Description Usage Arguments Value Author(s) Examples
Inference on relative risk under multinomial logistic regression
1 2 
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 
boot 
a logical value whether bootstrap samples are generated or not. Defaults to 
n.boot 
if 

an object of class 

(adjusted) relative risk ratio of 

(adjusted) relative risk of 

estimated variance of relative risk ( 

if 

if 

estimated sampled variance using bootstraps if 

a data frame of fixed value for each of adjsuted confounders. 
Youjin Lee
1 2 3 4 5 6 7 8 9 10 11 12 13 14  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 < multinRR(multiY ~ W + X, basecov = 0, comparecov = 1,
data = dat, boot = TRUE)
print(apply(result$boot.rr, 2, sd)) # estimated standard errors using Delta method
print(sqrt(result$delta.var)) # estimated standard errors using bootstrap

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.