The Gaussian Process (GP) combines a highly flexible non-linear regression approach with rigorous handling of uncertainty. A key feature of this approach is that, while it can be used to produce a conditional expectation function representing the mode of a posterior conditional distribution, it does not ``choose a particular model fit’’ and construct uncertainty estimates conditional on putting full faith in that model. This is valuable because the uncertainty estimates reflect the lesser knowledge we have at locations near, at, or beyond the edge of the observed data, where results from other approaches would become highly model-dependent. We first offer an accessible explanation of GPs, and provide an implementation more suitable to social science inference problems, which reduces the number of user-chosen hyperparameters from three to zero. We then illustrate the settings in which GPs can be most valuable: those where conventional approaches have poor properties due to model-dependency/extrapolation in data-sparse regions. Specifically, we demonstrate the usefulness of GPs in contexts where (i) treated and control models are needed by these groups have poor covariate overlap; (ii) regression discontinuity, which depends on model estimates taken at or just beyond the edge of their supporting data; and (iii) interrupted time-series designs, where models are fitted prior to an event by extrapolated after it.
Installation
You can install the latest version by running:
devtools::install_github('doeun-kim/gpss')Troubleshooting installation
This version uses Rcpp extensively for speed reasons. These means you need to have the right compilers on your machine.
Windows
If you are on Windows, you will need to install RTools if you haven’t already. If you still are having difficulty with installing and it says that the compilation failed, try installing it without support for multiple architectures:
devtools::install_github('doeun-kim/gpss', args=c('--no-multiarch'))Mac OSX
In order to compile the C++ in this package, RcppArmadillo will require you to have compilers installed on your machine. You may already have these, but you can install them by running:
If you are having problems with this install on Mac OSX, specifically if you are getting errors with either lgfortran or lquadmath, then try open your Terminal and try the following:
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2
sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /Also see section 2.16 here
Example
library(gpss)
data(lalonde)
cat_vars <- c("race_ethnicity", "married")
all_vars <- c("age","educ","re74","re75","married", "race_ethnicity")
X <- lalonde[,all_vars]
Y <- lalonde$re78
D <- lalonde$nsw
X_train <- X[D==0,]
Y_train <- Y[D==0]
X_test <- X[D == 1,]
Y_test <- Y[D == 1]
gp_train.out <- gp_train(X = X_train, Y = Y_train, optimize=TRUE,
mixed_data = TRUE,
cat_columns = cat_vars)
gp_predict.out <- gp_predict(gp_train.out, X_test)Optional: Accelerate R (faster matrix operations in R)
Mac users can see a significant speed up (5-10x) by using Apple’s native Accelerate BLAS library (vecLib). Upgrade to the latest version of R and RStudio, then follow the steps outlined here:
cd /Library/Frameworks/R.framework/Resources/lib
# for vecLib use
ln -sf libRblas.vecLib.dylib libRblas.dylibFor Window Users and to learn more details regarding installation and reversion, please see this blog post.
Here is another Blog Post that explains about the benefits of using Accelerate BLAS libraries: MacOS and MacOS with M1.