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.

Usage

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

Value

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)
#> Iteration: 10 (10% completed; 0.00476 secs elapsed)
#> Iteration: 20 (20% completed; 0.00992 secs elapsed)
#> Iteration: 30 (30% completed; 0.015 secs elapsed)
#> Iteration: 40 (40% completed; 0.02004 secs elapsed)
#> Iteration: 50 (50% completed; 0.02508 secs elapsed)
#> Iteration: 60 (60% completed; 0.03013 secs elapsed)
#> Iteration: 70 (70% completed; 0.03523 secs elapsed)
#> Iteration: 80 (80% completed; 0.0483 secs elapsed)
#> Iteration: 90 (90% completed; 0.0534 secs elapsed)
#> Iteration: 100 (100% completed; 0.05844 secs elapsed)

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