Get the coefficients

Get the coefficients

coefficient_variance_matrices(object, ...)

Arguments

object	the object to get from
...	additional arguments used by the concrete implementation

Value

a list with as many entries as rows in the data. Each element is a p*p matrix

See also

accessor_methods for the implementation for a 'proDAFit' object

Examples

  syn_data <- generate_synthetic_data(n_proteins = 10)
  fit <- proDA(syn_data$Y, design = syn_data$groups)
  coefficient_variance_matrices(fit)
#> [[1]]
#>             Condition_1 Condition_2
#> Condition_1 0.002859237 0.000000000
#> Condition_2 0.000000000 0.002859642
#> 
#> [[2]]
#>             Condition_1 Condition_2
#> Condition_1  0.02704272 0.000000000
#> Condition_2  0.00000000 0.009472642
#> 
#> [[3]]
#>             Condition_1 Condition_2
#> Condition_1  0.01444376  0.00000000
#> Condition_2  0.00000000  0.01444404
#> 
#> [[4]]
#>             Condition_1 Condition_2
#> Condition_1  0.08874854  0.00000000
#> Condition_2  0.00000000  0.06406862
#> 
#> [[5]]
#>             Condition_1 Condition_2
#> Condition_1  0.01407351 0.000000000
#> Condition_2  0.00000000 0.009478986
#> 
#> [[6]]
#>             Condition_1 Condition_2
#> Condition_1 0.003931589 0.000000000
#> Condition_2 0.000000000 0.003930584
#> 
#> [[7]]
#>             Condition_1 Condition_2
#> Condition_1 0.003437091 0.000000000
#> Condition_2 0.000000000 0.003436644
#> 
#> [[8]]
#>             Condition_1 Condition_2
#> Condition_1  0.07466749  0.00000000
#> Condition_2  0.00000000  0.07687061
#> 
#> [[9]]
#>             Condition_1 Condition_2
#> Condition_1  0.01508273 0.000000000
#> Condition_2  0.00000000 0.007553562
#> 
#> [[10]]
#>             Condition_1 Condition_2
#> Condition_1  0.00425475 0.000000000
#> Condition_2  0.00000000 0.004255852
#>