Rdistance (version 4.1.1)

oneStep.start.limits: oneStep likelihood start and limit values

Description

Compute starting values and limits for the oneStep distance function.

Usage

oneStep.start.limits(ml)

Value

A list containing the following components

start

Vector of starting values for parameters of the likelihood and expansion terms.

lowlimit

Vector of lower limits for the likelihood parameters and expansion terms.

uplimit

Vector of upper limits for the likelihood parameters and expansion terms.

names

Vector of names for the likelihood parameters and expansion terms.

The length of each vector in the return is:

(Num expansions) + 1 + 1*(like %in% c("hazrate")) + (Num Covars).

Arguments

ml

Either a Rdistance 'model frame' or an Rdistance 'fitted object'. Both are of class "dfunc". Rdistance 'model frames' are lists containing components necessary to estimate a distance function, but no estimates. Rdistance 'model frames' are typically produced by calls to parseModel. Rdistance 'fitted objects' are typically produced by calls to dfuncEstim. 'Fitted objects' are 'model frames' with additional components such as the parameters estimates, log likelihood value, convergence information, and the variance- covariance matrix of the parameters.

See Also

oneStep.like

Examples

# make 'model list' object
# Boundary is 10, p is 100 / 120 = 0.833
library(Rdistance)
whi <- 50
x <- c( runif(100, min=0, max=10), runif(20, min=10, max=whi))
x <- setUnits(x, "m")
detectDf <- data.frame(transect = 1, dist = x)
siteDf <- data.frame(transect = 1, length = setUnits(10,"m"))
distDf <- RdistDf(siteDf, detectDf)
ml <- parseModel(distDf
            , formula = dist ~ 1
            , w.lo = 0
            , w.hi = setUnits(whi, "m")
            )
            

sl <- oneStep.start.limits(ml)
hist(x, n = 20)
abline(v = exp(sl$start["(Intercept)"]))