mincLogLikRatioParametricBootstrap | R Documentation |
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.
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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.