fitVsDatCorrelation=0.935155021474393 cont.fitVsDatCorrelation=0.242366205919887 fstatistic=9059.55170769542,57,807 cont.fstatistic=1195.51519743863,57,807 residuals=-0.773512677772756,-0.093022013881663,-0.0028167850384674,0.100847270437514,0.932833366454236 cont.residuals=-0.898670719316519,-0.409798426933302,-0.112286639655791,0.403849477690830,1.53832904060108 predictedValues: Include Exclude Both Lung 67.7106036857163 102.188684877447 114.643866054805 cerebhem 66.0398049144421 111.396396451917 154.425622251731 cortex 79.0374218035254 126.762715776569 156.441628389956 heart 69.3195564301614 136.931608622871 119.105120586287 kidney 63.4260432479182 87.902067284438 88.3078095582802 liver 64.036839337316 99.9862561713508 75.7999694430412 stomach 66.17501309383 117.884974703164 103.463222420792 testicle 66.5618945370824 108.082395811756 102.808075659785 diffExp=-34.4780811917309,-45.3565915374752,-47.7252939730437,-67.6120521927101,-24.4760240365197,-35.9494168340347,-51.7099616093338,-41.5205012746733 diffExpScore=0.997141451738826 diffExp1.5=-1,-1,-1,-1,0,-1,-1,-1 diffExp1.5Score=0.875 diffExp1.4=-1,-1,-1,-1,0,-1,-1,-1 diffExp1.4Score=0.875 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 85.3216201260147 62.6050638906276 86.5732775929921 cerebhem 83.9456817284 67.7747940400305 86.1809432028528 cortex 81.4133582747338 72.9491928228485 85.140513600767 heart 84.561812121264 61.3050061975433 95.1999832641333 kidney 84.6690093985104 85.4324325203031 77.4053728869684 liver 87.3477379504136 93.027398705101 94.2539338451366 stomach 82.0104257614802 70.467734943743 110.561400629524 testicle 87.9206336748916 98.408340688359 77.6211322184951 cont.diffExp=22.7165562353871,16.1708876883694,8.46416545188524,23.2568059237207,-0.763423121792684,-5.67966075468742,11.5426908177373,-10.4877070134674 cont.diffExpScore=1.49624622998503 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=1,0,0,1,0,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=1,1,0,1,0,0,0,0 cont.diffExp1.2Score=0.75 tran.correlation=0.65400872064862 cont.tran.correlation=0.633254994811256 tran.covariance=0.00649934341628092 cont.tran.covariance=0.00282621726820260 tran.mean=89.590142296844 cont.tran.mean=80.5725151777665 weightedLogRatios: wLogRatio Lung -1.81960016886573 cerebhem -2.3275024916755 cortex -2.17591009665044 heart -3.11724600727298 kidney -1.40755821225994 liver -1.95261561881651 stomach -2.58736168635483 testicle -2.15259021902758 cont.weightedLogRatios: wLogRatio Lung 1.32861234856298 cerebhem 0.925072303401275 cortex 0.476939117595305 heart 1.37546870147190 kidney -0.039883179723418 liver -0.283574056864852 stomach 0.65697571833457 testicle -0.51080372444565 varWeightedLogRatios=0.2634138585205 cont.varWeightedLogRatios=0.512208040480236 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.6746000644306 0.0862520450088413 42.6030485892124 1.07614231740604e-208 *** df.mm.trans1 0.406154221432076 0.0748985466520179 5.4227250005142 7.76016356669548e-08 *** df.mm.trans2 0.946604743256715 0.0665740485262253 14.2188249657646 4.15307083605534e-41 *** df.mm.exp2 -0.236593171890636 0.0865259100144885 -2.73436213327336 0.00638712111181258 ** df.mm.exp3 0.0593221893225069 0.0865259100144885 0.685600293745233 0.49316209315233 df.mm.exp4 0.277968945581279 0.0865259100144885 3.21255154132137 0.0013678045864497 ** df.mm.exp5 0.045036084740601 