nominalRR: Calculate adjusted relative risks under nominal exposure...
In logisticRR: Adjusted Relative Risk from Logistic Regression

Description Usage Arguments Value Author(s) Examples

View source: R/nominalRR.R

When response variable is binary and exposure variable is categorical this function derives adjusted relative risks conditional on fixed other confounders' value from logistic regression.

nominalRR(
  formula,
  basecov = NULL,
  comparecov = NULL,
  fixcov = NULL,
  data,
  boot = FALSE,
  n.boot = 100
)

`formula`	a formula term that is passed into `glm()` having a form of `response ~ terms` where `response` is binary response vector and `terms` is a collection of terms connected by `'+'`. The first term of predictors will be used as a predictor of interest to calculate relative risks with respect to response variable.
`basecov`	a baseline value of exposure variable. Defaults to the first level.
`comparecov`	a value of exposure variable for comparison. Defaults to the first level.
`fixcov`	a data frame of fixed value for each of adjusted confounders. If there is no confounder other than an exposure variable of interest, `fixcov` = `NULL`; if `fixcov` is missing for covariates, they are all set to `0` (for numerical covariates) or first levels (for factor covariates).
`data`	a data frame containing response variable and all the terms used in `formula`.
`boot`	a logical value whether bootstrap samples are generated or not. Defaults to `FALSE`.
`n.boot`	if `boot = TRUE`, the number of bootstrap samples. Defaults to `100`.

`fit`	an object of class `glm`.
`RR`	(conditional) relative risk in response under exposure at baseline (`basecov`) and `comparecov`.
`delta.var`	estimated variance of relative risk (`RR`) using Delta method.
`boot.rr`	if `boot = TRUE`, a vector of `RR`'s using bootstrap samples.
`boot.var`	estimated sampled variance using bootstraps if `boot = TRUE`.
`fix.cov`	a data frame of fixed value for each of adjsuted confounders.

Youjin Lee

n <- 500
set.seed(1234)
W <- rbinom(n, 1, 0.3); W[sample(1:n, n/3)] = 2
dat <- as.data.frame(W)
dat$X <- sample( c("low", "medium", "high"), size = n, replace = TRUE)
dat$Y <- ifelse(dat$X == "low", rbinom(n, 1, plogis(W + 0.5)),
               ifelse(dat$X == "medium", rbinom(n, 1, plogis(W + 0.2)),
               rbinom(n, 1, plogis(W - 0.4)) ))
dat$X <- as.factor(dat$X)
result <- nominalRR(Y ~ X + W, basecov = "low", comparecov = "high", data = dat,
boot = TRUE, n.boot = 200)