fitVsDatCorrelation=0.883174703287742 cont.fitVsDatCorrelation=0.272011821128945 fstatistic=10982.1746510702,52,692 cont.fstatistic=2599.01194590234,52,692 residuals=-0.580675472138839,-0.076112344187346,-6.94830937468055e-05,0.0755208839160675,1.38207412431966 cont.residuals=-0.568387056451972,-0.206302943074794,-0.0549908355180887,0.141764744856353,1.41635283250994 predictedValues: Include Exclude Both Lung 57.8013204879219 60.4406329017073 65.1821006974409 cerebhem 55.4714316452773 58.2685304092392 75.5009526986054 cortex 57.8880940953391 58.1996929724169 75.2867196710969 heart 60.3120691534609 60.1898238966056 67.318612196434 kidney 57.5830947568956 63.4166630259158 71.5134286423777 liver 59.1541784172312 58.3080054234507 74.949991456267 stomach 59.7393002517764 57.1482878096941 67.2615842522238 testicle 58.1761400427018 54.9900668992728 68.9183310159775 diffExp=-2.63931241378542,-2.79709876396187,-0.311598877077842,0.122245256855329,-5.83356826902027,0.846172993780513,2.59101244208231,3.18607314342899 diffExpScore=3.14030984330715 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 58.2503046719746 60.444119535269 61.7903967281605 cerebhem 57.3903795423411 65.6623252941787 53.2051352508427 cortex 54.0965504246662 61.3631722836044 53.5633818164786 heart 61.9370635528386 58.8771888455554 53.5820565614325 kidney 57.085845159494 57.5462231721084 52.7824762265795 liver 59.6107976926802 58.5444125834382 60.9035595153895 stomach 58.1845124852482 51.2082447233783 59.0076956541749 testicle 59.1797405510704 55.4585180595046 46.9768974498517 cont.diffExp=-2.19381486329438,-8.27194575183757,-7.2666218589382,3.05987470728314,-0.460378012614392,1.06638510924204,6.97626776186989,3.72122249156585 cont.diffExpScore=7.55697684543517 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=-0.071582384385513 cont.tran.correlation=-0.264960411195843 tran.covariance=-7.51996292641393e-05 cont.tran.covariance=-0.000743330334457836 tran.mean=58.5679582618067 cont.tran.mean=58.4274624110844 weightedLogRatios: wLogRatio Lung -0.182142329649782 cerebhem -0.198766712879346 cortex -0.0218018993265473 heart 0.00831562918099445 kidney -0.395782849472333 liver 0.0586822778719162 stomach 0.180369850904295 testicle 0.227279932358468 cont.weightedLogRatios: wLogRatio Lung -0.150957055267696 cerebhem -0.554375516524756 cortex -0.510937416119632 heart 0.207766482866846 kidney -0.0325194117116260 liver 0.073626911807894 stomach 0.510843980126296 testicle 0.262900053030612 varWeightedLogRatios=0.0434851563143147 cont.varWeightedLogRatios=0.138030429839127 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.11528192035327 0.0783325770080048 52.5360211235324 1.07872594120519e-243 *** df.mm.trans1 -0.0309406844547369 0.070354109432523 -0.439785034652627 0.660230121154778 df.mm.trans2 0.190130319201923 0.0647001585874667 2.938637606968 0.0034063026919624 ** df.mm.exp2 -0.224703299221019 0.0886231942242841 -2.53549086317458 0.0114479775227922 * df.mm.exp3 -0.180400267729569 0.0886231942242841 -2.03558751530687 0.0421722525186164 * df.mm.exp4 0.006110453097147 0.0886231942242841 0.0689486894557524 0.945050380252053 df.mm.exp5 -0.0484178918484187 0.0886231942242842 -0.546334312052492 0.58501225157847 df.mm.exp6 -0.152422811452882 0.0886231942242842 -1.71989751426852 0.0858981840948604 . df.mm.exp7 -0.0544380585748043 0.0886231942242841 -0.614264234676924 0.539242625575965 df.mm.exp8 -0.143782650335024 0.0886231942242841 -1.62240428810481 0.105172277973710 df.mm.trans1:exp2 0.183559821087346 0.0848502485262846 2.16333863807699 0.0308567909990796 * df.mm.trans2:exp2 0.188103850771394 0.0738526618535701 2.54701517928159 0.0110802149380289 * df.mm.trans1:exp3 0.181900381184240 0.0848502485262846 2.14378136002621 0.0323982638744669 * df.mm.trans2:exp3 0.142618738145035 0.0738526618535702 1.93112522373005 0.0538756829204148 . df.mm.trans1:exp4 0.0364101611778141 0.0848502485262846 0.429110837153707 0.667976123699324 df.mm.trans2:exp4 -0.0102687622533002 0.0738526618535701 -0.139043901676291 0.889455911709725 df.mm.trans1:exp5 0.0446353014394649 0.0848502485262846 0.526047975282452 0.599023415745808 