fitVsDatCorrelation=0.853368272311004 cont.fitVsDatCorrelation=0.266665406032325 fstatistic=5265.53729643506,50,646 cont.fstatistic=1531.38332251807,50,646 residuals=-0.682479381092793,-0.134192482884897,-0.0156659543102024,0.125610891349623,1.12630634647659 cont.residuals=-0.909532186407485,-0.314190927861895,-0.0643830189383683,0.300728844139919,1.42065922973591 predictedValues: Include Exclude Both Lung 62.7429888204979 132.709744203002 50.6779550325154 cerebhem 69.3946522529274 113.444227864719 74.5252334168975 cortex 74.4001581274982 109.411945424063 89.938952343573 heart 59.3860837654282 104.324609663626 49.6176929583551 kidney 63.7122212911986 149.817965953945 51.3399884754594 liver 66.4543001611227 131.711515578961 52.9095864811218 stomach 62.8877766240127 113.911696704106 51.7316247442087 testicle 60.1883575627612 110.227614969211 54.2726843352909 diffExp=-69.9667553825041,-44.0495756117914,-35.0117872965646,-44.9385258981976,-86.1057446627466,-65.2572154178381,-51.0239200800931,-50.0392574064499 diffExpScore=0.99776482759495 diffExp1.5=-1,-1,0,-1,-1,-1,-1,-1 diffExp1.5Score=0.875 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 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 66.619269290671 78.0709123627283 62.0252431134785 cerebhem 82.9878416412486 79.0304810157026 68.903107841478 cortex 78.7703303012099 67.7100342196875 63.6686783925226 heart 79.5627528332727 62.6748058465327 77.913104662539 kidney 78.643066509376 67.4689680253374 67.5561494734685 liver 75.4455627524923 73.9454064432794 67.7771017818226 stomach 73.7495693687444 82.9756383818623 73.564968289827 testicle 69.9617778095588 72.3573742563853 74.3189529567677 cont.diffExp=-11.4516430720573,3.95736062554596,11.0602960815224,16.8879469867399,11.1740984840386,1.50015630921288,-9.22606901311786,-2.39559644682652 cont.diffExpScore=3.0059323687617 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=0,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.0545087909125798 cont.tran.correlation=-0.348707999627902 tran.covariance=-0.000176219659980423 cont.tran.covariance=-0.00251698525328732 tran.mean=92.7953661854424 cont.tran.mean=74.3733619411306 weightedLogRatios: wLogRatio Lung -3.38122107784591 cerebhem -2.20465985613474 cortex -1.73636183989432 heart -2.45988927124143 kidney -3.91772772039059 liver -3.10482976107366 stomach -2.63672071068324 testicle -2.66230946049902 cont.weightedLogRatios: wLogRatio Lung -0.678640604348523 cerebhem 0.214705916799523 cortex 0.649219636825598 heart 1.01572307862666 kidney 0.657188329960157 liver 0.0866310138857078 stomach -0.513875622719252 testicle -0.143588047768385 varWeightedLogRatios=0.473733110092352 cont.varWeightedLogRatios=0.353822166306864 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.96137840049733 0.110303213378667 44.9794548003339 3.49280027108807e-201 *** df.mm.trans1 -1.02611635607567 0.0965887800215366 -10.6235564404776 2.08740635486113e-24 *** df.mm.trans2 0.0519191757971414 0.0879797977430425 0.590126109959683 0.555312466154879 df.mm.exp2 -0.441736836977617 0.117478274587652 -3.7601576847133 0.000185210354032523 *** df.mm.exp3 -0.596273196027711 0.117478274587652 -5.07560396269547 5.05725456548946e-07 *** df.mm.exp4 -0.274500483914931 0.117478274587652 -2.33660636299288 0.0197644211491389 * df.mm.exp5 0.123607242314230 0.117478274587652 1.05217107374186 0.293114475417743 df.mm.exp6 0.00682379258484382 0.117478274587652 0.058085570364353 0.953698430452667 df.mm.exp7 -0.171014123961388 0.117478274587652 -1.45570850918305 0.145959005252386 df.mm.exp8 -0.29571486165108 0.117478274587652 -2.51718764758028 0.0120704826584764 * df.mm.trans1:exp2 0.54249980482194 0.109476960446026 4.95537876290787 9.23200829426446e-07 *** df.mm.trans2:exp2 0.284883799719546 0.0913965240330212 3.11700912845021 0.00190804781104761 ** df.mm.trans1:exp3 0.76668442340894 0.109476960446026 7.00315774465561 6.29556709384984e-12 *** df.mm.trans2:exp3 0.403228901443875 0.0913965240330213 4.41186254849462 1.20064499889012e-05 *** df.mm.trans1:exp4 