| umx_polypairwise | R Documentation |
Compute polychoric/polyserial/Pearson correlations with FIML in OpenMx, but pair by pair, not across the whole dataset at once.
umx_polypairwise(
data,
useDeviations = TRUE,
printFit = FALSE,
use = "any",
tryHard = c("no", "yes", "ordinal", "search")
)
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" |
matrix of correlations
Barendse, M. T., Ligtvoet, R., Timmerman, M. E., & Oort, F. J. (2016). Model Fit after Pairwise Maximum Likelihood. Frontiers in Psychology, 7, 528. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3389/fpsyg.2016.00528")}.
Other Data Functions:
noNAs(),
prolific_anonymize(),
prolific_check_ID(),
prolific_read_demog(),
prolific_scoring_stub(),
umx,
umxFactor(),
umxGSEM_sim_snps(),
umxHetCor(),
umx_as_numeric(),
umx_cont_2_quantiles(),
umx_lower2full(),
umx_make_GxE_data(),
umx_make_MR_data(),
umx_make_TwinData(),
umx_make_fake_data(),
umx_make_raw_from_cov(),
umx_merge_randomized_columns(),
umx_polychoric(),
umx_polytriowise(),
umx_read_lower(),
umx_rename(),
umx_reorder(),
umx_score_scale(),
umx_select_valid(),
umx_stack(),
umx_strings2numeric()
## Not run:
# Mixed continuous + binary (polyserial / polychoric). Ordinal FIML can be
# platform-sensitive; keep out of --run-donttest on win-builder.
tmp = mtcars
tmp$am = umxFactor(mtcars$am)
tmp$vs = umxFactor(mtcars$vs)
# Scale continuous only (leave ordered factors alone)
tmp[, c("hp", "mpg")] = umx_scale(tmp[, c("hp", "mpg")])
x = umx_polypairwise(tmp[, c("hp", "mpg", "am", "vs")], tryHard = "yes")
x$R
cor(mtcars[, c("hp", "mpg", "am", "vs")])
## End(Not run)
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