predictLong: Obtain predictions

In davidbolin/ngme: Linear Mixed Effects Models With Flexible Distributions

Obtain predictions

Description

A function to obtain predictions based on either filtering or smoothing distributions.

Usage

predictLong(
  Y,
  locs,
  pInd,
  locs.pred,
  Brandom.pred = NULL,
  Bfixed.pred,
  return.samples = FALSE,
  type = "Filter",
  quantiles = NULL,
  predict.derivatives = NULL,
  excursions = NULL,
  crps = FALSE,
  crps.skip = 10,
  Y.val,
  mixedEffect_list,
  measurment_list,
  processes_list,
  operator_list = NULL,
  nSim = 1,
  nBurnin = 10,
  silent = FALSE,
  max.num.threads = 2,
  repeat.mix = 10,
  seed = NULL
)

Arguments

Y	A numeric list that contains outcome values.
locs	A numeric list that contains the timings at which the outcomes are collected.
pInd	A numeric vector that contains the indices of longitudinal subjects for whom the predictions are to be obtained.
locs.pred	A numeric list that contains the timings of the repeated measurements.
Brandom.pred	A numeric list that contains random effects covaraite matrices.
Bfixed.pred	A numeric list that contains fixed effects covaraite matrices.
type	A character string for the type of prediction: "Filter" for filtering, "Smoothing" for smoothing.
quantiles	A two-elemnent vector that contains the quantiles of the predictions to be calculated.
predict.derivatives	STUFF
excursions	A list of excursion probabilities to compute. Each list should contain: "type" - type of excursion '>' or '<', "level" - level to compute excursion probability for, "process" - which expression for the model, x\alpha + dU + W + Z with x \alpha being fixed effects, dU random effects and Z noise, to compute the probability for. 'X' for x\alpha + dU + W, 'W' for W, 'Y' for x\alpha + dU + W + Z, 'Xderivative' for the first derivarive of x\alpha + dU + W, and 'Wderivative' for the first derivariate of W.
crps	A logical variable for calculating continuous ranked probability score (CRPS); "TRUE" indicates calculate, "FALSE" do not calculate.
crps.skip	A numerical value, say a, that indicates every ath element of the sample to be used to compute the crps score.
Y.val	Observations to use when calculating CRPS
mixedEffect_list	A list of inputs for random effects. noise The distribution of the mixed effects. B_random A list that contains the random effect covariates (needs to be matrix, can be NULL). B_fixed A list that contains the fixed effect covariates (needs to be matrix, can be NULL). beta_random Initial values for the parameters of the random effects (mean parameter) (if not specified set to zero). beta_fixed Initial values for the parameters of the fixed effects (if not specified set to zero). Sigma Initial values for the parameters of the variance-covariance matrix of the random effects (if not specified set to I ). nu Shape parameter for noise (NIG only) mu Shift parameter for noise (NIG only) U A list of inital values of the random effects. V A list of inital values of the variance effects.
measurment_list	A list of inputs for measurement error. sigma Measurement noise variance parameter. nu Shape parameter for noise (NIG only). Vs A list of inital values for the noise of the measurement.
processes_list	A list of inputs for the process. noise Distribution of the process. nu Shape parameter (for NIG or GAL). mu Asymmetry parameter (for NIG or GAL).
nSim	A numeric value for the number of samples of the Gibbs sampler to estimate the gradient.
silent	A logical value for printing the details of the iterations; "TRUE" indicates do not print, "FALSE" indicates print.
max.num.threads	STUFF
repeat.mix	STUFF
seed	A numerical value for starting the Gibbs samplers from fixed seed.

Details

This function calls "predictLong_cpp" internally. It is wrapped by "predict.ngme", and not advised to be used.

Value

.samples A logical variable for returning the Monte Carlo samples used to compute the predictions; "TRUE" indicates return, "FALSE" do not return.

A list of output.

See Also

predict.ngme

Examples

  ## Not run: 
  predictLong(...)
  
## End(Not run)

davidbolin/ngme documentation built on Dec. 5, 2023, 11:48 p.m.