models: Internal Functions: Models

In psolymos/moosecounter: Adaptive Moose Surveys

Internal Functions: Models

Description

These functions power the total Moose estimation and prediction.

Usage

wzi(object, pass_data = FALSE, ...)

loo(object, ...)

zeroinfl2(
  formula,
  data,
  subset,
  na.action,
  weights,
  offset,
  dist = "ZIP",
  link = c("logit", "probit", "cloglog"),
  control = NULL,
  model = TRUE,
  y = TRUE,
  x = FALSE,
  solveH = TRUE,
  robust = FALSE,
  ...
)

## S3 method for class 'non_zeroinfl'
summary(object, ...)

## S3 method for class 'summary.non_zeroinfl'
print(x, digits = max(3, getOption("digits") - 3), ...)

## S3 method for class 'zeroinfl'
nobs(object, ...)

## S3 method for class 'hurdle'
nobs(object, ...)

Arguments

object	A model as returned by 'zeroinfl2()'.
pass_data	Logical, to pass the data or not.
...	Other parameters passes to underlying functions.
formula	Model formula as in 'pscl::zeroinfl()'.
data	Moose data frame.
subset, na.action, weights, offset, model, y	Arguments as in 'pscl::zeroinfl()'.
dist	Count distribution, one of '"ZIP"', '"ZINB"', '"P"', '"NB"'.
link	Link function for the zero model.
control	See 'pscl::zeroinfl.control()'.
x	Arguments for 'pscl::zeroinfl()' or a model as returned by 'zeroinfl2()' for the methods.
solveH	Logical, to use robust matrix inversion to get VCV.
robust	Logical, use CL/PL robust regression approach.
digits	Digits for print method.

Details

'zeroinfl2' is a customized version of the 'pscl::zeroinfl()' function, but this also fits the non-ZI counterparts in a way that simplifies downstream analyses (i.e. PI calculations). Intended for internal use.

'wzi' applies a leave-one-out approach to temper influential observations. The process finds weights that are related to leverage (how much each observation contributes to the model likelihood).

The robust version (CL/PL = conditional likelihood / pseudo likelihood) applies the conditioning described in Solymos et al. (2021) <https://doi.org/10.1002/env.1149>.

psolymos/moosecounter documentation built on Feb. 25, 2024, 4:43 p.m.