TimeStratPetersenDiagErrorWHChinook2_fit {BTSPAS}

Wrapper (*_fit) to fit the Time Stratified Petersen Estimator with Diagonal Entries and separating Wild from Hatchery Chinook function.

Description

Takes the number of marked fish released, the number of recaptures, and the number of unmarked fish and uses Bayesian methods to fit a fit a spline through the population numbers and a hierarchical model for the trap efficiencies over time. The output is written to files and an MCMC object is also created with samples from the posterior.

Usage

TimeStratPetersenDiagErrorWHChinook2_fit(
  title = "TSPDE-WHChinook2",
  prefix = "TSPDE-WHChinook2-",
  time,
  n1,
  m2,
  u2.A.YoY,
  u2.N.YoY,
  u2.A.1,
  u2.N.1,
  clip.frac.H.YoY,
  clip.frac.H.1,
  sampfrac = rep(1, length(u2.A.YoY)),
  hatch.after.YoY = NULL,
  bad.m2 = c(),
  bad.u2.A.YoY = c(),
  bad.u2.N.YoY = c(),
  bad.u2.A.1 = c(),
  bad.u2.N.1 = c(),
  logitP.cov = as.matrix(rep(1, length(n1))),
  n.chains = 3,
  n.iter = 2e+05,
  n.burnin = 1e+05,
  n.sims = 2000,
  tauU.alpha = 1,
  tauU.beta = 0.05,
  taueU.alpha = 1,
  taueU.beta = 0.05,
  prior.beta.logitP.mean = c(logit(sum(m2, na.rm = TRUE)/sum(n1, na.rm = TRUE)), rep(0,
    ncol(as.matrix(logitP.cov)) - 1)),
  prior.beta.logitP.sd = c(stats::sd(logit((m2 + 0.5)/(n1 + 1)), na.rm = TRUE), rep(10,
    ncol(as.matrix(logitP.cov)) - 1)),
  tauP.alpha = 0.001,
  tauP.beta = 0.001,
  run.prob = seq(0, 1, 0.1),
  debug = FALSE,
  debug2 = FALSE,
  InitialSeed = ceiling(stats::runif(1, min = 0, 1e+06)),
  save.output.to.files = TRUE,
  trunc.logitP = 15
)

TimeStratPetersenDiagErrorWHChinook_fit(
  title = "TSPDE-WHChinook",
  prefix = "TSPDE-WHChinook-",
  time,
  n1,
  m2,
  u2.A,
  u2.N,
  clip.frac.H,
  sampfrac = rep(1, length(u2.A)),
  hatch.after = NULL,
  bad.n1 = c(),
  bad.m2 = c(),
  bad.u2.A = c(),
  bad.u2.N = c(),
  logitP.cov = as.matrix(rep(1, length(n1))),
  n.chains = 3,
  n.iter = 2e+05,
  n.burnin = 1e+05,
  n.sims = 2000,
  tauU.alpha = 1,
  tauU.beta = 0.05,
  taueU.alpha = 1,
  taueU.beta = 0.05,
  prior.beta.logitP.mean = c(logit(sum(m2, na.rm = TRUE)/sum(n1, na.rm = TRUE)), rep(0,
    ncol(as.matrix(logitP.cov)) - 1)),
  prior.beta.logitP.sd = c(stats::sd(logit((m2 + 0.5)/(n1 + 1)), na.rm = TRUE), rep(10,
    ncol(as.matrix(logitP.cov)) - 1)),
  tauP.alpha = 0.001,
  tauP.beta = 0.001,
  run.prob = seq(0, 1, 0.1),
  debug = FALSE,
  debug2 = FALSE,
  InitialSeed = ceiling(stats::runif(1, min = 0, max = 1e+06)),
  save.output.to.files = TRUE,
  trunc.logitP = 15
)

Arguments

title

A character string used for a title on reports and graphs

prefix

A character string used as the prefix for created files. All created graph files are of the form prefix-xxxxx.pdf.

time

A numeric vector of time used to label the strata. For example, this could be julian week for data stratified at a weekly level.

n1

A numeric vector of the number of marked fish released in each time stratum.

m2

A numeric vector of the number of marked fish from n1 that are recaptured in each time stratum. All recaptures take place within the stratum of release. Use the TimeStratPetersenNonDiagError_fit function for cases where recaptures take place outside the stratum of release.

u2.A.YoY, u2.N.YoY

Number of YoY unmarked fish with/without adipose fin clips All YoY wild fish have NO adipose fin clips; however, hatchery fish are a mixture of fish with adipose fin clips (a known percentage are marked) and unmarked fish. So u2.A.YoY MUST be hatchery fish. u2.N.YoY is a mixture of wild and hatchery fish.

u2.A.1, u2.N.1

Number of Age1 unmarked fish with/with out adipose fin clips All Age1 wild fish have NO adipose fin clips; however, hatchery fish are a mixture of fish with adipose fin clips (a known percentage are marked) and unmarked fish. So u2.A.1 MUST be hatchery fish. u2.N.1 is a mixture of wild and hatchery fish.

