SPQR (version 0.1.0)

predict.SPQR: predict method for class SPQR

Description

Computes the predicted values for different functions based on the fitted "SPQR" object.

Usage

# S3 method for SPQR
predict(
  object,
  X,
  Y = NULL,
  nY = 101,
  type = c("QF", "PDF", "CDF"),
  tau = seq(0.1, 0.9, 0.1),
  ci.level = 0,
  getAll = FALSE,
  ...
)

Arguments

object

An object of class SPQR.

X

The covariate vector/matrix for which the predictions are computed.

Y

The response vector for which the predictions are computed. Default is NULL indicating that a equi-distant grid vector on [0,1] of length nY is used.

nY

An integer number indicating length of grid when Y is not specified. Default: 101.

type

The function to be predicted; "PDF": probability density function, "CDF": cumulative distribution function, and "QF": the quantile function (default).

tau

The grid of quantiles for which the quantile function is computed. Default: seq(0.1,0.9,0.1).

ci.level

The credible level for computing the pointwise credible intervals. The default is 0 indicating no credible intervals should be computed.

getAll

If TRUE, extracts all posterior samples of the prediction. Default: FALSE.

...

Other arguments.

Value

A named array containing all predicted values.

Examples

# NOT RUN {
set.seed(919)
n <- 200
X <- rbinom(n, 1, 0.5)
Y <- rnorm(n, X, 0.8)
control <- list(iter = 200, warmup = 150, thin = 1)
fit <- SPQR(X = X, Y = Y, method = "MCMC", control = control,
            normalize = TRUE, verbose = FALSE)

## compute the estimated PDF of Y conditioned on X = 0
pdf <- predict(fit, type = "PDF", X = 0, Y = seq(0, 1, 0.01))
plot(seq(0, 1, 0.01), pdf, xlab = "Y", ylab = "Density")
# }