These functions are rbind() methods for objects resulting from calls to
match.data() and get_matches(). They function nearly identically to
rbind.data.frame(); see Details for how they differ.
Two or more matchdata or getmatches objects the
output of calls to match.data() and get_matches(), respectively.
Supplied objects must either be all matchdata objects or all
getmatches objects.
Passed to rbind().
An object of the same class as those supplied to it (i.e., a
matchdata object if matchdata objects are supplied and a
getmatches object if getmatches objects are supplied).
rbind() is called on the objects after adjusting the variables so that the
appropriate method will be dispatched corresponding to the class of the
original data object.
rbind() appends two or more datasets row-wise. This can be useful
when matching was performed separately on subsets of the original data and
they are to be combined into a single dataset for effect estimation. Using
the regular data.frame method for rbind() would pose a
problem, however; the subclass variable would have repeated names
across different datasets, even though units only belong to the subclasses
in their respective datasets. rbind.matchdata() renames the
subclasses so that the correct subclass membership is maintained.
The supplied matched datasets must be generated from the same original dataset, that is, having the same variables in it. The added components (e.g., weights, subclass) can be named differently in different datasets but will be changed to have the same name in the output.
rbind.getmatches() and rbind.matchdata() are identical.
See vignettes("estimating-effects") for details on using
rbind() for effect estimation after subsetting the data.
data("lalonde")
# Matching based on race subsets
m.out_b <- matchit(treat ~ age + educ + married +
nodegree + re74 + re75,
data = subset(lalonde, race == "black"))
#> Warning: Fewer control units than treated units; not all treated units will get a match.
md_b <- match.data(m.out_b)
m.out_h <- matchit(treat ~ age + educ + married +
nodegree + re74 + re75,
data = subset(lalonde, race == "hispan"))
md_h <- match.data(m.out_h)
m.out_w <- matchit(treat ~ age + educ + married +
nodegree + re74 + re75,
data = subset(lalonde, race == "white"))
md_w <- match.data(m.out_w)
#Bind the datasets together
md_all <- rbind(md_b, md_h, md_w)
#Subclass conflicts are avoided
levels(md_all$subclass)
#> [1] "1_1" "1_2" "1_3" "1_4" "1_5" "1_6" "1_7" "1_8" "1_9" "1_10"
#> [11] "1_11" "1_12" "1_13" "1_14" "1_15" "1_16" "1_17" "1_18" "1_19" "1_20"
#> [21] "1_21" "1_22" "1_23" "1_24" "1_25" "1_26" "1_27" "1_28" "1_29" "1_30"
#> [31] "1_31" "1_32" "1_33" "1_34" "1_35" "1_36" "1_37" "1_38" "1_39" "1_40"
#> [41] "1_41" "1_42" "1_43" "1_44" "1_45" "1_46" "1_47" "1_48" "1_49" "1_50"
#> [51] "1_51" "1_52" "1_53" "1_54" "1_55" "1_56" "1_57" "1_58" "1_59" "1_60"
#> [61] "1_61" "1_62" "1_63" "1_64" "1_65" "1_66" "1_67" "1_68" "1_69" "1_70"
#> [71] "1_71" "1_72" "1_73" "1_74" "1_75" "1_76" "1_77" "1_78" "1_79" "1_80"
#> [81] "1_81" "1_82" "1_83" "1_84" "1_85" "1_86" "1_87" "2_1" "2_2" "2_3"
#> [91] "2_4" "2_5" "2_6" "2_7" "2_8" "2_9" "2_10" "2_11" "3_1" "3_2"
#> [101] "3_3" "3_4" "3_5" "3_6" "3_7" "3_8" "3_9" "3_10" "3_11" "3_12"
#> [111] "3_13" "3_14" "3_15" "3_16" "3_17" "3_18"