file in progress

R/building_new_table.R

-
-get_datatable <- function(model,matrix, type = c('raw','ordered','predicted')){
-  UseMethod("get_datatable",model)
-}
-
-get_datatable.default <- function(model,matrix, type = c('raw','ordered','predicted')){
-  return(NULL)
-}
-
-### still need to make table and build them as dataframe as doing this for simple sbm
-get_datatable.BipartiteSBM <- function(model,matrix, type = c('raw','ordered','predicted')){
-  if(type[[1]] == 'raw'){
-    return(shinySbm:::as.data.frame.sbmMatrix(matrix))
-  }else{
-    clustering <- setNames(model$memberships, c("row", "col"))
-    oRow <- order(clustering$row, decreasing = TRUE)
-    oCol <- order(clustering$col)
-    uRow <- cumsum(table(clustering$row)) + 0.5
-    uCol <- cumsum(table(clustering$col)) + 0.5
-    if(type[[1]] == 'ordered'){
-      oMat <- model$networkData[oRow, oCol] %>%
-        `colnames<-`(matrix$nodes_names$col[oCol]) %>%
-        `rownames<-`(matrix$nodes_names$row[oRow]) %>%
-        as.data.frame()
-      return(oMat)
-    }else{
-      pMat <- model$connectParam$mean[clustering$row, clustering$col][oRow, oCol] %>%
-        `colnames<-`(matrix$nodes_names$col[oCol]) %>%
-        `rownames<-`(matrix$nodes_names$row[oRow]) %>%
-        as.data.frame()
-      return(pMat)
-    }
-  }
-}
-
-
-get_datatable.SimpleSBM <- function(model,matrix, type = c('raw','ordered','predicted')){
-  if(type[[1]] == 'raw'){
-    return(shinySbm:::as.data.frame.sbmMatrix(matrix))
-  }else{
-    clustering <- list(row = model$memberships, col = model$memberships)
-    if (ordered) {
-      oRow <- order(clustering$row)
-      uRow <- cumsum(table(clustering$row)) + 0.5
-      uCol <- uRow[-length(uRow)]
-      uRow <- uRow[length(uRow)] - uRow[-length(uRow)] + 0.5
-      nb_rows <- dim(model$networkData)[1]
-      if(type[[1]] == 'ordered'){
-        oMat <- model$networkData[oRow, oRow][nb_rows:1, ] %>%
-          `colnames<-`(matrix$nodes_names[oRow]) %>%
-          `rownames<-`(matrix$nodes_names[oRow][nb_rows:1]) %>%
-          as.data.frame()
-        return(oMat)
-      }else{
-        pMat <-  model$connectParam$mean[clustering$row, clustering$col][oRow, oRow][nb_rows:1, ] %>%
-          `colnames<-`(matrix$nodes_names[oRow]) %>%
-          `rownames<-`(matrix$nodes_names[oRow][nb_rows:1]) %>%
-          as.data.frame()
-        return(pMat)
-      }
-    }
-  }
-}
-
-
-
-library(shinySbm)
-
-binetmat <- fungusTreeNetwork$fungus_tree %>%
-  `colnames<-`(fungusTreeNetwork$tree_names) %>%
-  `rownames<-`(fungusTreeNetwork$fungus_names)
-
-bisbmmat <- binetmat %>%
-  as.data.frame() %>%
-  shinySbm:::buildSbmMatrix()
-
-bisbm <- estimateBipartiteSBM(netMat = bisbmmat$matrix, model = 'bernoulli',
-                              estimOptions = list(plot = T, verbosity = 3))
-
-
-
-
-uninetmat <- fungusTreeNetwork$tree_tree %>%
-  `colnames<-`(fungusTreeNetwork$tree_names) %>%
-  `rownames<-`(fungusTreeNetwork$tree_names)
-
-unisbmmat <- uninetmat %>%
-  as.data.frame() %>%
-  shinySbm:::buildSbmMatrix()
-
-unisbm <- estimateBipartiteSBM(netMat = unisbmmat$matrix, model = 'poisson',
-                               estimOptions = list(plot = T, verbosity = 3))
-
-
-get_datatable(bisbm,bisbmmat, type = c('raw','ordered','predicted')[3]) %>% View
-
-get_datatable(unisbm,unisbmmat, type = c('raw','ordered','predicted')[3]) %>% View
-
-
-
-
+# remember to change my ref from row and col to first and scnd col
+
+
