Nothing
R/matchit2optimal.R
In MatchIt: Nonparametric Preprocessing for Parametric Causal Inference
Defines functions
.subset_infsm
matchit2optimal
#' Optimal Pair Matching
#' @name method_optimal
#' @aliases method_optimal
#' @usage NULL
#'
#' @description
#' In [matchit()], setting `method = "optimal"` performs optimal pair
#' matching. The matching is optimal in the sense that that sum of the absolute
#' pairwise distances in the matched sample is as small as possible. The method
#' functionally relies on \pkgfun{optmatch}{fullmatch}.
#'
#' Advantages of optimal pair matching include that the matching order is not
#' required to be specified and it is less likely that extreme within-pair
#' distances will be large, unlike with nearest neighbor matching. Generally,
#' however, as a subset selection method, optimal pair matching tends to
#' perform similarly to nearest neighbor matching in that similar subsets of
#' units will be selected to be matched.
#'
#' This page details the allowable arguments with `method = "optmatch"`.
#' See [matchit()] for an explanation of what each argument means in a general
#' context and how it can be specified.
#'
#' Below is how `matchit()` is used for optimal pair matching:
#' \preformatted{
#' matchit(formula,
#' data = NULL,
#' method = "optimal",
#' distance = "glm",
#' link = "logit",
#' distance.options = list(),
#' estimand = "ATT",
#' exact = NULL,
#' mahvars = NULL,
#' antiexact = NULL,
#' discard = "none",
#' reestimate = FALSE,
#' s.weights = NULL,
#' ratio = 1,
#' min.controls = NULL,
#' max.controls = NULL,
#' verbose = FALSE,
#' ...) }
#'
#' @param formula a two-sided [formula] object containing the treatment and
#' covariates to be used in creating the distance measure used in the matching.
#' This formula will be supplied to the functions that estimate the distance
#' measure.
#' @param data a data frame containing the variables named in `formula`.
#' If not found in `data`, the variables will be sought in the
#' environment.
#' @param method set here to `"optimal"`.
#' @param distance the distance measure to be used. See [`distance`]
#' for allowable options. Can be supplied as a distance matrix.
#' @param link when `distance` is specified as a method of estimating
#' propensity scores, an additional argument controlling the link function used
#' in estimating the distance measure. See [`distance`] for allowable
#' options with each option.
#' @param distance.options a named list containing additional arguments
#' supplied to the function that estimates the distance measure as determined
#' by the argument to `distance`.
#' @param estimand a string containing the desired estimand. Allowable options
#' include `"ATT"` and `"ATC"`. See Details.
#' @param exact for which variables exact matching should take place.
#' @param mahvars for which variables Mahalanobis distance matching should take
#' place when `distance` corresponds to a propensity score (e.g., for
#' caliper matching or to discard units for common support). If specified, the
#' distance measure will not be used in matching.
#' @param antiexact for which variables anti-exact matching should take place.
#' Anti-exact matching is processed using \pkgfun{optmatch}{antiExactMatch}.
#' @param discard a string containing a method for discarding units outside a
#' region of common support. Only allowed when `distance` is not
#' `"mahalanobis"` and not a matrix.
#' @param reestimate if `discard` is not `"none"`, whether to
#' re-estimate the propensity score in the remaining sample prior to matching.
#' @param s.weights the variable containing sampling weights to be incorporated
#' into propensity score models and balance statistics.
#' @param ratio how many control units should be matched to each treated unit
#' for k:1 matching. For variable ratio matching, see section "Variable Ratio
#' Matching" in Details below.
#' @param min.controls,max.controls for variable ratio matching, the minimum
#' and maximum number of controls units to be matched to each treated unit. See
#' section "Variable Ratio Matching" in Details below.
#' @param verbose `logical`; whether information about the matching
#' process should be printed to the console. What is printed depends on the
#' matching method. Default is `FALSE` for no printing other than
#' warnings.
#' @param \dots additional arguments passed to \pkgfun{optmatch}{fullmatch}.
#' Allowed arguments include `tol` and `solver`. See the
#' \pkgfun{optmatch}{fullmatch} documentation for details. In general, `tol`
#' should be set to a low number (e.g., `1e-7`) to get a more precise
#' solution (default is `1e-3`).
#'
#' The arguments `replace`, `caliper`, and `m.order` are ignored with a warning.
#'
#' @section Outputs:
#'
#' All outputs described in [matchit()] are returned with
#' `method = "optimal"`. When `include.obj = TRUE` in the call to
#' `matchit()`, the output of the call to `optmatch::fullmatch()` will be
#' included in the output. When `exact` is specified, this will be a list
#' of such objects, one for each stratum of the `exact` variables.
