postsamples {BHMSMAfMRI} | R Documentation |

Generates samples from the posterior distribution of the GLM coefficients.

postsamples(nsample, nsubject, grid, GLMCoeffStandardized, WaveletCoefficientMatrix, hyperparam, pklj.bar, analysis, wave.family="DaubLeAsymm", filter.number=6, bc="periodic")

`nsample` |
Number of samples to be generated. |

`nsubject` |
Number of subjects included in the analysis. |

`grid` |
The number of voxels in one row (or, one column) of the brain slice of interest. Must be a power of 2. The total number of voxels is grid^2. The maximum grid value for this package is 512. |

`GLMCoeffStandardized ` |
An array of dimension (nsubject, grid, grid), containing for each subject the standardized GLM coefficients obtained by fitting GLM to the time-series corresponding to the voxels. |

`WaveletCoefficientMatrix ` |
A matrix of dimension (nsubject, grid^2-1), containing for each subject the wavelet coefficients of all levels stacked together (by the increasing order of resolution level). |

`hyperparam ` |
A vector containing the estimates of the six hyperparameters. |

`pklj.bar ` |
A matrix of dimension (nsubject, grid^2-1), containing the piklj bar values (see References for details). |

`analysis` |
"MSA" or "SSA", depending on whether performing multi-subject analysis or single subject analysis. |

`wave.family` |
The family of wavelets to use - "DaubExPhase" or "DaubLeAsymm". Default is "DaubLeAsymm". |

`filter.number` |
The number of vanishing moments of the wavelet. By default 6. |

`bc` |
The boundary condition to use - "periodic" or "symmetric". Default is "periodic". |

The wavelet computations are performed by using R package 'wavethresh'. For details, check wavethresh package help.

A list containing the following.

`samples ` |
An array of dimension (nsubject,grid,grid,nsample), containing for each subject the posterior samples of the GLM coefficients. |

`postdiscovery ` |
An array of dimension (nsubject,grid,grid), containing for each subject the posterior discovery maps of the GLM coefficients (for details see Morris et al. (2011)). |

Nilotpal Sanyal <nsanyal@stanford.edu>, Marco Ferreira <marf@vt.edu>

Sanyal, Nilotpal, and Ferreira, Marco A.R. (2012). Bayesian hierarchical multi-subject multiscale analysis of functional MRI data. Neuroimage, 63, 3, 1519-1531.

Morris, J.S. et al. (2011). Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomic data. Ann. Appl. Stat. 5, 894-923.

nsubject <- 3 grid <- 8 nsample <- 5 GLMCoeffStandardized <- array(rnorm(3*8*8),dim=c(3,8,8)) WaveletCoefficientMatrix <- array(rnorm(3*63),dim=c(3,63)) hyperparam <- rep(.2,6) pklj.bar <- array(runif(3*63),dim=c(3,63)) analysis <- "multi" post.samples <- postsamples(nsample, nsubject, grid, GLMCoeffStandardized, WaveletCoefficientMatrix, hyperparam, pklj.bar, analysis) dim(post.samples$samples) #[1] 3 8 8 5

[Package *BHMSMAfMRI* version 1.3 Index]