MatchIt/0000755000175100001440000000000013075407325011627 5ustar hornikusersMatchIt/inst/0000755000175100001440000000000013075401340012573 5ustar hornikusersMatchIt/inst/CITATION0000644000175100001440000000154313060361515013736 0ustar hornikuserscitHeader("To cite MatchIt in publications use:") citEntry(entry = "Article", title = "{MatchIt}: Nonparametric Preprocessing for Parametric Causal Inference", author = personList(as.person("Daniel E. Ho"), as.person("Kosuke Imai"), as.person("Gary King"), as.person("Elizabeth A. Stuart")), journal = "Journal of Statistical Software", year = "2011", volume = "42", number = "8", pages = "1--28", url = "http://www.jstatsoft.org/v42/i08/", textVersion = paste("Daniel E. Ho, Kosuke Imai, Gary King, Elizabeth A. Stuart (2011).", "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference.", "Journal of Statistical Software, Vol. 42, No. 8, pp. 1-28.", "URL http://www.jstatsoft.org/v42/i08/") ) MatchIt/inst/doc/0000755000175100001440000000000013075401340013340 5ustar hornikusersMatchIt/inst/doc/matchit.pdf0000644000175100001440000104635513075401340015502 0ustar hornikusers%PDF-1.5 % 1 0 obj << /Type /ObjStm /Length 4305 /Filter /FlateDecode /N 100 /First 814 >> stream x\rܸ}W-3X HR%ɒG^E-O8EӋǞϹ\I=T&Ź+@E,`Q$Bf8˘fLbfÐYJ*sB$dQ` D3zTXd"tYk&Nb&"N,B1.(dBt*TƲtL 61[^Ѝb"d*2[1Wc󆊘E(N| +W׿.7OLGro>ƛj "t%G/x 8c0x$?U^BUh>1xz_9O__WxzݏFndӟ߽̆T]dloZXmZ=77&72zJד:e2MIzzɛN~.Q|̗dyOs>|ۜ)f>|}3qtYtR:tpyȏ'')ƟCK 5/%?ohO55u?U@k3ƿw4tNl2N @脞G!%^{m!lW(A5AMaa4PŃAX"V$  xىa L TOW/NYf nd(4!) 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Ċvl?$dt NYePBT-C0+! G>u/xiSH1|+B=) /݁Nl{JE^ܝf ֒r27 -)\4=iBOyPL5:g}8~KXi=J)F]l VyC?COLcԖ P#(߸7lhrdߦ~sn2JCw i4u<ɓC 8]LٴZFGyQv䨀%bI|Xذ taʊ''ȣoQLLҹޟ.`WHەx5$*&+e Zi,x|,^OAWkcm)mtep(pq(GcVvcdmk!v䌕MC![D82mv=o-WFs/b"/ D\ok MwQq~쳻xe0x]hWmc~q,DUގikb$M@>4e$>]VN3ޯ-yrit[ɟ}2;`wii b,VfD,ElwwםKԘމAA`ajJ86DöU8a><]_TlU^o_)Fx~4־#|[Y3uHJKzjzuӈv{42Fq>}uPӣ[mmE',{$vb{2#g\U xXSc޽0zGgiIӗss0 ۍU\[+JM3$fkCYoDk⿓<02tU٣]< _l"s"keI:QEb4\hZ+=~J> stream xSU uLOJu+53Rp 4S03RUu.JM,sI,IR04Tp,MW04U002226RUp/,L(Qp)2WpM-LNSM,HZRQZZTeh\ǥrg^Z9D8&UZT tБ @'T*qJB7ܭ4'/1d<(0s3s* s JKR|SRЕB曚Y.Y옗khg`l ,vˬHM ,IPHK)N楠;z`GhCb,WRY`P "0*ʬP6300*B+.׼̼t#S3ĢJ.QF Ն y) @(CV!- f IE Q.L_89WT(ZX(Y*[բK.-*J+'`PiKMHMy-?ٺ%ku/byb˛"vL 6^G[g_J*\Eׯ'"5[,`_Fxes4<͘HjY:7(ן)jq iMR2.xWH+r6ϋ=!zik^J7 ⱼ딥b%h䉪[EK2&R1wz )_$>H]h+#$('i~jTwUa~EO ՕyZ?u54Nf&پqY#4?l9`֖tGm3Iw; j7h:G(W苳i5j{o/[vn?u{\T jR7% (\ sRJsqendstream endobj 254 0 obj << /Filter /FlateDecode /Length 3577 >> stream xZK6Q IH $;@Lj[=_cbe=sۋ-QTXϯﴙ jcak3=Z5׻Ϊ|2\z팇YVTk? .Ec _v9\-aw?v[G]l7xw)ݦwFW8U|qCTS-W7'i8_൯Ml؞ ʹ6>n{n@#,}sCixdsqm*ƅrQ>d^-5}`-l a!3DWoQ^$B]כNljw;7Ձ9B:ˌUf*EyY䤍T )A9S* vXYmqnv[70}'+`8,ߢ%p6#ն{uhQ\56A\ DŞQAK4WV}:DAOwhF"N߮h I= *BSMCŴȥ%Q ?4)})eG !,kՃI-`L:9?[ ]Mլv&-4\Z\:He c.zUdw>-[d$ջ8g,&Wx)r*%ceRڛf;r7Xdgy5Vb}_lyg?pqC3j1[LZͫ<'UU ƕc!1h130YiP6Jt3tDmô [Qk 7J"N B b'8_3e] WC D y M@0fdHZ@RϗNZ6)CM]pxsq碳gq6>k_˾f5ɺfb Y_(s羖 Nt^'M\iJOi:Oyi^!4`#L -1*ǁez@a('"|" b(aGO p㸣OzPY B=190[5@L'Ԓ7VKF'?xMϤ=:q0#R_jD!E)4R|`6oYe1gr-IH~R(WZcd!X{ 'aKs PA\BJdA-:7`6Dliu><<͡SA)qF*$и *] ^ B==MG^o Ǝ!T5PN> خwąd .E=v5:*A~=]vaA ̈""T5O?*(~o"zI ~-,u Md-,Z`Xh NO7;\`=5fov38cR&/ݛ.U#~j{= #?ކ8M  1Me bmV? 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U.YwSt =i(:*Xm&E&ɢ;\֖]xg#bnl(V=v>ւ`)'l#^Wnߝg0ot.A7G erPuCc%AQu,GZ3Ԃ [h2+x/h沃OMG~x E.N cN!,ȭ՛ʋ_wK ㎣De($\beR m6+C!CUݗ䶟'D(}9ߗgxO}9"z lq2B+JYX%[z#&FAsfDd#񍟆:tw:|~~D)98d?nn_+@o O ˎM7ƮӘfFH$tb]&'i&L*[TGøD\d0~MDUFNg?I%.>y!O܌Q-K0  nkqLP* Qh`m1BJ· ϒ '$z 2lDo?Z¶& Qm􎶯$-cYMCoy/m", pɌ/˅Yգcyr_AiqN\Ogy>O)_ ՁenA{n^~0xCeG08h!@a"FCC2q'+g 4p ޓ.?%s!_8w>OѬymP[{,"WZ((R0TC{WzmK7ys I02L/IEV>Gwx'DjANC Lygk\ԣ}gqZUχ5!K%8A=O\kN#F[ʕcXٌ/xs/vmisj1^z61W^IbRt@H‡|u/W5޿ V&r2(XWu$c# lXoJ\rs`-[Ḁ!F4rѮr[Cv\_.'z _>BΰUWvSw=օXlU $FiՀ" !-m)fhf!C/ڒK.T j=сԛε|K?Y+tz}|op ND+^- F4pëS;endstream endobj 257 0 obj << /Filter /FlateDecode /Length 1769 >> stream xڵXI6WXIQKhmS㢇$ıh1$顿o!exPbs{B{HVBr[%X 0LV'ݯoGjTЌU@kFx ~x~ }%tbVƞlFĻkᄎ2vcYȽp7㖬_<4C.8#sd6+Md村W;╧j౲_T.~J~S.26M}JAiR]E#E(/T>ش7UW[lYn`Z-Z̎~Ma8ܟ:zBȩ0c۲=Y@)h1Dtb5L,fkJفI`c4 6Ym[lr*kKذ)^29hr jq:̲s̞qX"N{dRz@ˑA*ϖ"|0& ڇcIɰ_2L!++e 顯G)`EN A3Ωˍ)C{ZGΩ'',FKhM8OOtk^el2x9esV{,:+ jbŋX5X89@Uђ5dF "A%v1vd"M/(c~6 ȒZa] a!C+`+Er 4S;]]&F" JH |DU螄 /!TݩWƮAaҼt*>ؒa`@d}D71$Fκe=E5Dc+%S{T6\H@}lx_vpsΎU07mSQfY$ ! jZ֪Z񹘤GЩ;P#n(ۡpVUec#LU|ݻ\S ^v}V #Gsz1hJ+ctCa9V(܇g<օ_e)haZ`GA$YW;a]W[n]EdIa')'nI a];wmwOWEFb㧆xpUXyIr|.Q4VvYoki 04dޗ5kx|[/UIh :a=etI#U5T1jd S/q?7t1 4dV q.=N ‡}sR>u~.  {/bOmTNG<d_..Vo7(y6\v Kx,$e> stream x}T;0 +L2l)zCz[IXJ.%E9/]l)}^/^yqRzUu*eYfBTѺξUtfMpbQ,#8K41?#bzC kKDۇdN`d4ʍvzhU^OW)j+ P6^6%M'\BHjG%4n閐X&Z8"dܞy͹a-2Bp+!hpA?Oxj\@a2n;/+y $H[`l?'!u~@<)*ԘiQSH.XxDsB^popLf}Ҙ‘_y> stream xڍX[sۺ~ϯ F=9I|2nci,CBN_߽dS.]ͫ7tLݬfeHf/q0_HQu3am%ԊF};#:ǾjZ yIVKcs䖿>8mzI@!KQ ;rqu4,=/us|_7q(S5ж]S*hISP!.AL( f ўഭdlA[1FmvVB]b_j׵PY= +2&߫{xƲڰՎ,nxWTwb0cQ#TG\\}7\lVOѲ㳗ڢ𿶛:4 Brtkh汯)q&QF(@ZX@#P3@#< uIK,TbA5oء7ysw7K5Nՙ`q<>lTݣpE[Xbc=b{?%p'PP`e)V TsQЪf58vO^/=ri8!JݎzL3A&"kq$*pSt92:OOt|dFkfS&Hvv\`J0R<t#p%֛KtкHփ/w']E{Ɯ:P܁u ?XMr@$DZ\X@,1DA|eçAux3iNRS:Dd5a\htWM2%R AjV?ZkdfV9(Uh'.ZU(uBB Ւqs|RKBąWp\MfT.w1TL`+N%} 'I{aֱ`µ2tT<En96:dh̋L|&!Tn61w,>& bpaThzv يL]l`ES>ReC5  *]&נ#72⊣,O|(9. q'<EX HfO` t%'o 95A#wkJ$+@-FIʻDx vPE8(ԷdɯtYbw,ufBR8In}7.j@m~Pcb+<#v\A/ӟ#!lјtd vک%"ruʌLvd]@02٩A{*MG-r(-D!2[q&qz>ibqn9 AFLHMC zI `4`f{tl 7Dm/r@<=R }!@LO1np.`*y9ᚈgB} (+3 AD mO}]H]ls!Z#^Ol`Z&p܋jJ&j}׃ۆ[Ǵw:N=EKߓx \>=R#-g1 :Wo(I_t܊1(oU!|; Uj#tOEt kځKLu|Q}0-IVz=CT5\| w3Es+pް13` N[q/1hkնVx۩ZD#MZzk]]:&|@i1SK xV 5};dX(n[>?eG;q;tDޣcFx]3J_% > m-KC˥g>B> stream xڍ˒۸P%T%WWR̖=p(jeԒdz_~9^Dn_}^ts{&O81մUQ,fFvi}ݦy9phxi?O۟oLUn<&! |Nl!8i`kSƩVBy=>4CFǤqĻ 05iNec;"gWdpG.Wj&Faj$٥e'IRnvXz]mwƼ$+NT#~Z4ݪr+* hpN$.TmvK3kYTj`ou=TU5?>CV >mKN?l"}wtRAY eqZjt.0 a^ 7# b-2x6^F߅4H3*(udQ@:tu_v#y8FG#E4]<l~j!Aw( 4B<3x3*>o_Uߡ&^hܑc5DTZdG~HqMyYJ_S& <h䠕~˥HYş;)@šĽ~,[&lYa8r><;H tdST]IۜAGZWm\|R<5.<4X*<¡Hx#@!9+U:c1J{iя4YkXG Mݴ FRU< $x?7!:+ፁ4=yK׭hg,NkL()WN[&V$?Nåf@%J(? yeݜլ6ݽ Ҟ)MQ}t,w "IzͺqSW,bF}Nrݍ9Z(xmiRnyu _d~qre"TGQ(]yG;*c>a #HKο1xr ĘpԪJA`9),Ŋ2p,c}y3qXe|Hb(r.3`5Zx?ChPq evXi å?=Fo[2K %tOk?Si\dnW{h K"=>rLkH~K4=m 4(nMs 4ReWDKԌzzXDs0^WL(IIcMĪi]BeM-xP93=5tPtV*K6gUNKzBIؓ@+ͤJc u; AHrWݜo}Arz\)fPₛK(UPc=]\h2Vv@w%(fjq)lI78o:io2Ai,RJzRwɳޱ; ${m\p<:p/m?o=&!=gX6+c\ H1g]ʭ Sv-le%7; ovo{~xR}7f/'LTál3]-혨-U\LI6ڞwA[lfv5x 끟H$Ho)f,+8LH۟'sOKLDoyJB#{386)pG˺=< s߸ gz1l =twƬY > stream xڕXrF}WReNԖN޵-'8M9$Cr$k>8șDya g?&ygכ48K(;.?9k,N@` 8srj4t<ex>ơwhGH<_;[EhҞ+{+"jGvNrND7O)"2H xj*O݀pUW^Z b?oaܑ4v^ҸZN~ u[8#Uw]/Qώ 7ԍ4 f5x5l7 F("HU9-Cr1 ny ڪ"%˰5WgolͤBYkԳEtVg@4v"شDW;ɺ&.2euiHk 6.Έ6Jêܪgn`{#cw(! 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MatchIt/inst/doc/matchit.pdf.asis0000644000175100001440000000017113075401340016421 0ustar hornikusers%\VignetteIndexEntry{MatchIt: Nonparametric Preprocessing for Parametric Causal Inference} %\VignetteEngine{R.rsp::asis} MatchIt/tests/0000755000175100001440000000000013075246077012776 5ustar hornikusersMatchIt/tests/testthat.R0000644000175100001440000000007213075246077014760 0ustar hornikuserslibrary(testthat) library(MatchIt) test_check("MatchIt") MatchIt/tests/testthat/0000755000175100001440000000000013075407325014631 5ustar hornikusersMatchIt/tests/testthat/test-get_matches.R0000644000175100001440000003016713075246077020230 0ustar hornikusers library(testthat) library(MatchIt) data("lalonde") context("get_matches") test_that("can get correct matches, exact", { # preliminaries m.out <- matchit(treat ~ educ + black + hispan, data = lalonde, method = "exact") n_matched <- sum(m.out$nn[2,]) nms_matched <- names(m.out$subclass[!is.na(m.out$subclass)]) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% m.out$weights)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) expect_true(all(matches2$weight %in% m.out$weights)) }) test_that("can get correct matches, nearest", { m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "nearest") n_matched <- sum(m.out$nn[2,]) nms_matched <- c(rownames(m.out$match.matrix), c(m.out$match.matrix)) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% m.out$weights)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) expect_true(all(matches2$weight %in% m.out$weights)) }) test_that("can get correct matches, full", { # preliminaries m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "full") n_matched <- sum(m.out$nn[2,]) nms_matched <- names(m.out$subclass[!is.na(m.out$subclass)]) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% m.out$weights)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) expect_true(all(matches2$weight %in% m.out$weights)) }) test_that("can get correct matches, optimal", { ratio <- 2 m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "optimal", ratio = ratio) n_matched <- sum(m.out$nn[2,]) nms_matched <- c(rownames(m.out$match.matrix), c(m.out$match.matrix)) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% c(1, 1 / ratio))) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) expect_true(all(matches2$weight %in% c(1, 1 / ratio))) }) test_that("can get correct matches, subclass", { m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "subclass") n_matched <- sum(m.out$nn[2,]) nms_matched <- names(m.out$subclass[!is.na(m.out$subclass)]) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% m.out$weights)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) expect_true(all(matches2$weight %in% m.out$weights)) }) test_that("can get correct matches, genetic", { ## this one is problematic -- problematic returns m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "genetic") n_matched <- sum(m.out$nn[2,]) nms_matched <- c(rownames(m.out$match.matrix), m.out$match.matrix[,1], m.out$match.matrix[which(!is.na(m.out$match.matrix[,2])), 2]) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) }) test_that("can get correct matches, cem", { library(cem) m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "cem") n_matched <- sum(m.out$nn[2,]) nms_matched <- names(m.out$subclass[!is.na(m.out$subclass)]) lalonde$id <- 1:nrow(lalonde) lalonde2 <- lalonde[sample.int(500, 3000, replace=TRUE),] matches <- get_matches(m.out, lalonde) matches2 <- get_matches(m.out, model_frame= lalonde, id_cols= "id", newdata= lalonde2) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% m.out$weights)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) # tests -- newdata expect_equal(sum(is.na(matches2)), 0L) expect_true(all(names(matches2) %in% c(names(lalonde2), "weight"))) expect_true(all(c(names(lalonde2), "weight") %in% names(matches2))) expect_equal(ncol(matches2), ncol(lalonde2) + 1) expect_true(all(matches2[matches2$treat == 1,]$weight == 1)) expect_true(all(matches2$weight %in% m.out$weights)) }) l_treat <- do.call("rbind",replicate(10, lalonde[lalonde$treat == 1, ], simplify = FALSE)) l_contr <- do.call("rbind",replicate(3, lalonde[lalonde$treat == 0, ], simplify = FALSE)) l2 <- do.call("rbind", list(l_treat, l_contr)); rm(l_treat, l_contr) rownames(l2) <- 1:nrow(l2) test_that("correct weights for replace= TRUE", { m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = l2, method = "nearest", replace= TRUE) n_matched <- sum(m.out$nn[2,]) nms_matched <- c(rownames(m.out$match.matrix), c(m.out$match.matrix)) matches <- get_matches(m.out, l2) exp_wts <- c(1, unique(as.vector(table(m.out$match.matrix)))) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% exp_wts)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) }) test_that("correct weights for replace= TRUE; test2", { m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = l2, method = "cem") n_matched <- sum(m.out$nn[2,]) nms_matched <- names(m.out$subclass[!is.na(m.out$subclass)]) matches <- get_matches(m.out, l2) exp_wts <- c(1, unique(as.vector(table(m.out$match.matrix)))) # tests -- no newdata expect_equal(n_matched, nrow(matches)) expect_equal(sum(matches$treat), m.out$nn[2,2]) expect_equal(nrow(matches) - sum(matches$treat), m.out$nn[2,1]) expect_equal(sum(is.na(matches)), 0L) expect_equal(names(matches), c(names(lalonde), "weight")) expect_equal(ncol(matches), ncol(lalonde) + 1) expect_true(all(matches[matches$treat == 1,]$weight == 1)) expect_true(all(matches$weight %in% m.out$weights)) expect_true(all(rownames(matches) %in% nms_matched)) expect_true(all(nms_matched %in% rownames(matches))) }) MatchIt/NAMESPACE0000644000175100001440000000125613075246077013057 0ustar hornikusers# Generated by roxygen2: do not edit by hand S3method(get_matches,matchit) S3method(plot,matchit) S3method(plot,matchit.subclass) S3method(plot,summary.matchit) S3method(print,matchit) S3method(print,matchit.exact) S3method(print,matchit.full) S3method(print,matchit.subclass) S3method(print,summary.matchit) S3method(print,summary.matchit.exact) S3method(print,summary.matchit.subclass) S3method(summary,matchit) S3method(summary,matchit.exact) S3method(summary,matchit.full) S3method(summary,matchit.subclass) export(get_matches) export(help.matchit) export(is.matchit) export(match.data) export(matchit) export(user.prompt) import(MASS) import(graphics) import(stats) import(utils) MatchIt/demo/0000755000175100001440000000000013060361515012545 5ustar hornikusersMatchIt/demo/genetic.R0000644000175100001440000000101713060361515014305 0ustar hornikusers#### #### demo file for Genetic Matching #### ## loading the lalonde data data(lalonde) ## using logistic propensity score as one of the covariates m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "genetic", distance = "logit") user.prompt() ## printing a short summary print(m.out) user.prompt() ## numerical balance diagonstics s.out <- summary(m.out, standardize=TRUE) print(s.out) user.prompt() ## graphical balance diagnostics plot(m.out) plot(s.out) MatchIt/demo/match.data.R0000644000175100001440000000204413060361515014674 0ustar hornikusers### ### An Example Script