Given two sets of observations (two samples), the function provides with the most plausible type of support for the underlying random variables to be studied. Basically, if less than 3 percent of the observations have different values, we consider that the support is discrete. Otherwise, we consider it as a continuous support.

detect_support_type(sample1, sample2)

## Arguments

sample1 The first sample of observations under study. The second sample of observations under study.

## Value

The type of support, either discrete or continuous.

## Author

Xavier Milhaud xavier.milhaud.research@gmail.com

## Examples

## Simulate the two mixture samples:
list.comp <- list(f1 = 'norm', g1 = 'norm',
f2 = 'norm', g2 = 'norm')
list.param <- list(f1 = list(mean = 3, sd = 0.5), g1 = list(mean = 0, sd = 1),
f2 = list(mean = 1, sd = 0.1), g2 = list(mean = 5, sd = 2))
sample1 <- rsimmix(n=1500, unknownComp_weight=0.5, comp.dist = list(list.comp$f1,list.comp$g1),
comp.param=list(list.param$f1,list.param$g1))
sample2 <- rsimmix(n=2000, unknownComp_weight=0.7, comp.dist = list(list.comp$f2,list.comp$g2),
comp.param=list(list.param$f2,list.param$g2))
## Test the type of support:
detect_support_type(sample1[['mixt.data']], sample2[['mixt.data']])
#> [1] "continuous"