ddf.ds: CDS/MCDS Distance Detection Function Fitting

View source: R/ddf.ds.R

ddf.dsR Documentation

CDS/MCDS Distance Detection Function Fitting

Description

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 ddf,ddf.io, or ddf.trial.

Usage

## S3 method for class 'ds'
ddf(model, data, meta.data = list(), control = list(), call, method = "ds")

Arguments

model

model list with key function and scale formula if any

data

data.frame; see ddf for details

meta.data

list containing settings controlling data structure

control

list containing settings controlling model fitting

call

original function call if this function not called directly from ddf (e.g., called via ddf.io)

method

analysis method; only needed if this function called from ddf.io or ddf.trial

Details

For a complete description of each of the calling arguments, see ddf. The argument model in this function is the same as dsmodel in ddf. The argument dataname is the name of the dataframe specified by the argument data in ddf. The arguments control,meta.data,and method are defined the same as in ddf.

Value

result: a ds model object

Note

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.

Author(s)

Jeff Laake

References

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.

See Also

flnl, summary.ds, coef.ds, plot.ds,gof.ds

Examples


# 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))


mrds documentation built on March 18, 2022, 5:26 p.m.