# mnl_pred_ova: Multinomial Prediction Function (Observed Value Approach) In MNLpred: Simulated Predicted Probabilities for Multinomial Logit Models

## Description

This function predicts probabilities for all choices of a multinomial logit model over a specified span of values.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```mnl_pred_ova( model, data, x, by = NULL, z = NULL, z_value = NULL, xvari, scenname, scenvalue, 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) `by` define the steps of `x`. `z` if you want to hold a specific variable stable over all scenarios, you can name it here (optional). `z_value` determine at which value you want to fix the `z`. `xvari` former argument for `x` (deprecated). `scenname` former argument for `z` (deprecated). `scenvalue` former argument for `z_value` (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', and a data set for plotting 'plotdata'.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```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) pred <- mnl_pred_ova(model = mod, data = dataset, x = "x1", nsim = 10) ```

MNLpred documentation built on July 16, 2021, 9:06 a.m.