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
)
A data.frame()
of columns for which to compute heterochoric correlations. OR an existing covariance matrix.
Whether to use Maximum likelihood computation of correlations (default = FALSE)
How to handle missing data: Default= "pairwise.complete.obs". Alternative ="complete.obs".
Whether to treat all columns as factors, whether they are or not (Default = FALSE)
How much to tell the user about what was done.
Return just the correlations (default) or the hetcor object (contains, method, SEs etc.)
Compute the SEs? (default = FALSE)
A matrix of correlations
Other Data Functions:
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 Helpers:
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
# NOT RUN {
umxHetCor(mtcars[,c("mpg", "am")])
umxHetCor(mtcars[,c("mpg", "am")], treatAllAsFactor = TRUE, verbose = TRUE)
# }
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