Package intRvals calculates means and variances of arrival intervals (and arrival rates) corrected for missed arrival observations, and compares means and variances of groups of interval data.
library(devtools)
install_github("adokter/intRvals")The central function of package intRvals is
estinterval, which is used to estimate the mean arrival
interval (and its standard deviation) from interval data with missed
arrivals. This is achieved by fitting the theoretical probability
density intervalpdf to the interval data
The package can be used to analyse general interval data where intervals are derived from distinct arrival observations. For example, the authors have used it to analyze dropping intervals of grazing geese for estimating their faecal output.
Intervals are defined as the time between observed arrival events (e.g. the time between one excreted droppings to the next) The package provides a way of taking into account missed observations (e.g. defecations), which lead to occasional observed intervals at integer multiples of the true arrival interval.
m to an interval dataset
d using estinterval, as in
m=estinterval(d).plot.intRvals, as in
plot(m).anova.intRvals to check whether the missed event
probability was signficantly different from zero, as in
anova(m)anova.intRvals to perform model selection
between competing models m1,m2 for the same
interval dataset d, as in anova(m1,m2)d1,d2 using ttest and
vartestfold provides functionality to fold observed intervals
back to their fundamental intervalfundamental tests which intervals are fundamental,
i.e. intervals not containing a missed arrival observationinterval2rate converts interval estimates to ratespartition estimates and tests for the presence of
within-subject variationintervalsim simulates a set of observed intervalsThe package comes with a example interval dataset
goosedrop