Find the maximum duration that an animal spent at a single
station/location before a station change (i.e., the animal can be assumed
to be alive). Differs from resmax
in that the duration is cumulative -
the time of residence events and intervals between residence events are all
included, provided there are no intervening residence events at other
stations/locations.
resmaxcml(
data,
ID,
station,
res.start,
res.end,
residences,
units,
stnchange,
verbose = TRUE
)
a dataframe of residence events. Residence events must include tag ID, location name, start time, and duration.
a string of the name of the column in data
that holds the tag or
sample IDs.
a string of the name of the column in data
that holds the
station name or receiver location.
a string of the name of the column in data
that holds the
start date and time. Must be specified and in POSIXt if type="manual"
.
a string of the name of the column in data
that holds the
end date and time. Must be specified and in POSIXt if type="manual"
.
a character string with the name of the column in data
that holds the duration of the residence events.
units of the duration of the residence events in data
.
a dataframe with the start time and location of the most
recent station or location change. Must use the same column names as data
.
option to display progress bar as function is run. Default is TRUE.
a dataframe with the cumulative residence information for each period where an animal was consecutively detected at a single station/location. Records are only given for cumulative residences that occurred before the most recent station/location change (i.e., the animal can be assumed to be alive).
# Identify most recent station change
station.change<-stationchange(data=events[events$ID=="A",],type="mort",
ID="ID",station="Station.Name",verbose=FALSE)
cumulative_events<-resmaxcml(data=events[events$ID=="A",],ID="ID",
station="Station.Name",res.start="ResidenceStart",res.end="ResidenceEnd",
residences="ResidenceLength.days",units="days",
stnchange=station.change,verbose=FALSE)