fitVsDatCorrelation=0.861142006258904 cont.fitVsDatCorrelation=0.288510419603745 fstatistic=9074.35509227636,53,715 cont.fstatistic=2548.36629674066,53,715 residuals=-0.764008364438976,-0.0815923983436765,-0.00367554141978199,0.0806663175036999,2.45566718291967 cont.residuals=-0.610096951176561,-0.212598873921338,-0.0593648728777567,0.152228728516654,2.29537514731046 predictedValues: Include Exclude Both Lung 59.6960202430139 67.898778514306 57.5312803903915 cerebhem 62.3880949250651 61.8086477138 57.1722154423192 cortex 56.3557674762504 68.5718855369167 60.1814972761599 heart 59.2959344479593 69.4141417027197 56.5656624018265 kidney 59.8518833838703 73.7751245017273 63.9518001315404 liver 59.0950550284358 72.8530369400922 60.9168117218468 stomach 59.3565372666216 70.2394594679189 63.2675279394102 testicle 59.0479218764517 62.4865917948679 54.7090513926477 diffExp=-8.20275827129215,0.579447211265077,-12.2161180606662,-10.1182072547604,-13.9232411178571,-13.7579819116563,-10.8829222012974,-3.4386699184162 diffExpScore=1.00217781577841 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,-1,0,-1,-1,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 67.536414085444 61.6214019168214 68.4400767504263 cerebhem 59.5298022294375 64.0737800544203 52.1111614819124 cortex 60.8334886930863 58.4262498899893 55.9403758072789 heart 60.1045486581174 69.2105723058054 65.5596131676245 kidney 54.2555531065353 62.6380444869663 75.4076798954323 liver 64.0481712435829 58.8652936939199 57.815039270974 stomach 63.8504729257016 71.5895513491008 63.333626895514 testicle 67.1415338011792 59.0646928264638 55.1297941120224 cont.diffExp=5.91501216862265,-4.54397782498277,2.40723880309699,-9.10602364768797,-8.38249138043102,5.18287754966293,-7.7390784233992,8.07684097471543 cont.diffExpScore=5.58822264552438 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=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=-0.350339895380235 cont.tran.correlation=-0.178318318354932 tran.covariance=-0.00063004958768678 cont.tran.covariance=-0.000983653112761742 tran.mean=63.883430051251 cont.tran.mean=62.6743482041607 weightedLogRatios: wLogRatio Lung -0.53479253947207 cerebhem 0.0385257479941625 cortex -0.810255418307184 heart -0.65561475745042 kidney -0.877681523511168 liver -0.875655769079017 stomach -0.70163002648786 testicle -0.232447323324253 cont.weightedLogRatios: wLogRatio Lung 0.381922696699421 cerebhem -0.303299167559931 cortex 0.165051770842599 heart -0.587777133924335 kidney -0.58408577358494 liver 0.347445535733192 stomach -0.482074278552609 testicle 0.53097012337825 varWeightedLogRatios=0.107850701443563 cont.varWeightedLogRatios=0.221631614220615 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.26916435627391 0.0841476096902574 50.7342320475705 3.92238510913919e-239 *** df.mm.trans1 -0.354400017729957 0.0747247059377034 -4.74274222002845 2.54585440044143e-06 *** df.mm.trans2 0.0189974096198833 0.0679718372842588 0.279489423545165 0.779950081440643 df.mm.exp2 -0.0436048741527542 0.0916361374711246 -0.475848015380336 0.634328012772062 df.mm.exp3 -0.0927523395771322 0.0916361374711246 -1.0121808070136 0.311794007849683 