# interactionRCS

## #### Version 1.1 (February 25,
2022)

### Description

`interactionRCS`

facilitates interpretation and
presentation of results from a regression model (linear, logistic, Cox)
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)
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.

### Installation

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")
library(interactionRCS)
```

### Usage

After estimating a regression model (linear, logistic, Cox) 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

### Authors

Giorgio Melloni, Andrea Bellavia

TIMI study group, Department of Cardiovascular Medicine, Brigham and
Womens Hospital / Harvard Medical School