General covariance estimator dispatcher — estimate

Dispatches to the requested covariance estimator. For MEC, the function supports continuous, categorical, and censored survival responses by passing response_type and delta to cov_mec().

Usage

estimate_cov(
  X,
  y = NULL,
  method = c("sample", "ridge", "oas", "lw", "mec"),
  nslices = 6,
  response_type = c("continuous", "categorical", "survival"),
  delta = NULL,
  ...
)

Arguments

X: Numeric matrix or data frame.
y: Optional response vector required for MEC.
method: Covariance estimator.
nslices: Number of slices for MEC.
response_type: Response type. One of "continuous", "categorical", or "survival".
delta: Optional event indicator for survival data.
...: Additional arguments passed to internal covariance estimators.

Value

A covariance matrix.

Examples

X <- as.matrix(mtcars[, c("disp", "hp", "wt")])

estimate_cov(X, method = "oas")
#>             disp         hp        wt
#> disp 14251.72868 6023.65641  96.50905
#> hp    6023.65641 4698.05255  39.60648
#> wt      96.50905   39.60648 485.88591
#> attr(,"shrinkage")
#> [1] 0.07486667
estimate_cov(
  X,
  y = mtcars$mpg,
  method = "mec",
  nslices = 4
)
#>            [,1]        [,2]       [,3]
#> [1,] 6453.60813  119.899005 65.6710881
#> [2,]  119.89900 3094.797811 -3.3171616
#> [3,]   65.67109   -3.317162  0.8010292
#> attr(,"alpha")
#> [1] 0.95
#> attr(,"entropy_slice")
#> [1] 1
#> attr(,"slice_entropy")
#> [1] 14.550077 13.234394 11.199660  9.706499
#> attr(,"response_type")
#> [1] "continuous"