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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | 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)
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