#'
#' @details
#'
#' ## Mahalanobis Distance Matching
#'
#' Mahalanobis distance matching can be done one of two ways:
#' \enumerate{
#' \item{If no propensity score needs to be estimated, `distance` should be
#' set to `"mahalanobis"`, and Mahalanobis distance matching will occur
#' using all the variables in `formula`. Arguments to `discard` and
#' `mahvars` will be ignored. For example, to perform simple Mahalanobis
#' distance matching, the following could be run:
#'
#' \preformatted{
#' matchit(treat ~ X1 + X2, method = "nearest",
#' distance = "mahalanobis") }
#'
#' With this code, the Mahalanobis distance is computed using `X1` and
#' `X2`, and matching occurs on this distance. The `distance`
#' component of the `matchit()` output will be empty.
#' }
#' \item{If a propensity score needs to be estimated for common support with
#' `discard`, `distance` should be whatever method is used to
#' estimate the propensity score or a vector of distance measures, i.e., it
#' should not be `"mahalanobis"`. Use `mahvars` to specify the
#' variables used to create the Mahalanobis distance. For example, to perform
#' Mahalanobis after discarding units outside the common support of the
#' propensity score in both groups, the following could be run:
#'
#' \preformatted{
#' matchit(treat ~ X1 + X2 + X3, method = "nearest",
#' distance = "glm", discard = "both",
#' mahvars = ~ X1 + X2) }
#'
#' With this code, `X1`, `X2`, and `X3` are used to estimate the
#' propensity score (using the `"glm"` method, which by default is
#' logistic regression), which is used to identify the common support. The
#' actual matching occurs on the Mahalanobis distance computed only using
#' `X1` and `X2`, which are supplied to `mahvars`. The estimated
#' propensity scores will be included in the `distance` component of the
#' `matchit()` output.
#' }
#' }
#' ## Estimand
#'
#' The `estimand` argument controls whether control units are selected to be matched with treated units
#' (`estimand = "ATT"`) or treated units are selected to be matched with
#' control units (`estimand = "ATC"`). The "focal" group (e.g., the
#' treated units for the ATT) is typically made to be the smaller treatment
#' group, and a warning will be thrown if it is not set that. Setting `estimand = "ATC"` is equivalent to
#' swapping all treated and control labels for the treatment variable. When
#' `estimand = "ATC"`, the `match.matrix` component of the output
#' will have the names of the control units as the rownames and be filled with
#' the names of the matched treated units (opposite to when `estimand = "ATT"`). Note that the argument supplied to `estimand` doesn't
#' necessarily correspond to the estimand actually targeted; it is merely a
#' switch to trigger which treatment group is considered "focal".
#'
#' ## Variable Ratio Matching
#'
#' `matchit()` can perform variable
#' ratio matching, which involves matching a different number of control units
#' to each treated unit. When `ratio > 1`, rather than requiring all
#' treated units to receive `ratio` matches, the arguments to
#' `max.controls` and `min.controls` can be specified to control the
#' maximum and minimum number of matches each treated unit can have.
#' `ratio` controls how many total control units will be matched: `n1 * ratio` control
#' units will be matched, where `n1` is the number of
#' treated units, yielding the same total number of matched controls as fixed
#' ratio matching does.
#'
#' Variable ratio matching can be used with any `distance` specification.
#' `ratio` does not have to be an integer but must be greater than 1 and
#' less than `n0/n1`, where `n0` and `n1` are the number of
#' control and treated units, respectively. Setting `ratio = n0/n1`
#' performs a restricted form of full matching where all control units are
#' matched. If `min.controls` is not specified, it is set to 1 by default.
#' `min.controls` must be less than `ratio`, and `max.controls`
#' must be greater than `ratio`. See the Examples section of
#' [method_nearest()] for an example of their use, which is the same
#' as it is with optimal matching.
#'
#' @note
#' Optimal pair matching is a restricted form of optimal full matching
#' where the number of treated units in each subclass is equal to 1, whereas in
#' unrestricted full matching, multiple treated units can be assigned to the
#' same subclass. \pkgfun{optmatch}{pairmatch} is simply a wrapper for
#' \pkgfun{optmatch}{fullmatch}, which performs optimal full matching and is the
#' workhorse for [`method_full`]. In the same way, `matchit()`
#' uses `optmatch::fullmatch()` under the hood, imposing the restrictions that
#' make optimal full matching function like optimal pair matching (which is
#' simply to set `min.controls >= 1` and to pass `ratio` to the
#' `mean.controls` argument). This distinction is not important for
#' regular use but may be of interest to those examining the source code.
#'
#' The option `"optmatch_max_problem_size"` is automatically set to
#' `Inf` during the matching process, different from its default in
#' *optmatch*. This enables matching problems of any size to be run, but
#' may also let huge, infeasible problems get through and potentially take a
#' long time or crash R. See \pkgfun{optmatch}{setMaxProblemSize} for more details.
#'
#' A preprocessing algorithm describe by Sävje (2020; \doi{10.1214/19-STS739}) is used to improve the speed of the matching when 1:1 matching on a propensity score. It does so by adding an additional constraint that guarantees a solution as optimal as the solution that would have been found without the constraint, and that constraint often dramatically reduces the size of the matching problem at no cost. However, this may introduce differences between the results obtained by *MatchIt* and by *optmatch*, though such differences will shrink when smaller values of `tol` are used.
