fitVsDatCorrelation=0.850186147909288 cont.fitVsDatCorrelation=0.26407460247599 fstatistic=9174.9781151599,52,692 cont.fstatistic=2724.45244068432,52,692 residuals=-0.943526714887924,-0.088011569629641,-0.00709291503479784,0.0856579604793603,0.755267909629469 cont.residuals=-0.538243650441857,-0.167671320180063,-0.0614038132265711,0.083162021820226,1.73482437072531 predictedValues: Include Exclude Both Lung 52.443141051643 61.1411326996783 106.764949742303 cerebhem 58.4840183104426 69.3446247490102 77.1159749701985 cortex 51.5043795384249 73.7994387915337 178.506323758531 heart 53.2557753726782 56.7504858203839 121.843916808496 kidney 51.2510139186851 57.565857905851 82.8945048629626 liver 50.4909020194475 52.0604973322852 77.8504364213716 stomach 52.73920473472 58.1775829255436 132.025359084352 testicle 51.7857336142121 60.9214425149234 115.550453771947 diffExp=-8.69799164803537,-10.8606064385677,-22.2950592531088,-3.49471044770571,-6.31484398716594,-1.56959531283768,-5.43837819082358,-9.13570890071136 diffExpScore=0.985466572616995 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 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,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 57.3944818787727 65.0073882899209 68.8791191474248 cerebhem 58.0818998963222 55.6705355152722 51.7496636518404 cortex 63.0307594735765 53.1848240002889 57.4463151828501 heart 59.5504257908658 58.2736881454407 58.9001992496296 kidney 59.3637071520268 63.7746006672779 51.7642224898145 liver 56.6314363661745 61.5379987939695 55.3052351537699 stomach 60.9292477883887 55.9085711187175 55.0655338386387 testicle 57.6110513677088 57.9479089558321 61.9642385311643 cont.diffExp=-7.61290641114823,2.41136438104999,9.84593547328764,1.27673764542506,-4.41089351525116,-4.90656242779504,5.02067666967115,-0.336857588123252 cont.diffExpScore=15.6599014271228 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=0.44887727179338 cont.tran.correlation=-0.635073706049837 tran.covariance=0.00237134532516380 cont.tran.covariance=-0.00160950018271542 tran.mean=56.9822019562164 cont.tran.mean=58.9936578250347 weightedLogRatios: wLogRatio Lung -0.619415699962234 cerebhem -0.707558600346823 cortex -1.48244200631115 heart -0.254669454105312 kidney -0.464176551743322 liver -0.120527580365672 stomach -0.393980158328389 testicle -0.654488063621489 cont.weightedLogRatios: wLogRatio Lung -0.512188938621079 cerebhem 0.171336032938759 cortex 0.689368877995545 heart 0.088337941047943 kidney -0.295253976009442 liver -0.338852596572015 stomach 0.349719724662919 testicle -0.0236504426661032 varWeightedLogRatios=0.171762621428243 cont.varWeightedLogRatios=0.156787370475436 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.51818591775566 0.0858120191826699 40.9987546181197 2.43500397556014e-187 *** df.mm.trans1 0.421251731276184 0.0770717422916699 5.46570920483413 6.44068456178665e-08 *** df.mm.trans2 0.53334149998028 0.0708779343396569 7.52478899038498 1.64737639402090e-13 *** df.mm.exp2 0.560246992203675 0.0970852170742015 5.77067249873358 1.19142213555762e-08 *** df.mm.exp3 -0.343890768051648 0.0970852170742016 -3.54215377392435 0.000423511870482852 *** df.mm.exp4 -0.191255092772955 0.0970852170742015 -1.96997131527017 0.0492400171227695 * df.mm.exp5 0.169811564257720 0.0970852170742016 1.74909805401098 0.0807174031993868 . df.mm.exp6 0.117125399407218 0.0970852170742016 1.20641847375899 0.228068451817729 df.mm.exp7 -0.256419520140471 0.0970852170742015 -2.64117986103375 0.00844827350338383 ** df.mm.exp8 -0.095292069710994 0.0970852170742016 -0.98153017094418 0.326674393322364 df.mm.trans1:exp2 -0.451223021675542 0.092952018589249 -4.85436495649951 1.49390300134791e-06 *** df.mm.trans2:exp2 -0.434343200092166 0.0809043475618346 -5.36860148041118 1.08419842391827e-07 *** df.mm.trans1:exp3 0.325828056575086 0.092952018589249 3.50533599506759 0.000485439691218491 *** df.mm.trans2:exp3 0.532057052559574 0.0809043475618347 6.57637158686592 9.50445890607303e-11 *** df.mm.trans1:exp4 0.206631794072597 0.092952018589249 2.22299415557282 0.0265384950796105 * df.mm.trans2:exp4 0.116734467190611 0.0809043475618347 1.44287013873255 