@@ -7,7 +7,6 @@ S3method(FunctionalityScore,COMPASSResult)

 S3method(FunctionalityScore,default)

 S3method(PolyfunctionalityScore,COMPASSResult)

 S3method(PolyfunctionalityScore,default)

-S3method(Response,COMPASSResult)

 S3method(UniqueCombinations,COMPASSContainer)

 S3method(UniqueCombinations,default)

 S3method(markers,COMPASSContainer)

@@ -23,6 +22,7 @@ S3method(print,COMPASSResult)

 S3method(subset,COMPASSContainer)

 S3method(summary,COMPASSContainer)

 S3method(summary,COMPASSResult)

+export(">=")

 export("COMPASSDescription<-")

 export("metadata<-")

 export(COMPASS)

@@ -45,9 +45,12 @@ export(SimpleCOMPASS)

 export(TotalCellCounts)

 export(UniqueCombinations)

 export(categories)

+export(condition)

+export(degree)

 export(getCounts)

 export(markers)

 export(metadata)

+export(on)

 export(pheatmap)

 export(plot2)

 export(plotCOMPASSResultStack)

@@ -57,6 +60,7 @@ export(shinyCOMPASS)

 export(shinyCOMPASSDeps)

 export(translate_marker_names)

 export(transpose_list)

+export(x)

 import(data.table)

 import(grid)

 import(magrittr)

@@ -4,6 +4,8 @@

 #' @param markers a \code{vector} of marker names.

 #' @param degree the \code{numeric} degree of functionality to test.

 #' @param max.prob \code{logical} Use the max probability rather than the average across subsets. Defaults to FALSE.

+#' @param cond_response \code{logical} Renormalize the probabilities to condition on response. i.e. given that there

+#' is a response, what's the probability of a response of degree X or greater?

 #' @description

 #' Compute a response probability based on the selected markers, evaluating the probability

 #' that a subject exhibits a response of size \code{degree} or greater.

@@ -16,13 +18,13 @@

 #'

 #' @examples

 #' Response(CR, markers = c("M1","M2","M3"), degree = 2)

-Response <- function(x, markers, degree, max.prob){

+Response <- function(x, markers, degree, max.prob, cond_response){

   UseMethod("Response")

 }

 ##' @rdname Response

 ##' @export

-Response.COMPASSResult <- function(x, markers = NULL, degree = 1, max.prob = FALSE) {

+Response.COMPASSResult <- function(x, markers = NULL, degree = 1, max.prob = FALSE, cond_response = FALSE) {

   ## we drop the last column as it is the 'NULL' category

   if (is.null(markers)) {

     markers <- markers(x)

@@ -44,25 +46,55 @@ Response.COMPASSResult <- function(x, markers = NULL, degree = 1, max.prob = FAL

     }

     new_mean_gamma = apply(cat_indices, 2, function(i)

       apply(Gamma(x)[, i, ], 1, mean))

+    # new_gamma <- Gamma(x)[,cat_indices,]

     new_categories = cbind(new_categories, Counts = rowSums(new_categories))

     reord = c(setdiff(1:nrow(new_categories), which(new_categories[, "Counts"] ==

                                                       0)), which(new_categories[, "Counts"] == 0))

     new_categories = new_categories[reord, ]

     new_mean_gamma = new_mean_gamma[, reord]

+    # new_gamma = new_gamma[,reord,]

     colnames(new_mean_gamma) = apply(new_categories[, -ncol(new_categories)], 1, function(x)

       paste0(x, collapse = ""))

+    # dimnames(new_gamma)[[2]] = apply(new_categories[, -ncol(new_categories)], 1, function(x)

+      # paste0(x, collapse = ""))

     include_cols <- new_categories[,"Counts"] >= degree

     if (sum(include_cols) == 0) {

-      response <- matrix(rep(0,nrow(new_mean_gamma)),nrow=nrow(new_mean_gamma),ncol=1)

+      response <- matrix(rep(0,nrow(new_mean_gamma)),nrow = nrow(new_mean_gamma),ncol=1)

       rownames(response) <- rownames(new_mean_gamma)

       colnames(response) <- paste0("Pr(response|degree >=",degree,")")

     }else{

+      #' condition on degree >= x

       response <- new_mean_gamma[,include_cols, drop = FALSE]

-      if(!max.prob){

-        #polyfunctional response is the average over subsets

+      if (!max.prob & !cond_response) {

+        # probability of response from total probability

+        # response <- structure(

+        #   rowMeans(new_gamma[, include_cols, , drop = FALSE]) / (

+        #   rowMeans(new_gamma[, !include_cols, , drop = FALSE]) +

+        #     rowMeans(new_gamma[, include_cols, , drop = FALSE]) +

+        #     rowMeans(1 - new_gamma[, !include_cols, , drop = FALSE]) +

+        #     rowMeans(1 - new_gamma[,include_cols, , drop = FALSE])),

+        #   dim = c(nrow(response),1),

+        #   names = NULL,

+        #   dimnames = list(rownames(response),paste0("Pr(response | degree >=",degree,")")))

+

         response <- structure(rowMeans(response), dim = c(nrow(response),1), names = NULL, dimnames = list(rownames(response),paste0("Pr(response | degree >=",degree,")")))

-      }else{

+      }else if (max.prob & !cond_response){

         response <- structure(apply(response,1,max),dim=c(nrow(response),1), names = NULL, dimnames = list(rownames(response), paste0("Pr(response | degree >=",degree,")")))

+      }else if (cond_response & ! max.prob){

+        response <-

+          structure(

+            rowSums(exp(log(

+              apply(Gamma(scr), 1:2, mean)

+            ) - log(

+              rowSums(apply(Gamma(scr), 1:2, mean)) + 1e-10

+            ))[, include_cols, drop = FALSE]),

+            dim = c(nrow(response), 1),

+            names = NULL,

+            dimnames = list(

+              rownames(response),

+              paste0("Pr(degree >= ",degree," | response )")

+            )

+          )

       }

       # response <- matrix(response, ncol = 1, nrow = length(response))

       # rownames(response) <- rownames(new_mean_gamma)

@@ -5,10 +5,10 @@

 \alias{Response.COMPASSResult}

 \title{Compute a response probability from COMPASS mcmc samples.}

 \usage{

-Response(x, markers, degree, max.prob)

+Response(x, markers, degree, max.prob, cond_response)

 \method{Response}{COMPASSResult}(x, markers = NULL, degree = 1,

-  max.prob = FALSE)

+  max.prob = FALSE, cond_response = FALSE)

 }

 \arguments{

 \item{x}{a \code{COMPASSResult} object.}

@@ -18,6 +18,9 @@ Response(x, markers, degree, max.prob)

 \item{degree}{the \code{numeric} degree of functionality to test.}

 \item{max.prob}{\code{logical} Use the max probability rather than the average across subsets. Defaults to FALSE.}

+

+\item{cond_response}{\code{logical} Renormalize the probabilities to condition on response. i.e. given that there

+is a response, what's the probability of a response of degree X or greater?}

 }

 \value{

 A \code{vector} of response probabilities for each subject.