fitVsDatCorrelation=0.904949769872323 cont.fitVsDatCorrelation=0.288809850612816 fstatistic=8985.0203168043,52,692 cont.fstatistic=1764.25096949228,52,692 residuals=-0.668841231883889,-0.0867763706989358,-0.000287716211128017,0.083701458191532,1.08327739360381 cont.residuals=-0.680876970088705,-0.263608128850906,-0.0705601149875117,0.207594505289632,1.72279357081731 predictedValues: Include Exclude Both Lung 57.638317217075 64.9010512424156 80.0210119290034 cerebhem 62.7562358203755 75.1085435833498 76.3810516838398 cortex 54.8121946163628 99.336970471931 111.080510418772 heart 52.6636332844467 63.2547460383127 65.3629391455412 kidney 58.7181090320876 61.0097103692425 80.6935283491815 liver 56.2255191428375 59.2185175056338 76.1852398264712 stomach 55.7653950924447 62.5953406248289 67.3461706408499 testicle 53.7314941853466 65.3461793210033 79.2145101738904 diffExp=-7.2627340253406,-12.3523077629743,-44.5247758555682,-10.5911127538661,-2.29160133715494,-2.99299836279636,-6.82994553238414,-11.6146851356567 diffExpScore=0.989945723068402 diffExp1.5=0,0,-1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,-1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,-1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,-1,-1,0,0,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 64.3769902582556 60.2896649878779 66.4523000544425 cerebhem 57.8171637663206 61.8538617474562 75.4059986584857 cortex 59.0919675980849 65.8381365553297 74.384372179085 heart 67.0746470309447 67.9705191567218 54.5803806633387 kidney 59.5983943864996 57.5791422934969 61.3071954092101 liver 62.055560291692 60.8104558316046 56.9891332492151 stomach 59.6455023817689 61.6774699655135 83.8811225841775 testicle 61.5716960106966 73.8434275606875 65.0296997558423 cont.diffExp=4.08732527037772,-4.0366979811356,-6.74616895724485,-0.895872125777103,2.01925209300268,1.24510446008743,-2.03196758374464,-12.2717315499909 cont.diffExpScore=1.69805581535250 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=0.0143069573551758 cont.tran.correlation=0.26906234446517 tran.covariance=0.000382353238846883 cont.tran.covariance=0.00106844841811727 tran.mean=62.6926223467309 cont.tran.mean=62.5684124889344 weightedLogRatios: wLogRatio Lung -0.488177785532435 cerebhem -0.759868625974205 cortex -2.55752458727816 heart -0.743158887169541 kidney -0.156657447775983 liver -0.210322415187513 stomach -0.471267766015926 testicle -0.798816489538292 cont.weightedLogRatios: wLogRatio Lung 0.271038210627523 cerebhem -0.276098923978722 cortex -0.446810771332514 heart -0.0558903829901388 kidney 0.140299354136640 liver 0.0834630482094108 stomach -0.137523005136442 testicle -0.765340596247696 varWeightedLogRatios=0.579272465348039 cont.varWeightedLogRatios=0.115383461390773 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.74089841616291 0.0881542569562556 42.4358226741023 9.44091999241686e-195 *** df.mm.trans1 0.069125999388992 0.0791754143389076 0.873074046611245 0.382925571102312 df.mm.trans2 0.371923553515947 0.0728125464919554 5.10795970522798 4.21574401337697e-07 *** df.mm.exp2 0.277695464799573 0.0997351566147675 2.78432875853584 0.00551040998456087 ** df.mm.exp3 0.0474131057095816 0.0997351566147675 0.47539009631997 0.634658970224169 df.mm.exp4 0.0863777415422622 0.0997351566147675 0.866071147568364 0.386751316032159 df.mm.exp5 -0.0516393077373368 0.0997351566147675 -0.517764341984206 0.604788178376495 df.mm.exp6 -0.067324869226398 0.0997351566147675 -0.675036481733758 0.499878063520278 df.mm.exp7 0.103236169998111 0.0997351566147675 1.03510310207729 0.30098234810016 df.mm.exp8 -0.0532233524687128 0.0997351566147675 -0.533646853078007 0.593757248900802 df.mm.trans1:exp2 -0.19262509209951 0.0954891425393022 -2.01724601328604 0.0440554745162565 * df.mm.trans2:exp2 -0.131624971197910 0.0831126305123063 -1.58369396307846 0.113720256934186 df.mm.trans1:exp3 -0.097687983013869 0.0954891425393023 -1.02302712555683 0.306652514542738 df.mm.trans2:exp3 0.378240885487706 0.0831126305123063 4.55094349867438 6.30766029302271e-06 *** df.mm.trans1:exp4 -0.176640170704960 0.0954891425393022 -1.84984560555937 0.0647619581565966 . df.mm.trans2:exp4 -0.112071402029764 0.0831126305123063 -1.34842804684385 0.177961905150275 df.mm.trans1:exp5 0.0701999123951278 0.0954891425393023 0.735161197685216 0.462490398851631 df.mm.trans2:exp5 -0.0101914758360505 0.0831126305123063 -0.122622467526659 0.902441664744569 df.mm.trans1:exp6 0.0425080243409299 0.0954891425393023 0.445160813161916 0.656342758023398 df.mm.trans2:exp6 -0.0243046635496606 0.0831126305123063 -0.292430445286675 0.770045188589071 df.mm.trans1:exp7 -0.136270228620893 0.0954891425393022 -1.42707563391100 0.154009145405791 df.mm.trans2:exp7 -0.139409147034368 0.0831126305123063 -1.67735212055075 0.0939252294532816 . df.mm.trans1:exp8 -0.016964910030403 0.0954891425393023 -0.177663235623049 0.85903945118978 df.mm.trans2:exp8 0.0600585045048354 0.0831126305123063 0.72261585434891 0.470160062897207 df.mm.trans1:probe2 0.0548441198593705 0.0477445712696511 1.14869855149861 0.251077202741598 df.mm.trans1:probe3 0.0666265246305914 0.0477445712696511 1.39547854046692 0.163319206642972 df.mm.trans1:probe4 0.0979505304246054 0.0477445712696511 2.05155325139275 0.0405889656712884 * df.mm.trans1:probe5 0.229873956986054 0.0477445712696511 4.81466166462728 1.81198966708968e-06 *** df.mm.trans1:probe6 0.0669734923914907 0.0477445712696511 1.40274570721850 0.161141161038836 df.mm.trans1:probe7 -0.0404989989322093 0.0477445712696511 -0.84824301182808 0.396595953905219 df.mm.trans1:probe8 0.187220510899521 0.0477445712696511 3.92129421043787 9.68450577881042e-05 *** df.mm.trans1:probe9 0.078892450705162 0.0477445712696511 1.65238578140317 0.0989095491462437 . df.mm.trans1:probe10 0.101849435393938 0.0477445712696511 2.13321499566337 0.033258267613254 * df.mm.trans1:probe11 0.197845488833406 0.0477445712696511 4.14383213781556 3.83867594229679e-05 *** df.mm.trans1:probe12 0.0636984668688763 0.0477445712696511 1.33415098669796 0.182593255706339 df.mm.trans1:probe13 0.00705395031301142 0.0477445712696511 0.147743505186636 0.882588233054086 df.mm.trans1:probe14 0.142872859579409 0.0477445712696511 2.99244198408429 0.00286603879971616 ** df.mm.trans1:probe15 0.0568661374030692 0.0477445712696511 1.19104928352799 0.234042608067662 df.mm.trans1:probe16 -0.0199674007386505 0.0477445712696511 -0.418213007419816 0.675921135056049 df.mm.trans1:probe17 0.642667178107163 0.0477445712696511 13.4605288311736 7.49306866989288e-37 *** df.mm.trans1:probe18 0.719457340431938 0.0477445712696511 15.0688826247616 1.38462896589964e-44 *** df.mm.trans1:probe19 0.970315335896149 0.0477445712696511 20.3230505603666 2.31290579103593e-72 *** df.mm.trans1:probe20 0.772821481868971 0.0477445712696511 16.1865833395852 3.25702273266470e-50 *** df.mm.trans1:probe21 0.718045643247054 0.0477445712696511 15.0393149242389 1.93905270412959e-44 *** df.mm.trans1:probe22 0.988670506981368 0.0477445712696511 20.7074957569012 1.69546925258557e-74 *** df.mm.trans2:probe2 -0.0338900229984676 0.0477445712696511 -0.709819401394642 0.478055245291146 df.mm.trans2:probe3 0.0563282792713866 0.0477445712696511 1.17978395812283 0.238491570132119 df.mm.trans2:probe4 0.0196726604755209 0.0477445712696511 0.41203973462059 0.68043790204822 df.mm.trans2:probe5 0.282748627161329 0.0477445712696511 5.92211050685586 5.00626563086163e-09 *** df.mm.trans2:probe6 0.215517122110742 0.0477445712696511 4.51396077877729 7.4769914080172e-06 *** df.mm.trans3:probe2 -0.116408983451226 0.0477445712696511 -2.43816166645990 0.0150127450966604 * df.mm.trans3:probe3 0.71743282414823 0.0477445712696511 15.0264795571485 2.24406270844926e-44 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19249982688083 0.198340876561157 21.1378506517193 6.74010904373165e-77 *** df.mm.trans1 0.00335566904940977 0.178139112327432 0.0188373513574145 0.984976286933182 df.mm.trans2 -0.119331145329867 0.163823107295100 -0.728414613177323 0.466606238564302 df.mm.exp2 -0.208259185962768 0.224397086084605 -0.928083290191421 0.35368796051846 df.mm.exp3 -0.110384185020588 0.224397086084605 -0.4919145205788 0.622935777041215 df.mm.exp4 0.357773140607024 0.224397086084605 1.59437516257289 0.111308631405495 df.mm.exp5 -0.0425407663309371 0.224397086084605 -0.189578069275364 0.849695365656487 