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.
The first sample of observations under study.
The second sample of observations under study.
The type of support, either discrete or continuous.
Xavier Milhaud firstname.lastname@example.org
## 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']])#>  "continuous"