FIML-based Pairwise polychoric, polyserial, and Pearson correlations
Compute polychoric/polyserial/Pearson correlations with FIML in OpenMx
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. doi: 10.3389/fpsyg.2016.00528.
Other Data Functions:
noNAs(),
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
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")])Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.