LP_for_rev_fit {Petersen}

Fit a combined FORWARD and REVERSE simple Lincoln-Petersen Model using pseudo-likelihood

Description

EXPERIMENTAL. This will take a data frame of capture histories, frequencies, and additional covariates (e.g., strata and/or continuous covariates) for a simple forward Petersen estimate plus estimates of escapement and associated stock proportions with SE for backwards estimation. DO NOT USE YET.

Usage

LP_for_rev_fit(
  data,
  E,
  E.SE,
  G,
  G.SE,
  min.G = 0.01,
  n.boot = 100,
  trace = FALSE
)

Arguments

data

Data frame containing the variables:

• cap_hist Capture history (see details below)

• freq Number of times this capture history was observed

plus any other covariates (e.g. discrete strata and/or continuous covariates) to be used in the model fitting.

E

Escapement at one or more terminal areas. E, E.SE, G, G.SE must all have the same length

E.SE

SE of the estimates of escapement

G

Estimated proportion of the stock at the first capture location estimated using GSI and other methods

G.SE

SE of the estimated stock proportion.

min.G

Miniumum acceptable stock proportion during the bootstrap estimation of uncertainty

n.boot

Number of bootstrap samples used to estimate the uncertainty

trace

Should intermediate tracing be enabled (e.g. browser() stops)

Details

The frequency variable (freq in the data argument) is the number of animals with the corresponding capture history.

Capture histories (cap_hist in the data argument) are character values of length 2.

• 10 Animals tagged but never seen again.

• 11 Animals tagged and recaptured and tag present at event 2.

• 01 Animals captured at event 2 that appear to be untagged.

A pseudo-likelihood is constructed consisting of the usual likelihood for a forward capture recapture and marginal likelihoods for each of the escapement (E) and stock proportions (G) point estimates. I have not integrated over the uncertainty in G and E.

Value

An list object of class LP_for_rev_est with abundance estimates and measures of uncertainty

• summary A data frame with the pseudo-likelihood value, abundance estimates and SE

• data A data frame with the raw data used in the fit

• fit Results of the fit from the optimizer

• datetime Date and time the fit was done

Unlike other routine, it is not necessary to use a XX_est() function to get estimates of abundance.

Author(s)

Schwarz, C. J. [e-mail].

Examples

# Example of combined forward and reverse MR

# get some data
n1 <-  500
n2 <- 1500
m2 <-  150

f.data <- data.frame(cap_hist=c("10","11","01"), freq=c(n1 - m2,  m2,  n2 - m2))
f.data

E = 1500
E.SE = 150
G = .2
G.SE = .05

res <- LP_for_rev_fit(data=f.data,
                      E=E,
                      E.SE=E.SE,
                      G=G,
                      G.SE=G.SE)