Package 'localsp'

local indicator of stratified power

Description

local indicator of stratified power

Usage

lisp(
  formula,
  data,
  threshold,
  distmat,
  discvar = NULL,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  ...
)

Arguments

formula	A formula.
data	The observation data.
threshold	The distance threshold employed to select "local" data.
distmat	The distance matrices.
discvar	(optional) Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.
discnum	(optional) A vector of number of classes for discretization. Default is 3:8.
discmethod	(optional) A vector of methods for discretization, default is using c("sd","equal","geometric","quantile","natural") by invoking sdsfun.
cores	(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
...	(optional) Other arguments passed to gdverse::gd_optunidisc(). A useful parameter is seed, which is used to set the random number seed.

Value

A tibble.

Examples

gtc = readr::read_csv(system.file("extdata/gtc.csv", package = "localsp"))
gtc


# Sample 100 observations from the original data to save runtime;
# This is unnecessary in practice;
set.seed(42)
gtc1 = gtc[sample.int(nrow(gtc),size = 100),]
distmat = as.matrix(dist(gtc1[, c("X","Y")]))
gtc1 = gtc1[, -c(1,2)]
gtc1

# Use 2 cores for parallel computing;
# Increase cores in practice to speed up;
lisp(GTC ~ ., data = gtc1, threshold = 4.2349, distmat = distmat,
     discnum = 3:5, discmethod = "quantile", cores = 2)

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