fitVsDatCorrelation=0.860198819935128 cont.fitVsDatCorrelation=0.245321812927188 fstatistic=8936.67228775944,62,922 cont.fstatistic=2462.12171550109,62,922 residuals=-0.592209892290989,-0.0960499854434505,-0.00439274874735109,0.0829532251727908,1.20885113660702 cont.residuals=-0.761350928957596,-0.223381077037524,-0.0261838950856533,0.190830972766976,1.79342403365521 predictedValues: Include Exclude Both Lung 69.9828086002455 83.6032942919 87.3134656116614 cerebhem 73.7255005479343 118.584791085373 118.710028770925 cortex 60.8894514835334 94.1459936601056 111.807650211520 heart 63.45912130701 79.977579857163 88.064024712218 kidney 68.00031704975 68.8301738638802 77.4341208932064 liver 67.1396268717341 66.7767908222162 70.2996616515765 stomach 69.5680285850932 72.3542213190007 78.7775245506868 testicle 64.6641384889078 69.7477169124237 72.0130007735854 diffExp=-13.6204856916545,-44.8592905374383,-33.2565421765722,-16.5184585501531,-0.829856814130238,0.362836049517853,-2.78619273390754,-5.08357842351589 diffExpScore=0.997667112501499 diffExp1.5=0,-1,-1,0,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,-1,-1,0,0,0,0,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,-1,-1,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,-1,-1,-1,0,0,0,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 81.7265420293144 83.8872158966862 81.3879020488281 cerebhem 85.4944527068497 84.3959449275303 76.6896204822864 cortex 83.5056552603106 82.6858654278615 76.6619471766111 heart 75.9404930336718 75.6514383069646 80.6169564465505 kidney 79.5547519225102 91.5355174599652 78.5872348876335 liver 81.1131275987838 82.2319270582935 75.6600266675336 stomach 78.3400783134281 84.0442567748856 88.697745034423 testicle 82.0353973372682 73.1351172316974 87.2096124517484 cont.diffExp=-2.16067386737187,1.09850777931945,0.81978983244909,0.289054726707164,-11.9807655374550,-1.11879945950973,-5.70417846145753,8.90028010557076 cont.diffExpScore=2.95410198499556 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=0.345318462494929 cont.tran.correlation=0.149043610708383 tran.covariance=0.00320920316668884 cont.tran.covariance=0.000422884173880368 tran.mean=74.4655971716419 cont.tran.mean=81.5798613303763 weightedLogRatios: wLogRatio Lung -0.771292609371187 cerebhem -2.15681288917191 cortex -1.88562893658837 heart -0.986957281931649 kidney -0.0512556387617199 liver 0.0227812257448652 stomach -0.167360799457028 testicle -0.318380443456330 cont.weightedLogRatios: wLogRatio Lung -0.115244021773650 cerebhem 0.0574443713870633 cortex 0.0436061392382474 heart 0.0165054018896217 kidney -0.623774139132704 liver -0.0603116997843795 stomach -0.308983494051423 testicle 0.499532435663022 varWeightedLogRatios=0.703454324080281 cont.varWeightedLogRatios=0.103761049257494 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.44922756194105 0.0847371404782462 40.7050266562457 3.84570905072926e-208 *** df.mm.trans1 0.621859871309142 0.0725645518329394 8.56974728846682 4.32183414417838e-17 *** df.mm.trans2 0.932343771585298 0.0637878020207914 14.6163332494417 1.13691382131129e-43 *** df.mm.exp2 0.0944653499629658 0.0810242036582299 1.16589050799478 0.243960222788168 df.mm.exp3 -0.267701184271379 0.0810242036582299 -3.30396563230137 0.00098998988892366 *** df.mm.exp4 -0.150749669189931 0.0810242036582299 -1.86055107466174 0.0631257897832288 . df.mm.exp5 -0.103100780775300 0.0810242036582299 -1.27246891817896 0.20352736202688 df.mm.exp6 -0.0494648233885746 0.0810242036582299 -0.610494409759623 0.541684796308068 df.mm.exp7 -0.0475766841342529 0.0810242036582299 -0.58719101189735 0.557219186792679 df.mm.exp8 -0.0675830561549999 0.0810242036582299 -0.834109477213422 0.404435343398543 df.mm.trans1:exp2 -0.0423662263543323 0.0739646400791752 -0.572790272608391 0.566926359095215 df.mm.trans2:exp2 0.255079966603607 0.0523008985680071 4.87716222068202 1.26722215061237e-06 *** df.mm.trans1:exp3 0.128511513071773 0.0739646400791752 1.73747229668404 0.0826377587765543 . df.mm.trans2:exp3 0.386464961015485 0.0523008985680071 7.38926044478879 3.30687267780130e-13 *** df.mm.trans1:exp4 0.0528959883803071 0.0739646400791752 0.715152379889698 0.474695937103909 df.mm.trans2:exp4 0.106413088080695 0.0523008985680071 2.03463211903187 0.0421736014091302 * df.mm.trans1:exp5 0.0743635279750779 0.0739646400791752 1.00539295392333 0.314971452168167 df.mm.trans2:exp5 -0.0913399215475693 0.0523008985680071 -1.74643120956707 0.0810690646078538 . df.mm.trans1:exp6 0.00798963699725831 0.0739646400791752 0.108019683306859 0.91400358012263 df.mm.trans2:exp6 -0.175262523942032 0.0523008985680071 -3.35104230980157 0.00083778167426141 *** df.mm.trans1:exp7 0.0416321660654091 0.0739646400791752 0.562865796694801 0.573663110092115 df.mm.trans2:exp7 -0.096932443404966 0.0523008985680071 -1.85336095667504 0.0641499337757432 . df.mm.trans1:exp8 -0.0114597902081677 0.0739646400791752 -0.154936063988152 0.876905632244234 df.mm.trans2:exp8 -0.113615180837399 0.0523008985680071 -2.17233707160241 0.0300844186617315 * df.mm.trans1:probe2 -0.217927792133919 0.0535924732641815 -4.06638803661204 5.18261899145494e-05 *** df.mm.trans1:probe3 0.221102434502589 0.0535924732641815 4.12562475728028 4.03161024823769e-05 *** df.mm.trans1:probe4 0.0566981909407994 0.0535924732641815 1.05795063163643 0.290355047762278 df.mm.trans1:probe5 0.0062588241822329 0.0535924732641815 0.116785507386090 0.907055461740342 df.mm.trans1:probe6 -0.140976670993390 0.0535924732641815 -2.63053116243493 0.00866733657592638 ** df.mm.trans1:probe7 0.220066751738290 0.0535924732641815 4.1062996039291 4.37735978589913e-05 *** df.mm.trans1:probe8 0.0151586826217862 0.0535924732641815 0.282850962056971 0.777354575456952 df.mm.trans1:probe9 0.124181825946696 0.0535924732641815 2.31715049489409 0.0207138070828295 * df.mm.trans1:probe10 0.40451947377604 0.0535924732641815 7.54806503857324 1.05999537025791e-13 *** df.mm.trans1:probe11 0.326418923888148 0.0535924732641815 6.09076058645551 1.64809459207594e-09 *** df.mm.trans1:probe12 0.55154426354905 0.0535924732641815 10.2914500853551 1.37356203503783e-23 *** df.mm.trans1:probe13 0.298509210665220 0.0535924732641815 5.56998385190647 3.34268928213855e-08 *** df.mm.trans1:probe14 0.417994774964424 0.0535924732641815 7.79950521977104 1.67772735808690e-14 *** df.mm.trans1:probe15 0.324013911568877 0.0535924732641815 6.04588465196719 2.15596500259838e-09 *** df.mm.trans1:probe16 0.757291101509163 0.0535924732641815 14.1305495974432 3.50585747883839e-41 *** df.mm.trans1:probe17 0.638198414281648 0.0535924732641815 11.9083590551173 1.62493899673998e-30 *** df.mm.trans1:probe18 0.404347807719363 0.0535924732641815 7.54486186383208 1.0848231929617e-13 *** df.mm.trans1:probe19 0.620114136256592 0.0535924732641815 11.5709184235586 5.22628240143672e-29 *** df.mm.trans1:probe20 0.593147390913077 0.0535924732641815 11.0677368441121 8.0638287366837e-27 *** df.mm.trans1:probe21 0.580014896508912 0.0535924732641815 10.8226932101035 8.82750842268006e-26 *** df.mm.trans2:probe2 -0.0182239440761126 0.0535924732641815 -0.340046707422487 0.733898806447563 df.mm.trans2:probe3 0.461446238828427 0.0535924732641815 8.61028071150495 3.11755353505374e-17 *** df.mm.trans2:probe4 0.142796666949144 0.0535924732641815 2.66449107965655 