R: Use results from function estimateModel to make predictions.

predictModel {demest}

R Documentation

Use results from function estimateModel to make predictions.

Description

Typically predict along time dimension, but can predict along other dimenions. When predicting along time dimension, typically predict forwards, but can predict backwards.

Usage

predictModel(
  filenameEst,
  filenamePred,
  along = NULL,
  labels = NULL,
  n = NULL,
  exposure = NULL,
  weights = NULL,
  data = NULL,
  aggregate = NULL,
  lower = NULL,
  upper = NULL,
  nBurnin = 0L,
  parallel = TRUE,
  outfile = NULL,
  verbose = FALSE,
  useC = TRUE
)

Arguments

`filenameEst`	Filename used to `estimateModel`.
`filenamePred`	Filename to be used by `predictModel`.
`along`	Name or index along which prediction should occur.
`labels`	Labels of new categories.
`n`	Number of new categories. Can only be used when predicting along `"time"`, `"age"`, or `"cohort"` dimensions, and when the units have equal length.
`exposure`	A `Counts` object specifying exposure or sample size.
`weights`	A `Counts` object containing weights.
`data`	A named list containing future values of covariates.
`aggregate`	An object of class `SpecAggregate`.
`lower`	A lower bound for estimates of data-level rates, probabilities, or means.
`upper`	An upper bound for estimates of data-level rate, probabilities, or means.
`nBurnin`	Number of iteration discarded before recording begins.
`parallel`	Logical. If `TRUE` (the default), parallel processing is used.
`outfile`	Where to direct the ‘stdout’ and ‘stderr’ connection output from the workers when parallel processing. Passed to function `[parallel]{makeCluster}`.
`verbose`	Logical. If `TRUE` (the default) a message is printed at the end of the calculations.
`useC`	Logical. If `TRUE` (the default), the calculations are done in C. Setting `useC` to `FALSE` may be useful for debugging.

Details

If an along argument is not supplied, predictModel looks for a dimension with dimtype "time", and, failing that, a dimension with dimtype "age", or "cohort".

When predicting along "time", "age" or "cohort" dimensions, specifying an n argument is usually more convenient than specifying a labels argument.

exposure or weights arguments are needed if predictions for outcome variable y, and not just the model parameters, are needed.

[Package demest version 0.0.0.3.1 Index]