logistic | R Documentation |
Logistic regression
logistic(
dataset,
rvar,
evar,
lev = "",
int = "",
wts = "None",
check = "",
form,
ci_type,
data_filter = "",
arr = "",
rows = NULL,
envir = parent.frame()
)
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 |
See https://radiant-rstats.github.io/docs/model/logistic.html for an example in Radiant
A list with all variables defined in logistic as an object of class logistic
summary.logistic
to summarize the results
plot.logistic
to plot the results
predict.logistic
to generate predictions
plot.model.predict
to plot prediction output
logistic(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
logistic(titanic, "survived", c("pclass", "sex")) %>% str()
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