SingVarIntSummaries function

Single Variable Interaction Summaries

Compare the single-predictor health risks when all of the other predictors in Z are fixed to their a specific quantile to when all of the other predictors in Z are fixed to their a second specific quantile.

SingVarIntSummaries(
  fit,
  y = NULL,
  Z = NULL,
  X = NULL,
  which.z = 1:ncol(Z),
  qs.diff = c(0.25, 0.75),
  qs.fixed = c(0.25, 0.75),
  method = "approx",
  sel = NULL,
  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 indicating which variables (columns of Z) for which the summary should be computed
• qs.diff: vector indicating the two quantiles at which to compute the single-predictor risk summary
• qs.fixed: vector indicating the two quantiles at which to fix all of the remaining exposures in Z
• 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
• sel: logical expression indicating samples to keep; defaults to keeping the second half of all samples
• z.names: optional vector of names for the columns of z
• ...: other arguments to pass on to the prediction function

Returns

a data frame containing the (posterior mean) estimate and posterior standard deviation of the single-predictor risk measures

Details

• If method == "approx", the argument sel defaults to the second half of the MCMC iterations.
• If method == "exact", 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

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)

risks.int <- SingVarIntSummaries(fit = fitkm, method = "exact")