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
)
Arguments
- formula
A formula with syntax
y ~ t
, wherey
is the (unquoted) name of the outcome andt
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
, whered
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.
Examples
post_sens(formula = angry_bin ~ t_commonality,
data = delponte,
moderator = ~ itaid_bin,
g_by = 0.1
)
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#> gamma lower upper ci_lower ci_upper
#> 1 0.0615942 -1.015053 0.1817998 -1.071183 0.8056042
#> 2 0.1615942 -1.015053 0.7984021 -1.102876 1.1498132
#> 3 0.2615942 -1.278600 0.8184618 -1.578838 1.0033118
#> 4 0.3615942 -1.399967 0.9215294 -1.723996 0.9981251
#> 5 0.4615942 -1.112325 1.0542587 -1.211475 1.1380851
#> 6 0.5615942 -1.147707 1.1477073 -1.195345 1.1957869
#> 7 0.6615942 -1.147707 1.1477073 -1.194633 1.1946334
#> 8 0.7615942 -1.147707 1.1477073 -1.194633 1.1946334
#> 9 0.8615942 -1.147707 1.1477073 -1.194633 1.1946334
#> 10 0.9615942 -1.147707 1.1477073 -1.194633 1.1946334