nominalRR {logisticRR} | R Documentation |
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 |
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, |
data |
a data frame containing response variable and all the terms used in |
boot |
a logical value whether bootstrap samples are generated or not. Defaults to |
n.boot |
if |
fit |
an object of class |
RR |
(conditional) relative risk in response under exposure at baseline ( |
delta.var |
estimated variance of relative risk ( |
boot.rr |
if |
boot.var |
estimated sampled variance using bootstraps if |
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)