robcat (version 0.2)

polycormat_mle: Maximum likelihood estimation of polychoric correlation matrix

Description

A useful wrapper of polycor_mle to estimate a polychoric correlation matrix via maximum likelihood by calculating all unique pairwise polychoric correlation coefficients.

Usage

polycormat_mle(
  data,
  parallel = FALSE,
  num_cores = 1L,
  return_polycor = TRUE,
  variance = TRUE,
  constrained = "ifneeded",
  method = NULL,
  maxcor = 0.999,
  tol_thresholds = 0.01
)

Value

If return_polycor = TRUE, returns a list with a polychoric correlation matrix and list of "polycor" objects. If return_polycor = FALSE, then only a correlation matrix is returned.

Arguments

data

Data matrix or data.frame of integer-valued responses, individual respondents are in rows and responses to the items in the columns.

parallel

Logical. Shall parallelization be used? Default is FALSE.

num_cores

Number of cores to be used, only relevant if parallel = TRUE. Defaults to the number of system cores.

return_polycor

Logical. Shall the individual "polycor" objects for each item pair estimate be returned? Default is TRUE.

variance

Shall an estimated asymptotic covariance matrix be returned? Default is TRUE.

constrained

Shall strict monotonicity of thresholds be explicitly enforced by linear constraints? This can be a logical (TRUE or FALSE), or "ifneeded" to first try unconstrained optimization and in case of an error perform constrained optimization. Default is "ifneeded".

method

Numerical optimization method, see optim() and constrOptim(). Default is to use "L-BFGS-B" in case of unconstrained optimization and "Nelder-Mead" in case of constrained optimization.

maxcor

Maximum absolute correlation (to ensure numerical stability). Default is 0.999.

tol_thresholds

Minimum distance between consecutive thresholds (to enforce strict monotonicity); only relevant in case of constrained optimization. Default is 0.01.

Examples

## example data
set.seed(123)
data <- matrix(sample(c(1,2,3), size = 3*100, replace = TRUE), nrow = 100)
polycormat(data)     # robust 
polycormat_mle(data) # non-robust MLE