MNLpred (version 0.0.8)

mnl_fd2_ova: Multinomial First Differences Predictions For Two Values (Observed Value Approach)

Description

Multinomial First Differences Predictions For Two Values (Observed Value Approach)

Usage

mnl_fd2_ova(
  model,
  data,
  x,
  value1,
  value2,
  xvari,
  nsim = 1000,
  seed = "random",
  probs = c(0.025, 0.975)
)

Arguments

model

the multinomial model, from a multinom()-function call (see the nnet package)

data

the data with which the model was estimated

x

the name of the variable that should be varied

value1

first value for the difference

value2

second value for the difference

xvari

former argument for x (deprecated).

nsim

numbers of simulations

seed

set a seed for replication purposes.

probs

a vector with two numbers, defining the significance levels. Default to 5% significance level: c(0.025, 0.975)

Value

The function returns a list with several elements. Most importantly the list includes the simulated draws `S`, the simulated predictions `P`, the first differences of the predictions `P_fd`, a data set for plotting `plotdata` the predicted probabilities, and one for the first differences `plotdata_fd`.

Examples

# NOT RUN {
library(nnet)
library(MASS)

dataset <- data.frame(y = c(rep("a", 10), rep("b", 10), rep("c", 10)),
                      x1 = rnorm(30),
                      x2 = rnorm(30, mean = 1),
                      x3 = sample(1:10, 30, replace = TRUE))

mod <- multinom(y ~ x1 + x2 + x3, data = dataset, Hess = TRUE)

fdi1 <- mnl_fd2_ova(model = mod, data = dataset,
                    x = "x1",
                    value1 = min(dataset$x1),
                    value2 = max(dataset$x1),
                    nsim = 10)



# }