for Obtaining Mathced Data ### ## load the Lalonde data data(lalonde) user.prompt() ## perform nearest neighbor matching m.out1 <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "nearest", distance = "logit") user.prompt() ## obtain matched data m.data1 <- match.data(m.out1) user.prompt() ## summarize the resulting matched data summary(m.data1) user.prompt() ## obtain matched data for the treatment group m.data2 <- match.data(m.out1, group = "treat") user.prompt() summary(m.data2) user.prompt() ## obtain matched data for the control group m.data3 <- match.data(m.out1, group = "control") user.prompt() summary(m.data3) user.prompt() ## run a subclassification method m.out2 <- matchit(treat ~ re74 + re75 + age + educ, data=lalonde, method = "subclass") user.prompt() ## specify different names m.data4 <- match.data(m.out2, subclass = "block", weights = "w", distance = "pscore") user.prompt() ## print the variable names of the matched data names(m.data4) MatchIt/demo/analysis.R0000644000175100001440000000641713060361515014523 0ustar hornikusers### ### Example 1: calculating the average treatment effect for the treated ### ## load the Lalonde data data(lalonde) ## load Zelig package: if not already installed, try install.package("Zelig") library(Zelig) ## propensity score matching m.out1 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, method = "nearest", data = lalonde) user.prompt() ## fit the linear model to the control group controlling for propensity score and ## other covariates z.out1 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75 + distance, data = match.data(m.out1, "control"), model = "ls") user.prompt() ## set the covariates to the covariates of matched treated units ## use conditional prediction by setting cond = TRUE. x.out1 <- setx(z.out1, data = match.data(m.out1, "treat"), fn = NULL, cond = TRUE) user.prompt() ## simulate quantities of interest s.out1 <- sim(z.out1, x = x.out1) user.prompt() ## obtain a summary print(summary(s.out1)) user.prompt() ### ### Example 2: calculating the average treatment effect for the entire sample ### ## fit the linear model to the treatment group controlling for propensity score and ## other covariates z.out2 <- zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75 + distance, data = match.data(m.out1, "treat"), model = "ls") user.prompt() ## conducting the simulation procedure for the control group x.out2 <- setx(z.out2, data = match.data(m.out1, "control"), fn = NULL, cond = TRUE) user.prompt() s.out2 <- sim(z.out2, x = x.out2) user.prompt() ## Note that Zelig calculates the difference between observed and ## either predicted or expected values. This means that the treatment ## effect for the control units is actually the effect of control ## (observed control outcome minus the imputed outcome under treatment ## from the model). Hence, to combine treatment effects just reverse ## the signs of the estimated treatment effect of controls. ate.all <- c(s.out1$qi$att.ev, -s.out2$qi$att.ev) user.prompt() ## some summaries ## point estimate print(mean(ate.all)) user.prompt() ## standard error print(sd(ate.all)) user.prompt() ## 95% confidence interval print(quantile(ate.all, c(0.025, 0.975))) user.prompt() ### ### Example 3: subclassification ### ## subclassification with 4 subclasses m.out2 <- matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = lalonde, method = "subclass", subclass = 4) user.prompt() ## controlling only for the estimated prpensity score and lagged Y within each subclass ## one can potentially control for more z.out3 <- zelig(re78 ~ re74 + re75 + distance, data = match.data(m.out2, "control"), model = "ls", by = "subclass") user.prompt() ## conducting simulations x.out3 <- setx(z.out3, data = match.data(m.out2, "treat"), fn = NULL, cond = TRUE) user.prompt() ## for the demonstration purpose, we set the number of simulations to be 100 s.out3 <- sim(z.out3, x = x.out3, num = 100) user.prompt() ## overall results print(summary(s.out3)) user.prompt() ## summary for each subclass print(summary(s.out3, subset = 1)) user.prompt() print(summary(s.out3, subset = 2)) user.prompt() print(summary(s.out3, subset = 3)) MatchIt/demo/exact.R0000644000175100001440000000054413060361515013777 0ustar hornikusers### ### An Example Script for Exact Matching ### ## laod the Lalonde data data(lalonde) ## exact matching m.out <- matchit(treat ~ educ + black + hispan, data = lalonde, method = "exact") user.prompt() ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics print(summary(m.out, covariates = T)) MatchIt/demo/nearest.R0000644000175100001440000000555013060361515014336 0ustar hornikusers### ### An Example Script for Nearest Neighbor Matching ### data(lalonde) user.prompt() ## 1:1 Nearest neighbor matching m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "nearest") ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics s.out <- summary(m.out, standardize=TRUE) print(s.out) user.prompt() ## balance diagnostics through graphics plot(m.out) user.prompt() plot(m.out, type="jitter") user.prompt() plot(m.out, type="hist") user.prompt() plot(s.out) ## 2:1 Nearest neighbor matching m.out1 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", ratio=2) user.prompt() ## print a short summary print(m.out1) user.prompt() ## balance diagnostics through statistics print(summary(m.out1)) user.prompt() ## balance diagnostics through graphics plot(m.out) user.prompt() plot(m.out, type="jitter") ## 1:1 Nearest neighbor matching with Mahalanobis matching on re74 and re75 and exact matching on married m.out2 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", mahvars=c("re74", "re75"), exact=c("married"), caliper=.25) user.prompt() ## print a short summary print(m.out2) user.prompt() ## balance diagnostics through statistics s.out2 <- summary(m.out2, standardize=TRUE) print(s.out2) user.prompt() ## balance diagnostics through graphics plot(m.out2) user.prompt() plot(m.out2, type="jitter") user.prompt() plot(s.out2) ## 1:1 Nearest neighbor matching with units outside the common support discarded m.out3 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", discard= "both") user.prompt() ## print a short summary print(m.out3) user.prompt() ## balance diagnostics through statistics print(summary(m.out3)) user.prompt() ## balance diagnostics through graphics plot(m.out3) plot(m.out3, type="jitter") ## 2:1 Nearest neighbor matching with replacement m.out4 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", replace=TRUE, ratio=2) user.prompt() ## print a short summary print(m.out4) user.prompt() ## balance diagnostics through statistics print(summary(m.out4)) user.prompt() ## balance diagnostics through graphics plot(m.out4) plot(m.out4, type="jitter") plot(m.out4, type="hist") ## 1:1 Nearest neighbor matching followed by subclassification m.out5 <- matchit(treat ~ re74+re75+age+educ, data=lalonde, method = "nearest", distance = "logit", subclass=5) user.prompt() ## print a short summary print(m.out5) user.prompt() ## balance diagnostics through statistics print(summary(m.out5)) user.prompt() ## balance diagnostics through graphics plot(m.out5) user.prompt() s.out5 <- summary(m.out5, standardize=TRUE) plot(s.out5) MatchIt/demo/00Index0000644000175100001440000000103613060361515013677 0ustar hornikusersexact Demo of exact matching, using Lalonde dataset full Demo of full matching, using Lalonde dataset genetic Demo of genetic matching, using Lalonde dataset match.data Demo of obtaining matched data nearest Demos of nearest neighbor matching, using Lalonde dataset subclass Demo of subclassification, using Lalonde dataset optimal Demo of optimal matching, using Lalonde dataset analysis Demo of using Zelig with MatchIt for parametric causal inference after matching cem Demo of coarsened exact matching MatchIt/demo/cem.R0000644000175100001440000000247713060361515013446 0ustar hornikusers### ### An Example Script for Coarsened Exact Matching ### ## load the Lalonde data data(lalonde) ## coarsened exact matching with automatic coarsening m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "cem") user.prompt() ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics; standardize = T for plotting s.out <- summary(m.out, covariates = T, standardize = T) print(s.out) user.prompt() ## graphical balance checks plot(m.out) plot(s.out) ## create some cutpoints for continuous variables re74cut <- hist(lalonde$re74, br=seq(0,max(lalonde$re74)+1000, by=1000),plot=FALSE)$breaks re75cut <- hist(lalonde$re75, br=seq(0,max(lalonde$re75)+1000, by=1000),plot=FALSE)$breaks agecut <- hist(lalonde$age, br=seq(15,55, length=14),plot=FALSE)$breaks mycp <- list(re75=re75cut, re74=re74cut, age=agecut) ## coarsened exact matching with user-given cutpoints m.out2 <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "cem", cutpoints = mycp) user.prompt() ## print a short summary print(m.out2) user.prompt() ## balance diagnostics through statistics s.out2 <- summary(m.out2, covariates = T) print(s.out2) user.prompt() MatchIt/demo/subclass.R0000644000175100001440000000077213060361515014515 0ustar hornikusers### ### An Example Script for Subclassification ### ## load the Lalonde data data(lalonde) ## sublclassification m.out <- matchit(treat ~ re74 + re75 + educ + black + hispan + age, data = lalonde, method = "subclass") user.prompt() ## a short summary print(m.out) user.prompt() ## balance diagnostics print(summary(m.out)) user.prompt() ## balance diagnostics through plots plot(m.out) user.prompt() plot(m.out, type="jitter") s.out <- summary(m.out, standardize=TRUE) plot(s.out) MatchIt/demo/full.R0000644000175100001440000000114513060361515013633 0ustar hornikusers### ### An Example Script for Full Matching ### ## load the Lalonde data data(lalonde) ## conduct full matching using the propensity score based on logistic regression m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde, method = "full", distance = "logit") ## print a short summary print(m.out) user.prompt() ## balance diagnostics through statistics s.out <- summary(m.out) print(s.out) s.out <- summary(m.out, standardize=TRUE) print(s.out) user.prompt() ## balance diagnostics through graphics plot(m.out) plot(s.out) MatchIt/demo/optimal.R0000644000175100001440000000112713060361515014336 0ustar hornikusers### ### An Example Script for Optimal Matching ### ## load the Lalonde data data(lalonde) ## optimal ratio matching using the propensity score based on logistic regression m.out <- matchit(treat ~ re74 + re75 + age + educ, data = lalonde, method = "optimal", distance = "logit", ratio = 2) user.prompt() ## a short summary print(m.out) user.prompt() ## balance diagnostics through statistics print(summary(m.out)) user.prompt() ## balance diagnostics through graphics plot(m.out) user.prompt() plot(m.out, type="jitter") s.out <- summary(m.out, standardize=TRUE) plot(s.out) MatchIt/data/0000755000175100001440000000000013075401340012527 5ustar hornikusersMatchIt/data/lalonde.tab.gz0000644000175100001440000002263713075401340015266 0ustar hornikusers}]tN_l9&U N (YKdNH'mm[%y__~O˿_ϿG]/?r?{S+fccD~ H{Y_c$TS1*N~Ux 1_}iCZRRmWm&ُ^!+ac~GnV^Id\k[cx%Wkook,x;GcFO$}mn{9=WMARG~%<\'6d\>&K)A.͒/_NG| ~*2\&6נÔ<4^seryK3-IRzuι+/ZZ.#4:罆ofe;'4DAq]a۷'<ܲ^Y4/(C=&:R>O&8qÉ6}[K~à ̂`ooV3? 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'<&ǀ;tip8r=l,\*l4\隆d4]ow~E¯>ݞ 3K}}&p<oa,DX~Z$rCּ) z+x|T5~iϻ5Ry Hf~WV 4WC)-?==wreogbnYyorJ2le~$o%}?qI?Ev{}](,{]Wm~⎽'6!VVnMƏ򒭯)G5DuQV:^7zo]|Bf/T)i?ǽ?XDtR"/]Ϫdo}xco,xlW9N\IGdV(Ƃո"οq@kMatchIt/R/0000755000175100001440000000000013075246077012035 5ustar hornikusersMatchIt/R/matchit2exact.R0000644000175100001440000000111213060361514014677 0ustar hornikusersmatchit2exact <- function(treat, X, data, distance, discarded, is.full.mahalanobis, verbose=FALSE, ...){ if(verbose) cat("Exact matching... \n") n <- length(treat) xx <- apply(X, 1, function(x) paste(x, collapse = "\r")) xx1 <- xx[treat==1] xx0 <- xx[treat==0] cc <- unique(xx1) cc <- cc[cc%in%xx0] ncc <- length(cc) psclass <- rep(NA,n) names(psclass) <- names(treat) for(i in 1:ncc) psclass[xx==cc[i]] <- i res <- list(subclass = psclass, weights = weights.subclass(psclass, treat)) class(res) <- c("matchit.exact", "matchit") return(res) } MatchIt/R/weights.subclass.R0000644000175100001440000000213413060361515015435 0ustar hornikusersweights.subclass <- function(psclass, treat) { ttt <- treat[!is.na(psclass)] classes <- na.omit(psclass) n <- length(ttt) labels <- names(ttt) tlabels <- labels[ttt==1] clabels <- labels[ttt==0] weights <- rep(0, n) names(weights) <- labels weights[tlabels] <- 1 for(j in unique(classes)){ qn0 <- sum(ttt==0 & classes==j) qn1 <- sum(ttt==1 & classes==j) weights[ttt==0 & classes==j] <- qn1/qn0 } if (sum(weights[ttt==0])==0) weights[ttt==0] <- rep(0, length(weights[clabels])) else { ## Number of C units that were matched to at least 1 T num.cs <- sum(weights[clabels] > 0) weights[clabels] <- weights[clabels]*num.cs/sum(weights[clabels]) } if (any(is.na(psclass))) { tmp <- rep(0, sum(is.na(psclass))) names(tmp) <- names(treat[is.na(psclass)]) weights <- c(weights, tmp)[names(treat)] } if (sum(weights)==0) stop("No units were matched") else if (sum(weights[tlabels])==0) stop("No treated units were matched") else if (sum(weights[clabels])==0) stop("No control units were matched") return(weights) } MatchIt/R/matchit2cem.R0000644000175100001440000000345613060361514014354 0ustar hornikusers# matchit2cem - matchit wrapper for cem matching algorithm # # 06/10/2008 - m.blackwell # # this function takes inputs from matchit() and returns the # strata for each observation in the subclass entry and the # weight for each observation in the weight entry. No match # matrix is returned since matches are not unique within # strata. # matchit2cem <- function(treat, X, data, distance, discarded, is.full.mahalanobis, ratio = 1, verbose = FALSE, k2k.method=NULL, ...) { if (!requireNamespace("cem", quietly = TRUE)) stop("cem package is required. Please install it.") if (verbose) cat("Coarsened exact matching...