Package 'SSVS'

Example dataset for ssvs function @format A data frame with 74 records and 76 variables

Description

Example dataset for ssvs function @format A data frame with 74 records and 76 variables

Usage

dat

Format

An object of class data.frame with 74 rows and 76 columns.

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Imputed mtcars Dataset

Description

This dataset is a version of the mtcars dataset where random missing values were introduced, and multiple imputation was performed using the mice package.

Usage

imputed_mtcars

Format

A data frame with 160 rows and 13 variables

Details

Random missingness was introduced into 10% of the values in the original mtcars dataset. Multiple imputation was then performed using the mice package with the following parameters:

• 5 multiple imputations (m = 5).

• 50 iterations per imputation (maxit = 50).

• Seed set to 123 for reproducibility.

The dataset included here is the first completed dataset resulting from the multiple imputation process.

Source

Original dataset from datasets::mtcars, with missing values introduced and imputed.

Examples

data(imputed_mtcars)
head(imputed_mtcars)

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Run an interactive analysis tool (Shiny app) that lets you perform SSVS in a browser

Description

Run an interactive analysis tool (Shiny app) that lets you perform SSVS in a browser

Usage

launch()

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Plot results of an SSVS model

Description

Plot results of an SSVS model

Usage

## S3 method for class 'ssvs'
plot(x, threshold = 0.5, legend = TRUE, title = NULL, color = TRUE, ...)

Arguments

x	An ssvs result object obtained from ssvs()
threshold	An MIP threshold to show on the plot, must be between 0-1. If NULL, no threshold is used.
legend	If TRUE, show a legend for the shapes based on the threshold. Ignored if threshold = NULL.
title	The title of the plot. Set to NULL to use a default title.
color	If TRUE, the data points will be colored based on the threshold.
...	Ignored

Value

Creates a plot of the inclusion probabilities by variable

Examples

outcome <- "qsec"
predictors <- c("cyl", "disp", "hp", "drat", "wt", "vs", "am", "gear", "carb", "mpg")
results <- ssvs(x = predictors, y = outcome, data = mtcars, progress = FALSE)
plot(results)

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Perform SSVS for continuous and binary outcomes

Description

For continuous outcomes, a basic Gibbs sampler is used. For binary outcomes, BoomSpikeSlab::logit.spike() is used.

Usage

ssvs(
  data,
  y,
  x,
  continuous = TRUE,
  inprob = 0.5,
  runs = 20000,
  burn = 5000,
  a1 = 0.01,
  b1 = 0.01,
  prec.beta = 0.1,
  progress = TRUE
)

Arguments

data	The dataframe used to extract predictors and response values
y	The response variable
x	The set of predictor variables
continuous	If TRUE, treat the response variable as continuous. If FALSE, treat the response variable as binary.
inprob	Prior inclusion probability value, which applies to all predictors. The prior inclusion probability reflects the prior belief that each predictor should be included in the model. A prior inclusion probability of .5 reflects the belief that each predictor has an equal probability of being included or excluded. Note that a value of .5 also implies a prior belief that the true model contains half of the candidate predictors. The prior inclusion probability will influence the magnitude of the marginal inclusion probabilities (MIPs), but the relative pattern of MIPs is expected to remain fairly consistent, see Bainter et al. (2020) for more information.
runs	Total number of iterations (including burn-in). Results are based on the Total - Burn-in iterations.
burn	Number of burn-in iterations. Burn-in iterations are discarded warmup iterations used to achieve MCMC convergence. You may increase the number of burn-in iterations if you are having convergence issues.
a1	Prior parameter for Gamma(a,b) distribution on the precision (1/variance) residual variance. Only used when continuous = TRUE.
b1	Prior parameter for Gamma(a,b) distribution on the precision (1/variance) residual variance. Only used when continuous = TRUE.
prec.beta	Prior precision (1/variance) for beta coefficients. Only used when continuous = TRUE.
progress	If TRUE, show progress of the model creation. When continuous = TRUE, progress plots will be created for every 1000 iterations. When continuous = FALSE, 10 progress messages will be printed. Only used when continuous = TRUE.

