fitVsDatCorrelation=0.88875783989221 cont.fitVsDatCorrelation=0.255612175791765 fstatistic=11852.7257747537,53,715 cont.fstatistic=2654.00174430588,53,715 residuals=-0.596684781001268,-0.0865646353421195,9.19007644448425e-05,0.085797249004949,0.677390573857474 cont.residuals=-0.631020473773332,-0.21153465500272,-0.0491187447430452,0.170920964057306,1.17427984189618 predictedValues: Include Exclude Both Lung 61.3181204346609 75.7234702797725 76.5722479915367 cerebhem 59.2821109266141 68.4375826017642 54.4262297388124 cortex 56.2677437632215 62.0841206485222 62.2437980301434 heart 61.9720131240147 69.5153757255858 69.9836448742748 kidney 63.639375928103 77.9395641702013 74.7635094552447 liver 64.3904354337973 75.0868287389754 72.3333723959718 stomach 60.5016435229473 67.8713660283888 67.6959524198762 testicle 60.6685206019143 76.803768924239 63.8286555516463 diffExp=-14.4053498451116,-9.15547167515011,-5.81637688530068,-7.54336260157114,-14.3001882420983,-10.6963933051781,-7.36972250544146,-16.1352483223247 diffExpScore=0.988428887458725 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=-1,0,0,0,-1,0,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 68.97503889764 71.7663684974383 70.0260632665658 cerebhem 66.7176940673711 62.2830833253018 67.7875376842485 cortex 66.9754632377683 66.1686588938648 66.1116883405546 heart 71.0590181012406 67.3960177388724 70.6639136867779 kidney 72.2637506270818 64.1252093546442 68.9824565590564 liver 71.1476702558617 63.8719493220189 69.2344718248019 stomach 71.6348431266848 82.1448951352962 72.9122630202706 testicle 72.1612784489925 61.2442653418945 66.7047728262666 cont.diffExp=-2.79132959979832,4.43461074206935,0.806804343903579,3.66300036236819,8.13854127243764,7.27572093384283,-10.5100520086113,10.9170131070980 cont.diffExpScore=2.11635205776949 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=0.79521742925732 cont.tran.correlation=0.142799648886915 tran.covariance=0.00269198795779188 cont.tran.covariance=0.000416937269621837 tran.mean=66.3438775532951 cont.tran.mean=68.7459502732482 weightedLogRatios: wLogRatio Lung -0.890807597085064 cerebhem -0.596591282858788 cortex -0.401276478036217 heart -0.480608751209989 kidney -0.862409562067225 liver -0.651880852154359 stomach -0.478182188544436 testicle -0.99598486235055 cont.weightedLogRatios: wLogRatio Lung -0.168744962997413 cerebhem 0.286544412650952 cortex 0.0508805218825892 heart 0.224245107964995 kidney 0.504296269301761 liver 0.454251746272313 stomach -0.594164036713622 testicle 0.688429521424725 varWeightedLogRatios=0.0489479314090991 cont.varWeightedLogRatios=0.169973248356066 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.25077500568691 0.0694088201601354 46.835186049654 4.85927133798199e-220 *** df.mm.trans1 0.727393563927743 0.059261746776519 12.2742511568349 1.46120600021438e-31 *** df.mm.trans2 1.07908470206080 0.053382381960918 20.2142478927002 3.24910375295688e-72 *** df.mm.exp2 0.206454670620260 0.0691630277763739 2.98504384868451 0.00293211352862067 ** df.mm.exp3 -0.0773760583826202 0.0691630277763739 -1.11874885860697 0.263623007007721 df.mm.exp4 0.0150404292152853 