https://github.com/carloscinelli/sensemakr
Suite of sensitivity analysis tools for OLS
https://github.com/carloscinelli/sensemakr
Last synced: 7 months ago
JSON representation
Suite of sensitivity analysis tools for OLS
- Host: GitHub
- URL: https://github.com/carloscinelli/sensemakr
- Owner: carloscinelli
- Created: 2017-07-17T16:06:05.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2024-07-14T04:00:37.000Z (almost 2 years ago)
- Last Synced: 2024-07-18T18:56:57.382Z (almost 2 years ago)
- Language: R
- Homepage: https://carloscinelli.com/sensemakr/
- Size: 19.3 MB
- Stars: 83
- Watchers: 7
- Forks: 15
- Open Issues: 11
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
editor_options:
chunk_output_type: console
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.align = "center",
comment = "#>",
fig.path = "man/figures/figures-"
)
```
# sensemakr: Sensitivity Analysis Tools for OLS 
[](https://CRAN.R-project.org/package=sensemakr)
[](https://cran.r-project.org/package=sensemakr)
[](https://github.com/chadhazlett/sensemakr/actions/workflows/R-CMD-check.yaml)
`sensemakr` implements a suite of sensitivity analysis tools that extends the traditional omitted variable bias framework and makes it easier to understand the impact of omitted variables in regression models, as discussed in [Cinelli, C. and Hazlett, C. (2020) "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology).]( https://doi.org/10.1111/rssb.12348)
# News
- Watch the [useR! 2020 presentation](https://www.youtube.com/watch?v=p3dfHj6ki68) for a quick introduction on sensemakr.
- Check out the [software paper preprint](https://www.researchgate.net/publication/340965014_sensemakr_Sensitivity_Analysis_Tools_for_OLS_in_R_and_Stata)!
- Check out the [Stata version](https://github.com/resonance1/sensemakr-stata) of the package!
- Check out the [Python version](https://github.com/nlapier2/PySensemakr) of the package!
- Check out the Robustness Value Shiny App at: https://carloscinelli.shinyapps.io/robustness_value/
- Check out the [package website](http://carloscinelli.com/sensemakr/)!
# Details
For theoretical details, [please see the JRSS-B paper](https://www.researchgate.net/publication/322509816_Making_Sense_of_Sensitivity_Extending_Omitted_Variable_Bias).
For a practical introduction, [please see the software paper](https://www.researchgate.net/publication/340965014_sensemakr_Sensitivity_Analysis_Tools_for_OLS_in_R_and_Stata) or [see the package vignettes](http://carloscinelli.com/sensemakr/articles/sensemakr.html).
For a quick start, watch the 15 min tutorial on sensitivity analysis using sensemakr prepared for useR! 2020:
# CRAN
To install the current CRAN version run:
```{r, eval = FALSE}
install.packages("sensemakr")
```
# Development version
To install the development version on GitHub make sure you have the package `devtools` installed.
```{r, eval=FALSE}
# install.packages("devtools")
devtools::install_github("carloscinelli/sensemakr")
```
# Citation
Please use the following citations:
- [Cinelli, C., & Hazlett, C. (2020). "Making sense of sensitivity: Extending omitted variable bias." Journal of the Royal Statistical Society: Series B (Statistical Methodology), 82(1), 39-67.](https://doi.org/10.1111/rssb.12348)
- [Cinelli, C., & Ferwerda, J., & Hazlett, C. (2020). "sensemakr: Sensitivity Analysis Tools for OLS in R and Stata."](https://www.researchgate.net/publication/340965014_sensemakr_Sensitivity_Analysis_Tools_for_OLS_in_R_and_Stata)
# Basic usage
```{r basic-usage, fig.align='center', collapse=T, dpi=300}
# loads package
library(sensemakr)
# loads dataset
data("darfur")
# runs regression model
model <- lm(peacefactor ~ directlyharmed + age + farmer_dar + herder_dar +
pastvoted + hhsize_darfur + female + village, data = darfur)
# runs sensemakr for sensitivity analysis
sensitivity <- sensemakr(model = model,
treatment = "directlyharmed",
benchmark_covariates = "female",
kd = 1:3)
# short description of results
sensitivity
# long description of results
summary(sensitivity)
# plot bias contour of point estimate
plot(sensitivity)
# plot bias contour of t-value
plot(sensitivity, sensitivity.of = "t-value")
# plot bias contour of lower limit of confidence interval
plot(sensitivity, sensitivity.of = "lwr")
# plot extreme scenario
plot(sensitivity, type = "extreme")
# latex code for sensitivity table
ovb_minimal_reporting(sensitivity)
```
```{r results='asis'}
# html code for sensitivity table
ovb_minimal_reporting(sensitivity, format = "pure_html")
```