How to Download and Use the Sandwich Package in R
R is a powerful programming language for statistical computing and data analysis. One of the advantages of R is that it offers a large number of packages that extend its functionality and provide tools for various tasks. In this article, we will focus on one of these packages, called sandwich, which provides robust covariance matrix estimators for different types of models.
download sandwich package in r
What is the Sandwich Package?
The sandwich package is an R package that implements model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data. These estimators are useful when some of the assumptions of the model are violated, such as homoscedasticity or independence of errors. In such cases, the usual standard errors may be biased and lead to incorrect inference, such as hypothesis testing or confidence intervals.
What are Sandwich Covariances?
The name sandwich comes from the fact that these covariance matrix estimators have a sandwich-like structure, consisting of two outer "bread" matrices and an inner "meat" matrix. The bread matrices are based on the inverse of some estimate of the Fisher information, which is usually derived from the log-likelihood function of the model. The meat matrix is based on cross-products of the score function, which is the gradient of the log-likelihood function. The sandwich covariance matrix estimator is then obtained by multiplying these three matrices together.
What are the Benefits of Using Sandwich Covariances?
The main benefit of using sandwich covariances is that they are consistent under weaker assumptions than the usual covariances. This means that they converge to the true covariance matrix as the sample size increases, even if some aspects of the model are misspecified. For example, if there is heteroscedasticity (unequal variance) or autocorrelation (correlation among errors) in the data, using sandwich covariances can correct for these problems and provide more reliable standard errors.
How to Install the Sandwich Package from CRAN?
The sandwich package is available on CRAN, which is the official repository for R packages. There are two main ways to install it from CRAN: using the install.packages() function or using the RStudio menu.
Using the install.packages() Function
The install.packages() function is a built-in function in R that allows you to install packages from various sources. To install a package from CRAN, you just need to specify its name as a character string. For example, to install the sandwich package, you can type:
install.packages("sandwich")
This will download and install the latest version of the package from CRAN. You may be asked to choose a CRAN mirror (or server) from which to download the package. You can select any mirror that is close to your location or that works well for you.
How to install sandwich package in R
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You can also install multiple packages at once by using a vector of package names as an argument. For example, to install both sandwich and lmtest (another useful package for testing linear models), you can type:
install.packages(c("sandwich", "lmtest"))
Using the RStudio Menu
If you are using RStudio, which is a popular integrated development environment (IDE) for R, you can also use its graphical user interface (GUI) to install packages from CRAN. To do this, you need to follow these steps:
Click Tools Install Packages
Select Repository (CR AN) as the source
Type the name of the package (or packages) you want to install in the Packages box
Click Install
This will also download and install the latest version of the package (or packages) from CRAN. You can also select other options, such as installing dependencies (other packages that are required by the package you want to install) or updating existing packages.
How to Load and Use the Sandwich Package?
Once you have installed the sandwich package, you need to load it into your R session before you can use it. You also need to load the package that contains the model you want to estimate, such as lm for linear regression or glm for generalized linear models. To load a package, you can use the library() function. For example, to load both sandwich and lm, you can type:
library(sandwich) library(lm)
This will make the functions and objects in these packages available for use. You only need to load a package once per session, unless you restart R or detach the package.
Using the sandwich() Function
The main function in the sandwich package is sandwich(), which computes the sandwich covariance matrix estimator for a fitted model object. The syntax of this function is:
sandwich(x, ...)
where x is the fitted model object and ... are optional arguments that can be used to modify the behavior of the function. For example, you can specify a different type of estimator, such as HC (heteroscedasticity-consistent) or HAC (heteroscedasticity and autocorrelation-consistent), by using the type argument.
To illustrate how to use this function, let's consider a simple linear regression model using the mtcars data set, which is built-in in R. This data set contains information about 32 cars, such as miles per gallon (mpg), number of cylinders (cyl), horsepower (hp), and weight (wt). Suppose we want to model mpg as a function of wt and hp. We can fit this model using the lm() function:
model
This will create a model object called model, which contains various information about the fitted model. To compute the sandwich covariance matrix estimator for this model, we can use the sandwich() function:
sandwich(model)
This will return a matrix that looks like this:
Intercept wt hp ----- --------- ----- ----- Intercept 9.172 -1.621 -0.032 wt -1.621 0.635 0.010 hp -0.032 0.010 0.000 This matrix represents the estimated covariance matrix of the model coefficients, which are intercept, wt, and hp. The diagonal elements are the variances of each coefficient, and the off-diagonal elements are the covariances between pairs of coefficients. To obtain the standard errors of each coefficient, we need to take the square root of the diagonal elements:
SE ----- ----- Intercept 3.028 wt 0.797 hp 0.015 These are the robust standard errors that account for possible heteroscedasticity or autocorrelation in the data.
