PredictorResponseUnivar {bkmr}R Documentation

Plot univariate predictor-response function on a new grid of points

Description

Plot univariate predictor-response function on a new grid of points

Usage

PredictorResponseUnivar(fit, y = NULL, Z = NULL, X = NULL,
  which.z = 1:ncol(Z), method = "approx", ngrid = 50, q.fixed = 0.5,
  sel = NULL, min.plot.dist = Inf, center = TRUE, z.names = colnames(Z),
  ...)

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.

which.z

vector identifying which predictors (columns of Z) should be plotted

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

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

z.names

optional vector of names for the columns of z

...

other argumentd to pass on to the prediction function

Details

For guided examples, go to https://jenfb.github.io/bkmr/overview.html


[Package bkmr version 0.2.0 Index]