vignettte update

vennLasso.Rproj

@@ -17,4 +17,4 @@ PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
 PackageBuildArgs: --compact-vignettes=both
 PackageCheckArgs: --compact-vignettes=both
-PackageRoxygenize: rd,collate,namespace
+PackageRoxygenize: rd,collate,namespace,vignette

vignettes/refs.bib

@@ -3,7 +3,8 @@ @preamble{
 @unpublished{huling18,
   author = "Huling, Jared and Yu, Menggang and Liang, Muxuan and Smith, Maureen",
   title = "Risk Prediction for Heterogeneous Populations with Application to Hospital Admission Prediction",
-  Note  = {To appear in \textit{Biometrics}},
+  note  = {To appear in \textit{Biometrics}},
+  journal = {Biometrics},
   year = {2018},
   doi = {10.1111/biom.12769},
   url = {http://dx.doi.org/10.1111/biom.12769}

vignettes/using_the_vennLasso_package.R

@@ -0,0 +1,69 @@
+## ---- eval = FALSE-------------------------------------------------------
+#  install.packages("devtools")
+#  devtools::install_github("jaredhuling/vennLasso")
+
+## ---- warning=FALSE, message=FALSE---------------------------------------
+library(vennLasso)
+
+## ----simdata-------------------------------------------------------------
+set.seed(123)
+dat.sim <- genHierSparseData(ncats = 3,   # number of binary stratifying factors
+                             nvars = 50,  # number of variables
+                             nobs = 150,  # number of observations per subpopulation
+                             nobs.test = 5000,
+                             hier.sparsity.param = 0.6, # the following two parameters
+                             prop.zero.vars = 0.5,      # determine how many variables 
+                             family = "gaussian")       # have no impact on response
+
+# design matrices
+x        <- dat.sim$x      # one for training
+x.test   <- dat.sim$x.test # one for testing
+
+# response vectors
+y        <- dat.sim$y
+y.test   <- dat.sim$y.test
+
+# binary stratifying factors
+grp      <- dat.sim$group.ind
+grp.test <- dat.sim$group.ind.test
+
+## ----fitmodel------------------------------------------------------------
+fit1 <- vennLasso(x = x, y = y, groups = grp, adaptive.lasso = TRUE)
+
+## ----lookatcoefs---------------------------------------------------------
+round(fit1$beta[,1:10,35], 3)
+
+## ----truecoefs-----------------------------------------------------------
+round(dat.sim$beta.mat[,1:9], 3)
+
+## ----plotpaths, fig.height = 8, fig.cap = "Coefficient paths for each subpopulation. The subpopulation denoted by '0,1,1' is the subpopulation of samples who have the second and third binary factor but not the first, the '0,1,0' subpopulation is the subpopulation of those who have only the second binary factor, and so on."----
+layout(matrix(1:9, ncol = 3))
+for (i in 1:nrow(fit1$beta)) plot(fit1, which.subpop = i, xvar = "loglambda")
+
+## ----cvvennlasso---------------------------------------------------------
+cvfit1 <- cv.vennLasso(x = x, y = y, groups = grp, adaptive.lasso = TRUE, nfolds = 5)
+
+## ----minlam--------------------------------------------------------------
+cvfit1$lambda.min
+
+## ----plotcv--------------------------------------------------------------
+plot(cvfit1)
+
+## ----predict_cv----------------------------------------------------------
+preds <- predict(cvfit1, 
+                 newx = x.test, 
+                 group.mat = grp.test, 
+                 s = "lambda.min")
+mean((y.test - preds) ^ 2)
+mean((y.test - mean(y.test)) ^ 2)
+
+## ----fitmodel_confint----------------------------------------------------
+fit2 <- vennLasso(x = x, y = y, groups = grp, 
+                  adaptive.lasso = TRUE,
+                  gamma = 1,
+                  conf.int = 0.90)   # specify the confidence level (90% here) for CIs
+
+## ----lookatcis-----------------------------------------------------------
+round(fit2$lower.ci[,7:11,35], 3)
+round(fit2$upper.ci[,7:11,35], 3)
+