Calculate Single Variable Risk Summaries when fixing multiple effect modifiers at certain levels 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 a set of effect modifiers (in Z) fixed to a specific level (quantile)

Source: R/SingVarRishSummaries.fixEY.R

Calculate Single Variable Risk Summaries when fixing multiple effect modifiers at certain levels 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 a set of effect modifiers (in Z) fixed to a specific level (quantile)

SingVarRiskSummaries.fixEY(
  list.fit.y.TE,
  which.z = 1:length(z.names),
  qs.diff = c(0.25, 0.75),
  q.fixed = c(0.25, 0.5, 0.75),
  q.alwaysfixed = NULL,
  EY.alwaysfixed.name = NULL,
  sel = NULL,
  z.names = colnames(BKMRfits[[1]]$Z),
  method = "approx",
  ...
)

Arguments

list.fit.y.TE

The Total Effect BKMR model fit in a 'List' form.

which.z

vector indicating which variables (columns of Z) for which the summary should be computed, effect modifiers are not included

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

q.alwaysfixed

the quantile values in the point which we want to keep fixed for all comparisons

EY.alwaysfixed.name

names of all the effect modifiers that we want to fixed for all comparisons

sel

selects which iterations of the MCMC sampler to use for inference

z.names

column names of the selected columns of Z in which.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

Value

a list of data frames containing the (posterior mean) estimate and posterior standard deviation of the predictor risk measures, for each of the comparisons specified

Details

For guided examples, go to https://zc2326.github.io/causalbkmr/articles/BKMRCMA_Effectof_singleZ.html