Description Usage Arguments Details Value Author(s) See Also
Makes predicted probabilities from a strategic model.
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, ...)
|
object |
a fitted model of class |
... |
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 |
na.action |
how to deal with |
n.sim |
number of simulation draws to use per observation for
|
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
.
A data frame of predicted probabilities.
Brenton Kenkel (brenton.kenkel@gmail.com)
predProbs
provides a more full-featured and
user-friendly wrapper, including plots and confidence bands.
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