radiant.model (version 1.4.3)

logistic: Logistic regression

Description

Logistic regression

Usage

logistic(
  dataset,
  rvar,
  evar,
  lev = "",
  int = "",
  wts = "None",
  check = "",
  form,
  ci_type,
  data_filter = "",
  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")

envir

Environment to extract data from

Value

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

Details

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

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

# NOT RUN {
logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
logistic(titanic, "survived", c("pclass", "sex")) %>% str()
# }