Fixed a small bug in the coef functions for fs and lar: use of dec instead of decreasing in the call to coef.interpolate.

Author: ryantibs

selectiveInference/R/funs.common.R

@@ -56,14 +56,14 @@ standardize <- function(x, y, intercept, normalize) {
 
 # Interpolation function to get coefficients
 
-coef.interpolate <- function(betas, s, knots, dec=TRUE) {
+coef.interpolate <- function(beta, s, knots, decreasing=TRUE) {
   # Sort the s values
-  o = order(s,dec=dec)
+  o = order(s,decreasing=decreasing)
   s = s[o]
 
   k = length(s)
   mat = matrix(rep(knots,each=k),nrow=k)
-  if (dec) b = s >= mat
+  if (decreasing) b = s >= mat
   else b = s <= mat
   blo = max.col(b,ties.method="first")
   bhi = pmax(blo-1,1)
@@ -73,7 +73,7 @@ coef.interpolate <- function(betas, s, knots, dec=TRUE) {
   p[i] = 0
   p[!i] = ((s-knots[blo])/(knots[bhi]-knots[blo]))[!i]
 
-  beta = t((1-p)*t(betas[,blo,drop=FALSE]) + p*t(betas[,bhi,drop=FALSE]))
+  beta = t((1-p)*t(beta[,blo,drop=FALSE]) + p*t(beta[,bhi,drop=FALSE]))
   colnames(beta) = as.character(round(s,3))
   rownames(beta) = NULL
 

selectiveInference/R/funs.fs.R

@@ -225,8 +225,8 @@ coef.fs <- function(object, s, ...) {
 
   if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
   knots = 1:k
-  dec = FALSE
-  return(coef.interpolate(beta,s,knots,dec))
+  decreasing = FALSE
+  return(coef.interpolate(beta,s,knots,decreasing))
 }
 
 # Prediction function for fs

selectiveInference/R/funs.lar.R

@@ -295,14 +295,14 @@ coef.lar <- function(object, s, mode=c("step","lambda"), ...) {
   if (mode=="step") {
     if (min(s)<0 || max(s)>k) stop(sprintf("s must be between 0 and %i",k))
     knots = 1:k
-    dec = FALSE
+    decreasing = FALSE
   } else {
     if (min(s)<min(lambda)) stop(sprintf("s must be >= %0.3f",min(lambda)))
     knots = lambda
-    dec = TRUE
+    decreasing = TRUE
   }
 
-  return(coef.interpolate(beta,s,knots,dec))
+  return(coef.interpolate(beta,s,knots,decreasing))
 }
 
 # Prediction function for lar