\n") n <- length(treat) # cem takes the data all together and wants the treatment specified # with the column name of the data frame. Here we massage the matchit # inputs to this format. Note that X has its proper columnames, but # treat does not have the original column name. cem.data <- as.data.frame(cbind(treat,X)) mat <- cem::cem(treatment="treat",data=cem.data,verbose=as.integer(verbose)+1, method=k2k.method,...) # here we create a column vector where the matched entry get its stratum # and the unmatched entry gets an NA. strat <- rep(NA,n) names(strat) <- names(treat) strat[mat$matched] <- mat$strata[mat$matched] # here we just add the names onto the wieght from the cem output wh <- mat$w names(wh) <- names(treat) # weighting functions in matchit error-out on these conditions, # so we should too. if (sum(wh)==0) stop("No units were matched") else if (sum(wh[treat==1])==0) stop("No treated units were matched") else if (sum(wh[treat==0])==0) stop("No control units were matched") res <- list(subclass = strat, weights = mat$w) class(res) <- "matchit" return(res) } MatchIt/R/eqqplot.R0000644000175100001440000000102613060361514013630 0ustar hornikuserseqqplot <- function(x, y, plot.it = TRUE, xlab = deparse(substitute(x)), ylab = deparse(substitute(y)), ...) { ## empirical quantile-quantile plot; hacked from qqplot() in stats. sx <- sort(x) sy <- sort(y) lenx <- length(sx) leny <- length(sy) if (leny < lenx) sx <- approx(1:lenx, sx, n = leny, method = "constant")$y if (leny > lenx) sy <- approx(1:leny, sy, n = lenx, method = "constant")$y if (plot.it) plot(sx, sy, xlab = xlab, ylab = ylab, ...) invisible(list(x = sx, y = sy)) } MatchIt/R/match.data.R0000644000175100001440000000252413075246077014167 0ustar hornikusers#' @export match.data <- function(object, group = "all", distance = "distance", weights = "weights", subclass = "subclass") { if (!is.null(object$model)) { env <- attributes(terms(object$model))$.Environment } else { env <- parent.frame() } data <- eval(object$call$data, envir = env) treat <- object$treat wt <- object$weights vars <- names(data) if (distance %in% vars) stop("invalid input for distance. choose a different name.") else if (!is.null(object$distance)) { dta <- data.frame(cbind(data, object$distance)) names(dta) <- c(names(data), distance) data <- dta } if (weights %in% vars) stop("invalid input for weights. choose a different name.") else if (!is.null(object$weights)){ dta <- data.frame(cbind(data, object$weights)) names(dta) <- c(names(data), weights) data <- dta } if (subclass %in% vars) stop("invalid input for subclass. choose a different name.") else if (!is.null(object$subclass)){ dta <- data.frame(cbind(data, object$subclass)) names(dta) <- c(names(data), subclass) data <- dta } if (group == "all") return(data[wt > 0,]) else if (group == "treat") return(data[wt > 0 & treat == 1,]) else if (group == "control") return(data[wt > 0 & treat == 0,]) else stop("error: invalid input for group.") } MatchIt/R/hist.pscore.R0000644000175100001440000000431213060361514014405 0ustar hornikusershist.pscore <- function(x, numdraws=5000, xlab="Propensity Score", main=NULL, freq=F, xlim = NULL,...){ treat <- x$treat pscore <- x$distance weights <- x$weights matched <- weights!=0 q.cut <- x$q.cut cwt <- sqrt(weights) ratio <- x$call$ratio if(is.null(ratio)){ratio <- 1} ## For full or ratio matching, sample numdraws observations using the weights if(identical(x$call$method,"full") | (ratio!=1)) { pscore.treated.matched <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=x$weights[treat==1]) pscore.treated.matched <- pscore[pscore.treated.matched] pscore.control.matched <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=x$weights[treat==0]) pscore.control.matched <- pscore[pscore.control.matched] } else { pscore.treated.matched <- pscore[treat==1 & weights!=0] pscore.control.matched <- pscore[treat==0 & weights!=0] } par(mfrow=c(2,2)) if(!is.null(xlim)){warning("xlim may not be user specified. xlim returned to default.")} xlim <- range(na.omit(pscore)) if(is.null(main)){ hist(pscore[treat==1],xlim=xlim, xlab=xlab, freq=freq, main="Raw Treated", ...) hist(pscore.treated.matched,xlim=xlim, xlab=xlab, freq=freq, main="Matched Treated",...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} hist(pscore[treat==0],xlim=xlim, xlab=xlab, freq=freq, main="Raw Control",...) hist(pscore.control.matched,xlim=xlim, xlab=xlab, freq=freq, main="Matched Control",...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} }else{ hist(pscore[treat==1],xlim=xlim, xlab=xlab, freq=freq, main=main, ...) hist(pscore.treated.matched,xlim=xlim, xlab=xlab, freq=freq, main=main,...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} hist(pscore[treat==0],xlim=xlim, xlab=xlab, freq=freq, main=main,...) hist(pscore.control.matched,xlim=xlim, xlab=xlab, freq=freq, main=main,...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} } } MatchIt/R/matchit2subclass.R0000644000175100001440000000716213060361514015425 0ustar hornikusersmatchit2subclass <- function(treat, X, data, distance, discarded, is.full.mahalanobis, match.matrix=NULL, subclass=6, sub.by="treat", verbose = FALSE){ if(verbose) cat("Subclassifying... \n") # sub.by if(!is.vector(sub.by)|length(sub.by)!=1){ warning(sub.by," is not a valid sub.by option; sub.by=\"treat\" used instead",call.=FALSE); sub.by <- "treat"} if(!sub.by%in%c("treat","control","all")){ warning(sub.by, " is not a valid sub.by option; sub.by=\"treat\" used instead",call.=FALSE); sub.by <- "treat"} #subclass if(length(subclass)==1){ if(subclass<0 | subclass==1){ warning(subclass, " is not a valid subclass; subclass=6 used instead",call.=FALSE); subclass <- 6} } else { if(!is.vector(subclass)){ warning(subclass, " is not a valid subclass; subclass=6 used instead",call.=FALSE); subclass <- 6} if(sum(subclass<=1 & subclass>=0)!=length(subclass)){ warning("Subclass ", subclass, " is not bounded by 0 and 1; subclass=6 used instead", call.=FALSE); subclass <- 6} } in.sample <- !discarded n <- length(treat) ## Matching & Subclassification if(!is.null(match.matrix)){ #match.matrix <- match.matrix[in.sample[treat==1],,drop=F] t.units <- row.names(match.matrix)[in.sample[treat==1]==1] c.units <- na.omit(as.vector(as.matrix(match.matrix))) matched <-c(t.units,c.units) matched <- names(treat)%in%matched } else matched <- rep(TRUE,n) names(matched) <- names(treat) m1 <- matched[treat==1] m0 <- matched[treat==0] p1 <- distance[treat==1][m1] p0 <- distance[treat==0][m0] ## Settting Cut Points if(length(subclass)!=1 | (length(subclass)==1 & all(subclass<1))) { subclass <- sort(subclass) if (subclass[1]==0) subclass <- subclass[-1] if (subclass[length(subclass)]==1) subclass <- subclass[-length(subclass)] if(sub.by=="treat") q <- c(0,quantile(p1,probs=c(subclass)),1) else if(sub.by=="control") q <- c(0,quantile(p0,probs=c(subclass)),1) else if(sub.by=="all") q <- c(0,quantile(distance,probs=c(subclass)),1) else stop("Invalid input for sub.by") } else { if(subclass<=0){stop("Subclass must be a positive vector",call.=FALSE)} sprobs <- seq(0,1,length=(round(subclass)+1)) sprobs <- sprobs[2:(length(sprobs)-1)] min.dist <- min(distance,na.rm=TRUE)-0.01 max.dist <- max(distance,na.rm=TRUE)+0.01 if(sub.by=="treat") q <- c(min.dist,quantile(p1,probs=sprobs,na.rm=TRUE), max.dist) else if(sub.by=="control") q <- c(min.dist,quantile(p0,probs=sprobs,na.rm=TRUE), max.dist) else if(sub.by=="all") q <- c(min.dist, quantile(distance,probs=sprobs,na.rm=TRUE), max.dist) else stop("Must specify a valid sub.by",call.=FALSE) } ## Calculating Subclasses qbins <- length(q)-1 psclass <- rep(0,n) names(psclass) <- names(treat) for (i in 1:qbins){ q1 <- q[i] q2 <- q[i+1] psclass <- psclass+i*as.numeric(distance=q1) } ## No subclass for discarded or unmatched units psclass[in.sample==0] <- NA psclass[!matched] <- NA if(verbose){cat("Done\n")} res <- list(subclass = psclass, q.cut = q, weights = weights.subclass(psclass, treat)) #warning for discrete data unique.classes <- unique(psclass) unique.classes <- unique.classes[!is.na (unique.classes)] if(length(unique.classes)!=subclass){ warning("Due to discreteness in data, fewer subclasses generated",call.=F) } class(res) <- c("matchit.subclass", "matchit") return(res) } MatchIt/R/weights.matrix.R0000644000175100001440000000273713060361515015133 0ustar hornikusersweights.matrix <- function(match.matrix, treat, discarded){ n <- length(treat) labels <- names(treat) tlabels <- labels[treat==1] clabels <- labels[treat==0] in.sample <- !discarded names(in.sample) <- labels match.matrix <- match.matrix[tlabels,,drop=F][in.sample[tlabels],,drop=F] num.matches <- dim(match.matrix)[2]-apply(as.matrix(match.matrix), 1, function(x){sum(is.na(x))}) names(num.matches) <- tlabels[in.sample[tlabels]] t.units <- row.names(match.matrix)[num.matches>0] c.units <- na.omit(as.vector(as.matrix(match.matrix))) weights <- rep(0,length(treat)) names(weights) <- labels weights[t.units] <- 1 for (cont in clabels) { treats <- na.omit(row.names(match.matrix)[cont==match.matrix[,1]]) if (dim(match.matrix)[2]>1) for (j in 2:dim(match.matrix)[2]) treats <- c(na.omit(row.names(match.matrix)[cont==match.matrix[,j]]),treats) for (k in unique(treats)) weights[cont] <- weights[cont] + 1/num.matches[k] } if (sum(weights[clabels])==0) weights[clabels] <- rep(0, length(weights[clabels])) else weights[clabels] <- weights[clabels]*length(unique(c.units))/sum(weights[clabels]) weights[!in.sample] <- 0 if (sum(weights)==0) stop("No units were matched") else if (sum(weights[tlabels])==0) stop("No treated units were matched") else if (sum(weights[clabels])==0) stop("No control units were matched") return(weights) } MatchIt/R/distance2nnet.R0000644000175100001440000000047113060361514014707 0ustar hornikusersdistance2nnet <- function(formula, data, ...) { if (requireNamespace("nnet", quietly = TRUE)) { res <- nnet::nnet(formula, data, ...) return(list(model = res, distance = fitted(res))) } else { stop("nnet package is needed. Please install it.", call. = FALSE) } } MatchIt/R/distance2rpart.R0000644000175100001440000000050713060361514015073 0ustar hornikusersdistance2rpart <- function(formula, data, ...) { if (requireNamespace("rpart", quietly = TRUE)) { res <- rpart::rpart(formula, data, ...) return(list(model = res, distance = predict(res))) } else { stop("rpart package is needed. Please install it.", call. = FALSE) } } MatchIt/R/distance2GAM.R0000644000175100001440000000330313060361514014344 0ustar hornikusersdistance2GAMlogit <- function(formula, data, ...) { if (requireNamespace("mgcv", quietly = TRUE)) { res <- mgcv::gam(formula, data, family=binomial(logit), ...) return(list(model = res, distance = fitted(res))) } else { stop("mgcv package is needed. Please install it.", call. = FALSE) } } distance2GAMprobit <- function(formula, data, ...) { if (requireNamespace("mgcv", quietly = TRUE)) { res <- mgcv::gam(formula, data, family=binomial(probit), ...) return(list(model = res, distance = fitted(res))) } else { stop("mgcv package is needed. Please install it.", call. = FALSE) } } distance2GAMcloglog <- function(formula, data, ...) { if (requireNamespace("mgcv", quietly = TRUE)) { res <- mgcv::gam(formula, data, family=binomial(cloglog), ...) return(list(model = res, distance = fitted(res))) } else { stop("mgcv package is needed. Please install it.", call. = FALSE) } } distance2GAMlog <- function(formula, data, ...) { if (requireNamespace("mgcv", quietly = TRUE)) { res <- mgcv::gam(formula, data, family=binomial(log), ...) return(list(model = res, distance = fitted(res))) } else { stop("mgcv package is needed. Please install it.", call. = FALSE) } } distance2GAMcauchit <- function(formula, data, ...) { if (requireNamespace("mgcv", quietly = TRUE)) { res <- mgcv::gam(formula, data, family=binomial(cauchit), ...) return(list(model = res, distance = fitted(res))) } else { stop("mgcv package is needed. Please install it.", call. = FALSE) } } MatchIt/R/jitter.pscore.R0000644000175100001440000000351413060361514014742 0ustar hornikusersjitter.pscore <- function(x, interactive,pch=1,cex=NULL,...){ treat <- x$treat pscore <- x$distance weights <- x$weights matched <- weights!=0 q.cut <- x$q.cut jitp <- jitter(rep(1,length(treat)),factor=6)+(treat==1)*(weights==0)-(treat==0)-(weights==0)*(treat==0) cwt <- sqrt(weights) minp <- min(pscore,na.rm=T) maxp <- max(pscore,na.rm=T) plot(pscore,xlim=c(minp,maxp+0.1*(maxp-minp)),ylim=c(-1.5,2.5), type="n",ylab="",xlab="Propensity Score", axes=F,main="Distribution of Propensity Scores",...) if(!is.null(q.cut)){abline(v=q.cut,col="grey",lty=1)} if(is.null(cex)){ points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0], pch=pch,cex=cwt[treat==1&weights!=0],...) points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0], pch=pch,cex=cwt[treat==0&weights!=0],...) points(pscore[treat==1&weights==0],jitp[treat==1&weights==0], pch=pch,cex=1,...) points(pscore[treat==0&weights==0],jitp[treat==0&weights==0], pch=pch,cex=1,...) }else{ points(pscore[treat==1&weights!=0],jitp[treat==1&weights!=0], pch=pch,cex=cex,...) points(pscore[treat==0&weights!=0],jitp[treat==0&weights!