version 0.3.4 - fix cran error due to bug in vignette

Author: s3alfisc

R/boot_algo2.R

@@ -199,30 +199,9 @@ boot_algo2 <- function(preprocessed_object, boot_iter, point_estimate, impose_nu
         S_diag_XinvXXRu_S_b <- Matrix.utils::aggregate.Matrix(diag_XinvXXRuS_b, clustid[x])
         K_b <- S_diag_XinvXXRu_S_b - tcrossprod(SXinvXXrX_invXX, SuXb)
 
-        bigstatsr <- FALSE
-        if(bigstatsr == FALSE){
-          C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
-          D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
-        } else if(bigstatr == TRUE){
+        C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
+        D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
 
-          # matrix multiplication via bigstatsr
-          C_fbm <- FBM(dim(K_a)[1], B)
-          D_fbm <- FBM(dim(K_b)[1], B)
-          # C
-          big_apply(C, a.FUN = function(C_fbm, ind, K_a, v) {
-            C_fbm[, ind] <- tcrossprod(K_a, v[ind, ])
-            NULL  ## you don't want to return anything here
-          }, a.combine = 'c', ncores = nthreads, K_a = K_a, v = v)
-          # D
-          big_apply(D, a.FUN = function(D_fbm, ind, K_b, v) {
-            D_fbm[, ind] <- tcrossprod(K_b, v[ind, ])
-            NULL  ## you don't want to return anything here
-          }, a.combine = 'c', ncores = nthreads, K_b = K_b, v = v)
-          
-          C <- C_fbm[]
-          D <- D_fbm[]
-          
-        }
 
         CC[[x]] <- colSums(C * C)
         DD[[x]] <- colSums(D * D)
@@ -249,32 +228,9 @@ boot_algo2 <- function(preprocessed_object, boot_iter, point_estimate, impose_nu
         S_diag_XinvXXRu_S_b <- S_diag_XinvXXRu_S_b - prod_b
         K_b <- S_diag_XinvXXRu_S_b - tcrossprod(SXinvXXrX_invXX, SuXb)
 
-        bigstatsr <- FALSE
-        if(bigstatsr == FALSE){
-          
-          C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
-          D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
-          
-        } else if(bigstatr == TRUE){
-          
-          # matrix multiplication via bigstatsr
-          C_fbm <- FBM(dim(K_a)[1], B)
-          D_fbm <- FBM(dim(K_b)[1], B)
-          # C
-          big_apply(C, a.FUN = function(C_fbm, ind, K_a, v) {
-            C_fbm[, ind] <- tcrossprod(K_a, v[ind, ])
-            NULL  ## you don't want to return anything here
-          }, a.combine = 'c', ncores = nthreads, K_a = K_a, v = v)
-          # D
-          big_apply(D, a.FUN = function(D_fbm, ind, K_b, v) {
-            D_fbm[, ind] <- tcrossprod(K_b, v[ind, ])
-            NULL  ## you don't want to return anything here
-          }, a.combine = 'c', ncores = nthreads, K_b = K_b, v = v)
-          
-          C <- C_fbm[]
-          D <- D_fbm[]
+        C <- eigenMapMatMult(as.matrix(K_a), v, nthreads)
+        D <- eigenMapMatMult(as.matrix(K_b), v, nthreads)
 
-        }
         CC[[x]] <- colSums(C * C)
         DD[[x]] <- colSums(D * D)
         CD[[x]] <- colSums(C * D)