robumeta/0000755000176200001440000000000014410454062012070 5ustar liggesusersrobumeta/NAMESPACE0000644000176200001440000000026714410443122013307 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(predict,robu) S3method(print,robu) export(forest.robu) export(group.center) export(group.mean) export(robu) export(sensitivity) robumeta/data/0000755000176200001440000000000014410441300012770 5ustar liggesusersrobumeta/data/hierdat.RData0000755000176200001440000000421114410441300015326 0ustar liggesusersp?C $%YcFr`m!tY:aN9Ma: qøŀcƎ)6 P1P5-m:iK"]bW5#۷}{NW+9K2ƟtOMҙUUq\t4\Sw;2guFR3^ a|5a3>,mk& G7†9[: `%|y I@7Fầ0ߖk&)p;)'|4{q`mؗz'O%Ƕ$<0uo%aU}h"܌ש]pw~aJco:H>ω 1R]Mjm3MJ )x9y/>71i GCGA?Y:]O* gpOoyNyF/OvυXm̰-7$MMû߇u#yGa #m#g?M7\w߇_Xo{?>glsOZv 긲vCt\OEBބ?y?JuPWp wlg7B[wڑ v}w 8ߴ|6G!O n\N_/{g, vN?}2$t>pg74nU^,C϶@o?.B|tV"lyߺ>Kgg[IOz;e>ޑ u4`~j?+ǐWimOۀ'4}~r {@`]<׮DKG~Qgܹٯ}RR߬:n4Xuƻ119kޱ^^xy>β2tS߃$<_J&Ǖ#qĝ~YaKq>꺓u:&w1%S&Y%:亩~x~H7כCXAab"?,xkDzrH|rB\Gu4np63avҼ-<1"Gt>^}-4ob_sR.ͩy2D7SӊD["Y¦::SՕYjD*.>Rüy}Mm+&$M J+Ԅ<^9j>:*,i!ùZF( *ުW +"A$B%s&/ XZ Vl a)$Gq$tfрժ}똙-Cz=5JUkdP#ѹoXD *k#24si̵Y-54܇naꖶ.G~^~'_)haw/%ut)E载X-qU|6yG>IK||H¼/gQWz0e *'O8-aާG*UJW5Q_A肣*UG$U x)S63Fut5=]{@ߩq%r@)a*jUe%vIс@"AN:xC8HYFldg#9A6ZFLC`4!0 iLC`4D!2 iLCd"4L4L4L4L4L4L4L4L4L4L4L4L4L4L4 OTn0G_i{ JQzٓ8UsTl<robumeta/data/corrdat.sm.RData0000755000176200001440000001210614410441300015764 0ustar liggesusers 8ǷTSrhohB!ΕE<$Q!dʴ7RC$SކRBnRʜL{SPN4)u?s}νy]kְY5K"IB/‚I4ϱ,ؑ͜HB|(b?B_Ш!J(_c>iohoHO8D$I ɯ?Y Ӕ?XSHj WJf&|[7J1GCBC;~ԧc:5I e;/RӤj0p&]g& -87JQd-ٰs/gYl.Wj@Y=Pʴ]Q|ݏP]Cphã/E͵}*L1=Gyv tdHb/Q%UMRr@cj]z@W݆4LcjJHab}w>3@a) (ѧN`2' =^&^~x0붱IHH}njfTi qr!.%'; QЧFW B\u\n&-TצH,Dǣ9g8؉ȜaQa飢=-˙We%7(MXjjpJ-5"̞KUQ$fUkGqsk"O(6/~4R:22LERIYiTj+ZqF4tP)HQfGTɽF7E*'gf4GId?\خ%Sk "[4*W%Bn3S>iӣ6ۛ,Qk++ wiL:wtt="6Iv+zhSkC{w%q}]?6zQΑu-%2y^?!L;$s-QtBF=S(JgmikTb58=pѓ稷ej]ls"+(ZLQ*yd O lJđ:yY)xڥB?wfqĠqܦ;Js'J8:4T)y@2F!y)t(~h+ cuG9#y:F3pDlFy=y N3J \]EjxubNd~ ;wTt-"JeN$nF6&!kBDY'K!Ԙ( bp6_@TŲ,Y3qc /d9q>.fmٌ['Y򰛸xY)MA6$/9P3jOl_2B;F~uw>q;R'B!T4}yU)cWўL5bcPm E[Pݕב f^Y?:n!t{c\WONߊ0h:4b{ Q8Y :n W@U]Bu5QmxI^;YO]bMTZ&˹Nme'܀=Q2imC(D{Bၵ\ˊi %.Ԋ,F`VW!>+<,ݤ ¨xIk`jPv%ف:Zy?M}q8>n6F:3J`K6-+CkڬC\# xCLDo[^ ".z-3*~5 zue)fҁ%)`f߁lI?aF037fK!\`B } e*Do޸d9Uhȴ9dd,#BDd_, 1 L-YcXFRlvbru/ _)h\G]Fv6DN|P;"beVE.VCkmDd9eMY^ ޱop9A ֎E_qX.Sy+ԍ߀9a139r+&>GcSG0mF+aeb@ i <%~BM=D +D9?NSd|tD\^jLESQܡf)uȵ+/LINd[ c[02v*j7zw FK!?DSùajXyl u^YY+%BIB٫PeN]tlsY!` {X ?'%q/ڮ솗 >װ ^=%y(З#_"tnY+(>?D;Fb{Wmki?| yԅ!NYkeTY8{|7 GZSAo܅xi_uU[ZW=_t,łkLr@(Gl3N HJsI{ ՐT-?ga17ߑBi${:a"w#/f#ItnO&5-ANOg3$7V_Hjr.lB}4*G Y,Wv? Aվk~ B}kk/R}}5)K_"e^ӇH1*6sEEyY~}_LFi.{'E?$jT-K#uq bޠ[sP2.,7{ Ҧ"ez߉mX,d*Y;9*A>Z k͎CQt*b5}]*[r'Ĺ-'rڜgs:d{')ʝ߯8zۿMX#3$/v~xGqkyd&6 w 0v#^,ܶ,E՘X(Ao:!m>H4iTyaQ5\gjYG``"MI $i+_Gb8c$<!têWPK`B5cۿFClDN M9sF~ sI520`vB OwiO2[tWH =/3H7dnYm7i}K|/GZ'pwU3},.ۗ@aX" -,w$~-!z6 ?ۿ[ߠ_w>X8&nDz{vn,D|L_6K* Oiv~a]&ه[Q헄?]|i8n)=7{Wwa/c?ſݹ{}6NҰ0 Me_ϟ2?;n|i|=elI_4v`suب0 ܤc~}-=?Lx8Irӷ3@ `'03F(3au mk軂#،T `?RC?R?-@w߫s0R#o3c#ُT?C)֞_&L9/<[s>~joN7:@pO],ڻ}ʏڽYOEbfNeVNۛɻq\jxf>?x,Lyߵac)v9BA: 1Q*: u+robumeta/data/oswald2013.RData0000755000176200001440000005564314410441300015524 0ustar liggesusersRDA2 A 2 196611 131840 1026 1 262153 10 oswald2013 787 12 16 308 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 22 Amodio\040&\040Devine\040(2006) 262153 27 Ashburn-Nardo\040et\040al.\040(2003) 262153 22 Avenanti\040et\040al.\040(2010) 262153 22 Avenanti\040et\040al.\040(2010) 262153 21 Biernat\040et\040al.\040(2009) 262153 21 Biernat\040et\040al.\040(2009) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 27 Carney\040(Unpublished)\040(2006) 262153 34 Carney\040et\040al.\040(Unpublished)\040(2006) 262153 34 Carney\040et\040al.\040(Unpublished)\040(2006) 262153 24 Cunningham\040et\040al.\040(2004) 262153 21 Florack\040et\040al.\040(2001) 262153 21 Florack\040et\040al.\040(2001) 262153 21 Florack\040et\040al.\040(2001) 262153 21 Florack\040et\040al.\040(2001) 262153 21 Florack\040et\040al.\040(2001) 262153 21 Florack\040et\040al.\040(2001) 262153 23 Gawronski\040et\040al.\040(2003) 262153 23 Gawronski\040et\040al.\040(2003) 262153 23 Gawronski\040et\040al.\040(2003) 262153 23 Gawronski\040et\040al.\040(2003) 262153 23 Glaser\040&\040Knowles\040(2008) 262153 23 Glaser\040&\040Knowles\040(2008) 262153 19 Green\040et\040al.\040(2007) 262153 19 Green\040et\040al.\040(2007) 262153 19 Green\040et\040al.\040(2007) 262153 19 Green\040et\040al.\040(2007) 262153 19 Green\040et\040al.\040(2007) 262153 19 Green\040et\040al.\040(2007) 262153 23 Greenwald\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 16 He\040et\040al.\040(2009) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 26 Heider\040&\040Skowronski\040(2007) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 21 Hofmann\040et\040al.\040(2008) 262153 30 Hugenberg\040&\040Bodenhausen\040(2003) 262153 30 Hugenberg\040&\040Bodenhausen\040(2003) 262153 30 Hugenberg\040&\040Bodenhausen\040(2003) 262153 30 Hugenberg\040&\040Bodenhausen\040(2003) 262153 30 Hugenberg\040&\040Bodenhausen\040(2004) 262153 30 Hugenberg\040&\040Bodenhausen\040(2004) 262153 30 Hugenberg\040&\040Bodenhausen\040(2004) 262153 30 Hugenberg\040&\040Bodenhausen\040(2004) 262153 22 Hughes\040&\040Bigler\040(2011) 262153 22 Hughes\040&\040Bigler\040(2011) 262153 22 Hughes\040&\040Bigler\040(2011) 262153 22 Hughes\040&\040Bigler\040(2011) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Kang\040et\040al.\040(2010) 262153 18 Korn\040et\040al.\040(2012) 262153 18 Korn\040et\040al.\040(2012) 262153 18 Korn\040et\040al.\040(2012) 262153 18 Korn\040et\040al.\040(2012) 262153 22 Levinson\040et\040al.\040(2010) 262153 22 Levinson\040et\040al.\040(2010) 262153 22 Levinson\040et\040al.\040(2010) 262153 22 Levinson\040et\040al.\040(2010) 262153 22 Levinson\040et\040al.\040(2010) 262153 22 Levinson\040et\040al.\040(2010) 262153 31 Livingston\040(Unpublished)\040(2002) 262153 31 Livingston\040(Unpublished)\040(2002) 262153 31 Livingston\040(Unpublished)\040(2002) 262153 31 Livingston\040(Unpublished)\040(2002) 262153 31 Livingston\040(Unpublished)\040(2002) 262153 24 Ma-Kellums\040et\040al.\040(2010) 262153 24 Ma-Kellums\040et\040al.\040(2010) 262153 24 Ma-Kellums\040et\040al.\040(2010) 262153 24 Ma-Kellums\040et\040al.\040(2010) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 19 Maner\040et\040al.\040(2005) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 262153 26 Mcconnell\040&\040Leibold\040(2001) 32777 14 Pa\302\216rez\040(2010) 32777 14 Pa\302\216rez\040(2010) 32777 14 Pa\302\216rez\040(2010) 32777 14 Pa\302\216rez\040(2010) 32777 14 Pa\302\216rez\040(2010) 32777 14 Pa\302\216rez\040(2010) 262153 22 Perugini\040et\040al.\040(2007) 262153 22 Perugini\040et\040al.\040(2007) 262153 20 Phelps\040et\040al.\040(2000) 262153 20 Phelps\040et\040al.\040(2000) 262153 23 Prestwich\040et\040al.\040(2008) 262153 23 Prestwich\040et\040al.\040(2008) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 24 Rachlinski\040et\040al.\040(2009) 262153 25 Richeson\040&\040Shelton\040(2003) 262153 25 Richeson\040&\040Shelton\040(2003) 262153 25 Richeson\040&\040Shelton\040(2003) 262153 25 Richeson\040&\040Shelton\040(2003) 262153 25 Richeson\040&\040Shelton\040(2003) 262153 25 Richeson\040&\040Shelton\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 29 Richeson,\040Baird\040et\040al.\040(2003) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 23 Rudman\040&\040Ashmore\040(2007) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Rudman\040&\040Lee\040(2002) 262153 19 Sabin\040et\040al.\040(2008) 262153 19 Sabin\040et\040al.\040(2008) 262153 19 Sabin\040et\040al.\040(2008) 262153 19 Sabin\040et\040al.\040(2008) 262153 19 Sabin\040et\040al.\040(2008) 262153 19 Sabin\040et\040al.\040(2008) 262153 36 Sargent\040&\040Theil\040(Unpublished)\040(2001) 262153 36 Sargent\040&\040Theil\040(Unpublished)\040(2001) 262153 26 Sekaquaptewa\040et\040al.\040(2003) 262153 26 Sekaquaptewa\040et\040al.\040(2003) 262153 21 Shelton\040et\040al.\040(2005) 262153 21 Shelton\040et\040al.\040(2005) 262153 21 Shelton\040et\040al.\040(2005) 262153 21 Shelton\040et\040al.\040(2005) 262153 20 Sibley\040et\040al.\040(2010) 262153 20 Sibley\040et\040al.\040(2010) 262153 20 Sibley\040et\040al.\040(2010) 262153 20 Sibley\040et\040al.\040(2010) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 38 Spicer\040&\040Monteith\040(Unpublished)\040(2001) 262153 21 Stanley\040et\040al.\040(2011) 262153 21 Stanley\040et\040al.\040(2011) 262153 21 Stanley\040et\040al.\040(2011) 262153 21 Stanley\040et\040al.\040(2011) 262153 21 Stanley\040et\040al.\040(2011) 262153 21 Stanley\040et\040al.\040(2011) 262153 21 Stanley\040et\040al.\040(2011) 262153 25 Stepanikova\040et\040al.\040(2011) 262153 25 Stepanikova\040et\040al.\040(2011) 262153 27 Tuttle\040(Unpublished)\040(2009) 262153 20 Vanman\040et\040al.\040(2004) 262153 20 Vanman\040et\040al.\040(2004) 262153 20 Vanman\040et\040al.\040(2004) 262153 20 Vanman\040et\040al.\040(2004) 262153 27 Vezzali\040&\040Giovannini\040(2010) 262153 23 Ziegert\040&\040Hanges\040(2005) 262153 23 Ziegert\040&\040Hanges\040(2005) 781 308 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1026 1 262153 6 levels 16 2 262153 9 Ethnicity 262153 4 Race 1026 1 262153 5 class 16 1 262153 6 factor 254 13 308 1 2 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 4 1 1 2 3 2 3 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 4 5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 3 3 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 781 308 1 2 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1026 767 16 2 262153 8 Attitude 262153 10 Stereotype 1026 1023 16 1 262153 6 factor 254 13 308 3 3 1 2 1 2 1 2 1 2 1 1 1 2 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 1 1 1 1 1 1 2 2 2 1 2 1 2 1 1 1 1 1 1 1 1 1 1 2 3 4 5 1 2 3 4 5 6 1 1 2 1 1 1 1 2 2 1 2 3 4 1 2 3 4 1 2 3 1 2 3 4 4 1 1 1 1 1 1 1 1 1 2 1 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 1 2 3 4 5 6 7 8 9 10 11 12 13 14 2 3 1 1 1 2 3 1 2 3 1 1 1 1 1 1 1 1 1 1 2 1 2 3 4 3 4 5 6 5 6 7 8 7 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1 2 3 4 5 6 1 1 1 1 1 2 1 2 3 4 5 6 7 8 7 8 2 3 2 3 1 1 1 2 4 5 1 2 4 5 3 1 2 3 1 2 3 1 1 1 1 1 2 3 1 2 3 1 2 3 1 2 3 1 2 1 2 1 2 1 1 1 1 1 2 1 2 1 1 2 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 3 1 1 1 781 308 3 3 4 4 4 4 4 4 4 4 4 1 1 5 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 4 4 4 4 4 4 4 4 4 4 6 6 4 4 4 4 4 4 5 1 1 1 1 1 1 1 1 1 1 1 2 3 3 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 1 1 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 5 5 5 5 5 5 4 4 1 1 2 3 4 4 4 4 4 4 4 4 4 4 3 3 3 3 6 6 1 1 1 1 1 1 1 1 6 2 2 2 2 2 2 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 5 5 5 5 4 4 4 4 4 4 4 4 2 2 2 4 4 4 2 2 6 4 4 3 3 5 4 4 1026 767 16 6 262153 14 Brain\040Activity 262153 22 Interpersonal\040Behavior 262153 13 Microbehavior 262153 17 Person\040Perception 262153 17 Policy\040Preference 262153 13 Response\040Time 1026 1023 16 1 262153 6 factor 254 781 308 1 1 1 1 1 1 2 2 2 2 3 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 1 1 1 1 1 1 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 3 3 3 3 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 1 1 1 1 2 3 3 3 3 1 2 2 1026 767 16 3 262153 8 Absolute 262153 16 Difference\040Score 262153 15 Relative\040Rating 1026 1023 16 1 262153 6 factor 254 781 308 5 5 5 5 5 5 20 20 20 20 20 20 20 5 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 16 16 16 16 16 16 16 16 10 10 20 20 17 5 17 17 5 5 20 20 20 20 20 20 19 19 19 19 19 19 20 20 20 17 5 17 5 17 5 8 8 8 8 8 8 8 8 16 16 16 16 16 16 16 16 17 17 5 5 20 20 20 20 5 5 5 5 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 18 20 20 20 20 5 5 5 17 17 17 11 17 12 5 17 7 7 9 6 3 3 3 3 2 2 2 2 3 3 3 3 2 2 2 2 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 14 14 14 14 14 14 1 1 20 20 4 4 5 5 5 5 5 5 17 17 5 5 17 5 5 17 5 17 17 17 17 17 5 5 5 5 20 5 5 5 5 5 5 5 5 4 4 5 5 5 5 5 5 17 17 17 17 17 17 20 20 20 20 20 20 20 20 17 5 17 17 5 5 15 15 15 15 5 5 5 5 5 5 5 20 20 17 5 20 17 5 5 17 20 20 20 20 20 13 20 20 1026 767 16 20 262153 15 Afro-Carribbean 262153 8 American 262153 4 Arab 262153 5 Asian 262153 5 Black 262153 29 Chinese\040Vs.