@@ -22,6 +22,8 @@

 #' @param y ExpressionSet of -omics data, induces dual -omics analysis

 #' @param transform If TRUE v1 and v2 will be Fisher transformed

 #' @param subsampling If TRUE subsampling will be run

+#' @param subSize Indicates how many feature pairs to be used for subsampling. 

+#' Default is the feature size in x

 #' @param iter Number of iterations for subsampling. Default is 100

 #' @param components Number of components in mixture model.

 #' 

@@ -94,7 +96,7 @@

 #'                        TCGA_GBM_miRNA_microarray)

 #' @export

 discordantRun <- function(v1, v2, x, y = NULL, transform = TRUE, 

-                          subsampling = FALSE, iter = 100, 

+                          subsampling = FALSE, subSize = NULL, iter = 100, 

                           components = 3) {

     .checkDiscordantInputs(v1, v2, x, y, transform, subsampling, subSize, iter, 

@@ -113,23 +115,41 @@ discordantRun <- function(v1, v2, x, y = NULL, transform = TRUE,

                    .assignClass(v2, param2, components))

     if (subsampling) {

-        subSize <- .setSubSize(x, y)

+        subSize <- .setSubSize(x, y, subSize)

         total_mu <- total_sigma <- total_nu <- 

           total_tau <- total_pi <- rep(0, components)

-        for(i in 1:iter) {

+        i <- 0

+        repeats <- 0

+        

+        while(i < iter && repeats < floor(iter * .1)) {

             subSamples <- .createSubsamples(x, y, v1, v2, subSize)

             sub.pdata <- cbind(subSamples$v1, subSamples$v2)

             sub.class <- cbind(.assignClass(subSamples$v1, param1, components),

                                .assignClass(subSamples$v2, param2, components))

-            pd <- em.normal.partial.concordant(sub.pdata, sub.class, components)

-            total_mu <- total_mu + pd$mu_sigma[1,]

-            total_sigma <- total_sigma + pd$mu_sigma[2,]

-            total_nu <- total_nu + pd$nu_tau[1,]

-            total_tau <- total_tau + pd$nu_tau[2,]

-            total_pi <- total_pi + pd$pi

-      }

+            pd <- tryCatch({em.normal.partial.concordant(sub.pdata, sub.class, 

+                                                   components)},

+                error = function(unused) return(NULL))

+            

+            if(is.null(pd)) { 

+              repeats <- repeats + 1 

+            } else {

+              i <- i + 1

+              total_mu <- total_mu + pd$mu_sigma[1,]

+              total_sigma <- total_sigma + pd$mu_sigma[2,]

+              total_nu <- total_nu + pd$nu_tau[1,]

+              total_tau <- total_tau + pd$nu_tau[2,]

+              total_pi <- total_pi + pd$pi

+            }

+        }

+        

+        if (repeats >= floor(iter * .1)) {

+          stop("Insufficient data for subsampling. Increase number of

+               features, reduce number of components used, or increase 

+               subSize if not at default value. Alternatively, set

+               subsampling=FALSE.")

+        }

       mu <- total_mu / iter

       sigma <- total_sigma / iter

@@ -311,11 +331,23 @@ em.normal.partial.concordant <- function(data, class, components) {

 }

 # Internal function to set sub size based on data

-.setSubSize <- function(x, y) {

+.setSubSize <- function(x, y, subSize) {

   if (is.null(y)) {

-    sampleNames <- rownames(x)

-    subSize <- floor(length(sampleNames) / 2)

+    if (is.null(subSize)) {

+      subSize <- floor(length(rownames(x)) / 2)

+    } else if (subSize > floor(length(rownames(x)) / 2)) {

+      subSize <- floor(length(rownames(x)) / 2)

+      warning(paste0("subSize argument too large. Using subSize ", subSize,

+                     " See vignette for more information."))

+    }

   } else {

-    subSize <- min(nrow(x), nrow(y))

+    if (is.null(subSize)) {

+      subSize <- min(nrow(x), nrow(y))

+    } else if (subSize > min(nrow(x), nrow(y))) {

+      subSize <- min(nrow(x), nrow(y))

+      warning(paste0("subSize argument to large. Using subSize ", subSize,

+                     " See vignette for more information."))

+    }

   }

+  return(subSize)

 }

\ No newline at end of file

@@ -11,6 +11,7 @@ discordantRun(

   y = NULL,

   transform = TRUE,

   subsampling = FALSE,

+  subSize = NULL,

   iter = 100,

   components = 3

 )

@@ -28,6 +29,9 @@ discordantRun(

 \item{subsampling}{If TRUE subsampling will be run}

+\item{subSize}{Indicates how many feature pairs to be used for subsampling. 

+Default is the feature size in x}

+

 \item{iter}{Number of iterations for subsampling. Default is 100}

 \item{components}{Number of components in mixture model.}