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Calculate overall risk summaries

Source: R/ComputeMixtureSummaries.R
OverallRiskSummaries.Rd

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

Usage

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
)

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.

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

Value

a data frame containing the (posterior mean) estimate and posterior standard deviation of the overall 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; 0.00487 secs elapsed)
#> Iteration: 20 (20% completed; 0.01043 secs elapsed)
#> Iteration: 30 (30% completed; 0.01565 secs elapsed)
#> Iteration: 40 (40% completed; 0.0209 secs elapsed)
#> Iteration: 50 (50% completed; 0.03424 secs elapsed)
#> Iteration: 60 (60% completed; 0.03943 secs elapsed)
#> Iteration: 70 (70% completed; 0.04473 secs elapsed)
#> Iteration: 80 (80% completed; 0.05 secs elapsed)
#> Iteration: 90 (90% completed; 0.05545 secs elapsed)
#> Iteration: 100 (100% completed; 0.06063 secs elapsed)

risks.overall <- OverallRiskSummaries(fit = fitkm, qs = seq(0.25, 0.75, by = 0.05), 
q.fixed = 0.5, method = "exact")