#'
#' @seealso [matchit()] for a detailed explanation of the inputs and outputs of
#' a call to `matchit()`.
#'
#' \pkgfun{optmatch}{fullmatch}, which is the workhorse.
#'
#' [`method_full`] for optimal full matching, of which optimal pair
#' matching is a special case, and which relies on similar machinery.
#'
#' @references In a manuscript, be sure to cite the following paper if using
#' `matchit()` with `method = "optimal"`:
#'
#' Hansen, B. B., & Klopfer, S. O. (2006). Optimal Full Matching and Related
#' Designs via Network Flows. Journal of Computational and Graphical
#' Statistics, 15(3), 609–627. \doi{10.1198/106186006X137047}
#'
#' For example, a sentence might read:
#'
#' *Optimal pair matching was performed using the MatchIt package (Ho,
#' Imai, King, & Stuart, 2011) in R, which calls functions from the optmatch
#' package (Hansen & Klopfer, 2006).*
#'
#' @examplesIf requireNamespace("optmatch", quietly = TRUE)
#' data("lalonde")
#'
#' #1:1 optimal PS matching with exact matching on race
#' m.out1 <- matchit(treat ~ age + educ + race +
#' nodegree + married + re74 + re75,
#' data = lalonde,
#' method = "optimal",
#' exact = ~race)
#' m.out1
#' summary(m.out1)
#'
#' #2:1 optimal matching on the scaled Euclidean distance
#' m.out2 <- matchit(treat ~ age + educ + race +
#' nodegree + married + re74 + re75,
#' data = lalonde,
#' method = "optimal",
#' ratio = 2,
#' distance = "scaled_euclidean")
#' m.out2
#' summary(m.out2, un = FALSE)
NULL
matchit2optimal <- function(treat, formula, data, distance, discarded,
ratio = 1, s.weights = NULL, caliper = NULL,
mahvars = NULL, exact = NULL,
estimand = "ATT", verbose = FALSE,
is.full.mahalanobis, antiexact = NULL, ...) {
rlang::check_installed("optmatch")
.args <- c("tol", "solver")
A <- ...mget(.args)
A[lengths(A) == 0L] <- NULL
estimand <- toupper(estimand)
estimand <- match_arg(estimand, c("ATT", "ATC"))
if (estimand == "ATC") {
tc <- c("control", "treated")
focal <- 0
}
else {
tc <- c("treated", "control")
focal <- 1
}
treat_ <- setNames(as.integer(treat[!discarded] == focal), names(treat)[!discarded])
# if (is_not_null(data)) data <- data[!discarded,]
if (is.full.mahalanobis) {
if (is_null(attr(terms(formula, data = data), "term.labels"))) {
.err(sprintf("covariates must be specified in the input formula when `distance = \"%s\"`",
attr(is.full.mahalanobis, "transform")))
}
mahvars <- formula
}
if (is_not_null(caliper)) {
.wrn("calipers are currently not compatible with `method = \"optimal\"` and will be ignored")
caliper <- NULL
}
min.controls <- attr(ratio, "min.controls")
max.controls <- attr(ratio, "max.controls")
if (is_null(max.controls)) {
min.controls <- max.controls <- ratio
}
#Exact matching strata
if (is_not_null(exact)) {
ex <- factor(exactify(model.frame(exact, data = data),
sep = ", ", include_vars = TRUE)[!discarded])
cc <- Reduce("intersect", lapply(unique(treat_), function(t) unclass(ex)[treat_ == t]))
if (is_null(cc)) {
.err("no matches were found")
}
e_ratios <- vapply(levels(ex), function(e) {
sum(treat_[ex == e] == 0) / sum(treat_[ex == e] == 1)
}, numeric(1L))
if (any(e_ratios < 1)) {
.wrn(sprintf("fewer %s units than %s units in some `exact` strata; not all %s units will get a match",
tc[2L], tc[1L], tc[1L]))
}
if (ratio > 1 && any(e_ratios < ratio)) {
if (ratio == max.controls)
.wrn(sprintf("not all %s units will get %s matches",
tc[1L], ratio))
else
.wrn(sprintf("not enough %s units for an average of %s matches per %s unit in all `exact` strata",
tc[2L], ratio, tc[1L]))
}
}
else {
ex <- gl(1, length(treat_), labels = "_")
cc <- 1
e_ratios <- setNames(sum(treat_ == 0) / sum(treat_ == 1), levels(ex))
if (e_ratios < 1) {
.wrn(sprintf("fewer %s units than %s units; not all %s units will get a match",
tc[2L], tc[1L], tc[1L]))
}
else if (e_ratios < ratio) {
if (ratio == max.controls)
.wrn(sprintf("not all %s units will get %s matches",
tc[1L], ratio))
else
.wrn(sprintf("not enough %s units for an average of %s matches per %s unit",
tc[2L], ratio, tc[1L]))
}
}
#Create distance matrix; note that Mahalanobis distance computed using entire
#sample (minus discarded), like method2nearest, as opposed to within exact strata, like optmatch.
