use camelcaps

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: MetNet
 Type: Package
 Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data
-Version: 0.99.6
+Version: 0.99.7
 Date: 2018-06-12
 Authors@R: c(person(given = "Thomas", family = "Naake",
     email = "thomasnaake@googlemail.com",
@@ -16,7 +16,7 @@ Imports:
     bnlearn (>= 4.3),     
     methods (>= 3.5),
     mpmi (>= 0.42), 
-    parallel (>= 3.4),
+    parallel (>= 3.5),
     parmigene (>= 1.0.2),
     ppcor (>= 1.1),
     psych (>= 1.7.8),

NAMESPACE

@@ -1,19 +1,19 @@
 # Generated by roxygen2: do not edit by hand
 
-export(add_to_list)
+export(addToList)
 export(aracne)
 export(bayes)
 export(clr)
-export(combine_structural_statistical)
-export(consensus_network)
+export(combineStructuralStatistical)
+export(consensusAdjacency)
 export(correlation)
-export(create_statistical_network)
-export(create_statistical_networks_list)
-export(create_structural_network)
-export(in_range_which)
+export(createStatisticalAdjacency)
+export(createStatisticalAdjacencyList)
+export(createStructuralAdjacency)
+export(inRangeWhich)
 export(lasso)
 export(randomForest)
-export(threeDots_call)
+export(threeDotsCall)
 importFrom(bnlearn,arcs)
 importFrom(bnlearn,fast.iamb)
 importFrom(methods,formalArgs)

R/combine.R

@@ -1,26 +1,25 @@
-#' @name combine_structural_statistical
-#' @aliases combine_structural_statistical
-#' @title Combine structural and statistical network
-#' @description The function \code{combine\_structural\_statistical} takes as 
-#' input the structural and statistical network, that were create in former 
+#' @name combineStructuralStatistical
+#' @aliases combineStructuralStatistical
+#' @title Combine structural and statistical adjacency matrix
+#' @description The function \code{combineStructuralStatistical} takes as 
+#' input the structural and statistical adjacency matrix, created in former 
 #' steps, adds them together and will report a connection between metabolites
-#' when the edge is reported in networks. 
-#' \code{combine\_structural\_statistical} returns this consensus matrix of 
-#' the structural and statistical network.
-#' @usage combine_structural_statistical(structure, statistical, threshold = 1)
-#' @param structure matrix containing structural network
-#' @param statistical matrix containing statistical network
+#' in the returned when the sum exceeds the \code{threshold} . 
+#' \code{combineStructuralStatistical} returns this consensus matrix supported
+#' by the structural and statistical adjacency matrices.
+#' @usage combineStructuralStatistical(structure, statistical, threshold=1)
+#' @param structure matrix containing structural adjacency matrix
+#' @param statistical matrix containing statistical adjacency matrix
 #' @param threshold numeric, threshold value to be applied to define a 
 #' connection as present 
-#' @details \code{combine\_structural\_statistical} will call internally the 
-#' function \code{consensus\_network} to create the consensus network of the 
-#' statistical networks. 
-#' @return a matrix containing the consensus network as described above 
-#' harbouring connections reported by the structual network and statistcal 
-#' networks. 
+#' @details The matrices will be added and a unweighted connection will 
+#' be reported when the value exceeds a certain value. 
+#' @return a matrix containing the consensus adjacency matrix as described 
+#' above harbouring connections reported by the structual and 
+#' statistcal adjacency matrices. 
 #' @author Thomas Naake, \email{thomasnaake@@googlemail.com}
 #' @examples 
-#' data("x_test", package = "MetNet")
+#' data("x_test", package="MetNet")
 #' x_test <- as.matrix(x_test)
 #' functional_groups <- rbind(
 #'     c("Hydroxylation (-H)", "O", "15.9949146221"),
@@ -31,17 +30,17 @@
 #'     c("Glucuronic acid (-H2O)", "C6H8O6", "176.0320879894"),
 #'     c("Monosaccharide (-H2O)", "C6H10O5", "162.0528234315"),
 #'     c("Trisaccharide (-H2O)", "C18H30O15", "486.1584702945"))
-#' functional_groups <- data.frame(group = functional_groups[,1],
-#'                                 formula = functional_groups[,2],
-#'                                 mass = as.numeric(functional_groups[,3]))
-#' struct_net <- create_structural_network(x_test, functional_groups, ppm = 5)
-#' stat_net <- create_statistical_network(x_test, 
-#'     model = c("pearson", "spearman","bayes"), 
-#'     correlation_adjust = "bonferroni")
-#' combine_structural_statistical(struct_net[[1]], stat_net)
+#' functional_groups <- data.frame(group=functional_groups[,1],
+#'                                 formula=functional_groups[,2],
+#'                                 mass=as.numeric(functional_groups[,3]))
+#' struct_adj <- createStructuralAdjacency(x_test, functional_groups, ppm=5)
+#' stat_adj <- createStatisticalAdjacency(x_test, 
+#'     model=c("pearson", "spearman","bayes"), 
+#'     correlation_adjust="bonferroni")
+#' combineStructuralStatistical(struct_adj[[1]], stat_adj)
 #' @export
-combine_structural_statistical <- function(structure, statistical, 
-                    threshold = 1) {
+combineStructuralStatistical <- function(structure, statistical, 
+                    threshold=1) {
 
     if (!is.matrix(structure) & !is.numeric(structure)) 
         stop("structure is not a numeric matrix")
@@ -52,8 +51,8 @@ combine_structural_statistical <- function(structure, statistical,
     if (!all(colnames(structure) == colnames(statistical))) 
         stop("colnames are not identical")
     if (!is.numeric(threshold)) stop("threshold is not numeric")
-    ##l <- list(structure, statistical)
-    ##consensus_mat <- consensus_network(statistical, ...)
+
+    ## sum the matrices
     consensus_mat <- structure + statistical
     consensus_mat <- ifelse(consensus_mat > threshold, 1, 0)
     return(consensus_mat)