\040European-American 262153 34 Chinese/European-Americans/Latinos 262153 69 Comparison\040Was\040Italian\040(Euro-Amer)\040Confederate\040To\040African\040Confederate 262153 29 European-American\040Vs.\040Chinese 262153 6 German 262153 8 Hispanic 262153 9 Hispanic\040 262153 9 Immigrant 262153 16 Latino\040Immigrant 262153 5 Maori 262153 7 Turkish 262153 5 White 262153 17 White\040Minus\040Asian 262153 22 White-Asian\040Comparison 262153 11 White/Black 1026 1023 16 1 262153 6 factor 254 781 308 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1026 767 16 1 262153 3 ICC 1026 1023 16 1 262153 6 factor 254 13 308 2 2 1 1 1 1 2 2 2 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 3 1 2 3 1 1 2 2 1 1 1 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 2 2 2 2 1 1 1 1 2 2 2 2 1 1 1 2 2 2 1 2 1 2 1 2 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 2 2 1 2 1 2 3 1 1 2 3 1 1 2 2 1 1 2 2 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 1 1 2 2 1 1 2 2 1 1 1 1 1 1 1 2 2 3 3 1 1 1 2 2 2 3 3 3 4 4 4 1 2 1 2 1 2 1 2 2 1 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 1 2 1 1 1 1 2 13 308 21 21 32 32 32 32 21 21 21 21 77 16 16 136 136 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 29 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 33 21 21 13 20 26 21 20 26 21 35 35 34 34 48 48 109 101 109 109 100 102 1057 20 20 20 20 20 20 20 20 20 20 20 140 55 55 140 140 55 55 55 55 85 85 86 85 85 84 84 84 76 76 76 77 77 77 76 77 24 24 24 24 20 20 57 57 98 98 107 107 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 68 19 19 19 29 31 31 30 35 35 33 34 34 32 31 23 291 291 40 55 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 51 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 333 333 333 333 333 333 20 18 12 12 105 105 63 63 63 63 63 63 70 70 63 63 20 20 20 16 21 21 15 15 15 15 15 15 15 15 15 64 64 64 64 64 64 126 126 89 89 20 20 20 18 18 18 18 18 18 19 19 19 60 33 60 33 60 33 19 19 39 40 15 15 23 23 142 142 142 142 78 78 78 78 78 78 78 39 43 43 43 50 50 50 61 63 147 21 59 21 21 78 52 47 14 308 0.44 -0.08500000000000001 -0.138 -0.33 0.35 -0.06 -0.24 -0.057 0.461 0.437 0.23 0.5 0.71 0.05 0.14 0.36 -0.21 0.1 -0.01 0.09 -0.03 0.1 0.61 0.41 0.23 0.48 0.45 0.36 -0.27 0.35 0.36 0.09 -0.29 0.43 -0.23 -0.09 0.07000000000000001 -0.17 -0.1 -0.21 0.12 0.13 0.01 0.18 -0.1 -0.11 0 -0.02 0.44 0.39 -0.17 0 0.25 -0.05 -0.01 0.06 0.08 -0.38 0.02 0.41 0.11 0.79 0.58 -0.21 0.17 0.12 0.04 0.33 0.24 0.117 0.32 0.321 0.46 0.1 0.153 0.217 0.094 0.082 -0.017 0.12 0.17 0.14 0.27 0.5 0.52 0.34 0.11 0.28 -0.09 0.007 -0.19 0.5600000000000001 0.03 0.02 0.16 -0.18 0.21 0.01 0.01 0.27 -0.08 0.173 0.317 -0.019 0.013 -0.045 -0.08599999999999999 0.126 0.133 0.322 0.312 0.361 -0.073 -0.063 -0.146 0.059 0.135 -0.095 -0.192 0.46 0.424 0.345 -0.106 0.163 0.038 -0.032 -0.025 0.078 0.045 0.001 0.068 -0.121 -0.157 -0.091 0.27 0.102 0.232 0.079 0.079 0.118 0.09 -0.091 -0.043 0.227 0.398 0.196 0.145 0.298 0.137 0.195 0.204 0.364 0.364 0.328 0.258 0.334 0.329 -0.73 -0.77 0.1 -0.01 0.582 0.29 0.221 -0.093 -0.016 -0.03 0.37 0.19 0.04 0.43 0.03 0.068 0.035 -0.386 0.095 -0.356 -0.387 0.116 0.112 0.035 -0.08699999999999999 0.042 0.116 -0.12 -0.039 0.061 0.035 0.151 0.117 -0.168 0.179 0.51 0.39 0.42 0.35 0.32 -0.26 -0.03 0.17 0.09 0.25 0.26 -0.06 0.19 0.39 0.34 0.17 0.15 0.13 0.2 0.17 0.16 0.371 0.156 0.576 0.52 0.19 0.08 0.1135 -0.1542 -0.1961 0.0988 -0.1624 -0.042 0.0198 0.0892 0.1653 0.1603 -0.25124 0.16731 0.41669 0.14685 0.5246 0.0574 0.22 0.17 -0.13 0.24 0.53 0.44 0.7 0.5 0.67 0.41 0.15 0.12 0.34 0.3 0.25 0.23 0.18 0.25 0.3 0.48 0.47 -0.21 0.16 0.12 -0.27 -0.26 -0.22 0.01 0.05 0.04 -0.28 0 -0.04 0.16 -0.05 -0.28 0.09 -0.39 0.542 -0.146 0.104 0.41 -0.16 -0.47 -0.49 0.23 0.21 0.11 0.08 0.23 -0.17 0.07000000000000001 0.24 0.24 0.21 0.19 0.34 0.4072 -0.09320000000000001 0.2772 0.4182 0.3078 -0.018 0.2107 -0.0526 0.04 0.295 0.17 -0.27 0.045 0.04 0.112 0.326 1026 1 262153 5 names 16 12 262153 5 Study 262153 11 Crit.Domain 262153 6 IAT.ID 262153 9 IAT.Focus 262153 7 Crit.ID 262153 8 Crit.Cat 262153 7 Scoring 262153 6 Target 262153 4 Type 262153 9 Sample.ID 262153 1 N 262153 1 R 1026 1 262153 9 row.names 13 2 NA -308 1026 1023 16 1 262153 10 data.frame 254 254 robumeta/data/oswald2013.ex1.RData0000755000176200001440000000273714410441300016214 0ustar liggesusersW}lEWKKQC@`idlù7vo+bFH hBĄ!Ss`,j?^-峊of׃û@Guf׾fͫXO> R Q8Pu2$J$UTFͮ}2in,t״5aQWT]c#HK2xHq& #}:Rשl$lS(z[%5qtv8(Z<|mE3n'o`rr*SYu:~ ]2:@T$Tnw*w"1o)- Nb(ۄu*iESD (F'I׉ۭ'q [ի)RPV&P'**Os CAnN*r1ͻ c'^lR'VU,[:R^^ +5j1ҫ$,23Qs5AU ŒSIU4&YTRdcMpX7 EU,84p1,Eo79$ZÅ]k*tDbkRD9G-/̅AMs(1Ix^w]ًd|S]NgWR-[7)q܏\c]ʐ7Of0-t"T hf>\sHmWA*}39oA_5P]3>2{WG_\t)b.Nqai ˯\;XatVNy?y_?d~C1sMc{ŵ}"ypN$tAgˉkbƃ{ݽwS9U>MrTwT+~hxqUzjj"=ijX,sļg +0c6L 13\[JyO9m;4ε&bmb٪#ej2܀:Ph<x<`ppp ppp0+U4b;Tlfq77 0[robumeta/data/corrdat.RData0000755000176200001440000000701114410441300015345 0ustar liggesusers TTǏbo{x1g|ײRA-aP u+ҲRjZe>3Y)( >|f/[{[&Z{}{͙ F'FRUؒ V-쐤]G$!J$ͤ-/M 2 _&{la~I -'+#` !$ux 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vyV;IXUCw%V0%mJTɔh%XT1ǚmf˚rL1P]r2HD[rcRS)*k#1_FJ9tkr*9MjMsLGZlQ*ls$'$ހ.R)S _#f}<gې 6tކ0xFoiTG򖚷d𖖷t-oy3dΐ9C 3dΐ9C 3dΐ9Cp34 gh8Cr3 -gh9CZr3t g8C:q3t =g9Czs3 =g90p3 a g80r3a #g9F0r3La g8&0q3La2ԑMY45MhE F4M-hjAS ZԂ4M4YdAM4YdAM4Y44iM#hAF44iM+hZA Vд4iM'h:A Nt4M'h:A ^4M/hzA ^ f4z`hM4ᴐS'$oDcU%IWR G?KmP=_|˖ RvOt:"F2B?4robumeta/data/hedgesdat.RData0000755000176200001440000000436714410441300015652 0ustar liggesusers P H-bwfNfRӎMj|m\, Nl4dT`Y!ZMFj4Bj} Fcߞ{9+iMs_߹ۅTg&\>ȯ>W+n~X0tbGVm U F=PC}txT(%8P\u/jm4ȡ94Jۚ`+mRl4[:pȌ%-O&F(MdE^ESzяݝt־o9iw2\Dz:cGރދR쎷H{av 77Ώ΁W$7DCgW5-EںqW`HûGx$N^iϵEہqc+vNijW*Ή\yb[֭]"rK`L?Quu}"n8$;pr'32W|Yeza>;KN1?֛<\=߹zDܰ!{}r__brq| ۰H"ؽa8߅Xy.'1?~~K~Ԏ|UF&;_{y@;<84z n ~GyP|OW<_ɕu ccw?w|.]X?y?/󼸗.~.^O+]_pe']2Jqߗ àISa~!~AKbwx= ~Y0޳s>7ݞas[$$X/lBkwAޡP:9y<8o|sz:WS ; ^%o=Wyf?56xr.Mg -෩ u_]i#-(;A7~$Y~>1XX߹ﰨ`־Zȣ>+(8o`|$e/@B<.ؽ^!Գt&̯qо8?saaz$O7F=q aeA"෮X[.ю| ׻Ww ~3&zSC*7*M8_0Z8NmWeY+?NhX'̇Wm˹ nqEbP1l^$9SmJ;[1o5 컎8n8]‰b1&aVm U`\0;9Ѿ3 ~ jfsVkos{/! AW,\6L ffR(Aqfw<ӌ(׾ف|􊓓g<>Q$&x:4 g y$y;5ma-TB\;_n,I?+j-,BB^ΒTg}+q!FV4FMvt1dǔKvܲgQOJ=z: Գ1TbPC%J *1TbЈC#F 14bhЈC'N :1tbЉC'N 1 b0a A 1Lb0a$I &1Lbİa"E 1,bXİaM 71pM 71pM 71<C 1<C 1#߮R3g~7To7Ĝrobumeta/man/0000755000176200001440000000000014410441300012632 5ustar liggesusersrobumeta/man/sensitivity.Rd0000644000176200001440000000223214410441300015512 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sensitivity.R \name{sensitivity} \alias{sensitivity} \title{Sensitivity Analysis for Correlated Effects RVE} \usage{ sensitivity(x) } \arguments{ \item{x}{A dataframe containing values of rho, tau squared, coefficient estimates, and standard errors.} } \description{ \code{sensitivity} is used to assess the impact of differing rho values on the correlated effects meta-regression model. } \examples{ # Correlated Effects Model CorrMod <- robu(formula = effectsize ~ followup + males + binge + college, data = corrdat, studynum = studyid, var.eff.size = var, rho = .8, modelweights = "CORR", small = FALSE) sensitivity(CorrMod) # Output sensitivity } \references{ Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in meta-regression with dependent effect size estimates. \emph{Research Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. DOI: 10.1002/jrsm.5 Tipton, E. (in press) Small sample adjustments for robust variance estimation with meta-regression. \emph{Psychological Methods}. } \keyword{robu} robumeta/man/group.center.Rd0000644000176200001440000000126214410441300015535 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/group.center.R \name{group.center} \alias{group.center} \title{Convenience function for calculating group-centered covariates.} \usage{ group.center(var, grp) } \arguments{ \item{var}{The covariate to be group centered.} \item{grp}{A vector corresponding to the group identification.} } \value{ A column or vector containing the group.centered covariate. } \description{ Creates a within-study (or within-cluster) version of the covariate in question. } \examples{ # Load data data(corrdat) # Create a group centered covariate males_c <- group.center(corrdat$males, corrdat$studyid) } \keyword{robumeta} robumeta/man/hedgesdat.Rd0000644000176200001440000000175114410442521015063 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robumeta-data.R \docType{data} \name{hedgesdat} \alias{hedgesdat} \title{hedgesdat} \format{ A dataframe containing 179 effect sizes from 66 different studies } \source{ Hedges, Tipton, and Johnson (2010) } \description{ Data from a meta-analysis on the effectiveness of phonics reading instruction by Ehri, Nunes, Stahl and Willows (2001). Data reported in Hedges, Tipton, and Johnson (2010) with example. } \references{ Ehri, L.C., Nunes, S.R., Stahl, S.A., Willows, D.M. (2001) Systematic phonics instruction helps children learn to read: Evidence from the National Reading Panel's meta-analysis. \emph{Review of Educational Research}. \bold{71}, 393--447. Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in meta-regression with dependent effect size estimates. \emph{Research Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. DOI: 10.1002/jrsm.5 } \keyword{datasets} robumeta/man/print.robu.Rd0000644000176200001440000000231514410441300015224 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/print.robu.R \name{print.robu} \alias{print.robu} \title{Outputs Model Information} \usage{ \method{print}{robu}(x, digits = 3, ...) } \arguments{ \item{x}{Object from robu class.} \item{digits}{Controls the number of digits to print when printing numeric values.} \item{...}{Additional arguments to be passed to the fitting function.} } \description{ Prints relevant information from robu function. } \examples{ # Load data data(hierdat) ### Small-Sample Corrections - Hierarchical Dependence Model HierMod <- robu(formula = effectsize ~ binge + followup + sreport + age, data = hierdat, studynum = studyid, var.eff.size = var, modelweights = "HIER", small = FALSE) print(HierMod) # Output results } \references{ Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in meta-regression with dependent effect size estimates. \emph{Research Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. DOI: 10.1002/jrsm.5 Tipton, E. (in press) Small sample adjustments for robust variance estimation with meta-regression. \emph{Psychological Methods}. } \keyword{robu} robumeta/man/predict.robu.Rd0000644000176200001440000000620314410441300015522 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.robu.R \name{predict.robu} \alias{predict.robu} \title{Prediction method for a robumeta object.} \usage{ \method{predict}{robu}(object, pred.vector, level = 0.95, ...) } \arguments{ \item{object}{A fitted robumeta model object.} \item{pred.vector}{A prediction vector containing the new covariate values.} \item{level}{Confidence level.} \item{...}{Additional arguments to predict.