0.0865259100144885 0.520492471365627 0.602863139558502 df.mm.exp6 0.336160136157476 0.0865259100144885 3.88508062037356 0.000110680226605319 *** df.mm.exp7 0.222562848769796 0.0865259100144885 2.57221043653315 0.0102829295335252 * df.mm.exp8 0.147928968217715 0.0865259100144885 1.70964937777534 0.087715149284054 . df.mm.trans1:exp2 0.211608041674748 0.0804815876071519 2.62927270654318 0.00871961789119884 ** df.mm.trans2:exp2 0.322867194876896 0.0615258226296393 5.24766969505514 1.97045403295504e-07 *** df.mm.trans1:exp3 0.0953564494905752 0.0804815876071519 1.18482316671026 0.236436175403864 df.mm.trans2:exp3 0.156173813670201 0.0615258226296393 2.53834580335974 0.0113246934909361 * df.mm.trans1:exp4 -0.254484674838802 0.080481587607152 -3.16202354358362 0.00162534701122309 ** df.mm.trans2:exp4 0.0146916923138252 0.0615258226296393 0.238789043134998 0.811329791867083 df.mm.trans1:exp5 -0.110404326074062 0.0804815876071519 -1.37179607605369 0.170508167595262 df.mm.trans2:exp5 -0.195633717887070 0.0615258226296393 -3.17970096986280 0.00153054957595701 ** df.mm.trans1:exp6 -0.391944398966807 0.080481587607152 -4.86998841126212 1.34306912140255e-06 *** df.mm.trans2:exp6 -0.357948354056715 0.0615258226296393 -5.81785563780951 8.59151446280364e-09 *** df.mm.trans1:exp7 -0.245502698001932 0.080481587607152 -3.05042066516238 0.00235996933490908 ** df.mm.trans2:exp7 -0.079674446528003 0.0615258226296393 -1.29497572113113 0.195698962328806 df.mm.trans1:exp8 -0.165039503723849 0.0804815876071519 -2.05064920599532 0.0406236103503949 * df.mm.trans2:exp8 -0.0918560639446217 0.0615258226296393 -1.49296766818639 0.135836489970396 df.mm.trans1:probe2 -0.194023904268650 0.0526875667514277 -3.68253681526092 0.000246305440011098 *** df.mm.trans1:probe3 -0.0804691588205297 0.0526875667514277 -1.52728933564481 0.127080906262125 df.mm.trans1:probe4 -0.0461002637216314 0.0526875667514277 -0.874974240870256 0.381848251430367 df.mm.trans1:probe5 0.0694310723173514 0.0526875667514277 1.31778855237170 0.187948403260497 df.mm.trans1:probe6 0.312058084303687 0.0526875667514277 5.92280311170816 4.68186308137325e-09 *** df.mm.trans1:probe7 -0.0336963038512178 0.0526875667514277 -0.639549440766398 0.522647064648709 df.mm.trans1:probe8 -0.164720314762082 0.0526875667514277 -3.12636025761465 0.00183327862680402 ** df.mm.trans1:probe9 -0.116227895990203 0.0526875667514277 -2.20598336868638 0.0276662516488117 * df.mm.trans1:probe10 -0.167798968645758 0.0526875667514277 -3.18479252301419 0.00150420743757725 ** df.mm.trans1:probe11 -0.149175528063573 0.0526875667514277 -2.83132316144645 0.00475090404655762 ** df.mm.trans1:probe12 -0.0696020670835008 0.0526875667514277 -1.32103400052375 0.186864477271224 df.mm.trans1:probe13 -0.082413988691844 0.0526875667514277 -1.56420183685197 0.118162206725977 df.mm.trans1:probe14 -0.166728867196692 0.0526875667514276 -3.16448220095824 0.00161184566964473 ** df.mm.trans1:probe15 -0.132517392257933 0.0526875667514277 -2.51515491089446 0.0120912592499437 * df.mm.trans1:probe16 -0.0248478358458668 0.0526875667514277 -0.471607200292534 0.637334662024543 df.mm.trans1:probe17 0.75950966555698 0.0526875667514277 14.4153490545547 4.35747585624583e-42 *** df.mm.trans1:probe18 0.768025825327482 0.0526875667514277 14.5769841479095 6.73105389505069e-43 *** df.mm.trans1:probe19 0.779136836826435 0.0526875667514277 14.7878690337379 