Print an impact or attributable fraction

Method for printing a potential impact fraction or a population attributable fraction

Usage

# S3 method for pif_class
print(x, n = 10000, ...)

Arguments

x

A pif_class object

n

Maximum number of simulations to print

...

Additional arguments to pass to summary.pif_class

Examples

#Example 1
data(ensanut)
options(survey.lonely.psu = "adjust")
design <- survey::svydesign(data = ensanut, ids = ~1, weights = ~weight, strata = ~strata)
rr <- function(X, theta) {
  exp(-2 +
    theta[1] * X[, "age"] + theta[2] * X[, "systolic_blood_pressure"] / 100)
}
cft <- function(X) {
  X[, "systolic_blood_pressure"] <- X[, "systolic_blood_pressure"] - 5
  return(X)
}
pifsim <- pif(design,
  theta = log(c(1.05, 1.38)), rr, cft,
  additional_theta_arguments = c(0.01, 0.03), n_bootstrap_samples = 10,
)
print(pifsim)
#> ── Potential Impact Fraction (PIF) ─────────────────────────────────────────────
#>     counterfactual   relative_risk potential_impact_fraction
#> 1 Counterfactual_1 Relative_Risk_1               0.010532528
#> 2 Counterfactual_1 Relative_Risk_1              -0.001994805
#> 3 Counterfactual_1 Relative_Risk_1               0.023059861
#>   average_relative_risk average_counterfactual           type
#> 1              1327.588               1316.699 point_estimate
#> 2             -4684.146              -4644.551     Lower 2.5%
#> 3              7339.322               7277.949    Upper 97.5%
#> ────────────────────────────────────────────────────────────────────────────────
#> • Number of bootstrap simulations: 10
#> ✖ A low number of bootstrap simulations will result in an unstable estimate.
#> • Use `as.data.frame` to access values.
#> • Use `summary` to save list of main results.
print(pifsim, n = 1)
#> ── Potential Impact Fraction (PIF) ─────────────────────────────────────────────
#> ℹ Too many simulations to print.
#> ────────────────────────────────────────────────────────────────────────────────
#> • Number of bootstrap simulations: 10
#> ✖ A low number of bootstrap simulations will result in an unstable estimate.
#> • Use `as.data.frame` to access values.
#> • Use `summary` to save list of main results.