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.

# S3 method for matchdata
rbind(..., deparse.level = 1)

# S3 method for getmatches
rbind(..., deparse.level = 1)

Arguments

...

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.

deparse.level

Passed to rbind().

Details

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.

Value

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.

Author

Noah Greifer

See also

match.data(), rbind()

See vignettes("estimating-effects") for details on using rbind() for effect estimation after subsetting the data.

Examples

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" "1"    "2"    "3"    "4"   
#> [121] "5"    "6"    "7"    "8"    "9"    "10"   "11"   "12"   "13"   "14"  
#> [131] "15"   "16"   "17"   "18"   "19"   "20"   "21"   "22"   "23"   "24"  
#> [141] "25"   "26"   "27"   "28"   "29"   "30"   "31"   "32"   "33"   "34"  
#> [151] "35"   "36"   "37"   "38"   "39"   "40"   "41"   "42"   "43"   "44"  
#> [161] "45"   "46"   "47"   "48"   "49"   "50"   "51"   "52"   "53"   "54"  
#> [171] "55"   "56"   "57"   "58"   "59"   "60"   "61"   "62"   "63"   "64"  
#> [181] "65"   "66"   "67"   "68"   "69"   "70"   "71"   "72"   "73"   "74"  
#> [191] "75"   "76"   "77"   "78"   "79"   "80"   "81"   "82"   "83"   "84"  
#> [201] "85"   "86"   "87"