0.00784514833991108 ** df.mm.trans2:probe5 0.0989173798423532 0.0535924732641815 1.84573269001311 0.0652514699924663 . df.mm.trans2:probe6 0.205295482324946 0.0535924732641815 3.83067751534719 0.000136440898619523 *** df.mm.trans3:probe2 -0.576181518689832 0.0535924732641815 -10.7511649229095 1.76151560316249e-25 *** df.mm.trans3:probe3 -0.856770746711667 0.0535924732641815 -15.9867737860923 5.8130319133957e-51 *** df.mm.trans3:probe4 -0.586941985106931 0.0535924732641815 -10.9519480881882 2.51084020482557e-26 *** df.mm.trans3:probe5 -0.177930579545345 0.0535924732641815 -3.32006658226511 0.000935260094607147 *** df.mm.trans3:probe6 -0.726047368356574 0.0535924732641815 -13.5475622626625 2.88951303879188e-38 *** df.mm.trans3:probe7 -0.368970829489748 0.0535924732641815 -6.88475091774407 1.06959879705285e-11 *** df.mm.trans3:probe8 -1.06192454858066 0.0535924732641815 -19.8148076381165 4.50621764753132e-73 *** df.mm.trans3:probe9 -0.707595583549483 0.0535924732641815 -13.2032641983404 1.39639004287585e-36 *** df.mm.trans3:probe10 -0.261874508102486 0.0535924732641815 -4.88640460408663 1.21061935173412e-06 *** df.mm.trans3:probe11 -0.569972935058665 0.0535924732641815 -10.6353168708787 5.35343034646255e-25 *** df.mm.trans3:probe12 -0.872691294403072 0.0535924732641815 -16.2838406449574 1.36439295764026e-52 *** df.mm.trans3:probe13 -0.367991507316897 0.0535924732641815 -6.86647741564193 1.20831094335508e-11 *** df.mm.trans3:probe14 -0.340077715757519 0.0535924732641815 -6.34562458204013 3.46865136334484e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43743365447116 0.161086965215082 27.5468201200907 2.3248045812972e-122 *** df.mm.trans1 -0.0381144618702916 0.137946635571938 -0.276298597006488 0.782380654423795 df.mm.trans2 -0.00687790105239799 0.121261862121813 -0.0567194081638698 0.95478100246756 df.mm.exp2 0.110579063519221 0.154028599532736 0.717912542571157 0.472993074568679 df.mm.exp3 0.0669321598263079 0.154028599532736 0.434543714799424 0.663995336097403 df.mm.exp4 -0.167247916460939 0.154028599532736 -1.08582378187107 0.277840771110711 df.mm.exp5 0.0953378625066175 0.154028599532736 0.618962081041029 0.536094235593291 df.mm.exp6 0.0455130680979924 0.154028599532736 0.295484528432133 0.767690299185484 df.mm.exp7 -0.126457023466156 0.154028599532736 -0.820997034640182 0.411860178728234 df.mm.exp8 -0.202480490349408 0.154028599532736 -1.31456424952026 0.188983293734992 df.mm.trans1:exp2 -0.065506391249347 0.140608230775015 -0.465878781692109 0.641412303790035 df.mm.trans2:exp2 -0.104532937714426 0.0994250334716563 -1.05137442819394 0.293362132660812 df.mm.trans1:exp3 -0.0453966235408837 0.140608230775015 -0.322858934293271 0.746875280285615 df.mm.trans2:exp3 -0.0813567149752485 0.0994250334716563 -0.818271939515528 0.413413349524672 df.mm.trans1:exp4 0.0938191427079024 0.140608230775015 0.667237914813257 0.504787130936187 df.mm.trans2:exp4 0.0639111404039296 0.0994250334716564 0.642807331034484 0.520508943141375 df.mm.trans1:exp5 -0.122271195412267 0.140608230775015 -0.869587752710654 0.384752053303167 df.mm.trans2:exp5 -0.00808402534238751 0.0994250334716564 -0.0813077457468703 0.935214860240574 df.mm.trans1:exp6 -0.0530470717402372 0.140608230775015 -0.37726860972397 0.706060817239788 df.mm.trans2:exp6 -0.065442663171677 0.0994250334716564 -0.658211125373501 0.510566789606494 df.mm.trans1:exp7 0.0841375304177941 0.140608230775015 0.598382683247193 0.549731583102974 df.mm.trans2:exp7 0.128327321171269 0.0994250334716563 1.29069427175855 0.197133265844993 df.mm.trans1:exp8 0.206252498367253 0.140608230775015 1.46685935261694 0.14275531421823 df.mm.trans2:exp8 