0.149509538378583 df.mm.trans1:exp5 -0.192805717767334 0.0929520185892491 -2.07424992693633 0.0384252992725015 * df.mm.trans2:exp5 -0.230066759607368 0.0809043475618346 -2.84368846101292 0.00459104209496245 ** df.mm.trans1:exp6 -0.155061792498712 0.092952018589249 -1.66819177089551 0.0957300449107934 . df.mm.trans2:exp6 -0.277903789445608 0.0809043475618347 -3.43496731412620 0.000627997247233434 *** df.mm.trans1:exp7 0.262049066862639 0.092952018589249 2.81918640218695 0.00495219303740177 ** df.mm.trans2:exp7 0.206734784960202 0.0809043475618346 2.55529883362814 0.0108224034437638 * df.mm.trans1:exp8 0.0826772130786406 0.092952018589249 0.889461190122052 0.374064367553523 df.mm.trans2:exp8 0.0916924336786392 0.0809043475618346 1.13334371318623 0.257462436903694 df.mm.trans1:probe2 0.0563780679543224 0.0464760092946245 1.21305742059147 0.225521786002045 df.mm.trans1:probe3 0.0398055392639972 0.0464760092946245 0.856474982859622 0.39203158594751 df.mm.trans1:probe4 -0.0165254535636256 0.0464760092946245 -0.355569546835790 0.722271311390113 df.mm.trans1:probe5 -0.0451239491864612 0.0464760092946245 -0.970908429344865 0.331933114109902 df.mm.trans1:probe6 0.0524801756967475 0.0464760092946245 1.12918851022817 0.259209601904774 df.mm.trans1:probe7 -0.0993781354643498 0.0464760092946245 -2.13826739801096 0.0328446367715696 * df.mm.trans1:probe8 -0.0375729187072141 0.0464760092946245 -0.808436853281201 0.419116979853324 df.mm.trans1:probe9 0.269866231307791 0.0464760092946245 5.80657064587953 9.7175952343162e-09 *** df.mm.trans1:probe10 0.0349536352384369 0.0464760092946245 0.75207909992564 0.452259218129464 df.mm.trans1:probe11 0.158763085993032 0.0464760092946245 3.41602233932324 0.000672567641320997 *** df.mm.trans1:probe12 0.0446516622343092 0.0464760092946245 0.960746477849458 0.337015225453071 df.mm.trans1:probe13 -0.0121747220220648 0.0464760092946245 -0.26195713028814 0.793432448465661 df.mm.trans1:probe14 0.0808298659058526 0.0464760092946245 1.73917397669514 0.0824488451860014 . df.mm.trans1:probe15 0.0367922115040984 0.0464760092946245 0.791638784450321 0.428842588727431 df.mm.trans1:probe16 -0.0509723898179889 0.0464760092946245 -1.09674626956158 0.273133832354247 df.mm.trans1:probe17 -0.0474866988427843 0.0464760092946245 -1.02174647874246 0.307257963455330 df.mm.trans1:probe18 -0.0119307401033708 0.0464760092946245 -0.256707498867609 0.797480884290362 df.mm.trans1:probe19 -0.0783018481042396 0.0464760092946245 -1.68477993899739 0.0924819298556034 . df.mm.trans1:probe20 -0.0266767063665076 0.0464760092946245 -0.57398874755783 0.566161928022147 df.mm.trans1:probe21 0.113763195616265 0.0464760092946245 2.44778321854375 0.0146212117571604 * df.mm.trans1:probe22 0.0451575434590222 0.0464760092946245 0.971631259748569 0.331573518713628 df.mm.trans2:probe2 0.0590043237238062 0.0464760092946245 1.26956519329706 0.204666334442745 df.mm.trans2:probe3 0.146672109555148 0.0464760092946245 3.15586711899794 0.00166960254606576 ** df.mm.trans2:probe4 0.101455515229788 0.0464760092946245 2.18296529262298 0.0293737114769295 * df.mm.trans2:probe5 0.190149472674829 0.0464760092946245 4.09134681657838 4.79382561767944e-05 *** df.mm.trans2:probe6 0.0576354027623459 0.0464760092946245 1.24011083647434 0.215354732007928 df.mm.trans3:probe2 0.411216793356175 0.0464760092946245 8.84793680863078 7.37846251492756e-18 *** df.mm.trans3:probe3 0.182334819790883 0.0464760092946245 3.92320301502246 9.60972982720659e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1276461706774 0.157205476579612 26.2563764347426 5.73456466575417e-106 *** df.mm.trans1 0.00328619704873290 0.141193507543403 0.0232744203746246 0.981438085295093 df.mm.trans2 0.0218589562095791 0.129846606022929 0.168344455654999 0.866361492155622 df.mm.exp2 0.142791148854907 0.17785769364657 0.80283931455141 0.422343263485116 df.mm.exp3 0.0744490608578173 0.17785769364657 0.418587800906486 0.675647285828132 df.mm.exp4 0.0840336238349887 0.17785769364657 0.472476743131372 0.636735506660758 df.mm.exp5 0.300242804439598 0.177857693646570 1.68810692573258 0.091841276635431 . df.mm.exp6 0.151255500340174 0.177857693646570 0.85042989841497 