removing useC

Author: jonathan-taylor

selectiveInference/R/funs.fixed.R

@@ -159,8 +159,7 @@ sigma=NULL, alpha=0.1,
       hsigmaSinv <- solve(hsigmaS) # pinv(hsigmaS)
 
       # Approximate inverse covariance matrix for when (n < p) from lasso_Inference.R
-      useC = TRUE
-      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE, useC=useC)
+      htheta <- InverseLinfty(hsigma, n, length(S), verbose=FALSE)
       # htheta <- InverseLinfty(hsigma, n, verbose=FALSE)
 
       FS = rbind(diag(length(S)),matrix(0,pp-length(S),length(S)))
@@ -270,7 +269,7 @@ fixedLasso.poly=
 ### Functions borrowed and slightly modified from lasso_inference.R
 
 ## Approximates inverse covariance matrix theta
-InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE, useC = FALSE) {
+InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
   # InverseLinfty <- function(sigma, n, resol=1.5, mu=NULL, maxiter=50, threshold=1e-2, verbose = TRUE) {
     isgiven <- 1;
   if (is.null(mu)){
@@ -295,12 +294,7 @@ InverseLinfty <- function(sigma, n, e, resol=1.5, mu=NULL, maxiter=50, threshold
     incr <- 0;
     while ((mu.stop != 1)&&(try.no<10)){
       last.beta <- beta
-      useC = FALSE
-      if (useC == FALSE) {
-            output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
-      } else {
-            output <- InverseLinftyOneRowC(sigma, i, mu, maxiter=maxiter)
-      }
+      output <- InverseLinftyOneRow(sigma, i, mu, maxiter=maxiter, threshold=threshold)
       beta <- output$optsol
       iter <- output$iter
       if (isgiven==1){