rob added logistic+cox features

Author: tibshirani

selectiveInference/NAMESPACE

@@ -1,19 +1,18 @@
 export(lar,fs,
-       larInf,fsInf,fsInf_maxZ,
+       larInf,fsInf,
        coef.lar,coef.fs,
        predict.lar,predict.fs,
        print.lar,print.fs,
        print.larInf,print.fsInf,
        plot.lar,plot.fs,
        fixedLassoInf,print.fixedLassoInf,
- #     fixedLogitLassoInf,print.fixedLogitLassoInf,
-  #     fixedCoxLassoInf,print.fixedCoxLassoInf,
+#      fixedLogitLassoInf,print.fixedLogitLassoInf,
+#       fixedCoxLassoInf,print.fixedCoxLassoInf,
        forwardStop,
        estimateSigma,
        manyMeans,print.manyMeans,
        groupfs,groupfsInf,
-       scaleGroups,factorDesign,
-       sample_from_constraints
+       scaleGroups,factorDesign
     )
 
 S3method("coef", "lar")
@@ -26,7 +25,6 @@ S3method("predict", "fs")
 S3method("print", "fs")
 S3method("plot", "fs")
 S3method("print", "fsInf")
-S3method("print", "fsInf_maxZ")
 S3method("print", "fixedLassoInf")
 S3method("print", "fixedLogitLassoInf")
 S3method("print", "fixedCoxLassoInf")
@@ -37,8 +35,9 @@ S3method("print", "groupfsInf")
 useDynLib("selectiveInference")
 import(glmnet)
 import(intervals)
+import(survival)
 importFrom("graphics", abline, axis, matplot)
-importFrom("stats", dnorm, lsfit, pexp, pnorm, predict,
+importFrom("stats", dnorm, lsfit, pexp, pnorm, predict, 
            qnorm, rnorm, sd, uniroot, dchisq, model.matrix, pchisq)
 importFrom("stats", "coef", "df", "lm", "pf")
 

selectiveInference/R/funs.constraints.R

@@ -25,7 +25,7 @@ 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)
+    s2=sign(bhat)
 
  #check KKT
 

selectiveInference/R/funs.fixedCox.R

@@ -74,7 +74,7 @@ fixedLogitLassoInf=function(x,y,beta,lambda,alpha=.1, type=c("partial"), tol.bet
   b1= (s2 * dbeta)[-1]
 
   tol.poly = 0.01 
-  if (max((A1 %*% bbar)[-1] - b1) > tol.poly)
+  if (max((A1 %*% bbar) - b1) > tol.poly)
     stop(paste("Polyhedral constraints not satisfied; you must recompute beta",
                "more accurately. With glmnet, make sure to use exact=TRUE in coef(),",
                "and check whether the specified value of lambda is too small",