cma_separation.RdFirst CMA step: decomposes the the pure "individual" features in the marginals from the pure "joint" information available in the copulas.
cma_separation(x, p = NULL) # S3 method for default cma_separation(x, p = NULL) # S3 method for matrix cma_separation(x, p = NULL) # S3 method for xts cma_separation(x, p = NULL) # S3 method for data.frame cma_separation(x, p = NULL) # S3 method for tbl cma_separation(x, p = NULL)
| x | A rectangular (non-tidy) data structure. |
|---|---|
| p | A probability vector. If |
An S3 list of the cma class that contains two elements:
marginal and copula.
Arguments x and p must have the same size.
set.seed(123) margins <- matrix(stats::rnorm(30), ncol = 3) cma <- cma_separation(x = margins) cma #> # CMA Decomposition #> marginal: << tbl 10 x 3 >> #> cdf : << tbl 10 x 3 >> #> copula : << tbl 10 x 3 >> # Access the elements with `$` cma$copula #> # A tibble: 10 × 3 #> ...1 ...2 ...3 #> <dbl> <dbl> <dbl> #> 1 0.273 0.818 0.273 #> 2 0.455 0.455 0.636 #> 3 0.818 0.545 0.364 #> 4 0.545 0.364 0.455 #> 5 0.636 0.182 0.545 #> 6 0.909 0.909 0.0909 #> 7 0.727 0.636 0.818 #> 8 0.0909 0.0909 0.727 #> 9 0.182 0.727 0.182 #> 10 0.364 0.273 0.909 cma$marginal #> # A tibble: 10 × 3 #> ...1 ...2 ...3 #> <dbl> <dbl> <dbl> #> 1 -0.560 1.22 -1.07 #> 2 -0.230 0.360 -0.218 #> 3 1.56 0.401 -1.03 #> 4 0.0705 0.111 -0.729 #> 5 0.129 -0.556 -0.625 #> 6 1.72 1.79 -1.69 #> 7 0.461 0.498 0.838 #> 8 -1.27 -1.97 0.153 #> 9 -0.687 0.701 -1.14 #> 10 -0.446 -0.473 1.25 cma$cdf #> # A tibble: 10 × 3 #> ...1 ...2 ...3 #> <dbl> <dbl> <dbl> #> 1 0.0909 0.0909 0.0909 #> 2 0.182 0.182 0.182 #> 3 0.273 0.273 0.273 #> 4 0.364 0.364 0.364 #> 5 0.455 0.455 0.455 #> 6 0.545 0.545 0.545 #> 7 0.636 0.636 0.636 #> 8 0.727 0.727 0.727 #> 9 0.818 0.818 0.818 #> 10 0.909 0.909 0.909