LP_SPAS_fit {Petersen}

Fit a Stratified-Petersen SPAS model.

Description

This function is a wrapper to fits a SPAS model(Schwarz, 2023; Schwarz and Taylor, 1998). Consult the SPAS package for more details.

Usage

LP_SPAS_fit(
  data,
  model.id = "Base model",
  autopool = FALSE,
  row.pool.in = NULL,
  col.pool.in = NULL,
  min.released = 100,
  min.inspected = 50,
  min.recaps = 50,
  min.rows = 1,
  min.cols = 1,
  quietly = 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.

model.id

Character string identifying the name of the model.

autopool

Should the automatic pooling algorithms be used. Give more details here on these rule work.

row.pool.in, col.pool.in

Vectors (character/numeric) of length s and t respectively. These identify the rows/columns to be pooled before the analysis is done. The vectors consists of entries where pooling takes place if the entries are the same. For example, if s=4, then row.pool.in = c(1,2,3,4) implies no pooling because all entries are distinct; row.pool.in=c("a","a","b","b") implies that the first two rows will be pooled and the last two rows will be pooled. It is not necessary that row/columns be continuous to be pooled, but this is seldom sensible. A careful choice of pooling labels helps to remember what as done, e.g. row.pool.in=c("123","123","123","4") indicates that the first 3 rows are pooled and the 4th row is not pooled. Character entries ensure that the resulting matrix is sorted properly (e.g. if row.pool.in=c(123,123,123,4), then the same pooling is done, but the matrix rows are sorted rather strangely.

min.released

Minimum number of releases in a pooled row

min.inspected

Minimum number of inspections in a pooled column

min.recaps

Minimum number of recaptures before any rows can be pooled

min.rows, min.cols

Minimum number or rows and columns after pooling

quietly

Suppress all console messages that occur during the fit. This includes the progress bar when a model that requires MCMC is fit (LP_BTSPAS_fit_Diag and LP_BTSPAS_fit_NonDiag), or a trace of the likelihood during the fit (LP_SPAS_fit).

Value

An list object of class LP_SPAS_fit with abundance estimates and other information with the following elements

• summary A data frame with the model for the capture probabilities; the conditional log-likelihood; the number of parameters; the number of parameters, condition factor of the data matrix, and method used to fit the model

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

• fit Results of the fit including the estimates, SE, vcov, etc.

• row.pool.in, col.pool.in, autopool Arguments used in the fit to indicate row, column, or automatic pooling used in the fit.

• datetime Date and time the fit was done

After the fit is complete, use the LP_SPAS_est() function to extract the estimates, and the SPAS::SPAS.print.model() function to get a nicely formatted report on the fit.

References

Schwarz CJ (2023). SPAS: Stratified-Petersen Analysis System. R package version 2023.3.31, https://CRAN.R-project.org/package=SPAS.

Schwarz, C. J. and Taylor, C. G. (1998). The use of the stratified-Petersen estimator in fisheries management: estimating the number of pink salmon (Oncorhynchus gorbuscha) that spawn in the Fraser River. Canadian Journal of Fisheries and Aquatic Sciences 55, 281-297. https://doi.org/10.1139/f97-238

Examples

data(data_spas_harrison)

fit <- Petersen::LP_SPAS_fit(data=data_spas_harrison,
                              model.id="Pooling rows 5/6",
                              row.pool.in=c(1,2,3,4,56,56),
                              col.pool.in=c(1,2,3,4,5,6),quietly=TRUE)
fit$summary
est <- Petersen::LP_SPAS_est(fit)
est$summary

# make a nice report using the SPAS package functions
SPAS::SPAS.print.model(fit$fit)