df.mm.exp6 0.125498684609644 0.224397086084605 0.559270562730599 0.576158035449944 df.mm.exp7 -0.286495706977516 0.224397086084605 -1.27673541567067 0.202123937203152 df.mm.exp8 0.179872634532827 0.224397086084605 0.801581863968631 0.423070028207228 df.mm.trans1:exp2 0.100788593395203 0.214843853118933 0.469124864090985 0.639128150650384 df.mm.trans2:exp2 0.233873024351648 0.186997571737171 1.25067412469057 0.211476103094018 df.mm.trans1:exp3 0.0247229130107334 0.214843853118933 0.115073867145025 0.90841995502593 df.mm.trans2:exp3 0.198422742558518 0.186997571737171 1.06109796354685 0.289015571951504 df.mm.trans1:exp4 -0.316723281752486 0.214843853118933 -1.47420220385432 0.140881919741822 df.mm.trans2:exp4 -0.23785976705804 0.186997571737171 -1.27199388124867 0.203802579689801 df.mm.trans1:exp5 -0.0345868749235618 0.214843853118934 -0.160986104193613 0.872151330936476 df.mm.trans2:exp5 -0.00345954031385435 0.186997571737171 -0.0185004558172381 0.985244946699563 df.mm.trans1:exp6 -0.162224842254789 0.214843853118933 -0.755082539713084 0.450456366397555 df.mm.trans2:exp6 -0.116897635316061 0.186997571737171 -0.625129161999832 0.532092369737016 df.mm.trans1:exp7 0.21015817740163 0.214843853118933 0.978190319856583 0.32832204328165 df.mm.trans2:exp7 0.309253720866160 0.186997571737171 1.65378468818206 0.098624782912921 . df.mm.trans1:exp8 -0.224426625071716 0.214843853118933 -1.04460342622638 0.296571056650072 df.mm.trans2:exp8 0.0229136775292075 0.186997571737171 0.122534626072114 0.902511201633377 df.mm.trans1:probe2 -0.0883389751072602 0.107421926559467 -0.82235515538215 0.411158058603183 df.mm.trans1:probe3 0.157538104297903 0.107421926559467 1.46653583065923 0.142956640964306 df.mm.trans1:probe4 -0.0660260543887296 0.107421926559467 -0.614642247662342 0.53899304588107 df.mm.trans1:probe5 -0.0846877558677242 0.107421926559467 -0.788365639866295 0.430752843314089 df.mm.trans1:probe6 0.0360291699558644 0.107421926559467 0.335398657516344 0.73742603325073 df.mm.trans1:probe7 -0.000747804774390731 0.107421926559467 -0.00696137928579004 0.994447674130053 df.mm.trans1:probe8 -0.0298346470824118 0.107421926559467 -0.277733308626669 0.781300043889898 df.mm.trans1:probe9 -0.128284385008018 0.107421926559467 -1.19421042906918 0.232804849332898 df.mm.trans1:probe10 0.0300384510043734 0.107421926559467 0.279630536953223 0.779844535071865 df.mm.trans1:probe11 -0.0947879846115497 0.107421926559467 -0.882389542316362 0.377872596283358 df.mm.trans1:probe12 -0.0610892063572984 0.107421926559467 -0.568684702591706 0.5697546357952 df.mm.trans1:probe13 -0.0299635889968615 0.107421926559467 -0.278933640054148 0.78037908836772 df.mm.trans1:probe14 -0.098597699491834 0.107421926559467 -0.917854507452463 0.359014827994252 df.mm.trans1:probe15 -0.041567543101371 0.107421926559467 -0.386955851870335 0.698907859786721 df.mm.trans1:probe16 -0.056257870531753 0.107421926559467 -0.523709379766241 0.600648366213092 df.mm.trans1:probe17 0.0297103104983477 0.107421926559467 0.276575848617839 0.782188397490374 df.mm.trans1:probe18 0.0715195272119297 0.107421926559467 0.665781461034753 0.505772658403761 df.mm.trans1:probe19 -0.0507877725216684 0.107421926559467 -0.472787764549663 0.636513684679334 df.mm.trans1:probe20 -0.0460599487848241 0.107421926559467 -0.428776044705605 0.668219653962548 df.mm.trans1:probe21 -0.106256639464573 0.107421926559467 -0.989152241705057 0.322934392019656 df.mm.trans1:probe22 -0.119028206657019 0.107421926559467 -1.10804386468648 0.268227843890919 df.mm.trans2:probe2 -0.104516308359699 0.107421926559467 -0.972951349013842 0.330917449166717 df.mm.trans2:probe3 0.148668993882369 0.107421926559467 1.38397251514632 0.166813063956711 df.mm.trans2:probe4 0.092487012136163 0.107421926559467 0.860969590644643 0.389553002102673 df.mm.trans2:probe5 0.186949289493941 0.107421926559467 1.74032709598118 0.082246125260663 . df.mm.trans2:probe6 -0.0896608588413972 0.107421926559467 -0.834660685328173 0.404196830854472 df.mm.trans3:probe2 0.187559788742645 0.107421926559467 1.74601028625954 0.0812529224806692 . df.mm.trans3:probe3 0.172320353066602 0.107421926559467 1.60414506223931 0.109138351476339