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Run sensitivity analysis on post-measurement design

Source: R/post_bounds.R
post_sens.Rd

Run sensitivity analysis on post-measurement design

Usage

post_sens(
  formula,
  data,
  moderator,
  g_by,
  g_max = 1,
  q_by,
  sims = 1000,
  conf_level = 0.95,
  moderator_mono = NULL,
  stable_mod = FALSE,
  progress = TRUE,
  solver = "Rglpk"
)

Arguments

formula

A formula with syntax y ~ t, where y is the (unquoted) name of the outcome and t is the (unquoted) name of the treatment.

data

A data.frame containing variables in the formula, moderator, and covariates arguments.

moderator

A one-sided formuala with syntax ~ d, where d is the (unquoted) name of the moderator variable for the CATE.

g_by

Numeric indicating the grid spacing for the \(\gamma\) parameter that places an upper bound on the proportion of units whose moderator is affected by treatment.

g_max

Numeric indicating the maximum value of the \(\gamma\) parameter.

q_by

Numeric indicating the grid spacing for the mean of the moderator under a pre-test measurement.

sims

An integer indicating the number of simulations for the bootstrap confidence intervals for the bounds.

conf_level

A numeric indicating the confidence level for the bootstrap confidence intervals.

moderator_mono

A integer or vector of length 2 indicating if the bounds should assume monotonicity of the effect of the post-test on the moderator with 1 indicating that the post-test effect is positive and -1 indicating that it is negative. The vector of length 2 allows the monotonicity assumption to vary by treatment status with the first entry being for control and the second for treated.

stable_mod

A logical value indicating if the bounds should assume that the moderator is unaffected by pre-vs-post measurement under the control condition.

progress

A logical indicating if progress bars should be displayed. Defaults to TRUE.

solver

A character indicating what linear programming solver to use: "Rglpk" (the default) or "lpSolve".

Value

A list object containing sensitivity output.

Examples

data(delponte)
post_sens(formula = angry_bin ~ t_commonality,
  data = delponte,
  moderator = ~ itaid_bin,
  g_by = 0.1,
  sims = 50
)
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#>        gamma     lower     upper  ci_lower  ci_upper
#> 1  0.0615942 -1.015053 0.1817998 -1.076013 0.9159451
#> 2  0.1615942 -1.015053 0.7984021 -1.099627 1.1495981
#> 3  0.2615942 -1.278600 0.8184618 -1.605928 0.9823337
#> 4  0.3615942 -1.399967 0.9215294 -1.701747 1.0131734
#> 5  0.4615942 -1.112325 1.0542587 -1.200810 1.1526079
#> 6  0.5615942 -1.147707 1.1477073 -1.197402 1.1964693
#> 7  0.6615942 -1.147707 1.1477073 -1.196555 1.1965553
#> 8  0.7615942 -1.147707 1.1477073 -1.196555 1.1965553
#> 9  0.8615942 -1.147707 1.1477073 -1.196555 1.1965553
#> 10 0.9615942 -1.147707 1.1477073 -1.196555 1.1965553