bkmr: SingVarRiskSummaries – R documentation

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SingVarRiskSummaries

Single Variable Risk Summaries

Description

Compute summaries of the risks associated with a change in a single variable in Z from a single level (quantile) to a second level (quantile), for the other variables in Z fixed to a specific level (quantile)

Usage

SingVarRiskSummaries(fit, y = NULL, Z = NULL, X = NULL,
  which.z = 1:ncol(Z), qs.diff = c(0.25, 0.75), q.fixed = c(0.25, 0.5,
  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 `q_1` and `q_2` at which to compute `h(z_{q2}) - h(z_{q1})`
`q.fixed`	vector of quantiles at which to fix the remaining predictors 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

bkmr

Bayesian Kernel Machine Regression

v0.2.0

GPL-2

Authors

Jennifer F. Bobb [aut, cre]

Initial release