Predict from a fitted panglm model

Pooled predictions use the ordinary GLM mean. Random-effects response predictions are population averages over the fitted latent-effect distribution. Fixed-effects predictions include the estimated individual and, where applicable, time effects. New fixed-effect levels produce an error unless allow.new.levels = TRUE, in which case their contribution is set to zero.

Usage

# S3 method for class 'panglm'
predict(
  object,
  newdata = NULL,
  type = c("link", "response"),
  allow.new.levels = FALSE,
  ...
)

Arguments

object

a fitted "panglm" object

newdata

optional data frame; when omitted, return in-sample values

type

either "link" or "response"

allow.new.levels

if TRUE, assign zero to unseen fixed-effect levels; otherwise unseen levels are an error

...

unused

Value

a numeric vector of predictions

Details

Exact conditional binomial fits provide in-sample conditional inclusion probabilities. They cannot provide predictions for arbitrary newdata because the individual intercept is conditioned out and the probability depends on the complete response set and its success total.

See also

Other panglm methods: coefplot(), confint.panglm(), effectcurve(), fitted.panglm(), fixef(), glance.panglm(), logLik.panglm(), model.matrix.panglm(), partialeffects.panordered(), plot.panglm(), plot.panglm_ape(), plot.panglm_effectcurve(), predict.panordered(), residuals.panglm(), summary.panglm(), tidy.panglm(), vcov.panglm()

Examples

data(copd)
fit <- panglm(fev1 ~ crp, data = copd, index = c("id", "visit"),
              model = "random", family = "gaussian")
predict(fit, newdata = copd[1:4, ], type = "response")
#> [1] 2.921594 3.125174 2.984774 2.949674