ddf.ds  R Documentation 
Fits a conventional distance sampling (CDS) (likelihood eq 6.6 in Laake and
Borchers 2004) or multicovariate 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
.
## S3 method for class 'ds'
ddf(
dsmodel,
mrmodel = NULL,
data,
method = "ds",
meta.data = list(),
control = list(),
call
)
dsmodel 
model list with key function and scale formula if any 
mrmodel 
not used 
data 

method 
analysis method; only needed if this function called from

meta.data 

control 

call 
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
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
.
result: a 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.
Jeff Laake
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.
flnl
, summary.ds
, coef.ds
,
plot.ds
,gof.ds
# 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))
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