hsarML: Estimation of HSAR models by Quasi-Maximum Likelihood

In gpiras/hspm: Heterogeneous Spatial Models

Estimation of HSAR models by Quasi-Maximum Likelihood

Description

Estimation of HSAR models by Quasi-Maximum Likelihood

Usage

hsarML(
  formula,
  data,
  listw = NULL,
  index = NULL,
  gradient = TRUE,
  average = FALSE,
  init.values = NULL,
  print.init = FALSE,
  otype = c("maxLik", "optim"),
  ...
)

## S3 method for class 'hsarML'
coef(object, ...)

## S3 method for class 'hsarML'
summary(object, MG = TRUE, ...)

## S3 method for class 'summary.hsarML'
print(x, digits = max(5, getOption("digits") - 3), ...)

Arguments

formula	a symbolic description of the model.
data	the data of class pdata.frame.
listw	object. An object of class listw, matrix, or Matrix.
index	index.
gradient	logical. Only for testing procedures. Should the analytic gradient be used in the ML optimization procedure? TRUE as default. If FALSE, then the numerical gradient is used.
average	logical. Should the sample log-likelihood function be divided by N?
init.values	if not NULL, the user must provide a vector of initial parameters for the optimization procedure.
print.init	logical. If TRUE the initial parameters used in the optimization of the first step are printed.
otype	string. A string indicating whether package maxLik or optim is used in for the numerical optimization.
...	additional arguments passed to maxLik
MG	logical. If TRUE, the Mean Group estimator is returned
x, object	an object of class hsarML
digits	the number of digits

gpiras/hspm documentation built on Nov. 10, 2023, 5:37 p.m.