MNLpred (version 0.0.8)

mnl_fd_ova: Multinomial First Differences Prediction (Observed Value Approach)

Description

This function predicts values for two different scenarios over a range of values. It then takes the differences between the different simulations to return first differences for each value.

Usage

mnl_fd_ova(
  model,
  data,
  x,
  z,
  z_values,
  xvari,
  scenname,
  scenvalues,
  by = NULL,
  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 (the x-axis variable in prediction plots)

z

define the variable for which you want to compute the difference.

z_values

determine the two values at which value you want to fix the scenario (z). The first differences will be computed by subtracting the values of the first supplied scenario from the second one.

xvari

former argument for x (deprecated).

scenname

former argument for z (deprecated).

scenvalues

former argument for z_values (deprecated).

by

define the steps of x.

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`, and a data set for plotting `plotdata`.

Details

The function uses the mnl_pred_ova function for each scenario. The results of these predictions are also returned and can therefore be easily accessed. If you need predictions for multiple scenarios, you can use this function to both plot the predictions for each scenario and the differences between them.

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)

fdif <- mnl_fd_ova(model = mod, data = dataset,
                   x = "x1", z = "x3",
                   z_values = c(min(dataset$x3), max(dataset$x3)),
                   nsim = 10)

# }