=0], pch=pch,cex=cex,...) points(pscore[treat==1&weights==0],jitp[treat==1&weights==0], pch=pch,cex=cex,...) points(pscore[treat==0&weights==0],jitp[treat==0&weights==0], pch=pch,cex=cex,...) } axis(1) text(sum(range(na.omit(pscore)))/2,2.5,"Unmatched Treatment Units") text(sum(range(na.omit(pscore)))/2,1.5,"Matched Treatment Units") text(sum(range(na.omit(pscore)))/2,0.5,"Matched Control Units") text(sum(range(na.omit(pscore)))/2,-0.5,"Unmatched Control Units") box() if(interactive==TRUE) { print("To identify the units, use first mouse button; to stop, use second.") identify(pscore,jitp,names(treat),atpen=T) } } MatchIt/R/user.prompt.R0000644000175100001440000000013513075246077014455 0ustar hornikusers#' @export user.prompt <- function() silent <- readline("\nPress to continue: ") MatchIt/R/matchit.qqplot.R0000644000175100001440000000666413060361514015130 0ustar hornikusersmatchit.qqplot <- function(x,discrete.cutoff, which.subclass=NULL, numdraws=5000, interactive = T, which.xs = NULL,...){ X <- x$X ## Fix X matrix so that it doesn't have any factors varnames <- colnames(X) for(var in varnames) { if(is.factor(X[,var])) { tempX <- X[,!colnames(X)%in%c(var)] form<-formula(substitute(~dummy-1,list(dummy=as.name(var)))) X <- cbind(tempX, model.matrix(form, X)) } } covariates <- X if(!is.null(which.xs)){ if(sum(which.xs%in%dimnames(covariates)[[2]])!=length(which.xs)){ stop("which.xs is incorrectly specified") } covariates <- covariates[,which.xs,drop=F] } treat <- x$treat matched <- x$weights!=0 ratio <- x$call$ratio if(is.null(ratio)){ratio <- 1} ## For full or ratio matching, sample numdraws observations using the weights if(identical(x$call$method,"full") | (ratio!=1)) { t.plot <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=x$weights[treat==1]) c.plot <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=x$weights[treat==0]) m.covariates <- x$X[c(t.plot, c.plot),] m.treat <- x$treat[c(t.plot, c.plot)] } else { m.covariates <- covariates[matched,,drop=F] m.treat <- treat[matched] } if(!is.null(which.subclass)){ subclass <- x$subclass sub.index <- subclass==which.subclass & !is.na(subclass) sub.covariates <- covariates[sub.index,,drop=F] sub.treat <- treat[sub.index] sub.matched <- matched[sub.index] ## Matched units in each subclass m.covariates <- sub.covariates[sub.matched,,drop=F] m.treat <- sub.treat[sub.matched] ## Compare to full sample--reset covariates and treat to full data set # covariates <- x$X # treat <- x$treat } nn <- dimnames(covariates)[[2]] nc <- length(nn) covariates <- data.matrix(covariates) # oma <- c(4, 4, 6, 4) oma <- c(2.25,0,3.75,1.5) opar <- par(mfrow = c(3, 3), mar = rep.int(1/2, 4), oma = oma) on.exit(par(opar)) # par(oma=c(2.25,0,3.75,1.5)) for (i in 1:nc){ xi <- covariates[,i] m.xi <- m.covariates[,i] ni <- nn[i] plot(xi,type="n",axes=F) if(((i-1)%%3)==0){ htext <- "QQ Plots" if(!is.null(which.subclass)){ htext <- paste(htext,paste(" (Subclass ",which.subclass,")",sep=""),sep="") } mtext(htext, 3, 2, TRUE, 0.5, cex=1.1,font=2) mtext("All", 3, .25, TRUE, 0.5, cex=1,font = 1) mtext("Matched", 3, .25, TRUE, 0.83, cex=1,font = 1) mtext("Control Units", 1, 0, TRUE, 2/3, cex=1,font = 1) mtext("Treated Units", 4, 0, TRUE, 0.5, cex=1,font = 1) } par(usr = c(0, 1, 0, 1)) l.wid <- strwidth(nn, "user") cex.labels <- max(0.75, min(1.45, 0.85/max(l.wid))) text(0.5,0.5,ni,cex=cex.labels) if(length(table(xi))<=discrete.cutoff){ xi <- jitter(xi) m.xi <- jitter(m.xi) } rr <- range(xi) eqqplot(xi[treat==0],xi[treat==1], xlim=rr,ylim=rr,axes=F,ylab="",xlab="",...) abline(a=0,b=1) abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2) abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2) axis(2) box() eqqplot(m.xi[m.treat==0],m.xi[m.treat==1],xlim=rr,ylim=rr,axes=F,ylab="",xlab="",...) abline(a=0,b=1) abline(a=(rr[2]-rr[1])*0.1,b=1,lty=2) abline(a=-(rr[2]-rr[1])*0.1,b=1,lty=2) box() if(interactive){ par(ask=T) } else { par(ask=F) } } par(ask=F) } MatchIt/R/distance2glm.R0000644000175100001440000000317513060361514014526 0ustar hornikusersdistance2logit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(logit), ...) return(list(model = res, distance = fitted(res))) } distance2linear.logit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(logit), ...) return(list(model = res, distance = predict(res))) } distance2probit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(probit), ...) return(list(model = res, distance = fitted(res))) } distance2linear.probit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(probit), ...) return(list(model = res, distance = predict(res))) } distance2cloglog <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cloglog), ...) return(list(model = res, distance = fitted(res))) } distance2linear.cloglog <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cloglog), ...) return(list(model = res, distance = predict(res))) } distance2log <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(log), ...) return(list(model = res, distance = fitted(res))) } distance2linear.log <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(log), ...) return(list(model = res, distance = predict(res))) } distance2cauchit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cauchit), ...) return(list(model = res, distance = fitted(res))) } distance2linearcauchit <- function(formula, data, ...) { res <- glm(formula, data, family=binomial(cauchit), ...) return(list(model = res, distance = predict(res))) } MatchIt/R/match.qoi.R0000644000175100001440000000561513060361514014036 0ustar hornikusers## Function to calculate summary stats qoi <- function(xx,tt,ww, t.plot=NULL, c.plot=NULL, sds=NULL, standardize = FALSE, std=F){ weighted.var <- function(x, w) { sum(w * (x - weighted.mean(x,w))^2)/(sum(w) - 1)} xsum <- matrix(NA,2,7) xsum <- as.data.frame(xsum) row.names(xsum) <- c("Full","Matched") if (standardize) names(xsum) <- c("Means Treated","Means Control", "SD Control", "Std. Mean Diff.", "eCDF Med", "eCDF Mean", "eCDF Max") else names(xsum) <- c("Means Treated","Means Control", "SD Control", "Mean Diff", "eQQ Med", "eQQ Mean", "eQQ Max") x1 <- xx[tt==1] x0 <- xx[tt==0] ww1 <- ww[tt==1] ww0 <- ww[tt==0] xsum[1,1] <- mean(x1,na.rm=T) xsum[1,2] <- mean(x0,na.rm=T) xsum[1,3] <- sd(x0,na.rm=T) X.t.m <- xx[tt==1][ww1>0] X.c.m <- xx[tt==0][ww0>0] xsum[2,1] <- weighted.mean(X.t.m, ww1[ww1>0]) xsum[2,2] <- weighted.mean(X.c.m, ww0[ww0>0]) xsum[2,3] <- sqrt(weighted.var(X.c.m, ww0[ww0>0])) if(!(sum(tt==1)<2|(sum(tt==0)<2))){ xsd <- sd(x1,na.rm=T) qqall <- qqsum(x1,x0,standardize=standardize) xsum[1,5:7] <- c(qqall$meddiff,qqall$meandiff,qqall$maxdiff) if (standardize) if (!is.null(sds)) xsum[1,4] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/sds else xsum[1,4] <- (mean(x1,na.rm=T)-mean(x0,na.rm=T))/xsd else xsum[1,4] <- mean(x1,na.rm=T)-mean(x0,na.rm=T) if(!is.null(t.plot)) qqmat <- qqsum(xx[t.plot],xx[c.plot],standardize=standardize) else qqmat <- qqsum(x1[ww1>0],x0[ww0>0],standardize=standardize) xsum[2,5:7] <- c(qqmat$meddiff,qqmat$meandiff,qqmat$maxdiff) if (standardize) if (!is.null(sds)) xsum[2,4] <- (xsum[2,1]-xsum[2,2])/sds else xsum[2,4] <- (xsum[2,1]-xsum[2,2])/xsd else xsum[2,4] <- xsum[2,1]-xsum[2,2] } if(!std){ xsum <- xsum[,c(1:2,4:7)] } xsum } ## By subclass qoi.by.sub <- function(xx,tt,ww,qq,standardize=FALSE){ qbins <- max(qq,na.rm=TRUE) q.table <- matrix(0,6,qbins) qn <- matrix(0,3,qbins) matched <- ww!=0 for (i in 1:qbins) { qi <- qq[matched]==i & (!is.na(qq[matched])) qx <- xx[matched][qi] qt <- tt[matched][qi] qw <- as.numeric(ww[matched][qi]!=0) if(sum(qt==1)<2|(sum(qt==0)<2)){ if(sum(qt==1)<2) warning("Not enough treatment units in subclass ",i,call.=FALSE) else if(sum(qt==0)<2) warning("Not enough control units in subclass ",i,call.=FALSE) } qoi.i <- qoi(qx,qt,qw, sds=sd(xx[tt==1],na.rm=T), standardize=standardize) q.table[,i] <- as.numeric(qoi.i[1,]) qn[,i] <- c(sum(qt),sum(qt==0),length(qt)) } q.table <- as.data.frame(q.table) qn <- as.data.frame(qn) names(q.table) <- names(qn) <- paste("Subclass",1:qbins) row.names(q.table) <- names(qoi.i) row.names(qn) <- c("Treated","Control","Total") list(q.table=q.table,qn=qn) } MatchIt/R/qqsum.R0000644000175100001440000000120013060361514013303 0ustar hornikusers## Function for QQ summary stats qqsum <- function (x, y, standardize = FALSE){ sx <- sort(x) sy <- sort(y) lenx <- length(sx) leny <- length(sy) if (standardize) { vals <- sort(unique(c(sx, sy))) sx <- ecdf(sx) sx <- sx(vals) sy <- ecdf(sy) sy <- sy(vals) } else { if (leny < lenx) sx <- approx(1:lenx, sx, n = leny, method = "constant")$y if (leny > lenx) sy <- approx(1:leny, sy, n = lenx, method = "constant")$y } dxy <- abs(sx-sy) meandiff <- mean(dxy) meddiff <- median(dxy) maxdiff <- max(dxy) invisible(list(meandiff=meandiff, meddiff=meddiff, maxdiff=maxdiff)) } MatchIt/R/matchit2full.R0000644000175100001440000000206213075246077014556 0ustar hornikusersmatchit2full <- function(treat, X, data, distance, discarded, is.full.mahalanobis, verbose=FALSE, ...) { if(verbose) cat("Full matching... \n") ## full matching for undiscarded units ttt <- treat[!discarded] ddd <- distance[!discarded] n0 <- length(ttt[ttt==0]) n1 <- length(ttt[ttt==1]) d1 <- ddd[ttt==1] d0 <- ddd[ttt==0] d <- matrix(0, ncol=n0, nrow=n1) rownames(d) <- names(ttt[ttt==1]) colnames(d) <- names(ttt[ttt==0]) for (i in 1:n1) d[i,] <- abs(d1[i]-d0) full <- optmatch::fullmatch(d, ...) psclass <- full[pmatch(names(ttt), names(full))] psclass <- as.numeric(as.factor(psclass)) names(psclass) <- names(ttt) ## add psclass = NA for discarded units if (any(discarded)) { tmp <- rep(NA, sum(discarded)) names(tmp) <- names(treat[discarded]) psclass <- c(psclass, tmp)[names(treat)] } ## calculate weights and return the results res <- list(subclass = psclass, weights = weights.subclass(psclass, treat)) class(res) <- c("matchit.full", "matchit") return(res) } MatchIt/R/help.matchit.R0000644000175100001440000000401513075246077014540 0ustar hornikusers#' @export help.matchit <- function (object=NULL) { under.unix <- !(version$os == "Microsoft Windows" || version$os == "Win32" || version$os == "mingw32") sys <- function(command, text = NULL) { cmd <- if (length(text)) paste(command, text) else command if (under.unix) system(cmd) else shell(cmd, wait = TRUE) } browser <- .Options$help.browser if (!length(browser)) browser <- .Options$browser if (!length(browser)) browser <- getOption("browser") url <- NULL if (is.null(object)) url <- c("http://gking.harvard.edu/matchit") if (!is.null(object)) { if (object == "matchit") url <- c("http://gking.harvard.edu/matchit/docs/Reference_Manual.html") if (object == "exact") url <- c("http://gking.harvard.edu/matchit/docs/Exact_Matching2.html") if (object == "subclass") url <- c("http://gking.harvard.edu/matchit/docs/Subclassification2.html") if (object == "nearest") url <- c("http://gking.harvard.edu/matchit/docs/Nearest_Neighbor_Match2.html") if (object == "optimal") url <- c("http://gking.harvard.edu/matchit/docs/Optimal_Matching2.html") if (object == "full") url <- c("http://gking.harvard.edu/matchit/docs/Full_Matching2.html") if (object == "match.data") url <- c("http://gking.harvard.edu/matchit/docs/_TT_match_data_TT.html") if (object == "summary") url <- c("http://gking.harvard.edu/matchit/docs/_TT_summary_TT.html") if (object == "plot") url <- c("http://gking.harvard.edu/matchit/docs/_TT_plot_TT.html") } if (is.null(url)) { cat("Error:", object, "currently not documented in help.matchit. \n Please check http://gking.harvard.edu/matchit. \n", sep = " ") url <- c("http://gking.harvard.edu/matchit") } if (under.unix) { sys(paste(browser, url, "&")) invisible() } if (!under.unix) { browseURL(url, browser = browser) invisible("") } } MatchIt/R/matchit2optimal.R0000644000175100001440000000362513060361514015253 0ustar hornikusersmatchit2optimal <- function(treat, X, data, distance, discarded, is.full.mahalanobis, ratio = 1, verbose=FALSE, ...) { if (!requireNamespace("optmatch", quietly = TRUE)) stop("optmatch package is required. Please install it.") if(verbose) cat("Optimal matching... \n") ## optimal matching for undiscarded units ttt <- treat[!discarded] n0 <- length(ttt[ttt==0]) n1 <- length(ttt[ttt==1]) d1 <- distance[ttt==1] d0 <- distance[ttt==0] d <- matrix(0, ncol=n0, nrow=n1) tlabels <- rownames(d) <- names(ttt[ttt==1]) clabels <- colnames(d) <- names(ttt[ttt==0]) for (i in 1:n1) d[i,] <- abs(d1[i]-d0) full <- optmatch::fullmatch(d, min.controls = ratio, max.controls = ratio, omit.fraction = (n0-ratio*n1)/n0, ...) psclass <- full[pmatch(names(ttt), names(full))] psclass <- as.numeric(as.factor(psclass)) names(psclass) <- names(ttt) mm <- matrix(0, nrow = n1, ncol = ratio, dimnames = list(tlabels, 1:ratio)) for (i in 1:n1) mm[i,] <- names(which(psclass[tlabels[i]] == psclass[-pmatch(tlabels[i], names(psclass))])) if (any(discarded)) { ## add psclass = NA for discarded units tmp <- rep(NA, sum(discarded)) names(tmp) <- names(treat[discarded]) psclass <- c(psclass, tmp)[names(treat)] ## add match.matrix = NA for discarded units tdisc <- discarded[treat==1] if (any(tdisc)) { tmp <- matrix(NA, nrow = sum(tdisc), ncol= ratio, dimnames = list(names(treat[treat==1 & discarded]), 1:ratio)) mm <- as.matrix(rbind(mm, tmp)[names(treat[treat==1]),]) } } ## calculate weights and return the results res <- list(match.matrix = mm, subclass = psclass, weights = weights.matrix(mm, treat, discarded)) class(res) <- "matchit" return(res) } MatchIt/R/discard.R0000644000175100001440000000306313060361514013557 0ustar hornikusersdiscard <- function(treat, pscore, option, X) { n.obs <- length(treat) pmax0 <- max(pscore[treat==0]) pmax1 <- max(pscore[treat==1]) pmin0 <- min(pscore[treat==0]) pmin1 <- min(pscore[treat==1]) if (is.logical(option)) # user input return(option) else if (option == "none") # keep all units discarded <- rep(FALSE, n.obs) else if (option == "both") # discard units outside of common support discarded <- (pscore < max(pmin0, pmin1) | pscore > min(pmax0, pmax1)) else if (option == "control") # discard control units only discarded <- (pscore < pmin1 | pscore > pmax1) else if (option == "treat") # discard treated units only discarded <- (pscore < pmin0 | pscore > pmax0) else if (any(grep(option, c("hull.control", "hull.treat", "hull.both")))) { ## convext hull stuff requireNamespace("WhatIf") discarded <- rep(FALSE, n.obs) if (option == "hull.control"){ # discard units not in T convex hull wif <- WhatIf::whatif(cfact = X[treat==0,], data = X[treat==1,]) discarded[treat==0] <- !wif$in.hull } else if (option == "hull.treat") { wif <- WhatIf::whatif(cfact = X[treat==1,], data = X[treat==0,]) discarded[treat==1] <- !wif$in.hull } else if (option == "hull.both"){ # discard units not in T&C convex hull wif <- WhatIf::whatif(cfact = cbind(1-treat, X), data = cbind(treat, X)) discarded <- !wif$in.hull } else stop("invalid input for `discard'") } else stop("invalid input for `discard'") names(discarded) <- names(treat) return(discarded) } MatchIt/R/matchit2nearest.R0000644000175100001440000002371213060361514015246 0ustar hornikusersmatchit2nearest <- function(treat, X, data, distance, discarded, ratio=1, replace = FALSE, m.order = "largest", caliper = 0, calclosest = FALSE, mahvars = NULL, exact = NULL, subclass=NULL, verbose=FALSE, sub.by=NULL, is.full.mahalanobis,...){ if(verbose) cat("Nearest neighbor matching... \n") #replace if(!