inst/examples/affinity_example.R

In kpmainali/CooccurrenceAffinity: Affinity in Co-Occurrence Data

# when you have a binary presence absence occurrence data
# -------------------------------------------------------

data(finches)
head(finches)
# this dataset carries the occurrence records of 13 species in row in 17 islands in columns
dim(finches)


# the remainder of the script has been enclosed under \donttest{}
# to bypass the CRAN's 5 second limit on example files
# --------------------------------------------------------------

\donttest{

  # compute alpha and other quantities for island-pair affinity (beta diversity)
  myout <- affinity(data = finches, row.or.col = "col")
  myout

  # you can simply flip the analysis to rows to compute species-pair affinity
  myout <- affinity(data = finches, row.or.col = "row")
  myout


  # the rows of the outputs above include every single pair of the entities,
  # producing many columns for various quantities.
  # # can output an NxN square matrix for selected columns.
  # an example is here
  myout <- affinity(data = finches, row.or.col = "col", squarematrix = c("alpha_mle", "jaccard"))
  # it is a list of three elements: one main dataframe and two square matrices
  length(myout)
  myout
  head(myout)

  # you can also compute all the square matrices with an "all"
  myout <- affinity(data = finches, row.or.col = "col", squarematrix = c("all"))
  # this one has 12 elements
  length(myout)
  myout

  # when you want to compute for only certain pairs
  myout <- affinity(data = finches, row.or.col = "col", which.row.or.col = 4:6,
                    squarematrix = c("alpha_mle"))
  myout

  myout <- affinity(data = finches, row.or.col = "col",
                    which.row.or.col = c("Isabella", "Espanola"), squarematrix = c("alpha_mle"))
  print(myout)

} #end of \donttest{}



# if you select only one column, the computation stops
\dontrun{
  myout <- affinity(data = finches, row.or.col = "col",
                    which.row.or.col = c("Darwin"), squarematrix = c("alpha_mle"))
}



\donttest{

  # you can also add additional arguments bringing them from ML.Alpha() or AlphInts()
  myout1 <- affinity(data = finches, row.or.col = "col",
                     which.row.or.col = c("Isabella", "Espanola"), lev=0.95, pvalType="Blaker")
  myout1
  myout2 <- affinity(data = finches, row.or.col = "col",
                     which.row.or.col = c("Isabella", "Espanola"), lev=0.90, pvalType="Blaker")
  myout2
  identical(myout1, myout2)
  # myout1 and myout2 were generated with identical arguments except a difference in "lev",
  # which gave different confidence intervals

  myout3 <- affinity(data = finches, row.or.col = "col",
                     which.row.or.col = 4:6, lev=0.95, pvalType="Blaker")
  myout3
  myout4 <- affinity(data = finches, row.or.col = "col",
                     which.row.or.col = 4:6, lev=0.95, pvalType="midP")
  myout4
  myout3$all$p_value
  myout4$all$p_value
  # the p values are (or, can be) different

  # when you have abundance data requiring conversion to binary
  # -----------------------------------------------------------
  # abundance data is converted to binary based on a threshold supplied.
  # it might be a good idea to explore dataprep() function and its examples
  # first before workign on affinity() for abundance data.
  matrix.data <- matrix(runif(400, 0, 10), nrow = 100, ncol = 4)
  row.names(matrix.data) <- paste0("id_", 1:nrow(matrix.data))
  colnames(matrix.data) <- paste0("variable_", 1:ncol(matrix.data))

  # add some missing data and zero abundance
  matrix.data[1,1] <- matrix.data[2,3] <- matrix.data[1,4] <- matrix.data[1,2] <- NA
  matrix.data[10,4] <- 0
  head(matrix.data)
  # now this is an abundance data with some missing and some zero occurrences

  # inspecting how the abundance is converted to binary first
  dataprep(data = matrix.data, row.or.col = "col", datatype = "abundance",
           threshold = 5, class0.rule = "less")
  myout10 <- affinity(data = matrix.data, row.or.col = "col",
                      datatype = "abundance", threshold = 5, class0.rule = "less")
  myout10

  # you can also feed the output of dataprep() to affinity()
  myinput <- dataprep(data = matrix.data, row.or.col = "col",
                      datatype = "abundance", threshold = 5, class0.rule = "less")
  myout11 <- affinity(data = myinput, row.or.col = "col", datatype = "binary")
  myout11
  # myout 10 and myout11 are identical
  identical(myout10, myout11)

} # end of \donttest{}