gss_gwbr: Golden Section Search Algorithm

In gwbr: Local and Global Beta Regression

Golden Section Search Algorithm

Description

The Golden Section Search (GSS) algorithm is used in searching for the best bandwidth for geographically weighted regression. For more details see Da Silva and Mendes (2018).

Usage

gss_gwbr(
  yvar,
  xvar,
  lat,
  long,
  data,
  method = c("fixed_g", "fixed_bsq", "adaptive_bsq"),
  link = c("logit", "probit", "loglog", "cloglog"),
  type = c("cv", "aic"),
  globalmin = TRUE,
  distancekm = TRUE,
  maxint = 100
)

Arguments

yvar	A vector with the response variable name.
xvar	A vector with descriptive variable(s) name(s).
lat	A vector with the latitude variable name.
long	A vector with the longitude variable name.
data	A data set object with yvar and xvar.
method	Kernel function used to set bandwidth parameter. The options are: "fixed_g", "fixed_bsq" or "adaptive_bsq". The default is "fixed_g".
link	The link function used in modeling. The options are: "logit", "probit", "loglog" or "cloglog". The default is "logit".
type	Can be "cv", when the Cross-Validation function is used to estimate the bandwidth or "aic", when the AIC function is used. The default is "cv".
globalmin	Logical. If TRUE search for the global minimum. The default is TRUE.
distancekm	Logical. If TRUE use the distance in kilometers otherwise, use the Euclidean distance. The default is TRUE.
maxint	A maximum number of iterations to numerically maximize the log-likelihood function in search of parameter estimates. The default is maxint=100.

Value

A list that contains:

• global_min - Global minimum of the function, giving the best bandwidth (h).

• local_mins - Local minimums of the function.

• type - Function used to estimate the bandwidth.

Examples

data(saopaulo)
output_list=gss_gwbr("prop_landline",c("prop_urb","prop_poor"),"y","x",saopaulo,"fixed_g")

## Best bandwidth
output_list$global_min

gwbr documentation built on July 9, 2023, 6:12 p.m.