fitVsDatCorrelation=0.862254278775626 cont.fitVsDatCorrelation=0.25422379663329 fstatistic=9517.23765253056,53,715 cont.fstatistic=2600.23282984328,53,715 residuals=-0.636626589404771,-0.0865383003630842,-0.00380209009625759,0.0839802429267112,0.80521537305695 cont.residuals=-0.689403777889344,-0.219430829975431,-0.0511209990365864,0.169587941362547,1.11536031366904 predictedValues: Include Exclude Both Lung 67.500026577265 70.2317024950023 72.2196403539186 cerebhem 62.8450419470634 73.008314860129 58.6600884920895 cortex 60.0408344323884 67.0498792950736 56.8059352352084 heart 61.5621775550654 67.8751821602612 61.1844052923436 kidney 68.2614777418422 68.6957370568556 66.2400817327783 liver 67.0781216929067 72.7127492431307 58.9529092257976 stomach 62.5986868768864 77.1962721536468 60.9327654774627 testicle 61.3539166767907 67.457687447223 57.9645001298841 diffExp=-2.73167591773725,-10.1632729130657,-7.00904486268517,-6.31300460519584,-0.434259315013364,-5.63462755022404,-14.5975852767604,-6.1037707704323 diffExpScore=0.98147710500543 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=0,0,0,0,0,0,-1,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 68.8193098091172 64.1994060051602 65.7091444327081 cerebhem 70.3592426169973 70.9701693839465 68.6366614154414 cortex 68.0172083721405 72.9706095705574 63.7796441736062 heart 66.568497806473 64.2310374617883 68.0799115069329 kidney 68.1391739895932 65.7250012617215 73.7398839278545 liver 70.1588252112165 67.9141301141356 68.6167429918365 stomach 72.7896542051015 81.3948946350107 69.7097780522382 testicle 66.8708546986715 77.2728563044717 61.6824721025596 cont.diffExp=4.619903803957,-0.610926766949163,-4.9534011984169,2.33746034468469,2.4141727278717,2.24469509708086,-8.6052404299092,-10.4020016058002 cont.diffExpScore=2.59311540167696 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=0.177233973772553 cont.tran.correlation=0.467467334203917 tran.covariance=0.000494056007805203 cont.tran.covariance=0.00118176971730862 tran.mean=67.2167380132206 cont.tran.mean=69.7750544653814 weightedLogRatios: wLogRatio Lung -0.167889710012333 cerebhem -0.631927287986881 cortex -0.458234894675765 heart -0.406975615238737 kidney -0.0268026807768669 liver -0.342492381309494 stomach -0.889057528574598 testicle -0.394927730902917 cont.weightedLogRatios: wLogRatio Lung 0.291633137793037 cerebhem -0.0368119245434297 cortex -0.299103124670592 heart 0.149426582626233 kidney 0.151633006926955 liver 0.137695399113462 stomach -0.485330778474918 testicle -0.618085146699848 varWeightedLogRatios=0.070163168919225 cont.varWeightedLogRatios=0.113556971037101 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79324748212768 0.0818357470935466 46.3519625206282 1.28896139851569e-217 *** df.mm.trans1 0.112014640378204 0.0719973815367859 1.55581547533047 0.120194474428158 df.mm.trans2 0.429903784038429 0.0650976677905986 6.60398135031983 7.81029913034614e-11 *** df.mm.exp2 0.175270042363345 0.0867415175649948 2.02060152143426 0.0436933392879665 * df.mm.exp3 0.0766051330377565 0.0867415175649948 0.883142642510915 0.377456038494947 df.mm.exp4 0.0396100344529961 0.0867415175649948 