dp.to.ddf: Conversion of double platform samples for analysis by MRDS
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value Author(s) References See Also Examples

Convert a Wisp object of type 'sample.dp' into format acceptable to codeddf

1	dp.to.ddf(the.sample)

the.sample

Object of class 'sample.dp' to be converted for analysis by ddf

More sophisticated models for line transect data can be constructed using algorithms available in the MRDS library, written by Jeff Laake. In particular, models incorporating multiple covariates can be analyzed. This routine transforms line transect samples generated by Wisp into a format compatible with MRDS, more specifically the model-fitting component of MRDS, namely ddf.

A data frame consisting of:

`object`	Sequential number of objects detected during the survey
`observer`	Observer who made the detection (presently always 1)
`detected`	Whether the object was detected by the specified observer, presently always 1
`distance`	Distance of object from transect line
`size`	Group size of the detected cluster
`sex`	Gender of detected cluster (species exhibits sexual segregation, hence travel in single-sex groups)
`exposure`	Detectability factor of the detected group

Eric Rexstad, RUWPA ericr@mcs.st-and.ac.uk

Laake, J.L. and D.L. Borchers. 2004. Methods for incomplete detection at distance zero. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.

Marques, F.F.C. and S.T. Buckland. 2004. Covariate models for the detection function. In: Advanced Distance Sampling, eds. S.T. Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L. Thomas. Oxford University Press.

generate.population, generate.sample.dp, also consult documentation for the MRDS library, specifically ddf, plot.ddf, and ddf.gof

library(mrds)
data(harb.samp.dp)
test <- dp.to.ddf(harb.samp.dp)
double.obs <- ddf(dsmodel = ~cds(key = "hn"), mrmodel = ~glm(~distance), data = test, method = "trial", meta.data = list(width = 1))
summary(double.obs)