POLRModel: Ordered Logistic or Probit Regression Model

In MachineShop: Machine Learning Models and Tools

Ordered Logistic or Probit Regression Model

Description

Fit a logistic or probit regression model to an ordered factor response.

Usage

POLRModel(method = c("logistic", "probit", "loglog", "cloglog", "cauchit"))

Arguments

method

logistic or probit or (complementary) log-log or cauchit (corresponding to a Cauchy latent variable).

Details

Response types:

ordered

Further model details can be found in the source link below.

In calls to varimp for POLRModel, numeric argument base may be specified for the (negative) logarithmic transformation of p-values [defaul: exp(1)]. Transformed p-values are automatically scaled in the calculation of variable importance to range from 0 to 100. To obtain unscaled importance values, set scale = FALSE.

Value

MLModel class object.

See Also

polr, fit, resample

Examples

data(Boston, package = "MASS")

df <- within(Boston,
             medv <- cut(medv,
                         breaks = c(0, 10, 15, 20, 25, 50),
                         ordered = TRUE))
fit(medv ~ ., data = df, model = POLRModel)

MachineShop documentation built on Sept. 11, 2024, 6:28 p.m.