if (is_not_null(mahvars)) {
transform <- if (is.full.mahalanobis) attr(is.full.mahalanobis, "transform") else "mahalanobis"
mahcovs <- transform_covariates(mahvars, data = data, method = transform,
s.weights = s.weights, treat = treat,
discarded = discarded)
mo <- eucdist_internal(mahcovs, treat)
}
else if (is.matrix(distance)) {
mo <- distance
}
else {
mo <- eucdist_internal(setNames(as.numeric(distance), names(treat)), treat)
}
#Transpose distance mat as needed
if (focal == 0) {
mo <- t(mo)
}
#Remove discarded units from distance mat
mo <- mo[!discarded[treat == focal], !discarded[treat != focal], drop = FALSE]
dimnames(mo) <- list(names(treat_)[treat_ == 1], names(treat_)[treat_ == 0])
mo <- optmatch::match_on(mo, data = as.data.frame(data)[!discarded, , drop = FALSE])
mo <- optmatch::as.InfinitySparseMatrix(mo)
if (is_null(mahvars) && !is.matrix(distance) && nlevels(ex) == 1L &&
ratio == 1 && max.controls == 1) {
.cat_verbose("Preprocessing to reduce problem size...\n", verbose = verbose)
#Preprocess by pruning unnecessary edges as in Sävje (2020) https://doi.org/10.1214/19-STS699
keep <- preprocess_matchC(treat_, distance[!discarded])
if (length(keep) < length(mo)) {
mo <- .subset_infsm(mo, keep)
}
}
.cat_verbose("Optimal matching...\n", verbose = verbose)
#Process antiexact
if (is_not_null(antiexact)) {
antiexactcovs <- model.frame(antiexact, data)
for (i in seq_len(ncol(antiexactcovs))) {
mo <- mo + optmatch::antiExactMatch(antiexactcovs[[i]][!discarded], z = treat_)
}
}
#Initialize pair membership; must include names
pair <- rep_with(NA_character_, treat)
p <- setNames(vector("list", nlevels(ex)), levels(ex))
t_df <- data.frame(treat_)
for (e in levels(ex)[cc]) {
if (nlevels(ex) > 1L) {
.cat_verbose(sprintf("Matching subgroup %s/%s: %s...\n",
match(e, levels(ex)[cc]), length(cc), e),
verbose = verbose)
mo_ <- mo[ex[treat_ == 1] == e, ex[treat_ == 0] == e]
}
else {
mo_ <- mo
}
if (any(dim(mo_) == 0) || !any(is.finite(mo_))) {
next
}
if (all_equal_to(dim(mo_), 1) && all(is.finite(mo_))) {
pair[ex == e] <- paste(1, e, sep = "|")
next
}
#Process ratio, etc., when available ratio in exact matching categories
#(e_ratio) differs from requested ratio
if (e_ratios[e] < 1) {
#Switch treatment and control labels; unmatched treated units are dropped
ratio_ <- min.controls_ <- max.controls_ <- 1
mo_ <- t(mo_)
}
else if (e_ratios[e] < ratio) {
#Lower ratio and min.controls.
ratio_ <- e_ratios[e]
min.controls_ <- min(min.controls, floor(e_ratios[e]))
max.controls_ <- max.controls
}
else {
ratio_ <- ratio
min.controls_ <- min.controls
max.controls_ <- max.controls
}
A$x <- mo_
A$mean.controls <- ratio_
A$min.controls <- min.controls_
A$max.controls <- max.controls_
A$data <- t_df[ex == e, , drop = FALSE] #just to get rownames; not actually used in matching
rlang::with_options({
matchit_try({
p[[e]] <- do.call(optmatch::fullmatch, A)
}, from = "optmatch")
}, optmatch_max_problem_size = Inf)
pair[names(p[[e]])[!is.na(p[[e]])]] <- paste(as.character(p[[e]][!is.na(p[[e]])]), e, sep = "|")
}
if (length(p) == 1L) {
p <- p[[1L]]
}
psclass <- factor(pair)
levels(psclass) <- seq_len(nlevels(psclass))
names(psclass) <- names(treat)
mm <- nummm2charmm(subclass2mmC(psclass, treat, focal), treat)
.cat_verbose("Calculating matching weights...", verbose = verbose)
## calculate weights and return the results
res <- list(match.matrix = mm,
subclass = psclass,
weights = get_weights_from_subclass(psclass, treat, estimand),
obj = p)
.cat_verbose("Done.\n", verbose = verbose)
class(res) <- "matchit"
res
}
#Subset InfinitySparseMatrix using vector indices
.subset_infsm <- function(y, ss) {
y@.Data <- y[ss]
y@cols <- y@cols[ss]
y@rows <- y@rows[ss]
y
}
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Defines functions .subset_infsm matchit2optimal
#' Optimal Pair Matching
#' @name method_optimal
#' @aliases method_optimal
#' @usage NULL
#'
#' @description
#' In [matchit()], setting `method = "optimal"` performs optimal pair
#' matching. The matching is optimal in the sense that that sum of the absolute
#' pairwise distances in the matched sample is as small as possible. The method
#' functionally relies on \pkgfun{optmatch}{fullmatch}.
