Package: semidist 0.1.0

Zhuoxi Li

semidist: Measure Dependence Between Categorical and Continuous Variables

Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable. Test of independence and feature screening for classification problems can be implemented via the two dependence measures. For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>; Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>; Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.

Authors:Wei Zhong [aut], Zhuoxi Li [aut, cre, cph], Wenwen Guo [aut], Hengjian Cui [aut], Runze Li [aut]

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NEWS

# Install 'semidist' in R:
install.packages('semidist', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/wzhong41/semidist/issues1 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

1.70 score 145 downloads 10 exports 20 dependencies

Last updated 1 years agofrom:9f2a02f817. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 16 2025
R-4.5-linux-x86_64OKMar 16 2025
R-4.4-linux-x86_64OKMar 16 2025

Exports:MINTsemiautoMINTsemipermmvmv_sismv_testsd_sissd_testsdcorsdcovswitch_cat_repr

Dependencies:bootclicodetoolsdigestenergyFNNfurrrfutureglobalsgluegsllifecyclelistenvmagrittrparallellypurrrRcppRcppArmadillorlangvctrs

Citation

To cite package ‘semidist’ in publications use:

Zhong W, Li Z, Guo W, Cui H, Li R (2023). semidist: Measure Dependence Between Categorical and Continuous Variables. R package version 0.1.0, https://CRAN.R-project.org/package=semidist.

Corresponding BibTeX entry: 

  @Manual{,
    title = {semidist: Measure Dependence Between Categorical and
      Continuous Variables},
    author = {Wei Zhong and Zhuoxi Li and Wenwen Guo and Hengjian Cui
      and Runze Li},
    year = {2023},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=semidist},
  }

Readme and manuals

Semi-Distance Correlation and MV Index: Measure Dependence Between Categorical and Continuous Variables

The goal of package `semidist` is to provide an

easy way to implement the semi-distance methods (Zhong et al., 2023) and MV index methods (Cui, Li and Zhong, 2015; Cui and Zhong, 2019).

Installation

To install semidist,

install.packages("semidist")

Example

Here is a simple example showing how to use semidist to measure the dependence between a categorical variable and a multivariate continuous variable, and apply the measure on testing the independence and conduct groupwise feature screening.

library(semidist)
X <- mtcars[, c("mpg", "disp", "drat", "wt")]
y <- factor(mtcars[, "am"])

sdcov(X, y)
#> [1] 31.78288
sdcor(X, y)
#> [1] 0.3489821

sd_test(X, y)
#> 
#>  Semi-Distance Independence Test (Permutation Test with K = 10000)
#> 
#> Data: X and y,   Sample size = 32
#> Test statistic = 940.344,    p-value = 0.0005999
#> Alternative hypothesis: Two random variables are not independent

sd_sis(X, y, d = 2)
#> $group_info
#> $group_info$`Grp mpg`
#> [1] "mpg"
#> 
#> $group_info$`Grp disp`
#> [1] "disp"
#> 
#> $group_info$`Grp drat`
#> [1] "drat"
#> 
#> $group_info$`Grp wt`
#> [1] "wt"
#> 
#> 
#> $measurement
#>   Grp mpg  Grp disp  Grp drat    Grp wt 
#> 0.3447938 0.3488447 0.5054821 0.5358834 
#> 
#> $selected
#> [1] "wt"   "drat"
#> 
#> $ordering
#> [1] "Grp wt"   "Grp drat" "Grp disp" "Grp mpg"

# Suppose we have prior information for the group structure as
# ("mpg", "drat"), ("disp", "hp") and ("wt", "qsec")
group_info <- list(
  mpg_drat = c("mpg", "drat"),
  disp_wt = c("disp", "wt")
)
sd_sis(X, y, group_info, d = 2)
#> $group_info
#> $group_info$mpg_drat
#> [1] "mpg"  "drat"
#> 
#> $group_info$disp_wt
#> [1] "disp" "wt"  
#> 
#> 
#> $measurement
#>  mpg_drat   disp_wt 
#> 0.3518051 0.3488598 
#> 
#> $selected
#> [1] "mpg"  "drat"
#> 
#> $ordering
#> [1] "mpg_drat" "disp_wt"

References

  1. Wei Zhong, Zhuoxi Li, Wenwen Guo and Hengjian Cui. (2023) “Semi-Distance Correlation and Its Applications.” Journal of the American Statistical Association.
  2. Hengjian Cui and Wei Zhong (2019). “A Distribution-Free Test of Independence Based on Mean Variance Index.” Computational Statistics & Data Analysis, 139, 117-133.
  3. Hengjian Cui, Runze Li and Wei Zhong (2015). “Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis.” Journal of the American Statistical Association, 110, 630-641.