robcat (version 0.1.0)

polycormat: Robust estimation of polychoric correlation matrix

Description

A useful wrapper of polycor to robustly estimate a polychoric correlation matrix by calculating all unique pairwise polychoric correlation coefficients.

Usage

polycormat(
  data,
  c = 0.6,
  parallel = FALSE,
  num_cores = 1L,
  return_polycor = TRUE,
  variance = TRUE,
  constrained = TRUE,
  method = ifelse(constrained, "Nelder-Mead", "L-BFGS-B"),
  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.

c

tuning constant that governs robustness; takes values in [0, Inf]. Defaults to 0.6.

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? Deafult 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?

method

Numerical optimization method.

maxcor

Maximum absolute correlation (to ensure numerical stability).

tol_thresholds

Minimum distance between consecutive thresholds (to enforce strict monotonicity); only relevant if constrained = TRUE.

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