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Compute the posterior mean and variance of h at a new predictor values

Source: R/ComputePostmeanHnew.R
ComputePostmeanHnew.Rd

Compute the posterior mean and variance of h at a new predictor values

Usage

ComputePostmeanHnew(
  fit,
  y = NULL,
  Z = NULL,
  X = NULL,
  Znew = NULL,
  sel = NULL,
  method = "approx"
)

Arguments

fit

An object containing the results returned by a the kmbayes function

y

a vector of outcome data of length n.

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.

Znew

matrix of new predictor values at which to predict new h, where each row represents a new observation. If set to NULL then will default to using the observed exposures Z.

sel

selects which iterations of the MCMC sampler to use for inference; see details

method

method for obtaining posterior summaries at a vector of new points. Options are "approx" and "exact"; defaults to "approx", which is faster particularly for large datasets; see details

Value

a list of length two containing the posterior mean vector and posterior variance matrix

Details

  • If method == "approx", the argument sel defaults to the second half of the MCMC iterations.

  • If method == "exact", the argument sel defaults 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

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

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)
#> Iteration: 10 (10% completed; 0.01172 secs elapsed)
#> Iteration: 20 (20% completed; 0.01792 secs elapsed)
#> Iteration: 30 (30% completed; 0.02422 secs elapsed)
#> Iteration: 40 (40% completed; 0.0304 secs elapsed)
#> Iteration: 50 (50% completed; 0.03642 secs elapsed)
#> Iteration: 60 (60% completed; 0.04241 secs elapsed)
#> Iteration: 70 (70% completed; 0.04858 secs elapsed)
#> Iteration: 80 (80% completed; 0.0545 secs elapsed)
#> Iteration: 90 (90% completed; 0.06064 secs elapsed)
#> Iteration: 100 (100% completed; 0.0666 secs elapsed)

med_vals <- apply(Z, 2, median)
Znew <- matrix(med_vals, nrow = 1)
h_true <- dat$HFun(Znew)
h_est1 <- ComputePostmeanHnew(fitkm, Znew = Znew, method = "approx")
h_est2 <- ComputePostmeanHnew(fitkm, Znew = Znew, method = "exact")