I ans S is
correlation or covariance, set to |

`fixed` |
vector of lenght 3 with mean, variance and estimate of fixed effect to simulate |

`n.X` |
number of different values to simulate for the fixed effect (covariate).
If |

`autocorr.X` |
correlation between two successive covariate value for a group. Default: |

`X.dist` |
specify the distribution of the fixed effect. Only "gaussian" (normal distribution) and
"unif" (uniform distribution) are accepted actually. Default: |

`intercept` |
a numeric value giving the expected intercept value. Default:0 |

`exgr` |
a vector specifying minimum and maximum value for number of group.
Default: |

`exrepl` |
a vector specifying minimum and maximum value for number
of replicates. Default: |

`heteroscedasticity` |
a vector specifying heterogeneity in residual variance
across X. If |

P-values for random effects are estimated using a log-likelihood ratio test between two models with and without the effect. Power represent the percentage of simulations providing a significant p-value for a given random structure

data frame reporting estimated P-values and power with CI for random intercept and random slope

the simulation is based on a balanced data set with unrelated group

Julien Martin

Martin, Nussey, Wilson and Reale Submitted Measuring between-individual variation in reaction norms in field and experimental studies: a power analysis of random regression models. Methods in Ecology and Evolution.

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