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"))
#> Rows: 908 Columns: 11
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (11): X, Y, GTC, Slope, Elev, LakeArea, WinTem, SumTem, Pre, Aspect, Sur...
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
gtc
#> # A tibble: 908 × 11
#>        X     Y    GTC  Slope  Elev LakeArea    WinTem  SumTem   Pre   Aspect
#>    <dbl> <dbl>  <dbl>  <dbl> <dbl>    <dbl>     <dbl>   <dbl> <dbl>    <dbl>
#>  1  75.7  34.5 -0.766 0.0369  139.    4750.  0.00245   0.318  14.3  0.000233
#>  2  76.1  34.3 -0.948 0.124   137.    4559.  0.00131   0.281  12.6  0.000379
#>  3  90.2  28.1 -2.05  0.411   154.    4428.  0.000229 -0.192   7.70 0.000429
#>  4  72.7  35.7 -0.525 0.0101  296.    4272.  0.00215   0.234  17.1  0.00130 
#>  5  88.7  28.0 -0.950 0.0597  229.    5845.  0.000472  0.0204 28.7  0.00174 
#>  6  88.7  28.0 -1.88  0.991   137.    5371.  0.000472 -0.0492 16.3  0.00174 
#>  7  88.7  28.0 -2.55  1.28    126.    5477.  0.000472 -0.294  14.5  0.00174 
#>  8  75.4  35.4 -0.909 0.0444  217.    4691.  0.00155   0.326  17.3  0.00175 
#>  9  96.5  29.5 -3.94  0.406   161.    3912. -0.000868  0.0490  7.88 0.00177 
#> 10  71.9  36.3 -0.819 0.0122  153.    4066. -0.000529 -0.0356  6.40 0.00178 
#> # ℹ 898 more rows
#> # ℹ 1 more variable: SurAlbedo <dbl>

# \donttest{
# 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
#> # A tibble: 100 × 9
#>       GTC   Slope  Elev LakeArea     WinTem  SumTem   Pre  Aspect SurAlbedo
#>     <dbl>   <dbl> <dbl>    <dbl>      <dbl>   <dbl> <dbl>   <dbl>     <dbl>
#>  1 -3.00  0.194   121.     5125.  0.000359   0.147   7.18 0.0325   0.00310 
#>  2 -1.17  0.0569  107.     5090.  0.00170    0.248  14.6  0.0251  -0.0175  
#>  3 -0.413 0.0585  119.     5639. -0.00149    0.109  29.7  0.0170   0.000883
#>  4 -0.718 0.0877  279.     5396. -0.00104    0.114  25.3  0.0116   0.0114  
#>  5 -1.21  0.0234  159.     5438. -0.000516  -0.0733 18.4  0.0208   0.0177  
#>  6 -0.358 0.0171  111.     5014.  0.00194    0.323  14.8  0.0169   0.00164 
#>  7 -0.703 0.0146  263.     5831.  0.0000967  0.138  20.5  0.0352   0.0110  
#>  8 -0.415 0.177    90.1    5704. -0.00155    0.192  12.7  0.00856 -0.00664 
#>  9 -0.220 0.0144   80.2    5816. -0.00169    0.132  24.3  0.0161  -0.00377 
#> 10 -1.01  0.00602  97.2    5419.  0.000879   0.118  12.2  0.0245  -0.00623 
#> # ℹ 90 more rows

# 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)
#> # A tibble: 100 × 16
#>    pd_Slope sig_Slope pd_Elev sig_Elev pd_LakeArea sig_LakeArea pd_WinTem
#>       <dbl>     <dbl>   <dbl>    <dbl>       <dbl>        <dbl>     <dbl>
#>  1   0.247     0.458   0.121     0.763      0.304       0.320       0.565
#>  2   0.182     0.801   0.223     0.727      0.181       0.795       0.351
#>  3   0.213     0.224   0.0693    0.749      0.0664      0.596       0.283
#>  4   0.575     0.0482  0.229     0.340      0.693       0.00959     0.249
#>  5   0.575     0.0482  0.229     0.340      0.693       0.00959     0.249
#>  6   0.0923    0.549   0.0920    0.550      0.0721      0.795       0.138
#>  7   0.295     0.296   0.0862    0.687      0.265       0.230       0.372
#>  8   0.212     0.239   0.0703    0.755      0.136       0.486       0.320
#>  9   0.212     0.239   0.0703    0.755      0.136       0.486       0.320
#> 10   0.373     0.229   0.0406    0.956      0.331       0.0726      0.442
#> # ℹ 90 more rows
#> # ℹ 9 more variables: sig_WinTem <dbl>, pd_SumTem <dbl>, sig_SumTem <dbl>,
#> #   pd_Pre <dbl>, sig_Pre <dbl>, pd_Aspect <dbl>, sig_Aspect <dbl>,
#> #   pd_SurAlbedo <dbl>, sig_SurAlbedo <dbl>
# }