Golden {gwzinbr}

Golden Section Search

Description

Runs a Golden Section Search (GSS) algorithm for determining the optimum bandwidth for the geographically weighted zero inflated negative binomial regression and other spatial regression models.

Usage

Golden(
  data,
  formula,
  xvarinf = NULL,
  weight = NULL,
  lat,
  long,
  globalmin = TRUE,
  method,
  model = "zinb",
  bandwidth = "cv",
  offset = NULL,
  force = FALSE,
  maxg = 100,
  distancekm = FALSE
)

Arguments

data

name of the dataset.

formula

regression model formula as in lm.

xvarinf

name of the covariates for the zero inflated part of the model, default value is NULL.

weight

name of the variable containing the sample weights, default value is NULL.

lat

name of the variable containing the latitudes in the dataset.

long

name of the variable containing the longitudes in the dataset.

globalmin

logical value indicating whether to find a global minimum in the optimization process, default value is TRUE.

method

indicates the method to be used for the bandwidth calculation (adaptive_bsq or fixed_g).

model

indicates the model to be used for the regression (zinb, zip, negbin, poisson), default value is"zinb".

bandwidth

indicates the criterion to be used for the bandwidth calculation (cv, aic), default value is "cv".

offset

name of the variable containing the offset values, if null then is set to a vector of zeros, default value is NULL.

force

logical value indicating whether to force the indicated model even if it is not the best fit for the data, default value is FALSE.

maxg

integer indicating the maximum number of iterations for the zero inflated part of the model, default value is 100.

distancekm

logical value indicating whether to calculate the distances in km, default value is FALSE.

Value

A list that contains:

• h_values - Initial values tested for the bandwidth.

• iterations - All bandwidth values tested and respective cv/aic results for each Golden Section Search executed.

• gss_results - Optimum bandwidth found and respective cv/aic.

• min_bandwidth - Optimum bandwidth.

Examples

## Data


data(southkorea_covid19)


## GSS algorithm

gss <- Golden(data = southkorea_covid19[1:122, ],
formula = n_covid1~diff_sd,
xvarinf = NULL, weight = NULL, lat = "y", long = "x",
offset = NULL, model = "poisson", method = "fixed_g",
bandwidth = "cv", globalmin = FALSE, distancekm = FALSE,
force=FALSE)

## Bandwidth
gss$min_bandwidth

## Iterations
gss$iterations