Fits a conventional distance sampling (CDS) (likelihood eq 6.6 in Laake and
Borchers 2004) or multi-covariate distance sampling (MCDS)(likelihood eq
6.14 in Laake and Borchers 2004) model for the detection function of
observed distance data. It only uses key functions and does not incorporate
adjustment functions as in CDS/MCDS analysis engines in DISTANCE (Marques
and Buckland 2004). Distance can be grouped (binned), ungrouped (unbinned)
or mixture of the two. This function is not called directly by the user and
is called from
## S3 method for class 'ds' ddf( dsmodel, mrmodel = NULL, data, method = "ds", meta.data = list(), control = list(), call )
model list with key function and scale formula if any
analysis method; only needed if this function called from
original function call if this function not called directly from
For a complete description of each of the calling arguments, see
ddf. The argument
model in this function is the same
ddf. The argument
dataname is the name
of the dataframe specified by the argument
method are defined the
same as in
ds model object
If mixture of binned and unbinned distance, width must be set to be >= largest interval endpoint; this could be changed with a more complicated analysis; likewise, if all binned and bins overlap, the above must also hold; if bins don't overlap, width must be one of the interval endpoints; same holds for left truncation Although the mixture analysis works in principle it has not been tested via simulation.
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.
# ddf.ds is called when ddf is called with method="ds" data(book.tee.data) region <- book.tee.data$book.tee.region egdata <- book.tee.data$book.tee.dataframe samples <- book.tee.data$book.tee.samples obs <- book.tee.data$book.tee.obs result <- ddf(dsmodel = ~mcds(key = "hn", formula = ~1), data = egdata[egdata$observer==1, ], method = "ds", meta.data = list(width = 4)) summary(result,se=TRUE) plot(result,main="cds - observer 1") print(dht(result,region,samples,obs,options=list(varflag=0,group=TRUE), se=TRUE)) print(ddf.gof(result))
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.