df.mm.trans2:exp5 0.0964829335921172 0.0738526618535701 1.30642459148482 0.191842244110672 df.mm.trans1:exp6 0.175558419188335 0.0848502485262846 2.06903836155473 0.0389132270100224 * df.mm.trans2:exp6 0.116500600758494 0.0738526618535702 1.57747328037387 0.115143665912422 df.mm.trans1:exp7 0.0874165368372866 0.0848502485262846 1.03024491213137 0.303254996993154 df.mm.trans2:exp7 -0.00157412013856410 0.0738526618535701 -0.0213143317932826 0.983001055524026 df.mm.trans1:exp8 0.150246334882632 0.0848502485262846 1.77072356878353 0.0770468534626388 . df.mm.trans2:exp8 0.0492736085056308 0.0738526618535701 0.667187983059121 0.504874476982251 df.mm.trans1:probe2 -0.0534034806444751 0.0424251242631423 -1.25877016442520 0.208537841907746 df.mm.trans1:probe3 0.240453676610465 0.0424251242631423 5.66771885260838 2.12465914308426e-08 *** df.mm.trans1:probe4 -0.203403687699884 0.0424251242631423 -4.79441583808384 1.99836937664588e-06 *** df.mm.trans1:probe5 0.827358644428815 0.0424251242631423 19.5016198254863 7.78232539723095e-68 *** df.mm.trans1:probe6 -0.25588518319424 0.0424251242631423 -6.03145394712598 2.64522427788374e-09 *** df.mm.trans1:probe7 -0.140668298305615 0.0424251242631423 -3.31568382529932 0.00096191422186298 *** df.mm.trans1:probe8 -0.215975454476379 0.0424251242631423 -5.09074418113164 4.6018973293271e-07 *** df.mm.trans1:probe9 0.336747086337573 0.0424251242631423 7.9374449029046 8.33417242355068e-15 *** df.mm.trans1:probe10 0.489682509434959 0.0424251242631423 11.5422763737284 2.61429732635320e-28 *** df.mm.trans1:probe11 0.281954490937717 0.0424251242631423 6.64593199984261 6.10888422695307e-11 *** df.mm.trans1:probe12 0.0389824125972253 0.0424251242631423 0.91885205463245 0.35849311991599 df.mm.trans1:probe13 -0.0179857073010505 0.0424251242631423 -0.423940002850528 0.671741316507634 df.mm.trans1:probe14 -0.120468453265480 0.0424251242631423 -2.83955451770212 0.00465024712915897 ** df.mm.trans1:probe15 -0.265475042131827 0.0424251242631423 -6.25749592352907 6.85617344919294e-10 *** df.mm.trans1:probe16 -0.234225431207233 0.0424251242631423 -5.52091326249152 4.77311625329882e-08 *** df.mm.trans1:probe17 -0.204150090057759 0.0424251242631423 -4.81200924224795 1.83541851565595e-06 *** df.mm.trans1:probe18 -0.147085761134307 0.0424251242631423 -3.46694944773777 0.000558949338208437 *** df.mm.trans1:probe19 -0.157390221110637 0.0424251242631423 -3.70983524136364 0.000224029990917028 *** df.mm.trans1:probe20 -0.269741165173143 0.0424251242631423 -6.35805244788609 3.70984252918093e-10 *** df.mm.trans1:probe21 -0.306437840442833 0.0424251242631423 -7.22302752826718 1.34469034711804e-12 *** df.mm.trans1:probe22 -0.306123370935931 0.0424251242631423 -7.21561518682178 1.41459977280396e-12 *** df.mm.trans2:probe2 -0.306995753692917 0.0424251242631423 -7.23617806724083 1.22892472766225e-12 *** df.mm.trans2:probe3 -0.182291522421457 0.0424251242631423 -4.29678228614703 1.98126675483650e-05 *** df.mm.trans2:probe4 -0.41647943605304 0.0424251242631423 -9.8168112241657 2.19725552561171e-21 *** df.mm.trans2:probe5 -0.445467619508997 0.0424251242631423 -10.5000899171439 4.94355540002449e-24 *** df.mm.trans2:probe6 -0.482521344040426 0.0424251242631423 -11.3734809837582 1.34473440167726e-27 *** df.mm.trans3:probe2 -0.0828128333221771 0.0424251242631423 -1.95197621127824 0.0513439911073186 . df.mm.trans3:probe3 -0.209047125792526 0.0424251242631423 -4.9274369709776 1.04346755588686e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98077801024293 0.160707594091607 24.7703167528835 1.79891277433118e-97 *** df.mm.trans1 0.0495533894533678 0.144338921215410 0.343312732533278 0.731467476290606 df.mm.trans2 0.116907420041768 0.132739241080689 0.880729911441204 0.378769798155094 df.mm.exp2 0.217526434757603 0.181819887312561 1.19638416882120 0.231956417976439 df.mm.exp3 0.0839939592123215 0.181819887312561 0.461962442358851 0.644253449332667 df.mm.exp4 0.177637591214430 0.181819887312561 0.976997587227952 0.328911761186416 df.mm.exp5 0.0882447930603099 0.181819887312561 0.485341809219203 0.627587519194271 df.mm.exp6 0.0056101037445541 0.181819887312561 