formatDistData: Bin distance data

Description

Convert individual-level distance data to the transect-level format required by distsamp or gdistsamp

Usage

formatDistData(distData, distCol, transectNameCol, dist.breaks, occasionCol)

Arguments

distData

data.frame where each row is a detected individual. Must have at least 2 columns. One for distances and the other for transect names.

distCol

character, name of the column in distData that contains the distances. The distances should be numeric.

transectNameCol

character, column name containing transect names. The transect column should be a factor.

dist.breaks

numeric vector of distance interval cutpoints. Length must equal J+1.

occasionCol

optional character. If transects were visited more than once, this can be used to format data for gdistsamp. It is the name of the column in distData that contains the occasion numbers. The occasion column should be a factor.

Value

data. Transect names will become rownames and colnames will describe the distance intervals.

Details

This function creates a site (M) by distance interval (J) response matrix from a data.frame containing the detection distances for each individual and the transect names. Alternatively, if each transect was surveyed T times, the resulting matrix is M x JT, which is the format required by gdistsamp, seeunmarkedFrameGDS.

Examples

Run this code

# Create a data.frame containing distances of animals detected
# along 4 transects.
dat <- data.frame(transect=gl(4,5, labels=letters[1:4]),
                  distance=rpois(20, 10))
dat

# Look at your transect names.
levels(dat$transect)

# Suppose that you also surveyed a transect named "e" where no animals were
# detected. You must add it to the levels of dat$transect
levels(dat$transect) <- c(levels(dat$transect), "e")
levels(dat$transect)

# Distance cut points defining distance intervals
cp <- c(0, 8, 10, 12, 14, 18)

# Create formated response matrix
yDat <- formatDistData(dat, "distance", "transect", cp)
yDat

# Now you could merge yDat with transect-level covariates and
# then use unmarkedFrameDS to prepare data for distsamp


## Example for data from multiple occasions

dat2 <- data.frame(distance=1:100, site=gl(5, 20),
                   visit=factor(rep(1:4, each=5)))
cutpt <- seq(0, 100, by=25)
y2 <- formatDistData(dat2, "distance", "site", cutpt, "visit")
umf <- unmarkedFrameGDS(y=y2, numPrimary=4, survey="point",
                        dist.breaks=cutpt, unitsIn="m")