hmmlt.gof.y: Goodness-of-fit in forward dimension.

In DistanceDevelopment/hsltm: Line transect estimation with hidden state availability models

Description

Calculates goodness-of-fit in forward dimension, plots fit, and returns p-value and other stuff. Returns two p-values: p.ks is the Kolmogarov-Smirnov p-value (which is based on only the largest difference between emprical and theoretical cdfs), and Cramer-von Mises p-value (which is based on all cdf values).

Usage

hmmlt.gof.y(hmltm, ks.plot = TRUE, seplots = FALSE, smult = 5,
  ymax = hmmlt$fitpars$survey.pars$ymax)

Arguments

hmltm	fitted model, as output by est.hmltm
ks.plot	If TRUE, does Q-Q plot. Point corresponding to largest difference between empirical and theoretical cdf (on which the Kolmogarov-Smirnov test is based) is circled in red.
seplots	if TRUE does additional diagnostic plots
smult	multiplier to size circles in third plot.
ymax	forward distance at which detection probability is assumed to be zero.

DistanceDevelopment/hsltm documentation built on June 21, 2019, 2:22 p.m.