sgee (version 0.6-0)

sgee-package: sgee: Stagewise Generalized Estimating Equations

Description

Provides functions to perform Boosting / Functional Gradient Descent / Forward Stagewise regression with grouped covariates setting using Generalized Estimating Equations.

Details

Package:	sgee
Type:	Package
Version:	0.6-0
Date:	2018-01-08
License:	GPL (>= 3)

sgee provides several stagewise regression approaches that are designed to address variable selection with grouped covariates in the context of Generalized Estimating Equations. Given a response and design matrix stagewise techniques perform a sequence of small learning steps wherein a subset of the covariates are selected as being the most important at that iteration and are then subsequently updated by a small amount, epsilon. different techniques this optimal update in different ways that achieve different structural goals (i.e. groups of covariates are fully included or not).

The resulting path can then be analyzed to determine an optimal model along the path of coefficient estimates. The analyzeCoefficientPath function provides such functionality based on various possible metrics, primarily focused on the Mean Squared Error. Furthermore, the plot.sgee function can be used to examine the path of coefficient estimates versus the iteration number, or some desired penalty.

References

Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017). Stagewise Generalized Estimating Equations with Grouped Variables. Biometrics 73, 1332-1342. URL: http://dx.doi.org/10.1111/biom.12669, doi:10.1111/biom.12669.

Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017). Efficient interaction selection for clustered data via stagewise generalized estimating equations. Department of Statistics, University of Connecticut. Technical Report.

Wolfson, J. (2011). EEBoost: A general method for prediction and variable selection based on estimating equations. Journal of the American Statistical Association 106, 296--305.

Tibshirani, R. J. (2015). A general framework for fast stagewise algorithms. Journal of Machine Learning Research 16, 2543--2588.

Simon, N., Friedman, J., Hastie, T., and Tibshirani, R. (2013). A sparse-group lasso. Journal of Computational and Graphical Statistics 22, 231--245.

Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York.

Liang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73, 13--22.

Examples

# NOT RUN {


#####################
## Generate test data
#####################

## Initialize covariate values
p <- 50 
beta <- c(rep(2.4,5),
          c(1.2, 0, 1.6, 0, .4),
          rep(0.5,5),
          rep(0,p-15))
groupSize <- 5
numGroups <- length(beta)/groupSize


generatedData <- genData(numClusters = 50,
                         clusterSize = 4,
                         clusterRho = 0.6,
                         clusterCorstr = "exchangeable",
                         yVariance = 1,
                         xVariance = 1,
                         numGroups = numGroups,
                         groupSize = groupSize,
                         groupRho = 0.3,
                         beta = beta,
                         family = gaussian(),
                         intercept = 0)


coefMat1 <- hisee(y = generatedData$y, x = generatedData$x,
                  family = gaussian(),
                  clusterID = generatedData$clusterID,
                  groupID = generatedData$groupID, 
                  corstr="exchangeable",
                  control = sgee.control(maxIt = 100, epsilon = 0.2))

## interceptLimit allows for compatibility with older R versions
coefMat2 <- bisee(y = generatedData$y, x = generatedData$x,
                  family = gaussian(),
                  clusterID = generatedData$clusterID,
                  groupID = generatedData$groupID, 
                  corstr="exchangeable", 
                  control = sgee.control(maxIt = 100, epsilon = 0.2,
                                         interceptLimit = 10),
                  lambda1 = .5,
                  lambda2 = .5)


par(mfrow = c(2,1))
plot(coefMat1)
plot(coefMat2)

# }