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

Usage

PredictorResponseBivarPair(
  fit,
  y = NULL,
  Z = NULL,
  X = NULL,
  whichz1 = 1,
  whichz2 = 2,
  whichz3 = NULL,
  method = "approx",
  prob = 0.5,
  q.fixed = 0.5,
  sel = NULL,
  ngrid = 50,
  min.plot.dist = 0.5,
  center = TRUE,
  ...
)

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.
whichz1: vector identifying the first predictor that (column of Z) should be plotted
whichz2: vector identifying the second predictor that (column of Z) should be plotted
whichz3: vector identifying the third predictor that will be set to a pre-specified fixed quantile (determined by prob)
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
prob: pre-specified quantile to set the third predictor (determined by whichz3); defaults to 0.5 (50th percentile)
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
ngrid: number of grid points to cover the range of each predictor (column in Z)
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
...: other arguments to pass on to the prediction function

Value

a data frame with value of the first predictor, the value of the second predictor, the posterior mean estimate, and the posterior standard deviation

Examples

## First generate dataset
set.seed(111)
dat <- SimData(n = 50, M = 4)
y <- dat$y
Z <- dat$Z
X <- dat$X

## Fit model with component-wise variable selection
## Using only 100 iterations to make example run quickly
## Typically should use a large number of iterations for inference
set.seed(111)
fitkm <- kmbayes(y = y, Z = Z, X = X, iter = 100, verbose = FALSE, varsel = TRUE)
#> Iteration: 10 (10% completed; 0.00472 secs elapsed)
#> Iteration: 20 (20% completed; 0.01 secs elapsed)
#> Iteration: 30 (30% completed; 0.01531 secs elapsed)
#> Iteration: 40 (40% completed; 0.02049 secs elapsed)
#> Iteration: 50 (50% completed; 0.02558 secs elapsed)
#> Iteration: 60 (60% completed; 0.03072 secs elapsed)
#> Iteration: 70 (70% completed; 0.0434 secs elapsed)
#> Iteration: 80 (80% completed; 0.04857 secs elapsed)
#> Iteration: 90 (90% completed; 0.05379 secs elapsed)
#> Iteration: 100 (100% completed; 0.0589 secs elapsed)

## Obtain predicted value on new grid of points
## Using only a 10-by-10 point grid to make example run quickly
pred.resp.bivar12 <- PredictorResponseBivarPair(fit = fitkm, min.plot.dist = 1, ngrid = 10)