Value

An ssvs object that can be used in summary() or plot().

Examples

# Example 1: continuous response variable
outcome <- "qsec"
predictors <- c("cyl", "disp", "hp", "drat", "wt", "vs", "am", "gear", "carb", "mpg")
results <- ssvs(data = mtcars, x = predictors, y = outcome, progress = FALSE)

# Example 2: binary response variable
library(AER)
data(Affairs)
Affairs$hadaffair[Affairs$affairs > 0] <- 1
Affairs$hadaffair[Affairs$affairs == 0] <- 0
outcome <- "hadaffair"
predictors <- c("gender", "age", "yearsmarried", "children", "religiousness",
"education", "occupation", "rating")
results <- ssvs(data = Affairs, x = predictors, y = outcome, continuous = FALSE, progress = FALSE)

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Perform SSVS on Multiply Imputed Datasets

Description

This function performs Stochastic Search Variable Selection (SSVS) analysis on multiply imputed datasets for a given set of predictors and a response variable. It supports continuous response variables and calculates aggregated results across multiple imputations.

Usage

ssvs_mi(
  data,
  y,
  x,
  imp,
  imp_num = 5,
  interval = 0.9,
  continuous = TRUE,
  progress = FALSE
)

Arguments

data	A dataframe containing the variables of interest, including an .imp column for imputation identifiers.
y	The response variable (character string).
x	A vector of predictor variable names.
imp	The imputation variable.
imp_num	The number of imputations to process (default is 5).
interval	Confidence interval level for summary results (default is 0.9).
continuous	Logical indicating if the response variable is continuous (default is TRUE).
progress	Logical indicating whether to display progress (default is FALSE).

Value

An ssvs_mi object containing aggregated results across imputations that can be used in summary().

Examples

data(imputed_mtcars)
outcome <- 'qsec'
predictors <- c('cyl', 'disp', 'hp', 'drat', 'wt', 'vs', 'am', 'gear', 'carb','mpg')
imputation <- '.imp'
agg_results <- ssvs_mi(data = imputed_mtcars, y = outcome, x = predictors, imp = imputation)

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Summarize results of an SSVS model

Description

Summarize results from SSVS including marginal inclusion probabilities, Bayesian model averaged parameter estimates, and 95% highest posterior density credible intervals. Estimates and credible intervals are based on standardized X variables.

Usage

## S3 method for class 'ssvs'
summary(object, interval = 0.89, threshold = 0, ordered = FALSE, ...)

Arguments

object	An SSVS result object obtained from ssvs()
interval	The desired probability for the credible interval, specified as a decimal
threshold	Minimum MIP threshold where a predictor will be shown in the output, specified as a decimal
ordered	If TRUE, order the results based on MIP (in descending order)
...	Ignored

Value

A dataframe with results

Examples

outcome <- "qsec"
predictors <- c("cyl", "disp", "hp", "drat", "wt", "vs", "am", "gear", "carb", "mpg")
results <- ssvs(data = mtcars, x = predictors, y = outcome, progress = FALSE)
summary(results, interval = 0.9, ordered = TRUE)

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Calculate Summary Statistics for SSVS-MI Results

Description

Computes summary statistics (average, minimum, and maximum) for beta coefficients, MIP and average nonzero beta coefficients from an SSVS result object.

Usage

## S3 method for class 'ssvs_mi'
summary(object, ...)

Arguments

object	An ssvs_mi result object obtained from ssvs_mi()
...	Ignored

Value

A data frame with results

Examples

data(imputed_mtcars)
outcome <- 'qsec'
predictors <- c('cyl', 'disp', 'hp', 'drat', 'wt', 'vs', 'am', 'gear', 'carb','mpg')
imputation <- '.imp'
results <- ssvs_mi(data = imputed_mtcars, y = outcome, x = predictors, imp = imputation)
summary_MI<-summary(results)
print(summary_MI)

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