mnl | R Documentation |
Multinomial logistic regression
mnl(
dataset,
rvar,
evar,
lev = "",
int = "",
wts = "None",
check = "",
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 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 |
See https://radiant-rstats.github.io/docs/model/mnl.html for an example in Radiant
A list with all variables defined in mnl as an object of class mnl
summary.mnl
to summarize the results
plot.mnl
to plot the results
predict.mnl
to generate predictions
plot.model.predict
to plot prediction output
result <- mnl(
ketchup,
rvar = "choice",
evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"),
lev = "heinz28"
)
str(result)
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