umx_polychoric: FIML-based polychoric, polyserial, and Pearson correlations

Description

Compute polychoric/polyserial/Pearson correlations with FIML.

Usage

umx_polychoric(
  data,
  useDeviations = TRUE,
  tryHard = c("no", "yes", "ordinal", "search")
)

Value

- list of output and diagnostics. matrix of correlations = $polychorics

Arguments

data: Dataframe
useDeviations: Whether to code the mode using deviation thresholds (default = TRUE)
tryHard: 'no' uses normal mxRun (default), "yes" uses mxTryHard, and others used named versions: "mxTryHardOrdinal", "mxTryHardWideSearch"

References

- Barendse, M. T., Ligtvoet, R., Timmerman, M. E., & Oort, F. J. (2016). Model Fit after Pairwise Maximum Likelihood. *Frontiers in psychology*, **7**, 528. tools:::Rd_expr_doi("10.3389/fpsyg.2016.00528").

Other Data Functions: noNAs(), prolific_anonymize(), prolific_check_ID(), prolific_read_demog(), umxFactor(), umxHetCor(), umx_as_numeric(), umx_cont_2_quantiles(), umx_lower2full(), umx_make_MR_data(), umx_make_TwinData(), umx_make_fake_data(), umx_make_raw_from_cov(), umx_merge_randomized_columns(), umx_polypairwise(), umx_polytriowise(), umx_read_lower(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx_strings2numeric(), umx

Examples

Run this code

if (FALSE) {
tmp = mtcars
tmp$am = umxFactor(mtcars$am)
tmp$vs = umxFactor(mtcars$vs)
tmp = umx_scale(tmp)
x = umx_polychoric(tmp[, c("am", "vs")], tryHard = "yes")
x$polychorics
cor(mtcars[, c("am", "vs")])

}