variable rename

Author: jonathan-taylor

selectiveInference/R/funs.fixedCox.R

@@ -29,7 +29,7 @@ if( sum(status==0)+sum(status==1)!=length(y)) stop("status vector must have valu
 vars=which(m)
 if(sum(m)>0){
     bhat=beta[beta!=0] #penalized coefs just for active variables
-    s2=sign(bhat)
+    sign_bhat=sign(bhat)
 
  #check KKT
 
@@ -40,7 +40,7 @@ if(sum(m)>0){
     res=residuals(aaa,type="score")
 if(!is.matrix(res)) res=matrix(res,ncol=1)
 scor=colSums(res)
-    g=(scor+lambda*s2)/(2*lambda)
+    g=(scor+lambda*sign_bhat)/(2*lambda)
 #    cat(c(g,lambda,tol.kkt),fill=T)
      if (any(abs(g) > 1+tol.kkt) )
     warning(paste("Solution beta does not satisfy the KKT conditions",
@@ -49,9 +49,9 @@ scor=colSums(res)
 # Hessian of partial likelihood at the LASSO solution    
 MM=vcov(aaa)
 
-bbar=(bhat+lambda*MM%*%s2)
-A1=-(mydiag(s2))
-b1= -(mydiag(s2)%*%MM)%*%s2*lambda
+bbar=(bhat+lambda*MM%*%sign_bhat)
+A1=-(mydiag(sign_bhat))
+b1= -(mydiag(sign_bhat)%*%MM)%*%sign_bhat*lambda
 
    temp=max(A1%*%bbar-b1)
 
@@ -63,7 +63,7 @@ b1= -(mydiag(s2)%*%MM)%*%s2*lambda
 # the one sided p-values are a bit off
 
     for(jj in 1:length(bbar)){
-      vj=rep(0,length(bbar));vj[jj]=s2[jj]
+      vj=rep(0,length(bbar));vj[jj]=sign_bhat[jj]
 
 
       junk=TG.pvalue(bbar, A1, b1, vj,MM)

selectiveInference/R/funs.fixedLogit.R

@@ -32,7 +32,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  m=beta[-1]!=0  #active set
 
     bhat=c(beta[1],beta[-1][beta[-1]!=0]) # intcpt plus active vars
-     s2=sign(bhat)
+     sign_bhat=sign(bhat)
      lam2m=diag(c(0,rep(lambda,sum(m))))
 
 
@@ -66,14 +66,14 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
  # MM=solve(t(xxm)%*%w%*%xxm)
    MM=solve(scale(t(xxm),F,1/ww)%*%xxm)
   gm = c(0,-g[vars]*lambda) # gradient at LASSO solution, first entry is 0 because intercept is unpenalized
-                            # at exact LASSO solution it should be s2[-1]
+                            # at exact LASSO solution it should be sign_bhat[-1]
   dbeta = MM %*% gm
 
-  # bbar=(bhat+lam2m%*%MM%*%s2)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
+  # bbar=(bhat+lam2m%*%MM%*%sign_bhat)  # JT: this is wrong, shouldn't use sign of intercept anywhere...
   bbar = bhat - dbeta
 
-  A1=-(mydiag(s2))[-1,]
-  b1= (s2 * dbeta)[-1]
+  A1=-(mydiag(sign_bhat))[-1,]
+  b1= (sign_bhat * dbeta)[-1]
 
   tol.poly = 0.01 
   if (max((A1 %*% bbar) - b1) > tol.poly)
@@ -87,7 +87,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
 
 
     for(jj in 1:sum(m)){
-       vj=c(rep(0,sum(m)+1));vj[jj+1]=s2[jj+1]
+       vj=c(rep(0,sum(m)+1));vj[jj+1]=sign_bhat[jj+1]
       # compute p-values
       junk=TG.pvalue(bbar, A1, b1, vj, MM)
       pv[jj] = junk$pv