Merge pull request #22 from sahirbhatnagar/jasa

First CRAN Release

.Rbuildignore

@@ -23,3 +23,6 @@
 ^_pkgdown\.yml$
 ^manuscript$
 ^rda/scott/$
+^appveyor\.yml$
+^CRAN-RELEASE$
+^cran-comments\.md$

CODE_OF_CONDUCT.md

@@ -21,5 +21,5 @@ Instances of abusive, harassing, or otherwise unacceptable behavior may be repor
 opening an issue or contacting one or more of the project maintainers.
 
 This Code of Conduct is adapted from the Contributor Covenant 
-(http://contributor-covenant.org), version 1.0.0, available at 
-http://contributor-covenant.org/version/1/0/0/
+(https://www.contributor-covenant.org), version 1.0.0, available at 
+https://contributor-covenant.org/version/1/0/0/.

CRAN-RELEASE

@@ -0,0 +1,2 @@
+This package was submitted to CRAN on 2019-12-02.
+Once it is accepted, delete this file and tag the release (commit 357d86d705).

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: sail
 Title: Sparse Additive Interaction Learning
-Version: 0.0.0.9000
+Version: 0.1.0
 Authors@R: c(
   person("Sahir", "Bhatnagar", email = "[email protected]",
   role = c("aut", "cre"), comment = "http://sahirbhatnagar.com/"),
@@ -9,10 +9,12 @@ Authors@R: c(
   person("Celia", "Greenwood", email = "[email protected]",
   role = c("aut"), comment = "https://www.mcgill.ca/statisticalgenetics/")
   )
-Description: Sparse Additive Interaction Learning with the strong heredity property, i.e., 
+Description: Sparse additive interaction learning with the strong heredity property, i.e., 
     an interaction is selected only if its corresponding main effects are also included. 
     Fits a linear model with non-linear interactions via penalized maximum likelihood. 
-    Interactions are limited to a single exposure or environment variable. 
+    Interactions are limited to a single exposure or environment variable. For more information, 
+    see the website below and the accompanying paper: Bhatnagar et al., "A sparse additive model for 
+    high-dimensional interactions with an exposure variable", 2019, <DOI:10.1101/445304>.
 Depends: R (>= 3.4.0)
 Imports:
     glmnet, 
@@ -22,7 +24,7 @@ Suggests:
     grpreg,
     truncnorm,
     foreach,
-    doMC,
+    doParallel,
     testthat,
     covr,
     vdiffr,
@@ -32,6 +34,6 @@ License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 BugReports: https://github.com/sahirbhatnagar/sail/issues
-URL: https://github.com/sahirbhatnagar/sail
-RoxygenNote: 6.0.1
+URL: https://sahirbhatnagar.com/sail
+RoxygenNote: 6.1.1
 VignetteBuilder: knitr

NAMESPACE

@@ -1,27 +1,27 @@
-# Generated by roxygen2: do not edit by hand
-
-S3method(coef,cv.sail)
-S3method(coef,sail)
-S3method(plot,cv.sail)
-S3method(plot,sail)
-S3method(predict,cv.sail)
-S3method(predict,sail)
-S3method(print,sail)
-export(cv.sail)
-export(gendata)
-export(plotInter)
-export(plotMain)
-export(sail)
-importFrom(grDevices,hcl)
-importFrom(graphics,abline)
-importFrom(graphics,axis)
-importFrom(graphics,legend)
-importFrom(graphics,lines)
-importFrom(graphics,mtext)
-importFrom(graphics,par)
-importFrom(graphics,plot.default)
-importFrom(graphics,segments)
-importFrom(graphics,text)
-importFrom(stats,approx)
-importFrom(stats,coef)
-importFrom(stats,predict)
+# Generated by roxygen2: do not edit by hand
+
+S3method(coef,cv.sail)
+S3method(coef,sail)
+S3method(plot,cv.sail)
+S3method(plot,sail)
+S3method(predict,cv.sail)
+S3method(predict,sail)
+S3method(print,sail)
+export(cv.sail)
+export(gendata)
+export(plotInter)
+export(plotMain)
+export(sail)
+importFrom(grDevices,hcl)
+importFrom(graphics,abline)
+importFrom(graphics,axis)
+importFrom(graphics,legend)
+importFrom(graphics,lines)
+importFrom(graphics,mtext)
+importFrom(graphics,par)
+importFrom(graphics,plot.default)
+importFrom(graphics,segments)
+importFrom(graphics,text)
+importFrom(stats,approx)
+importFrom(stats,coef)
+importFrom(stats,predict)

