fitVsDatCorrelation=0.854309870743623 cont.fitVsDatCorrelation=0.3002973903578 fstatistic=7656.54811201356,42,462 cont.fstatistic=2265.73646741227,42,462 residuals=-0.599452707727281,-0.0857914048271119,-0.00868238296039619,0.0819739772169944,0.654064534972656 cont.residuals=-0.530643875144624,-0.177080523370781,-0.0604255996047557,0.107088568274908,1.31712149515636 predictedValues: Include Exclude Both Lung 42.9564287139683 45.8856695618443 61.6882117173228 cerebhem 47.456255387124 44.7111679844243 68.178914603395 cortex 65.3544070983352 48.5260847158931 108.832976960545 heart 45.8073175476883 47.9792002379814 59.876852510075 kidney 42.1216005020807 46.1916580584544 54.4213263269874 liver 47.2536681044623 52.594442821884 55.7993539906718 stomach 42.4473028246395 44.5974251881097 65.2694113844303 testicle 44.9975373226711 49.0122415861219 58.7102937303194 diffExp=-2.92924084787600,2.74508740269961,16.8283223824421,-2.17188269029312,-4.07005755637375,-5.34077471742168,-2.15012236347020,-4.01470426345085 diffExpScore=19.1360252556221 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=0,0,1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 54.5073488773015 61.1899443603124 53.3540413212285 cerebhem 54.8731166488307 62.4622863094703 54.8613154604436 cortex 52.5034463298991 61.0880152843211 51.3419232690255 heart 56.5767636081575 56.6449731712774 54.3657005351128 kidney 60.3846134803177 56.8742336017955 52.0944895673337 liver 60.2080039702092 54.8596287968216 51.2288663111003 stomach 54.0926528737901 69.2377058234795 51.0681108069627 testicle 55.2860419496904 54.7612107379347 56.9924966015914 cont.diffExp=-6.6825954830109,-7.58916966063961,-8.584568954422,-0.0682095631199076,3.51037987852224,5.34837517338756,-15.1450529496895,0.524831211755668 cont.diffExpScore=1.59850321142925 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,-1,0 cont.diffExp1.2Score=0.5 tran.correlation=0.292612748517962 cont.tran.correlation=-0.606157898213641 tran.covariance=0.00255901242232355 cont.tran.covariance=-0.00248855106553176 tran.mean=47.3682754784802 cont.tran.mean=57.8468741139755 weightedLogRatios: wLogRatio Lung -0.250222281220905 cerebhem 0.228211623599077 cortex 1.20011203732160 heart -0.178235481107395 kidney -0.349277811771343 liver -0.418583965572890 stomach -0.186432822737098 testicle -0.328973485685394 cont.weightedLogRatios: wLogRatio Lung -0.469083997160472 cerebhem -0.527198598506221 cortex -0.611289717989307 heart -0.0048631567541928 kidney 0.243807358546063 liver 0.376881847746837 stomach -1.01555959831287 testicle 0.0382273770954554 varWeightedLogRatios=0.28812531453732 cont.varWeightedLogRatios=0.231617536203330 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.63890264291857 0.0800080011883868 45.4817341874397 1.00250368535869e-172 *** df.mm.trans1 0.106790996082796 0.0642785768104474 1.66137773083736 0.0973159916834123 . df.mm.trans2 0.185980442390134 0.0642785768104474 2.89335034499248 0.00399172895624098 ** df.mm.exp2 -0.0263500060420020 0.086306213435471 -0.305308331730985 0.760268834476832 df.mm.exp3 -0.0921343468194845 0.086306213435471 -1.06752854924369 0.286290728325502 df.mm.exp4 0.138675123885549 0.086306213435471 1.60678030428520 0.108785774126805 df.mm.exp5 0.112357480342609 0.086306213435471 1.30184694554600 0.193617430339984 df.mm.exp6 0.332132135066421 0.086306213435471 3.84829923415362 0.000135674206271787 *** df.mm.exp7 -0.0968303152490422 0.0863062134354709 -1.12193909794733 0.262471097949049 df.mm.exp8 