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

View source: R/LP_est_adjust.R

LP_est_adjustR Documentation

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. cschwarz.stat.sfu.ca@gmail.com.

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 June 22, 2024, 10:55 a.m.