R/cv.sail.R

@@ -33,7 +33,7 @@
 #'   Default: FALSE
 #' @param parallel If \code{TRUE}, use parallel \code{foreach} to fit each fold.
 #'   Must register parallel before hand using the
-#'   \code{\link[doMC]{registerDoMC}} function from the \code{doMC} package. See
+#'   \code{\link[doParallel]{registerDoParallel}} function from the \code{doParallel} package. See
 #'   the example below for details. Default: FALSE
 #' @return an object of class \code{"cv.sail"} is returned, which is a list with
 #'   the ingredients of the cross-validation fit. \describe{ \item{lambda}{the
@@ -80,42 +80,27 @@
 #'   models with the strong heredity property (2018+). Preprint.
 #' @seealso \code{\link[splines]{bs}} \code{\link{sail}}
 #' @examples
-#' \dontrun{
-#' if(interactive()){
-#' f.basis <- function(i) splines::bs(i, degree = 5)
+#' f.basis <- function(i) splines::bs(i, degree = 3)
 #' data("sailsim")
-#' cvfit <- cv.sail(x = sailsim$x, y = sailsim$y, e = sailsim$e,
-#'                  basis = f.basis, nfolds = 10)
-#'
 #' # Parallel
-#' library(doMC)
-#' registerDoMC(cores = 4)
+#' library(doParallel)
+#' cl <- makeCluster(2)
+#' registerDoParallel(cl)
 #' cvfit <- cv.sail(x = sailsim$x, y = sailsim$y, e = sailsim$e,
-#'                  parallel = TRUE, nlambda = 100, nfolds = 10)
+#'                  parallel = TRUE, nlambda = 10,
+#'                  maxit = 25, basis = f.basis,
+#'                  nfolds = 3, dfmax = 5)
+#' stopCluster(cl)
 #' # plot cross validated curve
 #' plot(cvfit)
-#' # plot solution path
-#' plot(cvfit$sail.fit)
-#'
 #' # solution at lambda.min
 #' coef(cvfit, s = "lambda.min")
 #' # solution at lambda.1se
 #' coef(cvfit, s = "lambda.1se")
 #' # non-zero coefficients at lambda.min
 #' predict(cvfit, s = "lambda.min", type = "nonzero")
 #'
-#' # predicted response
-#' predict(cvfit, s = "lambda.min")
-#' predict(cvfit, s = "lambda.1se")
-#' # predict response at any value for lambda
-#' predict(cvfit, s = 0.457)
 #'
-#' # predict response for new data set
-#' newx <- sailsim$x * 1.10
-#' newe <- sailsim$e * 2
-#' predict(cvfit, newx = newx, newe = newe, s = "lambda.min")
-#'  }
-#' }
 #' @rdname cv.sail
 #' @export
 cv.sail <- function(x, y, e, ...,
@@ -259,7 +244,7 @@ cv.sail <- function(x, y, e, ...,
 #' @return A vector of CV fold ID's for each observation in \code{y}
 #' @details For numeric y, the sample is split into groups sections based on
 #'   percentiles and sampling is done within these subgroups
-#' @references \url{http://topepo.github.io/caret/splitting.html}
+#' @references \url{https://topepo.github.io/caret/data-splitting.html}
 createfolds <- function(y, k = 10, list = FALSE, returnTrain = FALSE) {
   if (class(y)[1] == "Surv") {
     y <- y[, "time"]
@@ -335,7 +320,7 @@ createfolds <- function(y, k = 10, list = FALSE, returnTrain = FALSE) {
 #' @rdname cv.lspath
 #' @seealso \code{\link{cv.sail}}
 #' @details The output of the \code{cv.lspath} function only returns values for
-#'   those tuning paramters that converged. \code{cvcompute, getmin,
+#'   those tuning parameters that converged. \code{cvcompute, getmin,
 #'   lambda.interp} are taken verbatim from the \code{glmnet} package
 #' @references Jerome Friedman, Trevor Hastie, Robert Tibshirani (2010).
 #'   Regularization Paths for Generalized Linear Models via Coordinate Descent.

