# predict.game: Predicted probabilities for strategic models In games: Statistical Estimation of Game-Theoretic Models

## Description

Makes predicted probabilities from a strategic model.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## S3 method for class 'game' predict(object, ...) ## S3 method for class 'egame12' predict(object, newdata, type=c("outcome", "action"), na.action = na.pass, ...) ## S3 method for class 'egame122' predict(object, newdata, type=c("outcome", "action"), na.action = na.pass, ...) ## S3 method for class 'egame123' predict(object, newdata, type=c("outcome", "action"), na.action = na.pass, ...) ## S3 method for class 'ultimatum' predict(object, newdata, na.action = na.pass, n.sim = 1000, ...) ```

## Arguments

 `object` a fitted model of class `game`. `...` other arguments, currently ignored. `newdata` data frame of values to make the predicted probabilities for. If this is left empty, the original dataset is used. `type` whether to provide probabilities for outcomes (e.g., L, RL, or RR in `egame12`) or for actions (e.g., whether 2 moves L or R given that 1 moved R). `na.action` how to deal with `NA`s in `newdata` `n.sim` number of simulation draws to use per observation for `ultimatum` models (see Details).

## Details

This method uses a fitted strategic model to make predictions for a new set of data. This is useful for cross-validating or for graphical analysis. For many uses, such as analyzing the marginal effect of a particular independent variable, the function `predProbs` will be more convenient.

In the `ultimatum` model, there is not an analytic expression for the expected value of Player 1's offer. Therefore, predicted values are instead generating via simulation by drawing errors from a logistic distribution. The number of draws per observation can be controlled via the `n.sim` argument. For replicability, we recommend seeding the random number generator via `set.seed` before using `predict.ultimatum`.

## Value

A data frame of predicted probabilities.

## Author(s)

Brenton Kenkel (brenton.kenkel@gmail.com)

`predProbs` provides a more full-featured and user-friendly wrapper, including plots and confidence bands.