rob fixed type=full option

Author: tibshirani

selectiveInference/R/funs.fixed.R

@@ -82,10 +82,11 @@ fixedLassoInf <- function(x, y, beta,
 
     tol.coef = tol.beta * sqrt(n^2 / colSums(x^2))
     # print(tol.coef)
-    vars = which(abs(beta) > tol.coef)
+   #   vars = which(abs(beta) > tol.coef)
+       vars = abs(beta) > tol.coef
     # print(beta)
     # print(vars)
-    if(length(vars)==0){
+    if(sum(vars)==0){
       cat("Empty model",fill=T)
       return()
     }
@@ -96,10 +97,17 @@ fixedLassoInf <- function(x, y, beta,
                     "'thresh' parameter, for a more accurate convergence."))
 
     # Get lasso polyhedral region, of form Gy >= u
+<<<<<<< HEAD
+    if (type == 'full') out = fixedLasso.poly(x,y,beta,lambda,vars,inactive=TRUE)
+    else out = fixedLasso.poly(x,y,beta,lambda,vars)
+    G = out$G
+    u = out$u
+=======
     if (type == 'full' & p > n) out = fixedLassoPoly(x,y,lambda,beta,vars,inactive=TRUE)
     else out = fixedLassoPoly(x,y,lambda,beta,vars)
     A = out$A
     b = out$b
+>>>>>>> 726b917649c7aaabd030b2cab062836ca774ef57
 
     # Check polyhedral region
     tol.poly = 0.01
@@ -127,7 +135,8 @@ fixedLassoInf <- function(x, y, beta,
     # add additional targets for inference if provided
     if (!is.null(add.targets)) vars = sort(unique(c(vars,add.targets,recursive=T)))
 
-    k = length(vars)
+      k = length(vars)
+      k=sum(vars)
     pv = vlo = vup = numeric(k)
     vmat = matrix(0,k,n)
     ci = tailarea = matrix(0,k,2)