SingVarIntSummaries: Single Variable Interaction Summaries

Description Usage Arguments Details

Description

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

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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 argumentd to pass on to the prediction function

Details

  • If method == "approx" then calls the function ComputePostmeanHnew.approx. In this case, the argument sel defaults to the second half of the MCMC iterations.

  • If method == "exact" then calls the function ComputePostmeanHnew.exact. In this case, 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



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