Checks whether proposed tolerance values for tols are suitable in number and order for submission to optweight() and optweight.svy(), and returns an object that can supplied to the tols argument of these functions.
Usage
process_tols(formula, data = NULL, tols = 0)
check.tols(...)
# S3 method for class 'optweight.tols'
print(x, internal = FALSE, digits = 5, ...)Arguments
- formula
a formula with the covariates to be balanced on the right-hand side. Interactions and functions of covariates are allowed. Lists of formulas are not allowed; multiple formulas must be checked one at a time.
- data
an optional data set in the form of a data frame that contains the variables in
formula.- tols
a vector of balance tolerance values in standardized mean difference units for each covariate. These should be in the order corresponding to the order of the corresponding variable in
formula, except for interactions, which will appear after all lower-order terms. If only one value is supplied, it will be applied to all covariates.- ...
ignored.
- x
an
optweight.tolsobject; the output of a call toprocess_tols().- internal
logical; whether to print the tolerance values that are to be used internally byoptweight(). See Value section.- digits
how many digits to print.
Value
An optweight.tols object, which is a named vector of tolerance values, one for each variable specified in formula. This should be used as an input to the tols argument of optweight(). The "internal.tols" attribute contains the tolerance values to be used internally by optweight(). These will differ from the vector values when there are factor variables that are split up; the user only needs to submit one tolerance per factor variable, but separate tolerance values are produced for each new dummy created.
Details
The purpose of process_tols() is to allow users to ensure that their proposed input to tols in optweight() is correct both in the number of entries and their order. This is especially important when factor variables and interactions are included in the formula because factor variables are split into several dummies and interactions are moved to the end of the variable list, both of which can cause some confusion and potential error when entering tols values.
Factor variables are internally split into a dummy variable for each level, but the user only needs to specify one tolerance value per original variable; process_tols() automatically expands the tols input to match the newly created variables.
Interactions (e.g., a:b or a*b in the formula input) are always sent to the end of the variable list even if they are specified elsewhere in the formula. It is important to run process_tols() to ensure the order of the proposed tols corresponds to the represented order of covariates used in optweight(). You can run process_tols() with no tols input to see the order of covariates that is required.
Note that only one formula and vector of tolerance values can be assessed at a time; for multiple treatments, each formula and tolerance vector must be entered separately.
Examples
library("cobalt")
data("lalonde", package = "cobalt")
# Generating tols; 0 by default
tols <- process_tols(treat ~ age + educ + married +
nodegree + re74,
data = lalonde)
tols
#> - tols:
#> age educ married nodegree re74
#> 0 0 0 0 0
tols <- process_tols(treat ~ age + educ + married +
nodegree + re74,
data = lalonde,
tols = .05)
tols
#> - tols:
#> age educ married nodegree re74
#> 0.05 0.05 0.05 0.05 0.05
# Checking the order of interactions; notice they go
# at the end even if specified at the beginning.
tols <- process_tols(treat ~ age:educ + married*race +
nodegree + re74,
data = lalonde,
tols = .05)
tols
#> - tols:
#> married race nodegree re74 age:educ married:race
#> 0.05 0.05 0.05 0.05 0.05 0.05
# Internal tolerances for expanded covariates
print(tols, internal = TRUE)
#> - tols:
#> married race nodegree re74 age:educ married:race
#> 0.05 0.05 0.05 0.05 0.05 0.05
#>
#> - tols used internally by optweight:
#> married race_black race_hispan race_white
#> 0.05 0.05 0.05 0.05
#> nodegree re74 age:educ married:race_black
#> 0.05 0.05 0.05 0.05
#> married:race_hispan married:race_white
#> 0.05 0.05