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Effect curve objects

Source: R/effect_curve.R
effect_curve.Rd

An effect_curve object is a function that takes in values of the treatment and produces estimates of the effect curve at those values. effect_curve objects are produces by adrf() and functions that modify effect curves, such as amef(), curve_contrast(), reference_curve(), and curve_projection(). The output of an effect_curve object is a curve_est object containing the effect curve estimates. This page describes effect_curve and curve_est objects.

Usage

f <- adrf(x, ...)

f({treat}, subset = NULL)

Arguments

{treat}

the values of the treatment at which to evaluate the effect curve.

subset

an optional logical expression indicating the subset of the subgroups for which to compute estimates. Can only be used when by was supplied to the original call to adrf(), and only to refer to variables defining subgroups.

x

a curve_est object; the output of an effect_curve object call.

digits

the number of digits to display.

...

arguments passed to print.data.frame().

Value

A call to an effect_curve object returns a curve_est object, which is a data.frame containing a column for the treatment and a column for the effect curve estimates. curve_est objects have print(), summary(), ceof(), and vcov() methods.

Details

An effect_curve object contains a set of grid points on which the effect curve is initially evaluated. The effect curve estimates produced by a call to the effect_curve object are interpolated using 3rd-degree local polynomial regression with a Gaussian kernel and bandwidth equal to half the distance between grid points, unless they coincide with the grid points; this means the produced estimates are linear combinations of the grid point estimates.

See also

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")

adrf1
#> An <effect_curve> object
#> 
#>  - curve type: ADRF
#>  - response: Math
#>  - treatment: logBLL
#>    + range: -0.3567 to 2.4248
#>  - inference: unconditional
#> 

# Compute estimates along the ADRF
adrf1(logBLL = c(0, 1, 2))
#>  ADRF Estimates
#> ────────────────
#>  logBLL Estimate
#>       0    8.470
#>       1    8.012
#>       2    7.029
#> ────────────────

# Perform inference on the estimates
adrf1(logBLL = c(0, 1, 2)) |>
  summary()
#>               ADRF Estimates
#> ──────────────────────────────────────────
#>  logBLL Estimate Std. Error CI Low CI High
#>       0    8.470     0.2197  7.946   8.993
#>       1    8.012     0.0985  7.777   8.247
#>       2    7.029     0.2298  6.482   7.577
#> ──────────────────────────────────────────
#> Inference: unconditional, simultaneous
#> Confidence level: 95% (t* = 2.384, df = 2473)

# ADRF within groups defined by `Male`
adrf2 <- adrf(fit, treat = "logBLL",
              by = ~Male)

adrf2
#> An <effect_curve> object
#> 
#>  - curve type: ADRF
#>  - response: Math
#>  - treatment: logBLL
#>    + range: -0.3567 to 2.4248
#>  - by: Male
#>  - inference: unconditional
#> 

# Estimates in both groups
adrf2(logBLL = c(0, 1, 2))
#>    ADRF Estimates
#> ─────────────────────
#>  Male logBLL Estimate
#>     0      0    8.367
#>     0      1    8.406
#>     0      2    7.281
#>     1      0    8.569
#>     1      1    7.631
#>     1      2    6.786
#> ─────────────────────

# Estimates in one group
adrf2(logBLL = c(0, 1, 2), subset = Male == 1)
#>    ADRF Estimates
#> ─────────────────────
#>  Male logBLL Estimate
#>     1      0    8.569
#>     1      1    7.631
#>     1      2    6.786
#> ─────────────────────