#'
#' Advantages of optimal pair matching include that the matching order is not
#' required to be specified and it is less likely that extreme within-pair
#' distances will be large, unlike with nearest neighbor matching. Generally,
#' however, as a subset selection method, optimal pair matching tends to
#' perform similarly to nearest neighbor matching in that similar subsets of
#' units will be selected to be matched.
#'
#' This page details the allowable arguments with `method = "optmatch"`.
#' See [matchit()] for an explanation of what each argument means in a general
#' context and how it can be specified.
#'
#' Below is how `matchit()` is used for optimal pair matching:
#' \preformatted{
#' matchit(formula,
#' data = NULL,
#' method = "optimal",
#' distance = "glm",
#' link = "logit",
#' distance.options = list(),
#' estimand = "ATT",
#' exact = NULL,
#' mahvars = NULL,
#' antiexact = NULL,
#' discard = "none",
#' reestimate = FALSE,
#' s.weights = NULL,
#' ratio = 1,
#' min.controls = NULL,
#' max.controls = NULL,
#' verbose = FALSE,
#' ...) }
#'
#' @param formula a two-sided [formula] object containing the treatment and
#' covariates to be used in creating the distance measure used in the matching.
#' This formula will be supplied to the functions that estimate the distance
#' measure.
#' @param data a data frame containing the variables named in `formula`.
#' If not found in `data`, the variables will be sought in the
#' environment.
#' @param method set here to `"optimal"`.
#' @param distance the distance measure to be used. See [`distance`]
#' for allowable options. Can be supplied as a distance matrix.
#' @param link when `distance` is specified as a method of estimating
#' propensity scores, an additional argument controlling the link function used
#' in estimating the distance measure. See [`distance`] for allowable
#' options with each option.
#' @param distance.options a named list containing additional arguments
#' supplied to the function that estimates the distance measure as determined
#' by the argument to `distance`.
#' @param estimand a string containing the desired estimand. Allowable options
#' include `"ATT"` and `"ATC"`. See Details.
#' @param exact for which variables exact matching should take place.
#' @param mahvars for which variables Mahalanobis distance matching should take
#' place when `distance` corresponds to a propensity score (e.g., for
#' caliper matching or to discard units for common support). If specified, the
#' distance measure will not be used in matching.
#' @param antiexact for which variables anti-exact matching should take place.
#' Anti-exact matching is processed using \pkgfun{optmatch}{antiExactMatch}.
#' @param discard a string containing a method for discarding units outside a
#' region of common support. Only allowed when `distance` is not
#' `"mahalanobis"` and not a matrix.
#' @param reestimate if `discard` is not `"none"`, whether to
#' re-estimate the propensity score in the remaining sample prior to matching.
#' @param s.weights the variable containing sampling weights to be incorporated
#' into propensity score models and balance statistics.
#' @param ratio how many control units should be matched to each treated unit
#' for k:1 matching. For variable ratio matching, see section "Variable Ratio
#' Matching" in Details below.
#' @param min.controls,max.controls for variable ratio matching, the minimum
#' and maximum number of controls units to be matched to each treated unit. See
#' section "Variable Ratio Matching" in Details below.
#' @param verbose `logical`; whether information about the matching
#' process should be printed to the console. What is printed depends on the
#' matching method. Default is `FALSE` for no printing other than
#' warnings.
#' @param \dots additional arguments passed to \pkgfun{optmatch}{fullmatch}.
#' Allowed arguments include `tol` and `solver`. See the
#' \pkgfun{optmatch}{fullmatch} documentation for details. In general, `tol`
#' should be set to a low number (e.g., `1e-7`) to get a more precise
#' solution (default is `1e-3`).
#'
#' The arguments `replace`, `caliper`, and `m.order` are ignored with a warning.
#'
#' @section Outputs:
#'
#' All outputs described in [matchit()] are returned with
#' `method = "optimal"`. When `include.obj = TRUE` in the call to
#' `matchit()`, the output of the call to `optmatch::fullmatch()` will be
#' included in the output. When `exact` is specified, this will be a list
#' of such objects, one for each stratum of the `exact` variables.
