printmRR: Print adjusted relative risk using multinomial logistic...

In youjin1207/logisticRR: Adjusted Relative Risk from Logistic Regression

Description

Print adjusted relative risk using multinomial logistic regression under binary or ordinal exposure variable.

Usage

printmRR(formula, basecov = 0, fixcov = NULL, data)

Arguments

formula	a formula term that is passed into multinom() where response should be a factor having K different levels. Every term appearing in the formula should be well-defined as a column of data. Reference response should be specified as the first level.
basecov	a baseline value of exposure variable. Defaults to 0.
fixcov	a data frame of fixed value for each of adjusted confounders. If there is no confounder other than the exposure variable of interest, fixcov = NULL; if fixcov is missing for existing covariates, they are all set to 0 (for numerical covariates) or to the first level (for factor covariates).
data	a data frame containing response variable and all the terms used in formula.

Value

fit	an object of class multinom.
RRR	(adjusted) relative risk ratio of K different responses compared to reference response under exposure at baseline (basecov) and basecov + 1.
RR	(adjusted) relative risk of K different responses under exposure at baseline (basecov) and basecov + 1.
delta.var	estimated variance of relative risk (RR) using Delta method.
fix.cov	a data frame of fixed value for each of adjsuted confounders.

Author(s)

Youjin Lee

Examples

n <- 500
set.seed(1234)
X <- rbinom(n, 1, 0.3)
W <- rbinom(n, 1, 0.3)
W[sample(1:n, n/3)] = 2
Y <- rbinom(n, 1, plogis(X - W))
dat <- as.data.frame(cbind(Y, X, W))
result <- printmRR(W ~ X + Y, basecov = 0, data = dat)

youjin1207/logisticRR documentation built on March 16, 2020, 3:37 a.m.