plotEventCorrs: Plot correlations between events in each subsampling dataset...

In AMBarbosa/DeadCanMove: Assess How Spatial Roadkill Patterns Change with Temporal Sampling Scheme

Description

This function plots the correlation with baseline against sampling interval for each group

Usage

plotEventCorrs(event.corrs, sep.plots = FALSE, ...)

Arguments

event.corrs	a matrix of correlations resulting from the sequential.corr function
sep.plots	logical, whether to place each plot in a separate window
...	additional arguments to pass to the plot function

Value

This function produces plots.

Author(s)

A. Marcia Barbosa

See Also

plot, binary.comparison, sequential.corr

Examples

data(roadkills)

submats <- sequential.submatrix(dataset = roadkills,
sampl.columns = 4:ncol(roadkills), sampl.intervals = 1:3,
group.column = "taxon", include.all.together = TRUE, 
remove.zeros = TRUE, keep.nonsampl.columns = TRUE, 
n.subsampl.columns = 80)

names(submats)

hsl <- sequential.hotspots(dataset = roadkills, submats = submats,
region.column = "segment", first.subsampl.col = 4, confidence = 0.95)

hsn <- hotspot.numbers(hotspots.list = hsl, sampl.intervals = 1:3,
groups = as.character(unique(roadkills$taxon)), include.all.together = TRUE,
min.total.events = 0, min.hotspot.threshold = 2)

seqcorr <- sequential.corr(hotspots.list = hsl,
hotspots.thresholds = hsn$HS.threshold, comp.method = "Phi",
baseline.interval = 1, messages = "TRUE")

plotEventCorrs(event.corrs = seqcorr, sep.plots = FALSE, ylim = c(0, 1),
pch = 20)

AMBarbosa/DeadCanMove documentation built on Nov. 3, 2021, 10:03 a.m.