em_mixedgc_ppca | R Documentation |
fit the Gaussian copula model from incomplete mixed data
em_mixedgc_ppca( rank, Z, Lower, Upper, d_index, dcat_index = NULL, cat_input = NULL, start = NULL, trunc_method = "Iterative", n_sample = 5000, n_update = 1, maxit = 50, eps = 0.01, verbose = FALSE, runiter = 0 )
rank |
the number of latent factors |
Z |
Transformed latent matrix |
Lower |
Lower boundary of truncated intervals |
Upper |
Upper boundary of truncated intervals |
d_index |
Boolean vector with |
dcat_index |
Boolean vector with |
cat_input |
Input for categorical dimensions |
start |
Initial value of copula correlation |
trunc_method |
Method for evaluating truncated normal moments: |
n_sample |
Number of MC samples, only used when |
n_update |
The number of updates, only used when |
maxit |
Maximum number of iterations |
eps |
Convergence threshold |
verbose |
Whether to print progress information |
runiter |
When set as a positive integer, the algorithm will run the specified number of iterations exactly. |
A list containing fitted copula parameters, the likelihood (objective function), Z matrix with updated ordinal entries and the conditional variance corresponding to the observed Z matrix.
W
Fitted latent low rank subspace matrix
sigma
Fitted noise variance
loglik
The log-likelihood achieved during iteration.
Z
Incomplete Z
with approximated observed ordinal entries
C
The conditional variance corresponding to the observed Z matrix. Useful for quantifying imputation uncertainty.
Yuxuan Zhao, yz2295@cornell.edu and Madeleine Udell, udell@cornell.edu
Zhao, Y., & Udell, M. (2020). Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula. arXiv preprint arXiv:2006.10829.
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