} } \value{ \code{prediction} the predicted value based on the prediction vector. \code{se} The standard error for the predicted mean. \code{t} The t-statistic calculated based on the predicted mean. \code{df} The small sample corrected degrees of freedom of the distribution of the t-statistic. \code{lower} The lower bound of the confidence interval for the predicted mean. \code{upper} The upper bound of the confidence interval for the predicted mean. } \description{ \code{predict.robu} produces the predicted mean and confidence interval of a fitted robumeta model object given a prediction vector. } \details{ \itemize{ \item{\code{intercept}} { If an intercept is included in the robumeta model, the first element should always be 1, representing the intercept, followed by the covariate values in appropriate order. If the robumeta model does not have an intercept, the prediction vector should begin with the first covariate value. } \item{\code{variable}} { For continuous variables, use the variable value as the corresponding element value in \code{ pred.vector}. For a categorical variable the original variable value should be transformed to match the coding system used in the robumeta model (e.g. dummy coding, deviation coding, etc.). } \item{\code{NA}} { If the vector contains NAs, \code{predict.robu} will remove the corresponding covariates from the original data, and refit a new robumeta model. The prediction and confidence interval will be estimated based on the new model. } } \preformatted{ robu_mod <- robu(LOR1 ~ study_design + duration + service_hrs, data = dropoutPrevention, studynum = studyID, var.eff.size = varLOR, modelweights = "HIER", small = TRUE) } In this robumeta model, the first covariate is a categorical variable that contains three levels: "Matched" (33 percent, dummy code: 00), "Randomized"(24 percent, 01) and "non-match non-randomized"(43 percent, 10). The corresponding prediction vector begins with 1 (intercept), and followed by 0, 0, the dummy code for "Matched". The last two elements are 38 and 5, the values for duration and sevice_hrs. \preformatted{ predict(object = robu_mod, pred.vector = c(1,0,0,38,5),level = 0.95) } If we do not know the value of duration, the prediction vector should be c(1,0,0,NA,5). predict.robu() will refit a new model without the covariate duration, and the prediction will be based on it. \preformatted{ predict(object = robu_mod, pred.vector = c(1,0,0,NA,5),level = 0.95) } } robumeta/man/group.mean.Rd0000644000176200001440000000126214410441300015175 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/group.mean.R \name{group.mean} \alias{group.mean} \title{Convenience function for calculating group-mean covariates.} \usage{ group.mean(var, grp) } \arguments{ \item{var}{The covariate cotaining the values to be group averaged.} \item{grp}{The group from which the average should be calculated.} } \value{ A column or vector containing the group.mean covariate. } \description{ Creates a between-study (or between-cluster) version of the covariate in question. } \examples{ # Load data data(corrdat) # Create a group mean covariate age_m <- group.mean(corrdat$age, corrdat$studynum) } \keyword{robumeta} robumeta/man/hierdat.Rd0000644000176200001440000000127614410442521014555 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robumeta-data.R \docType{data} \name{hierdat} \alias{hierdat} \title{Data for Fitting Hierarchical Effects Model} \format{ A dataframe containing 68 effect sizes from 15 studies used in Tanner- Smith and Tipton (2013). } \source{ https://my.vanderbilt.edu/emilytannersmith/training-materials/ } \description{ Fictional data used in TannerTanner-Smith and Tipton (2013). } \references{ Tanner-Smith E.E., Tipton, E. (2013) Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and SPSS. \emph{Research Synthesis Methods}. ISSN 1759-2887. } \keyword{datasets} robumeta/man/forest.robu.Rd0000644000176200001440000000723414410441300015377 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/forest.robu.R \name{forest.robu} \alias{forest.robu} \title{Forest Plots for Robust Variance Estimation Meta-Analysis} \usage{ forest.robu(x, es.lab, study.lab, ...) } \arguments{ \item{x}{An intercept-only RVE model previously fit using the \code{robu()} function..} \item{es.lab}{A vector of labels to be used to individual effect sizes in the forest plot. Labels for individual effect sizes might be ``Math Score'' or ``Reading Score'' for a meta-analysis that included such measures or as simple as ``Effect Size 1'' and ``Effect Size 2.''} \item{study.lab}{A vector of labels to be used to identify study (or cluster) level groupings in the forest plot. For instance, labels for the study column might be author names with corresponding publication years.} \item{...}{Additional arguments to be passed to the forest function. Any number of additional columns can be specified to be plotted along side the confidence interval column and can be specified with the following syntax \code{``arg1'' = ``arg2''} where \code{``arg1''} is the title of the column on the forest plot, and \code{``arg2''} is the name of the column from the original \code{data.frame} that contains the information to be displayed alongside the estimates and confidence intervals.} } \description{ \code{forest.robu} In meta-analysis, forest plots provide a graphical depiction of effect size estimates and their corresponding confidence intervals. The \code{forest.robu()} function in \pkg{robumeta} can be used to produce forest plots for RVE meta-analyses. The function requires the \pkg{grid} package and is based on examples provided in (Murrell, 2011). As is the case with traditional forest plots, point estimates of individual effect sizes are plotted as boxes with areas proportional to the weight assigned to that effect size. Importantly, here the weight is not necessarily proportional to the effect size variance or confidence intervals, since the combined study weight is divided evenly across the study effect sizes. Two-sided 95\% confidence intervals are calculated for each effect size using a standard normal distribution and plotted along with each block. The overall effect is included at the bottom of the plot as a diamond with width equivalent to the confidence interval for the estimated effect. The RVE forest function is designed to provide users with forest plots which display each individual effect size used in the meta-analysis, while taking into account the study- or cluster-level properties inherent to the RVE analysis. As such, the user must specify columns from their original dataset that contain labels for the study or cluster and for the individual effect sizes. } \examples{ # Load data data(oswald2013.ex1) # Run intercept only model. oswald_intercept <- robu(formula = effect.size ~ 1, data = oswald2013.ex1, studynum = Study, var.eff.size = var.eff.size, rho = 0.8, small = TRUE) # Create forest plot. forest.robu(oswald_intercept, es.lab = "Crit.Cat", study.lab = "Study", "Effect Size" = effect.size, # optional column "Weight" = r.weights) # optional column } \references{ Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in meta-regression with dependent effect size estimates. \emph{Research Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. DOI: 10.1002/jrsm.5 Murrell P (2011). R Graphics. CRC/Taylor & Francis. ISBN 9781439831762. Tipton, E. (in press) Small sample adjustments for robust variance estimation with meta-regression. \emph{Psychological Methods}. } \keyword{forest.robu} robumeta/man/corrdat.sm.Rd0000644000176200001440000000105014410442521015177 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robumeta-data.R \docType{data} \name{corrdat.sm} \alias{corrdat.sm} \title{Data for Fitting Correlated Effects Model With Small-Sample Corrections} \format{ A dataframe containing 300 effect sizes from 28 studies used in Tipton (2013). } \source{ Elizabeth Tipton } \description{ Data used in Tipton (2013). } \references{ Tipton, E. (in press) Small sample adjustments for robust variance estimation with meta-regression. \emph{Psychological Methods}. } \keyword{datasets} robumeta/man/corrdat.Rd0000644000176200001440000000127514410442521014572 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robumeta-data.R \docType{data} \name{corrdat} \alias{corrdat} \title{Data for Fitting Correlated Effects Model} \format{ A dataframe containing 172 effect sizes from 39 studies used in Tanner- Smith and Tipton (2013). } \source{ https://my.vanderbilt.edu/emilytannersmith/training-materials/ } \description{ Fictional data used in TannerTanner-Smith and Tipton (2013). } \references{ Tanner-Smith E.E., Tipton, E. (2013) Robust variance estimation with dependent effect sizes: practical considerations including a software tutorial in Stata and SPSS. \emph{Research Synthesis Methods}. ISSN 1759-2887. } \keyword{datasets} robumeta/man/robu.Rd0000644000176200001440000001151614410442521014102 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robu.R \name{robu} \alias{robu} \alias{CORR} \alias{HIER} \alias{USER} \title{Fitting Robust Variance Meta-Regression Models} \usage{ robu( formula, data, studynum, var.eff.size, userweights, modelweights = c("CORR", "HIER"), rho = 0.8, small = TRUE, ... ) } \arguments{ \item{formula}{An object of class \code{"formula"}. A typical meta-regression formula will look similar to \code{y ~ x1 + x2...}, where \code{y} is a vector of effect sizes and \code{x1 + x2...} are (optional) user-specified covariates. An intercept only model can be specified with \code{y ~ 1} and the intercept can be ommitted as follows \code{y ~ -1 +...}.} \item{data}{A data frame, list or environment or an object coercible by as.data.frame to a data frame.} \item{studynum}{A vector of study numbers to be used in model fitting. \code{studynum} must be a numeric or factor variable that uniquely identifies each study.} \item{var.eff.size}{A vector of user-calculated effect-size variances.} \item{userweights}{A vector of user-specified weights if non-efficient weights are of interest. Users interested in non-efficient weights should see the Appendix of Tipton (2013) for a discussion of the role of non-efficient weights in RVE).} \item{modelweights}{User-specified model weighting scheme. The two two avialable options are \code{modelweights = "CORR"} and \code{modelweights = "HIER"}. The default is \code{"CORR"}. See Hedges, Tipton and Johnson (2010) and Tipton (2013) for extended explanations of each weighting scheme.} \item{rho}{User-specified within-study effect-size correlation used to fit correlated (\code{modelweights = "CORR"}) effects meta-regression models. The value of \code{rho} must be between 0 and 1. The default value for \code{rho} is 0.8. \code{rho} is not specified for hierarchical (\code{modelweights = "HIER"}) effects models.} \item{small}{\code{small = TRUE} is used to fit the meta-regression models with the small- sample corrections for both the residuals and degrees of freedom, as detailed in Tipton (2013). Users wishing to use the original RVE estimator must specify \code{small = FALSE} as the corrected estimator is the default option.} \item{...}{Additional arguments to be passed to the fitting function.} } \value{ \item{output}{ A data frame containing some combination of the robust coefficient names and values, standard errors, t-test value, confidence intervals, degrees of freedom and statistical significance. } \item{n}{The number of studies in the sample \code{n}}. \item{k}{The number of effect sizes in the sample \code{k}}. \item{k descriptives}{the minimum \code{min.k}, mean \code{mean.k}, median \code{median .k}, and maximum \code{max.k} number of effect sizes per study. } \item{tau.sq.}{ \code{tau.sq} is the between study variance component in the correlated effects meta-regression model and the between-cluster variance component in the hierarchical effects model. \code{tau.sq} is calculated using the method-of-moments estimator provided in Hedges, Tipton, and Johnson (2010). For the correlated effects model the method-of-moments estimar depends on the user-specified value of rho. } \item{omega.sq.