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Ordinal Regression with plssem

This vignette demonstrates how to fit an ordinal regression model with plssem using the titanic dataset.

In plssem, regression-style model syntax like y ~ x1 + x2 is supported. When the dependent variable (and/or predictors) are ordinal. Ordinal variables can be supplied via the ordered argument, or by making sure they are ordered in the dataset.

Data

head(titanic[, c("Survived", "Age", "Sex", "Female", "Pclass")])

Linear ordinal regression

This model predicts survival as a function of age and sex.

m_linear <- "Survived ~ Age + Female"

fit_linear <- pls(
  m_linear,
  data          = titanic,
  ordered       = "Survived",
  boot.R        = 50,
  bootstrap     = TRUE,
  boot.parallel = "multicore",
  boot.ncores   = 2
)

summary(fit_linear)

Optional: evaluate predictive performance.

pls_predict(fit_linear, benchmark = "acc")

Non-linear ordinal regression (interaction)

To include a non-linear (interaction) effect, add an interaction term. With ordinal indicators and interactions, plssem automatically switches to the Monte-Carlo ordinal PLSc estimator.

m_int <- "Survived ~ Age + Female + Age:Female"

fit_int <- pls(
  m_int,
  data          = titanic,
  ordered       = "Survived",
  boot.R        = 50,
  bootstrap     = TRUE,
  boot.parallel = "multicore",
  boot.ncores   = 2
)

summary(fit_int)

pls_predict(fit_int, benchmark = "acc")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.