LP_est_adjust: Estimate abundance after empirical adjustments for various...

In Petersen: Estimators for Two-Sample Capture-Recapture Studies

Estimate abundance after empirical adjustments for various factors.

Description

This will take a previous fit and return estimates of abundance after making various empirical adjustments

Usage

LP_est_adjust(
  N_hat,
  N_hat_SE,
  conf_level = 0.95,
  tag.retention.est = 1,
  tag.retention.se = 0,
  tag.reporting.est = 1,
  tag.reporting.se = 0,
  n1.adjust.est = 1,
  n1.adjust.se = 0,
  n2.adjust.est = 1,
  n2.adjust.se = 0,
  m2.adjust.est = 1,
  m2.adjust.se = 0,
  n.sim = 10000,
  trace = FALSE
)

Arguments

N_hat	Estimate of N that will be adjusted
N_hat_SE	SE of the N_hat
conf_level	The expected coverage for confidence intervals on N.
tag.retention.est	Estimated tag retention probability
tag.retention.se	Estimated SE of tag retention probability
tag.reporting.est	Estimated tag reporting probability
tag.reporting.se	Estimated SE of tag reporting probability
n1.adjust.est	Adjustment to "n1". This should typically be a ratio of new n1 to old n1
n1.adjust.se	Adjustment to "n1" uncertainty
n2.adjust.est	Adjustment to "n2" This should typically be a ratio of new n2 to old n2
n2.adjust.se	Adjustment to "n2" uncertainty
m2.adjust.est	Adjustment to "m2" This should typically be a ratio of new m2 to old m2
m2.adjust.se	Adjustment to "m2" uncertainty
n.sim	Number of simulation runs to make
trace	If trace flag is set in call when estimating functions

Details

The estimate and SE are converted to a beta distribution for adjustment factors between 0 and 1 with equivalent mean and SD as the estimate and se. The estimate and se are used in normal distribution for adjustment factors for n1, n2, and m2. These adjustment factors are then simulated a large number of times and then multiplied together to get the mean and sd of all adjustments applied together. Then the abundance is simulated (on the log scale), the product taken, and the mean, sd, ci estimated directly.

Value

An list object with a summary data frame and a data frame with the adjustment factors with the following objects summary A data frame with the adjusted abundance estimates, SE, and CI adjustment a data frame showing the adjustment factors applied for tag retention, tag reporting, n1 n2 or m2. datetime Date and time the adjustment was done

Author(s)

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

Examples

data(data_rodli)
rodli.fit <- Petersen::LP_fit(data=data_rodli, p_model=~..time)
rodli.est <- Petersen::LP_est(rodli.fit)
res <- Petersen::LP_est_adjust(rodli.est$summary$N_hat, rodli.est$summary$N_hat_SE,
          tag.retention.est=.90, tag.retention.se=.05)
res$summary

Petersen documentation built on April 4, 2025, 3:05 a.m.