inst/doc/v04-latent-and-mixed-scale-correlation.R

In matrixCorr: Collection of Correlation and Association Estimators

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE
)

## -----------------------------------------------------------------------------
library(matrixCorr)

set.seed(30)
n <- 500
Sigma <- matrix(c(
  1.00, 0.55, 0.35, 0.20,
  0.55, 1.00, 0.40, 0.30,
  0.35, 0.40, 1.00, 0.45,
  0.20, 0.30, 0.45, 1.00
), 4, 4, byrow = TRUE)

Z <- matrix(rnorm(n * 4), n, 4) %*% chol(Sigma)

X_bin <- data.frame(
  b1 = Z[, 1] > qnorm(0.70),
  b2 = Z[, 2] > qnorm(0.55),
  b3 = Z[, 3] > qnorm(0.50)
)

X_ord <- data.frame(
  o1 = ordered(cut(Z[, 2], breaks = c(-Inf, -0.5, 0.4, Inf),
    labels = c("low", "mid", "high")
  )),
  o2 = ordered(cut(Z[, 3], breaks = c(-Inf, -1, 0, 1, Inf),
    labels = c("1", "2", "3", "4")
  ))
)

X_cont <- data.frame(x1 = Z[, 1], x2 = Z[, 4])

## -----------------------------------------------------------------------------
fit_tet <- tetrachoric(X_bin, ci = TRUE, p_value = TRUE)
fit_pol <- polychoric(X_ord, ci = TRUE, p_value = TRUE)

print(fit_tet, digits = 2)
summary(fit_pol)

## -----------------------------------------------------------------------------
fit_bin_naive <- pearson_corr(data.frame(lapply(X_bin[, 1:2], as.numeric)))
fit_ord_naive <- pearson_corr(data.frame(lapply(X_ord, as.numeric)))

round(c(
  b1_b2_pearson = fit_bin_naive[1, 2],
  b1_b2_tetrachoric = fit_tet[1, 2],
  o1_o2_pearson = fit_ord_naive[1, 2],
  o1_o2_polychoric = fit_pol[1, 2]
), 2)

## -----------------------------------------------------------------------------
fit_ps <- polyserial(X_cont, X_ord, ci = TRUE, p_value = TRUE)
fit_bis <- biserial(X_cont, X_bin[, 1:2], ci = TRUE, p_value = TRUE)

summary(fit_ps)
summary(fit_bis)