0.0691630277763739 0.217463429506236 0.82790924519259 df.mm.exp5 0.0899073365711625 0.0691630277763739 1.29993349715489 0.194042671079275 df.mm.exp6 0.0973958153230249 0.0691630277763739 1.40820635611756 0.159504627653688 df.mm.exp7 0.000329532694035337 0.0691630277763739 0.00476457877322579 0.99619975954574 df.mm.exp8 0.185547590546748 0.0691630277763739 2.68275690802147 0.00747052728902634 ** df.mm.trans1:exp2 -0.240222482626480 0.062251588594087 -3.85889722739217 0.000124197655757700 *** df.mm.trans2:exp2 -0.30762069935958 0.0485951307708993 -6.33027824968414 4.3199789066903e-10 *** df.mm.trans1:exp3 -0.00857790737893414 0.062251588594087 -0.137794192448109 0.890441869137794 df.mm.trans2:exp3 -0.121221847235125 0.0485951307708993 -2.4945266184512 0.0128371640807867 * df.mm.trans1:exp4 -0.0044329490913312 0.062251588594087 -0.0712102163406038 0.94325036354504 df.mm.trans2:exp4 -0.100580623367215 0.0485951307708993 -2.06976752138811 0.0388327146005719 * df.mm.trans1:exp5 -0.0527503412167589 0.062251588594087 -0.847373415009836 0.397070566575766 df.mm.trans2:exp5 -0.0610617842550167 0.0485951307708993 -1.25654120662605 0.209330179816326 df.mm.trans1:exp6 -0.0485061131347252 0.062251588594087 -0.779194784104394 0.436122798270759 df.mm.trans2:exp6 -0.105838810619858 0.0485951307708993 -2.17797151568194 0.0297342589717166 * df.mm.trans1:exp7 -0.0137344041410341 0.062251588594087 -0.220627367930956 0.82544557300091 df.mm.trans2:exp7 -0.109803450765629 0.0485951307708993 -2.25955664741999 0.0241489982890479 * df.mm.trans1:exp8 -0.196198035016143 0.062251588594087 -3.15169523295954 0.00169101404193452 ** df.mm.trans2:exp8 -0.171382032778703 0.0485951307708993 -3.52673261826747 0.000447580776545847 *** df.mm.trans1:probe2 0.137371365874452 0.042620749141891 3.22311007291593 0.00132563019051042 ** df.mm.trans1:probe3 -0.0182275719821136 0.042620749141891 -0.427668972251784 0.669020915852369 df.mm.trans1:probe4 -0.0460892248163549 0.042620749141891 -1.08137997910165 0.279892710849267 df.mm.trans1:probe5 -0.0521374285747689 0.042620749141891 -1.22328747439880 0.221624143665682 df.mm.trans1:probe6 0.130332267478345 0.042620749141891 3.0579534640381 0.00231167728137086 ** df.mm.trans1:probe7 0.055040594480122 0.042620749141891 1.29140373147557 0.196981042965952 df.mm.trans1:probe8 0.165875586268312 0.042620749141891 3.8918974820477 0.00010876051187572 *** df.mm.trans1:probe9 0.246591346453767 0.042620749141891 5.78571121856227 1.07996963608016e-08 *** df.mm.trans1:probe10 0.205213469612144 0.042620749141891 4.81487242115237 1.79842303316748e-06 *** df.mm.trans1:probe11 0.352164724311503 0.042620749141891 8.26275303465673 6.89888062776733e-16 *** df.mm.trans1:probe12 0.458103406734983 0.042620749141891 10.7483659006060 4.41236650337680e-25 *** df.mm.trans1:probe13 0.443199516219504 0.042620749141891 10.3986796370947 1.11030477471987e-23 *** df.mm.trans1:probe14 0.30093369311431 0.042620749141891 7.06073213571296 3.92985826522827e-12 *** df.mm.trans1:probe15 0.46748316178323 0.042620749141891 10.9684407523412 5.57652716348301e-26 *** df.mm.trans1:probe16 0.739722729428969 0.042620749141891 17.3559297835502 