df.mm.exp4 0.0322746386915524 0.0916361374711246 0.352204267685578 0.724788861193016 df.mm.exp5 -0.0201897621784269 0.0916361374711246 -0.220325329456285 0.825680688135047 df.mm.exp6 0.00312768400625198 0.0916361374711246 0.0341315565296229 0.972781770581523 df.mm.exp7 -0.0668542881214266 0.0916361374711246 -0.729562484478277 0.465896591172727 df.mm.exp8 -0.0436824110642193 0.0916361374711246 -0.476694154399339 0.633725574649489 df.mm.trans1:exp2 0.0877139892883703 0.0870128116612039 1.00805832628299 0.313767479009551 df.mm.trans2:exp2 -0.0503698854517757 0.0732713445580985 -0.687443171072647 0.492026424478891 df.mm.trans1:exp3 0.0351715701988587 0.087012811661204 0.404211397464134 0.686178149434691 df.mm.trans2:exp3 0.102616913558776 0.0732713445580985 1.40050539781496 0.161795834549867 df.mm.trans1:exp4 -0.0389992496749126 0.0870128116612039 -0.448201235316489 0.654143751598743 df.mm.trans2:exp4 -0.0102020659275191 0.0732713445580985 -0.139236777884288 0.889302255490176 df.mm.trans1:exp5 0.0227973065090421 0.0870128116612039 0.261999423691841 0.793397347307313 df.mm.trans2:exp5 0.103193325670149 0.0732713445580985 1.40837221280039 0.159455553549331 df.mm.trans1:exp6 -0.0132457899369207 0.0870128116612039 -0.152228041871523 0.879050022089053 df.mm.trans2:exp6 0.0672984904217283 0.0732713445580985 0.918483082678602 0.358675742165599 df.mm.trans1:exp7 0.0611511952414222 0.0870128116612039 0.702783809348934 0.482419234419947 df.mm.trans2:exp7 0.100746496993575 0.0732713445580985 1.37497813915084 0.169568918270374 df.mm.trans1:exp8 0.0327664048991136 0.0870128116612039 0.376569889807652 0.706604988409455 df.mm.trans2:exp8 -0.0393836314134749 0.0732713445580985 -0.5375038720935 0.591086852789264 df.mm.trans1:probe2 0.114386101190655 0.0476588797387899 2.40010050210130 0.0166452898426711 * df.mm.trans1:probe3 -0.0327487684062562 0.0476588797387899 -0.68714935360937 0.492211428467136 df.mm.trans1:probe4 0.0738228135301216 0.0476588797387899 1.54898339899577 0.121828198438014 df.mm.trans1:probe5 0.167467341768673 0.0476588797387899 3.5138749103322 0.000469423280435748 *** df.mm.trans1:probe6 0.0509743967479032 0.0476588797387899 1.06956766561206 0.285174894253942 df.mm.trans1:probe7 0.0185302456631847 0.0476588797387899 0.388809929329975 0.6975325099677 df.mm.trans1:probe8 0.068662013640655 0.0476588797387899 1.44069717997946 0.150108003326488 df.mm.trans1:probe9 -0.0297713869396671 0.0476588797387899 -0.624676599677518 0.532382617745655 df.mm.trans1:probe10 -0.0879541290097739 0.0476588797387899 -1.84549300134278 0.0653791355991579 . df.mm.trans1:probe11 0.834763158261817 0.0476588797387899 17.5153751585646 2.00615610103447e-57 *** df.mm.trans1:probe12 0.7028614578784 0.0476588797387899 14.7477544946642 3.58373272541397e-43 *** df.mm.trans1:probe13 0.812961497037462 0.0476588797387899 17.0579229199923 5.39965695154937e-55 *** df.mm.trans1:probe14 0.457419095272107 0.0476588797387899 9.59777270844681 1.33634583985857e-20 *** df.mm.trans1:probe15 0.578206355681964 0.0476588797387899 12.1321852055905 6.19387235489335e-31 *** df.mm.trans1:probe16 0.781155319954022 0.0476588797387899 16.3905514404745 