}{ \code{omega.sq} is the between-studies-within-cluster variance component for the hierarchical effects meta-regression model. \code{omega.sq} is calculated using the method-of-moments estimator provided in Hedges, Tipton, and Johnson (2010) erratum. } \item{I.2}{ \code{I.2} is a test statistics used to quantify the amount of variability in effect size estimates due to effect size heterogeneity as opposed to random variation. } } \description{ \code{robu} is used to meta-regression models using robust variance estimation (RVE) methods. \code{robu} can be used to estimate correlated and hierarchical effects models using the original (Hedges, Tipton and Johnson, 2010) and small-sample corrected (Tipton, 2013) RVE methods. In addition, \code{robu} contains options for fitting these models using user-specified weighting schemes (see the Appendix of Tipton (2013) for a discussion of non- efficient weights in RVE). } \examples{ # Load data data(hierdat) # Small-Sample Corrections - Hierarchical Dependence Model HierModSm <- robu(formula = effectsize ~ binge + followup + sreport + age, data = hierdat, studynum = studyid, var.eff.size = var, modelweights = "HIER", small = TRUE) print(HierModSm) # Output results } \references{ Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in meta-regression with dependent effect size estimates. \emph{Research Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. DOI: 10.1002/jrsm.5 Tipton, E. (in press) Small sample adjustments for robust variance estimation with meta-regression. \emph{Psychological Methods}. } \keyword{robu} robumeta/man/oswald2013.ex1.Rd0000644000176200001440000000340514410442521015424 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robumeta-data.R \docType{data} \name{oswald2013.ex1} \alias{oswald2013.ex1} \title{IAT Criterion-Related Correlations} \format{ A dataframe containing 32 effect sizes from 9 studies. \tabular{rlll}{ [,1] \tab Study \tab Factor \cr [,2] \tab Crit.Domain \tab Factor \cr [,3] \tab IAT.ID \tab Integer \cr [,4] \tab IAT.Focus \tab Factor \cr [,5] \tab Crit.ID \tab Integer \cr [,6] \tab Crit.Cat \tab Factor \cr [,7] \tab Scoring \tab Factor \cr [,8] \tab Target \tab Factor \cr [,9] \tab Type \tab Factor \cr [,10] \tab Sample.ID \tab Integer \cr [,11] \tab N \tab Integer \cr [,12] \tab R \tab Numeric \cr [,13] \tab effect.size \tab Numeric \cr [,14] \tab var.eff.size \tab Numeric } } \source{ Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies. \emph{Journal of Personality and Social Psychology}, 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. } \description{ Data from a meta-analysis on IAT conducted by Oswald et al., (2013) examining the predictive validity of the Implicit Association Test (IAT) and various explicit measures of bias for a variety of criterion measures of discrimination. Included in the dataset are the study-level correlations between IAT scores and criterion measures of neurological activity or response latency from the original \code{oswald2013} dataset. } \references{ Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies. \emph{Journal of Personality and Social Psychology}, 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. } \keyword{datasets} robumeta/man/oswald2013.Rd0000644000176200001440000000315314410442521014730 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/robumeta-data.R \docType{data} \name{oswald2013} \alias{oswald2013} \title{IAT Criterion-Related Correlations} \format{ A dataframe containing 308 effect sizes from 46 studies. \tabular{rlll}{ [,1] \tab Study \tab Factor \cr [,2] \tab Crit.Domain \tab Factor \cr [,3] \tab IAT.ID \tab Integer \cr [,4] \tab IAT.Focus \tab Factor \cr [,5] \tab Crit.ID \tab Integer \cr [,6] \tab Crit.Cat \tab Factor \cr [,7] \tab Scoring \tab Factor \cr [,8] \tab Target \tab Factor \cr [,9] \tab Type \tab Factor \cr [,10] \tab Sample.ID \tab Integer \cr [,11] \tab N \tab Integer \cr [,12] \tab R \tab Numeric } } \source{ Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies. \emph{Journal of Personality and Social Psychology}, 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. } \description{ Data from a meta-analysis on IAT conducted by Oswald et al., (2013) examining the predictive validity of the Implicit Association Test (IAT) and various explicit measures of bias for a variety of criterion measures of discrimination. Included in the dataset are the study level correlations between IAT scores and some criterion measure of discrimination. } \references{ Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) Predicting ethnic and racial discrimination: a meta-analysis of IAT criterion studies. \emph{Journal of Personality and Social Psychology}, 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. } \keyword{datasets} robumeta/DESCRIPTION0000755000176200001440000000233714410454062013606 0ustar liggesusersPackage: robumeta Type: Package Title: Robust Variance Meta-Regression Version: 2.1 Date: 2023-03-29 Authors@R: c( person("Zachary","Fisher", email = "fish.zachary@gmail.com", role = c("aut", "cre")), person("Elizabeth", "Tipton", role = "aut"), person("Hou", "Zhipeng", role = "aut") ) Suggests: clubSandwich, R.rsp Description: Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots. URL: https://github.com/zackfisher/robumeta BugReports: https://github.com/zackfisher/robumeta/issues License: GPL-2 LazyData: true RoxygenNote: 7.2.2 VignetteBuilder: R.rsp NeedsCompilation: no Packaged: 2023-03-28 01:44:22 UTC; zff100 Author: Zachary Fisher [aut, cre], Elizabeth Tipton [aut], Hou Zhipeng [aut] Maintainer: Zachary Fisher Repository: CRAN Date/Publication: 2023-03-28 03:00:02 UTC robumeta/build/0000755000176200001440000000000014410443166013172 5ustar liggesusersrobumeta/build/vignette.rds0000644000176200001440000000033514410443166015532 0ustar liggesusersb```b`abd`b2 1# ',O*M-I LK-)I+HIK,,FS(SS@Ihj@!@„$Ϛn3KjAj^ H~f7( <(,ULN,/ -3fkHf d/cs1G*gQ~̻i=LsÄ+%$Q/nrobumeta/tests/0000755000176200001440000000000014410441300013221 5ustar liggesusersrobumeta/tests/testthat/0000755000176200001440000000000014410442673015077 5ustar liggesusersrobumeta/tests/testthat/testHierarchical.R0000644000176200001440000000263714410441300020472 0ustar liggesuserscontext("HierarchicalModel") test_that("Tau_square_Est", { New_result <- robu(effectsize ~ followup, data = hierdat, studynum = studyid, var.eff.size = var, modelweights ="HIER",small = T) expect_equivalent(New_result$mod_info$tau.sq[1,1], 0.06541402) }) test_that("Omega_square_Est", { New_result <- robu(effectsize ~ followup, data = hierdat, studynum = studyid, var.eff.size = var, modelweights ="HIER",small = T) expect_equivalent(New_result$mod_info$omega.sq[1,1], 0.16360794) }) test_that("Coef_Est", { New_result <- robu(effectsize ~ followup, data = hierdat, studynum = studyid, var.eff.size = var, modelweights ="HIER",small = T) expect_equivalent(as.vector(New_result$b.r), c(0.2610277292, -0.0001810645)) }) test_that("df_Est", { #Without Small Sample Correction New_result <- robu(effectsize ~ followup, data = hierdat, studynum = studyid, var.eff.size = var, modelweights ="HIER",small = F) expect_equivalent(as.vector(New_result$dfs), 13) #With Small Sample Correction New_result <- robu(effectsize ~ followup, data = hierdat, studynum = studyid, var.eff.size = var, modelweights ="HIER",small = T) expect_equivalent(as.vector(New_result$dfs), c(3.1683243, 1.5765336)) }) robumeta/tests/testthat/testCorrelated.R0000644000176200001440000000232214410441300020167 0ustar liggesuserscontext("CorrelatedModel") test_that("Tau_square_Est", { New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, modelweights ="CORR",small = T, rho = 1) expect_equivalent(New_result$mod_info$tau.sq[1,1], 0.12361242975) }) test_that("Coef_Est", { New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, modelweights ="CORR",small = T, rho = 1) expect_equivalent(as.vector(New_result$b.r), c(0.334200417, -0.002179469)) }) test_that("df_Est", { #Without Small Sample Correction New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, modelweights ="CORR",small = F, rho = 1) expect_equivalent(as.vector(New_result$dfs), 37) #With Small Sample Correction New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, modelweights ="CORR",small = T, rho = 1) expect_equivalent(as.vector(New_result$dfs), c(22.946909551160, 1.853700618431)) }) robumeta/tests/testthat/testUserWeights.R0000644000176200001440000000255014410441300020357 0ustar liggesuserscontext("UserWeights_Model") test_that("Coef_Est", { User_Weights <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, modelweights ="CORR", small = T, rho = 1)$data.full$r.weights New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, userweights = User_Weights, small = T, rho = 1) expect_equivalent(as.vector(New_result$b.r), c(0.334200417, -0.002179469)) }) test_that("df_Est", { User_Weights <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, modelweights ="CORR", small = T, rho = 1)$data.full$r.weights #Without Small Sample Correction New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, userweights = User_Weights, small = F, rho = 1) expect_equivalent(as.vector(New_result$dfs), 37) #With Small Sample Correction New_result <- robu(effectsize ~ followup, data = corrdat, studynum = studyid, var.eff.size = var, userweights = User_Weights, small = T, rho = 1) expect_equivalent(as.vector(New_result$dfs), c(9.88413624629, 1.32957029417)) }) robumeta/vignettes/0000755000176200001440000000000014410443166014103 5ustar liggesusersrobumeta/vignettes/robumetaVignette.pdf.asis0000644000176200001440000000022414410441300021042 0ustar liggesusers%\VignetteIndexEntry{robumeta Vignette} %\VignetteEngine{R.rsp::asis} %\VignetteKeyword{PDF} %\VignetteKeyword{vignette} %\VignetteKeyword{robumeta}robumeta/R/0000755000176200001440000000000014410441300012260 5ustar liggesusersrobumeta/R/robu.R0000644000176200001440000006271514410441300013365 0ustar liggesusers#' Fitting Robust Variance Meta-Regression Models #' #' \code{robu} is used to meta-regression models using robust variance #' estimation (RVE) methods. \code{robu} can be used to estimate correlated and #' hierarchical effects models using the original (Hedges, Tipton and Johnson, #' 2010) and small-sample corrected (Tipton, 2013) RVE methods. In addition, #' \code{robu} contains options for fitting these models using user-specified #' weighting schemes (see the Appendix of Tipton (2013) for a discussion of #' non- efficient weights in RVE). #' #' #' @aliases robu CORR HIER USER #' @param formula An object of class \code{"formula"}. A typical #' meta-regression formula will look similar to \code{y ~ x1 + x2...}, where #' \code{y} is a vector of effect sizes and \code{x1 + x2...} are (optional) #' user-specified covariates. An intercept only model can be specified with #' \code{y ~ 1} and the intercept can be ommitted as follows \code{y ~ -1 #' +...}. #' @param data A data frame, list or environment or an object coercible by #' as.data.frame to a data frame. #' @param studynum A vector of study numbers to be used in model fitting. #' \code{studynum} must be a numeric or factor variable that uniquely #' identifies each study. #' @param var.eff.size A vector of user-calculated effect-size variances. #' @param rho User-specified within-study effect-size correlation used to fit #' correlated (\code{modelweights = "CORR"}) effects meta-regression models. #' The value of \code{rho} must be between 0 and 1. The default value for #' \code{rho} is 0.8. \code{rho} is not specified for hierarchical #' (\code{modelweights = "HIER"}) effects models. #' @param modelweights User-specified model weighting scheme. The two two #' avialable options are \code{modelweights = "CORR"} and \code{modelweights = #' "HIER"}. The default is \code{"CORR"}. See Hedges, Tipton and Johnson #' (2010) and Tipton (2013) for extended explanations of each weighting scheme. #' @param userweights A vector of user-specified weights if non-efficient #' weights are of interest. Users interested in non-efficient weights should #' see the Appendix of Tipton (2013) for a discussion of the role of #' non-efficient weights in RVE). #' @param small \code{small = TRUE} is used to fit the meta-regression models #' with the small- sample corrections for both the residuals and degrees of #' freedom, as detailed in Tipton (2013). Users wishing to use the original RVE #' estimator must specify \code{small = FALSE} as the corrected estimator is #' the default option. #' @param ... Additional arguments to be passed to the fitting function. #' @return #' #' \item{output}{ A data frame containing some combination of the robust #' coefficient names and values, standard errors, t-test value, confidence #' intervals, degrees of freedom and statistical significance. } #' #' \item{n}{The number of studies in the sample \code{n}}. #' #' \item{k}{The number of effect sizes in the sample \code{k}}. #' #' \item{k descriptives}{the minimum \code{min.k}, mean \code{mean.k}, median #' \code{median .k}, and maximum \code{max.k} number of effect sizes per study. #' } #' #' \item{tau.sq.