ComputePostmeanHnew {bkmr} R Documentation

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

### Description

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

### 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

[Package bkmr version 0.2.0 Index]