clip.frac.H.YoY, clip.frac.H.1

Fraction of the YoY hatchery/Age1 (from last year's releases) hatchery fish are clipped?\ (between 0 and 1)

sampfrac

Deprecated because it really doesn't work as intended. You must remove all references to sampfrac from your code. Contact [e-mail] for more information.

hatch.after.YoY

A numeric vector with elements belonging to time. At which point do YoY hatchery fish arrive? They arrive in the immediate stratum AFTER these entries.

bad.m2

A numeric vector with elements belonging to time. In some cases, something goes wrong in the stratum, and the number of recovered marked fish should be ignored. For example, poor handling is suspected to induce handling induced mortality in the marked fish and so only very few are recovered. The values of n1 and m2 will be set to 0 for these strata.

bad.u2.A.YoY, bad.u2.N.YoY

List of julian weeks where the value of u2.A.YoY/u2.N.YoY is suspect. These are set to NA prior to the fit.

bad.u2.A.1, bad.u2.N.1

List of julian weeks where the value of u2.A.1/u2.N.1 is suspect. These are set to NA prior to the fit.

logitP.cov

A numeric matrix for covariates to fit the logit(catchability). Default is a single intercept, i.e. all strata have the same mean logit(catchability).

n.chains

Number of parallel MCMC chains to fit.

n.iter

Total number of MCMC iterations in each chain.

n.burnin

Number of burn-in iterations.

n.sims

Number of simulated values to keeps for posterior distribution.

tauU.alpha

One of the parameters along with tauU.beta for the prior for the variance of the random noise for the smoothing spline.

tauU.beta

One of the parameters along with tauU.alpha for the prior for the variance of the random noise for the smoothing spline.

taueU.alpha

One of the parameters along with taueU.beta for the prior for the variance of noise around the spline.

taueU.beta

One of the parameters along with taueU.alpha for the prior for the variance of noise around the spline.

prior.beta.logitP.mean

Mean of the prior normal distribution for logit(catchability) across strata

prior.beta.logitP.sd

SD of the prior normal distribution for logit(catchability) across strata

tauP.alpha

One of the parameters for the prior for the variance in logit(catchability) among strata

tauP.beta

One of the parameters for the prior for the variance in logit(catchability) among strata

run.prob

Numeric vector indicating percentiles of run timing should be computed.

debug

Logical flag indicating if a debugging run should be made. In the debugging run, the number of samples in the posterior is reduced considerably for a quick turn around.

debug2

Logical flag indicated if additional debugging information is produced. Normally the functions will halt at browser() calls to allow the user to peek into the internal variables. Not useful except to package developers.

InitialSeed

Numeric value used to initialize the random numbers used in the MCMC iterations.

save.output.to.files

Should the plots and text output be save to the files in addition to being stored in the MCMC object?

trunc.logitP

Truncate logit(P) between c(=trunc.logitP, trunc.logitP) when plotting the logitP over time. Actual values of logit(P) are not affected.

u2.A

A numeric vector of the number of unmarked fish with adipose clips captured in each stratum.

u2.N

A numeric vector of the number of unmarked fish with NO-adipose clips captured in each stratum.

clip.frac.H

A numeric value for the fraction of the hatchery fish that have the adipose fin clipped (between 0 and 1).

hatch.after

A numeric vector with elements belonging to time. At which point do hatchery fish arrive? They arrive in the immediate stratum AFTER these entries.

bad.n1

A numeric vector with elements belonging to time. In some cases, something goes wrong in the stratum, and the number of marked fish releases should be discarded. The values of n1 and m2 will be set to 0 for these strata.

bad.u2.A

A numeric vector with elements belonging to time. In some cases, something goes wrong in the stratum, and the number of unmarked fish with an adipose fin clip should be ignored.

bad.u2.N

A numeric vector with elements belonging to time. In some cases, something goes wrong in the stratum, and the number of unmarked fish with NO adipose fin clip should be ignored.

Details

Normally use the *_fit to pass the data to the fitting function.

Value

An MCMC object with samples from the posterior distribution. A series of graphs and text file are also created in the working directory.

Author(s)

Bonner, S.J. [e-mail] and Schwarz, C. J. [e-mail].

References

Bonner, S. J., & Schwarz, C. J. (2011). Smoothing population size estimates for Time-Stratified Mark-Recapture experiments Using Bayesian P-Splines. Biometrics, 67, 1498-1507. doi:10.1111/j.1541-0420.2011.01599.x

Schwarz, C. J., & Dempson, J. B. (1994). Mark-recapture estimation of a salmon smolt population. Biometrics, 50, 98-108.

Schwarz, C.J., D. Pickard, K. Marine and S.J. Bonner. 2009. Juvenile Salmonid Outmigrant Monitoring Evaluation, Phase II - December 2009. Final Technical Memorandum for the Trinity River Restoration Program, Weaverville, CA. 155 pp. + appendices available at https://www.trrp.net/library/document/?id=369

Examples

 
##---- See the vignettes for examples on how to run this analysis.