#'
#' @details
#'
#' ## Mahalanobis Distance Matching
#'
#' Mahalanobis distance matching can be done one of two ways:
#' \enumerate{
#' \item{If no propensity score needs to be estimated, `distance` should be
#' set to `"mahalanobis"`, and Mahalanobis distance matching will occur
#' using all the variables in `formula`. Arguments to `discard` and
#' `mahvars` will be ignored. For example, to perform simple Mahalanobis
#' distance matching, the following could be run:
#'
#' \preformatted{
#' matchit(treat ~ X1 + X2, method = "nearest",
#' distance = "mahalanobis") }
#'
#' With this code, the Mahalanobis distance is computed using `X1` and
#' `X2`, and matching occurs on this distance. The `distance`
#' component of the `matchit()` output will be empty.
#' }
#' \item{If a propensity score needs to be estimated for common support with
#' `discard`, `distance` should be whatever method is used to
#' estimate the propensity score or a vector of distance measures, i.e., it
#' should not be `"mahalanobis"`. Use `mahvars` to specify the
#' variables used to create the Mahalanobis distance. For example, to perform
#' Mahalanobis after discarding units outside the common support of the
#' propensity score in both groups, the following could be run:
#'
#' \preformatted{
#' matchit(treat ~ X1 + X2 + X3, method = "nearest",
#' distance = "glm", discard = "both",
#' mahvars = ~ X1 + X2) }
#'
#' With this code, `X1`, `X2`, and `X3` are used to estimate the
#' propensity score (using the `"glm"` method, which by default is
#' logistic regression), which is used to identify the common support. The
#' actual matching occurs on the Mahalanobis distance computed only using
#' `X1` and `X2`, which are supplied to `mahvars`. The estimated
#' propensity scores will be included in the `distance` component of the
#' `matchit()` output.
#' }
#' }
#' ## Estimand
#'
#' The `estimand` argument controls whether control units are selected to be matched with treated units
#' (`estimand = "ATT"`) or treated units are selected to be matched with
#' control units (`estimand = "ATC"`). The "focal" group (e.g., the
#' treated units for the ATT) is typically made to be the smaller treatment
#' group, and a warning will be thrown if it is not set that. Setting `estimand = "ATC"` is equivalent to
#' swapping all treated and control labels for the treatment variable. When
#' `estimand = "ATC"`, the `match.matrix` component of the output
#' will have the names of the control units as the rownames and be filled with
#' the names of the matched treated units (opposite to when `estimand = "ATT"`). Note that the argument supplied to `estimand` doesn't
#' necessarily correspond to the estimand actually targeted; it is merely a
#' switch to trigger which treatment group is considered "focal".
#'
#' ## Variable Ratio Matching
#'
#' `matchit()` can perform variable
#' ratio matching, which involves matching a different number of control units
#' to each treated unit. When `ratio > 1`, rather than requiring all
#' treated units to receive `ratio` matches, the arguments to
#' `max.controls` and `min.controls` can be specified to control the
#' maximum and minimum number of matches each treated unit can have.
#' `ratio` controls how many total control units will be matched: `n1 * ratio` control
#' units will be matched, where `n1` is the number of
#' treated units, yielding the same total number of matched controls as fixed
#' ratio matching does.
#'
#' Variable ratio matching can be used with any `distance` specification.
#' `ratio` does not have to be an integer but must be greater than 1 and
#' less than `n0/n1`, where `n0` and `n1` are the number of
#' control and treated units, respectively. Setting `ratio = n0/n1`
#' performs a restricted form of full matching where all control units are
#' matched. If `min.controls` is not specified, it is set to 1 by default.
#' `min.controls` must be less than `ratio`, and `max.controls`
#' must be greater than `ratio`. See the Examples section of
#' [method_nearest()] for an example of their use, which is the same
#' as it is with optimal matching.
#'
#' @note
#' Optimal pair matching is a restricted form of optimal full matching
#' where the number of treated units in each subclass is equal to 1, whereas in
#' unrestricted full matching, multiple treated units can be assigned to the
#' same subclass. \pkgfun{optmatch}{pairmatch} is simply a wrapper for
#' \pkgfun{optmatch}{fullmatch}, which performs optimal full matching and is the
#' workhorse for [`method_full`]. In the same way, `matchit()`
#' uses `optmatch::fullmatch()` under the hood, imposing the restrictions that
#' make optimal full matching function like optimal pair matching (which is
#' simply to set `min.controls >= 1` and to pass `ratio` to the
#' `mean.controls` argument). This distinction is not important for
#' regular use but may be of interest to those examining the source code.
#'
#' The option `"optmatch_max_problem_size"` is automatically set to
#' `Inf` during the matching process, different from its default in
#' *optmatch*. This enables matching problems of any size to be run, but
#' may also let huge, infeasible problems get through and potentially take a
#' long time or crash R. See \pkgfun{optmatch}{setMaxProblemSize} for more details.
#'
#' A preprocessing algorithm describe by Sävje (2020; \doi{10.1214/19-STS739}) is used to improve the speed of the matching when 1:1 matching on a propensity score. It does so by adding an additional constraint that guarantees a solution as optimal as the solution that would have been found without the constraint, and that constraint often dramatically reduces the size of the matching problem at no cost. However, this may introduce differences between the results obtained by *MatchIt* and by *optmatch*, though such differences will shrink when smaller values of `tol` are used.
