optweight (version 2.0.0)

plot.optweight: Plot Dual Variables for Covariate Constraints

Description

Plots the dual variables resulting from optweight(), optweightMV(), or optweight.svy() in a way similar to figure 2 of Zubizarreta (2015), which explains how to interpret these values.

Usage

# S3 method for optweight
plot(x, type = "variables", ...)
# S3 method for optweightMV
plot(x, which.treat = 1L, type = "variables", ...)
# S3 method for optweight.svy
plot(x, type = "variables", ...)

Value

A ggplot object that can be used with other ggplot2 functions.

Arguments

x

an optweight, optweightMV, or optweight.svy object; the output of a call to optweight(), optweightMV(), or optweight.svy().

type

the type of plot to display; allowable options include "variables" (the default), which produces a row for each covariate, and "constraints", which produces a row for each type of constraint (computed as the sum of the absolute dual variables for each constraint type).

...

ignored.

which.treat

for optweightMV objects, an integer corresponding to which treatment to display. Only one may be displayed at a time.

Details

Dual variables represent the cost of changing the constraint on the objective function minimized to estimate the weights. For covariates with large values of the dual variable, tightening the constraint will increase the variability of the weights, and relaxing the constraint will decrease the variability of the weights, both to a greater extent than would doing the same for covariate with small values of the dual variable. See optweight() and vignette("optweight") for more information on interpreting dual variables.

References

Zubizarreta, J. R. (2015). Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data. Journal of the American Statistical Association, 110(511), 910–922. tools:::Rd_expr_doi("10.1080/01621459.2015.1023805")

See Also

optweight(), optweightMV(), or optweight.svy() to estimate the weights and the dual variables.

plot.summary.optweight() for plots of the distribution of weights.

Examples

if (FALSE) { # rlang::is_installed("cobalt")
library("cobalt")
data("lalonde", package = "cobalt")

tols <- process_tols(treat ~ age + educ + married +
                       nodegree + re74, data = lalonde,
                     tols = .1)

#Balancing covariates between treatment groups (binary)
ow1 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

# Note the L2 divergence and effective sample
# size (ESS)
summary(ow1, weight.range = FALSE)

# age has a low value, married is high
plot(ow1)

tols["age"] <- 0
ow2 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

# Notice that tightening the constraint on age has
# a negligible effect on the variability of the
# weights and ESS
summary(ow2, weight.range = FALSE)

tols["age"] <- .1
tols["married"] <- 0
ow3 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 tols = tols,
                 estimand = "ATT")

# In contrast, tightening the constraint on married
# has a large effect on the variability of the
# weights, shrinking the ESS
summary(ow3, weight.range = FALSE)

# More duals are displayed when targeting other
# estimands:
ow4 <- optweight(treat ~ age + educ + married +
                   nodegree + re74, data = lalonde,
                 estimand = "ATE")

plot(ow4)

# Display duals by constraint type
plot(ow4, type = "constraints")
}