Model evaluation metrics

Classification and calibration metrics: the confusion-matrix summaries, Matthews correlation, the Brier score, and principal-component and penalised-regression baselines.

Usage

morie_binary_metrics(y_true, y_pred, positive = 1) 

morie_class_metrics(conf, i) 

morie_mcc(y_true, y_pred, positive = 1) 

morie_brier(probs, y_true, halved = FALSE) 

morie_pca(X, k = NULL) 

morie_ridge(X, y, lambda, add_intercept = TRUE)

Arguments

X

predictor matrix

y

response vector

y_true

argument y_true; not described in the source.

y_pred

argument y_pred; not described in the source.

positive

argument positive; not described in the source.

conf

argument conf; not described in the source.

i

argument i; not described in the source.

probs

argument probs; not described in the source.

halved

argument halved; not described in the source.

k

argument k; not described in the source.

lambda

argument lambda; not described in the source.

add_intercept

argument add_intercept; not described in the source.

Value

A list holding the fitted quantities for the method called: point estimates, fitted values and any diagnostics it produces. See the individual method descriptions above for the components each returns.

Details

These functions mirror, in R, the implementations in the Python package morie; the two are checked against each other by the parity tests in tests/testthat/.

See also

morie_ols

Examples

set.seed(3)
X <- matrix(rnorm(40), 10, 4)
morie_pca(X, k = 2)
#> $eigenvalues
#> [1] 1.921200 1.224633 0.684857 0.169310
#> 
#> $sd_pc
#> [1] 1.3860737 1.1066313 0.8275609 0.4114730
#> 
#> $loadings
#>            [,1]       [,2]       [,3]         [,4]
#> [1,] -0.4029045  0.4605630  0.7908539 -0.009988788
#> [2,]  0.6640477  0.1480371  0.2607423  0.684937301
#> [3,] -0.2807207  0.7118363 -0.5534056  0.328979024
#> [4,] -0.5638302 -0.5091717  0.0174860  0.650026092
#> 
#> $scores
#>             [,1]        [,2]        [,3]        [,4]
#>  [1,] -0.3062552 -1.06041104 -0.84688479 -0.11810897
#>  [2,] -0.8191189 -1.03789644 -0.15057118 -0.67303265
#>  [3,] -0.8247978  0.02158231  0.04162656 -0.12994133
#>  [4,]  1.1771499 -1.70403742  0.01083765  0.27527424
#>  [5,]  0.9751565  0.73945391  0.44480029 -0.42585448
#>  [6,] -0.1548377 -0.42864664  0.31332530  0.04168444
#>  [7,] -1.8595433  0.54689675 -1.03431258  0.69026459
#>  [8,] -0.9803263  1.97124228  0.07804003 -0.27652625
#>  [9,]  3.0036138  0.99906493 -0.73163465  0.16972038
#> [10,] -0.2110411 -0.04724864  1.87477337  0.44652003
#> 
#> $compressed
#>             [,1]        [,2]
#>  [1,] -0.3062552 -1.06041104
#>  [2,] -0.8191189 -1.03789644
#>  [3,] -0.8247978  0.02158231
#>  [4,]  1.1771499 -1.70403742
#>  [5,]  0.9751565  0.73945391
#>  [6,] -0.1548377 -0.42864664
#>  [7,] -1.8595433  0.54689675
#>  [8,] -0.9803263  1.97124228
#>  [9,]  3.0036138  0.99906493
#> [10,] -0.2110411 -0.04724864
#> 
#> $prop_variance
#> [1] 0.4803000 0.3061582 0.1712142 0.0423275
#> 
#> $cum_variance
#> [1] 0.4803000 0.7864582 0.9576725 1.0000000
#>