dsm {dsm}  R Documentation 
Fits a density surface model (DSM) to detection adjusted counts from a
spatiallyreferenced distance sampling analysis. dsm
takes observations of
animals, allocates them to segments of line (or strip transects) and
optionally adjusts the counts based on detectability using a supplied
detection function model. A generalized additive model, generalized mixed
model or generalized linear model is then used to model these adjusted
counts based on a formula involving environmental covariates.
dsm(
formula,
ddf.obj,
segment.data,
observation.data,
engine = "gam",
convert.units = 1,
family = quasipoisson(link = "log"),
group = FALSE,
control = list(keepData = TRUE),
availability = 1,
segment.area = NULL,
weights = NULL,
method = "REML",
...
)
formula 
formula for the surface. This should be a valid formula. See "Details", below, for how to define the response. 
ddf.obj 
result from call to 
segment.data 
segment data, see 
observation.data 
observation data, see 
engine 
which fitting engine should be used for the DSM
( 
convert.units 
conversion factor to multiply the area of the segments by. See 'Units' below. 
family 
response distribution (popular choices include

group 
if 
control 
the usual 
availability 
an estimate of availability bias. For count models used
to multiply the effective strip width (must be a vector of length 1 or
length the number of rows in 
segment.area 
if 
weights 
weights for each observation used in model fitting. The
default, 
method 
The smoothing parameter estimation method. Default is

... 
anything else to be passed straight to 
The response (LHS of formula
) can be one of the following (with
restrictions outlined below):
count
count in each segment
abundance.est
estimated abundance per segment, estimation is via a
HorvitzThompson estimator
density.est
density per segment
The offset used in the model is dependent on the response:
count
area of segment multiplied by average probability of detection
in the segment
abundance.est
area of the segment
density
zero
The count
response can only be used when detection function covariates
only vary between segments/points (not within). For example, weather
conditions (like visibility or sea state) or foliage cover are usually
acceptable as they do not change within the segment, but animal sex or
behaviour will not work. The abundance.est
response can be used with any
covariates in the detection function.
In the density case, observations can be weighted by segment areas via the
weights=
argument. By default (weights=NULL
), when density is estimated
the weights are set to the segment areas (using segment.area
or by
calculated from detection function object metadata and Effort
data).
Alternatively weights=1
will set the weights to all be equal. A third
alternative is to pass in a vector of length equal to the number of
segments, containing appropriate weights.
A example analyses are available at http://examples.distancesampling.org.
a glm
, gam
, gamm
or
bam
object, with an additional element, $ddf
which holds the
detection function object.
It is often the case that distances are collected in metres and segment
lengths are recorded in kilometres. dsm
allows you to provide a conversion
factor (convert.units
) to multiply the areas by. For example: if distances
are in metres and segment lengths are in kilometres setting
convert.units=1000
will lead to the analysis being in metres. Setting
convert.units=1/1000
will lead to the analysis being in kilometres. The
conversion factor will be applied to segment.area
if that is specified.
For large models, engine="bam"
with method="fREML"
may be useful. Models
specified for bam
should be as gam
. Read bam
before using
this option; this option is considered EXPERIMENTAL at the moment. In
particular note that the default basis choice (thin plate regression
splines) will be slow and that in general fitting is less stable than when
using gam
. For negative binomial response, theta must be
specified when using bam
.
David L. Miller
Hedley, S. and S. T. Buckland. 2004. Spatial models for line transect sampling. JABES 9:181199.
Miller, D. L., Burt, M. L., Rexstad, E. A., Thomas, L. (2013), Spatial models for distance sampling data: recent developments and future directions. Methods in Ecology and Evolution, 4: 10011010. doi: 10.1111/2041210X.12105 (Open Access)
Wood, S.N. 2006. Generalized Additive Models: An Introduction with R. CRC/Chapman & Hall.
## Not run:
library(Distance)
library(dsm)
# load the Gulf of Mexico dolphin data (see ?mexdolphins)
data(mexdolphins)
# fit a detection function and look at the summary
hr.model < ds(distdata, truncation=6000,
key = "hr", adjustment = NULL)
summary(hr.model)
# fit a simple smooth of x and y to counts
mod1 < dsm(count~s(x,y), hr.model, segdata, obsdata)
summary(mod1)
# predict over a grid
mod1.pred < predict(mod1, preddata, preddata$area)
# calculate the predicted abundance over the grid
sum(mod1.pred)
# plot the smooth
plot(mod1)
## End(Not run)