Construct and Check Targets Input

Checks whether proposed target population means values for targets are suitable in number and order for submission to optweight(), optweightMV(), and optweight.svy(), and returns an object that can supplied to the targets argument of these functions.

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 nothing on the left hand side and the covariates to be targeted on the right hand side. Interactions and functions of covariates are allowed. Can be omitted, in which case all variables in data are assumed targeted. If data is NULL and formula is a data.frame, data will be replaced with formula.

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. If NULL, the current sample means will be produced (weighted by s.weights). If NA, an NA vector named with the covariate names will be produced.

s.weights

a vector of sampling weights. For optweight(), can also be the name of a variable in data that contains sampling weights.

...

for optweight(), additional arguments passed to optweight.fit(), including options that are passed to the settings function corresponding to solver.

x

an optweight.targets object; the output of a call to process_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 an input to the targets argument of optweight(), optweightMV(), and optweight.svy().

Details

The purpose of process_targets() is to allow users to ensure that their proposed input to targets in optweight(), optweightMV(), 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.

See also

process_tols()

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
#>  - variables:
#> 	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