LogRegEquiv (version 0.1.5)

performance_equiv: performance_equiv function

Description

This function takes two logistic regression models \(M_A, M_B\), test data, significance level \(\alpha\) and acceptable score degradation \(\delta_B\). It checks whether the models perform equivalently on the test set and returns various figures.

Usage

performance_equiv(
  model_a,
  model_b,
  test_data,
  dv_index,
  delta_B = 1.1,
  alpha = 0.05
)

Arguments

model_a

logistic regression model \(M_A\)

model_b

logistic regression model \(M_B\)

test_data

testing dataset

dv_index

column number of the dependent variable

delta_B

acceptable score degradation (defaults to 1.1)

alpha

significance level \(\alpha\) (defaults to 0.05)

Value

equivalence

Are models \(M_A,M_B\) producing equivalent Brier scores for the given test data? (boolean)

brier_score_ac

\(M_A\) Brier score on the testing data

brier_score_bc

\(M_B\) Brier score on the testing data

diff_sd_l

SD of the lower Brier difference \(BS^A-\delta_B^2BS^B\)

diff_sd_u

SD of the upper Brier difference \(BS^A-\delta_B^{-2}BS^B\)

test_stat_l

\(t_L\) equivalence boundary for the test

test_stat_u

\(t_U\) equivalence boundary for the test

crit_val

a level-\(\alpha\) critical value for the test

delta_B

Calculated equivalence parameter

p_value_l

P-value for \(t_L\)

p_value_u

P-value for \(t_U\)