chest_nb | R Documentation |
It fits negative binomial regression models to present rate ratios for the association between exposure and outcome variables by adding other variables (potential confounders) to the model sequentially. The order of variables to be added is based on the magnitudes of the changes in effect estimates.
chest_nb(
crude,
xlist,
data,
method = "glm.fit",
na_omit = TRUE,
indicate = FALSE,
plus = " + ",
...
)
crude |
An object of formula for initial model, generally crude model. However, any other variables can also be included here as the initial model. |
xlist |
A vector of characters with all variable names of potential confounders. |
data |
Data frame. |
method |
Method to detect for singularity. |
na_omit |
Remove all missing values, default: 'na_omit = TRUE'. |
indicate |
indicate progress. |
plus |
Change the |
... |
Further optional arguments. |
A table with effect estimates and their changes at all steps.
'glm.nb'
of 'MASS'
library(MASS)
df <- quine
results <- chest_nb("Days ~ Lrn", xlist = c("Sex", "Age", "Eth"), data = df)
results$data
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.