controlPopVar function

Control parameters for population size estimation

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

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: a significance level, 0.05 used by default.

• bootType: the bootstrap type to be used. Default is "parametric", other possible values are: "semiparametric" and "nonparametric".

• B: a number of bootstrap samples to be performed (default 500).

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

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

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

• bootstrapVisualTrace: a 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: a 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: a character indicating how to compute standard deviation of population size estimator either as: \mjsdeqn \hat \sigma=\sqrt \hat \text var(\hat N)

  for sqrt (which is slightly biased if \mjseqn \hat N

  has a normal distribution) or for normalMVUE as the unbiased minimal variance estimator for normal distribution: \mjsdeqn \hat \sigma=\sqrt \hat \text var(\hat N)

  \frac \Gamma \left (\frac N_obs-12\right )\Gamma \left (\frac N_obs2\right )

  \sqrt \frac N_obs2

  where the ration involving gamma functions is computed by log gamma function.

• covType: a type of covariance matrix for regression parameters by default observed information matrix.

• cores: for bootstrap only, a 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.

Returns

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

See Also

estimatePopsize() controlModel() controlMethod()

Author(s)

Piotr Chlebicki, Maciej Beręsewicz \loadmathjax