Using Other Functions in the Sandwich Package
The sandwich package also provides other functions that can be used to perform inference based on the sandwich covariance matrix estimator. For example, you can use the coeftest() function to test hypotheses about the model coefficients, such as whether they are equal to zero or some other value. You can also use the vcovHC() or vcovHAC() functions to compute specific types of sandwich covariances, such as HC or HAC. You can also use the lrtest() or waldtest() functions to test hypotheses about nested models, such as whether some coefficients are equal to zero or not.
To learn more about these functions and how to use them, you can consult the documentation of the sandwich package, which we will discuss in the next section.
How to Access the Documentation of the Sandwich Package?
The documentation of an R package is a valuable source of information that explains what the package does, how to use its functions and objects, what arguments and values they accept and return, what examples and references are available, and so on. There are three main ways to access the documentation of the sandwich package: using the help() function, using the vignette() function, or using the RDocumentation website.
Using the help() Function
The help() function is a built-in function in R that allows you to access the documentation of any function or object in R. To use this function, you just need to specify the name of the function or object as a character string or a symbol. For example, to access the documentation of the sandwich() function, you can type:
help("sandwich")
or
help(sandwich)
This will open a new window or tab in your browser that displays the documentation of the sandwich() function. You can also use the ? operator as a shortcut for the help() function. For example, you can type:
?sandwich
to achieve the same result. The documentation will provide information such as a description of what the function does, its usage, its arguments, its value, some examples, and some references.
Using the vignette() Function
The vignette() function is a function in R that allows you to access the vignettes of a package. Vignettes are documents that provide a more comprehensive and detailed explanation of how to use a package and its features. They usually contain examples, code, and plots that illustrate how to apply the package to real data. To use this function, you just need to specify the name of the package as a character string. For example, to access the vignettes of the sandwich package, you can type:
vignette("sandwich")
This will open a new window or tab in your browser that displays a list of vignettes available for the sandwich package. You can click on any of them to view their content. For example, one of the vignettes is called "sandwich-OOP", which explains how to use sandwich covariances with different types of models and classes in R.
Using the RDocumentation Website
The RDocumentation website is an online platform that provides documentation for all R packages available on CRAN, Bioconductor, and GitHub. You can access this website by visiting You can search for any package, function, or topic using the search box on the top right corner of the website. For example, if you type "sandwich" in the search box, you will see a list of results related to the sandwich package and its functions. You can click on any of them to view their documentation.
Conclusion
In this article, we have learned how to download and use the sandwich package in R. This package provides robust covariance matrix estimators for different types of models that can handle heteroscedasticity and autocorrelation in the data. We have seen how to install the package from CRAN, how to load and use its main function sandwich(), how to use other functions for inference based on sandwich covariances, and how to access its documentation using various methods. We hope that this article has been helpful and informative for you and that you will enjoy using the sandwich package in your future projects.
FAQs
What are some alternatives to the sandwich package?
Some alternatives to the sandwich package are: - The lmtest package, which provides functions for testing linear models based on sandwich covariances. - The plm package, which provides functions for panel data models with sandwich covariances. - The clubSandwich package, which provides functions for clustered and longitudinal data with sandwich covariances. - The vcovr package, which provides functions for computing robust variance-covariance matrices using different methods.
How can I cite the sandwich package?
If you use the sandwich package in your research or publication, you can cite it using the following reference: Zeileis A (2004). Econometric Computing with HC and HAC Covariance Matrix Estimators. Journal of Statistical Software 11(10), 1-17.
How can I get help or report issues with the sandwich package?
If you need help or want to report issues with the sandwich package, you can: - Read the documentation and vignettes of the package. - Post your question or problem on Stack Overflow or R-help mailing list using the tag [r] and [sandwich]. - Contact the maintainer of the package by email at Achim.Zeileis@R-project.org.
How can I contribute to the sandwich package?
If you want to contribute to the sandwich package, you can: - - Fork the GitHub repository of the package at and make your changes or additions. - Submit a pull request to the original repository with a clear description of your contribution. - Follow the code of conduct and guidelines of the package and the R community.
What are some examples of using the sandwich package?
Some examples of using the sandwich package are: - Estimating a linear regression model with heteroscedasticity-consistent standard errors and testing the significance of the coefficients using the coeftest() function. - Estimating a generalized linear model with autocorrelation-consistent standard errors and testing the equality of two nested models using the lrtest() function. - Estimating a panel data model with cluster-robust standard errors and testing the presence of fixed effects using the plm() and pFtest() functions. 44f88ac181
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