interactionRCS facilitates interpretation and
presentation of results from a regression model (linear, logistic, Cox
or Poisson) where an interaction between the main predictor of interest
X (binary or continuous) and another continuous covariate Z has been
specified. In particular, interactionRCS allows for basic
interaction assessment (i.e. log-linear/linear interaction models where
a product term between the two predictors is included) as well as
settings where the second covariate is flexibly modeled with restricted
cubic splines. Confidence intervals for the predicted effect measures
(beta, OR, HR, RR) can be calculated with either bootstrap or the delta
method. Lastly, interactionRCS produces a plot of the
effect measure over levels of the other covariate.
To install the latest version of interactionRCS, type
the following lines in a web-aware R environment.
if(!"devtools" %in% rownames(installed.packages())){
install.packages("devtools")
}
devtools::install_github("https://github.com/gmelloni/interactionRCS.git")
# or alternative devtools::install_git("https://github.com/gmelloni/interactionRCS.git")
library(interactionRCS)After estimating a regression model (linear, logistic, Cox or
Poisson) such as model<-glm(y~ ...) estimate and plot
interactions with:
int<-estINT(model=model, ...)
plotINT(int, ...)For a detailed introduction to interactionRCS and code
examples please refer to this vignette
Giorgio Melloni, Hong Xiong, Andrea Bellavia
TIMI study group, Department of Cardiovascular Medicine, Brigham and Womens Hospital / Harvard Medical School