0.219513562967220 0.109476960446026 2.00511196212326 0.0453677772222614 * df.mm.trans2:exp4 0.0338433998846922 0.0913965240330212 0.370291980387183 0.711286323283037 df.mm.trans1:exp5 -0.108277680958784 0.109476960446026 -0.989045370986227 0.323011243703403 df.mm.trans2:exp5 -0.00235061444441301 0.0913965240330212 -0.0257188604192840 0.979489522917943 df.mm.trans1:exp6 0.0506438633886987 0.109476960446026 0.462598369395423 0.643808053345147 df.mm.trans2:exp6 -0.0143741186597092 0.0913965240330213 -0.157272049585998 0.875079589413262 df.mm.trans1:exp7 0.173319098656180 0.109476960446026 1.58315592568566 0.113875345271265 df.mm.trans2:exp7 0.0182733129321313 0.0913965240330213 0.199934440893280 0.841594774566122 df.mm.trans1:exp8 0.254146959469865 0.109476960446026 2.32146525108509 0.0205719439609547 * df.mm.trans2:exp8 0.110097947576038 0.0913965240330212 1.20461854256361 0.228791733212040 df.mm.trans1:probe2 -0.125865605174675 0.0639207735879328 -1.96908763942801 0.0493700162703892 * df.mm.trans1:probe3 -0.175203845774197 0.0639207735879328 -2.74095315090606 0.00629554677652173 ** df.mm.trans1:probe4 0.00729997463736009 0.0639207735879328 0.114203477642802 0.909111979585045 df.mm.trans1:probe5 0.2294700851508 0.0639207735879328 3.58991408067252 0.00035586442416452 *** df.mm.trans1:probe6 0.00961091170166802 0.0639207735879328 0.150356623710862 0.880530195073502 df.mm.trans1:probe7 0.629353595123643 0.0639207735879328 9.8458382118588 2.09636853208489e-21 *** df.mm.trans1:probe8 0.683051562738218 0.0639207735879328 10.6859088899883 1.18056514093165e-24 *** df.mm.trans1:probe9 0.628376501634622 0.0639207735879328 9.83055220334895 2.39244964293970e-21 *** df.mm.trans1:probe10 0.773729448983847 0.0639207735879328 12.1045069631309 1.53202928687697e-30 *** df.mm.trans1:probe11 0.696792790791202 0.0639207735879328 10.9008816958802 1.62624013796553e-25 *** df.mm.trans1:probe12 0.601127585939047 0.0639207735879328 9.40426018330496 8.9688062359077e-20 *** df.mm.trans1:probe13 0.0449055255336217 0.0639207735879328 0.702518493019289 0.482608932615361 df.mm.trans1:probe14 0.117006708069796 0.0639207735879328 1.83049580757711 0.0676363768374291 . df.mm.trans1:probe15 0.113898017021020 0.0639207735879328 1.78186230591117 0.0752413397728027 . df.mm.trans1:probe16 0.0574688070161155 0.0639207735879328 0.899063071210463 0.368954057607669 df.mm.trans1:probe17 0.19882504325068 0.0639207735879328 3.11049181808111 0.00195001561027313 ** df.mm.trans1:probe18 0.197203187637639 0.0639207735879328 3.08511891468829 0.00212162168412637 ** df.mm.trans2:probe2 -0.366335940700556 0.0639207735879328 -5.73109366701587 1.53143197431248e-08 *** df.mm.trans2:probe3 -0.150242604626443 0.0639207735879328 -2.35045034959975 0.0190504980303192 * df.mm.trans2:probe4 -0.335035758501695 0.0639207735879328 -5.24142214957399 2.16079075314387e-07 *** df.mm.trans2:probe5 -0.228017723938679 0.0639207735879328 -3.56719280978359 0.000387520357864097 *** df.mm.trans2:probe6 -0.296833252924973 0.0639207735879328 -4.64376815647629 4.14607512052673e-06 *** df.mm.trans3:probe2 -0.189761554359372 0.0639207735879328 -2.96869927736913 0.00310140030787979 ** df.mm.trans3:probe3 0.115550185350268 0.0639207735879328 1.80770943254169 0.0711167364450687 . df.mm.trans3:probe4 -0.281755248748846 0.0639207735879328 -4.40788233517306 1.22225725863299e-05 *** df.mm.trans3:probe5 0.0475155369491952 0.0639207735879328 0.743350467807314 0.457539776774746 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.5935467431969 0.203927299375381 22.525413504061 2.29298913111459e-83 *** df.mm.trans1 -0.308308215001671 0.178572214321040 -1.72651840698682 0.0847322748917525 . df.mm.trans2 -0.266688535847674 0.162656027904993 -1.63958593654731 0.101578133314362 df.mm.exp2 0.126756479207919 0.217192469177626 0.583613601741663 0.559684010893517 df.mm.exp3 -0.000991906588515532 0.217192469177626 -0.0045669474281096 0.996357525740113 df.mm.exp4 -0.270158524276683 0.217192469177626 -1.24386690431582 0.213999715122021 df.mm.exp5 -0.0654418293268905 