FuzzyQ (version 0.1.0)

sortClus: Sort Species by fuzzyq Clustering

Description

Sort species in a matrix or data frame to match the resulting species order of a fuzzyq object. This is useful prior to plotting Commonness Indices derived from bootstrap replicates.

Usage

sortClus(M, fq)

Arguments

M

A matrix or data frame with information of species in columns.

fq

A list of class fuzzyq returned by FuzzyQ::fuzzyq.

Value

A matrix or data frame with information of species in columns sorted according to fq$spp.

Examples

# NOT RUN {
data(antsA)
FQAnts <- fuzzyq(antsA, sorting = TRUE)
# Compute species Commonness Indices of species of 1,000 bootstrap
# replicates:
# }
# NOT RUN {
BS.FQAnts <- fuzzyqBoot (antsA, N = 1e3, level='spp')
# }
# NOT RUN {
# Compute 95 % confidence intervals, percentile method, default values:
# }
# NOT RUN {
BS.sppCI1 <- fuzzyqCI(BS.FQAnts)
# }
# NOT RUN {
# Plot Commonness Indices and their respective confidence intervals:
# }
# NOT RUN {
BS.sppCI1 <- sortClus(BS.sppCI1, FQAnts)
# }
# NOT RUN {
spp <- FQAnts$spp
col.RC <- c("brown2", "turquoise3") # two colors to plot rare and common
# species
# }
# NOT RUN {
plot(spp[, 3], cex.axis = 0.8, xaxt= 'n', ylab = "Commoness index",
   ylim = c(0, max(BS.sppCI1)), xlab = "Species", col = col.RC[spp[, 1] + 1],
   pch = 16, cex = 0.8, las = 1)
ebar.int <- seq_len(nrow(spp))
arrows(ebar.int, BS.sppCI1["Lower", ], ebar.int, BS.sppCI1["Upper", ],
   length= 0, col = col.RC[spp[, 1] + 1])
axis(1, at = ebar.int, labels = rownames(spp), las = 2, cex.axis = 0.6)
# }
# NOT RUN {
# }