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

In Rdistance: Density and Abundance from Distance-Sampling Surveys

oneStep likelihood start and limit values

Description

Compute starting values and limits for the oneStep distance function.

Usage

oneStep.start.limits(ml)

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.

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).

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)"]))

Rdistance documentation built on Jan. 10, 2026, 1:07 a.m.