5.78098951627015e-44 *** df.mm.trans1:probe20 0.92751586842683 0.0526875667514277 17.6040748437429 5.9503140306887e-59 *** df.mm.trans1:probe21 0.924837010402405 0.0526875667514277 17.5532306277428 1.13795186722474e-58 *** df.mm.trans1:probe22 0.922463406518026 0.0526875667514277 17.5081800772861 2.01986013459296e-58 *** df.mm.trans2:probe2 -0.0244843993069755 0.0526875667514277 -0.464709243880808 0.642265039477811 df.mm.trans2:probe3 -0.186932903200971 0.0526875667514277 -3.54795096313507 0.000410658482828804 *** df.mm.trans2:probe4 -0.0472050463107064 0.0526875667514276 -0.895942804370014 0.370550478121309 df.mm.trans2:probe5 -0.0247944054404643 0.0526875667514277 -0.470593101356166 0.638058497893951 df.mm.trans2:probe6 0.362042832811344 0.0526875667514277 6.87150413530027 1.27041993674743e-11 *** df.mm.trans3:probe2 -0.905121448298824 0.0526875667514277 -17.1790330073328 1.31001555729831e-56 *** df.mm.trans3:probe3 -0.0183861344186434 0.0526875667514277 -0.34896533570029 0.727206362245813 df.mm.trans3:probe4 -0.693177272946693 0.0526875667514277 -13.1563728539031 5.88882529795455e-36 *** df.mm.trans3:probe5 0.207616406775059 0.0526875667514277 3.94051992863066 8.83481951830389e-05 *** df.mm.trans3:probe6 0.224280989665369 0.0526875667514276 4.25681054362396 2.31733265148332e-05 *** df.mm.trans3:probe7 -0.419590453523537 0.0526875667514277 -7.9637470354838 5.66441396848166e-15 *** df.mm.trans3:probe8 -0.679850269102381 0.0526875667514277 -12.9034288546635 9.10865888574117e-35 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1349287313811 0.236226072055208 17.504116693837 2.12708083893914e-58 *** df.mm.trans1 0.338525484919664 0.205131246180149 1.65028727326292 0.09927330553868 . df.mm.trans2 -0.0635988119900942 0.182332210007901 -0.348807333533325 0.727324940835682 df.mm.exp2 0.0676283686166718 0.236976130266011 0.285380508748951 0.775425866753918 df.mm.exp3 0.122716728636649 0.236976130266011 0.517844259246267 0.604708862464282 df.mm.exp4 -0.124918342661316 0.236976130266011 -0.527134705597109 0.598244921541439 df.mm.exp5 0.415136398521253 0.236976130266011 1.75180680879232 0.0801868615984053 . df.mm.exp6 0.334515780708501 0.236976130266011 1.41160116140389 0.158452864860408 df.mm.exp7 -0.165852555915115 0.236976130266011 -0.699870302249184 0.484209999240645 df.mm.exp8 0.591437513735544 0.236976130266011 2.49576829983527 0.0127670744345488 * df.mm.trans1:exp2 -0.0838863072711954 0.220422012153518 -0.380571370579681 0.703621480306636 df.mm.trans2:exp2 0.0117158202293157 0.168506189137606 0.06952753658056 0.94458693891631 df.mm.trans1:exp3 -0.169605244764556 0.220422012153518 -0.769456929947771 0.44184725584848 df.mm.trans2:exp3 0.0302003138151146 0.168506189137606 0.179223766021154 0.857806990451754 df.mm.trans1:exp4 0.115973231757563 0.220422012153518 0.526141788764682 0.598934254103584 df.mm.trans2:exp4 0.10393368178333 0.168506189137606 0.61679444722624 0.53754427889098 df.mm.trans1:exp5 -0.422814632734091 0.220422012153518 -1.91820512208922 0.0554375929462906 . df.mm.trans2:exp5 -0.104256765577252 0.168506189137606 -0.618711788040699 0.536280840034874 df.mm.trans1:exp6 -0.311046523094439 0.220422012153518 -1.41114092941772 0.158588455450490 df.mm.trans2:exp6 0.0615321111321652 0.168506189137606 0.365162321022622 0.71508592315254 df.mm.trans1:exp7 0.126271056288411 0.220422012153518 0.572860464591287 