r.pc: Compute confidence intervals for a set of polychoric...

In sjpierce/piercer: Functions for Research and Statistical Computing

Compute confidence intervals for a set of polychoric correlations.

Description

This function computes confidence intervals for polychoric correlations obtained from a hetcor object.

Usage

r.pc(x, ord, digits = NULL, pdigits = NULL)

Arguments

x	A hetcor object produced by hetcor().
ord	A character vector of names for ordinal variables.
digits	An integer specifying the number of decimal places to used when rounding the correlation, SE, CI bounds, z-statistic, s-value, BFB, and posterior probability. Defaults to NULL, which does not round the result.
pdigits	An integer specifying the number of decimal places to used when rounding the p-value. Defaults to NULL, which does not round the result.

Details

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This function applies ci.rpc() to all the polyserial correlations in the hetcor object supplied by the user. If the ord argument is shorter than the ord argument used to create the hetcor object, r.pc will extract the subset of polychoric correlations for variables named in the r.pc() ord argument where the two variables have different names.

Value

A data frame containing the results.

References

Raykov, T., & Marcoulides, G. A. (2011). Introduction to psychometric theory. New York, NY: Routledge.

See Also

ci.rpc for the function used to get the CIs, p2s for s-values, p2bfb for BFBs, and p2pp for posterior probabilities.

Examples

library(mvtnorm)
library(polycor)
set.seed(12475)
# Create a population correlation matrix.
R <- matrix(0, 4, 4)
R[upper.tri(R)] <- c(.2, .3, .4, .5, .6, .7)
diag(R) <- 1
R <- cov2cor(t(R) %*% R)
# Show population correlations.
round(R, 4)
# Simulate data with normal distributions and correlation structure R.
mydf <- rmvnorm(1000, mean = rep(0, 4), sigma = R)
mydf <- data.frame(mydf)
names(mydf) <- c("x1", "x2", "y1", "y2")
# Show sample correlations.
Rhat <- round(cor(mydf), 4)
Rhat
# Convert y1 & y2  into ordinal categorical variables.
mydf$y1 <- cut(mydf$y1, c(-Inf, .75, Inf))
mydf$y2 <- cut(mydf$y2, c(-Inf, -1, .5, 1.5, Inf))
# Pearson, polychoric, and polyserial correlations, ML estimates.
HC <- hetcor(mydf, ML = TRUE)
HC

# Polychoric correlation, y1 & y2
r.pc(x = HC, ord = c("y1", "y2"))

sjpierce/piercer documentation built on Dec. 30, 2024, 3:28 p.m.