mcnnm_wc: This function trains all models for given vectors of lambda_L...
In susanathey/MCPanel: Matrix Completion algorithms for Causal Panel Data Models

Description Usage Arguments Value See Also Examples

This function trains all models for given vectors of lambda_L and lambda_H. Each model contains L, H, u, v, lambda_H, and lambda_L.

mcnnm_wc(M, X, Z, mask, num_lam_L = 30L, num_lam_H = 30L,
  lambda_L = as.numeric(c()), lambda_H = as.numeric(c()),
  to_normalize = 1L, to_estimate_u = 1L, to_estimate_v = 1L,
  to_add_ID = 1L, niter = 100L, rel_tol = 1e-05, is_quiet = 1L)

`M`	Matrix of observed entries. The input should be N (number of units) by T (number of time periods).
`X`	Matrix containing unit-related covariates. The number of rows of X should match with the number of units (number of rows of M). If unit-related covariates do not exist X = matrix(0L,0,0) should be used as input.
`Z`	Matrix containing time-related covariates. The number of rows of Z should match with the number of time periods (number of columns in M). If time-related covariates do not exist use Z = matrix(0L,0,0)
`mask`	Binary mask with the same shape as M containing observed entries.
`num_lam_L`	Optional parameter on the number of lambda_Ls to consider for learning. The default number is 30 and lambda_L values are from minimum number which makes L zero to 1e-3 times this minimum number.
`num_lam_H`	Optional parameter on the number of lambda_Hs to consider for learning. The default number is 30 and lambda_H values are from minimum number which makes H zero to 1e-3 times this minimum number.
`lambda_L`	Optional numeric vector containing all lambda_L values that user want to train model on sorted decreasingly (important for warm-start). By default this is empty (user need not to provide this) and num_lam_L and the rule explained above is used. However, once this vector is passed by user manually, num_lam_L argument will not be used.
`lambda_H`	Optional numeric vector containing all lambda_H values that user want to train model on sorted decreasingly (important for warm-start). By default this is empty (user need not to provide this) and num_lam_H and the rule explained above is used. However, once this vector is passed by user manually, num_lam_H argument will not be used.
`to_normalize`	Optional boolean parameter indicating whether to normalize covariates or not (columns of X and Z). The default value is 1. If this value is set to 0, the result would be sensitive to scales in covariates.
`to_estimate_u`	Optional boolean input for wheter estimating fixed unit effects (row means of M) or not. Default is 1.
`to_add_ID`	Optional boolean parameter indicating whether identity matrices are concatenated with X and Z in the model X * H * Z'. The default value is true (identity matrices are concatenated) and the model becomes XH_X + XH_XZ*Z^T+ H_Z Z^T (the rest of matrix in H forced to zero).
`niter`	Optional parameter on the number of iterations taken in the algorithm for each fixed value of lambda_L. The default value is 1000 and it is sufficiently large as the algorithm is using warm-start strategy.
`rel_tol`	Optional parameter on the stopping rule. Once the relative improve in objective value drops below rel_tol, execution is halted. Default value is 1e-5.
`is_quiet`	Optional boolean input which indicates whether to print the status of learning and convergence results for Cyclic Coordinate Descent algorithm or not. The default value is 1 (no output is printed).

The list of all models trained with the given vector of lambda_Ls and lambda_Hs.

mcnnm

1	mcnnm_wc(M = replicate(5,rnorm(5)), X = replicate(3, rnorm(5)), Z = matrix(0L, 0, 0), mask = matrix(rbinom(5*5,1,0.8),5,5), lambda_L=c(5,1,0.5,0.1), lambda_H=c(5,1,0.5,0.1))