polycor (version 0.5)

hetcor: Heterogeneous Correlation Matrix

Description

Compute a heterogenous correlation matrix, consisting of Pearson product-moment correlations between numeric variables, polyserial correlations between numeric and ordinal variables, and polychoric correlations between ordinal variables.

Usage

hetcor(data, ..., ML = FALSE)
## S3 method for class 'data.frame':
hetcor(data, ML = FALSE, use = c("complete.obs", "pairwise.complete.obs"), ...)
## S3 method for class 'default':
hetcor(data, ..., ML = FALSE)
## S3 method for class 'hetcor':
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

data

a data frame consisting of factors and/or numeric variables, or the first of several variables.

...

variables and/or arguments to be passed down.

ML

if TRUE compute maximum-likelihood estimates and their standard errors; if FALSE, compute quick two-step estimates.

use

if "complete.obs", remove observations with any missing data; if "pairwise.complete.obs", compute each correlation using all observations with valid data for that pair of variables.

x

an object of class "hetcor" to be printed.

digits

number of significant digits.

Value

• Returns an object of class "hetcor" with the following components:
• correlations
  the correlation matrix.
• type
  the type of each correlation: "Pearson", "Polychoric", or "Polyserial".
• std.errors
  the standard errors of the correlations, if estimated by ML.
• n
  the number (or numbers) of observations on which the correlations are based.
• NA.method
  the method by which any missing data were handled: "complete.obs" or "pairwise.complete.obs".

References

Drasgow, F. (1986) Polychoric 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 polychoric correlation coefficient. Psychometrika 44, 443-460. Rodriguez, R.N. (1982) Correlation. Pp. 193-204 in S. Kotz and N. Johnson, eds., The Encyclopedia of Statistics, Volume 2. Wiley. Ghosh, B.K. (1966) Asymptotic expansion for the moments of the distribution of correlation coefficient. Biometrika 53, 258-262. Olkin, I., and Pratt, J.W. (1958) Unbiased estimation of certain correlation coefficients. Annals of Mathematical Statistics 29, 201-211.

See Also

polychor, polyserial

Examples

R <- matrix(0, 4, 4)
R[upper.tri(R)] <- runif(6)
diag(R) <- 1
R <- cov2cor(t(R) %*% R)
round(R, 4)  # population correlations
data <- rmvnorm(1000, rep(0, 4), R)
round(cor(data), 4)   # sample correlations
x1 <- data[,1]
x2 <- data[,2]
y1 <- cut(data[,3], c(-Inf, .75, Inf))
y2 <- cut(data[,4], c(-Inf, -1, .5, 1.5, Inf))
data <- data.frame(x1, x2, y1, y2)
hetcor(data)  # Pearson, polychoric, and polyserial correlations, 2-step est.
hetcor(x1, x2, y1, y2, ML=TRUE) # Pearson, polychoric, polyserial correlations, ML est.