Added last fixes to cran check errors #38

R/algorithms.R

@@ -300,50 +300,6 @@ matrix(vector_sample(speciesData,weights=matrixWeights),ncol=ncol(speciesData))
 
 
 
-###############################################
-######################################################################
-
-# sim9 function
-# @description Takes a binary presence absence matrix returns a new matrix 
-# with same number of rows and columns uses swapping function from Denitz
-
-# Not used preserved for posterity
-# sim9 <- function(speciesData=matrix(rbinom(100,1,0.5),nrow=10)) 
-
-#{
-#  speciesData <- speciesData[which(rowSums(speciesData)>0),] # make 
-# calculation on submatrix with no missing species
-#  
-#  Burn.In <- max(c(10*nrow(speciesData),1000)) # set the burn-in
-#  # select two random rows and create submatrix
-#  for(i in 1:Burn.In)
-#  {
-#    ran.rows <- sample(nrow(speciesData),2)
-#    
-#    speciesData.pair <- speciesData[ran.rows,]
-#    
-#    
-#    
-#    # find columns if any in pair for which colsum =1; these can be swapped
-#    
-#    Sum.Is.One <- which(colSums(speciesData.pair)==1)
-#    
-#    if(length(Sum.Is.One)>1)
-#    {
-#      # swap them in speciesData.pair.swapped
-#      speciesData.pair.swapped <- speciesData.pair
-#      speciesData.pair.swapped[,Sum.Is.One] <- speciesData.pair[,sample(Sum.Is.One)]
-#      
-#      # return the two swapped rows to the original speciesData matrix
-#      
-#      speciesData[ran.rows,] <- speciesData.pair.swapped
-#      
-#    }
-#  }
-#  return(speciesData)
-#}
-
-
 #' Sim10 Co-occurrence Randomization Algorithm
 #' @description Randomizes a binary matrix speciesData by reshuffling all 
 #' elements. Rows and column probabilities are proportional to user-supplied 
@@ -401,7 +357,7 @@ matrix(vector_sample(speciesData, weights =matrixWeights),ncol=ncol(speciesData)
 #' resource categories) may include zeroes, but no negative numbers or missing
 #' values. Relative resource within a species is first calculated, so the rows
 #' need not sum to 1.0.
-#' @param m a resource utilization matrix (rows = species, columns = discrete 
+#' @param speciesData a resource utilization matrix (rows = species, columns = discrete 
 #' resource states) filled with non-negative real numbers. 
 #' @return Returns a random utilization matrix with the same dimensions as the 
 #' input matrix.
@@ -435,7 +391,7 @@ matrix(runif(prod(dim(speciesData))),ncol=ncol(speciesData))
 #' resource categories) may include zeroes, but no negative numbers or missing
 #' values. Relative resource within a species is first calculated, so the rows
 #' need not sum to 1.0.
-#' @param m a resource utilization matrix (rows = species, columns = discrete 
+#' @param speciesData a resource utilization matrix (rows = species, columns = discrete 
 #' resource states) filled with non-negative real numbers. 
 #' @return Returns a random utilization matrix with the same dimensions as the 
 #' input matrix.
@@ -475,7 +431,7 @@ return(RM)
 #' resource categories) may include zeroes, but no negative numbers or missing
 #' values. Relative resource within a species is first calculated, so the rows
 #' need not sum to 1.0.
-#' @param m a resource utilization matrix (rows = species, columns = discrete 
+#' @param speciesData a resource utilization matrix (rows = species, columns = discrete 
 #' resource states) filled with non-negative real numbers. 
 #' @return Returns a random utilization matrix with the same dimensions as the 
 #' input matrix.
@@ -507,7 +463,7 @@ return(RM)
 #' resource categories) may include zeroes, but no negative numbers or missing
 #' values. Relative resource within a species is first calculated, so the rows
 #' need not sum to 1.0.
-#' @param m a resource utilization matrix (rows = species, columns = discrete 
+#' @param speciesData a resource utilization matrix (rows = species, columns = discrete 
 #' resource states) filled with non-negative real numbers. 
 #' @return Returns a random utilization matrix with the same dimensions as the 
 #' input matrix.

R/coccurrence_null.R

@@ -73,7 +73,7 @@ cooc_null_model <- function(speciesData, algo = "sim1", metric = "c_score", nRep
 #' @param object the null model object to print a summary.
 #' @export
 
-summary.coocnullmod <- function(object,...)
+summary.coocnullmod <- function(object)
 { 
   nullmodObj <- object
   cat("Time Stamp: " , nullmodObj$Time.Stamp,   "\n") 

R/niche_overlap_null.R

@@ -34,16 +34,17 @@ niche_null_model <- function(speciesData, algo = "ra3", metric = "pianka", nReps
   output <- do.call(null_model_engine,params)
   class(output) <- "nichenullmod"
   return(output)
-  
+
 }
 
 
 #' Generic function for calculating null model summary statistics
 #' @description Takes as input a list of Null.Model.Out, with Obs, Sim, Elapsed Time, and Time Stamp values
 #' @param object the null model object to print a summary of. 
+#' @aliases 
 #' @export
 
-summary.nichenullmod <- function(object,...)
+summary.nichenullmod <- function(object)
 { 
 
   nullmodObj <- object 

R/sim9fast.R

@@ -1,6 +1,13 @@
 #' Sim9.fast function
 #' @description Special implementation of sequential swap algorithm.
-
+#'@param speciesData a dataframe <put some guidelines in here>
+#'@param algo the algorithm to use, must be "RA1", "RA2", "RA3", "RA4"
+#'@param metric the metric used to caluclate the null model: choices are "Pianka", "Czekanowski", "Pianka.var", "Czekanowski.var", "Pianka.skew", "Czekanowski.skew"; default is Pianka
+#'@param nReps the number of replicates to run the null model.
+#'@param rowNames Does your dataframe have row names? If yes, they are stripped, otherwise FALSE for data that has no row names
+#'@param saveSeed Should the existing random seed be saved to make the model reproducible? 
+#'@param burn_in The number of reps to burn in the swap algo
+#' @details Due to the extra swapping steps, this needs it's own special null model type engine for sim9.  So this fulfills that need.
 #' @export
 
 

R/sizeratio_null.R

@@ -57,7 +57,7 @@ size_null_model <- function(speciesData, algo = "size_uniform", metric = "var_ra
 #' @param object the null model object to print a summary of. 
 #' @export
 
-summary.sizenullmod <- function(object,...)
+summary.sizenullmod <- function(object)
 { 
   nullmodObj <- object
   #if (!is.null(Output.File)) outfile <- file(p$Output.File, "w") else outfile <-""
@@ -101,6 +101,7 @@ summary.sizenullmod <- function(object,...)
 #' @description plot a variety of size null models
 #' @param x the null model to plot
 #' @param type the type of null plot to make.  See details for more information
+#' @param ... Other variables to be passed on to base plotting
 #' @details the valid types for size are "hist" to show a histogram and "size" to show a sample size null model.
 #' @export