This page explains the details of estimating weights using a user-defined function. The function must take in arguments that are passed to it by weightit() or weightitMSM() and return a vector of weights or a list containing the weights.
To supply a user-defined function, the function object should be entered directly to method; for example, for a function fun, method = fun.
Point Treatments
The following arguments are automatically passed to the user-defined function, which should have named parameters corresponding to them:
treat: a vector of treatment status for each unit. This comes directly from the left hand side of the formula passed toweightit()and so will have it's type (e.g., numeric, factor, etc.), which may need to be converted.covs: a data frame of covariate values for each unit. This comes directly from the right hand side of the formula passed toweightit(). The covariates are processed so that all columns are numeric; all factor variables are split into dummies and all interactions are evaluated. All levels of factor variables are given dummies, so the matrix of the covariates is not full rank. Users can usemake_full_rank(), which accepts a numeric matrix or data frame and removes columns to make it full rank, if a full rank covariate matrix is desired.s.weights: a numeric vector of sampling weights, one for each unit.ps: a numeric vector of propensity scores.subset: a logical vector the same length astreatthat isTRUEfor units to be included in the estimation andFALSEotherwise. This is used to subset the input objects whenexactis used.treat,covs,s.weights, andps, if supplied, will already have been subsetted bysubset.estimand: a character vector of length 1 containing the desired estimand. The characters will have been converted to uppercase. If "ATC" was supplied to estimand,weightit()setsfocalto the control level (usually 0 or the lowest level oftreat) and setsestimandto "ATT".focal: a character vector of length 1 containing the focal level of the treatment when the estimand is the ATT (or the ATC as detailed above).weightit()ensures the value of focal is a level oftreat.stabilize: a logical vector of length 1. It is not processed byweightit()before it reaches the fitting function.moments: a numeric vector of length 1. It is not processed byweightit()before it reaches the fitting function except thatas.integer()is applied to it. This is used in other methods to determine whether polynomials of the entered covariates are to be used in the weight estimation.int: a logical vector of length 1. It is not processed byweightit()before it reaches the fitting function. This is used in other methods to determine whether interactions of the entered covariates are to be used in the weight estimation.
None of these parameters are required to be in the fitting function. These are simply those that are automatically available.
In addition, any additional arguments supplied to weightit() will be passed on to the fitting function. weightit() ensures the arguments correspond to the parameters of the fitting function and throws an error if an incorrectly named argument is supplied and the fitting function doesn't include \dots as a parameter.
The fitting function must output either a numeric vector of weights or a list (or list-like object) with an entry named wither "w" or "weights". If a list, the list can contain other named entries, but only entries named "w", "weights", "ps", and "fit.obj" will be processed. "ps" is a vector of propensity scores and "fit.obj" should be an object used in the fitting process that a user may want to examine and that is included in the weightit output object as "obj" when include.obj = TRUE. The "ps" and "fit.obj" components are optional, but "weights" or "w" is required.
Longitudinal Treatments
Longitudinal treatments can be handled either by running the fitting function for point treatments for each time point and multiplying the resulting weights together or by running a method that accommodates multiple time points and outputs a single set of weights. For the former, weightitMSM() can be used with the user-defined function just as it is with weightit(). The latter method is not yet accommodated by weightitMSM(), but will be someday, maybe.
Examples
library("cobalt")
data("lalonde", package = "cobalt")
#A user-defined version of method = "ps"
my.ps <- function(treat, covs, estimand, focal = NULL) {
covs <- make_full_rank(covs)
d <- data.frame(treat, covs)
f <- formula(d)
ps <- glm(f, data = d, family = "binomial")$fitted
w <- get_w_from_ps(ps, treat = treat, estimand = estimand,
focal = focal)
list(w = w, ps = ps)
}
#Balancing covariates between treatment groups (binary)
(W1 <- weightit(treat ~ age + educ + married +
nodegree + re74, data = lalonde,
method = my.ps, estimand = "ATT"))
#> A weightit object
#> - method: "my.ps" (a user-defined method)
#> - number of obs.: 614
#> - sampling weights: none
#> - treatment: 2-category
#> - estimand: ATT (focal: 1)
#> - covariates: age, educ, married, nodegree, re74
summary(W1)
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0000 || 1.0000
#> control 0.0222 |---------------------------| 2.0438
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 5 4 3 2 1
#> treated 1 1 1 1 1
#> 411 595 269 409 296
#> control 1.3303 1.4365 1.5005 1.6369 2.0438
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 0.000 0.000 0.00 0
#> control 0.823 0.701 0.33 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 429. 185
#> Weighted 255.99 185
bal.tab(W1)
#> Balance Measures
#> Type Diff.Adj
#> prop.score Distance 0.0199
#> age Contin. 0.0459
#> educ Contin. -0.0360
#> married Binary 0.0044
#> nodegree Binary 0.0080
#> re74 Contin. -0.0275
#>
#> Effective sample sizes
#> Control Treated
#> Unadjusted 429. 185
#> Adjusted 255.99 185
data("msmdata")
(W2 <- weightitMSM(list(A_1 ~ X1_0 + X2_0,
A_2 ~ X1_1 + X2_1 +
A_1 + X1_0 + X2_0,
A_3 ~ X1_2 + X2_2 +
A_2 + X1_1 + X2_1 +
A_1 + X1_0 + X2_0),
data = msmdata,
method = my.ps))
#> A weightitMSM object
#> - method: "my.ps" (a user-defined method)
#> - number of obs.: 7500
#> - sampling weights: none
#> - number of time points: 3 (A_1, A_2, A_3)
#> - treatment:
#> + time 1: 2-category
#> + time 2: 2-category
#> + time 3: 2-category
#> - covariates:
#> + baseline: X1_0, X2_0
#> + after time 1: X1_1, X2_1, A_1, X1_0, X2_0
#> + after time 2: X1_2, X2_2, A_2, X1_1, X2_1, A_1, X1_0, X2_0
summary(W2)
#> Time 1
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0791 |---------------------------| 403.4833
#> control 1.2761 |-------------------| 284.7636
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 5488 3440 3593 1286 5685
#> treated 166.992 170.5549 196.4136 213.1934 403.4833
#> 2594 2932 5226 1875 2533
#> control 155.6248 168.964 172.4195 245.8822 284.7636
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 1.914 0.816 0.649 0
#> control 1.706 0.862 0.670 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 3306. 4194.
