detTime {AICcmodavg}  R Documentation 
Compute Summary Statistics from Time to Detection Data
Description
This function extracts various summary statistics from time to detection
data of various unmarkedFrame
and unmarkedFit
classes.
Usage
detTime(object, plot.time = TRUE, plot.seasons = FALSE,
cex.axis = 1, cex.lab = 1, cex.main = 1, ...)
## S3 method for class 'unmarkedFrameOccuTTD'
detTime(object, plot.time = TRUE,
plot.seasons = FALSE, cex.axis = 1, cex.lab = 1,
cex.main = 1, ...)
## S3 method for class 'unmarkedFitOccuTTD'
detTime(object, plot.time = TRUE,
plot.seasons = FALSE, cex.axis = 1, cex.lab = 1,
cex.main = 1, ...)
Arguments
object 
an object of various 
plot.time 
logical. Specifies if the time to detection data (pooled across seasons) should be plotted. 
plot.seasons 
logical. Specifies if the time to detection data should be plotted for each season separately. This argument is only relevant for data collected across more than a single season. 
cex.axis 
expansion factor influencing the size of axis annotations on plots produced by the function. 
cex.lab 
expansion factor influencing the size of axis labels on plots produced by the function. 
cex.main 
expansion factor influencing the size of the main title above plots produced by the function. 
... 
additional arguments passed to the function. 
Details
This function computes a number of summary statistics in data sets used for the time to detection models of Garrard et al. (2008, 2013).
detTime
can take data frames of the unmarkedFrameOccuTTD
class as input, or can also extract the raw data from model objects of
the unmarkedFitOccuTTD
class. Note that different model
objects using the same data set will have identical values.
Value
detTime
returns a list with the following components:
time.table.full 
a table with the quantiles of time to detection data pooled across seasons, but excluding censored observations. 
time.table.seasons 
a list of tables with the quantiles of seasonspecific time to detection data, but excluding censored observations. 
out.freqs 
a matrix where the rows correspond to each sampling
season and where columns consist of the number of sites sampled in
season 
out.props 
a matrix where the rows correspond to each sampling
season and where columns consist of the proportion of sites in
season t with at least one detection ( 
n.seasons 
the number of seasons (primary periods) in the data set. 
n.visits.season 
the maximum number of visits per season in the data set. 
missing.seasons 
logical vector indicating whether data were
collected or not during a given season (primary period), where

Author(s)
Marc J. Mazerolle
References
Garrard, G. E., Bekessy, S. A., McCarthy, M. A., Wintle, B. A. (2008) When have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveys. Austral Ecology 33, 986–998.
Garrard, G. E., McCarthy, M. A., Williams, N. S., Bekessy, S. A., Wintle, B. A. (2013) A general model of detectability using species traits. Methods in Ecology and Evolution 4, 45–52.
See Also
countDist
, countHist
, detHist
,
Nmix.chisq
, Nmix.gof.test
Examples
##example from ?occuTTD
## Not run:
if(require(unmarked)){
N < 500; J < 1
##Simulate occupancy
scovs < data.frame(elev=c(scale(runif(N, 0,100))),
forest=runif(N,0,1),
wind=runif(N,0,1))
beta_psi < c(0.69, 0.71, 0.5)
psi < plogis(cbind(1, scovs$elev, scovs$forest)
z < rbinom(N, 1, psi)
##Simulate detection
Tmax < 10 #Same survey length for all observations
beta_lam < c(2, 0.2, 0.7)
rate < exp(cbind(1, scovs$elev, scovs$wind)
ttd < rexp(N, rate)
ttd[z==0] < Tmax #Censor unoccupied sites
ttd[ttd>Tmax] < Tmax #Censor when ttd was greater than survey length
##Build unmarkedFrame
umf < unmarkedFrameOccuTTD(y=ttd, surveyLength=Tmax, siteCovs=scovs)
##compute descriptive stats from data object
detTime(umf)
##Fit model
fit.occuTTD < occuTTD(psiformula=~elev+forest, detformula=~elev+wind, data=umf)
##extract info from model object
detTime(fit.occuTTD)
}
## End(Not run)