}{ \code{tau.sq} is the between study variance component in the #' correlated effects meta-regression model and the between-cluster variance #' component in the hierarchical effects model. \code{tau.sq} is calculated #' using the method-of-moments estimator provided in Hedges, Tipton, and #' Johnson (2010). For the correlated effects model the method-of-moments #' estimar depends on the user-specified value of rho. } #' #' \item{omega.sq.}{ \code{omega.sq} is the between-studies-within-cluster #' variance component for the hierarchical effects meta-regression model. #' \code{omega.sq} is calculated using the method-of-moments estimator provided #' in Hedges, Tipton, and Johnson (2010) erratum. } #' #' \item{I.2}{ \code{I.2} is a test statistics used to quantify the amount of #' variability in effect size estimates due to effect size heterogeneity as #' opposed to random variation. } #' @references #' #' Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in #' meta-regression with dependent effect size estimates. \emph{Research #' Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. #' DOI: 10.1002/jrsm.5 #' #' Tipton, E. (in press) Small sample adjustments for robust variance #' estimation with meta-regression. \emph{Psychological Methods}. #' @keywords robu #' @examples #' #' #' # Load data #' data(hierdat) #' #' # Small-Sample Corrections - Hierarchical Dependence Model #' HierModSm <- robu(formula = effectsize ~ binge + followup + sreport #' + age, data = hierdat, studynum = studyid, #' var.eff.size = var, modelweights = "HIER", small = TRUE) #' #' print(HierModSm) # Output results #' #' @export robu <- function(formula, data, studynum,var.eff.size, userweights, modelweights = c("CORR", "HIER"), rho = 0.8, small = TRUE, ...) { # Evaluate model weighting scheme. modelweights <- match.arg(modelweights) if (modelweights == "CORR" && rho > 1 | rho < 0) stop ("Rho must be a value between 0 and 1.") if (missing(userweights)){ user_weighting = FALSE } else { user_weighting = TRUE } cl <- match.call() # Full model call mf <- match.call(expand.dots = FALSE) ml <- mf[[2]] # Model formula m <- match(c("formula", "data", "studynum", "var.eff.size", "userweights"), names(mf)) mf <- mf[c(1L, m)] mf$drop.unused.levels <- TRUE mf[[1L]] <- as.name("model.frame") mf <- eval(mf, parent.frame()) if(!user_weighting){ dframe <- data.frame(effect.size = mf[,1], stats::model.matrix(formula, mf), studynum = mf[["(studynum)"]], var.eff.size = mf[["(var.eff.size)"]]) X.full.names <- names(dframe)[-match(c("effect.size", "studynum", "var.eff.size"), names(dframe))] } else { # Begin userweights dframe <- data.frame(effect.size = mf[,1], stats::model.matrix(formula, mf), studynum = mf[["(studynum)"]], var.eff.size = mf[["(var.eff.size)"]], userweights = mf[["(userweights)"]]) X.full.names <- names(dframe)[-match(c("effect.size", "studynum", "userweights", "var.eff.size"), names(dframe))] } # End userweights study_orig_id <- dframe$studynum dframe$study <- as.factor(dframe$studynum) dframe$study <- as.numeric(dframe$study) dframe <- dframe[order(dframe$study),] k_temp <- as.data.frame(unclass(rle(sort(dframe$study)))) dframe$k <- k_temp[[1]][ match(dframe$study, k_temp[[2]])] dframe$avg.var.eff.size <- stats::ave(dframe$var.eff.size, dframe$study) dframe$sd.eff.size <- sqrt(dframe$var.eff.size) switch(modelweights, HIER = { # Begin HIER dframe$weights <- 1 / dframe$var.eff.size }, # End HIER CORR = { # Begin CORR dframe$weights <- 1 / (dframe$k * dframe$avg.var.eff.size) } # End CORR ) X.full <- dframe[c("study", X.full.names)] data.full.names <- names(dframe)[-match(c("studynum",X.full.names), names(dframe))] data.full <- dframe[c(data.full.names)] k <- data.full[ !duplicated(data.full$study), ]$k k_list <- as.list(k) M <- nrow(data.full) # Number of units in analysis p <- ncol(X.full) - 2 # Number of (non-intercept) covariates N <- max(data.full$study) # Number of studies W <- as.matrix(by(data.full$weights, data.full$study, function(x) diag(x, nrow = length(x)), simplify = FALSE)) X <- data.matrix(X.full) X <- lapply(split(X[,2:(p + 2)], X[,1]), matrix, ncol = p + 1) y <- by(data.full$effect.size, data.full$study, function(x) matrix(x)) J <- by(rep(1, nrow(X.full)), X.full$study, function(x) matrix(x, nrow = length(x), ncol = length(x))) sigma <- by(data.full$sd.eff.size, data.full$study, function(x) tcrossprod(x)) vee <- by(data.full$var.eff.size, data.full$study, function(x) diag(x, nrow = length(x))) SigmV <- Map(function(sigma, V) sigma - V, sigma, vee) sumXWX <- Reduce("+", Map(function(X, W) t(X) %*% W %*% X, X, W)) sumXWy <- Reduce("+", Map(function(X, W, y) t(X) %*% W %*% y, X, W, y)) sumXWJWX <- Reduce("+", Map(function(X, W, J) t(X) %*% W %*% J %*% W %*% X, X, W, J)) Matrx_WKXX <- Reduce("+", Map(function(X, W, k) { t(X) %*% (W / k) %*% X}, X, W, k_list)) Matrx_wk_XJX_XX <- Reduce("+", Map(function(X, W, J, k) {(W / k)[1,1] * ( t(X) %*% J %*% X - t(X) %*% X) }, X, W, J, k_list)) switch(modelweights, HIER = { # Begin HIER tr.sumJJ <- Reduce("+", Map(function(J) sum(diag(J %*% J)), J)) sumXJX <- Reduce("+", Map(function(X, J) t(X) %*% J %*% X, X, J)) sumXWJJX <- Reduce("+", Map(function(X, W, J) t(X) %*% W %*% J %*% J %*% X, X, W, J)) sumXJJWX <- Reduce("+", Map(function(X, W, J) t(X) %*% J %*% J %*% W %*% X, X, W, J)) sumXWWX <- Reduce("+", Map(function(X, W) t(X) %*% W %*% W %*% X, X, W)) sumXJWX <- Reduce("+", Map(function(X, W, J) t(X) %*% J %*% W %*% X, X , W, J)) sumXWJX <- Reduce("+", Map(function(X, W, J) t(X) %*% W %*% J %*% X, X, W, J)) } # End HIER ) b <- solve(sumXWX) %*% sumXWy Xreg <- as.matrix(X.full[-c(1)], dimnames = NULL) data.full$pred <- Xreg %*% b data.full$e <- data.full$effect.size - data.full$pred if (!user_weighting) { switch(modelweights, HIER = { # Begin HIER # Sigma_aj = tau.sq * J_j + omega.sq * I_j + V_j # Qe is sum of squares 1 # Qe = Sigma(T'WT)-(Sigma(T'WX)(Sigma(X'WX))^-1(Sigma(X'WT) # where W = V^(-1) and V = data.full$var.eff.size # Also, Qe = (y-xb)' W (y-xb) sumV <- sum(data.full$var.eff.size) W <- diag(1 / data.full$var.eff.size) sumW <- sum(W) Qe <- t(data.full$e) %*% W %*% data.full$e # Qa is sum of squares 2 # Qa = sum(T-XB.hat)'J(T-XB.hat) # where B.hat = (X'WX)^-1(X'WT) # Also, Qa = (y-xb)'A (y-xb), A=diag(J) e <- by(data.full$e, data.full$study, function(x) matrix(x)) sumEJE <- Reduce("+", Map(function(e, J) t(e) %*% J %*% e, e, J)) Qa <- sumEJE # MoM estimators for tau.sq and omega.sq can be written as # omega.sq.h = A2(Qa-C1)-A1(Qe-C2) / B1A2-B2A1 # tau.sq.h = Qe-C2/A2 - omega.sq.h(B2/A2) where # Vi = (t(X)WX)^-1 V.i <- solve(sumXWX) # A1 = Sigma(kj^2) - tr(V*Sigma(kj*t(Xj)*Jj*Wj*Xj)) - # tr(V*Sigma(kj*t(Xj)*Jj*Wj*Xj)) + # tr(V*[Sigma(t(Xj)*Jj*Xj)]*V*Sigma(t(Xj)*Wj*Jj*Wj*Xj)) # B1 = Sigma(kj) - tr(V Sigma(t(Xj)*Jj*Wj*Xj)) - # tr(V Sigma(t(Xj)*Wj*Jj*Xj)) + # tr(V*[Sigma(t(Xj)*Jj*Xj)]*V*Sigma(t(Xj)*Wj^2*Xj)) # C1 = tr(W^-1) - tr(V*Sigma(t(X)*Jj*Xj)) A1 <- tr.sumJJ - sum(diag(V.i %*% sumXJJWX)) - sum(diag(V.i %*% sumXWJJX)) + sum(diag(V.i %*% sumXJX %*% V.i %*% sumXWJWX)) B1 <- length(data.full$study) - sum(diag(V.i %*% sumXWJX)) - sum(diag(V.i %*% sumXJWX)) + sum(diag(V.i %*% sumXJX%*%V.i %*% sumXWWX)) C1 <- sumV - sum(diag(V.i %*% sumXJX)) # A2 = tr(W) - tr(V*Sigma(t(X)*Wj*Jj*Wj*Xj)) # B2 = tr(W) - tr(V*Sigma(t(X)*Wj^2*Xj)) # C2 = Sigma(kj-p) A2 <- sumW - sum(diag(V.i %*% sumXWJWX)) B2 <- sumW - sum(diag(V.i %*% sumXWWX)) C2 <- length(data.full$study) - (p + 1) # MoM estimator for omega.sq.h = A2(Qa-C1)-A1(Qe-C2) / B1A2-B2A1 # Estimate of between-studies-wthin-cluster variance component omega.sq1 <- ((Qa - C1) * A2 - (Qe - C2) * A1) / (B1 * A2 - B2 * A1) omega.sq <- ifelse(omega.sq1 < 0, 0, omega.sq1) # MoM estimators for tau.sq: Qe-C2/A2 - omega.sq.h(B2/A2) # Estimate of between-clusters variance component tau.sq1 <- ((Qe - C2) / A2) - omega.sq * (B2 / A2) tau.sq <- ifelse(tau.sq1 < 0, 0, tau.sq1) # Approximate inverse variance weights data.full$r.weights <- (1 / (as.vector(data.full$var.eff.size) + as.vector(tau.sq) + as.vector(omega.sq))) # Model info list for hierarchical effects mod_info <- list(omega.sq = omega.sq, tau.sq = tau.sq) }, # End HIER CORR = { # Begin CORR W <- diag (data.full$weights) sumW <- sum(data.full$weights) # Sum (k.j*w.j) Qe <- t(data.full$e) %*% W %*% data.full$e # The following components (denom, termA, termB, term1, term2) # are used in the calculation of the estimate of the residual # variance component tau.sq.hat. # Note: The effect of correlation on the estimates occurs entirely # through the rho*term2 component. denom <- sumW - sum(diag(solve(sumXWX) %*% sumXWJWX)) termA <- sum(diag(solve(sumXWX) %*% Matrx_WKXX)) #ZH_edit termB <- sum(diag(solve(sumXWX) %*% Matrx_wk_XJX_XX ))#ZH_edit term1 <- (Qe - N + termA) / denom term2 <- termB / denom tau.sq1 <- term1 + rho * term2 tau.sq <- ifelse(tau.sq1 < 0, 0, tau.sq1) df <- N - termA - rho * (termB) I.2.1 <- ((Qe - df) / Qe) * 100 I.2 <- ifelse(I.2.1 < 0, 0, I.2.1) # Approximate inverse variance weights data.full$r.weights <- 1 / (as.vector(data.full$k) * (as.vector(data.full$avg.var.eff.size) + as.vector(tau.sq))) # Model info list for correlated effects mod_info <- list(rho = rho, I.2 = I.2, tau.sq = tau.sq, term1 = term1, term2 = term2) } # End CORR ) } else { # Begin userweights data.full$r.weights <- data.full$userweights # Model info list for userweights mod_info <- list(k = k, N = N, p = p, M = M) } # End userweights W.r.big <- diag(data.full$r.weights) # W W.r <- by(data.full$r.weights, data.full$study, # Wj function(x) diag(x, nrow = length(x))) sumXWX.r <- Reduce("+", Map(function(X, W) t(X) %*% W %*% X, X, W.r)) sumXWy.r <- Reduce("+", Map(function(X, W, y) t(X) %*% W %*% y, X, W.r, y)) b.r <- solve(sumXWX.r) %*% sumXWy.r data.full$pred.r <- Xreg %*% b.r data.full$e.r <- cbind(data.full$effect.size) - data.full$pred.r data.full$e.r <- as.numeric(data.full$e.r) sigma.hat.r <- by(data.full$e.r, data.full$study, function(x) tcrossprod(x)) if (!small) { # Begin small = FALSE sumXWeeWX.r <- Reduce("+", Map(function(X, W, V) t(X) %*% W %*% V %*% W %*% X, X, W.r, sigma.hat.r)) VR.r <- solve(sumXWX.r) %*% sumXWeeWX.r %*% solve(sumXWX.r) SE <- sqrt(diag(VR.r)) * sqrt(N / (N - (p + 1))) t <- b.r / SE dfs <- N - (p + 1) prob <- 2 * (1 - stats::pt(abs(t), dfs)) CI.L <- b.r - stats::qt(.975, dfs) * SE CI.U <- b.r + stats::qt(.975, dfs) * SE } else { # Begin small = TRUE Q <- solve(sumXWX.r) # Q = (X'WX)^(-1) Q.list <- rep(list(Q), N) H <- Xreg %*% Q %*% t(Xreg) %*% W.r.big # H = X * Q * X' * W ImH <- diag(c(1), dim(Xreg)[1], dim(Xreg)[1]) - H data.full$ImH <- cbind(ImH) ImHj <- lapply(split(x = ImH,f = as.factor(data.full$study)), function(x){matrix(x, ncol =M)}) #ImHj <- by(data.full$ImH, data.full$study, # function(x) as.matrix(x)) diag_one <- by(rep(1, M), X.full$study, function(x) diag(x, nrow = length(x))) ImHii <- Map(function(X, Q, W, D) D - X %*% Q %*% t(X) %*% W, X, Q.list, W.r, diag_one) if (!user_weighting){ Working_Matrx_E <- diag(1/data.full$r.weights) #1/W Working_Matrx_E_j <- by(data.full$r.weights, data.full$study, # Wj function(x) diag(1/x, nrow = length(x))) #1/W_j switch(modelweights, HIER = { # Inside Matrix = E_j^0.5 * ImH_j *E * t(ImH_j) * E_j^0.5 # In this case, the formula can be simplified to # Inside Matrix = E_j^0.5 * ImH_jj * E_j^1.5 InsideMatrx_list <- Map( function (W_E_j, ImH_jj) { sqrt(W_E_j) %*% ImH_jj %*% (W_E_j^1.5) }, Working_Matrx_E_j, ImHii) eigenres_list <- lapply(InsideMatrx_list, function(x) eigen(x)) eigenval_list <- lapply(eigenres_list, function(x) x$values) eigenvec_list <- lapply(eigenres_list, function(x) x$vectors) A.MBB <- Map(function (eigenvec, eigenval, k, W_E_j) { eigenval_InvSqrt <- ifelse(eigenval< 10^-10, 0, 1/sqrt(eigenval)) # Pseudo_Inverse sqrt(W_E_j) %*% eigenvec %*% diag(eigenval_InvSqrt, k, k) %*% t(eigenvec) %*%sqrt(W_E_j) # Pseudo_Inverse }, eigenvec_list, eigenval_list, k_list, Working_Matrx_E_j) }, CORR = { # In this case, the formula can be simplified to # A_MBB = ImH_jj ^ (-0.5) eigenres_list <- lapply(ImHii, function(x) eigen(x)) eigenval_list <- lapply(eigenres_list, function(x) x$values) eigenvec_list <- lapply(eigenres_list, function(x) x$vectors) A.MBB <- Map(function (eigenvec, eigenval, k, W_E_j) { eigenval_InvSqrt <- ifelse(eigenval< 10^-10, 0, 1/sqrt(eigenval)) # Pseudo_Inverse eigenvec %*% diag(eigenval_InvSqrt, k, k) %*% t(eigenvec) # Pseudo_Inverse }, eigenvec_list, eigenval_list, k_list, Working_Matrx_E_j) }) } else { # Begin userweights V.big <- diag(c(1), dim(Xreg)[1], dim(Xreg)[1]) %*% diag(data.full$avg.var.eff.size) v.j <- by(data.full$avg.var.eff.size, data.full$study, function(x) diag(x, nrow = length(x))) v.j.sqrt_list <- lapply(v.j, function (x) sqrt(x)) Working_Matrx_E_j <- v.j Working_Matrx_E <- V.big InsideMatrx_list <- Map( function (ImH_j) { ImH_j %*% Working_Matrx_E %*% t(ImH_j) }, ImHj) eigenres_list <- lapply(InsideMatrx_list, function(x) eigen(x)) eigenval_list <- lapply(eigenres_list, function(x) x$values) eigenvec_list <- lapply(eigenres_list, function(x) x$vectors) A.MBB <- Map(function (eigenvec, eigenval, k, v.j.sqrt) { eigenval_InvSqrt <- ifelse(eigenval< 10^-10, 0, 1/sqrt(eigenval)) # Pseudo_Inverse v.j.sqrt %*% eigenvec %*% diag(eigenval_InvSqrt, k, k) %*% t(eigenvec) # Pseudo_Inverse }, eigenvec_list, eigenval_list, k_list, v.j.sqrt_list) } # End userweights sumXWA.MBBeeA.MBBWX.r <- Map(function(X,W,A,S) t(X) %*% W %*% A %*% S %*% A %*% W %*%X, X, W.r, A.MBB, sigma.hat.r) sumXWA.MBBeeA.MBBWX.r <- Reduce("+", sumXWA.MBBeeA.MBBWX.r) giTemp <- Map(function(I, A, W, X, Q) t(I) %*% A %*% W %*% X %*% Q, ImHj, A.MBB, W.r, X, Q.list) giTemp <- do.call(rbind,giTemp) gi_matrix <- lapply(X = 1:(p+1), FUN = function(i){ matrix(giTemp[,i], nrow = M) }) if (!user_weighting) { W.mat <- matrix(rep(1/sqrt(data.full$r.weights),times = N),nrow = M) B_matrix_half <- lapply(X = gi_matrix, FUN = function(gi_mat){ W.mat * gi_mat}) }else{ B_matrix_half <- gi_matrix # B_matrix_half <- lapply(X = gi_matrix, FUN = function(gi_mat){ solve(sqrt(V.big)) %*% gi_mat}) } B_mat <- lapply(X = B_matrix_half, FUN = tcrossprod) B_trace_square <- sapply(X = B_mat, FUN = function(B){ (sum(diag(B)))^2}) B_square_trace <- sapply(X = B_mat, FUN = function(B){sum(B * B)}) dfs <- B_trace_square/B_square_trace VR.MBB1 <- solve(sumXWX.r) %*% sumXWA.MBBeeA.MBBWX.r %*% solve(sumXWX.r) VR.r <- VR.MBB1 SE <- sqrt(diag(VR.r)) t <- b.r / SE prob <- 2 * (1 - stats::pt(abs(t), df = dfs)) CI.L <- b.r - stats::qt(.975, dfs) * SE CI.U <- b.r + stats::qt(.975, dfs) * SE } # End small = TRUE reg_table <- data.frame(cbind(b.r, SE, t, dfs, prob, CI.L, CI.U)) #names(X.full)[2] <- "intercept" labels <- c(colnames(X.full[2:length(X.full)])) sig <- ifelse(prob < .01, "***", ifelse(prob > .01 & prob < .05, "**", ifelse(prob > .05 & prob < .10, "*", ""))) reg_table <- cbind(labels, reg_table, sig) colnames(reg_table) <- c("labels", "b.r", "SE", "t", "dfs", "prob", "CI.L", "CI.U", "sig") if (!small) { # Begin small = FALSE mod_label_sm <- "" mod_notice <- "" } else { # Begin small = TRUE mod_label_sm <- "with Small-Sample Corrections" mod_notice <- "Note: If df < 4, do not trust the results" } # End small = TRUE if (!user_weighting) { switch(modelweights, HIER = { # Begin HIER mod_label <- c("RVE: Hierarchical Effects Model", mod_label_sm) }, # End HIER CORR = { # Begin CORR mod_label <- c("RVE: Correlated Effects Model", mod_label_sm) } # End CORR ) } else { # Begin userweights mod_label <- c("RVE: User Specified Weights", mod_label_sm) } # End userweights res <- list(data.full = data.full, X.full = X.full, reg_table = reg_table, mod_label = mod_label, mod_notice = mod_notice, modelweights = modelweights, mod_info = mod_info, user_weighting = user_weighting, ml = ml, cl = cl, N = N, M = M, k = k, k_list = k_list, p = p, X = X, y = y, Xreg = Xreg, b.r = b.r, VR.r = VR.r, dfs = dfs, small = small, data = data, labels = labels, study_orig_id = study_orig_id) class(res) <- "robu" res } robumeta/R/group.mean.R0000644000176200001440000000132314410441300014455 0ustar liggesusers#' Convenience function for calculating group-mean covariates. #' #' Creates a between-study (or between-cluster) version of the covariate in #' question. #' #' #' @param var The covariate cotaining the values to be group averaged. #' @param grp The group from which the average should be calculated. #' @return A column or vector containing the group.mean covariate. #' @keywords robumeta #' @examples #' #' #' # Load data #' data(corrdat) #' #' # Create a group mean covariate #' age_m <- group.mean(corrdat$age, corrdat$studynum) #' #' #' @export group.mean <- function(var, grp) { grp <- as.factor(grp) grp <- as.numeric(grp) var <- as.numeric(var) return(tapply(var, grp, mean, na.rm = TRUE)[grp]) } robumeta/R/forest.robu.R0000644000176200001440000002764014410441300014664 0ustar liggesusers#' Forest Plots for Robust Variance Estimation Meta-Analysis #' #' \code{forest.robu} In meta-analysis, forest plots provide a graphical #' depiction of effect size estimates and their corresponding confidence #' intervals. The \code{forest.robu()} function in \pkg{robumeta} can be used #' to produce forest plots for RVE meta-analyses. The function requires the #' \pkg{grid} package and is based on examples provided in (Murrell, 2011). As #' is the case with traditional forest plots, point estimates of individual #' effect sizes are plotted as boxes with areas proportional to the weight #' assigned to that effect size. Importantly, here the weight is not #' necessarily proportional to the effect size variance or confidence #' intervals, since the combined study weight is divided evenly across the #' study effect sizes. Two-sided 95\% confidence intervals are calculated for #' each effect size using a standard normal distribution and plotted along with #' each block. The overall effect is included at the bottom of the plot as a #' diamond with width equivalent to the confidence interval for the estimated #' effect. The RVE forest function is designed to provide users with forest #' plots which display each individual effect size used in the meta-analysis, #' while taking into account the study- or cluster-level properties inherent to #' the RVE analysis. As such, the user must specify columns from their original #' dataset that contain labels for the study or cluster and for the individual #' effect sizes. #' #' #' @param x An intercept-only RVE model previously fit using the \code{robu()} #' function.. #' @param study.lab A vector of labels to be used to identify study (or #' cluster) level groupings in the forest plot. For instance, labels for the #' study column might be author names with corresponding publication years. #' @param es.lab A vector of labels to be used to individual effect sizes in #' the forest plot. Labels for individual effect sizes might be ``Math Score'' #' or ``Reading Score'' for a meta-analysis that included such measures or as #' simple as ``Effect Size 1'' and ``Effect Size 2.'' #' @param ... Additional arguments to be passed to the forest function. Any #' number of additional columns can be specified to be plotted along side the #' confidence interval column and can be specified with the following syntax #' \code{``arg1'' = ``arg2''} where \code{``arg1''} is the title of the column #' on the forest plot, and \code{``arg2''} is the name of the column from the #' original \code{data.frame} that contains the information to be displayed #' alongside the estimates and confidence intervals. #' @references #' #' Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in #' meta-regression with dependent effect size estimates. \emph{Research #' Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. #' DOI: 10.1002/jrsm.5 #' #' Murrell P (2011). R Graphics. CRC/Taylor & Francis. ISBN 9781439831762. #' #' Tipton, E. (in press) Small sample adjustments for robust variance #' estimation with meta-regression. \emph{Psychological Methods}. #' @keywords forest.robu #' @examples #' #' #' # Load data #' data(oswald2013.ex1) #' #' # Run intercept only model. #' oswald_intercept <- robu(formula = effect.size ~ 1, data = oswald2013.ex1, #' studynum = Study, var.eff.size = var.eff.size, #' rho = 0.8, small = TRUE) #' #' # Create forest plot. #' forest.robu(oswald_intercept, es.lab = "Crit.Cat", study.lab = "Study", #' "Effect Size" = effect.size, # optional column #' "Weight" = r.weights) # optional column #' #' @export forest.robu <- function(x, es.lab, study.lab, ...){ if (paste(x$ml[3]) != 1){ stop("Requires an intercept-only model.") } ellipsis <- lapply(substitute(list(...))[-1L], deparse) n_user_cols <- length(ellipsis) # num. of additional columns reg_table <- x$reg_table N <- x$N # num. of studies M <- x$M # num. of clusters n_rows <- M + (2 * N) + 4 data <- as.data.frame(x$data) data.full <- as.data.frame(x$data.full) data$orig.study <- as.factor(x$study_orig_id) data <- data[order(data$orig.study),] data$r.weights <- data.full$r.weights data$effect.size <- data.full$effect.size data$var.eff.size <- data.full$var.eff.size data$study.num <- data.full$study add_col_titles <- as.list(names(ellipsis)) # user supplied titles add_col_values <- as.list(data[, unlist(ellipsis, use.names = FALSE)]) id_col_title <- "Studies" id_col_study_values <- unique(data[,study.lab]) id_col_es_values <- as.character(data[,es.lab]) data$obs_num <- seq(1, M) data$study_num <- data$study.num data$es_rows <- as.numeric(data$obs_num + (2 * data$study_num) + 1) data$study_rows <- as.numeric(stats::ave(data$es_rows, data$study_num, FUN = min)- 1) es_rows <- data$es_rows study_rows <- unique(data$study_rows) total_row <- max(n_rows) title_row <- min(n_rows) data_col_values <- data[, c("r.weights", "effect.size", "var.eff.size")] data_col_values <- cbind(data_col_values, es_rows) grand.ES <- reg_table$b.r grand.CI.L <- reg_table$CI.L grand.CI.U <- reg_table$CI.U is.numeric_df <- function(x) all(sapply(x, is.numeric)) specify_decimal <- function(x, k) format(round(x, k), nsmall = k) makeTextGrob <- function(values, rows, just = "left", bold = FALSE){ if (is.numeric_df(values)) values <- lapply(values, function (x) specify_decimal(x, 3)) if (!bold){ t <- lapply(values, function(x) grid::textGrob(paste(x), x = 0, just = just)) } else { t <- lapply(values, function(x) grid::textGrob(paste(x), x = 0, just = just, gp = grid::gpar(fontface = "bold"))) } return(list(values = t, rows = rows)) } addTitleToGrob <- function(col, title){ titleGrob <- makeTextGrob(values = title, rows = 1, bold = TRUE) values <- c(col$values, titleGrob$values) rows <- c(col$rows, titleGrob$rows) return(list(values = values, rows = rows)) } addGrobToGrob <- function(col1, col2){ values <- c(col1$values, col2$values) rows <- c(col1$rows, col2$rows) return(list(values = values, rows = rows)) } makeDataGrob <- function(x){ ES <- x$effect.size size <- x$r.weights / max(x$r.weights) CI.U <- x$effect.size + (1.96 * sqrt(x$var.eff.size)) CI.L <- x$effect.size - (1.96 * sqrt(x$var.eff.size)) type <- rep("n", M) rows <- x$es_rows return(list(type = type, rows = rows, size = size, CI.L = CI.L, CI.U = CI.U, ES = ES)) } addSummaryToDataGrob <- function(x){ type <- c(x$type, "s") rows <- c(x$rows, max(x$rows) + 2) size <- as.numeric(x$size) size <- x$size / max(x$size) ES <- c(x$ES, grand.ES) CI.L <- c(x$CI.L, grand.CI.L) CI.U <- c(x$CI.U, grand.CI.U) min <- floor(as.numeric(min(CI.L))) max <- ceiling(as.numeric(max(CI.U))) range <- c(min, max) return(list(type = type, rows = rows, size = size, CI.L = CI.L, CI.U = CI.U, ES = ES, min = min, max = max, range = range)) } if (n_user_cols > 1){ add_col <- lapply(add_col_values, function(x) makeTextGrob(x, es_rows)) add_col <- Map(function(x, y) addTitleToGrob(x, y), add_col, add_col_titles) } if (n_user_cols == 1){ add_col <- makeTextGrob(add_col_values, es_rows) add_col <- addTitleToGrob(add_col, add_col_titles) } id_col_study_grob <- makeTextGrob(id_col_study_values, study_rows, bold =TRUE) id_col_es_grob <- makeTextGrob(id_col_es_values, es_rows) id_col <- addGrobToGrob(id_col_study_grob, id_col_es_grob) id_col <- addTitleToGrob(id_col, id_col_title) data_col <- makeDataGrob(data_col_values) data_col <- addSummaryToDataGrob(data_col) drawLabelCol <- function(col, j) { for (i in 1:length(col$rows)) { grid::pushViewport(grid::viewport(layout.pos.row = col$rows[i], layout.pos.col = j)) grid::grid.draw(col$values[[i]]) grid::popViewport() } } drawNormalCI <- function(CI.L, ES, CI.U, size) { grid::grid.rect(x = grid::unit(ES, "native"), width = grid::unit(size, "snpc"), height = grid::unit(size, "snpc"), gp = grid::gpar(fill = "black")) if (grid::convertX(grid::unit(CI.U, "native"), "npc", valueOnly = TRUE) > 1) grid::grid.lines(x = grid::unit(c(CI.L, 1), c("native", "npc")), y = .5, arrow = grid::arrow(length = grid::unit(0.05, "inches"))) else { lineCol <- "black" grid::grid.lines(x = grid::unit(c(CI.L, CI.U), "native"), y = 0.5, gp = grid::gpar(col = lineCol)) } } drawSummaryCI <- function(CI.L, ES, CI.U) { grid::grid.polygon(x=grid::unit(c(CI.L, ES, CI.U, ES), "native"), y=grid::unit(0.5 + c(0, 0.25, 0, -0.25), "npc")) } drawDataCol <- function(col, j) { # j = col_place grid::pushViewport(grid::viewport(layout.pos.col = j, xscale = col$range)) grid::grid.lines(x = grid::unit(col$ES[length(col$ES)], "native"), y = grid::unit(0:(n_rows - 2), "lines"), gp = grid::gpar(lty = "dashed")) grid::grid.xaxis(gp=grid::gpar(cex = 1)) grid::grid.text("Effect Size", y = grid::unit(-3, "lines"), x = grid::unit(0.5, "npc"), just = "centre", gp = grid::gpar(fontface = "bold")) grid::popViewport() x = grid::unit(0.5, "npc") for (i in 1:length(col$rows)) { grid::pushViewport(grid::viewport(layout.pos.row = col$rows[i], layout.pos.col = j, xscale = col$range)) if (col$type[i] == "n") drawNormalCI(col$CI.L[i], col$ES[i], col$CI.U[i], col$size[i]) else drawSummaryCI(col$CI.L[i], col$ES[i], col$CI.U[i]) grid::popViewport() } } id_col_width <- max(grid::unit(rep(1, length(id_col$values)), "grobwidth", id_col$values)) data_col_width <- grid::unit(3, "inches") gap_col <- grid::unit(10, "mm") cols <- grid::unit.c(id_col_width, gap_col, data_col_width, gap_col) add_col_widths <- c() if (n_user_cols > 1){ for (i in 1:n_user_cols) { add_col_widths[[i]] <- max(grid::unit(rep(1, length(add_col[[i]]$values)), "grobwidth", add_col[[i]]$values)) cols <- grid::unit.c(cols, add_col_widths[[i]]) cols <- grid::unit.c(cols, gap_col) } } if (n_user_cols == 1){ add_col_widths <- max(grid::unit(rep(1, length(add_col[1]$values)), "grobwidth", add_col[1]$values)) cols <- grid::unit.c(cols, add_col_widths[1]) cols <- grid::unit.c(cols, gap_col) } grid::pushViewport(grid::viewport(layout = grid::grid.layout(n_rows, (4 + (2 * n_user_cols)), widths = cols, heights = grid::unit(c(1, rep(1, n_rows)), "lines")))) grid::pushViewport(grid::viewport(layout.pos.row = 1)) grid::grid.text("Forest Plot", y = grid::unit(+3, "lines"), just = "center", gp = grid::gpar(fontface = "bold")) grid::grid.text(paste(x$model.lab1), y = grid::unit(+2, "lines"), just = "center", gp = grid::gpar(fontface = "italic")) grid::popViewport() drawLabelCol(id_col, 1) if (n_user_cols > 1){ for (i in 1:n_user_cols) { drawLabelCol(add_col[[i]], ((i * 2) + 3)) } } if (n_user_cols == 1){ for (i in 1:n_user_cols) { drawLabelCol(add_col, 5) } } drawDataCol(data_col, 3) grid::popViewport() } robumeta/R/print.robu.R0000644000176200001440000000745314410441300014516 0ustar liggesusers#' Outputs Model Information #' #' Prints relevant information from robu function. #' #' #' @param x Object from robu class. #' @param digits Controls the number of digits to print when printing numeric #' values. #' @param ... Additional arguments to be passed to the fitting function. #' @references #' #' Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in #' meta-regression with dependent effect size estimates. \emph{Research #' Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. #' DOI: 10.1002/jrsm.5 #' #' Tipton, E. (in press) Small sample adjustments for robust variance #' estimation with meta-regression. \emph{Psychological Methods}. #' @keywords robu #' @examples #' #' #' # Load data #' data(hierdat) #' #' ### Small-Sample Corrections - Hierarchical Dependence Model #' HierMod <- robu(formula = effectsize ~ binge + followup + sreport #' + age, data = hierdat, studynum = studyid, #' var.eff.size = var, modelweights = "HIER", small = FALSE) #' #' print(HierMod) # Output results #' #' @export print.robu <- function(x, digits = 3,...){ user_weighting <- x$user_weighting modelweights <- x$modelweights mod_info <- x$mod_info output <- x$reg_table output <- format(output, trim = TRUE, digits = digits, scientific = FALSE) colnames(output) <- c("", "Estimate","StdErr", "t-value", "dfs", "P(|t|>)", "95% CI.L","95% CI.U", "Sig") if(!user_weighting){ switch(modelweights, HIER = { # Begin HIER cat(x$mod_label, "\n") cat("\nModel:",paste(x$ml[2]), paste(x$ml[[1]]), paste(x$ml[3]),"\n\n") cat(paste("Number of clusters ="), x$N, "\n") cat(paste("Number of outcomes ="), sum(x$k), paste("(min ="), min(x$k), paste(", mean ="), format(mean(x$k), digits = 3), paste(", median ="), stats::median(x$k), paste(", max ="), max(x$k),")\n") cat(paste("Omega.sq ="), mod_info$omega.sq, "\n") cat(paste("Tau.sq ="), mod_info$tau.sq, "\n\n") print(output) cat("---\n") cat("Signif. codes: < .01 *** < .05 ** < .10 *\n") cat("---\n") cat(x$mod_notice) }, # End HIER CORR = { # Begin CORR cat(x$mod_label, "\n") cat("\nModel:",paste(x$ml[2]), paste(x$ml[[1]]), paste(x$ml[3]),"\n\n") cat(paste("Number of studies ="), x$N, "\n") cat(paste("Number of outcomes ="), sum(x$k), paste("(min ="), min(x$k), paste(", mean ="), format(mean(x$k), digits = 3), paste(", median ="), stats::median(x$k), paste(", max ="), max(x$k),")\n") cat(paste("Rho ="), mod_info$rho, "\n") cat(paste("I.sq ="), mod_info$I.2, "\n") cat(paste("Tau.sq ="), mod_info$tau.sq, "\n\n") print(output) cat("---\n") cat("Signif. codes: < .01 *** < .05 ** < .10 *\n") cat("---\n") cat(x$mod_notice) } # End CORR ) } else { # Begin userweights cat(x$mod_label, "\n") cat("\nModel:",paste(x$ml[2]), paste(x$ml[[1]]), paste(x$ml[3]),"\n\n") cat(paste("Number of studies ="), x$N, "\n") cat(paste("Number of outcomes ="), sum(x$k), paste("(min ="), min(x$k), paste(", mean ="), format(mean(x$k), digits = 3), paste(", median ="), stats::median(x$k), paste(", max ="), max(x$k),")\n") print(output) cat("---\n") cat("Signif. codes: < .01 *** < .05 ** < .10 *\n") cat("---\n") cat(x$mod_notice) } } robumeta/R/group.center.R0000644000176200001440000000132314410441300015015 0ustar liggesusers#' Convenience function for calculating group-centered covariates. #' #' Creates a within-study (or within-cluster) version of the covariate in #' question. #' #' #' @param var The covariate to be group centered. #' @param grp A vector corresponding to the group identification. #' @return A column or vector containing the group.centered covariate. #' @keywords robumeta #' @examples #' #' #' # Load data #' data(corrdat) #' #' # Create a group centered covariate #' males_c <- group.center(corrdat$males, corrdat$studyid) #' #' #' @export group.center <- function(var, grp) { grp <- as.factor(grp) grp <- as.numeric(grp) var <- as.numeric(var) return(var - tapply(var, grp, mean, na.rm = TRUE)[grp]) } robumeta/R/robumeta-data.R0000644000176200001440000001351214410441300015132 0ustar liggesusers #' Data for Fitting Correlated Effects Model #' #' Fictional data used in TannerTanner-Smith and Tipton (2013). #' #' #' @name corrdat #' @docType data #' @format A dataframe containing 172 effect sizes from 39 studies used in #' Tanner- Smith and Tipton (2013). #' @references Tanner-Smith E.E., Tipton, E. (2013) Robust variance estimation #' with dependent effect sizes: practical considerations including a software #' tutorial in Stata and SPSS. \emph{Research Synthesis Methods}. ISSN #' 1759-2887. #' @source https://my.vanderbilt.edu/emilytannersmith/training-materials/ #' @keywords datasets NULL #' Data for Fitting Correlated Effects Model With Small-Sample Corrections #' #' Data used in Tipton (2013). #' #' #' @name corrdat.sm #' @docType data #' @format A dataframe containing 300 effect sizes from 28 studies used in #' Tipton (2013). #' @references Tipton, E. (in press) Small sample adjustments for robust #' variance estimation with meta-regression. \emph{Psychological Methods}. #' @source Elizabeth Tipton #' @keywords datasets NULL #' hedgesdat #' #' Data from a meta-analysis on the effectiveness of phonics reading #' instruction by Ehri, Nunes, Stahl and Willows (2001). Data reported in #' Hedges, Tipton, and Johnson (2010) with example. #' #' #' @name hedgesdat #' @docType data #' @format A dataframe containing 179 effect sizes from 66 different studies #' @references #' #' Ehri, L.C., Nunes, S.R., Stahl, S.A., Willows, D.M. (2001) Systematic #' phonics instruction helps children learn to read: Evidence from the National #' Reading Panel's meta-analysis. \emph{Review of Educational Research}. #' \bold{71}, 393--447. #' #' Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in #' meta-regression with dependent effect size estimates. \emph{Research #' Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. #' DOI: 10.1002/jrsm.5 #' @source Hedges, Tipton, and Johnson (2010) #' @keywords datasets NULL #' Data for Fitting Hierarchical Effects Model #' #' Fictional data used in TannerTanner-Smith and Tipton (2013). #' #' #' @name hierdat #' @docType data #' @format A dataframe containing 68 effect sizes from 15 studies used in #' Tanner- Smith and Tipton (2013). #' @references Tanner-Smith E.E., Tipton, E. (2013) Robust variance estimation #' with dependent effect sizes: practical considerations including a software #' tutorial in Stata and SPSS. \emph{Research Synthesis Methods}. ISSN #' 1759-2887. #' @source https://my.vanderbilt.edu/emilytannersmith/training-materials/ #' @keywords datasets NULL #' IAT Criterion-Related Correlations #' #' Data from a meta-analysis on IAT conducted by Oswald et al., (2013) #' examining the predictive validity of the Implicit Association Test (IAT) and #' various explicit measures of bias for a variety of criterion measures of #' discrimination. Included in the dataset are the study-level correlations #' between IAT scores and criterion measures of neurological activity or #' response latency from the original \code{oswald2013} dataset. #' #' #' @name oswald2013.ex1 #' @docType data #' @format A dataframe containing 32 effect sizes from 9 studies. #' #' \tabular{rlll}{ [,1] \tab Study \tab Factor \cr [,2] \tab Crit.Domain \tab #' Factor \cr [,3] \tab IAT.ID \tab Integer \cr [,4] \tab IAT.Focus \tab Factor #' \cr [,5] \tab Crit.ID \tab Integer \cr [,6] \tab Crit.Cat \tab Factor \cr #' [,7] \tab Scoring \tab Factor \cr [,8] \tab Target \tab Factor \cr [,9] \tab #' Type \tab Factor \cr [,10] \tab Sample.ID \tab Integer \cr [,11] \tab N \tab #' Integer \cr [,12] \tab R \tab Numeric \cr [,13] \tab effect.size \tab #' Numeric \cr [,14] \tab var.eff.size \tab Numeric } #' @references Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) #' Predicting ethnic and racial discrimination: a meta-analysis of IAT #' criterion studies. \emph{Journal of Personality and Social Psychology}, #' 