1.69329819008187e-51 *** df.mm.trans1:probe17 -0.045570195992045 0.0476588797387899 -0.956174300399156 0.339307355192439 df.mm.trans1:probe18 0.00141038902611163 0.0476588797387899 0.0295934154105537 0.976399574911231 df.mm.trans1:probe19 -0.0629588098984475 0.0476588797387899 -1.32102999993944 0.186913973907071 df.mm.trans1:probe20 0.0176911413082768 0.0476588797387899 0.371203465235417 0.7105959543715 df.mm.trans1:probe21 0.123468932665418 0.0476588797387899 2.59068054772017 0.00977386141003294 ** df.mm.trans1:probe22 -0.00775052643229156 0.0476588797387899 -0.162625023390622 0.870859632609163 df.mm.trans2:probe2 -0.275858175512642 0.0476588797387899 -5.78818002069233 1.06492042688728e-08 *** df.mm.trans2:probe3 -0.273142116186655 0.0476588797387899 -5.7311904451741 1.47038196936292e-08 *** df.mm.trans2:probe4 -0.0626498921767447 0.0476588797387899 -1.31454814968623 0.189083311978268 df.mm.trans2:probe5 -0.0602939762502685 0.0476588797387899 -1.2651152645788 0.206242160524360 df.mm.trans2:probe6 -0.0294930495835203 0.0476588797387899 -0.618836400376312 0.536221243585606 df.mm.trans3:probe2 0.209469870770216 0.0476588797387899 4.39519082106596 1.27467720164882e-05 *** df.mm.trans3:probe3 0.163435715381023 0.0476588797387899 3.42928151640965 0.000639901120629946 *** df.mm.trans3:probe4 0.101362647892105 0.0476588797387899 2.12683656115411 0.0337754577163186 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13406862548007 0.158487430606231 26.0845204548198 6.52139128212671e-106 *** df.mm.trans1 0.0820468135255691 0.140739905630902 0.582967660506617 0.560098883525393 df.mm.trans2 0.0348585383975648 0.128021246051084 0.272287135712265 0.785479857718363 df.mm.exp2 0.185415546163658 0.172591664004919 1.07430186291253 0.283049818342037 df.mm.exp3 0.0439017118670075 0.172591664004919 0.254367510274171 0.799284785192747 df.mm.exp4 0.0425617062051302 0.172591664004919 0.246603487199227 0.805285888203752 df.mm.exp5 -0.299548537509298 0.172591664004919 -1.73559099297380 0.0830670106347392 . df.mm.exp6 0.0699206210725947 0.172591664004919 0.405121657964907 0.685509267766456 df.mm.exp7 0.171359109226510 0.172591664004919 0.992858549771134 0.321114770400746 df.mm.exp8 0.168028276078024 0.172591664004919 0.973559627267025 0.330604485890855 df.mm.trans1:exp2 -0.311605401554294 0.163883882153872 -1.9013791805452 0.0576541148919675 . df.mm.trans2:exp2 -0.146389557345723 0.138002578787706 -1.06077407126514 0.289150771157909 df.mm.trans1:exp3 -0.148428194007354 0.163883882153872 -0.905691225132158 0.365404399366197 df.mm.trans2:exp3 -0.097145682516051 0.138002578787706 -0.703941066677408 0.481698660921414 df.mm.trans1:exp4 -0.159143103051151 0.163883882153872 -0.971072328526673 0.331840644450424 df.mm.trans2:exp4 0.0735826798216099 0.138002578787706 0.533197860996529 0.594062312286425 df.mm.trans1:exp5 0.0805869657516106 0.163883882153872 0.491732101366424 0.62305966440947 df.mm.trans2:exp5 0.315912126511962 0.138002578787706 2.28917553053802 0.0223598170043934 * df.mm.trans1:exp6 -0.122952065500702 0.163883882153872 -0.750238912361505 0.453357628694015 df.mm.trans2:exp6 -0.115678189188674 0.138002578787706 -0.838232083812184 