glm.predict (version 4.3-2)

basepredict.mlogit: predicted value

Description

The function calculates the predicted value with the confidence interval. It can be used for any mlogit model.

Usage

# S3 method for mlogit
basepredict(model, values, sim.count=1000, conf.int=0.95, sigma=NULL, set.seed=NULL,
  type = c("any", "simulation", "bootstrap"), summary = TRUE)

Value

The output is a matrix have in the first column the predicted value, in the second column the lower value of the confidence interval and in the third column the upper value of the confidence interval.

Arguments

model

the model Object generated with mlogit()

values

the values of the case as vector in the order how they appear in the summary(model) Estimate

sim.count

OPTIONAL numbers of simulations to be done by the function. default: 1000

conf.int

OPTIONAL the confidence interval used by the function. default: 0.95

sigma

OPTIONAL the variance-covariance matrix, can be changed when having for example robust or clustered vcov. default: vcov(model)

set.seed

OPTIONAL set a seed for the random number generator

type

type is ignored as only simulation is implemented

summary

OPTIONAL if mean/quantiles should be return or all simulated values (default: TRUE)

Author

Benjamin Schlegel,

Details

The function makes a simulation for the two cases and compares them to each other.

Examples

if (FALSE) {
df_selects_withoutNA = selects2015 |>
  dplyr::filter(vote_choice != "other") |> 
  dplyr::mutate(vote_choice = factor(vote_choice)) |> 
  dplyr::select(age, gender, vote_choice, starts_with("lr_")) |>
  na.omit()
mlogit_data = dfidx::dfidx(df_selects_withoutNA, varying = 5:11, 
                    sep = "_", shape = "wide", 
                    choice = "vote_choice")

mlogit_data$distance = abs(mlogit_data$lr - mlogit_data$lr_self)

model1 = mlogit::mlogit(vote_choice ~ distance | lr_self + 
                          gender, data = mlogit_data)
summary(model1)
# predicted probability of a left male person with a distance of 2
basepredict(model1, list(1, c(2, 5, 5, 5, 5, 5), 0, 0))
}