DescTools (version 0.99.18)

CorPolychor: Polychoric Correlation

Description

Computes the polychoric correlation (and its standard error) between two ordinal variables or from their contingency table, under the assumption that the ordinal variables dissect continuous latent variables that are bivariate normal. Either the maximum-likelihood estimator or a (possibly much) quicker ``two-step'' approximation is available. For the ML estimator, the estimates of the thresholds and the covariance matrix of the estimates are also available.

Usage

CorPolychor(x, y, ML = FALSE, control = list(), std.err = FALSE, maxcor=.9999)
"print"(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

x

a contingency table of counts or an ordered categorical variable; the latter can be numeric, logical, a factor, or an ordered factor, but if a factor, its levels should be in proper order.

y

if x is a variable, a second ordered categorical variable.

ML

if TRUE, compute the maximum-likelihood estimate; if FALSE, the default, compute a quicker ``two-step'' approximation.

control

optional arguments to be passed to the optim function.

std.err

if TRUE, return the estimated variance of the correlation (for the two-step estimator) or the estimated covariance matrix (for the ML estimator) of the correlation and thresholds; the default is FALSE.

maxcor

maximum absolute correlation (to insure numerical stability).

digits

integer, determining the number of digits used to format the printed result

...

not used

Value

If std.err is TRUE, returns an object of class "polycor" with the following components:Othewise, returns the polychoric correlation.

References

Drasgow, F. (1986) CorPolychoric and polyserial correlations. Pp. 68--74 in S. Kotz and N. Johnson, eds., The Encyclopedia of Statistics, Volume 7. Wiley. Olsson, U. (1979) Maximum likelihood estimation of the CorPolychoric correlation coefficient. Psychometrika 44, 443-460.

See Also

hetcor, polyserial, print.CorPolychor, optim

Examples

set.seed(12345)
z <- RndPairs(1000, 0.6)
x <- z[,1]
y <- z[,2]

cor(x, y)                                  # sample correlation
x <- cut(x, c(-Inf, .75, Inf))
y <- cut(y, c(-Inf, -1, .5, 1.5, Inf))

CorPolychor(x, y)                          # 2-step estimate
CorPolychor(x, y, ML=TRUE, std.err=TRUE)   # ML estimate

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