Description Usage Arguments Value References
Maximum likelihood estimation of study-specific FA models parameters via the ECM
algorithm, adopting the upper-triangular zero constraint to achieve identification
for each loading matrix. Note: the function can also estimate a FA model for a single
study, by specifiyng X_s = list(data)
, where data
is the data matrix.
1 2 3 4 5 6 7 8 9 10 11 12 |
X_s |
List of lenght S, corresponding to number of different studies considered. Each element of the list contains a data matrix, with the same number of columns P for all the studies. |
tot_s |
Number of latent factors for each study. A vector of positive integers of length S. |
nIt |
Maximum number of iterations for the ECM algorithm. Default is 50000. |
tol |
Tolerance for declaring convergence of the ECM algorithm. Default is 10^-7. |
block_lower |
Should the upper-triangular zero constraint be enforced? Default is |
robust |
If |
corr |
If |
mcd |
If |
trace |
If |
traceIT |
Frequency of tracing information. |
A list containing the following components:
|
the estimated model matrices. |
loglik |
the value of the log likelihood function at the final estimates. |
|
model selection criteria at the estimate. |
|
number of model parameters. |
iter |
the number of ECM iterations performed. |
De Vito, R., Bellio, R., Trippa, L. and Parmigiani, G. (2019). Multi-study Factor Analysis. Biometrics, 75, 337-346.
Pison, G., Rousseeuw, P.J., Filzmoser, P. and Croux, C. (2003). Robust factor analysis. Journal Multivariate Analysis, 84, 145-172.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.