SamplePred: Obtain posterior samples of predictions at new points

In bkmr: Bayesian Kernel Machine Regression

Obtain posterior samples of predictions at new points

Description

Obtains posterior samples of E(Y) = h(Znew) + beta*Xnew or of g^{-1}[E(y)]

Usage

SamplePred(
  fit,
  Znew = NULL,
  Xnew = NULL,
  Z = NULL,
  X = NULL,
  y = NULL,
  sel = NULL,
  type = c("link", "response"),
  ...
)

Arguments

fit	An object containing the results returned by a the kmbayes function
Znew	optional matrix of new predictor values at which to predict new h, where each row represents a new observation. If not specified, defaults to using observed Z values
Xnew	optional matrix of new covariate values at which to obtain predictions. If not specified, defaults to using observed X values
Z	an n-by-M matrix of predictor variables to be included in the h function. Each row represents an observation and each column represents an predictor.
X	an n-by-K matrix of covariate data where each row represents an observation and each column represents a covariate. Should not contain an intercept column.
y	a vector of outcome data of length n.
sel	A vector selecting which iterations of the BKMR fit should be retained for inference. If not specified, will default to keeping every 10 iterations after dropping the first 50% of samples, or if this results in fewer than 100 iterations, than 100 iterations are kept
type	whether to make predictions on the scale of the link or of the response; only relevant for the binomial outcome family
...	other arguments; not currently used

Details

For guided examples, go to https://jenfb.github.io/bkmr/overview.html

Value

a matrix with the posterior samples at the new points

Examples

set.seed(111)
dat <- SimData(n = 50, M = 4)
y <- dat$y
Z <- dat$Z
X <- dat$X

## Fit model with component-wise variable selection
## Using only 100 iterations to make example run quickly
## Typically should use a large number of iterations for inference
set.seed(111)
fitkm <- kmbayes(y = y, Z = Z, X = X, iter = 100, verbose = FALSE, varsel = TRUE)

med_vals <- apply(Z, 2, median)
Znew <- matrix(med_vals, nrow = 1)
h_true <- dat$HFun(Znew)
set.seed(111)
samps3 <- SamplePred(fitkm, Znew = Znew, Xnew = cbind(0))
head(samps3)

bkmr documentation built on March 28, 2022, 9:11 a.m.