#'
#' @seealso [matchit()] for a detailed explanation of the inputs and outputs of
#' a call to `matchit()`.
#'
#' \pkgfun{optmatch}{fullmatch}, which is the workhorse.
#'
#' [`method_full`] for optimal full matching, of which optimal pair
#' matching is a special case, and which relies on similar machinery.
#'
#' @references In a manuscript, be sure to cite the following paper if using
#' `matchit()` with `method = "optimal"`:
#'
#' Hansen, B. B., & Klopfer, S. O. (2006). Optimal Full Matching and Related
#' Designs via Network Flows. Journal of Computational and Graphical
#' Statistics, 15(3), 609–627. \doi{10.1198/106186006X137047}
#'
#' For example, a sentence might read:
#'
#' *Optimal pair matching was performed using the MatchIt package (Ho,
#' Imai, King, & Stuart, 2011) in R, which calls functions from the optmatch
#' package (Hansen & Klopfer, 2006).*
#'
#' @examplesIf requireNamespace("optmatch", quietly = TRUE)
#' data("lalonde")
#'
#' #1:1 optimal PS matching with exact matching on race
#' m.out1 <- matchit(treat ~ age + educ + race +
#' nodegree + married + re74 + re75,
#' data = lalonde,
#' method = "optimal",
#' exact = ~race)
#' m.out1
#' summary(m.out1)
#'
#' #2:1 optimal matching on the scaled Euclidean distance
#' m.out2 <- matchit(treat ~ age + educ + race +
#' nodegree + married + re74 + re75,
#' data = lalonde,
#' method = "optimal",
#' ratio = 2,
#' distance = "scaled_euclidean")
#' m.out2
#' summary(m.out2, un = FALSE)
NULL
matchit2optimal <- function(treat, formula, data, distance, discarded,
ratio = 1, s.weights = NULL, caliper = NULL,
mahvars = NULL, exact = NULL,
estimand = "ATT", verbose = FALSE,
is.full.mahalanobis, antiexact = NULL, ...) {
rlang::check_installed("optmatch")
.args <- c("tol", "solver")
A <- ...mget(.args)
A[lengths(A) == 0L] <- NULL
estimand <- toupper(estimand)
estimand <- match_arg(estimand, c("ATT", "ATC"))
if (estimand == "ATC") {
tc <- c("control", "treated")
focal <- 0
}
else {
tc <- c("treated", "control")
focal <- 1
}
treat_ <- setNames(as.integer(treat[!discarded] == focal), names(treat)[!discarded])
# if (is_not_null(data)) data <- data[!discarded,]
if (is.full.mahalanobis) {
if (is_null(attr(terms(formula, data = data), "term.labels"))) {
.err(sprintf("covariates must be specified in the input formula when `distance = \"%s\"`",
attr(is.full.mahalanobis, "transform")))
}
mahvars <- formula
}
if (is_not_null(caliper)) {
.wrn("calipers are currently not compatible with `method = \"optimal\"` and will be ignored")
caliper <- NULL
}
min.controls <- attr(ratio, "min.controls")
max.controls <- attr(ratio, "max.controls")
if (is_null(max.controls)) {
min.controls <- max.controls <- ratio
}
#Exact matching strata
if (is_not_null(exact)) {
ex <- factor(exactify(model.frame(exact, data = data),
sep = ", ", include_vars = TRUE)[!discarded])
cc <- Reduce("intersect", lapply(unique(treat_), function(t) unclass(ex)[treat_ == t]))
if (is_null(cc)) {
.err("no matches were found")
}
e_ratios <- vapply(levels(ex), function(e) {
sum(treat_[ex == e] == 0) / sum(treat_[ex == e] == 1)
}, numeric(1L))
if (any(e_ratios < 1)) {
.wrn(sprintf("fewer %s units than %s units in some `exact` strata; not all %s units will get a match",
tc[2L], tc[1L], tc[1L]))
}
if (ratio > 1 && any(e_ratios < ratio)) {
if (ratio == max.controls)
.wrn(sprintf("not all %s units will get %s matches",
tc[1L], ratio))
else
.wrn(sprintf("not enough %s units for an average of %s matches per %s unit in all `exact` strata",
tc[2L], ratio, tc[1L]))
}
}
else {
ex <- gl(1, length(treat_), labels = "_")
cc <- 1
e_ratios <- setNames(sum(treat_ == 0) / sum(treat_ == 1), levels(ex))
if (e_ratios < 1) {
.wrn(sprintf("fewer %s units than %s units; not all %s units will get a match",
tc[2L], tc[1L], tc[1L]))
}
else if (e_ratios < ratio) {
if (ratio == max.controls)
.wrn(sprintf("not all %s units will get %s matches",
tc[1L], ratio))
else
.wrn(sprintf("not enough %s units for an average of %s matches per %s unit",
tc[2L], ratio, tc[1L]))
}
}
#Create distance matrix; note that Mahalanobis distance computed using entire
#sample (minus discarded), like method2nearest, as opposed to within exact strata, like optmatch.
