Checks whether proposed target population means values for targets are suitable in number and order for submission to optweight() and optweight.svy(). Users should include one value per variable in formula. For factor variables, one value per level of the variable is required. The output of process_targets() can also be used as an input to targets in optweight() and optweight.svy().
Usage
process_targets(formula, data = NULL, targets = NULL, s.weights = NULL)
check.targets(...)
# S3 method for class 'optweight.targets'
print(x, digits = 5, ...)Arguments
- formula
a formula with the covariates to be targeted on the right-hand side. See
glm()for more details. Interactions and functions of covariates are allowed. Can be omitted, in which case all variables indataare assumed targeted.- data
an optional data set in the form of a data frame that contains the variables in
formula.- targets
a vector of target population mean values 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. For factor variables, a target value must be specified for each level of the factor, and these values must add up to 1. IfNULL, the current sample means will be produced (weighted bys.weights). IfNA, anNAvector named with the covariate names will be produced.- s.weights
an optional vector of sampling weights. Default is a vector of 1s.
- ...
ignored.
- x
an
optweight.targetsobject; the output of a call toprocess_targets().- digits
how many digits to print.
Value
An optweight.targets object, which is a named vector of target population mean values, one for each (expanded) covariate specified in formula. This should be used as user inputs to optweight() and optweight.svy().
Details
The purpose of process_targets() is to allow users to ensure that their proposed input to targets in optweight() and optweight.svy() 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 targets values.
Factor variables are internally split into a dummy variable for each level, so the user must specify a target population mean value for each level of the factor. These must add up to 1, and an error will be displayed if they do not. These values represent the proportion of units in the target population with each factor level.
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_targets() to ensure the order of the proposed targets corresponds to the represented order of covariates used in the formula. You can run process_targets(., targets = NA) to see the order of covariates that is required without specifying any targets.
Examples
library("cobalt")
data("lalonde", package = "cobalt")
# Generating targets; means by default
targets <- process_targets(~ age + race + married +
nodegree + re74,
data = lalonde)
# Notice race is split into three values
targets
#> - targets:
#> age race_black race_hispan race_white married nodegree
#> 27.36319 0.39577 0.11726 0.48697 0.41531 0.63029
#> re74
#> 4557.54657
# Generating targets; NA by default
targets <- process_targets(~ age + race + married +
nodegree + re74,
data = lalonde,
targets = NA)
targets
#> - vars:
#> age race_black race_hispan race_white married nodegree re74
# Can also supply just a dataset
covs <- lalonde |>
subset(select = c(age, race, married,
nodegree, re74))
targets <- process_targets(covs)
targets
#> - targets:
#> age race_black race_hispan race_white married nodegree
#> 27.36319 0.39577 0.11726 0.48697 0.41531 0.63029
#> re74
#> 4557.54657