+# get_datatable <- function(model,matrix, type = c('raw','ordered','predicted')){
+#   UseMethod("get_datatable",model)
+# }
+#
+# get_datatable.default <- function(model,matrix, type = c('raw','ordered','predicted')){
+#   return(NULL)
+# }
+#
+# ### still need to make table and build them as dataframe as doing this for simple sbm
+# get_datatable.BipartiteSBM <- function(model,matrix, type = c('raw','ordered','predicted')){
+#   if(type[[1]] == 'raw'){
+#     return(shinySbm:::as.data.frame.sbmMatrix(matrix))
+#   }else{
+#     clustering <- setNames(model$memberships, c("row", "col"))
+#     oRow <- order(clustering$row, decreasing = TRUE)
+#     oCol <- order(clustering$col)
+#     uRow <- cumsum(table(clustering$row)) + 0.5
+#     uCol <- cumsum(table(clustering$col)) + 0.5
+#     if(type[[1]] == 'ordered'){
+#       oMat <- model$networkData[oRow, oCol] %>%
+#         `colnames<-`(matrix$nodes_names$col[oCol]) %>%
+#         `rownames<-`(matrix$nodes_names$row[oRow]) %>%
+#         as.data.frame()
+#       return(oMat)
+#     }else{
+#       pMat <- model$connectParam$mean[clustering$row, clustering$col][oRow, oCol] %>%
+#         `colnames<-`(matrix$nodes_names$col[oCol]) %>%
+#         `rownames<-`(matrix$nodes_names$row[oRow]) %>%
+#         as.data.frame()
+#       return(pMat)
+#     }
+#   }
+# }
+#
+#
+# get_datatable.SimpleSBM <- function(model,matrix, type = c('raw','ordered','predicted')){
+#   if(type[[1]] == 'raw'){
+#     return(shinySbm:::as.data.frame.sbmMatrix(matrix))
+#   }else{
+#     clustering <- list(row = model$memberships, col = model$memberships)
+#     if (ordered) {
+#       oRow <- order(clustering$row)
+#       uRow <- cumsum(table(clustering$row)) + 0.5
+#       uCol <- uRow[-length(uRow)]
+#       uRow <- uRow[length(uRow)] - uRow[-length(uRow)] + 0.5
+#       nb_rows <- dim(model$networkData)[1]
+#       if(type[[1]] == 'ordered'){
+#         oMat <- model$networkData[oRow, oRow][nb_rows:1, ] %>%
+#           `colnames<-`(matrix$nodes_names[oRow]) %>%
+#           `rownames<-`(matrix$nodes_names[oRow][nb_rows:1]) %>%
+#           as.data.frame()
+#         return(oMat)
+#       }else{
+#         pMat <-  model$connectParam$mean[clustering$row, clustering$col][oRow, oRow][nb_rows:1, ] %>%
+#           `colnames<-`(matrix$nodes_names[oRow]) %>%
+#           `rownames<-`(matrix$nodes_names[oRow][nb_rows:1]) %>%
+#           as.data.frame()
+#         return(pMat)
+#       }
+#     }
+#   }
+# }
+#
+#
+#
+# library(shinySbm)
+#
+# binetmat <- fungusTreeNetwork$fungus_tree %>%
+#   `colnames<-`(fungusTreeNetwork$tree_names) %>%
+#   `rownames<-`(fungusTreeNetwork$fungus_names)
+#
+# bisbmmat <- binetmat %>%
+#   as.data.frame() %>%
+#   shinySbm:::buildSbmMatrix()
+#
+# bisbm <- estimateBipartiteSBM(netMat = bisbmmat$matrix, model = 'bernoulli',
+#                               estimOptions = list(plot = T, verbosity = 3))
+#
+#
+#
+#
+# uninetmat <- fungusTreeNetwork$tree_tree %>%
+#   `colnames<-`(fungusTreeNetwork$tree_names) %>%
+#   `rownames<-`(fungusTreeNetwork$tree_names)
+#
+# unisbmmat <- uninetmat %>%
+#   as.data.frame() %>%
+#   shinySbm:::buildSbmMatrix()
+#
+# unisbm <- estimateBipartiteSBM(netMat = unisbmmat$matrix, model = 'poisson',
+#                                estimOptions = list(plot = T, verbosity = 3))
+#
+#
+# get_datatable(bisbm,bisbmmat, type = c('raw','ordered','predicted')[3]) %>% View
+#
+# get_datatable(unisbm,unisbmmat, type = c('raw','ordered','predicted')[3]) %>% View
+#
+#
+#
+#