Check if a distribution is unimodal or multimodal — check

For univariate distributions (one-dimensional vectors), this functions performs a Ameijeiras-Alonso et al. (2018) excess mass test. For multivariate distributions (dataframes), it uses mixture modelling. However, it seems that it always returns a significant result (suggesting that the distribution is multimodal). A better method might be needed here.

Usage

check_multimodal(x, ...)

Arguments

x: A numeric vector or a data frame.
...: Arguments passed to or from other methods.

References

Ameijeiras-Alonso, J., Crujeiras, R. M., and Rodríguez-Casal, A. (2019). Mode testing, critical bandwidth and excess mass. Test, 28(3), 900-919.

Examples

# \dontrun{
if (require("multimode")) {
  # Univariate
  x <- rnorm(1000)
  check_multimodal(x)
}
#> Loading required package: multimode
#> # Is the variable multimodal?
#> 
#> The Ameijeiras-Alonso et al. (2018) excess mass test suggests that the
#>   hypothesis of a multimodal distribution cannot be rejected (excess mass
#>   = 0.01, p = 0.726).
#> 

if (require("multimode") && require("mclust")) {
  x <- c(rnorm(1000), rnorm(1000, 2))
  check_multimodal(x)

  # Multivariate
  m <- data.frame(
    x = rnorm(200),
    y = rbeta(200, 2, 1)
  )
  plot(m$x, m$y)
  check_multimodal(m)

  m <- data.frame(
    x = c(rnorm(100), rnorm(100, 4)),
    y = c(rbeta(100, 2, 1), rbeta(100, 1, 4))
  )
  plot(m$x, m$y)
  check_multimodal(m)
}
#> Loading required package: mclust
#> Package 'mclust' version 6.0.0
#> Type 'citation("mclust")' for citing this R package in publications.
#> 
#> Attaching package: ‘mclust’
#> The following object is masked from ‘package:psych’:
#> 
#>     sim


#> # Is the data multimodal?
#> 
#> The parametric mixture modelling test suggests that the multivariate
#>   distribution is significantly multimodal (Chi2(14) = 94.32, p < .001).
#> 
# }