bkmr: ComputePostmeanHnew – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

ComputePostmeanHnew

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

Compute 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 `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

If method == "approx" then calls the function ComputePostmeanHnew.approx. In this case, the argument sel defaults to the second half of the MCMC iterations.
If method == "exact" then calls the function ComputePostmeanHnew.exact. In this case, 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

Bayesian Kernel Machine Regression

v0.2.0

GPL-2

Authors

Jennifer F. Bobb [aut, cre]

Initial release