multiRR: Inference on relative risk under multinomial logistic... In youjin1207/logisticRR: Adjusted Relative Risk from Logistic Regression

Description

Inference on relative risk under multinomial logistic regression

Usage

 ```1 2``` ```multiRR(formula, basecov = 0, fixcov = NULL, data, boot = FALSE, n.boot = 100) ```

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`. `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`.

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. `boot.rr` if `boot = TRUE`, a vector of `RR`'s using bootstrap samples. `boot.rrr` if `boot = TRUE`, a vector of relative risk ratio (`RRR`)'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

Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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 <- multiRR(W ~ X + Y, basecov = 0, data = dat, boot = TRUE, n.boot = 100) print(apply(result\$boot.rr, 2, sd)) # estimated standard errors using Delta method print(sqrt(result\$delta.var)) # estimated standard errors using bootstrap ```

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