fitVsDatCorrelation=0.87914557235742 cont.fitVsDatCorrelation=0.232130583843501 fstatistic=10002.740611363,61,899 cont.fstatistic=2389.83157313162,61,899 residuals=-0.594560961647835,-0.0910664504487419,0.000603731616124789,0.0870617292172213,0.969671034261664 cont.residuals=-0.790125070356868,-0.268214156503735,-0.0141940589232418,0.230557563703875,1.04197553583101 predictedValues: Include Exclude Both Lung 51.7671341272893 77.6745390632227 72.14308743151 cerebhem 48.3742671756342 65.9411750252678 71.1479487371951 cortex 64.084764536836 84.1654911925608 107.52264308241 heart 58.0205392335136 92.3579744043232 85.472772470292 kidney 52.9743274822201 84.4411975797521 73.9338984282747 liver 52.5712159224384 88.00274300018 68.9054471862869 stomach 53.1623360078042 84.3242916314454 68.896756833809 testicle 52.8915194455428 77.8565309227214 72.217771236211 diffExp=-25.9074049359334,-17.5669078496336,-20.0807266557247,-34.3374351708096,-31.466870097532,-35.4315270777416,-31.1619556236412,-24.9650114771787 diffExpScore=0.995493827783246 diffExp1.5=-1,0,0,-1,-1,-1,-1,0 diffExp1.5Score=0.833333333333333 diffExp1.4=-1,0,0,-1,-1,-1,-1,-1 diffExp1.4Score=0.857142857142857 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 64.8119783437677 65.4089073377266 63.1191618689702 cerebhem 68.2213795698436 65.4149230022609 68.1162391072834 cortex 66.5237489070066 64.2096125419971 73.184432650426 heart 64.2544065660992 58.9827764849431 64.3224167914346 kidney 69.0559242968749 56.9201005506751 65.841274650865 liver 61.15577686437 64.5242513895404 66.5365400683862 stomach 64.2100909102235 66.3616522425855 73.4512859054703 testicle 69.3696935016654 68.5049333498325 65.0876920041695 cont.diffExp=-0.596928993958912,2.8064565675827,2.31413636500952,5.27163008115611,12.1358237461999,-3.36847452517037,-2.15156133236208,0.864760151832982 cont.diffExpScore=1.61468739256573 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=0,0,0,0,1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.578643859245782 cont.tran.correlation=-0.0383387010889631 tran.covariance=0.00535824478566481 cont.tran.covariance=-0.000151030829186359 tran.mean=68.038127921922 cont.tran.mean=64.8706347412133 weightedLogRatios: wLogRatio Lung -1.68380853464793 cerebhem -1.24967159238828 cortex -1.17113170356935 heart -1.99581733207158 kidney -1.95960834773818 liver -2.17402145551769 stomach -1.93939310784934 testicle -1.6089604107937 cont.weightedLogRatios: wLogRatio Lung -0.0382862912196763 cerebhem 0.176505552235622 cortex 0.147992326412337 heart 0.3526959944432 kidney 0.799798155810749 liver -0.221986095177361 stomach -0.137723731112168 testicle 0.0531023244619094 varWeightedLogRatios=0.131971174179239 cont.varWeightedLogRatios=0.104207577250073 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01173326519442 0.0764264363308419 52.4914343490786 5.22259217838487e-276 *** df.mm.trans1 -0.269655489232287 0.0657405222785334 -4.10181543873038 4.47108258764793e-05 *** df.mm.trans2 0.459232163808349 0.0578269035147401 7.9414967064818 5.94479458865471e-15 *** df.mm.exp2 -0.21766195484287 0.0738112984718466 -2.94889751771392 0.00327169122555986 ** df.mm.exp3 -0.105341332196683 0.0738112984718466 -1.42717083126322 0.153877887246235 df.mm.exp4 0.117639730360579 0.0738112984718466 1.59379028409113 0.111334508746279 df.mm.exp5 0.0820598646913661 0.0738112984718466 1.11175208118938 0.26654198040384 df.mm.exp6 0.186169974050694 0.0738112984718466 2.52224222991692 0.0118326086313170 * df.mm.exp7 0.154779529662811 0.0738112984718466 2.09696256355452 0.0362757483745406 * df.mm.exp8 0.0227931239776567 0.0738112984718466 0.308802642001353 0.757543205359585 df.mm.trans1:exp2 0.149874485660962 0.0678965194548633 2.20739570841472 0.0275383584005723 * df.mm.trans2:exp2 0.0538974908505577 0.0487320860697576 1.10599597097908 0.269024139218406 df.mm.trans1:exp3 0.318792513546613 0.0678965194548633 4.6952703335337 3.07767544848616e-06 *** df.mm.trans2:exp3 0.185598805027554 0.0487320860697576 3.80855448629633 0.000149275567414939 *** df.mm.trans1:exp4 -0.00359812937877711 0.0678965194548633 -0.0529943126343773 0.957748212480225 df.mm.trans2:exp4 0.0555048012699883 0.0487320860697576 1.13897856107649 0.25501549959548 df.mm.trans1:exp5 -0.0590079272135195 0.0678965194548633 -0.86908618714612 0.385031952191669 df.mm.trans2:exp5 0.00146801971019450 0.0487320860697576 0.0301242944554661 0.975974611459361 df.mm.trans1:exp6 -0.170756701491884 0.0678965194548633 -2.51495515326674 0.0120782203692478 * df.mm.trans2:exp6 -0.0613295106916108 0.0487320860697576 -1.25850370131540 0.208536376728645 df.mm.trans1:exp7 -0.128184825415290 0.0678965194548633 -1.88794398364566 0.0593552933204577 . df.mm.trans2:exp7 -0.072637070297523 0.0487320860697576 -1.49053890682099 0.136433357267878 df.mm.trans1:exp8 -0.00130558249660654 0.0678965194548633 -0.0192290047720999 0.984662686163082 df.mm.trans2:exp8 -0.0204528591987044 0.0487320860697576 -0.419700054896626 0.674804862015491 df.mm.trans1:probe2 0.226130539037026 0.0480100893254982 4.71006286832563 2.86751404772186e-06 *** df.mm.trans1:probe3 0.821687419481832 0.0480100893254982 17.1148904537745 4.67966220585146e-57 *** df.mm.trans1:probe4 0.0655252268059418 0.0480100893254982 1.36482201400824 0.172650480490128 df.mm.trans1:probe5 0.729901986957628 0.0480100893254982 15.2030957911586 1.26115767230751e-46 *** df.mm.trans1:probe6 0.402180770932211 0.0480100893254982 8.37700526248786 2.07219405511475e-16 *** df.mm.trans1:probe7 0.421418164618781 0.0480100893254982 8.77770007386688 8.28944205229146e-18 *** df.mm.trans1:probe8 0.0115293307191086 0.0480100893254982 0.240143913104227 0.810273438764714 df.mm.trans1:probe9 0.496625934604256 0.0480100893254982 10.3441993460424 9.00107092104993e-24 *** df.mm.trans1:probe10 0.00917015311616451 0.0480100893254982 0.191004708489351 0.848565030547514 df.mm.trans1:probe11 0.62062385900799 0.0480100893254982 12.9269465590929 3.49546468679837e-35 *** df.mm.trans1:probe12 0.765815968858156 0.0480100893254982 15.9511465114361 1.25846150095761e-50 *** df.mm.trans1:probe13 0.759507890570104 0.0480100893254982 15.8197558313379 6.46249975437112e-50 *** df.mm.trans1:probe14 0.619238312009538 0.0480100893254982 12.8980870627262 4.79547979553574e-35 *** df.mm.trans1:probe15 0.463321731563277 0.0480100893254982 9.65050759272816 4.91011123849213e-21 *** df.mm.trans1:probe16 0.450238231781832 0.0480100893254982 9.37799196184186 5.31053752903348e-20 *** df.mm.trans1:probe17 0.0273123314627585 0.0480100893254982 0.568887328611007 0.569574693432038 df.mm.trans1:probe18 0.0345349635438886 0.0480100893254982 0.719327208698757 0.472126275772092 df.mm.trans1:probe19 -0.0144118932661313 0.0480100893254982 -0.300184679274844 0.76410568875146 df.mm.trans1:probe20 0.09164605250637 0.0480100893254982 1.90889152246790 0.0565942569605983 . df.mm.trans1:probe21 -0.0391550622359838 0.0480100893254982 -0.815559037404009 0.414968403617915 df.mm.trans1:probe22 -0.00380026062493905 0.0480100893254982 -0.0791554583282296 0.9369265878842 df.mm.trans2:probe2 -0.446958696513272 0.0480100893254982 -9.30968266863632 9.56364817602158e-20 *** df.mm.trans2:probe3 -0.340649010356336 0.0480100893254982 -7.09536297770264 2.61511534627033e-12 *** df.mm.trans2:probe4 -0.70374585032217 0.0480100893254982 -14.6582907928149 8.78900309432256e-44 *** df.mm.trans2:probe5 -0.391806375552982 0.0480100893254982 -8.1609174458439 1.11639548481373e-15 *** df.mm.trans2:probe6 -0.248722409686408 0.0480100893254982 -5.18062793010283 2.73063852698627e-07 *** df.mm.trans3:probe2 0.287910722255165 0.0480100893254982 5.99687953719877 2.911242331771e-09 *** df.mm.trans3:probe3 0.346530163726514 0.0480100893254982 7.21786125781006 1.12334076012225e-12 *** df.mm.trans3:probe4 0.103712943212869 0.0480100893254982 2.16023224847005 0.0310183582570829 * df.mm.trans3:probe5 -0.0574011607885614 0.0480100893254982 -1.19560620684110 0.232165539480774 df.mm.trans3:probe6 -0.133790498895170 0.0480100893254982 -2.78671630848463 0.00543666668874187 ** df.mm.trans3:probe7 0.0325901260924653 0.0480100893254982 0.678818276539982 0.497427720135488 df.mm.trans3:probe8 0.373840108146245 0.0480100893254982 7.78669886680877 1.89031701318234e-14 *** df.mm.trans3:probe9 -0.0219633544589907 0.0480100893254982 -0.457473726201253 0.647441117168464 df.mm.trans3:probe10 0.0348709186685428 0.0480100893254982 0.726324802941428 0.467828736220708 df.mm.trans3:probe11 -0.192793751077078 0.0480100893254982 -4.01569240519378 6.4218735994563e-05 *** df.mm.trans3:probe12 0.154592173890917 0.0480100893254982 3.21999346518218 0.00132792748764486 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1799934303959 0.155992661501677 26.7960902144808 9.92606198400554e-117 *** df.mm.trans1 0.0177402042409268 0.134181829365244 0.132210183188350 0.894847630620422 df.mm.trans2 -0.00157388277815976 0.118029480618668 -0.0133346581710775 0.989363755980028 df.mm.exp2 -0.0248317186030623 0.150654949390484 -0.164825109984942 0.869118692491769 df.mm.exp3 -0.1403952779539 0.150654949390484 -0.931899539456937 0.351638638218586 df.mm.exp4 -0.130936901443660 0.150654949390484 -0.869117821707161 0.385014660082373 df.mm.exp5 -0.117806189080453 0.150654949390484 -0.781960297733796 0.434443802683348 df.mm.exp6 -0.124410285935071 0.150654949390484 -0.825796208079502 0.409138793196155 df.mm.exp7 -0.146467155073296 0.150654949390484 -0.972202743194762 0.331211198306751 df.mm.exp8 0.0834958868329374 0.150654949390484 0.554219341420532 0.579566576019651 df.mm.trans1:exp2 0.0760992804961451 0.138582397465396 0.549126598240206 0.583054934967293 df.mm.trans2:exp2 0.0249236844860793 0.0994662079184577 0.250574391118959 0.802200437109323 df.mm.trans1:exp3 0.166463851022938 0.138582397465396 1.20119044025417 0.229993701366270 df.mm.trans2:exp3 0.12188975849654 0.0994662079184577 1.22543888067458 0.220730847760654 df.mm.trans1:exp4 0.122296770294075 0.138582397465396 0.882484157662324 0.377750944015907 df.mm.trans2:exp4 0.0275239317336254 0.0994662079184577 0.276716407608396 0.782061479497392 df.mm.trans1:exp5 0.181232425199035 0.138582397465396 1.30775934399814 0.191289262973895 df.mm.trans2:exp5 -0.021203718186828 0.0994662079184577 -0.213175093637939 0.831238725308002 df.mm.trans1:exp6 0.0663441767370964 0.138582397465396 0.478734514270923 0.632243919370509 df.mm.trans2:exp6 0.11079298259194 0.0994662079184577 1.11387560570086 0.265630268934819 df.mm.trans1:exp7 0.137137095445351 0.138582397465396 0.989570810965323 0.322650203266465 df.mm.trans2:exp7 0.160928071242315 0.0994662079184577 1.61791702539061 0.106031256707675 df.mm.trans1:exp8 -0.0155362450173988 0.138582397465396 -0.112108357926757 0.910762516600804 df.mm.trans2:exp8 -0.0372485714661073 0.0994662079184577 -0.3744846842522 0.708132063348063 df.mm.trans1:probe2 0.00505354807355007 0.097992553000871 0.0515707359262816 0.958882193808498 df.mm.trans1:probe3 -0.0416937324114758 0.097992553000871 -0.425478581123457 0.670589663116704 df.mm.trans1:probe4 0.0212202236843641 0.097992553000871 0.216549350277419 0.828608690232608 df.mm.trans1:probe5 0.0331787618168732 0.097992553000871 0.338584523015522 0.735001804445839 df.mm.trans1:probe6 -0.00627414854285904 0.097992553000871 -0.0640267892888072 0.948963129177396 df.mm.trans1:probe7 -0.0127179075461268 0.097992553000871 -0.129784429088339 0.896766014601665 df.mm.trans1:probe8 -0.0271728456198318 0.097992553000871 -0.277295006484730 0.781617342583204 df.mm.trans1:probe9 -0.0931448986166535 0.097992553000871 -0.95053037975065 0.342098289274767 df.mm.trans1:probe10 -0.0945912155394844 0.097992553000871 -0.965289837265936 0.334659248507213 df.mm.trans1:probe11 -0.062596988401028 0.097992553000871 -0.638793321370774 0.523120100725029 df.mm.trans1:probe12 -0.125031256705055 0.097992553000871 -1.27592610740475 0.20231119614886 df.mm.trans1:probe13 -0.0513831892758777 0.097992553000871 -0.524358103777753 0.600158749798637 df.mm.trans1:probe14 -0.110719724341009 0.097992553000871 -1.12987896478241 0.258828598347078 df.mm.trans1:probe15 0.0133083091308969 0.097992553000871 0.135809392891097 0.892002368734965 df.mm.trans1:probe16 -0.179884030796598 0.097992553000871 -1.83569082841427 0.0667335472257605 . df.mm.trans1:probe17 0.0783228662474139 0.097992553000871 0.799273657526993 0.424342869502074 df.mm.trans1:probe18 -0.0980133901529717 0.097992553000871 -1.00021264015951 0.317476751177266 df.mm.trans1:probe19 -0.0756159893764123 0.097992553000871 -0.771650365877703 0.440524314985976 df.mm.trans1:probe20 0.038848123174345 0.097992553000871 0.396439545503011 0.6918748773569 df.mm.trans1:probe21 -0.0642103264041976 0.097992553000871 -0.6552572051432 0.512469799057576 df.mm.trans1:probe22 -0.0391508992283589 0.097992553000871 -0.399529331866789 0.689598113075947 df.mm.trans2:probe2 -0.0081341758811988 0.097992553000871 -0.0830081024741391 0.933863575480783 df.mm.trans2:probe3 0.00571070475137741 0.097992553000871 0.0582769259142239 0.953540997369397 df.mm.trans2:probe4 0.0455043860436269 0.097992553000871 0.464365756887897 0.642498150581489 df.mm.trans2:probe5 -0.0221848958174704 0.097992553000871 -0.226393691541776 0.82094669756818 df.mm.trans2:probe6 0.0194041700297986 0.097992553000871 0.198016782251057 0.843076699476587 df.mm.trans3:probe2 -0.112533575777641 0.097992553000871 -1.14838905948946 0.251113458949093 df.mm.trans3:probe3 -0.0434237832451258 0.097992553000871 -0.443133502652389 0.657775785993854 df.mm.trans3:probe4 -0.107328248213830 0.097992553000871 -1.09526943555472 0.273691951298747 df.mm.trans3:probe5 -0.0694019230379858 0.097992553000871 -0.708236706899237 0.478981860676835 df.mm.trans3:probe6 -0.112872245109841 0.097992553000871 -1.15184513162789 0.249690939260036 df.mm.trans3:probe7 0.0714308109643199 0.097992553000871 0.728941218254463 0.466227470727141 df.mm.trans3:probe8 0.089627534640946 0.097992553000871 0.91463618301841 0.360627904030749 df.mm.trans3:probe9 -0.0557381431567503 0.097992553000871 -0.568799785798569 0.569634082858075 df.mm.trans3:probe10 -0.134927416545077 0.097992553000871 -1.37691500438689 0.168881297344326 df.mm.trans3:probe11 -0.0802322565830304 0.097992553000871 -0.818758713045442 0.413141069321001 df.mm.trans3:probe12 -0.0582249993464491 0.097992553000871 -0.594177797836654 0.552542688210476