Plot univariate predictor-response function on a new grid of point for MI BKMR

PredictorResponseUnivar.MI(
  BKMRfits,
  which.z = 1:ncol(BKMRfits[[1]]$Z),
  ngrid = 50,
  q.fixed = 0.5,
  sel = NULL,
  min.plot.dist = Inf,
  center = TRUE,
  method = "approx",
  ...
)

Arguments

BKMRfits: A list of multiple BKMR fits and that each of these fits were ran for the same number of MCMC iterations.
which.z: vector identifying which predictors (columns of Z) should be plotted
ngrid: number of grid points to cover the range of each predictor (column in Z)
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
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
...: other arguments to pass on to the prediction function

Value

a long data frame with the predictor name, predictor value, posterior mean estimate, and posterior standard deviation

Details

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

Examples

if (FALSE) {
library(causalbkmr)
data(BKMRfits10)
univar.MI <- PredictorResponseUnivar.MI(BKMRfits10, ngrid = 50, q.fixed = 0.5, sel = sel.MI, method="approx")
ggplot(univar.MI, aes(z, est, ymin = est - 1.96*se, ymax = est + 1.96*se)) +
geom_smooth(stat = "identity") + ylab("h(z)") + facet_wrap(~ variable)+ggtitle("")
}