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The goal of {SSVS} is to provide functions for performing stochastic search variable selection (SSVS) for binary and continuous outcomes and visualizing the results. SSVS is a Bayesian variable selection method used to estimate the probability that individual predictors should be included in a regression model. Using MCMC estimation, the method samples thousands of regression models in order to characterize the model uncertainty regarding both the predictor set and the regression parameters.
You can install the development version of {SSVS} from GitHub with:
# install.packages("remotes")
remotes::install_github("sabainter/SSVS")Consider a simple example using SSVS on the mtcars
dataset to predict quarter mile times. We first specify our response
variable (“qsec”), then choose our predictors and run the
ssvs() function.
library(SSVS)
outcome <- 'qsec'
predictors <- c('cyl', 'disp', 'hp', 'drat', 'wt',
'vs', 'am', 'gear', 'carb','mpg')
results <- ssvs(data = mtcars, x = predictors, y = outcome, progress = FALSE)The results can be summarized and printed using the
summary() function. This will display both the MIP for each
predictor, as well as the probable range of values for each
coefficient.
summary_results <- summary(results, interval = 0.9, ordered = TRUE)| Variable | MIP | Avg Beta | Lower CI (90%) | Upper CI (90%) | Avg Nonzero Beta |
|---|---|---|---|---|---|
| wt | 0.8433 | 1.0433 | 0.0000 | 1.9513 | 1.2372 |
| vs | 0.7512 | 0.6399 | 0.0000 | 1.1982 | 0.8519 |
| hp | 0.5413 | -0.4995 | -1.3349 | 0.0000 | -0.9228 |
| cyl | 0.4551 | -0.5173 | -1.7670 | 0.0005 | -1.1367 |
| am | 0.4240 | -0.3107 | -1.0805 | 0.0000 | -0.7328 |
| disp | 0.4130 | -0.4553 | -1.8170 | 0.0012 | -1.1023 |
| carb | 0.3938 | -0.2890 | -1.0068 | 0.0000 | -0.7338 |
| gear | 0.2013 | -0.0918 | -0.5464 | 0.0002 | -0.4560 |
| mpg | 0.1584 | 0.0563 | -0.0001 | 0.4160 | 0.3557 |
| drat | 0.1003 | -0.0180 | -0.0008 | 0.0000 | -0.1794 |
The MIPs for each predictor can then be visualized using the
plot() function.
plot(results)
In the example above, the response variable was a continuous
variable. The same workflow can be used for binary variables by
specifying continuous = FALSE to the ssvs()
function.
As an example, let’s create a binary variable:
library(AER)
data(Affairs)
Affairs$hadaffair[Affairs$affairs > 0] <- 1
Affairs$hadaffair[Affairs$affairs == 0] <- 0Then define the outcome and predictors.
outcome <- "hadaffair"
predictors <- c("gender", "age", "yearsmarried", "children", "religiousness", "education", "occupation", "rating")And finally run the model:
results <- ssvs(data = Affairs, x = predictors, y = outcome, continuous = FALSE, progress = FALSE)Now the results can be summarized or visualized in the same manner.
summary_results <- summary(results, interval = 0.9, ordered = TRUE)| Variable | MIP | Avg Beta | Lower CI (90%) | Upper CI (90%) | Avg Nonzero Beta |
|---|---|---|---|---|---|
| rating | 0.9993 | -0.5553 | -0.7173 | -0.4027 | -0.5557 |
| religiousness | 0.4024 | -0.1332 | -0.4032 | 0.0000 | -0.3309 |
| children | 0.0955 | 0.0268 | 0.0000 | 0.0000 | 0.2804 |
| yearsmarried | 0.0899 | 0.0272 | 0.0000 | 0.0000 | 0.3020 |
| gender | 0.0075 | 0.0008 | 0.0000 | 0.0000 | 0.1092 |
| occupation | 0.0063 | 0.0006 | 0.0000 | 0.0000 | 0.0986 |
| age | 0.0058 | -0.0007 | 0.0000 | 0.0000 | -0.1202 |
| education | 0.0041 | 0.0004 | 0.0000 | 0.0000 | 0.1011 |
plot(results)
You can launch an interactive (shiny) web application that lets you
run SSVS analyses without programming. Simply install this package and
run SSVS::launch() in an R console.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.