added print method, getting warning about doc

Author: jonathan-taylor

selectiveInference/NAMESPACE

@@ -1,23 +1,35 @@
-export(lar,fs,
-       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,
+export(lar,
+       fs,
+       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,
        forwardStop,
        estimateSigma,
-       manyMeans,print.manyMeans,
-       groupfs,groupfsInf,
-       scaleGroups,factorDesign,
+       manyMeans,
+       print.manyMeans,
+       groupfs,
+       groupfsInf,
+       scaleGroups,
+       factorDesign,
        TG.pvalue, 
        TG.limits, 
        TG.interval,
        debiasingMatrix,
        randomizedLasso,
        randomizedLassoInf,
-       ROSI
+       ROSI,
+       print.ROSI
     )
 
 S3method("coef", "lar")
@@ -31,6 +43,7 @@ S3method("print", "fs")
 S3method("plot", "fs")
 S3method("print", "fsInf")
 S3method("print", "fixedLassoInf")
+S3method("print", "ROSI")
 S3method("print", "fixedLogitLassoInf")
 S3method("print", "fixedCoxLassoInf")
 S3method("print", "manyMeans")

selectiveInference/R/funs.full.main.R renamed to selectiveInference/R/funs.ROSI.R

@@ -399,8 +399,11 @@ ROSI = function(X,
                 construct_ci=TRUE, 
                 debiasing_method=c("JM", "BN"),
                 verbose=FALSE,
+		level=0.9,
                 use_debiased=TRUE) {
 
+  this.call = match.call()
+
   family = match.arg(family)
   solver = match.arg(solver)
   debiasing_method = match.arg(debiasing_method)
@@ -502,29 +505,59 @@ ROSI = function(X,
         if (construct_ci) {
           sel_int = create_tnorm_interval(z=target_stat, 
                                           sd=sqrt(target_cov) * sigma_est, 
-                                          alpha=0.1, 
+                                          alpha=1-level, 
                                           intervals=intervals)
           if (verbose==TRUE){
             cat("sel interval", sel_int, "\n")
           }
-          sel_intervals = cbind(sel_intervals, sel_int)
+          sel_intervals = rbind(sel_intervals, sel_int)
         }
       } else {
            pvalues = c(pvalues, NA)
-	   sel_intervals = cbind(sel_intervals, c(NA, NA))
+	   sel_intervals = rbind(sel_intervals, c(NA, NA))
            warning("observation not within the truncation limits!")
       }
   }
 
-  return(list(pvalues=pvalues, 
-              active_set=active_set,
-              intervals=sel_intervals,
-	      estimate=beta_barE,
-              std_err=sqrt(diag(QiE) * dispersion),
-              lower_trunc=lower_trunc,
-	      upper_trunc=upper_trunc))
+  out = list(pvalues=pvalues, 
+             active_set=active_set,
+             intervals=sel_intervals,
+             estimate=as.numeric(beta_barE),
+             std_err=sqrt(diag(QiE) * dispersion),
+             dispersion=dispersion,
+             lower_trunc=as.numeric(lower_trunc),
+             upper_trunc=as.numeric(upper_trunc),
+             lambda=lambda,
+             penalty_factor=penalty_factor,
+             level=level,
+             call=this.call)
+   class(out) = "ROSI"
+   return(out)
+}
+
+print.ROSI <- function(x, ...) {
+  cat("\nCall:\n")
+  dput(x$call)
+
+  cat(sprintf("\nDispersion taken to be dispersion = %0.3f\n",
+              x$dispersion))
+
+  cat(sprintf("\nTesting results at lambda = %0.3f, with level = %0.2f\n",x$lambda,x$level))
+  cat("",fill=T)
+  tab = cbind(signif(x$std_err^2,3),
+              round(x$estimate,3),
+              round(x$estimate / x$std_err,3),
+              round(x$pvalues,3),
+              round(x$intervals,3))
+  colnames(tab) = c("Var", "Coef", "Z-score", "P-value", "LowConfPt", "UpConfPt")
+  rownames(tab) = rep("",nrow(tab))
+  print(tab)
+
+  cat("\nNote: coefficients shown are full regression coefficients.\n")
+  invisible()
 }
 
+
 # Some little used functions -- not exported
 
 

selectiveInference/R/funs.full.side.R renamed to selectiveInference/R/funs.ROSI.side.R

@@ -20,6 +20,7 @@ ROSI(X,
      construct_ci=TRUE, 
      debiasing_method=c("JM", "BN"),
      verbose=FALSE,
+     level=0.9,
      use_debiased=TRUE) 
 }
 \arguments{
@@ -79,6 +80,9 @@ or "BN" (method described in arxiv.org/1703.03282).
 \item{verbose}{
 Print out progress along the way? Default is FALSE.
 }
+\item{level}{
+Confidence level for intervals.
+}
 \item{use_debiased}{
 Use the debiased estimate of the parameter or not. When FALSE, this is the
 method desribed in arxiv.org/1801.09037. The default TRUE often
@@ -96,8 +100,13 @@ p is comparable to n. Ignored when n<p and set to TRUE.
 \item{intervals}{Confidence intervals}
 \item{estimate}{Relaxed (i.e. unshrunk) selected estimates.}
 \item{std_err}{Standard error of relaxed estimates (pre-selection).}
+\item{dispersion}{Dispersion parameter.}
 \item{lower_trunc}{Lower truncation point. The estimates should be outside the interval formed by the lower and upper truncation poitns.}
 \item{upper_trunc}{Lower truncation point. The estimates should be outside the interval formed by the lower and upper truncation poitns.}
+\item{lambda}{Value of tuning parameter lambda used.}
+\item{penalty_factor}{Penalty factor used for solving problem.}
+\item{level}{Confidence level.}
+\item{call}{The call to fixedLassoInf.}
 }
 
 \references{

selectiveInference/man/ROSI.Rd

@@ -142,7 +142,7 @@ for the model fit to the active set from the lasso.
 \item{sign}{Signs of active coefficients}
 \item{alpha}{Desired coverage (alpha/2 in each tail)}
 \item{sigma}{Value of error standard deviation (sigma) used}
-\item{call}{The call to lassoInf}
+\item{call}{The call to fixedLassoInf}
 }
 
 \references{