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. #' @source Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) #' Predicting ethnic and racial discrimination: a meta-analysis of IAT #' criterion studies. \emph{Journal of Personality and Social Psychology}, #' 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. #' @keywords datasets NULL #' IAT Criterion-Related Correlations #' #' Data from a meta-analysis on IAT conducted by Oswald et al., (2013) #' examining the predictive validity of the Implicit Association Test (IAT) and #' various explicit measures of bias for a variety of criterion measures of #' discrimination. Included in the dataset are the study level correlations #' between IAT scores and some criterion measure of discrimination. #' #' #' @name oswald2013 #' @docType data #' @format A dataframe containing 308 effect sizes from 46 studies. #' #' \tabular{rlll}{ [,1] \tab Study \tab Factor \cr [,2] \tab Crit.Domain \tab #' Factor \cr [,3] \tab IAT.ID \tab Integer \cr [,4] \tab IAT.Focus \tab Factor #' \cr [,5] \tab Crit.ID \tab Integer \cr [,6] \tab Crit.Cat \tab Factor \cr #' [,7] \tab Scoring \tab Factor \cr [,8] \tab Target \tab Factor \cr [,9] \tab #' Type \tab Factor \cr [,10] \tab Sample.ID \tab Integer \cr [,11] \tab N \tab #' Integer \cr [,12] \tab R \tab Numeric } #' @references Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) #' Predicting ethnic and racial discrimination: a meta-analysis of IAT #' criterion studies. \emph{Journal of Personality and Social Psychology}, #' 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. #' @source Oswald FL, Mitchell G, Blanton H, Jaccard J, Tetlock PE (2013) #' Predicting ethnic and racial discrimination: a meta-analysis of IAT #' criterion studies. \emph{Journal of Personality and Social Psychology}, #' 105(2), 171-192. ISSN 1939-1315. doi:10.1037/a0032734. PMID: 23773046. #' @keywords datasets NULL robumeta/R/predict.robu.R0000644000176200001440000001242314410443110015006 0ustar liggesusers#' Prediction method for a robumeta object. #' #' \code{predict.robu} produces the predicted mean and confidence interval of a fitted robumeta model object given a prediction vector. #' #' @param object A fitted robumeta model object. #' @param pred.vector A prediction vector containing the new covariate values. #' @param level Confidence level. #' @param ... Additional arguments to predict. #' #' @return \code{prediction} the predicted value based on the prediction vector. #' @return \code{se} The standard error for the predicted mean. #' @return \code{t} The t-statistic calculated based on the predicted mean. #' @return \code{df} The small sample corrected degrees of freedom of the distribution of the t-statistic. #' @return \code{lower} The lower bound of the confidence interval for the predicted mean. #' @return \code{upper} The upper bound of the confidence interval for the predicted mean. #' #' @details #' \itemize{ #' \item{\code{intercept}} { #' If an intercept is included in the robumeta model, #' the first element should always be 1, representing the intercept, #' followed by the covariate values in appropriate order. #' If the robumeta model does not have an intercept, the prediction #' vector should begin with the first covariate value. #' } #' \item{\code{variable}} { #' For continuous variables, use the variable value as the #' corresponding element value in \code{ pred.vector}. #' For a categorical variable the original variable value should #' be transformed to match the coding system used in the robumeta #' model (e.g. dummy coding, deviation coding, etc.). #' } #' \item{\code{NA}} { #' If the vector contains NAs, \code{predict.robu} will remove the #' corresponding covariates from the original data, and refit a new #' robumeta model. The prediction and confidence interval will be #' estimated based on the new model. #' } #' } #' #' \preformatted{ #' robu_mod <- robu(LOR1 ~ study_design + duration + service_hrs, #' data = dropoutPrevention, #' studynum = studyID, #' var.eff.size = varLOR, #' modelweights = "HIER", #' small = TRUE) #' } #' #' In this robumeta model, the first covariate is a categorical variable #' that contains three levels: "Matched" (33 percent, dummy code: 00), #' "Randomized"(24 percent, 01) and "non-match non-randomized"(43 percent, #' 10). The corresponding prediction vector begins with 1 (intercept), #' and followed by 0, 0, the dummy code for "Matched". The last two elements #' are 38 and 5, the values for duration and sevice_hrs. #' #' \preformatted{ #' predict(object = robu_mod, pred.vector = c(1,0,0,38,5),level = 0.95) #' } #' #' If we do not know the value of duration, the prediction vector should #' be c(1,0,0,NA,5). predict.robu() will refit a new model without the #' covariate duration, and the prediction will be based on it. #' #' \preformatted{ #' predict(object = robu_mod, pred.vector = c(1,0,0,NA,5),level = 0.95) #' } #' #' @export predict.robu <- function(object, pred.vector, level = 0.95, ...){ if (!requireNamespace("clubSandwich", quietly = TRUE)) { stop("clubSandwich needed for this function to work. Please install it.", call. = FALSE) } if (!inherits(object, "robu")) warning("calling predict.robu() ...") original_data <- object$Xreg combined_data <- rbind(object$Xreg, pred.vector) #remove columns with NAs. combined_data_noNA <- combined_data[,!colSums(is.na(combined_data))] #### #Check if combined_data_noNA is NULL #### #construct new formula and new data frame. original_data_noNA <- combined_data_noNA[- nrow(combined_data_noNA),] newformula <- stats::as.formula(paste0("effect.size ~",paste0(colnames(original_data_noNA),collapse = " + "), "-1")) data.full <- as.data.frame(cbind(original_data_noNA, effect.size = object$data.full$effect.size, var.eff.size=object$data.full$var.eff.size, study = object$data.full$study)) #Build new robu model. new_modelcall <- object$cl new_modelcall$formula <- newformula new_modelcall$data <- quote(data.full) new_modelcall$studynum <- quote(study) new_modelcall$var.eff.size <- quote(var.eff.size) new_robu_model <- eval(new_modelcall) #Calculate outcome prediction test_vec <- t(combined_data_noNA[nrow(combined_data_noNA),]) coef_est <- new_robu_model$b.r #estimated coefficients g_hat <- test_vec %*% coef_est #calculate F-test with small sample correction Test_result <- clubSandwich::Wald_test(new_robu_model, constraints = test_vec, vcov="CR2") #Retrieve t statistics and confidence interval. df <- if (utils::packageVersion("clubSandwich") > '0.4.2') Test_result$df_denom else Test_result$df Fvalue <- Test_result$Fstat tcrit <- stats::qt(1-(1-level)/2,df) SE_Yhat <- g_hat/sqrt(Fvalue) LB <- g_hat - tcrit*SE_Yhat UB <- g_hat + tcrit*SE_Yhat res <- c(prediction = g_hat, se = SE_Yhat, df = df, t = sqrt(Fvalue), lower = LB, upper = UB ) return(res) } robumeta/R/sensitivity.R0000644000176200001440000001500514410441300014776 0ustar liggesusers#' Sensitivity Analysis for Correlated Effects RVE #' #' \code{sensitivity} is used to assess the impact of differing rho values on #' the correlated effects meta-regression model. #' #' #' @aliases sensitivity #' @param x A dataframe containing values of rho, tau squared, coefficient #' estimates, and standard errors. #' @references #' #' Hedges, L.V., Tipton, E., Johnson, M.C. (2010) Robust variance estimation in #' meta-regression with dependent effect size estimates. \emph{Research #' Synthesis Methods}. \bold{1}(1): 39--65. Erratum in \bold{1}(2): 164--165. #' DOI: 10.1002/jrsm.5 #' #' Tipton, E. (in press) Small sample adjustments for robust variance #' estimation with meta-regression. \emph{Psychological Methods}. #' @keywords robu #' @examples #' #' #' # Correlated Effects Model #' CorrMod <- robu(formula = effectsize ~ followup + males + binge + college, #' data = corrdat, studynum = studyid, var.eff.size = var, #' rho = .8, modelweights = "CORR", small = FALSE) #' #' sensitivity(CorrMod) # Output sensitivity #' #' @export sensitivity <- function(x){ modelweights <- x$modelweights user_weighting <- x$user_weighting if(modelweights == "HIER") stop("Sensitivity analysis is not available for hierarchical effects.") if(user_weighting == TRUE) stop("Sensitivity analysis is not available for user specified weights.") mod_info <- x$mod_info p <- x$p N <- x$N Xreg <- x$Xreg y <- x$y X <- x$X data.full <- x$data.full X.full <- x$X.full k <- data.full$k k_list <- x$k_list ml <- x$ml term1 <- mod_info$term1 term2 <- mod_info$term2 small <- x$small labels <- x$labels mod_label <- x$mod_label rho.test <- seq(0, 1, .2) rho_labels <- c(paste("Rho = ", seq(0, 1, .2), sep="")) var_labels <- rep("", 2 * (p + 1)) var_labels[seq(1, length(var_labels), by = 2)] <- labels var_labels <- c(var_labels, "Tau.sq") col2_labels <- rep("", 2 * (p + 1)) col2_labels[seq(1, length(col2_labels), by = 2)] <- "Coefficient" col2_labels[seq(2, length(col2_labels), by = 2)] <- "Std. Error" col2_labels <- c(col2_labels, "Estimate") sen <- data.frame(cbind(var_labels, col2_labels)) for (i in (1: length(rho.test))){ tau.sq1 <- term1 + rho.test[i] * term2 tau.sq <- ifelse(tau.sq1 < 0, 0, tau.sq1) data.full$r.weights <- 1 / (as.vector(data.full$k) * (as.vector(data.full$avg.var.eff.size) + as.vector(tau.sq))) W.r.big <- diag(data.full$r.weights) # W W.r <- by(data.full$r.weights, data.full$study, function(x) diag(x, nrow = length(x))) sumXWX.r <- Reduce("+", Map(function(X, W) t(X) %*% W %*% X, X, W.r)) sumXWy.r <- Reduce("+", Map(function(X, W, y) t(X) %*% W %*% y, X, W.r, y)) b.r <- solve(sumXWX.r) %*% sumXWy.r data.full$pred.r <- Xreg %*% b.r data.full$e.r <- cbind(data.full$effect.size) - data.full$pred.r data.full$e.r <- as.numeric(data.full$e.r) sigma.hat.r <- by(data.full$e.r, data.full$study, function(x) tcrossprod(x)) if (!small) { # Begin small = FALSE sumXWeeWX.r <- Reduce("+", Map(function(X, W, V) t(X) %*% W %*% V %*% W %*% X, X, W.r, sigma.hat.r)) VR.r <- solve(sumXWX.r) %*% sumXWeeWX.r %*% solve(sumXWX.r) SE <- sqrt(diag(VR.r)) * sqrt(N / (N - (p + 1))) } else { Q <- solve(sumXWX.r) # Q.list <- rep(list(Q), N) H <- Xreg %*% Q %*% t(Xreg) %*% W.r.big ImH <- diag(c(1), dim(Xreg)[1], dim(Xreg)[1]) - H data.full$ImH <- cbind(ImH) ImHj <- by(data.full$ImH, data.full$study, function(x) as.matrix(x)) dfS <- c(rep(0, p + 1)) diag_one <- by(rep(1, nrow(X.full)), X.full$study, function(x) diag(x, nrow = length(x))) ImHii <- Map(function(X, Q, W, D) D - X %*% Q %*% t(X) %*% W, X, Q.list, W.r, diag_one) eigenvec <- lapply(ImHii, function(x) eigen(x)$vectors) eigenval <- lapply(ImHii, function(x) eigen(x)$values) I <- ImHii A.MBB <- Map(function (eigenvec, eigenval, k_list) eigenvec %*% diag(1/sqrt(eigenval), k_list, k_list) %*% t(eigenvec), eigenvec, eigenval, k_list) A.MBB1 <- Map(function(K, A, I) if (K > 1) A else matrix(sqrt(solve(I))), k_list, A.MBB, I) A.MBB2 <- A.MBB sumXWA.MBBeeA.MBBWX.r <- Reduce("+", Map(function(X,W,A,S) t(X) %*% W %*% A %*% S %*% A %*% W %*% X, X, W.r, A.MBB2, sigma.hat.r)) data.full$ImH <- ImH ImH <- lapply(split(data.full$ImH, data.full$study), matrix, ncol=nrow(data.full)) giTemp <- Map(function(I, A, W, X, Q) t(I) %*% A %*% W %*% X %*% Q, ImHj, A.MBB2, W.r, X, Q.list) dfs <- c(rep(0, p + 1)) for (i in 1:(p + 1)) { L <- c(rep(0,p + 1)) L[i] <- 1 Ll <- rep(list(L), N) gi <- Map(function(G, L) G %*% cbind(L), giTemp, Ll) G <- Reduce("+", lapply(gi, function(x) tcrossprod(x))) B <- solve(sqrt(W.r.big) )%*% G %*% solve(sqrt(W.r.big)) e.val2 <- eigen(B) dfs[i] <- sum(e.val2$values)^2 / sum(e.val2$values^2) } VR.MBB1 <- solve(sumXWX.r) %*% sumXWA.MBBeeA.MBBWX.r %*% solve(sumXWX.r) VR.r <- VR.MBB1 SE <- sqrt(diag(VR.r)) } vals <- c() temp_vals <- c() for (i in 1:(p + 1)) { temp_vals <- c(b.r[i], SE[i]) vals <- c(vals, temp_vals) } vals <- c(vals, tau.sq) vals <- format(vals, digits=3, justify="centre") sen <- cbind(sen, vals) } colnames(sen) <- c(" ", " ", rho_labels) format(sen[,1], justify = "left") 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