| predict.dsem {dsem} | R Documentation |
predictions using dsem
Description
Predict variables given new (counterfactual) values of data, or for future or past times
Usage
## S3 method for class 'dsem'
predict(object, newdata = NULL, type = c("link", "response"), ...)
Arguments
object |
Output from |
newdata |
optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted data are used to create predictions. If desiring predictions after the fitted data, the user must append rows with NAs for those future times. Similarly, if desiring predictions given counterfactual values for time-series data, then those individual observations can be edited while keeping other observations at their original fitted values. |
type |
the type of prediction required. The default is on the scale of the linear predictors; the alternative "response" is on the scale of the response variable. Thus for a Poisson-distributed variable the default predictions are of log-intensity and type = "response" gives the predicted intensity. |
... |
Not used |
Value
A matrix of predicted values with dimensions and order corresponding to
argument newdata is provided, or tsdata if not.
Predictions are provided on either link or response scale, and
are generated by re-optimizing random effects condition on MLE
for fixed effects, given those new data.