mnl: Multinomial logistic regression

In radiant-rstats/radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny

Multinomial logistic regression

Description

Multinomial logistic regression

Usage

mnl(
  dataset,
  rvar,
  evar,
  lev = "",
  int = "",
  wts = "None",
  check = "",
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset	Dataset
rvar	The response variable in the model
evar	Explanatory variables in the model
lev	The level in the response variable to use as the baseline
int	Interaction term to include in the model
wts	Weights to use in estimation
check	Use "standardize" to see standardized coefficient estimates. Use "stepwise-backward" (or "stepwise-forward", or "stepwise-both") to apply step-wise selection of variables in estimation.
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
envir	Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/model/mnl.html for an example in Radiant

Value

A list with all variables defined in mnl as an object of class mnl

See Also

summary.mnl to summarize the results

plot.mnl to plot the results

predict.mnl to generate predictions

plot.model.predict to plot prediction output

Examples

result <- mnl(
  ketchup,
  rvar = "choice",
  evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
  lev = "heinz28"
)
str(result)

radiant-rstats/radiant.model documentation built on Nov. 29, 2023, 5:59 a.m.