0.402180496035797 df.mm.trans1:exp7 -0.227482040302037 0.163883882153872 -1.38806841351520 0.165548562977766 df.mm.trans2:exp7 -0.0214192207003281 0.138002578787706 -0.155208843838186 0.876700481047809 df.mm.trans1:exp8 -0.173892360318080 0.163883882153872 -1.06107054600288 0.289016118446661 df.mm.trans2:exp8 -0.210404188205700 0.138002578787706 -1.52463953973912 0.127791204328652 df.mm.trans1:probe2 0.0826419140240888 0.0897628990671938 0.920668949899062 0.357533811467815 df.mm.trans1:probe3 -0.0402258697178799 0.0897628990671938 -0.448134698588199 0.654191744806659 df.mm.trans1:probe4 0.00804443844468524 0.0897628990671938 0.0896187459215574 0.928615284528992 df.mm.trans1:probe5 0.0381819673927921 0.0897628990671938 0.425364686185216 0.670698837277144 df.mm.trans1:probe6 -0.0354232104430198 0.0897628990671938 -0.394630864322944 0.69323301654895 df.mm.trans1:probe7 -0.0309326695627974 0.0897628990671938 -0.344604172595207 0.730493303158144 df.mm.trans1:probe8 0.0463296061837116 0.0897628990671938 0.516133131451455 0.605921003396415 df.mm.trans1:probe9 -0.00921419961865336 0.0897628990671938 -0.102650423665081 0.9182692039719 df.mm.trans1:probe10 0.0621544945929031 0.0897628990671938 0.692429670151096 0.488892353393284 df.mm.trans1:probe11 0.0442767742539858 0.0897628990671938 0.493263639143847 0.621977779141735 df.mm.trans1:probe12 -0.0366433536278782 0.0897628990671938 -0.408223820851063 0.683231579978369 df.mm.trans1:probe13 -0.0107473294355057 0.0897628990671938 -0.119730195294389 0.904730513333443 df.mm.trans1:probe14 0.0621692425388126 0.0897628990671938 0.692593969054794 0.488789273415916 df.mm.trans1:probe15 -0.00954349113622702 0.0897628990671938 -0.106318882694320 0.9153591721436 df.mm.trans1:probe16 -0.00503770167005276 0.0897628990671938 -0.0561223147024439 0.955260046275345 df.mm.trans1:probe17 0.0037461182476339 0.0897628990671938 0.0417334810546802 0.966722814095734 df.mm.trans1:probe18 -0.0489642168605391 0.0897628990671938 -0.545483906707224 0.585590871750059 df.mm.trans1:probe19 0.0193842720013258 0.0897628990671938 0.215949709766118 0.829088539240269 df.mm.trans1:probe20 -0.0421611547508377 0.0897628990671938 -0.469694664376616 0.638716389532569 df.mm.trans1:probe21 -0.0813640899442622 0.0897628990671938 -0.906433401659137 0.365011861359315 df.mm.trans1:probe22 -0.106333025946649 0.0897628990671938 -1.1845988381798 0.236569708441005 df.mm.trans2:probe2 -0.0971421416179244 0.0897628990671938 -1.08220815757306 0.279524885176709 df.mm.trans2:probe3 -0.147209656400382 0.0897628990671938 -1.63998331081292 0.101448481201632 df.mm.trans2:probe4 -0.106189303767442 0.0897628990671938 -1.18299770696969 0.237203260563298 df.mm.trans2:probe5 -0.0224998265076803 0.0897628990671938 -0.250658420589086 0.802150220773331 df.mm.trans2:probe6 -0.106138271353035 0.0897628990671938 -1.18242918239064 0.237428509016593 df.mm.trans3:probe2 0.0439241996296128 0.0897628990671938 0.489335795591144 0.624754062094341 df.mm.trans3:probe3 -0.0457144678028097 0.0897628990671938 -0.509280206832326 0.610712962701352 df.mm.trans3:probe4 -0.0982713596403642 0.0897628990671938 -1.09478816595263 0.273978105799082