logistic {radiant.model}

Logistic regression

Description

Logistic regression

Usage

logistic(
  dataset,
  rvar,
  evar,
  lev = "",
  int = "",
  wts = "None",
  check = "",
  form,
  ci_type,
  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 defined as _success_

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. Add "robust" for robust estimation of standard errors (HC1)

form

Optional formula to use instead of rvar, evar, and int

ci_type

To use the profile-likelihood (rather than Wald) for confidence intervals use "profile". For datasets with more than 5,000 rows the Wald method will be used, unless "profile" is explicitly set

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/logistic.html for an example in Radiant

Value

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

See Also

summary.logistic to summarize the results

plot.logistic to plot the results

predict.logistic to generate predictions

plot.model.predict to plot prediction output

Examples

logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
logistic(titanic, "survived", c("pclass", "sex")) %>% str()