View source: R/ComputePostmeanHnew.R
ComputePostmeanHnew | R Documentation |
h
at a new predictor valuesCompute the posterior mean and variance of h
at a new predictor values
ComputePostmeanHnew( fit, y = NULL, Z = NULL, X = NULL, Znew = NULL, sel = NULL, method = "approx" )
fit |
An object containing the results returned by a the |
y |
a vector of outcome data of length |
Z |
an |
X |
an |
Znew |
matrix of new predictor values at which to predict new |
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 |
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
a list of length two containing the posterior mean vector and posterior variance matrix
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) h_est1 <- ComputePostmeanHnew(fitkm, Znew = Znew, method = "approx") h_est2 <- ComputePostmeanHnew(fitkm, Znew = Znew, method = "exact")
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