start_msfa: Provides some starting values for the parameters of a MSFA...
In rdevito/MSFA: Maximum Likelihood Estimation and Bayesian analysis of MSFA models

Description Usage Arguments Details Value References

This is a supporting function for ecm_msfa. The method employed is documented in the reference.

start_msfa(
  X_s,
  k,
  j_s,
  constraint = "block_lower2",
  method = "adhoc",
  robust = FALSE,
  corr = FALSE,
  mcd = FALSE
)

`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. No standardization is carried out by the function.
`k`	Number of common factors.
`j_s`	Number of study-specific factors. A vector of positive integers of length S.
`constraint`	Constraint for ensuring identifiability. The default is "block_lower2", which corresponds to the main proposal of De Vito et al. (2018). An alternative identification strategy is triggered by "block_lower1"; this is more restrictive but may work also with smaller number of variables.
`method`	Which method should be used to find the starting values? The two possibilities are `"adhoc"` for the method described in De Vito et al. (2016), and `"fa"` for averaging over separate study-specific FA models. Default is `"adhoc"`.
`robust`	If `TRUE`, robust covariance matrix is used in place of the sample covariance. Default is `FALSE`.
`corr`	If `TRUE`, the analysis will employ the correlation matrix instead of the covariance matrix.
`mcd`	If `TRUE`, the robust estimator used for the covariance is the same proposed in Pison et al. (2003), otherwise the default value of the function `CovRob` of the `robust` library is employed. Default is `FALSE`.

The upper-triangular zero constraint is adopted to achieve identification, as detailed in the reference, though the function can also be run without such constraint.

A list containing Phi,Lambda_s and psi_s, starting values for the model matrices.

De Vito, R., Bellio, R., Parmigiani, G. and Trippa, L. (2019). Multi-study Factor Analysis. Biometrics, 75, 337-346.

rdevito/MSFA documentation built on March 18, 2020, 2:57 p.m.