1.41998139577909e-56 *** df.mm.trans2:probe2 0.188389476007069 0.042620749141891 4.42013525806155 1.13953010835303e-05 *** df.mm.trans2:probe3 -0.0232469272250559 0.042620749141891 -0.545436851606324 0.585623209577903 df.mm.trans2:probe4 -0.0246744333468326 0.042620749141891 -0.578930071470298 0.562818686254324 df.mm.trans2:probe5 -0.133547904310063 0.042620749141891 -3.13340114847493 0.00179850851774291 ** df.mm.trans2:probe6 -0.0512650436353651 0.042620749141891 -1.20281892429192 0.229444545295466 df.mm.trans3:probe2 -0.807563343891068 0.042620749141891 -18.9476571892851 3.46564037720235e-65 *** df.mm.trans3:probe3 -0.9867594459235 0.042620749141891 -23.1520906082253 6.31537630519802e-89 *** df.mm.trans3:probe4 -0.8935605624569 0.042620749141891 -20.9653884656532 1.92995044580390e-76 *** df.mm.trans3:probe5 -0.941939565243841 0.042620749141891 -22.1004929338051 6.81228768195005e-83 *** df.mm.trans3:probe6 -1.04251554142357 0.042620749141891 -24.4602819615599 1.75104760677934e-96 *** df.mm.trans3:probe7 -0.308795729487165 0.042620749141891 -7.24519713295365 1.12087666232053e-12 *** df.mm.trans3:probe8 -0.166398981408079 0.042620749141891 -3.90417777158517 0.000103493655069676 *** df.mm.trans3:probe9 -1.00601030927732 0.042620749141891 -23.6037688105425 1.57247328372199e-91 *** df.mm.trans3:probe10 -1.03664210424080 0.042620749141891 -24.3224749708096 1.10007741490615e-95 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21744940133634 0.146392627284022 28.809165321925 9.6546409351362e-122 *** df.mm.trans1 0.0871670027593983 0.124991071567555 0.697385834573682 0.485788059938395 df.mm.trans2 0.0593933565335465 0.112590692766542 0.52751568601411 0.597999174260677 df.mm.exp2 -0.142511458661084 0.1458742177686 -0.976947543171406 0.328925541093957 df.mm.exp3 -0.0531054470568516 0.1458742177686 -0.364049575512327 0.715928616811835 df.mm.exp4 -0.0421314630892612 0.1458742177686 -0.288820490239710 0.772802466006102 df.mm.exp5 -0.0509851621459188 0.1458742177686 -0.349514553879538 0.72680596270442 df.mm.exp6 -0.0741541591093835 0.1458742177686 -0.508343148252655 0.611369515114313 df.mm.exp7 0.132516311440263 0.1458742177686 0.90842860011406 0.363957907809124 df.mm.exp8 -0.0647958364451933 0.1458742177686 -0.444189778264853 0.657039786212065 df.mm.trans1:exp2 0.109236970732528 0.1312970539748 0.831983410332225 0.405696087303426 df.mm.trans2:exp2 0.000785350882577248 0.102493729908529 0.00766242855322114 0.993888463743347 df.mm.trans1:exp3 0.0236870949995295 0.131297053974800 0.180408427169096 0.85688305948325 df.mm.trans2:exp3 -0.0281035937000892 0.102493729908529 -0.274198175099788 0.7840115286022 df.mm.trans1:exp4 0.0718975516623375 0.1312970539748 0.547594553615323 0.584141220309555 df.mm.trans2:exp4 -0.0206985657209731 0.102493729908529 -0.201949580129883 0.840013584963077 df.mm.trans1:exp5 0.0975631070864765 0.131297053974800 0.743071562787707 0.457682428594425 df.mm.trans2:exp5 -0.0615932305686821 0.102493729908529 -0.600946327386575 0.548066188459978 df.mm.trans1:exp6 0.105167054947711 0.1312970539748 0.800985641063172 0.423406040498587 df.mm.trans2:exp6 -0.0423815146222253 0.102493729908529 -0.413503486116164 0.679361759231222 df.mm.trans1:exp7 -0.0946794040363625 0.1312970539748 -0.721108365877992 0.471078557273444 df.mm.trans2:exp7 0.00255242934676155 0.102493729908529 0.0249032730981639 0.980139064956488 df.mm.trans1:exp8 0.109954744866385 0.131297053974800 0.83745020575625 0.402619386741495 df.mm.trans2:exp8 -0.0937499065743461 0.102493729908529 -0.914689187894847 0.360663180236537 df.mm.trans1:probe2 -0.117288495878115 0.089892947744964 -1.30475747898350 0.192395223654318 df.mm.trans1:probe3 -0.0855878400871334 0.089892947744964 -0.952108504995913 0.341363697325449 df.mm.trans1:probe4 -0.228321489894845 0.089892947744964 -2.53992660851013 0.0112981331853444 * df.mm.trans1:probe5 -0.112944359959931 0.089892947744964 -1.25643182021760 0.209369791886270 df.mm.trans1:probe6 -0.237712963710539 0.089892947744964 -2.64440058618343 0.00836311308725039 ** df.mm.trans1:probe7 -0.070850803754335 0.089892947744964 -0.788168655402716 0.430859284251637 df.mm.trans1:probe8 -0.174918264217227 0.089892947744964 -1.94585079925835 0.052064142696382 . df.mm.trans1:probe9 -0.0653167573489929 0.089892947744964 -0.72660602402653 0.467705142017853 df.mm.trans1:probe10 -0.175559774819569 0.089892947744964 -1.95298718335114 0.0512108603589528 . df.mm.trans1:probe11 -0.0561518999893532 0.089892947744964 -0.624653005580173 0.532398097498891 df.mm.trans1:probe12 -0.0895954358953928 0.089892947744964 -0.996690376085838 0.319252003504527 df.mm.trans1:probe13 -0.0789713661997565 0.089892947744964 -0.878504578844236 0.379965074723862 df.mm.trans1:probe14 -0.0856055208289246 0.089892947744964 -0.95230519163524 0.341264036227735 df.mm.trans1:probe15 -0.126565664649426 0.089892947744964 -1.40795988811611 0.159577574389047 df.mm.trans1:probe16 -0.137274084178454 0.089892947744964 -1.52708402185137 0.127182345416713 df.mm.trans2:probe2 -0.00816107120294416 0.089892947744964 -0.0907865567619163 0.9276876190306 df.mm.trans2:probe3 0.0500023755892541 0.089892947744964 0.55624358577178 0.578218378378358 df.mm.trans2:probe4 0.0435589657883835 0.089892947744964 0.484564883910193 0.628133431871876 df.mm.trans2:probe5 0.00209267390904847 0.089892947744964 0.0232796227239717 0.981433721204903 df.mm.trans2:probe6 -0.142321694162283 0.089892947744964 -1.58323536754034 0.113810021714528 df.mm.trans3:probe2 -0.0722009465809191 0.089892947744964 -0.803188107544999 0.422132933903982 df.mm.trans3:probe3 -0.0866605477706416 0.089892947744964 -0.964041673396971 0.335350965230207 df.mm.trans3:probe4 -0.106517292870632 0.089892947744964 -1.18493492028800 0.236436876383151 df.mm.trans3:probe5 -0.193022250001048 0.089892947744964 -2.14724575000781 0.0321093146051589 * df.mm.trans3:probe6 -0.160873348224701 0.089892947744964 -1.78961033385084 0.0739396816530655 . df.mm.trans3:probe7 -0.0881009864291013 0.089892947744964 -0.980065607360582 0.327385229202044 df.mm.trans3:probe8 -0.112762859202733 0.089892947744964 -1.25441274350746 0.210101936813572 df.mm.trans3:probe9 -0.09881685394986 0.089892947744964 -1.09927259511184 0.272019177538692 df.mm.trans3:probe10 -0.232467469959502 0.089892947744964 -2.58604791355867 0.00990494873704407 **