Description Usage Arguments Value Author(s) Examples
Print adjusted relative risk using multinomial logistic regression under nominal exposure variable.
1 
formula 
a formula term that is passed into 
basecov 
a baseline value of exposure variable. Defaults to 
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 the exposure variable of interest, 
data 
a data frame containing response variable and all the terms used in 

an object of class 

(adjusted) relative risk ratio of 

(adjusted) relative risk of 

estimated variance of relative risk ( 

a data frame of fixed value for each of adjsuted confounders. 
Youjin Lee
1 2 3 4 5 6 7 8 9 10 11  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))
multiY < ifelse(X == 1 , rbinom(n, 1, 0.7) + Y, rbinom(n, 1, 0.2) + Y)
print(table(multiY))
dat < as.data.frame(cbind(multiY, X, W))
dat$W < as.factor(dat$W)
result < printmnRR(multiY ~ W + X, basecov = 0, comparecov = 1, data = dat)

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