point.est.dp: Double Platform Line Transect Method Abundance Estimation:...
In dill/wisp: Wildlife Simulation Package (wisp)

Description Usage Arguments Details Value References See Also Examples

This function estimates abundance and related parameters from a double platform line transect method sample object (of class ‘sample.dp’).

1	point.est.dp(samp, model = "~distance")

`samp`	sample of class 'sample.dp'
`model`	model specification allowing the selection of covariates

'model' - The default setting is model='~distance' which takes the perpendicular distance of the recorded observation as the sole explanatory variable of the response variable 'seen'. That is, whether an animal was observed or not is solely a function of its perpendicular distance from the observer. The WiSP sample object 'sample.dp' contains the variables distance, exposure, groupsize and one factor level variable named types. A platform variable is also created to denote which of the two 'platforms' observed the animal. A model including all these explanatory variables would then be defined as: model="~platform + distance + exposure + size + types" Note that the explanatory variable size refers to groupsize in the sample object.

An object of class 'point.est.dp´ containing the following items:

`sample`	Sample from which point.est.dp object was created (i.e. the data).
`Nhat.grp`	MLE of group abundance
`Nhat.ind`	MLE of individual abundance (= Nhat.grp * Es)
`Es`	mean group size
`phat`	MLE of probability of detecting a group - number of distinct groups detected / MLE of group abundance
`mu`	MLE of effective strip half-width
`nL`	"encounter rate" - the number of groups detected per unit distance surveyed along lines
`average.g0`	mean trackline detection probability for each platform and pooled across both platforms
`log.likelihood`	the value of the log-likelihood function at the maximum
`AIC`	Akaike´s Information Criterion
`model.summary`	coefficients output from numerical fitting routine
`model`	The model formula passed to the function in argument 'model'
`plotx`	A range of perpendicular distance values to use in plotting detection functions.
`plotp1`	Observer 1's mean detection probability evaluated at each value in plotx.
`plotp2`	Observer 2's mean detection probability evaluated at each value in plotx.
`plotp`	The combined Observer's mean detection probability evaluated at each value in plotx.
`parents`	Details of WiSP objects passed to function
`created`	Creation date and time

Borchers, D.L., Buckland, S.T. and Zucchini, W. 2002. Estimating animal abundance: closed populations. Springer. London. 314pp.

setpars.survey.dp, generate.sample.dp

dp.reg <- generate.region(x.length = 100, y.width = 50)
dp.dens <- generate.density(dp.reg)

#heterogeneous population
dp.poppars<-setpars.population(density.pop = dp.dens, number.groups = 1000, size.method = "poisson", 
						size.min = 1, size.max = 30, size.mean = 10, exposure.method = "beta", 
						exposure.min = 0, exposure.max = 1, exposure.mean = 0.4, exposure.shape = 0.5, 
						type.values=c("Male","Female"), type.prob=c(0.48,0.52))
dp.pop<-generate.population(dp.poppars)

dp.despars<-setpars.design.dp(dp.reg, n.transects=10, n.units=10, visual.range=2, percent.on.effort=1)
dp.des<-generate.design.dp(dp.despars, seed=3)
dp.survpars<-setpars.survey.dp(dp.pop, dp.des, adjust.interactive=FALSE, 
							theta.obs1=0.35, theta.obs2=0, theta.exp=3, theta.dist=-2)
dp.samp<-generate.sample.dp(dp.survpars)

dp.est<-point.est.dp(dp.samp,model="~platform + distance")
summary(dp.est)