(identical(replace,TRUE) | identical(replace,FALSE))){ warning("replace=",replace," is invalid; used replace=FALSE instead",call.=FALSE);replace=FALSE} #m.order if(!(identical(m.order,"largest") | identical(m.order,"smallest") | identical(m.order,"random"))){ warning("m.order=",m.order," is invalid; used m.order='largest' instead",call.=FALSE);m.order="largest"} #ratio ratio <- round(ratio) if(!is.numeric(ratio) | ratio[1]<1 | !identical(round(length(ratio)),1)){ warning("ratio=",ratio," is invalid; used ratio=1 instead",call.=FALSE);ratio=1} #caliper if(!is.vector(caliper) | !identical(round(length(caliper)),1)){ warning("caliper=",caliper," is invalid; Caliper matching not done",call.=FALSE);caliper=0} if(caliper<0){ warning("caliper=",caliper," is less than 0; Caliper matching not done",call.=FALSE);caliper=0} #calclosest if(!(identical(calclosest,TRUE)| identical(calclosest,FALSE))){ warning("calclosest=",calclosest," is invalid; used calclosest=FALSE instead",call.=FALSE) calclosest=FALSE} #mahvars & caliper if (!is.null(mahvars) & caliper[1]==0){ warning("No caliper size specified for Mahalanobis matching. Caliper=.25 used.",call. = FALSE);caliper=.25} #when mahalanobis distance is used for all covars if(is.full.mahalanobis){ mahvars <- X Sigma <- var(X) ## Note: caliper irrelevant, but triggers mahalanobis matching caliper <- .25 ## no subclass with full mahalanobis if(!is.null(subclass)){ warning("No subclassification with pure Mahalanobis distance.",call. = FALSE) subclass <- NULL } } # Sample sizes, labels n <- length(treat) n0 <- length(treat[treat==0]) n1 <- length(treat[treat==1]) d1 <- distance[treat==1] d0 <- distance[treat==0] if(is.null(names(treat))) names(treat) <- 1:n labels <- names(treat) tlabels <- names(treat[treat==1]) clabels <- names(treat[treat==0]) in.sample <- !discarded names(in.sample) <- labels ## 10/1/07: Warning for if fewer control than ratio*treated and matching without replacement if (n0 < ratio*n1 & replace==FALSE) { if (ratio > 1) warning(paste("Not enough control units for ", ratio, " matches for each treated unit when matching without replacement. Not all treated units will receive", ratio, "matches")) else warning(paste("Fewer control than treated units and matching without replacement. Not all treated units will receive a match. Treated units will be matched in the order specified by m.order:", m.order)) } ## Generating match matrix match.matrix <- matrix(0, nrow=n1, ncol=ratio, dimnames=list(tlabels, 1:ratio)) ## Vectors of whether unit has been matched: ## = 0 if not matched (unit # of match if matched) ## = -1 if can't be matched (if in.sample=0) matchedc <- rep(0,length(d0)) names(matchedc) <- clabels ## These are the units that are ineligible because of discard ## (in.sample==0) matchedc[in.sample[clabels]==0] <- -1 match.matrix[in.sample[tlabels]==0,] <- -1 matchedt <- match.matrix[,1] names(matchedt) <- tlabels ## total number of matches (including ratios) = ratio * n1 tr <- length(match.matrix[match.matrix!=-1]) r <- 1 ## Caliper for matching (=0 if caliper matching not done) sd.cal <- caliper*sqrt(var(distance[in.sample==1])) ## Var-covar matrix for Mahalanobis (currently set for full sample) if (!is.null(mahvars) & !is.full.mahalanobis) { if(!sum(mahvars%in%names(data))==length(mahvars)) { warning("Mahvars not contained in data. Mahalanobis matching not done.",call.=FALSE) mahvars=NULL } else { ww <- mahvars%in%dimnames(X)[[2]] nw <- length(mahvars) mahvars <- data[,mahvars,drop=F] Sigma <- var(mahvars) if(sum(ww)!=nw){ X <- cbind(X,mahvars[!ww]) } mahvars <- as.matrix(mahvars) } } ## Now for exact matching within nearest neighbor ## exact should not equal T for this type of matching--that would get sent to matchit2exact if (!is.null(exact)){ if(!sum(exact%in%names(data))==length(exact)) { warning("Exact variables not contained in data. Exact matching not done.",call.=FALSE) exact=NULL } else { ww <- exact%in%dimnames(X)[[2]] nw <- length(exact) exact <- data[,exact,drop=F] if(sum(ww)!=nw){ X <- cbind(X,exact[!ww]) } } } ## Looping through nearest neighbour matching for all treatment units ## Only do matching for units with in.sample==1 (matched!=-1) if(verbose){ trseq <- floor(seq(tr/10,tr,tr/10)) cat("Matching Treated: ") } for(i in 1:tr){ ## Make new matchedc column to be used for exact matching ## Will only be 0 (eligible for matching) if it's an exact match if(verbose) {if(i%in%trseq){cat(10*which(trseq==i),"%...",sep="")}} # a counter matchedc2 <- matchedc ##in cases there's no replacement and all controls have been used up if(!0%in%matchedc2){ match.matrix[match.matrix[,r]==0 & !is.na(match.matrix[,r]),r] <- NA if(r1){itert <- sample(itert,1)} ## Calculating all the absolute deviations in propensity scores ## Calculate only for those eligible to be matched (matchedc==0) ## this first if statement only applies to replacement ratio ## matching, so that each treatment unit is matched to a different ## control unit than from the previous round ## match number = NA if no units within caliper ## Set things up for exact matching ## Make matchedc2==-2 if it isn't an exact match ## There might be a more efficient way to do this, but I couldn't figure ## out another way to compare a vector with the matrix if (!is.null(exact)) { for (k in 1:dim(exact)[2]) matchedc2[exact[itert,k]!=exact[clabels,k]] <- -2 } ## Need to add a check in case there aren't any eligible matches left... if(replace & r!=1) { if (sum(!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0)==0) { deviation <- NULL mindev <- NA } else deviation <- abs(d0[!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0]-iterd1) } else { if (sum(matchedc2==0)==0) { deviation <- NULL mindev <- NA } else deviation <- abs(d0[matchedc2==0]-iterd1) } if (caliper!=0 & (!is.null(deviation))) { if(replace & r!=1) pool <- clabels[!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0][deviation <= sd.cal] else pool <- clabels[matchedc2==0][deviation <= sd.cal] if(length(pool)==0) { if (calclosest==FALSE) mindev <- NA else { if (replace & r!= 1){ mindev <- clabels[!clabels%in%match.matrix[itert,(1:r-1)]][min(deviation)==deviation] } else{mindev <- clabels[matchedc2==0][min(deviation)==deviation]} } } else if (length(pool)==1) mindev <- pool[1] else if (is.null(mahvars)) mindev <- sample(pool, 1) else { ## This has the important vars for the C's within the caliper poolvarsC <- mahvars[pool,,drop=F] ## Sigma is the full group var/covar matrix of Mahalvars mahal <- mahalanobis(poolvarsC, mahvars[itert,],Sigma) mindev <- pool[mahal==min(mahal)] } } else if(!is.null(deviation)) { if (replace & r!=1){ mindev <- clabels[!clabels%in%match.matrix[itert,(1:r-1)] & matchedc2==0][min(deviation)==deviation] } else {mindev <- clabels[matchedc2==0][min(deviation)==deviation]} } ## Resolving ties in minimum deviation by random draw if(length(mindev)>1){goodmatch <- sample(mindev,1)} else goodmatch <- mindev ## Storing which treatment unit has been matched to control, and ## vice versa matchedt[itert==tlabels] <- goodmatch matchedc[goodmatch==clabels] <- itert ## instead of the in.sample, we now have an index with dimensions n1 by # of ## matches (ratio) match.matrix[which(itert==tlabels),r] <- goodmatch ## If matching with replacement, set matchedc back to 0 so it can be reused if (replace) matchedc[goodmatch==clabels] <- 0 } if(verbose){cat("Done\n")} x <- as.matrix(match.matrix) x[x==-1] <- NA ## Calculate weights and return the results res <- list(match.matrix = match.matrix, weights = weights.matrix(match.matrix, treat, discarded), X=X) ## Subclassifying if(!is.null(subclass)){ if(is.null(sub.by)) sub.by="treat" psres <- matchit2subclass(treat,X,data,distance,discarded, match.matrix=match.matrix, subclass=subclass, verbose=verbose, sub.by=sub.by, ...) res$subclass <- psres$subclass res$q.cut <- psres$q.cut class(res) <- c("matchit.subclass", "matchit") } else{ class(res) <- "matchit" } return(res) } MatchIt/R/matchit2genetic.R0000644000175100001440000000242413075246077015234 0ustar hornikusersmatchit2genetic <- function(treat, X, data, distance, discarded, is.full.mahalanobis, ratio = 1, verbose = FALSE, ...) { if (verbose) cat("Genetic matching... \n") tt <- treat[!discarded] n <- length(tt) n1 <- length(tt[tt==1]) xx <- X[!discarded,] dd <- distance[!discarded] tind <- (1:n)[tt==1] cind <- (1:n)[tt==0] labels <- names(tt) tlabels <- names(tt[tt==1]) clabels <- names(tt[tt==0]) out <- Matching::GenMatch(tt, cbind(dd, xx), M = ratio, ...)$matches ## ratio matching does not seem to work with GenMatch mm <- matrix(0, nrow = n1, ncol = max(table(out[,1])), dimnames = list(tlabels, 1:max(table(out[,1])))) for (i in 1:n1) { tmp <- labels[c(out[out[,1]==tind[i],2:(ratio+1)])] if (length(tmp) < ncol(mm)) tmp <- c(tmp, rep(NA, ncol(mm)-length(tmp))) mm[i,] <- tmp } if (any(discarded)) { tdisc <- discarded[treat==1] tmp <- matrix(NA, nrow = sum(tdisc), ncol = ncol(mm), dimnames = list(names(treat[treat == 1 & discarded]), 1:ncol(mm))) mm <- as.matrix(rbind(mm, tmp)[names(treat[treat==1]),]) } res <- list(match.matrix = mm, weights = weights.matrix(mm, treat, discarded)) class(res) <- "matchit" return(res) } MatchIt/R/matchit.R0000644000175100001440000000736313075246077013622 0ustar hornikusers#' @export matchit <- function(formula, data, method = "nearest", distance = "logit", distance.options=list(), discard = "none", reestimate = FALSE, ...) { #Checking input format #data input mcall <- match.call() if(is.null(data)) stop("Dataframe must be specified",call.=FALSE) if(!is.data.frame(data)){ stop("Data must be a dataframe",call.=FALSE)} if(sum(is.na(data))>0) stop("Missing values exist in the data") # list-wise deletion # allvars <- all.vars(mcall) # varsindata <- colnames(data)[colnames(data) %in% all.vars(mcall)] # data <- na.omit(subset(data, select = varsindata)) ## 7/13/06: Convert character variables to factors as necessary ischar <- rep(0, dim(data)[2]) for (i in 1:dim(data)[2]) if(is.character(data[,i])) data[,i] <- as.factor(data[,i]) ## check inputs if (!is.numeric(distance)) { fn1 <- paste("distance2", distance, sep = "") if (!exists(fn1)) stop(distance, "not supported.") } if (is.numeric(distance)) { fn1 <- "distance2user" } fn2 <- paste("matchit2", method, sep = "") if (!exists(fn2)) stop(method, "not supported.") ## obtain T and X tryerror <- try(model.frame(formula), TRUE) if (distance %in% c("GAMlogit", "GAMprobit", "GAMcloglog", "GAMlog", "GAMcauchit")) { requireNamespace("mgcv") tt <- terms(mgcv::interpret.gam(formula)$fake.formula) } else { tt <- terms(formula) } attr(tt, "intercept") <- 0 mf <- model.frame(tt, data) treat <- model.response(mf) X <- model.matrix(tt, data=mf) ## estimate the distance measure if (method == "exact") { distance <- out1 <- discarded <- NULL if (!is.null(distance)) warning("distance is set to `NULL' when exact matching is used.") } else if (is.numeric(distance)){ out1 <- NULL discarded <- discard(treat, distance, discard, X) } else { if (is.null(distance.options$formula)) distance.options$formula <- formula if (is.null(distance.options$data)) distance.options$data <- data out1 <- do.call(fn1, distance.options) discarded <- discard(treat, out1$distance, discard, X) if (reestimate) { distance.options$data <- data[!discarded,] distance.options$weights <- distance.options$weights[!discarded] tmp <- out1 out1 <- do.call(fn1, distance.options) tmp$distance[!discarded] <- out1$distance out1$distance <- tmp$distance } distance <- out1$distance } ## full mahalanobis matching if(fn1=="distance2mahalanobis"){ is.full.mahalanobis <- TRUE } else {is.full.mahalanobis <- FALSE} ## matching! out2 <- do.call(fn2, list(treat, X, data, distance=distance, discarded, is.full.mahalanobis=is.full.mahalanobis, ...)) ## no