R: predict

predict.Hmsc {Hmsc}

R Documentation

predict

Description

Calculates predicted values from a fitted Hmsc model.

Usage

## S3 method for class 'Hmsc'
predict(
  object,
  post = poolMcmcChains(object$postList),
  XData = NULL,
  X = NULL,
  XRRRData = NULL,
  XRRR = NULL,
  studyDesign = object$studyDesign,
  ranLevels = object$ranLevels,
  Gradient = NULL,
  Yc = NULL,
  mcmcStep = 1,
  expected = FALSE,
  predictEtaMean = FALSE,
  predictEtaMeanField = FALSE,
  nParallel = 1,
  useSocket = TRUE,
  ...
)

Arguments

`object`	a fitted `Hmsc` model object
`post`	a list of posterior samples of the HMSC model. By default uses all samples from the pooled posterior of the hM object.
`XData`	a dataframe specifying the unpreprocessed covariates for the predictions to be made. Works only if the `XFormula` argument was specified in the `Hmsc()` model constructor call. Requirements are similar to those in the `Hmsc` model constructor.
`X`	a matrix specifying the covariates for the predictions to be made. Only one of XData and X arguments may be provided.
`XRRRData`	a dataframe of covariates for reduced-rank regression
`XRRR`	a matrix of covariates for reduced-rank regression
`studyDesign`	a matrix, specifying the structure of the study design for the prediction. Requirements are similar to those of the `Hmsc` constructor. By default this argument is assigned the study design of the training data in the fitted Hmsc model.
`ranLevels`	a list of `HmscRandomLevel` objects, futher specifying the structure of random levels. Requirements are similar to those of the `Hmsc` constructor. Each level must cover all units, specified in the correspondingly named column of `studyDesign` argument. By default this argument is assigned the list of `HmscRandomLevel` objects specified for fitting Hmsc model.
`Gradient`	an object returned by `constructGradient`. Providing `Gradient` is an alternative for providing `XData`, `studyDesign` and `ranLevels`. Cannot be used together with `Yc`.
`Yc`	a matrix of the outcomes that are assumed to be known for conditional predictions. Cannot be used together with `Gradient`.
`mcmcStep`	the number of extra mcmc steps used for updating the random effects
`expected`	boolean flag indicating whether to return the location parameter of the observation models or sample the values from those.
`predictEtaMean`	boolean flag indicating whether to use the estimated mean values of posterior predictive distribution for random effets corresponding for the new units.
`predictEtaMeanField`	boolean flag indicating whether to use draws from the mean-field of the posterior predictive distribution for random effets corresponding for the new units.
`nParallel`	Number of parallel processes. Parallel processing is only useful with new `Yc` data and extra `mcmcStep`.
`useSocket`	(logical) Use socket clusters in parallel proecessing; these are the only alternative in Windows, but in other systems this should be usually set `FALSE` for forking.
`...`	other arguments passed to functions.

Details

In mcmcStep,the number of extra mcmc steps used for updating the random effects for the Eta parameters, starting from the samples of the fitted Hmsc model in order to account for the conditional infromation provided in the Yc argument. The higher this number is, the more the obtained updated samples are unaffected by the posterior estimates of latent factors in the model fitted to the training data and more resembles the true conditional posterior. However, the elapsed time for conditional prediction grows approximately linearly as this parameter increases. The exact number for sufficient is problem-dependent and should be assessed by e.g. gradually increasing this parameter till the stationarity of the produced predictions.

Value