0.456644471585555 0.648065296159348 df.mm.exp5 0.0755312265015323 0.0867415175649948 0.870762105873203 0.384176316731684 df.mm.exp6 0.231419928853029 0.0867415175649948 2.66792575630958 0.00780497727420063 ** df.mm.exp7 0.189108650937332 0.0867415175649948 2.18013998654836 0.0295725118194692 * df.mm.exp8 0.0841130356612201 0.0867415175649948 0.969697533804326 0.332525184356820 df.mm.trans1:exp2 -0.246725989133846 0.0816042452235408 -3.02344551386981 0.00258853080758192 ** df.mm.trans2:exp2 -0.136496517090728 0.0670608511806068 -2.03541283308675 0.0421776112396033 * df.mm.trans1:exp3 -0.193708220043558 0.0816042452235408 -2.37375175167576 0.0178716217797716 * df.mm.trans2:exp3 -0.122968135117266 0.0670608511806068 -1.83367990343712 0.0671170282312745 . df.mm.trans1:exp4 -0.131690344834289 0.0816042452235408 -1.61376830915531 0.107018906143879 df.mm.trans2:exp4 -0.0737393840247027 0.0670608511806068 -1.09958914517964 0.271881263301171 df.mm.trans1:exp5 -0.0643136261498181 0.0816042452235408 -0.788116181623174 0.430889954375218 df.mm.trans2:exp5 -0.0976438923556207 0.0670608511806068 -1.45604910520220 0.145817992899838 df.mm.trans1:exp6 -0.237689984905922 0.0816042452235408 -2.9127159286237 0.00369469195322012 ** df.mm.trans2:exp6 -0.196703003415668 0.0670608511806068 -2.93320171087467 0.00346216351709181 ** df.mm.trans1:exp7 -0.264492341074518 0.0816042452235408 -3.24115908859872 0.00124563056770426 ** df.mm.trans2:exp7 -0.094557294820669 0.0670608511806068 -1.41002228805610 0.158967948193221 df.mm.trans1:exp8 -0.179582016645268 0.0816042452235408 -2.20064551976842 0.0280801224781136 * df.mm.trans2:exp8 -0.124412298553686 0.0670608511806068 -1.85521502282489 0.0639769190181217 . df.mm.trans1:probe2 0.434189918091938 0.0476466140592184 9.11271297373403 7.96576324480881e-19 *** df.mm.trans1:probe3 0.555393593059289 0.0476466140592184 11.6565175516776 7.20073260968268e-29 *** df.mm.trans1:probe4 0.229376140141527 0.0476466140592184 4.81411207638896 1.80506669710449e-06 *** df.mm.trans1:probe5 0.372508067623122 0.0476466140592184 7.81814353398008 1.92239293009820e-14 *** df.mm.trans1:probe6 -0.0130144557272725 0.0476466140592184 -0.273145447672258 0.784820286845557 df.mm.trans1:probe7 0.350790548773006 0.0476466140592184 7.36233950091437 4.98356428714544e-13 *** df.mm.trans1:probe8 0.133730535479205 0.0476466140592184 2.80671645026016 0.00514115580561456 ** df.mm.trans1:probe9 0.0980131606209305 0.0476466140592184 2.05708553600710 0.0400402042917466 * df.mm.trans1:probe10 0.760168641920518 0.0476466140592184 15.9543056086993 3.02368955502558e-49 *** df.mm.trans1:probe11 1.06048722650143 0.0476466140592184 22.2573470841683 8.6382200972138e-84 *** df.mm.trans1:probe12 0.763090545967197 0.0476466140592184 16.0156300932271 1.46447635725398e-49 *** df.mm.trans1:probe13 0.815774137675977 0.0476466140592184 17.1213454257648 2.4947542936884e-55 *** df.mm.trans1:probe14 0.699762783970762 0.0476466140592184 14.6865165088342 7.17978677364647e-43 *** df.mm.trans1:probe15 0.816170186693275 0.0476466140592184 17.1296576432248 2.25444657179668e-55 *** df.mm.trans1:probe16 0.194732430635060 0.0476466140592184 4.08701508974035 