Description Usage Arguments Value

The Log Likelihood Ratio tests closely approximates a Chi-squared distribution when the number of groups (i.e. individual subjects in a longitudinal study) is large (>50), but can be anticonservative when small. A parametric bootstrap test, in which data is randomly simulated from the null model and then fit with both models, can give the correct p-value. Here we compute the parametric boostrap on a small number of randomly chosen voxels to get a sense of biased the estimated p-values from the log likelihood ratio test really were.

1 2 | ```
mincLogLikRatioParametricBootstrap(logLikOutput, selection = "random",
nsims = 500, nvoxels = 50)
``` |

`logLikOutput` |
the output from mincLogLikRatio |

`selection` |
the algorithm for randomly chosing voxels. Only "random" works for now. |

`nsims` |
the number of simulations to run per voxel |

`nvoxels` |
the number of voxels to run the parametric bootstrap on |

a matrix containing the chi-square p-values and the bootstrapped p-values

Mouse-Imaging-Centre/RMINC documentation built on Oct. 5, 2018, 9:23 a.m.

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