0.217192469177626 -0.301308003793494 0.763276598448298 df.mm.exp6 -0.0185559246048496 0.217192469177626 -0.0854353959651982 0.93194172534933 df.mm.exp7 -0.00801669247043227 0.217192469177626 -0.0369105452909420 0.970567734290613 df.mm.exp8 -0.207869533571323 0.217192469177626 -0.957075235427808 0.338887311964317 df.mm.trans1:exp2 0.092943767220879 0.202399732553042 0.459208942860245 0.64623860680594 df.mm.trans2:exp2 -0.114540412048633 0.168972831773873 -0.677862889827854 0.498101342540675 df.mm.trans1:exp3 0.168534449143461 0.202399732553043 0.8326811849877 0.405332287334159 df.mm.trans2:exp3 -0.141391255062148 0.168972831773873 -0.836769163289894 0.403031761612319 df.mm.trans1:exp4 0.447710713996107 0.202399732553043 2.21201237940755 0.0273149577619277 * df.mm.trans2:exp4 0.0505005240004432 0.168972831773873 0.298867714237193 0.765137098336295 df.mm.trans1:exp5 0.231367434287708 0.202399732553043 1.14312124511861 0.253411706648175 df.mm.trans2:exp5 -0.0805079579962962 0.168972831773873 -0.476455043992135 0.633911337306283 df.mm.trans1:exp6 0.142973433264203 0.202399732553043 0.706391413964614 0.480199447114479 df.mm.trans2:exp6 -0.0357345517636035 0.168972831773873 -0.211481049281491 0.832578617699815 df.mm.trans1:exp7 0.109697984217818 0.202399732553043 0.54198680420227 0.588014488608874 df.mm.trans2:exp7 0.0689461971778715 0.168972831773873 0.40803125836311 0.683385925013863 df.mm.trans1:exp8 0.256824730811665 0.202399732553043 1.26889856805695 0.204934250924089 df.mm.trans2:exp8 0.131869359680720 0.168972831773873 0.780417528050863 0.435431013005203 df.mm.trans1:probe2 -0.114983954360277 0.118175983568339 -0.972989188567099 0.33092285566997 df.mm.trans1:probe3 0.0717148868210826 0.118175983568339 0.606848233081228 0.544164848736867 df.mm.trans1:probe4 -0.151537653270751 0.118175983568339 -1.28230498867073 0.200195634265107 df.mm.trans1:probe5 -0.177405255968171 0.118175983568339 -1.50119551038542 0.133793600832054 df.mm.trans1:probe6 -0.102487555804627 0.118175983568339 -0.867245210998058 0.386129688124538 df.mm.trans1:probe7 -0.0934666498062943 0.118175983568339 -0.790910699315183 0.429286371695416 df.mm.trans1:probe8 -0.131999718317825 0.118175983568339 -1.11697583833938 0.264419914360187 df.mm.trans1:probe9 -0.0899376038182037 0.118175983568339 -0.761048066642023 0.446906066257205 df.mm.trans1:probe10 -0.0579134425025322 0.118175983568339 -0.490061015392709 0.624257111519872 df.mm.trans1:probe11 -0.244754360022425 0.118175983568339 -2.07110068079855 0.0387458998299764 * df.mm.trans1:probe12 -0.070844553246721 0.118175983568339 -0.599483508472372 0.549060694461018 df.mm.trans1:probe13 -0.164670506282574 0.118175983568339 -1.39343461598819 0.163967626549749 df.mm.trans1:probe14 -0.149983781205096 0.118175983568339 -1.26915619126930 0.204842428035148 df.mm.trans1:probe15 -0.040153863756356 0.118175983568339 -0.339780237438311 0.734132473588623 df.mm.trans1:probe16 -0.109017377884495 0.118175983568339 -0.922500279605905 0.356612089729407 df.mm.trans1:probe17 -0.208243895134818 0.118175983568339 -1.76215072510393 0.078516732633848 . df.mm.trans1:probe18 -0.147941983014755 0.118175983568339 -1.25187858435892 0.211067190986407 df.mm.trans2:probe2 0.096957565867698 0.118175983568339 0.820450678217792 0.412261869020408 df.mm.trans2:probe3 0.20902830177368 0.118175983568339 1.76878833974590 0.0774010373222214 . df.mm.trans2:probe4 0.0884577091696124 0.118175983568339 0.748525262905548 0.454415825866868 df.mm.trans2:probe5 -0.0383454613174666 0.118175983568339 -0.324477615160209 0.74568150637648 df.mm.trans2:probe6 -0.0177453844597520 0.118175983568339 -0.150160666524009 0.880684728703445 df.mm.trans3:probe2 0.154831000418942 0.118175983568339 1.31017314807798 0.190602932687729 df.mm.trans3:probe3 0.224683463936765 0.118175983568339 1.90126163669148 0.0577124590424942 . df.mm.trans3:probe4 0.0536415314348719 0.118175983568339 0.453912290933904 0.650044411058097 df.mm.trans3:probe5 0.111801288345844 0.118175983568339 0.946057608068833 0.344472929474872