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.Rproj.user/902F8FED/sources/s-18DE930C/3B9010C1-contents

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+\name{EBEN-package}
+\alias{EBEN-package}
+\alias{EBEN}
+\docType{package}
+\title{Empirical Bayesian Elastic Net (EBEN)}
+
+\description{ Fast EBEN algorithms. \cr
+			EBEN implements a normal and generalized gamma hierearchical priors. \cr
+				(    **    )	Two parameters (alpha, lambda) are equivalent with elastic net priors. 	\cr
+				(    **    )	When parameter alpha = 1, it is equivalent with EBlasso-NE (normal + exponential) \cr					
+			Two models are available for both methods: 			\cr
+				(    **    )	General linear regression model.			\cr
+				(    **    )	Logistic regression model.			\cr
+			Multi-collinearity: 	\cr
+				(    **    )	for group of high correlated or collinear variables: EBEN identifies the group of variables estimates their effects together. \cr
+				(    **    )	group of variables can be selected together. \cr
+			*Epistasis (two-way interactions) can be included for all models/priors \cr
+			*model implemented with memory efficient c code. \cr
+			*LAPACK/BLAS are used for most linear algebra computations. 
+}
+\details{
+\tabular{ll}{
+Package: \tab EBEN\cr
+Type: \tab Package\cr
+Version: \tab 4.6\cr
+Date: \tab 2015-10-06\cr
+License: \tab gpl\cr
+}
+
+}
+\author{
+Anhui Huang
+}
+\references{
+key algorithms: \cr
+	Cai, X., Huang, A., and Xu, S. (2011). Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping. BMC Bioinformatics 12, 211.\cr
+	Huang A, Xu S, Cai X. (2013). Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC genetics  14(1):5. \cr
+	Huang, A., Xu, S., and Cai, X. (2014). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity 10.1038/hdy.2014.79 \cr
+Other publications: \cr
+	Huang, A., E. Martin, et al. (2014). "Detecting genetic interactions in pathway-based genome-wide association studies." Genet Epidemiol 38(4): 300-309.\cr
+	Huang, A., S. Xu, et al. (2014). "Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice." PLoS ONE 9(1): e87330.\cr
+	Huang, A. (2014). "Sparse model learning for inferring genotype and phenotype associations." Ph.D Dissertation. University of Miami(1186).
+}
+\keyword{ package }

.Rproj.user/902F8FED/sources/s-18DE930C/D6DE16C8

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+    "last_content_update" : 1525278909,
+    "path" : "C:/Users/Colby Ford/Desktop/parEBEN/R/CrossValidate.R",
+    "project_path" : "R/CrossValidate.R",
+    "properties" : {
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.Rproj.user/902F8FED/sources/s-18DE930C/D6DE16C8-contents

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+CrossValidate <- function(BASIS, Target, nFolds, foldId = 0, Epis = "no", prior = "gaussian", search = "global"){
+  if(search == "global"){
+    ParameterGrid <- BuildGrid(BASIS, Target, nFolds, Epis)
+
+    if(prior == "gaussian"){
+      MSE_temp <- foreach (row = 1:nrow(ParameterGrid), .combine = rbind, .packages = "parEBEN") %dopar% {
+        alpha <- ParameterGrid$alpha[row]
+        lambda <- ParameterGrid$lambda[row]
+        TestModel(BASIS, Target, lambda, alpha, nFolds, foldId, Epis, prior = "gaussian")
+      }
+
+      Error <- MSE_temp %>%
+        group_by(alpha, lambda) %>% 
+        summarise(SE = sd(MSE)/sqrt(max(foldId)),
+                  MSE = mean(MSE)
+        )
+
+      index <- which.min(Error$MSE)
+      lambda.optimal <- Error[index,]$lambda
+      alpha.optimal <- Error[index,]$alpha
+
+      out <- list(Results.Detail = MSE_temp,
+                  Results.Summary = Error,
+                  lambda.optimal = lambda.optimal,
+                  alpha.optimal = alpha.optimal)
+
+    }else{
+      logL_temp <- foreach (row = 1:nrow(ParameterGrid), .combine = rbind, .package = "parEBEN") %dopar% {
+        alpha <- ParameterGrid$alpha[row]
+        lambda <- ParameterGrid$lambda[row]
+        TestModel(BASIS, Target, lambda, alpha, nFolds, foldId, Epis, prior = "binomial")
+      }
+      Error <- logL_temp %>% 
+        group_by(alpha, lambda) %>% 
+        summarise(SE = sd(logL)/sqrt(max(foldId)),
+                  Likelihood = -mean(logL)
+        )
+
+      index <- which.min(Error$MSE)
+      lambda.optimal <- Error[index,]$lambda
+      alpha.optimal <- Error[index,]$alpha
+
+      out <- list(Results.Detail = logL_temp,
+                  Results.Summary = Error,
+                  lambda.optimal = lambda.optimal,
+                  alpha.optimal = alpha.optimal)
+    }
+
+    return(out)
+
+  }else{
+    out <- LocalSearch(BASIS, Target, nFolds, Epis, foldId, prior)
+
+    return(out)
+  }
+
+}

DESCRIPTION

@@ -26,3 +26,4 @@ Collate:
 	'LocalSearch.R'
 	'GetModelError.R'
 	'TestModel.R'
+RoxygenNote: 6.0.1

parEBEN.Rproj

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+
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+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
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+NumSpacesForTab: 2
+Encoding: UTF-8
+
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+LaTeX: pdfLaTeX
+
+BuildType: Package
+PackageUseDevtools: Yes
+PackageInstallArgs: --no-multiarch --with-keep.source
+PackageRoxygenize: rd,collate,namespace