Full console display of the object returned by
lago_optimization(), rendered with boxed, colour-accented cli
sections: an inputs recap (data dimensions, outcome, intervention components,
model family/link and fixed effects, goals, costs and bounds), the fitted
outcome-model coefficient table, the overall intervention-effect test, the
recommended intervention with its cost and the estimated outcome (and its
confidence interval, 95\
IQR, and first rows). Everything is shown on the console so results can be
read without further calls. summary.lago() renders the same output.
Usage
# S3 method for class 'lago'
print(x, ...)Arguments
- x
A "lago" object returned by
lago_optimization().- ...
Ignored.
Examples
# lago_optimization() already prints the result, so quiet = TRUE avoids
# rendering it twice here. The lower bounds start at 1 while the data also
# contains 0s, so the optimizer warns about that; the warning is expected.
result <- lago_optimization(
data = BB_data,
outcome_name = "pp3_oxytocin_mother",
outcome_type = "binary",
glm_family = "binomial",
intervention_components = c("coaching_updt", "launch_duration"),
center_characteristics = c("birth_volume_100"),
center_characteristics_optimization_values = 1.75,
intervention_lower_bounds = c(1, 1),
intervention_upper_bounds = c(40, 5),
cost_list_of_vectors = list(c(0, 1700), c(0, 8000)),
outcome_goal = 0.85,
outcome_goal_intention = "maximize",
include_confidence_set = TRUE,
confidence_set_grid_step_size = c(1, 1),
quiet = TRUE
)
#> Warning: The lower bound for the intervention component coaching_updt is greater than the minimum value in the data.
#> Warning: The lower bound for the intervention component launch_duration is greater than the minimum value in the data.
print(result)
#>
#> ── LAGO optimization result ────────────────────────────────────────────────────
#>
#> ── Inputs
#> Input data dimensions: 6124 rows, 21 columns
#> Outcome name: pp3_oxytocin_mother
#> Outcome type: binary
#> 2 intervention component(s): coaching_updt, launch_duration
#> 1 center characteristic(s): birth_volume_100
#> Outcome model family: binomial
#> Outcome model link: logit
#> Fixed center effects: FALSE
#> Fixed time effects: FALSE
#> Outcome goal: 0.85
#> Power goal: not specified
#> Intervention component costs: c(0, 1700), c(0, 8000)
#> Intervention lower bounds: 1, 1
#> Intervention upper bounds: 40, 5
#>
#> ── Outcome model fit
#> Estimate Std. Error
#> (Intercept) -2.29989 0.068371
#> coaching_updt 0.02514 0.006112
#> launch_duration 1.02447 0.074135
#> birth_volume_100 0.66451 0.029627
#> Standard errors are those of the fitted glm().
#>
#> ── Overall intervention-effect test
#> To see the overall test results, include a 'group' column in the data with
#> values 'treatment' or 'control' (binary outcomes only).
#>
#> ── Recommended intervention
#> coaching_updt: 1
#> launch_duration: 2.7785
#> Cost: 23928
#> Estimated outcome: 0.85
#> 95% CI for the estimated outcome: 0.802 - 0.898
#> Outcome goal: 0.85
#>
#> ── Confidence set
#> 95% confidence set size: 9% of the grid
#> IQR of the cost within the 95% confidence set: 62325 - 76775
#> First rows of the confidence set (use $cs for all):
#> coaching_updt launch_duration birth_volume_100 CI_lower_bound CI_upper_bound
#> 68 27 2 1.75 0.811 0.851
#> 69 28 2 1.75 0.814 0.855
#> 70 29 2 1.75 0.816 0.859
#> 71 30 2 1.75 0.819 0.863
#> 72 31 2 1.75 0.822 0.867
#> 73 32 2 1.75 0.824 0.871
#> cost
#> 68 61900
#> 69 63600
#> 70 65300
#> 71 67000
#> 72 68700
#> 73 70400