distance for full mahalanobis matching if(fn1=="distance2mahalanobis"){ distance[1:length(distance)] <- NA class(out2) <- c("matchit.mahalanobis","matchit") } ## putting all the results together out2$call <- mcall out2$model <- out1$model out2$formula <- formula out2$treat <- treat if (is.null(out2$X)){ out2$X <- X } out2$distance <- distance out2$discarded <- discarded ## basic summary nn <- matrix(0, ncol=2, nrow=4) nn[1,] <- c(sum(out2$treat==0), sum(out2$treat==1)) nn[2,] <- c(sum(out2$treat==0 & out2$weights>0), sum(out2$treat==1 & out2$weights>0)) nn[3,] <- c(sum(out2$treat==0 & out2$weights==0 & out2$discarded==0), sum(out2$treat==1 & out2$weights==0 & out2$discarded==0)) nn[4,] <- c(sum(out2$treat==0 & out2$weights==0 & out2$discarded==1), sum(out2$treat==1 & out2$weights==0 & out2$discarded==1)) dimnames(nn) <- list(c("All","Matched","Unmatched","Discarded"), c("Control","Treated")) out2$nn <- nn return(out2) } MatchIt/R/class_functions.R0000644000175100001440000006413113075246077015362 0ustar hornikusers #' @import MASS #' @import stats #' @import utils #' @import graphics in_frame <- function(df, col) { exists(col, envir= as.environment(df)) } int_rownames <- function(mat) { options(warn= -1) rn <- as.integer(rownames(mat)) options(warn= 0) if (any(is.na(rn))) return(FALSE) else return(TRUE) } #---------------------------------------------------------- ### Inheritance #---------------------------------------------------------- #' @title Checks matchit Class #' @description Function that checks if the target object is a \code{matchit} object. #' @param object any R object #' @return Returns \code{TRUE} if its argument has class "matchit" among its classes and #' \code{FALSE} otherwise. #' @export is.matchit <- function(object) { inherits(object, "matchit") } #---------------------------------------------------------- ### GET MATCHES #---------------------------------------------------------- #' @title Get matches from matchit object #' @description Get the resulting matches from a \code{matchit} model object. This function allows the #' user to extract the matches from the original dataset used in model building or from a new dataset #' that has a matching set of key column(s) (\code{id_cols}). #' @param object The \code{'matchit'} class model object #' @param model_frame The \code{'data.frame'} class object used in creation of \code{object}. #' @param id_cols A string indicating the ID for the datset used in the call to \code{\link{matchit}}. #' This can be used in combination with \code{newdata} to return the base dataset. Defaults to #' \code{NULL}. #' @param newdata A new \code{data.frame} object to extract matched observations from. Used in #' conjunction with \code{id_cols}. Defaults to \code{NULL}. #' @return If \code{newdata} is \code{NULL}, a subset of \code{model_frame} containing the rows #' corresponding to the matched treatement and control observations with weights appended. If #' \code{newdata} is not \code{NULL}, an equivalent subset of \code{newdata} is returned. #' @export get_matches <- function(object, model_frame, id_cols= NULL, newdata= NULL) { UseMethod("get_matches", object) } #' @export get_matches.matchit <- function(object, model_frame, id_cols= NULL, newdata= NULL) { # 00. error checking if (!is.matchit(object)) stop("object must be a class 'matchit' object.") if (!is.data.frame(model_frame)) stop("model_frame must be a data.frame") if (nrow(model_frame) != nrow(object$X)) stop("model_frame must have the same number of rows as the data used in the call to 'matchit'.") if ( (!is.null(id_cols) & is.null(newdata)) | (is.null(id_cols) & !is.null(newdata)) ) { stop("For identity returns, both id_cols and newdata must be supplied.") } if (!is.null(id_cols) & !is.null(newdata)) { if (!is.character(id_cols)) stop("id_cols must be a character vector.") if (!all(sapply(id_cols, in_frame, df= model_frame))) stop("all values in id_cols must exist in model_frame") if (!all(sapply(id_cols, in_frame, df= newdata))) stop("all values in id_cols must exist in newdata") } # 01. preliminaries if ( any(grepl(pattern= "replace", x= names(object$call))) ) { idx_call_replace <- which(names(object$call) == "replace") } else { idx_call_replace <- -1L } use_subclass_matching <- base::grepl(x = object$call[4], pattern= paste(c("exact", "full", "subclass", "cem"), collapse= "|"), ignore.case= TRUE) # [deprecated] use_genetic_matching <- base::grepl(x = object$call[4], pattern= "genetic", ignore.case= TRUE) use_newdata <- ifelse(is.null(newdata), FALSE, TRUE) has_int_rownames <- int_rownames(model_frame) # 02. extract pairs, either exact matching or otherwise if (use_subclass_matching) { if (has_int_rownames) { row_idx <- as.integer(names(object$subclass)[which(!is.na(object$subclass))]) match_wts <- object$weights[which(!is.na(object$subclass))] model_subset <- data.frame(model_frame[row_idx, ], weight= match_wts) } else { row_idx <- names(object$subclass)[which(!is.na(object$subclass))] match_wts <- object$weights[which(!is.na(object$subclass))] model_subset <- data.frame(model_frame[which(rownames(model_frame) %in% row_idx), ], weight= match_wts) } } else { model_subset <- get_matches_non_subclass(object= object, model_frame= model_frame, has_int_rownames= has_int_rownames, idx_call_replace= idx_call_replace) } # 03. return if (!use_newdata) { return(model_subset) } else { # using newdata via id_cols newdata <- base::as.data.frame(newdata) # in case of data.table unique_ids <- base::unique(x= model_subset[, c(id_cols, "weight")]) # use of all == FALSE for two cases: # a) newdata contains additional IDs not in the matching data -- most common # b) if model_subset contains IDs that newdata does not have -- less common return(merge(newdata, unique_ids, by= id_cols, all.x=FALSE, all.y=FALSE)) } } get_matches_non_subclass <- function(object, model_frame, has_int_rownames, use_genetic_matching, idx_call_replace) { ## get match weights n_matches <- ncol(object$match.matrix) # get control observations that have matches and their frequency (ie weight) control_units <- object$match.matrix attr(control_units, "dim") <- NULL control_units <- control_units[!is.na(control_units)] control_wts <- table(control_units); class(control_wts) <- "vector" control_wts <- data.frame(ob= names(control_wts), weight= control_wts / n_matches, stringsAsFactors= FALSE) if (has_int_rownames) { treated_obs <- data.frame(model_frame[as.integer(rownames(object$match.matrix)), ], weight= 1) control_obs <- data.frame(model_frame[as.integer(control_wts$ob), ], weight= control_wts$weight) } else { treated_obs <- data.frame( model_frame[which(rownames(model_frame) %in% rownames(object$match.matrix)), ], weight= 1) model_frame$rownames <- rownames(model_frame) control_obs <- merge(model_frame, control_wts, by.x= "rownames", by.y= "ob", all= FALSE) rownames(control_obs) <- control_obs$rownames control_obs$rownames <- NULL control_obs$ob <- NULL } model_subset <- do.call("rbind", list(control_obs, treated_obs)) return(model_subset) } #---------------------------------------------------------- ### PLOT METHODS #---------------------------------------------------------- # Need to account for weights -- how do we do qq plots with weights #' @export plot.matchit <- function(x, discrete.cutoff=5, type="QQ", numdraws=5000, interactive = T, which.xs = NULL, ...){ if ("matchit.exact" %in% class(x)){ stop("Not appropriate for exact matching. No plots generated.") } if(type=="QQ"){ matchit.qqplot(x=x,discrete.cutoff=discrete.cutoff, numdraws=numdraws, interactive=interactive, which.xs = which.xs, ...) } else if(type=="jitter"){ if("matchit.mahalanobis" %in% class(x)){ stop("Not appropriate for pure Mahalanobis matching. No plots generated.") } jitter.pscore(x, interactive=interactive,...) } else if(type=="hist"){ if("matchit.mahalanobis" %in% class(x)){ stop("Not appropriate for pure Mahalanobis matching. No plots generated.") } hist.pscore(x,...) } else { stop("Invalid type") } } #' @export plot.matchit.subclass <- function(x, discrete.cutoff=5, type="QQ", interactive = T, subclass = NULL, which.xs=NULL,...){ choice.menu <- function(choices,question) { k <- length(choices)-1 Choices <- data.frame(choices) row.names(Choices) <- 0:k names(Choices) <- "Choices" print.data.frame(Choices,right=FALSE) ans <- readline(question) while(!ans%in%c(0:k)) { print("Not valid -- please pick one of the choices") print.data.frame(Choices,right=FALSE) ans <- readline(question) } return(ans) } if(type=="QQ"){ if(interactive){ choices <- c("No",paste("Yes : Subclass ", 1:max(x$subclass,na.rm=T))) question <- "Would you like to see quantile-quantile plots of any subclasses?" ans <- -1 while(ans!=0) { ans <- as.numeric(choice.menu(choices,question)) if(ans!=0) { matchit.qqplot(x,discrete.cutoff,which.subclass=ans, interactive = interactive, which.xs=which.xs,...) } } } else { matchit.qqplot(x,discrete.cutoff,which.subclass=subclass, interactive=interactive, which.xs=which.xs,...) } } else if(type=="jitter"){ jitter.pscore(x, interactive=interactive,...) } else if(type=="hist"){ hist.pscore(x,...) } else { stop("Invalid type") } } #' @export plot.summary.matchit <- function(x, interactive = TRUE, ...) { if ("matchit.exact" %in% class(x)){ stop("Not appropriate for exact matching. No plots generated.") } if (!"Std. Mean Diff."%in%names(x$sum.all)){ stop(paste("Not appropriate for unstandardized summary. Run summary() with the ", "standardize=TRUE option, and then plot.")) } sd.pre <- abs(x$sum.all$"Std. Mean Diff.") sd.post <- abs(x$sum.matched$"Std. Mean Diff.") if (!is.null(x$q.table)) sd.post <- abs(x$sum.subclass$"Std. Mean Diff") ases.dat <- data.frame(es.unw = sd.pre, es.w = sd.post) par(mfrow=c(1,1)) plot(c(0.85, 2.15), c(0, min(3, max(unlist(ases.dat[, 1:2]), na.rm = TRUE))), type = "n", xaxt = "n", ylab = "Absolute Standardized Diff in Means", xlab = "", main = "") abline(h = c(0.2, 0.4, 0.6, 0.8, 1.0)) axis(side = 1, at = 1:2, labels = c("All Data", "Matched Data")) for (i in 1:nrow(ases.dat)) { points(1:2, abs(ases.dat[i, c("es.unw", "es.w")]), type = "b", col = "grey", pch=19) } temp1 <- ases.dat[abs(ases.dat$es.unw) < abs(ases.dat$es.w),] for (i in 1:nrow(temp1)) { points(1:2, abs(temp1[i, c("es.unw", "es.w")]), type = "b", col = "black", lwd = 2, pch=19) } if (max(ases.dat$es.w, na.rm = TRUE) > 3) mtext(text = "Some standardized diffs in means > 3 after matching!", side = 3, col = "red") if(interactive==TRUE) { print("To identify the variables, use first mouse button; to stop, use second.") identify(rep(1, length(sd.pre)),sd.pre,rownames(x$sum.all),atpen=T) identify(rep(2, length(sd.post)),sd.post,rownames(x$sum.all),atpen=T) } } #---------------------------------------------------------- ### PRINT METHODS #---------------------------------------------------------- #' @export print.matchit <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep="\n") cat("\nSample sizes:\n") #if(any(x$weights>0)) # nn <- rbind(table(x$treat), # table(x$weights>0, x$treat), # c(0,0)) #else # nn <- rbind(table(x$treat), # table(x$weights>0,x$treat)[2:1,]) print.table(x$nn, ...) invisible(x) cat("\n") } #' @export print.matchit.exact <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep = "\n") cat("\nExact Subclasses: ", max(x$subclass, na.rm=T),"\n",sep="") cat("\nSample sizes:\n") ntab <- table(factor(!is.na(x$subclass), levels=c("TRUE","FALSE")), x$treat) nn <- rbind(table(x$treat), ntab[c("TRUE","FALSE"),]) dimnames(nn) <- list(c("All","Matched","Unmatched"), c("Control","Treated")) print.table(nn, ...) invisible(x) cat("\n") } #' @export print.matchit.full <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep = "\n") cat("\nSample sizes:\n") if (any(x$weights>0)) nn <- rbind(table(x$treat), table(x$weights>0, x$treat), c(0,0)) else nn <- rbind(table(x$treat), table(x$weights>0,x$treat)[2:1,]) dimnames(nn) <- list(c("All","Matched","Discarded"), c("Control","Treated")) print.table(nn, ...) invisible(x) cat("\n") } #' @export print.matchit.subclass <- function(x, digits = getOption("digits"), ...){ cat("\nCall: ", deparse(x$call), sep = "\n") cat("\nSample sizes by subclasses:\n\n") nsub <- table(x$subclass,x$treat) nn <- rbind(table(x$treat),nsub) dimnames(nn) <- list(c("All",paste("Subclass",dimnames(nsub)[[1]])), c("Control","Treated")) print.table(nn, ...) invisible(x) cat("\n") } #' @export print.summary.matchit.exact <- function(x, digits = max(3, getOption("digits") - 3), ...){ cat("\nCall:", deparse(x$call), sep = "\n") cat("\nSample sizes:\n") print.table(x$nn,digits=digits) cat("\nMatched sample sizes by subclass:\n") print.data.frame(x$q.table, digits = digits) cat("\n") invisible(x) } #' @export print.summary.matchit <- function(x, digits = max(3, getOption("digits") - 3), ...){ sum.all <- x$sum.all sum.matched <- x$sum.matched q.table <- x$q.table xn <- x$nn cat("\nCall:", deparse(x$call), sep = "\n") cat("\nSummary of balance for all data:\n") print.data.frame(round(sum.all,digits)) cat("\n") xs1 <- sum.matched cc <- row.names(sum.all) if(!is.null(x$sum.matched) | identical(eval(x$call$method),"All")) { cat("\nSummary of balance for matched data:\n") print.data.frame(round(xs1,digits)) cat("\nPercent Balance Improvement:\n") print.data.frame(round(x$reduction,digits)) cat("\nSample sizes:\n") print.table(xn, digits=digits) cat("\n") } invisible(x) } #' @export print.summary.matchit.subclass <- function(x, digits = max(3, getOption("digits") - 3), ...){ sum.all <- x$sum.all sum.matched <- x$sum.matched q.table <- x$q.table cat("\nCall:", deparse(x$call), sep = "\n") cat("Summary of balance for all data:\n") print.data.frame(round(sum.all,digits)) cat("\n") cat("\nSummary of balance by subclasses:\n") print.table(round(q.table, digits)) cat("\nSample sizes by subclasses:\n") print.data.frame(x$qn, digits = digits) cat("\nSummary of balance across subclasses\n") print.data.frame(round(x$sum.subclass, digits)) cat("\nPercent Balance Improvement:\n") print.data.frame(round(x$reduction,digits)) cat("\n") } #---------------------------------------------------------- ### SUMMARY METHODS #---------------------------------------------------------- #' @export summary.matchit <- function(object, interactions = FALSE, addlvariables = NULL, standardize = FALSE, ...) { X <- object$X ## Fix X matrix so that it doesn't have any factors varnames <- colnames(X) for(var in varnames) { if(is.factor(X[,var])) { tempX <- X[,!colnames(X)%in%c(var)] form<-formula(substitute(~dummy-1,list(dummy=as.name(var)))) X <- cbind(tempX, model.matrix(form, X)) } } ## No distance output for pure Mahalanobis if("matchit.mahalanobis"%in%class(object)){ XX <- X } else{ XX <- cbind(distance=object$distance,X) } if (!is.null(addlvariables)) XX <- cbind(XX, addlvariables) treat <- object$treat weights <- object$weights nam <- dimnames(XX)[[2]] dupnam <- duplicated(nam) if(sum(dupnam)>0){ nam[dupnam] <- paste(nam[dupnam],".1",sep="") } kk <- ncol(XX) ## Summary Stats aa <- apply(XX,2,qoi,tt=treat,ww=weights,standardize=standardize,std=T) sum.all <- as.data.frame(matrix(0,kk,7)) sum.matched <- as.data.frame(matrix(0,kk,7)) row.names(sum.all) <- row.names(sum.matched) <- nam names(sum.all) <- names(sum.matched) <- names(aa[[1]]) sum.all.int <- sum.matched.int <- NULL for(i in 1:kk){ sum.all[i,] <- aa[[i]][1,] sum.matched[i,] <- aa[[i]][2,] if(interactions){ for(j in i:kk){ x2 <- XX[,i]*as.matrix(XX[,j]) jqoi <- qoi(x2,tt=treat,ww=weights,standardize=standardize,std=T) sum.all.int <- rbind(sum.all.int,jqoi[1,]) sum.matched.int <- rbind(sum.matched.int,jqoi[2,]) row.names(sum.all.int)[nrow(sum.all.int)] <- row.names(sum.matched.int)[nrow(sum.matched.int)] <- paste(nam[i],nam[j],sep="x") } } } xn <- aa[[1]]$xn sum.all <- rbind(sum.all,sum.all.int) sum.matched <- rbind(sum.matched,sum.matched.int) ## Imbalance Reduction stat0 <- abs(cbind(sum.all[,2]-sum.all[,1], sum.all[,5:7])) stat1 <- abs(cbind(sum.matched[,2]-sum.matched[,1], sum.matched[,5:7])) reduction <- as.data.frame(100*(stat0-stat1)/stat0) if(sum(stat0==0 & stat1==0, na.rm=T)>0){ reduction[stat0==0 & stat1==0] <- 0 } if(sum(stat0==0 & stat1>0,na.rm=T)>0){ reduction[stat0==0 & stat1>0] <- -Inf } if (standardize) names(reduction) <- c("Std. Mean Diff.", "eCDF Med","eCDF Mean", "eCDF Max") else names(reduction) <- c("Mean Diff.", "eQQ Med","eQQ Mean", "eQQ Max") ## output res <- list(call=object$call, nn = object$nn, sum.all = sum.all, sum.matched = sum.matched, reduction = reduction) class(res) <- "summary.matchit" return(res) } #' @export summary.matchit.exact <- function(object, covariates = FALSE, ...) { XX <- object$X treat <- object$treat qbins <- max(object$subclass,na.rm=TRUE) if(!covariates){ q.table <- as.data.frame(matrix(0,qbins,3)) names(q.table) <- c("Treated","Control","Total") for(i in 1:qbins){ qi <- object$subclass==i q.table[i,] <- c(sum(treat[qi]==1, na.rm=T), sum(treat[qi]==0, na.rm=T), length(treat[qi & !is.na(qi)])) } } else { kk <- ncol(XX) q.table <- as.data.frame(matrix(0,qbins,kk+3)) names(q.table) <- c("Treated","Control","Total",dimnames(XX)[[2]]) for(i in 1:qbins){ qi <- object$subclass==i & !is.na(object$subclass==i) q.table[i,] <- c(sum(treat[qi]==1, na.rm=T), sum(treat[qi]==0, na.rm=T), length(treat[qi & !is.na(qi)]),as.numeric(XX[qi,,drop=F][1,])) } } ntab <- table(factor(!is.na(object$subclass), levels=c("TRUE","FALSE")), treat) nn <- rbind(table(treat), ntab[c("TRUE","FALSE"),]) dimnames(nn) <- list(c("All","Matched","Discarded"), c("Control","Treated")) ## output res <- list(q.table = q.table, nn = nn, subclass = object$subclass, treat = object$treat, call = object$call) class(res) <- c("summary.matchit.exact", "summary.matchit") return(res) } #' @export summary.matchit.full <- function(object, interactions = FALSE, addlvariables = NULL, numdraws = 5000, standardize = FALSE, ...) { XX <- cbind(distance=object$distance,object$X) if (!is.null(addlvariables)) XX <- cbind(XX, addlvariables) treat <- object$treat weights <- object$weights nam <- dimnames(XX)[[2]] kk <- ncol(XX) ## Get samples of T and C units to send to qqplot t.plot <- sample(names(treat)[treat==1], numdraws/2, replace=TRUE, prob=weights[treat==1]) c.plot <- sample(names(treat)[treat==0], numdraws/2, replace=TRUE, prob=weights[treat==0]) ## Summary Stats aa <- apply(XX,2,qoi,tt=treat,ww=weights, t.plot=t.plot, c.plot=c.plot, standardize=standardize) sum.all <- as.data.frame(matrix(0,kk,6)) sum.matched <- as.data.frame(matrix(0,kk,6)) row.names(sum.all) <- row.names(sum.matched) <- nam names(sum.all) <- names(sum.matched) <- names(aa[[1]]) sum.all.int <- sum.matched.int <- NULL for(i in 1:kk){ sum.all[i,] <- aa[[i]][1,] sum.matched[i,] <- aa[[i]][2,] if(interactions){ for(j in i:kk){ x2 <- XX[,i]*as.matrix(XX[,j]) names(x2) <- names(XX[,1]) jqoi <- qoi(x2,tt=treat,ww=weights, t.plot=t.plot, c.plot=c.plot, standardize=standardize) sum.all.int <- rbind(sum.all.int,jqoi[1,]) sum.matched.int <- rbind(sum.matched.int,jqoi[2,]) row.names(sum.all.int)[nrow(sum.all.int)] <- row.names(sum.matched.int)[nrow(sum.matched.int)] <- paste(nam[i],nam[j],sep="x") } } } xn <- aa[[1]]$xn sum.all <- rbind(sum.all,sum.all.int) sum.matched <- rbind(sum.matched,sum.matched.int) ## Imbalance Reduction stat0 <- abs(cbind(sum.all[,2]-sum.all[,1], sum.all[,4:6])) stat1 <- abs(cbind(sum.matched[,2]-sum.matched[,1], sum.matched[,4:6])) reduction <- as.data.frame(100*(stat0-stat1)/stat0) if(sum(stat0==0 & stat1==0, na.rm=T)>0){ reduction[stat0==0 & stat1==0] <- 0 } if(sum(stat0==0 & stat1>0,na.rm=T)>0){ reduction[stat0==0 & stat1>0] <- -Inf } if (standardize) names(reduction) <- c("Std. Mean Diff.", "eCDF Med","eCDF Mean", "eCDF Max") else names(reduction) <- c("Mean Diff.", "eQQ Med","eQQ Mean", "eQQ Max") ## Sample sizes nn <- matrix(0, ncol=2, nrow=4) nn[1,] <- c(sum(object$treat==0), sum(object$treat==1)) nn[2,] <- c(sum(object$treat==0 & object$weights>0), sum(object$treat==1 & object$weights>0)) nn[3,] <- c(sum(object$treat==0 & object$weights==0 & object$discarded==0), sum(object$treat==1 & object$weights==0 & object$discarded==0)) nn[4,] <- c(sum(object$treat==0 & object$weights==0 & object$discarded==1), sum(object$treat==1 & object$weights==0 & object$discarded==1)) dimnames(nn) <- list(c("All","Matched","Unmatched","Discarded"), c("Control","Treated")) ## output res <- list(call=object$call, nn = nn, sum.all = sum.all, sum.matched = sum.matched, reduction = reduction) class(res) <- c("summary.matchit.full", "summary.matchit") return(res) } #' @export summary.matchit.subclass <- function(object, interactions = FALSE, addlvariables=NULL, standardize = FALSE, ...) { X <- object$X ## Fix X matrix so that it doesn't have any factors varnames <- colnames(X) for(var in varnames) { if(is.factor(X[,var])) { tempX <- X[,!colnames(X)%in%c(var)] form<-formula(substitute(~dummy-1,list(dummy=as.name(var)))) X <- cbind(tempX, model.matrix(form, X)) } } XX <- cbind(distance=object$distance,X) if (!is.null(addlvariables)) XX <- cbind(XX, addlvariables) treat <- object$treat weights <- object$weights nam <- dimnames(XX)[[2]] kk <- ncol(XX) ## Summary Stats aa <- apply(XX,2,qoi,tt=treat,ww=as.numeric(weights!=0),standardize=standardize) sum.all <- as.data.frame(matrix(0,kk,6)) sum.matched <- as.data.frame(matrix(0,kk,6)) row.names(sum.all) <- row.names(sum.matched) <- nam names(sum.all) <- names(sum.matched) <- names(aa[[1]]) sum.all.int <- sum.matched.int <- NULL for(i in 1:kk){ sum.all[i,] <- aa[[i]][1,] sum.matched[i,] <- aa[[i]][2,] if(interactions){ for(j in i:kk){ x2 <- XX[,i]*as.matrix(XX[,j]) jqoi <- qoi(x2,tt=treat,ww=as.numeric(weights!=0),standardize=standardize) sum.all.int <- rbind(sum.all.int,jqoi[1,]) sum.matched.int <- rbind(sum.matched.int,jqoi[2,]) row.names(sum.all.int)[nrow(sum.all.int)] <- row.names(sum.matched.int)[nrow(sum.matched.int)] <- paste(nam[i],nam[j],sep="x") } } } xn <- aa[[1]]$xn sum.all <- rbind(sum.all,sum.all.int) sum.matched <- rbind(sum.matched,sum.matched.int) ## By Subclass qbins <- max(object$subclass,na.rm=TRUE) if(interactions){ q.table <- array(0,dim=c(kk+sum(1:kk),6,qbins)) ii <- 0 nn <- NULL } else { q.table <- array(0,dim=c(kk,6,qbins)) } aa <- apply(XX,2,qoi.by.sub,tt=treat,ww=weights, qq=object$subclass,standardize=standardize) for(i in 1:kk){ if(!interactions){ q.table[i,,] <- as.matrix(aa[[i]]$q.table) nn <- names(aa) } else { ii <- ii + 1 q.table[ii,,] <- as.matrix(aa[[i]]$q.table) nn <- c(nn,names(aa)[i]) for(j in i:kk){ ii <- ii + 1 x2 <- XX[,i]*as.matrix(XX[,j]) q.table[ii,,] <- as.matrix(qoi.by.sub(x2,tt=treat,ww=weights,qq=object$subclass, standardize=standardize)$q.table) nn <- c(nn,paste(nam[i],nam[j],sep="x")) } } } qn <- aa[[1]]$qn dimnames(q.table) <- list(nn,row.names(aa[[i]]$q.table),paste("Subclass",1:qbins)) ## Aggregate Subclass if(is.null(object$call$sub.by)){ object$call$sub.by <- "treat" } if(object$call$sub.by=="treat") { wsub <- qn[1,]/sum(qn[1,]) } else if(object$call$sub.by=="control") { wsub <- qn[2,]/sum(qn[2,]) } else if(object$call$sub.by=="all") { wsub <- qn[3,]/sum(qn[3,]) } sum.subclass <- sum.all for(i in 1:kk){ for(j in 1:6){ if(j==3) { sum.subclass[i,j] <- sqrt(sum((wsub^2)*(q.table[i,j,]^2))) } else { sum.subclass[i,j] <- sum(wsub*q.table[i,j,]) } } } ## Imbalance Reduction stat0 <- abs(cbind(sum.all[,2]-sum.all[,1], sum.all[,4:6])) stat1 <- abs(cbind(sum.subclass[,2]-sum.subclass[,1], sum.subclass[,4:6])) reduction <- as.data.frame(100*(stat0-stat1)/stat0) if(sum(stat0==0 & stat1==0, na.rm=T)>0){ reduction[stat0==0 & stat1==0] <- 0 } if(sum(stat0==0 & stat1>0,na.rm=T)>0){ reduction[stat0==0 & stat1>0] <- -Inf } if (standardize) names(reduction) <- c("Std. Mean Diff.", "eCDF Med","eCDF Mean", "eCDF Max") else names(reduction) <- c("Mean Diff.", "eQQ Med","eQQ Mean", "eQQ Max") ## output res <- list(call=object$call, sum.all = sum.all, sum.matched = sum.matched, sum.subclass = sum.subclass, reduction = reduction, qn = qn, q.table = q.table) class(res) <- c("summary.matchit.subclass", "summary.matchit") return(res) } MatchIt/R/distance2mahalanobis.R0000644000175100001440000000037413060361514016223 0ustar hornikusersdistance2mahalanobis <- function(formula, data, ...) { X <- model.matrix(formula, data) ## Placeholder where real work is done on a unit by unit basis distance <- rep(1, nrow(X)) return(list(model = NULL, distance = distance)) } MatchIt/vignettes/0000755000175100001440000000000013075401340013626 5ustar hornikusersMatchIt/vignettes/matchit.pdf.asis0000644000175100001440000000017113060361515016712 0ustar hornikusers%\VignetteIndexEntry{MatchIt: Nonparametric Preprocessing for Parametric Causal Inference} %\VignetteEngine{R.rsp::asis} MatchIt/README.md0000644000175100001440000000131013060361515013073 0ustar hornikusers# MatchIt: Nonparametric Preprocessing for Parametric Causal Inference [![Build Status](https://travis-ci.org/kosukeimai/MatchIt.svg?branch=master)](https://travis-ci.org/kosukeimai/MatchIt) [![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/MatchIt)](https://cran.r-project.org/package=MatchIt) MatchIt selects matched samples of the original treated and control groups with similar covariate distributions -- can be used to match exactly on covariates, to match on propensity scores, or perform a variety of other matching procedures. The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) <[DOI:10.1093/pan/mpl013](https://doi.org/10.1093/pan/mpl013)>. 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The package also implements a series of recommendations offered in Ho, Imai, King, and Stuart (2007) . Authors@R: c( person("Daniel", "Ho", , "daniel.e.ho@gmail.com", c("aut")), person("Kosuke", "Imai", , "kimai@Princeton.Edu", c("aut", "cre")), person("Gary", "King", , "king@harvard.edu", c("aut")), person("Elizabeth", "Stuart", , "stuart@stat.harvard.edu", c("aut")), person("Alex", "Whitworth", , "whitworth.alex@gmail.com", c("ctb")) ) Maintainer: Kosuke Imai Depends: R (>= 2.6) Imports: MASS, Matching, rgenoud Suggests: cem, nnet, optmatch, rpart, mgcv, WhatIf, R.rsp, testthat VignetteBuilder: R.rsp LazyLoad: yes LazyData: yes License: GPL (>= 2) URL: http://gking.harvard.edu/matchit RoxygenNote: 5.0.1 NeedsCompilation: no Packaged: 2017-04-18 12:13:20 UTC; kimai Author: Daniel Ho [aut], Kosuke Imai [aut, cre], Gary King [aut], Elizabeth Stuart [aut], Alex Whitworth [ctb] Repository: CRAN Date/Publication: 2017-04-18 13:04:21 UTC MatchIt/man/0000755000175100001440000000000013075246077012407 5ustar hornikusersMatchIt/man/get_matches.Rd0000644000175100001440000000244013075246077015161 0ustar hornikusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/class_functions.R \name{get_matches} \alias{get_matches} \title{Get matches from matchit object} \usage{ get_matches(object, model_frame, id_cols = NULL, newdata = NULL) } \arguments{ \item{object}{The \code{'matchit'} class model object} \item{model_frame}{The \code{'data.frame'} class object used in creation of \code{object}.} \item{id_cols}{A string indicating the ID for the datset used in the call to \code{\link{matchit}}. This can be used in combination with \code{newdata} to return the base dataset. Defaults to \code{NULL}.