Obtains predictions and corresponding credible intervals from an object of class 'JointAI'.
# S3 method for JointAI predict(object, outcome = 1L, newdata, quantiles = c(0.025, 0.975), type = "lp", start = NULL, end = NULL, thin = NULL, exclude_chains = NULL, mess = TRUE, warn = TRUE, return_sample = FALSE, ...)
object  object inheriting from class 'JointAI' 

outcome  vector of variable names or integers identifying for which outcome(s) the prediction should be performed. 
newdata  optional new dataset for prediction. If left empty, the original data is used. 
quantiles  quantiles of the predicted distribution of the outcome 
type  the type of prediction. The default is on the scale of the linear
predictor ( 
start  the first iteration of interest
(see 
end  the last iteration of interest
(see 
thin  thinning interval (integer; see 
exclude_chains  optional vector of the index numbers of chains that should be excluded 
mess  logical; should messages be given? Default is

warn  logical; should warnings be given? Default is

return_sample  logical; should the full sample on which the summary (mean and quantiles) is calculated be returned?#' 
...  currently not used 
A list with entries dat
, fit
and quantiles
,
where fit
contains the predicted values (mean over the values
calculated from the iterations of the MCMC sample), quantiles
contain the specified quantiles (by default 2.5% and 97.5%), and dat
is newdata
, extended with fit
and quantiles
(unless
prediction for an ordinal outcome is done with type = "prob"
, in
which case the quantiles are an array with three dimensions and are
therefore not included in dat
).
A model.matrix
\(X\) is created from the model formula
(currently fixed effects only) and newdata
. \(X\beta\) is then
calculated for each iteration of the MCMC sample in object
, i.e.,
\(X\beta\) has n.iter
rows and nrow(newdata)
columns. A
subset of the MCMC sample can be selected using start
, end
and thin
.
So far, predict
cannot calculate
predicted values for cases with missing values in covariates. Predicted
values for such cases are NA
.
For repeated measures models prediction currently only uses fixed effects.
Functionality will be extended in the future.
# fit model mod < lm_imp(y ~ C1 + C2 + I(C2^2), data = wideDF, n.iter = 100) # calculate the fitted values fit < predict(mod) #> Warning: #> Prediction for cases with missing covariates is not yet implemented. #> I will report “NA” instead of predicted values for those cases. # create dataset for prediction newDF < predDF(mod, vars = ~ C2) # obtain predicted values pred < predict(mod, newdata = newDF) # plot predicted values and 95% confidence band matplot(newDF$C2, pred$fitted, lty = c(1, 2, 2), type = "l", col = 1, xlab = 'C2', ylab = 'predicted values')