Predict the exposure-response function at a new grid of points for MI BKMR

PredictorResponseBivar.MI(
  BKMRfits,
  z.pairs = NULL,
  method = "approx",
  ngrid = 50,
  q.fixed = 0.5,
  sel = NULL,
  min.plot.dist = 0.5,
  center = TRUE,
  z.names = colnames(BKMRfits[[1]]$Z),
  verbose = TRUE,
  ...
)

Arguments

BKMRfits: A list of multiple BKMR fits and that each of these fits were ran for the same number of MCMC iterations.
z.pairs: data frame showing which pairs of predictors to plot
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
ngrid: number of grid points in each dimension
q.fixed: vector of quantiles at which to fix the remaining predictors in Z
sel: logical expression indicating samples to keep; defaults to keeping the second half of all samples
min.plot.dist: specifies a minimum distance that a new grid point needs to be from an observed data point in order to compute the prediction; points further than this will not be computed
center: flag for whether to scale the exposure-response function to have mean zero
z.names: optional vector of names for the columns of z
verbose: TRUE or FALSE: flag of whether to print intermediate output to the screen
...: other arguments to pass on to the prediction function

Value

a long data frame with the name of the first predictor, the name of the second predictor, the value of the first predictor, the value of the second predictor, the posterior mean estimate, and the posterior standard deviation of the estimated exposure response function

Details

For guided examples, go to https://zc2326.github.io/causalbkmr/articles/MI_BKMR.html

Examples

if (FALSE) {
library(causalbkmr)
data(BKMRfits10)
bivar.MI <- PredictorResponseBivar.MI(BKMRfits = BKMRfits10,
min.plot.dist = 1, sel=seq(5001,10000,by=500),
method="approx")
Z.MI <- Z.complete.MI(BKMRfits10)
bivar.levels.MI <- PredictorResponseBivarLevels(pred.resp.df = bivar.MI,
                                               Z=Z.MI, both_pairs = TRUE,
                                               qs = c(0.25, 0.5, 0.75))

ggplot(bivar.levels.MI, aes(z1, est)) + geom_smooth(aes(col = quantile), stat = "identity") +
facet_grid(variable2 ~ variable1) + ggtitle("h(expos1 | quantiles of expos2)") + xlab("expos1")
}