version 0.4

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: rbounds
 Type: Package
 Title: Perform Rosenbaum bounds sensitivity tests for matched data.
-Version: 0.3
-Date: 2009-05-21
+Version: 0.4
+Date: 2009-10-21
 Author: Luke J. Keele
 Maintainer: Luke J. Keele <keele.4@osu.edu>
 Depends: R (>= 2.7.1), Matching (>= 4.6-2)
@@ -12,6 +12,6 @@ Description: Takes matched data and calculates Rosenbaum bounds for the
         for data with multiple matched controls.  Is designed to work
         with the Matching package and operate on Match() objects.
 License: GPL (>= 2)
-Packaged: Thu May 21 21:18:16 2009; Luke
+Packaged: Wed Oct 21 21:05:14 2009; Luke
 Repository: CRAN
-Date/Publication: 2009-05-24 08:37:13
+Date/Publication: 2009-10-23 06:41:12

R/binarysens.R

@@ -6,14 +6,24 @@ binarysens <- function(x, y=NULL, Gamma=6, GammaInc=1)
   		}
   	else
   	{
-		ctrl <- sum(x$mdata$Y[x$mdata$Tr==0])
-        trt <- sum(x$mdata$Y[x$mdata$Tr==1])
+		y.c <- x$mdata$Y[x$mdata$Tr==0]
+        y.t <- x$mdata$Y[x$mdata$Tr==1]
+        table(y.t, y.c)
+        y.tmp1 <- table(y.t, y.c)[2]
+		y.tmp2 <- table(y.t, y.c)[3]
+		if(y.tmp1 >= y.tmp2){
+			trt <- y.tmp1
+			ctrl <- y.tmp2
+			} else {
+			trt <- y.tmp2
+			ctrl <- y.tmp1
+			}
     }
   gamma <- seq(1, Gamma, by=GammaInc)
   mx <- ctrl + trt
   up <- c()
   lo <- c()
-  series <- seq(trt,mx, by=1)
+  series <- seq(trt, mx, by=1)
   n.it <- length(gamma)
   for(i in 1:n.it)
     {
@@ -33,4 +43,3 @@ binarysens <- function(x, y=NULL, Gamma=6, GammaInc=1)
   cat("Note: Gamma is Log Odds of Differential Assignment To Treatment Due To Unobserved Factors \n")
 } #end of binary.sens function
 
-

man/binarysens.Rd

@@ -33,7 +33,7 @@ binarysens(x,y, Gamma=6, GammaInc=1)
 #
 # Sensitivity Test
 #
-binarysens(12,100)
+binarysens(12,110)
 
 #
 # Example Using Match()

man/hlsens.Rd

@@ -1,6 +1,6 @@
 \name{hlsens} 
 \alias{hlsens} 
-\title{Demostration Autocorrelation Function} 
+\title{Rosenbaum Bounds for Hodges-Lehmann Point Estimate} 
 \description{ 
 Function to calculate Rosenbaum bounds for continuous or ordinal outcomes based on Hodges-Lehmann point estimate. 
 }