Description Usage Arguments Author(s) References See Also
Similar to that of Ghosh and Dunson (2009) but with two key differences: (1) the prior is order invariant (though this makes the factors and factor loadings unidentified), and (2) we place hierarchical priors on the uniquenesses (variances).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 |
Y21 |
Top left of matrix. |
Y31 |
Bottom left of matrix. |
Y32 |
Top right of matrix. |
k |
The rank of the mean matrix. |
nsamp |
A positive integer. The number of samples to draw. |
burnin |
A positive integer. The number of early samples to discard. |
thin |
A positive integer. We will same the updates of
|
display_progress |
A logical. Should we print a text progress bar
to keep track of the Gibbs sampler ( |
hetero_factors |
A logical. Should we assign colum-specific
variances for the factors ( |
rho_0 |
The prior sample size for column-specific the precisions. |
alpha_0 |
The prior sample size for the mean of the column-specific precisions. |
delta_0 |
The prior sample size of the column-specific precisions of the factors. |
lambda_0 |
The prior sample size of the mean of the column-specific precisions of the factors. |
nu_0 |
The prior mean of the mean of the column-specific precisions of the factors. |
beta_0 |
The prior mean of the mean of the column-specific precisions. |
eta_0 |
The prior sample size of the expanded parameters. |
tau_0 |
The prior mean of of the expanded parameters. |
David Gerard
Gerard, David, and Matthew Stephens. 2021. "Unifying and Generalizing Methods for Removing Unwanted Variation Based on Negative Controls." Statistica Sinica, 31(3), 1145-1166. doi: 10.5705/ss.202018.0345
Ghosh, J. and Dunson, D.B., 2009. "Default prior distributions and efficient posterior computation in Bayesian factor analysis." Journal of Computational and Graphical Statistics, 18(2), pp.306-320. doi: 10.1198/jcgs.2009.07145
gdfa
for the slower R implementation.
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