formatDistData: Convert individual-level distance data to the transect-level...
In ianfiske/unmarked: Models for Data from Unmarked Animals

Description Usage Arguments Details Value Note See Also Examples

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

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

`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, the column name containing distances
`transectNameCol`	character, column name containing transect names
`dist.breaks`	numeric vector of distance interval cutpoints. Length must equal J+1.

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.

An M x J data.frame containing the tabulated detections in each distance interval for each transect. Transect names will become rownames and colnames will be y.1, y.2, ..., y.J.

It is very important that the factor containing transect names contains levels for all the transects surveyed. This includes those where no individuals were detected. See the example for how to add levels to a factor.

distsamp, unmarkedFrame

# 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(6, 8, 10, 12, 14, 18)

# Create formated response data.frame
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