bkmr: OverallRiskSummaries – R documentation

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OverallRiskSummaries

Calculate overall risk summaries

Compare estimated h function when all predictors are at a particular quantile to when all are at a second fixed quantile

OverallRiskSummaries(fit, y = NULL, Z = NULL, X = NULL, qs = seq(0.25,
  0.75, by = 0.05), q.fixed = 0.5, method = "approx", sel = NULL)

`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.
`qs`	vector of quantiles at which to calculate the overall risk summary
`q.fixed`	a second quantile at which to compare the estimated `h` function
`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`	selects which iterations of the MCMC sampler to use for inference; see 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

Bayesian Kernel Machine Regression

v0.2.0

GPL-2

Authors

Jennifer F. Bobb [aut, cre]

Initial release