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Validate a saved binary logistic model

Source: R/lm-validate.R
validate_logistic.Rd

Scores a declared validation cohort with every fitted model retained in a binary fit_logistic() bundle. It does not refit or modify the source bundle.

Usage

validate_logistic(
  model,
  data,
  outcome_col = NULL,
  prediction_col = "predicted",
  groups = 10L
)

Arguments

model

A binary lm_fit bundle with bundle version 1.

data

Validation data containing the stored predictors and outcome.

outcome_col

Outcome column. Defaults to the bundle's outcome column.

prediction_col

Output column for averaged predicted probabilities.

groups

Number of equal-frequency calibration groups.

Value

An object of class c("lm_validation", "ps_data") with:

$data

data with prediction_col appended: the predicted probability of the bundle's event_level, averaged across the stored models on the probability scale. A row a model cannot score, such as one missing a predictor, gets NA.

$meta

Named list: bundle_version, model_family, formula, outcome_col, outcome_levels, event_level, predictors and package_versions, carried from model; prediction_col, groups, n_input, n_analyzed (rows with an outcome and a prediction) and n_excluded.

$tables

Named list: calibration, one row per equal-frequency group of the predicted probability (group, n, observed events, expected events); and performance, one row (n, observed, expected, oe_ratio, auc (the c-statistic) and brier).

$models

The stored models from model, unchanged.

Examples

dta <- sample_ps_data(n = 150, seed = 42)
odd <- seq_len(nrow(dta)) %% 2 == 1
# Fit on one half, then score the other half without refitting.
fit <- fit_logistic(
  tavr ~ age + female + ef,
  data           = dta[odd, ],
  family         = "binary",
  outcome_levels = c(0, 1),
  event_level    = 1
)
val <- validate_logistic(fit, dta[!odd, ])
val$tables$performance
#>     n observed expected  oe_ratio       auc    brier
#> 1 150       75 77.24495 0.9709373 0.7607111 0.199376
val$tables$calibration
#>    group  n observed  expected
#> 1      1 15        2  2.426137
#> 2      2 15        4  4.038097
#> 3      3 15        3  5.137954
#> 4      4 15        7  6.241516
#> 5      5 15        6  7.155727
#> 6      6 15        9  8.128755
#> 7      7 15       10  9.049067
#> 8      8 15       11 10.100560
#> 9      9 15        9 11.685894
#> 10    10 15       14 13.281238