hetcor: Heterogeneous Correlation Matrix

In polycor: Polychoric and Polyserial Correlations

Description

hetcor computes 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.

The detectCores function is imported from the parallel package and re-exported.

Usage

hetcor(data, ..., ML = FALSE, std.err = TRUE, 
  use=c("complete.obs", "pairwise.complete.obs"), 
  bins=4, pd=TRUE, parallel=FALSE, ncores=detectCores(logical=FALSE))
## S3 method for class 'data.frame'
hetcor(data, ML = FALSE, std.err = TRUE, 
  use = c("complete.obs", "pairwise.complete.obs"), 
  bins=4, pd=TRUE, parallel=FALSE, ncores=detectCores(logical=FALSE), ...)
## Default S3 method:
hetcor(data, ..., ML = FALSE, std.err = TRUE, 
  use=c("complete.obs", "pairwise.complete.obs"), 
  bins=4, pd=TRUE, parallel=FALSE, ncores=detectCores(logical=FALSE))
## S3 method for class 'hetcor'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'hetcor'
as.matrix(x, ...)
detectCores(all.tests=FALSE, logical=TRUE) 

Arguments

data	a data frame consisting of factors, ordered factors, logical variables, character variables, 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; if FALSE, compute quick two-step estimates.
std.err	if TRUE, compute standard errors.
bins	number of bins to use for continuous variables in testing bivariate normality; the default is 4.
pd	if TRUE and if the correlation matrix is not positive-definite, an attempt will be made to adjust it to a positive-definite matrix, using the nearPD function in the Matrix package. Note that default arguments to nearPD are used (except corr=TRUE); for more control call nearPD directly.
parallel	if TRUE (the default is FALSE), perform parallel computations on a computer with multiple CPUs/cores.
ncores	the number of cores to use for parallel computations; the default is the number of physical cores detected.
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, or from which to extract the correlation matrix.
digits	number of significant digits.
all.tests	logical, apply all known tests; default is FALSE.
logical	if TRUE, detect logical CPUs/cores; if FALSE, detect physical CPUs/cores.

Value

hetcor 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 requested.
n	the number (or numbers) of observations on which the correlations are based.
tests	p-values for tests of bivariate normality for each pair of variables.
NA.method	the method by which any missing data were handled: "complete.obs" or "pairwise.complete.obs".
ML	TRUE for ML estimates, FALSE for two-step estimates.

Warning

Be careful with character variables (as opposed to factors), the values of which are ordered alphabetically. Thus, e.g., the values "disagree", "neutral", "agree" are ordered "agree", "disagree", "neutral".

Note

Although the function reports standard errors for product-moment correlations, transformations (the most well known is Fisher's z-transformation) are available that make the approach to asymptotic normality much more rapid.

Author(s)

John Fox [e-mail]

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, nearPD, detectCores

Examples

if(require(mvtnorm)){
    set.seed(12345)
    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
    }
if(require(mvtnorm)){
    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.
    }
if(require(mvtnorm)){
    hetcor(x1, x2, y1, y2, ML=TRUE) # Pearson, polychoric, polyserial correlations, ML est.
    }

## Not run: 
    hc <- hetcor(data, ML=TRUE)
      # parallel computation:
    hc.m <- hetcor(data, ML=TRUE, parallel=TRUE,
                   ncores=min(2, detectCores()))
    hc.m
    all.equal(hc, hc.m)
    
      # error handling:
    data$y1[data$y2 == "(0.5,1.5]"] <- NA
    hetcor(data)
    
## End(Not run)

polycor documentation built on Dec. 11, 2021, 3:01 a.m.