0.395380269867688 df.mm.exp7 0.132810994505959 0.17785769364657 0.746726170698437 0.455482489811571 df.mm.exp8 -0.00539454369030093 0.17785769364657 -0.0303306738083577 0.97581207900466 df.mm.trans1:exp2 -0.130885231347993 0.170285777220248 -0.768621040961665 0.44238054069538 df.mm.trans2:exp2 -0.297841056681873 0.148214744705475 -2.00952379787671 0.0448693807917439 * df.mm.trans1:exp3 0.0192256278047880 0.170285777220249 0.112902134979374 0.910140887542085 df.mm.trans2:exp3 -0.275176897792885 0.148214744705475 -1.85660946446119 0.0637915210321938 . df.mm.trans1:exp4 -0.0471583421069053 0.170285777220248 -0.276936470424717 0.781911588709135 df.mm.trans2:exp4 -0.193383880048233 0.148214744705475 -1.30475466818444 0.192410127532825 df.mm.trans1:exp5 -0.266507919696651 0.170285777220249 -1.56506270839019 0.118025365035786 df.mm.trans2:exp5 -0.319388731453431 0.148214744705475 -2.15490524973143 0.0315135830358099 * df.mm.trans1:exp6 -0.164639420673862 0.170285777220249 -0.966841878173517 0.333960857051253 df.mm.trans2:exp6 -0.206101579194981 0.148214744705475 -1.39056056537786 0.164805746507485 df.mm.trans1:exp7 -0.0730458400147728 0.170285777220248 -0.428960311349402 0.668085612842585 df.mm.trans2:exp7 -0.283594226039549 0.148214744705475 -1.91340090085567 0.0561090830589457 . df.mm.trans1:exp8 0.00916079282317494 0.170285777220249 0.053796582267269 0.957112759140126 df.mm.trans2:exp8 -0.109561900202096 0.148214744705475 -0.739210531447543 0.460029831235454 df.mm.trans1:probe2 -0.0364077531521939 0.0851428886101243 -0.427607680999734 0.669069803604778 df.mm.trans1:probe3 -0.200427248660961 0.0851428886101243 -2.35401043977651 0.018850709414466 * df.mm.trans1:probe4 -0.109440629258474 0.0851428886101243 -1.28537604308460 0.199090914332526 df.mm.trans1:probe5 -0.077660875515024 0.0851428886101243 -0.912124039749685 0.362021067867953 df.mm.trans1:probe6 -0.177856506929830 0.0851428886101243 -2.08891793352524 0.0370799098267496 * df.mm.trans1:probe7 -0.152331305958413 0.0851428886101243 -1.78912541546422 0.0740318696233217 . df.mm.trans1:probe8 -0.168672130302646 0.0851428886101243 -1.98104777810638 0.0479813725478376 * df.mm.trans1:probe9 -0.0991771243505195 0.0851428886101243 -1.16483156690465 0.244488566912064 df.mm.trans1:probe10 -0.132915797693975 0.0851428886101243 -1.56109100670294 0.118959464440340 df.mm.trans1:probe11 -0.107596973432851 0.0851428886101243 -1.26372237528311 0.206755236966755 df.mm.trans1:probe12 -0.14430602915188 0.0851428886101243 -1.69486884351162 0.0905501979410193 . df.mm.trans1:probe13 -0.101387842211466 0.0851428886101243 -1.19079636440019 0.23414184106303 df.mm.trans1:probe14 -0.0645822170332343 0.0851428886101243 -0.758515691533102 0.4484005804388 df.mm.trans1:probe15 -0.0353530299225544 0.0851428886101243 -0.415219996639280 0.678109568604192 df.mm.trans1:probe16 -0.0899794584989587 0.0851428886101243 -1.05680533004913 0.290969228571153 df.mm.trans1:probe17 -0.0585458684761812 0.0851428886101243 -0.687619006494684 0.491923136024208 df.mm.trans1:probe18 -0.0236238011424886 0.0851428886101243 -0.277460649129063 0.781509285069456 df.mm.trans1:probe19 -0.123127903467538 0.0851428886101243 -1.44613255995283 0.148592778968791 df.mm.trans1:probe20 -0.0236421826606046 0.0851428886101243 -0.277676539362717 0.781343607790754 df.mm.trans1:probe21 -0.116363176182426 0.0851428886101243 -1.36668109435729 0.17216914442761 df.mm.trans1:probe22 0.0187927655525785 0.0851428886101243 0.220720319211062 0.825375314206557 df.mm.trans2:probe2 0.0312080523639051 0.0851428886101243 0.366537392298365 0.714076157262639 df.mm.trans2:probe3 0.0753384750225597 0.0851428886101243 0.8848475339795 0.376546211116289 df.mm.trans2:probe4 0.155210672727254 0.0851428886101243 1.82294346904268 0.0687432902587402 . df.mm.trans2:probe5 0.0230014808242346 0.0851428886101243 0.270151520575724 0.787124233178831 df.mm.trans2:probe6 -0.0597964580063849 0.0851428886101243 -0.702307133132368 0.482723805184583 df.mm.trans3:probe2 0.0952296127458107 0.0851428886101243 1.11846819270925 0.263755194891766 df.mm.trans3:probe3 0.143455630900977 0.0851428886101243 1.68488094828297 0.0924624264453965 .