#> Weighted 845.79 899.4
#>
#> Time 2
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0791 |---------------------------| 403.4833
#> control 1.2761 |----------------| 245.8822
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 2932 3440 3593 2533 5685
#> treated 168.964 170.5549 196.4136 284.7636 403.4833
#> 2594 5488 5226 1286 1875
#> control 155.6248 166.992 172.4195 213.1934 245.8822
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 1.892 0.819 0.652 0
#> control 1.748 0.869 0.686 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 3701. 3799.
#> Weighted 912.87 829.87
#>
#> Time 3
#> Summary of weights
#>
#> - Weight ranges:
#>
#> Min Max
#> treated 1.0791 |---------------------------| 403.4833
#> control 1.2761 |---------| 148.1547
#>
#> - Units with the 5 most extreme weights by group:
#>
#> 3593 1286 1875 2533 5685
#> treated 196.4136 213.1934 245.8822 284.7636 403.4833
#> 6862 168 3729 6158 3774
#> control 88.0721 97.8273 104.623 121.8451 148.1547
#>
#> - Weight statistics:
#>
#> Coef of Var MAD Entropy # Zeros
#> treated 1.832 0.975 0.785 0
#> control 1.254 0.683 0.412 0
#>
#> - Effective Sample Sizes:
#>
#> Control Treated
#> Unweighted 4886. 2614.
#> Weighted 1900.26 600.12
#>
bal.tab(W2)
#> Balance summary across all time points
#> Times Type Max.Diff.Adj
#> X1_0 1, 2, 3 Contin. 0.0342
#> X2_0 1, 2, 3 Binary 0.0299
#> X1_1 2, 3 Contin. 0.0657
#> X2_1 2, 3 Binary 0.0299
#> A_1 2, 3 Binary 0.0262
#> X1_2 3 Contin. 0.0643
#> X2_2 3 Binary 0.0096
#> A_2 3 Binary 0.0054
#>
#> Effective sample sizes
#> - Time 1
#> Control Treated
#> Unadjusted 3306. 4194.
#> Adjusted 845.79 899.4
#> - Time 2
#> Control Treated
#> Unadjusted 3701. 3799.
#> Adjusted 912.87 829.87
#> - Time 3
#> Control Treated
#> Unadjusted 4886. 2614.
#> Adjusted 1900.26 600.12
# Kernel balancing using the `kbal` package, available
# using `pak::pak_install("chadhazlett/KBAL")`.
# Only the ATT and ATC are available.
if (FALSE) { # \dontrun{
kbal.fun <- function(treat, covs, estimand, focal, verbose, ...) {
args <- list(...)
if (!estimand %in% c("ATT", "ATC"))
stop("`estimand` must be \"ATT\" or \"ATC\".", call. = FALSE)
treat <- as.numeric(treat == focal)
args <- args[names(args) %in% names(formals(kbal::kbal))]
args$allx <- covs
args$treatment <- treat
args$printprogress <- verbose
cat_cols <- apply(covs, 2, function(x) length(unique(x)) <= 2)
if (all(cat_cols)) {
args$cat_data <- TRUE
args$mixed_data <- FALSE
args$scale_data <- FALSE
args$linkernel <- FALSE
args$drop_MC <- FALSE
}
else if (any(cat_cols)) {
args$cat_data <- FALSE
args$mixed_data <- TRUE
args$cat_columns <- colnames(covs)[cat_cols]
args$allx[,!cat_cols] <- scale(args$allx[,!cat_cols])
args$cont_scale <- 1
}
else {
args$cat_data <- FALSE
args$mixed_data <- FALSE
}
k.out <- do.call(kbal::kbal, args)
w <- k.out$w
list(w = w, fit.obj = k.out)
}
(Wk <- weightit(treat ~ age + educ + married +
nodegree + re74, data = lalonde,
method = kbal.fun, estimand = "ATT",
include.obj = TRUE))
summary(Wk)
bal.tab(Wk, stats = c("m", "ks"))
} # }