predict.JointAI {JointAI}

Predict values from an object of class JointAI

Description

Obtains predictions and corresponding credible intervals from an object of class 'JointAI'.

Usage

## S3 method for class '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, ...)

Arguments

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 ("link" or "lp"). Additionally, for generalized linear (mixed) models (incl. beta and log-normal) type = "response" transforms the predicted values to the scale of the response, and for ordinal and multinomial (mixed) models type may be "prob" (to obtain probabilities per class), "class" to obtain the class with the highest posterior probability, or "lp". For parametric survival models type can be "lp" or "response", and for proportional hazards survival models the options are "lp", "risk" (= exp(lp)), "survival" or "expected" (= -log(survival)).

start

the first iteration of interest (see window.mcmc)

end

the last iteration of interest (see window.mcmc)

thin

thinning interval (integer; see window.mcmc). For example, thin = 1 (default) will keep the MCMC samples from all iterations; thin = 5 would only keep every 5th iteration.

exclude_chains

optional vector of the index numbers of chains that should be excluded

mess

logical; should messages be given? Default is TRUE.

warn

logical; should warnings be given? Default is TRUE.

return_sample

logical; should the full sample on which the summary (mean and quantiles) is calculated be returned?#'

...

currently not used

Details

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.

Value

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).

Note

• 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.

See Also

predDF.JointAI, *_imp

Examples

# fit model
mod <- lm_imp(y ~ C1 + C2 + I(C2^2), data = wideDF, n.iter = 100)

# calculate the fitted values
fit <- predict(mod)

# 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')