if (is_not_null(mahvars)) {
transform <- if (is.full.mahalanobis) attr(is.full.mahalanobis, "transform") else "mahalanobis"
mahcovs <- transform_covariates(mahvars, data = data, method = transform,
s.weights = s.weights, treat = treat,
discarded = discarded)
mo <- eucdist_internal(mahcovs, treat)
}
else if (is.matrix(distance)) {
mo <- distance
}
else {
mo <- eucdist_internal(setNames(as.numeric(distance), names(treat)), treat)
}
#Transpose distance mat as needed
if (focal == 0) {
mo <- t(mo)
}
#Remove discarded units from distance mat
mo <- mo[!discarded[treat == focal], !discarded[treat != focal], drop = FALSE]
dimnames(mo) <- list(names(treat_)[treat_ == 1], names(treat_)[treat_ == 0])
mo <- optmatch::match_on(mo, data = as.data.frame(data)[!discarded, , drop = FALSE])
mo <- optmatch::as.InfinitySparseMatrix(mo)
if (is_null(mahvars) && !is.matrix(distance) && nlevels(ex) == 1L &&
ratio == 1 && max.controls == 1) {
.cat_verbose("Preprocessing to reduce problem size...\n", verbose = verbose)
#Preprocess by pruning unnecessary edges as in Sävje (2020) https://doi.org/10.1214/19-STS699
keep <- preprocess_matchC(treat_, distance[!discarded])
if (length(keep) < length(mo)) {
mo <- .subset_infsm(mo, keep)
}
}
.cat_verbose("Optimal matching...\n", verbose = verbose)
#Process antiexact
if (is_not_null(antiexact)) {
antiexactcovs <- model.frame(antiexact, data)
for (i in seq_len(ncol(antiexactcovs))) {
mo <- mo + optmatch::antiExactMatch(antiexactcovs[[i]][!discarded], z = treat_)
}
}
#Initialize pair membership; must include names
pair <- rep_with(NA_character_, treat)
p <- setNames(vector("list", nlevels(ex)), levels(ex))
t_df <- data.frame(treat_)
for (e in levels(ex)[cc]) {
if (nlevels(ex) > 1L) {
.cat_verbose(sprintf("Matching subgroup %s/%s: %s...\n",
match(e, levels(ex)[cc]), length(cc), e),
verbose = verbose)
mo_ <- mo[ex[treat_ == 1] == e, ex[treat_ == 0] == e]
}
else {
mo_ <- mo
}
if (any(dim(mo_) == 0) || !any(is.finite(mo_))) {
next
}
if (all_equal_to(dim(mo_), 1) && all(is.finite(mo_))) {
pair[ex == e] <- paste(1, e, sep = "|")
next
}
#Process ratio, etc., when available ratio in exact matching categories
#(e_ratio) differs from requested ratio
if (e_ratios[e] < 1) {
#Switch treatment and control labels; unmatched treated units are dropped
ratio_ <- min.controls_ <- max.controls_ <- 1
mo_ <- t(mo_)
}
else if (e_ratios[e] < ratio) {
#Lower ratio and min.controls.
ratio_ <- e_ratios[e]
min.controls_ <- min(min.controls, floor(e_ratios[e]))
max.controls_ <- max.controls
}
else {
ratio_ <- ratio
min.controls_ <- min.controls
max.controls_ <- max.controls
}
A$x <- mo_
A$mean.controls <- ratio_
A$min.controls <- min.controls_
A$max.controls <- max.controls_
A$data <- t_df[ex == e, , drop = FALSE] #just to get rownames; not actually used in matching
rlang::with_options({
matchit_try({
p[[e]] <- do.call(optmatch::fullmatch, A)
}, from = "optmatch")
}, optmatch_max_problem_size = Inf)
pair[names(p[[e]])[!is.na(p[[e]])]] <- paste(as.character(p[[e]][!is.na(p[[e]])]), e, sep = "|")
}
if (length(p) == 1L) {
p <- p[[1L]]
}
psclass <- factor(pair)
levels(psclass) <- seq_len(nlevels(psclass))
names(psclass) <- names(treat)
mm <- nummm2charmm(subclass2mmC(psclass, treat, focal), treat)
.cat_verbose("Calculating matching weights...", verbose = verbose)
## calculate weights and return the results
res <- list(match.matrix = mm,
subclass = psclass,
weights = get_weights_from_subclass(psclass, treat, estimand),
obj = p)
.cat_verbose("Done.\n", verbose = verbose)
class(res) <- "matchit"
res
}
#Subset InfinitySparseMatrix using vector indices
.subset_infsm <- function(y, ss) {
y@.Data <- y[ss]
y@cols <- y@cols[ss]
y@rows <- y@rows[ss]
y
}
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