0.161816464175199 0.0863062134354709 1.87491094480915 0.0614355965101648 . df.mm.trans1:exp2 0.125972037288235 0.0682310526701784 1.84625668751104 0.0654943956771909 . df.mm.trans2:exp2 0.000420461170734724 0.0682310526701784 0.00616231399458525 0.995085875867198 df.mm.trans1:exp3 0.511772906748085 0.0682310526701784 7.50058641513184 3.28247078349536e-13 *** df.mm.trans2:exp3 0.148082971041098 0.0682310526701785 2.17031637716210 0.0304916218830126 * df.mm.trans1:exp4 -0.0744175899948342 0.0682310526701784 -1.09067040713208 0.275986438222576 df.mm.trans2:exp4 -0.0940603936041293 0.0682310526701784 -1.37855697549922 0.168698534904059 df.mm.trans1:exp5 -0.131983111424725 0.0682310526701784 -1.93435549151964 0.0536796895023752 . df.mm.trans2:exp5 -0.105711118400634 0.0682310526701784 -1.54931097005978 0.121991698633543 df.mm.trans1:exp6 -0.236788168550252 0.0682310526701784 -3.47038715194474 0.000568481366736059 *** df.mm.trans2:exp6 -0.195674529015368 0.0682310526701784 -2.8678222210822 0.00432216887235358 ** df.mm.trans1:exp7 0.0849073720370413 0.0682310526701784 1.24440952783587 0.213979804849455 df.mm.trans2:exp7 0.0683535830630369 0.0682310526701784 1.00179581565963 0.316966620074802 df.mm.trans1:exp8 -0.115395018316134 0.0682310526701784 -1.69123901508513 0.0914655561652642 . df.mm.trans2:exp8 -0.0958992273154715 0.0682310526701784 -1.4055070757744 0.160542648977549 df.mm.trans1:probe2 0.0557213305828453 0.0457707815840663 1.21739958668836 0.224073668928048 df.mm.trans1:probe3 0.0341794168173577 0.0457707815840663 0.746751871706213 0.455593194978747 df.mm.trans1:probe4 0.0832755077698668 0.0457707815840663 1.81940322816897 0.0694970365397129 . df.mm.trans1:probe5 0.00737069627421651 0.0457707815840663 0.161034966393984 0.872136253240418 df.mm.trans1:probe6 0.0368432072980672 0.0457707815840663 0.804950364030775 0.42126237219329 df.mm.trans2:probe2 0.0331787172646538 0.0457707815840663 0.72488858866688 0.4688873474185 df.mm.trans2:probe3 -0.0363650197921894 0.0457707815840663 -0.794502923778972 0.42731063438609 df.mm.trans2:probe4 -0.054748947913997 0.0457707815840663 -1.1961549709052 0.232249608105159 df.mm.trans2:probe5 0.0203428355630305 0.0457707815840663 0.444450255359246 0.656925207469853 df.mm.trans2:probe6 0.0566390103060771 0.0457707815840663 1.23744905255877 0.216548967853401 df.mm.trans3:probe2 0.174602586834247 0.0457707815840663 3.81471717963910 0.000154863686172444 *** df.mm.trans3:probe3 0.28363259136467 0.0457707815840663 6.19680463274869 1.27698032861535e-09 *** df.mm.trans3:probe4 0.50327927182996 0.0457707815840663 10.9956451345625 3.86189546182825e-25 *** df.mm.trans3:probe5 0.219776530262859 0.0457707815840663 4.80167746008882 2.12895044531085e-06 *** df.mm.trans3:probe6 0.362050216393806 0.0457707815840663 7.91007284262418 1.91553760499685e-14 *** df.mm.trans3:probe7 0.261105493305597 0.0457707815840663 5.70463261209622 2.08472540550499e-08 *** df.mm.trans3:probe8 -0.0689719403727275 0.0457707815840663 -1.50689889894163 0.132520011601909 df.mm.trans3:probe9 -0.0217057908198465 0.0457707815840663 -0.47422810073671 0.635561307304188 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11579854719531 0.146826786459454 28.0316599337403 9.86629319688915e-102 *** df.mm.trans1 -0.114306413831271 0.117960913047220 -0.96901940548319 0.333042517686852 df.mm.trans2 0.0479284882788192 0.117960913047220 0.406308217194227 0.684704283166789 df.mm.exp2 -0.000590634666143497 0.158384958779014 -0.00372910831114701 0.99702621858587 df.mm.exp3 -0.000681803153660082 0.158384958779014 -0.00430472160308711 0.99656719780287 df.mm.exp4 -0.0587005140520813 0.158384958779014 -0.370619246326181 0.71109101031326 df.mm.exp5 0.0531488775632231 0.158384958779014 0.335567707773179 0.737349206424654 df.mm.exp6 0.0309111740307979 0.158384958779014 0.195164833006186 0.8453497078616 df.mm.exp7 0.159715041988997 0.158384958779014 1.00839778739242 0.313791373054081 df.mm.exp8 -0.162785716397840 0.158384958779014 -1.02778519912971 0.304588672509326 df.mm.trans1:exp2 0.00727865035806015 0.125214304213391 0.0581295436155267 0.953670574994978 df.mm.trans2:exp2 0.0211707220492431 0.125214304213391 0.169075906959989 0.865810935755847 df.mm.trans1:exp3 -0.0367749193884393 0.125214304213391 -0.293695833071653 0.769122228792898 df.mm.trans2:exp3 -0.000985367281575393 0.125214304213391 -0.00786944660808179 0.993724551717557 df.mm.trans1:exp4 0.0959633436452915 0.125214304213391 0.766392819479714 0.443834012249001 df.mm.trans2:exp4 -0.0184791054319544 0.125214304213391 -0.147579827624663 0.882738759297317 df.mm.trans1:exp5 0.0492499168572979 0.125214304213391 0.393325005211592 0.694260892061973 df.mm.trans2:exp5 -0.126289343696401 0.125214304213391 -1.00858559642817 0.313701353683418 df.mm.trans1:exp6 0.0685585913976265 0.125214304213391 0.547530027246635 0.584279051152144 df.mm.trans2:exp6 -0.140116323096659 0.125214304213391 -1.11901211268860 0.263716430104579 df.mm.trans1:exp7 -0.167352206122740 0.125214304213391 -1.33652626330564 0.182035149077611 df.mm.trans2:exp7 -0.0361523140976281 0.125214304213391 -0.28872351545409 0.772922464963847 df.mm.trans1:exp8 0.17697065272912 0.125214304213391 1.41334214042770 0.158228541006046 df.mm.trans2:exp8 0.0517849582899753 0.125214304213391 0.413570626896772 0.67938049210404 df.mm.trans1:probe2 0.0342603934891936 0.0839963087929443 0.407879750688182 0.683550908538277 df.mm.trans1:probe3 0.0457847241440881 0.0839963087929443 0.545080192237376 0.585961396188748 df.mm.trans1:probe4 -0.0518263435708739 0.0839963087929443 -0.617007393725227 0.537533794742291 df.mm.trans1:probe5 0.0155949431438963 0.0839963087929443 0.185662243591427 0.852791122992404 df.mm.trans1:probe6 -0.0911627022037432 0.0839963087929443 -1.08531795639335 0.278346845318253 df.mm.trans2:probe2 0.00160406470100302 0.0839963087929443 0.0190968475169204 0.984772090727812 df.mm.trans2:probe3 -0.266993966474177 0.0839963087929443 -3.17863927964183 0.00157901265616671 ** df.mm.trans2:probe4 -0.157096091167081 0.0839963087929443 -1.87027374684204 0.0620779083575793 . df.mm.trans2:probe5 -0.177553294890719 0.0839963087929443 -2.11382258866158 0.035065746406995 * df.mm.trans2:probe6 -0.146123221577430 0.0839963087929443 -1.73963860647296 0.0825884639304527 . df.mm.trans3:probe2 -0.107402513250634 0.0839963087929443 -1.27865753619468 0.201659612024614 df.mm.trans3:probe3 -0.00323183309592801 0.0839963087929443 -0.0384758942669096 0.969324870792515 df.mm.trans3:probe4 -0.0821535548898762 0.0839963087929443 -0.978061489492227 0.328555773676636 df.mm.trans3:probe5 -0.0567327757107151 0.0839963087929443 -0.675419866967781 0.499746952847977 df.mm.trans3:probe6 -0.150058110436958 0.0839963087929443 -1.78648458001720 0.0746764403269087 . df.mm.trans3:probe7 -0.126750663739952 0.0839963087929443 -1.50900278311514 0.131981620987999 df.mm.trans3:probe8 -0.0341441570618122 0.0839963087929443 -0.406495922886082 0.684566483972829 df.mm.trans3:probe9 -0.0917644527833886 0.0839963087929443 -1.09248196857785 0.275190659022841