umx (version 4.9.0)

umx_polypairwise: FIML-based Pairwise polychoric, polyserial, and Pearson correlations

Description

Compute polychoric/polyserial/Pearson correlations with FIML in OpenMx

Usage

umx_polypairwise(
  data,
  useDeviations = TRUE,
  printFit = FALSE,
  use = "any",
  tryHard = c("no", "yes", "ordinal", "search")
)

Arguments

data

Dataframe

useDeviations

Whether to code the mode using deviation thresholds (default = TRUE)

printFit

Whether to print information about the fit achieved (default = FALSE)

use

parameter (default = "any")

tryHard

'no' uses normal mxRun (default), "yes" uses mxTryHard, and others used named versions: "mxTryHardOrdinal", "mxTryHardWideSearch"

Value

- matrix of correlations

References

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

See Also

Other Data Functions: 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_polychoric(), umx_polytriowise(), umx_read_lower(), umx_read_prolific_demog(), umx_rename(), umx_reorder(), umx_score_scale(), umx_select_valid(), umx_stack(), umx

Examples

# NOT RUN {
umx_set_optimizer("SLSQP")
tmp = mtcars
tmp$am = umxFactor(mtcars$am)
tmp$vs = umxFactor(mtcars$vs)
tmp = umx_scale(tmp)
x = umx_polypairwise(tmp[, c("hp", "mpg", "am", "vs")], tryHard = "yes")
x$R
cov2cor(x$R)
cor(mtcars[, c("hp", "mpg", "am", "vs")])
# }