0.0653159128759464 0.0994250334716564 0.656936292554192 0.511385821020407 df.mm.trans1:probe2 0.116312158842973 0.101880342288795 1.14165457467024 0.253894043323677 df.mm.trans1:probe3 -0.0221358028133536 0.101880342288795 -0.217272560300264 0.828044010092598 df.mm.trans1:probe4 -0.0608973176004957 0.101880342288795 -0.597733735796385 0.550164390652217 df.mm.trans1:probe5 -0.0957762016316592 0.101880342288795 -0.940085196810269 0.347420081264169 df.mm.trans1:probe6 -0.0199676421032076 0.101880342288795 -0.19599111717358 0.844660305699353 df.mm.trans1:probe7 -0.019962398397909 0.101880342288795 -0.195939647918758 0.844700579473565 df.mm.trans1:probe8 0.127614856195969 0.101880342288795 1.25259547945203 0.210670570941584 df.mm.trans1:probe9 0.0470098946527862 0.101880342288795 0.461422621839351 0.644604260764245 df.mm.trans1:probe10 -0.00469865834594517 0.101880342288795 -0.0461193812308377 0.963225085441629 df.mm.trans1:probe11 0.0554995546661263 0.101880342288795 0.544752338079163 0.586055594814913 df.mm.trans1:probe12 -0.0876555831090233 0.101880342288795 -0.860377783778452 0.38980449495334 df.mm.trans1:probe13 0.100007955479725 0.101880342288795 0.981621706729623 0.326543755259413 df.mm.trans1:probe14 -0.0858986861378308 0.101880342288795 -0.84313307364377 0.399372696514083 df.mm.trans1:probe15 0.0311661075439940 0.101880342288795 0.305908940270823 0.759743035758639 df.mm.trans1:probe16 -0.0437835980704416 0.101880342288795 -0.42975511356578 0.667474203225714 df.mm.trans1:probe17 -0.013373006862724 0.101880342288795 -0.131261895693443 0.895596769331251 df.mm.trans1:probe18 0.0684111791806795 0.101880342288795 0.671485564769286 0.50207945171727 df.mm.trans1:probe19 0.0174486892534265 0.101880342288795 0.171266496180054 0.86405180257174 df.mm.trans1:probe20 0.0180432697393637 0.101880342288795 0.177102563006879 0.859466725693538 df.mm.trans1:probe21 0.0147222216097849 0.101880342288795 0.144505026966366 0.885133267255819 df.mm.trans2:probe2 -0.0942337121177868 0.101880342288795 -0.9249449893942 0.355236539765848 df.mm.trans2:probe3 -0.182085817195059 0.101880342288795 -1.78725172201434 0.0742252097024805 . df.mm.trans2:probe4 0.0423698637814239 0.101880342288795 0.415878694844981 0.677595547312755 df.mm.trans2:probe5 0.177598087509965 0.101880342288795 1.74320269759732 0.0816315590358226 . df.mm.trans2:probe6 0.0347010848568586 0.101880342288795 0.34060628456168 0.73347758923505 df.mm.trans3:probe2 -0.105168795758139 0.101880342288795 -1.03227760523244 0.302212783633705 df.mm.trans3:probe3 0.115988926599013 0.101880342288795 1.13848190920114 0.25521503824797 df.mm.trans3:probe4 -0.0139329526789592 0.101880342288795 -0.136758008129421 0.891251932138908 df.mm.trans3:probe5 -0.0857216968117895 0.101880342288795 -0.84139584620553 0.400344380147244 df.mm.trans3:probe6 -0.0499473205756268 0.101880342288795 -0.490254738583856 0.624070291840192 df.mm.trans3:probe7 0.0288702303842831 0.101880342288795 0.283373904481457 0.776953844284192 df.mm.trans3:probe8 0.0838650821668411 0.101880342288795 0.823172363605859 0.410622839699845 df.mm.trans3:probe9 0.0336886376475245 0.101880342288795 0.330668673570306 0.740969894938099 df.mm.trans3:probe10 -0.0124018648167006 0.101880342288795 -0.121729712897369 0.903139605813693 df.mm.trans3:probe11 0.0298747454321763 0.101880342288795 0.293233657848263 0.769409545118362 df.mm.trans3:probe12 -0.0324915893855878 0.101880342288795 -0.318919122724241 0.74986007797068 df.mm.trans3:probe13 0.0195094840829542 0.101880342288795 0.191494096355229 0.848180667437596 df.mm.trans3:probe14 0.082861970813673 0.101880342288795 0.813326388115076 0.416240928169223