} \item{newdata}{A new \code{data.frame} object to extract matched observations from. Used in conjunction with \code{id_cols}. Defaults to \code{NULL}.} } \value{ If \code{newdata} is \code{NULL}, a subset of \code{model_frame} containing the rows corresponding to the matched treatement and control observations with weights appended. If \code{newdata} is not \code{NULL}, an equivalent subset of \code{newdata} is returned. } \description{ Get the resulting matches from a \code{matchit} model object. This function allows the user to extract the matches from the original dataset used in model building or from a new dataset that has a matching set of key column(s) (\code{id_cols}). } MatchIt/man/lalonde.Rd0000644000175100001440000000304313060361515014301 0ustar hornikusers\name{lalonde} \docType{data} \alias{lalonde} \title{Data from National Supported Work Demonstration and PSID, as analyzed by Dehejia and Wahba (1999).} \description{ This is a subsample of the data from the treated group in the National Supported Work Demonstration (NSW) and the comparison sample from the Current Population Survey (CPS). This data was previously analyzed extensively by Lalonde (1986) and Dehejia and Wahba (1999). } \usage{data(lalonde)} \format{ A data frame with 313 observations (185 treated, 429 control). There are 10 variables measured for each individual. "treat" is the treatment assignment (1=treated, 0=control). "age" is age in years. "educ" is education in number of years of schooling. "black" is an indicator for African-American (1=African-American, 0=not). "hispan" is an indicator for being of Hispanic origin (1=Hispanic, 0=not). "married" is an indicator for married (1=married, 0=not married). "nodegree" is an indicator for whether the individual has a high school degree (1=no degree, 0=degree). "re74" is income in 1974, in U.S. dollars. "re75" is income in 1975, in U.S. dollars. "re78" is income in 1978, in U.S. dollars. } \references{ Lalonde, R. (1986). Evaluating the econometric evaluations of training programs with experimental data. American Economic Review 76: 604-620. Dehejia, R.H. and Wahba, S. (1999). Causal Effects in Nonexperimental Studies: Re-Evaluating the Evaluation of Training Programs. Journal of the American Statistical Association 94: 1053-1062. } \keyword{datasets} MatchIt/man/help.matchit.Rd0000644000175100001440000000225113060361515015243 0ustar hornikusers\name{help.matchit} \alias{help.matchit} \title{HTML Help for Matchit Commands and Models} \description{ The \code{help.matchit} command launches html help for Matchit commands and supported methods. The full manual is available online at \url{http://gking.harvard.edu/matchit}. } \usage{ help.matchit(object) } \arguments{ \item{object}{a character string representing a Matchit command or model. \code{help.matchit("command")} will take you to an index of Matchit commands and \code{help.matchit("method")} will take you to a list of matching methods. The following inputs are currently available: \code{exact}, \code{nearest}, \code{subclass}, \code{full}, \code{optimal}. } } \seealso{The complete document is available online at \url{http://gking.harvard.edu/matchit}. } \author{ Daniel Ho <\email{daniel.ho@yale.edu}>; Kosuke Imai <\email{kimai@princeton.edu}>; Gary King <\email{king@harvard.edu}>; Elizabeth Stuart<\email{estuart@jhsph.edu}> } \keyword{documentation} MatchIt/man/matchit.Rd0000644000175100001440000001754113060361515014324 0ustar hornikusers\name{matchit} \alias{matchit} \alias{MatchIt} \alias{Matchit} \title{MatchIt: Matching Software for Causal Inference} \description{ \code{matchit} is the main command of the package \emph{MatchIt}, which enables parametric models for causal inference to work better by selecting well-matched subsets of the original treated and control groups. MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2004) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. Matched data sets created by MatchIt can be entered easily in Zelig (\url{http://gking.harvard.edu/zelig}) for subsequent parametric analyses. Full documentation is available online at \url{http://gking.harvard.edu/matchit}, and help for specific commands is available through \code{help.matchit}.} \details{The matching is done using the \code{matchit(treat ~ X, ...)} command, where \code{treat} is the vector of treatment assignments and \code{X} are the covariates to be used in the matching. There are a number of matching options, detailed below. The full syntax is \code{matchit(formula, data=NULL, discard=0, exact=FALSE, replace=FALSE, ratio=1, model="logit", reestimate=FALSE, nearest=TRUE, m.order=2, caliper=0, calclosest=FALSE, mahvars=NULL, subclass=0, sub.by="treat", counter=TRUE, full=FALSE, full.options=list(), \dots)} A summary of the results can be seen graphically using \code{plot(matchitobject)}, or numerically using \code{summary(matchitobject)}. \code{print(matchitobject)} also prints out the output. } \usage{matchit(formula, data, method = "nearest", distance = "logit", distance.options = list(), discard = "none", reestimate = FALSE, ...) } \arguments{ \item{formula}{This argument takes the usual syntax of R formula, \code{treat ~ x1 + x2}, where \code{treat} is a binary treatment indicator and \code{x1} and \code{x2} are the pre-treatment covariates. Both the treatment indicator and pre-treatment covariates must be contained in the same data frame, which is specified as \code{data} (see below). All of the usual R syntax for formula works. For example, \code{x1:x2} represents the first order interaction term between \code{x1} and \code{x2}, and \code{I(x1^2)} represents the square term of \code{x1}. See \code{help(formula)} for details.} \item{data}{This argument specifies the data frame containing the variables called in \code{formula}.} \item{method}{This argument specifies a matching method. Currently, \code{"exact"} (exact matching), \code{"full"} (full matching), \code{"genetic"} (genetic matching), \code{"nearest"} (nearest neighbor matching), \code{"optimal"} (optimal matching), and \code{"subclass"} (subclassification) are available. The default is \code{"nearest"}. Note that within each of these matching methods, \emph{MatchIt} offers a variety of options.} \item{distance}{This argument specifies the method used to estimate the distance measure. The default is logistic regression, \code{"logit"}. A variety of other methods are available.} \item{distance.options}{ This optional argument specifies the optional arguments that are passed to the model for estimating the distance measure. The input to this argument should be a list.} \item{discard}{This argument specifies whether to discard units that fall outside some measure of support of the distance score before matching, and not allow them to be used at all in the matching procedure. Note that discarding units may change the quantity of interest being estimated. The options are: \code{"none"} (default), which discards no units before matching, \code{"both"}, which discards all units (treated and control) that are outside the support of the distance measure, \code{"control"}, which discards only control units outside the support of the distance measure of the treated units, and \code{"treat"}, which discards only treated units outside the support of the distance measure of the control units.} \item{reestimate}{This argument specifies whether the model for distance measure should be re-estimated after units are discarded. The input must be a logical value. The default is \code{FALSE}.} \item{...}{Additional arguments to be passed to a variety of matching methods.} } \value{ \item{call}{The original \code{matchit} call.} \item{formula}{The formula used to specify the model for estimating the distance measure.} \item{model}{The output of the model used to estimate the distance measure. \code{summary(m.out$model)} will give the summary of the model where \code{m.out} is the output object from \code{matchit}.} \item{match.matrix}{An \eqn{n_1} by \code{ratio} matrix where the row names, which can be obtained through \code{row.names(match.matrix)}, represent the names of the treatment units, which come from the data frame specified in \code{data}. Each column stores the name(s) of the control unit(s) matched to the treatment unit of that row. For example, when the \code{ratio} input for nearest neighbor or optimal matching is specified as 3, the three columns of \code{match.matrix} represent the three control units matched to one treatment unit). \code{NA} indicates that the treatment unit was not matched.} \item{discarded}{A vector of length $n$ that displays whether the units were ineligible for matching due to common support restrictions. It equals \code{TRUE} if unit \eqn{i} was discarded, and it is set to \code{FALSE} otherwise.} \item{distance}{A vector of length \eqn{n} with the estimated distance measure for each unit.} \item{weights}{A vector of length \eqn{n} that provides the weights assigned to each unit in the matching process. Unmatched units have weights equal to \code{0}. Matched treated units have weight \code{1}. Each matched control unit has weight proportional to the number of treatment units to which it was matched, and the sum of the control weights is equal to the number of uniquely matched control units.} \item{subclass}{The subclass index in an ordinal scale from 1 to the total number of subclasses as specified in \code{subclass} (or the total number of subclasses from full or exact matching). Unmatched units have \code{NA}.} \item{q.cut}{The subclass cut-points that classify the distance measure.} \item{treat}{The treatment indicator from \code{data} (the left-hand side of \code{formula}).} \item{X}{The covariates used for estimating the distance measure (the right-hand side of \code{formula}).} \item{nn}{A basic summary table of matched data (e.g., the number of matched units)} } \seealso{Please use \code{help.matchit} to access the matchit reference manual. The complete document is available online at \url{http://gking.harvard.edu/matchit}. } \references{Daniel Ho, Kosuke Imai, Gary King, and Elizabeth Stuart (2007). Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference. Political Analysis 15(3): 199-236. \url{http://gking.harvard.edu/files/abs/matchp-abs.shtml} } \author{ Daniel Ho \email{daniel.ho@yale.edu}; Kosuke Imai \email{kimai@princeton.edu}; Gary King \email{king@harvard.edu}; Elizabeth Stuart\email{estuart@jhsph.edu} } \keyword{environment} MatchIt/man/match.data.Rd0000644000175100001440000000412513060361515014671 0ustar hornikusers\name{match.data} \alias{match.data} \title{Output Matched Data Sets} \description{\code{match.data} outputs matched data sets from \code{matchit()}. } \usage{ match.data(object, group="all", distance = "distance", weights = "weights", subclass = "subclass") } \arguments{ \item{object}{The output object from \code{matchit}. This is a required input.} \item{group}{This argument specifies for which matched group the user wants to extract the data. Available options are \code{"all"} (all matched units), \code{"treat"} (matched units in the treatment group), and \code{"control"} (matched units in the control group). The default is \code{"all"}.} \item{distance}{This argument specifies the variable name used to store the distance measure. The default is \code{"distance"}.} \item{weights}{This argument specifies the variable name used to store the resulting weights from matching. The default is \code{"weights"}.} \item{subclass}{This argument specifies the variable name used to store the subclass indicator. The default is \code{"subclass"}.} } \value{ Returns a subset of the original data set sent to \code{matchit()}, with just the matched units. The data set also contains the additional variables \code{distance}, \code{weights}, and \code{subclass}. The variable \code{distance} gives the estimated distance measure, and \code{weights} gives the weights for each unit, generated in the matching procedure. The variable \code{subclass} gives the subclass index for each unit (if applicable). See the \url{http://gking.harvard.edu/matchit/} for the complete documentation and type \code{demo(match.data)} at the R prompt to see a demonstration of the code. } \seealso{Please use \code{help.matchit} to access the matchit reference manual. The complete document is available online at \url{http://gking.harvard.edu/matchit}.} \author{ Daniel Ho \email{daniel.ho@yale.edu}; Kosuke Imai \email{kimai@princeton.edu}; Gary King \email{king@harvard.edu}; Elizabeth Stuart \email{estuart@jhsph.edu} } \keyword{methods} MatchIt/man/is.matchit.Rd0000644000175100001440000000066013075246077014743 0ustar hornikusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/class_functions.R \name{is.matchit} \alias{is.matchit} \title{Checks matchit Class} \usage{ is.matchit(object) } \arguments{ \item{object}{any R object} } \value{ Returns \code{TRUE} if its argument has class "matchit" among its classes and \code{FALSE} otherwise. } \description{ Function that checks if the target object is a \code{matchit} object. } MatchIt/man/user.prompt.Rd0000644000175100001440000000053313060361515015162 0ustar hornikusers\name{user.prompt} \alias{user.prompt} \title{Pause in demo files} \description{ Use \code{user.prompt} while writing demo files to force users to hit return before continuing. } \usage{ user.prompt() } \seealso{\code{readline}} \author{Olivia Lau \email{olau@fas.harvard.edu} } \examples{ \dontrun{ user.prompt() } } \keyword{file}