# Compute the posterior mean and variance of `h` at a new predictor values ```r 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 ## Returns 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](https://jenfb.github.io/bkmr/overview.html) ## Examples ```r 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") ```

ComputePostmeanHnew function