0.0308552811657503 0.975393850136078 df.mm.exp7 -0.120868767939185 0.181819887312561 -0.664771987958627 0.506417810184055 df.mm.exp8 0.203837987215922 0.181819887312561 1.12109841353884 0.262634868470664 df.mm.trans1:exp2 -0.232399072123431 0.174079288842248 -1.33501850604429 0.182309309207290 df.mm.trans2:exp2 -0.13472040331428 0.151516572760467 -0.889146321486956 0.374233423041904 df.mm.trans1:exp3 -0.157972861190297 0.174079288842248 -0.907476485232272 0.3644707780988 df.mm.trans2:exp3 -0.0689033981221195 0.151516572760467 -0.4547581618748 0.649425856117973 df.mm.trans1:exp4 -0.116268148575094 0.174079288842248 -0.667903398206417 0.504417948279495 df.mm.trans2:exp4 -0.203903155894752 0.151516572760467 -1.34574820549236 0.178824474379305 df.mm.trans1:exp5 -0.108437925612420 0.174079288842248 -0.622922613790587 0.533540605315442 df.mm.trans2:exp5 -0.137375581112076 0.151516572760467 -0.906670330573365 0.364896753725345 df.mm.trans1:exp6 0.0174773004055420 0.174079288842248 0.100398505312025 0.920057035997555 df.mm.trans2:exp6 -0.0375437422544594 0.151516572760467 -0.247786374588953 0.804373202427899 df.mm.trans1:exp7 0.119738655910240 0.174079288842248 0.687839758001013 0.491784182663717 df.mm.trans2:exp7 -0.0449499773954222 0.151516572760467 -0.296667068007694 0.766809720324831 df.mm.trans1:exp8 -0.188008047170138 0.174079288842248 -1.08001387425538 0.280512269464947 df.mm.trans2:exp8 -0.289921962443799 0.151516572760467 -1.91346700338937 0.0561006122418384 . df.mm.trans1:probe2 0.00648209714187234 0.0870396444211241 0.0744729276524843 0.940655604361935 df.mm.trans1:probe3 0.0929836320614573 0.0870396444211241 1.0682905781597 0.285761976055228 df.mm.trans1:probe4 0.0183143925890011 0.0870396444211241 0.210414377388659 0.833406211948444 df.mm.trans1:probe5 0.0510355343388966 0.0870396444211241 0.586348148344579 0.55783278821595 df.mm.trans1:probe6 0.0690319949382962 0.0870396444211241 0.7931097995334 0.427985693472785 df.mm.trans1:probe7 0.120746587823275 0.0870396444211241 1.38725966341345 0.165809210406433 df.mm.trans1:probe8 -0.0370599309221719 0.0870396444211241 -0.425782195787298 0.670398953068913 df.mm.trans1:probe9 0.0175472025556950 0.0870396444211241 0.201600117652094 0.840288599375872 df.mm.trans1:probe10 0.0482122392628732 0.0870396444211241 0.553911261741923 0.579818515335687 df.mm.trans1:probe11 -0.091779311350863 0.0870396444211241 -1.05445411641168 0.292043073503164 df.mm.trans1:probe12 0.217683622492385 0.0870396444211241 2.50097095341022 0.0126153796660069 * df.mm.trans1:probe13 0.0262041752329269 0.0870396444211241 0.301060228441918 0.763459004049901 df.mm.trans1:probe14 -0.0152489514281303 0.0870396444211241 -0.175195470173928 0.86097729205218 df.mm.trans1:probe15 0.0184126139470521 0.0870396444211241 0.211542844292496 0.832525989057015 df.mm.trans1:probe16 0.0255817700264242 0.0870396444211241 0.293909404117644 0.768915262855107 df.mm.trans1:probe17 0.0192773723368960 0.0870396444211241 0.221478068587071 0.824785549790815 df.mm.trans1:probe18 0.131768088873998 0.0870396444211241 1.51388588212130 0.130511366533624 df.mm.trans1:probe19 0.0274074748673209 0.0870396444211241 0.314884959027581 0.752943924387991 df.mm.trans1:probe20 0.0293546574967768 0.0870396444211241 0.337256174379 0.736026060663348 df.mm.trans1:probe21 -0.0173910403252239 0.0870396444211241 -0.199805967049691 0.841691033772014 df.mm.trans1:probe22 0.101883856941268 0.0870396444211241 1.17054541776760 0.242184491619453 df.mm.trans2:probe2 0.0335648599506021 0.0870396444211241 0.385627264148796 0.699891252710172 df.mm.trans2:probe3 -0.121014215209828 0.0870396444211241 -1.39033443914734 0.164874341635796 df.mm.trans2:probe4 0.108517274268267 0.0870396444211241 1.24675686567868 0.212908495997887 df.mm.trans2:probe5 0.00343169131205221 0.0870396444211241 0.0394267616196667 0.968561515454753 df.mm.trans2:probe6 0.0118051646435204 0.0870396444211241 0.13562974345809 0.89215343395563 df.mm.trans3:probe2 -0.0725129490070047 0.0870396444211241 -0.83310254182755 0.405074342996508 df.mm.trans3:probe3 0.00204036871500153 0.0870396444211241 0.023441831921206 0.981304594928652