0.56689874795115 df.mm.trans2:exp7 0.284161332946492 0.168506189137606 1.68635546504728 0.0921138677601478 . df.mm.trans1:exp8 -0.561430878504528 0.220422012153518 -2.54707264950248 0.0110476204597657 * df.mm.trans2:exp8 -0.139158117832378 0.168506189137606 -0.82583386725777 0.409142409628987 df.mm.trans1:probe2 0.0217719417240927 0.144300079361139 0.150879624047913 0.88010837930641 df.mm.trans1:probe3 0.0209367877900973 0.144300079361139 0.145092004680738 0.884674459974628 df.mm.trans1:probe4 0.0119309138635485 0.144300079361139 0.0826812702832203 0.934125481159353 df.mm.trans1:probe5 0.0268594729445653 0.144300079361139 0.186136231272225 0.852384707425389 df.mm.trans1:probe6 -0.120859221966194 0.144300079361139 -0.837554785148248 0.402528769324741 df.mm.trans1:probe7 0.0489941376561036 0.144300079361139 0.339529526754356 0.734299198416151 df.mm.trans1:probe8 0.0832446780582416 0.144300079361139 0.576885878558013 0.5641774316755 df.mm.trans1:probe9 -0.186844506928586 0.144300079361139 -1.29483301572532 0.195748173693121 df.mm.trans1:probe10 -0.0460194433564770 0.144300079361139 -0.318914885980793 0.749873581570663 df.mm.trans1:probe11 -0.0427365386770215 0.144300079361139 -0.296164346313802 0.767180772063709 df.mm.trans1:probe12 -0.104894082602352 0.144300079361139 -0.726916319566495 0.467488025223193 df.mm.trans1:probe13 0.115743377465220 0.144300079361139 0.802101966801764 0.422730107730683 df.mm.trans1:probe14 -0.0383036171952691 0.144300079361139 -0.265444186620347 0.790735092687668 df.mm.trans1:probe15 -0.109406423737607 0.144300079361139 -0.758186857706409 0.448560481153383 df.mm.trans1:probe16 -0.178620051368727 0.144300079361139 -1.23783751304596 0.216136321060761 df.mm.trans1:probe17 -0.0480022736964502 0.144300079361139 -0.332655906420642 0.739480430432947 df.mm.trans1:probe18 -0.0196060567108390 0.144300079361139 -0.13587003415134 0.891957909265294 df.mm.trans1:probe19 0.0498988787397183 0.144300079361139 0.345799385285414 0.729583612078561 df.mm.trans1:probe20 -0.0223519601183278 0.144300079361139 -0.154899153328861 0.876939553517998 df.mm.trans1:probe21 -0.182932787867342 0.144300079361139 -1.26772479043145 0.205261899957555 df.mm.trans1:probe22 -0.0895932457913117 0.144300079361139 -0.620881472747407 0.534852926354395 df.mm.trans2:probe2 0.214309616079253 0.144300079361139 1.48516630779462 0.13789028271653 df.mm.trans2:probe3 0.157969454293914 0.144300079361139 1.09472881091468 0.273962086717929 df.mm.trans2:probe4 0.16753293355862 0.144300079361139 1.16100375204463 0.245983679828473 df.mm.trans2:probe5 0.163751161605256 0.144300079361139 1.13479606061364 0.25679772117536 df.mm.trans2:probe6 0.213664310171886 0.144300079361139 1.48069433584406 0.139078344129205 df.mm.trans3:probe2 0.0144486368513951 0.144300079361139 0.100129098440996 0.920266702973208 df.mm.trans3:probe3 0.0169104759548649 0.144300079361139 0.117189651105757 0.906738926413638 df.mm.trans3:probe4 -0.0308307837188289 0.144300079361139 -0.213657427323161 0.830868179751315 df.mm.trans3:probe5 0.145183784226222 0.144300079361139 1.00612407747102 0.314657464359483 df.mm.trans3:probe6 0.00982281208100057 0.144300079361139 0.0680721183556461 0.945745087214873 df.mm.trans3:probe7 0.175966940749961 0.144300079361139 1.21945144818368 0.223029181231557 df.mm.trans3:probe8 0.0775844603364704 0.144300079361139 0.53766055209367 0.590959647661599