controlPopVar {singleRcapture}

Control parameters for population size estimation

Description

Creating control parameters for population size estimation and respective standard error and variance estimation.

Usage

controlPopVar(
  alpha = 0.05,
  bootType = c("parametric", "semiparametric", "nonparametric"),
  B = 500,
  confType = c("percentilic", "normal", "basic"),
  keepbootStat = TRUE,
  traceBootstrapSize = FALSE,
  bootstrapVisualTrace = FALSE,
  fittingMethod = c("optim", "IRLS"),
  bootstrapFitcontrol = NULL,
  sd = c("sqrtVar", "normalMVUE"),
  covType = c("observedInform", "Fisher"),
  cores = 1L
)

Arguments

alpha

significance level, 0.05 used by default.

bootType

bootstrap type. Default is "parametric", other possible values are: "semiparametric" and "nonparametric".

B

number of bootstrap samples to be performed (default 500).

confType

type of confidence interval for bootstrap confidence interval, "percentile" by default. Other possibilities: "studentized" and "basic".

keepbootStat

boolean value indicating whether to keep a vector of statistics produced by bootstrap.

traceBootstrapSize

boolean value indicating whether to print size of bootstrapped sample after truncation for semi- and fully parametric bootstraps.

bootstrapVisualTrace

boolean value indicating whether to plot bootstrap statistics in real time if cores = 1 if cores > 1 it instead indicates whether to make progress bar.

fittingMethod

method used for fitting models from bootstrap samples.

bootstrapFitcontrol

control parameters for each regression works exactly like controlMethod but for fitting models from bootstrap samples.

sd

indicates how to compute standard deviation of population size estimator either as: \[\hat{\sigma}=\sqrt{\hat{\text{var}}(\hat{N})}\] for sqrt (which is slightly biased if \(\hat{N}\) has a normal distribution) or for normalMVUE as the unbiased minimal variance estimator for normal distribution: \[\hat{\sigma}=\sqrt{\hat{\text{var}}(\hat{N})} \frac{\Gamma\left(\frac{N_{obs}-1}{2}\right)}{\Gamma\left(\frac{N_{obs}}{2}\right)} \sqrt{\frac{N_{obs}}{2}}\] where the ration involving gamma functions is computed by log gamma function.

covType

type of covariance matrix for regression parameters by default observed information matrix.

cores

for bootstrap only, number of processor cores to be used, any number greater than 1 activates code designed with doParallel, foreach and parallel packages. Note that for now using parallel computing makes tracing impossible so traceBootstrapSize and bootstrapVisualTrace parameters are ignored in this case.

Value

A list with selected parameters, it is also possible to call list directly.

Author(s)

Piotr Chlebicki, Maciej Beręsewicz

See Also

estimatePopsize() controlModel() controlMethod()