r.ps: Compute confidence intervals for a set of polyserial...

In sjpierce/piercer: Functions for Research and Statistical Computing

Compute confidence intervals for a set of polyserial correlations.

Description

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

Usage

r.ps(x, cont, ord, digits = NULL, pdigits = NULL)

Arguments

x	A hetcor object produced by hetcor().
cont	A character vector of names for continuous variables.
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.pc() to all the polyserial correlations in the hetcor object supplied by the user that are associated with the combinations of variables specified in cont and ord. You can extract a subset of the polyserial correlations from the hetcor object fed to this function by specifying a cont and/or ord argument shorter than used to create the hetcor object.

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(63247)
# 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

# Polyserial correlation, x2 & y2
r.ps(x = HC, cont = "x2", ord = "y2")

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