4.86489354803475e-05 *** df.mm.trans1:probe17 0.127050703353263 0.0476466140592184 2.66652113401713 0.00783733885052944 ** df.mm.trans1:probe18 0.133968128126699 0.0476466140592184 2.8117030091623 0.00506300299548409 ** df.mm.trans1:probe19 0.106598298124909 0.0476466140592184 2.23726911617311 0.0255761212787585 * df.mm.trans1:probe20 0.0306963478694856 0.0476466140592184 0.644250351794865 0.519619721489478 df.mm.trans1:probe21 0.309025657954058 0.0476466140592184 6.48578422739504 1.64706940724098e-10 *** df.mm.trans2:probe2 -0.0921518899371376 0.0476466140592184 -1.93407006471866 0.0534988018248705 . df.mm.trans2:probe3 0.00601234177855843 0.0476466140592184 0.126186128799958 0.899620065349395 df.mm.trans2:probe4 0.112783642131055 0.0476466140592184 2.36708618981571 0.0181941535447477 * df.mm.trans2:probe5 0.124994120754487 0.0476466140592184 2.62335788644154 0.00889238772710154 ** df.mm.trans2:probe6 0.163495784858427 0.0476466140592184 3.43142504638889 0.000634947405396796 *** df.mm.trans3:probe2 -0.0914855021459438 0.0476466140592184 -1.92008401755977 0.0552448296994738 . df.mm.trans3:probe3 -0.0669240412588209 0.0476466140592184 -1.40459175494912 0.160576970734440 df.mm.trans3:probe4 0.0921996436205828 0.0476466140592184 1.9350723118749 0.0533754739707848 . df.mm.trans3:probe5 -0.211059453093806 0.0476466140592184 -4.42968419186066 1.09151540124209e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.23785691809747 0.156270240840314 27.1187712728232 6.32528164158279e-112 *** df.mm.trans1 0.0508635333863046 0.137483294919571 0.369961553627735 0.711520692784683 df.mm.trans2 -0.054833509384957 0.124307879931138 -0.441110486441669 0.65926636264145 df.mm.exp2 0.0788067503878739 0.165638102200511 0.47577670439906 0.634378796208617 df.mm.exp3 0.146143176999292 0.165638102200511 0.882304101880982 0.377908901330767 df.mm.exp4 -0.0682044105711784 0.165638102200511 -0.411767640808963 0.680633148764527 df.mm.exp5 -0.101752368327395 0.165638102200511 -0.614305325740935 0.539208999363212 df.mm.exp6 0.0322289170400818 0.165638102200511 0.19457429547863 0.845781498850394 df.mm.exp7 0.234305559172086 0.165638102200511 1.41456317151262 0.157631932747044 df.mm.exp8 0.219865926738536 0.165638102200511 1.32738738139115 0.184804241448776 df.mm.trans1:exp2 -0.0566769663640798 0.155828174209708 -0.363714499329281 0.716178732955854 df.mm.trans2:exp2 0.0214589304469786 0.128056695724554 0.167573669815252 0.866966070707745 df.mm.trans1:exp3 -0.157866810932573 0.155828174209708 -1.01308259390963 0.311363409181984 df.mm.trans2:exp3 -0.0180803839390551 0.128056695724554 -0.141190461277756 0.88775925049748 df.mm.trans1:exp4 0.0349514989159218 0.155828174209708 0.224295119243876 0.822591751527079 df.mm.trans2:exp4 0.068696995606531 0.128056695724554 0.536457662114725 0.591809153553994 df.mm.trans1:exp5 0.0918202868422538 0.155828174209708 0.589240599833285 0.555886009776397 df.mm.trans2:exp5 0.125237799743278 0.128056695724554 0.977987125426543 0.328411469142052 df.mm.trans1:exp6 -0.0129516849304866 0.155828174209708 -0.0831151683331459 0.933783228096376 df.mm.trans2:exp6 0.0240212393055569 0.128056695724554 0.187582845001918 