lar_model_selection: Model selection for models of lagged association rate

In Alexhaoge/rCLIFII: Composite Likelihood Inference for Individual Identifications

Model selection for models of lagged association rate

Description

Model selection for models of lagged association rate

Usage

lar_model_selection(
  X,
  model,
  method,
  tp,
  mtau = 1000,
  ncores = 4,
  nboot = -1,
  bin_len = -1,
  group_id = NULL,
  model_cl_fun = NULL,
  cl.H = NULL,
  model.K = NULL,
  seed = NULL
)

Arguments

X	A list or matrix containing the identities of individuals within study area, and the states or status of individuals during each sampling period
model	Models of lagged identification rate, model = 'lar_1', 'lar_2', 'lar_3', or your model 'model_cl_fun'
method	The method = 'Bootstrap', 'BBootstrap', or 'Jackknife'
tp	A set of observed time
mtau	The maximum allowable lag time
ncores	doParallel
nboot	The number of bootstrap samples desired
bin_len	An integer represents len-time-unit intervals
group_id	Groups of individuals. If X is a list, please input group_id. If X is a matrix, this parameter can be skipped and takes the default NULL value
model_cl_fun	If you formulate your model, please input function to calculate the composite likelihood about your model
cl.H	If you formulate your model, please input the sensitivity matrix with respect to parameters in your model
model.K	If you formulate your model, please input the number of parameters in your model
seed	Random seed

Details

See Akaike (1973) for Akaike information criterion (AIC); See Burnhan and Anderson (2002) for Quasi-Akaike information criterion (QAIC); See this paper for composite likelihood information criterion (CLIC).

Value

The values of model selection criteria.

Alexhaoge/rCLIFII documentation built on Sept. 28, 2023, 11:23 p.m.