# Compute Summary Statistics from Time to Detection Data This function extracts various summary statistics from time to detection data of various `unmarkedFrame` and `unmarkedFit` classes. 1.1 ```r 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 `unmarkedFrame` or `unmarkedFit` classes containing time to detection data. - `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. ## Returns `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 season-specific 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 $t$ (`sampled`) and the number of sites with at least one detection in season $t$ (`detected`). For multiseason data, the matrix includes the number of sites sampled in season $t - 1$ with colonizations observed in season $t$ (`colonized`), the number of sites sampled in season c("$t -\n$", "$ 1$") with extinctions observed in season $t$ (`extinct`), the number of sites sampled in season $t - 1$ without changes observed in season $t$ (`static`), and the number of sites sampled in season $t$ that were also sampled in season c("$t -\n$", "$ 1$") (`common`). - **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 (`naive.occ`). For multiseason data, the matrix includes the proportion of sites sampled in season $t - 1$ with colonizations observed in season $t$ (`naive.colonization`), the proportion of sites sampled in season $t - 1$ with extinctions observed in season $t$ (`naive.extinction`), and the proportion of sites sampled in season $t - 1$ with no changes observed in season $t$. - **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 `TRUE` indicates that no data were collected during the season. ## 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. ## Author(s) Marc J. Mazerolle ## See Also `countDist`, `countHist`, `detHist`, `Nmix.chisq`, `Nmix.gof.test` ## Examples ```r ##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) ```

detTime function