gss_gwbr {gwbr} | R Documentation |
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 |
method |
Kernel function used to set bandwidth parameter. The options are: |
link |
The link function used in modeling. The options are: |
type |
Can be |
globalmin |
Logical. If |
distancekm |
Logical. If |
maxint |
A maximum number of iterations to numerically maximize the log-likelihood function in search of parameter estimates. The default is |
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