0.851256894288088 df.mm.trans1:exp7 -0.178216098025884 0.155828174209708 -1.14367057773550 0.253143034217985 df.mm.trans2:exp7 0.00301303400080592 0.128056695724554 0.0235289063469735 0.981234945708007 df.mm.trans1:exp8 -0.248587080648026 0.155828174209708 -1.59526402660335 0.111095094663176 df.mm.trans2:exp8 -0.0345171386284041 0.128056695724554 -0.269545754191950 0.787587507343356 df.mm.trans1:probe2 0.00229294868119467 0.0909840518196565 0.0252016549640988 0.979901148678441 df.mm.trans1:probe3 0.040923881370782 0.0909840518196565 0.449791810238337 0.652996890465566 df.mm.trans1:probe4 -0.121720879453019 0.0909840518196565 -1.3378265423295 0.181378333217251 df.mm.trans1:probe5 -0.083858930554323 0.0909840518196565 -0.921688239610866 0.357002103815821 df.mm.trans1:probe6 -0.0204781323560635 0.0909840518196565 -0.225073866754737 0.821986121141356 df.mm.trans1:probe7 0.00982064846316711 0.0909840518196565 0.107938130548781 0.91407505118526 df.mm.trans1:probe8 -0.117167772636023 0.0909840518196565 -1.28778363122656 0.198237931274699 df.mm.trans1:probe9 -0.0980490311578846 0.0909840518196565 -1.07765074424507 0.281553089243454 df.mm.trans1:probe10 -0.217865980298675 0.0909840518196565 -2.39455130807449 0.0168972249997812 * df.mm.trans1:probe11 -0.0810380705600296 0.0909840518196565 -0.890684344555887 0.373398140799294 df.mm.trans1:probe12 -0.0248916333805089 0.0909840518196565 -0.273582379358613 0.784484585594171 df.mm.trans1:probe13 -0.0461490716990714 0.0909840518196565 -0.507221549008891 0.612155778086303 df.mm.trans1:probe14 -0.156089210963644 0.0909840518196565 -1.71556671572547 0.0866745998002732 . df.mm.trans1:probe15 -0.100447105642544 0.0909840518196565 -1.10400783031344 0.269961142916977 df.mm.trans1:probe16 -0.128180712944506 0.0909840518196565 -1.40882616657454 0.159321295065422 df.mm.trans1:probe17 -0.116694777167840 0.0909840518196565 -1.28258496773859 0.200053158546745 df.mm.trans1:probe18 -0.0098086573681399 0.0909840518196565 -0.107806337176345 0.914179559646138 df.mm.trans1:probe19 -0.0555917169286797 0.0909840518196565 -0.611005069755198 0.541390411393311 df.mm.trans1:probe20 -0.0153293348549732 0.0909840518196565 -0.168483756750668 0.866250369478159 df.mm.trans1:probe21 -0.147814542982176 0.0909840518196565 -1.62462035956769 0.104684182960095 df.mm.trans2:probe2 -0.124272947946174 0.0909840518196565 -1.36587616687484 0.172407309339518 df.mm.trans2:probe3 0.0637499723774344 0.0909840518196565 0.70067194307631 0.483735713032925 df.mm.trans2:probe4 -0.144561510397834 0.0909840518196565 -1.58886648271474 0.112532603390762 df.mm.trans2:probe5 0.00143257587154598 0.0909840518196565 0.0157453514423116 0.98744193867704 df.mm.trans2:probe6 -0.0276720433751967 0.0909840518196565 -0.304141691008076 0.761108437517147 df.mm.trans3:probe2 0.00490155572114741 0.0909840518196565 0.0538726911268252 0.95705164294103 df.mm.trans3:probe3 -0.127496519107102 0.0909840518196565 -1.40130623507314 0.161556413926267 df.mm.trans3:probe4 -0.0571023783768979 0.0909840518196565 -0.627608654867153 0.530460716886723 df.mm.trans3:probe5 -0.0635468663633084 0.0909840518196565 -0.698439617629554 0.485129403420812