Contrast point estimates along an effect curve

point_contrast() computes pairwise contrasts of estimates from an effect curve.

Usage

point_contrast(object)

# S3 method for class 'curve_est_contrast'
summary(
  object,
  conf_level = 0.95,
  simultaneous = TRUE,
  null = 0,
  ci.type = "perc",
  df = NULL,
  ...
)

Arguments

object

for point_contrast(), a curve_est object; the output of a an effect_curve object. For summary(), a curve_est_contrast object; the output of a call to point_contrast().

conf_level

the desired confidence level. Set to 0 to omit confidence intervals. Default is .95.

simultaneous

logical; whether the computed p-values and confidence intervals should be simultaneous (TRUE) or pointwise (FALSE). Simultaneous (also known as uniform) intervals jointly cover all specified estimates at the desired confidence level, whereas pointwise confidence intervals only cover each estimate at the desired level. Simultaneous p-values are inversions of the simultaneous confidence intervals. Default is TRUE. See Details.

null

the null value for hypothesis tests. Default is 0. Set to NA to omit tests.

ci.type

string; when bootstrapping or Bayesian inference is used in the original effect curve, which type of confidence interval is to be computed. For bootstrapping, allowable options include "perc" for percentile intervals, "wald" for Wald intervals, and other options allowed by fwb::summary.fwb() . When simultaneous = TRUE, only "perc" and "wald" are allowed. For Bayesian models, allowable options include "perc" for equi-tailed intervals and "wald" for Wald intervals. Default is "perc". Ignored when bootstrapping is not used and the model is not Bayesian.

df

the "denominator" degrees of freedom to use for the tests and critical test statistics for confidence intervals. Default is to use the residual degrees of freedom from the original model if it is a linear model and Inf otherwise.

...

ignored.

Value

point_contrast() returns an object of class curve_est_contrast, which is like a curve_est object but with its own summary() method.

Details

point_contrast() computes all pairwise contrasts between effect curve estimates. Because pairwise contrasts are a linear operation over the original estimates, the delta method can be used to perform Wald inference for the contrasts. When by was specified in the original call to adrf() or the effect curve is a contrast_curve object resulting from curve_contrast(), pairwise contrasts occur only within subgroups or within subgroup contrasts, respectively. To compare points on an effect curve to a single point, use reference_curve().

See also

• adrf() for computing the ADRF

• reference_curve() for comparing points on an effect curve to a single point

• summary.curve_est() for inference on individual points on an effect curve

• marginaleffects::hypotheses() for general hypotheses on curve_est (and other) objects

Examples

data("nhanes3lead")

fit <- lm(Math ~ poly(logBLL, 5) *
            (Male + Age + Race + PIR +
               Enough_Food),
          data = nhanes3lead)

# ADRF of logBLL on Math, unconditional
# inference
adrf1 <- adrf(fit, treat = "logBLL")

# Differences among ADRF estimates at given points
adrf1(logBLL = c(0, 1, 2)) |>
  point_contrast() |>
  summary()
#>                           ADRF Point Contrasts
#> ────────────────────────────────────────────────────────────────────────
#>                         Term Estimate Std. Error      t  P-value  CI Low
#>  [logBLL = 1] - [logBLL = 0]  -0.4579     0.2662 -1.720   0.1962 -1.0810
#>  [logBLL = 2] - [logBLL = 0]  -1.4403     0.3074 -4.685 < 0.0001 -2.1601
#>  [logBLL = 2] - [logBLL = 1]  -0.9824     0.2610 -3.764   0.0005 -1.5934
#> ────────────────────────────────────────────────────────────────────────
#> Inference: unconditional, simultaneous
#> Confidence level: 95% (t* = 2.341, df = 2473)
#> Null value: 0