Create a matrix of correlations for variables of diverse types (binary, ordinal, continuous)
umxHetCor is a helper to:
return just the correlations from John Fox's polycor::hetcor function
If you give it a covariance matrix, return the nearest positive-definite correlation matrix.
umxHetCor(
data,
ML = FALSE,
use = c("pairwise.complete.obs", "complete.obs"),
treatAllAsFactor = FALSE,
verbose = FALSE,
return = c("correlations", "hetcor object"),
std.err = FALSE
)data |
A |
ML |
Whether to use Maximum likelihood computation of correlations (default = FALSE) |
use |
How to handle missing data: Default= "pairwise.complete.obs". Alternative ="complete.obs". |
treatAllAsFactor |
Whether to treat all columns as factors, whether they are or not (Default = FALSE) |
verbose |
How much to tell the user about what was done. |
return |
Return just the correlations (default) or the hetcor object (contains, method, SEs etc.) |
std.err |
Compute the SEs? (default = FALSE) |
A matrix of correlations
Other Data Functions:
noNAs(),
umxFactor(),
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_polypairwise(),
umx_polytriowise(),
umx_read_lower(),
umx_read_prolific_demog(),
umx_rename(),
umx_reorder(),
umx_score_scale(),
umx_select_valid(),
umx_stack(),
umx
Other Miscellaneous Stats Functions:
FishersMethod(),
SE_from_p(),
geometric_mean(),
harmonic_mean(),
oddsratio(),
reliability(),
umxCov2cor(),
umxWeightedAIC(),
umx_apply(),
umx_cor(),
umx_means(),
umx_r_test(),
umx_round(),
umx_scale(),
umx_var(),
umx
umxHetCor(mtcars[,c("mpg", "am")])
umxHetCor(mtcars[,c("mpg", "am")], treatAllAsFactor = TRUE, verbose = TRUE)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.