R/kkt.R

@@ -121,12 +121,12 @@ KKT <- function(b0, betaE, beta, gamma, alpha, y, phij, xe_phij, e, df,
 
   for (l in 1:length(lambda)) {
     if (abs(B0[l, ]) > thr) {
-      cat("violate at b0 ", B0[l, ], " lambda=", lambda[l], "\n")
+      warning("violate at b0 ", B0[l, ], " lambda=", lambda[l], "\n")
       ctr <- ctr + 1
     }
   }
 
-  cat("% of violations for beta0", ctr / length(lambda), "\n")
+  warning("% of violations for beta0", ctr / length(lambda), "\n")
 
 
   # KKT for betaE -----------------------------------------------------------
@@ -147,19 +147,19 @@ KKT <- function(b0, betaE, beta, gamma, alpha, y, phij, xe_phij, e, df,
     if (betaE[l] == 0) {
       BE <- dl_norm_betaE / (-lambda[l] * (1 - lambda2) * we)
       if (abs(BE) > 1 + thr) {
-        cat("violate at bE = 0", abs(BE), " lambda=", lambda[l], "\n")
+        warning("violate at bE = 0", abs(BE), " lambda=", lambda[l], "\n")
         ctr <- ctr + 1
       }
     } else {
       BE <- as.vector(dl_norm_betaE + lambda[l] * (1 - lambda2) * we * sign(betaE[l]))
       if (abs(BE) > thr) {
-        cat("violate at bE != 0", abs(BE), " lambda=", lambda[l], "\n")
+        warning("violate at bE != 0", abs(BE), " lambda=", lambda[l], "\n")
         ctr <- ctr + 1
-      } # else {cat("no violation at bE != 0", BE, " lambda=",lambda[l], "\n")}
+      } # else {warning("no violation at bE != 0", BE, " lambda=",lambda[l], "\n")}
     }
   }
 
-  cat("% of violations for betaE", ctr / length(lambda), "\n")
+  warning("% of violations for betaE", ctr / length(lambda), "\n")
   # return(list(v = ctr/length(lambda)))
 
   # browser()
@@ -181,20 +181,20 @@ KKT <- function(b0, betaE, beta, gamma, alpha, y, phij, xe_phij, e, df,
       if (gamma[g, l] == 0) {
         BE <- dl_norm_gammaj / (-lambda[l] * (1 - lambda2) * we)
         if (abs(BE) > 1 + thr) {
-          cat("violate at gamma_j = 0", BE, " lambda=", lambda[l], "\n")
+          warning("violate at gamma_j = 0", BE, " lambda=", lambda[l], "\n")
           ctr <- ctr + 1
         }
       } else {
         BE <- as.vector(dl_norm_gammaj + lambda[l] * (1 - lambda2) * we * sign(gamma[g, l]))
         if (abs(BE) > thr) {
-          cat("violate at gamma_j != 0", BE, " lambda=", lambda[l], "\n")
+          warning("violate at gamma_j != 0", BE, " lambda=", lambda[l], "\n")
           ctr <- ctr + 1
-        } # else {cat("no violation at bE != 0", BE, " lambda=",lambda[l], "\n")}
+        } # else {warning("no violation at bE != 0", BE, " lambda=",lambda[l], "\n")}
       }
     }
   }
 
-  cat("% of violations for gamma", ctr / length(lambda), "\n")
+  warning("% of violations for gamma", ctr / length(lambda), "\n")
 
 
 
@@ -223,22 +223,22 @@ KKT <- function(b0, betaE, beta, gamma, alpha, y, phij, xe_phij, e, df,
 
       if (b_norm != 0) {
         AA <- dl_prod + beta[ind, l] * lambda[l] * (1 - lambda2) * wj[g] / b_norm
-        # cat(AA,"\n")
+        # warning(AA,"\n")
         if (sum(abs(AA)) >= thr) {
-          cat("violate at bX != 0", sum(abs(AA)), " lambda=", lambda[l], "\n")
+          warning("violate at bX != 0", sum(abs(AA)), " lambda=", lambda[l], "\n")
           ctr <- ctr + 1
         }
       } else {
         BB <- dl_norm - lambda[l] * (1 - lambda2) * wj[g]
-        # cat(BB,"\n")
+        # warning(BB,"\n")
         if (BB > thr) {
-          cat("violate at bX = 0", BB, " lambda=", lambda[l], "\n")
+          warning("violate at bX = 0", BB, " lambda=", lambda[l], "\n")
           ctr <- ctr + 1
         }
       }
     }
   }
-  cat("% of violations for bTheta", ctr / length(lambda), "\n")
+  warning("% of violations for bTheta", ctr / length(lambda), "\n")
   return(ctr / length(lambda))
 }
 # nocov end