Fixed a small bug in fs function. Seems have been introduced when this function was modified notationally. The least squares solution was not being returned properly; now it is.

Author: ryantibs

selectiveInference/DESCRIPTION

@@ -12,7 +12,9 @@ Depends:
     survival
 Suggests:
     Rmpfr
-Description: New tools for post-selection inference, for use
-    with forward stepwise regression, least angle regression, the
-    lasso, and the many means problem. The lasso function implements  Gaussian,  logistic and Cox survival models.
+Description: New tools for post-selection inference, for use with forward
+    stepwise regression, least angle regression, the lasso, and the many means
+    problem. The lasso function implements Gaussian, logistic and Cox survival
+    models.
 License: GPL-2
+RoxygenNote: 5.0.1

selectiveInference/R/funs.fs.R

@@ -106,8 +106,8 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
     }
 
     # Key quantities for the next entry
-    keepLs=backsolve(R,t(Q_active)%*%X_inactive)
-    X_inactive_resid = X_inactive - X_active %*% keepLs
+    keepLs = backsolve(R,t(Q_active)%*%y)
+    X_inactive_resid = X_inactive - X_active %*% backsolve(R,t(Q_active)%*%X_inactive)
     working_x = scale(X_inactive_resid,center=F,scale=sqrt(colSums(X_inactive_resid^2)))
     score = as.numeric(t(working_x)%*%y)
 
@@ -127,7 +127,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     action[k] = I[i_hit] 
     df[k] = r
-    beta[A,k] = backsolve(R,t(Q_active)%*%y)
+    beta[A,k] = keepLs
 
     # Gamma matrix!
     if (gi + 2*p > nrow(Gamma)) Gamma = rbind(Gamma,matrix(0,2*p+gbuf,n))
@@ -188,8 +188,7 @@ fs <- function(x, y, maxsteps=2000, intercept=TRUE, normalize=TRUE,
 
     # Record the least squares solution. Note that
     # we have already computed this
-    bls = rep(0,p)
-   if(length(keepLs)>0)  bls[A] = keepLs
+    bls = rep(0,p); bls[A] = keepLs
   }
 
   if (verbose) cat("\n")