controlPopVar | R Documentation |
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
)
alpha |
a significance level, 0.05 used by default. |
bootType |
the bootstrap type to be used. Default is |
B |
a number of bootstrap samples to be performed (default 500). |
confType |
a type of confidence interval for bootstrap confidence interval,
|
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 |
fittingMethod |
a method used for fitting models from bootstrap samples. |
bootstrapFitcontrol |
control parameters for each regression works exactly
like |
sd |
a character indicating how to compute standard deviation of population
size estimator either as:
\mjsdeqn\hat\sigma=\sqrt\hat\textvar(\hatN)
for |
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 |
A list with selected parameters, it is also possible to call list directly.
Piotr Chlebicki, Maciej Beręsewicz \loadmathjax
estimatePopsize()
controlModel()
controlMethod()
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