fitVsDatCorrelation=0.85360950446703 cont.fitVsDatCorrelation=0.290465927161233 fstatistic=10413.5209998770,52,692 cont.fstatistic=3076.72852027359,52,692 residuals=-0.489263839285236,-0.0912983848998581,-0.00482698680689787,0.0869381942158598,0.714409925976651 cont.residuals=-0.606518760734115,-0.210544017678530,-0.0352896393961275,0.193944146451850,0.93874644533852 predictedValues: Include Exclude Both Lung 105.894496621818 61.4889151304732 97.0269519501321 cerebhem 71.2341774646427 61.9453280792022 75.322566978517 cortex 72.3568766459272 59.4586108044825 68.596944750248 heart 90.1652542968409 60.4828853669405 81.0015312192027 kidney 79.8929774378438 65.1010444955002 74.6225038191428 liver 87.0472587255209 61.4298026331716 74.9666204434035 stomach 77.6465688840698 63.7258033882218 78.4108422600459 testicle 82.060545586438 63.650307478726 74.2499720038972 diffExp=44.4055814913446,9.28884938544055,12.8982658414447,29.6823689299004,14.7919329423436,25.6174560923493,13.920765495848,18.4102381077121 diffExpScore=0.994118181898992 diffExp1.5=1,0,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=1,0,0,1,0,1,0,0 diffExp1.4Score=0.75 diffExp1.3=1,0,0,1,0,1,0,0 diffExp1.3Score=0.75 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 73.4358808667841 69.6928712492745 75.6423979614047 cerebhem 78.9100752103715 84.3663497366495 76.2057340282544 cortex 75.5019573301046 70.1627558652729 88.750831693052 heart 74.5773092386553 88.7634047225164 68.9144253473086 kidney 76.9289558901069 72.8767774967733 75.5889767943361 liver 84.2322187426709 71.9669248906281 80.6230586169767 stomach 79.4866911838878 74.4814829496066 82.780114508484 testicle 77.6327949338056 76.347191215044 86.2797634071985 cont.diffExp=3.74300961750964,-5.45627452627804,5.33920146483169,-14.1860954838610,4.05217839333358,12.2652938520427,5.00520823428124,1.28560371876155 cont.diffExpScore=3.93411806111936 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=-0.122402128827833 cont.tran.correlation=-0.101049552138204 tran.covariance=-0.000364312488953555 cont.tran.covariance=-0.000294203385360788 tran.mean=72.7238033149887 cont.tran.mean=76.8352275951345 weightedLogRatios: wLogRatio Lung 2.38669753576967 cerebhem 0.586283068914291 cortex 0.821336024616818 heart 1.71771942261299 kidney 0.875973567894013 liver 1.49606301218646 stomach 0.840373177235517 testicle 1.08745704209277 cont.weightedLogRatios: wLogRatio Lung 0.223396791759713 cerebhem -0.294298962838813 cortex 0.314448466976645 heart -0.76601729181292 kidney 0.233539323688336 liver 0.685333094295963 stomach 0.282469184918593 testicle 0.0725331961131536 varWeightedLogRatios=0.360764324617343 cont.varWeightedLogRatios=0.194410621033668 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27342592814728 0.0862475324845238 49.5483848064187 8.66732402227637e-230 *** df.mm.trans1 0.394450555464621 0.0774628969257733 5.0921224369209 4.56976376312596e-07 *** df.mm.trans2 -0.206325221052525 0.0712376541493858 -2.89629443187251 0.00389512745788469 ** df.mm.exp2 -0.135866274798563 0.097577944128662 -1.39238714252286 0.164252445788501 df.mm.exp3 -0.067668452596741 0.097577944128662 -0.693481023821493 0.488240427675697 df.mm.exp4 0.00322512030051123 0.097577944128662 0.0330517344806807 0.973642862742361 df.mm.exp5 0.0378750237976147 0.097577944128662 0.388151483778694 0.698023310568865 df.mm.exp6 0.0609919182086879 0.097577944128662 0.625058446899297 0.532138751589366 df.mm.exp7 -0.0615166982451016 0.097577944128662 -0.630436506880985 0.528617162473737 df.mm.exp8 0.0471126854644846 0.097577944128662 0.482821050240245 0.629375506079175 df.mm.trans1:exp2 -0.260604284954479 0.0934237688279144 -2.78948589019684 0.00542438692749346 ** df.mm.trans2:exp2 0.143261548864557 0.0813149534405517 1.761810624036 0.0785428100025042 . df.mm.trans1:exp3 -0.31316433547373 0.0934237688279145 -3.35208415805377 0.000845689654593326 *** df.mm.trans2:exp3 0.034091989577315 0.0813149534405518 0.419258551285271 0.675157298055113 df.mm.trans1:exp4 -0.164024258364218 0.0934237688279145 -1.75570157811069 0.0795817761233164 . df.mm.trans2:exp4 -0.0197215985665474 0.0813149534405518 -0.242533479170785 0.808438682811387 df.mm.trans1:exp5 -0.319630350348699 0.0934237688279145 -3.42129582609169 0.000659873819600663 *** df.mm.trans2:exp5 0.0192086529798801 0.0813149534405517 0.236225345611533 0.813327714111702 df.mm.trans1:exp6 -0.256984026863009 0.0934237688279145 -2.75073495842766 0.00610161945866765 ** df.mm.trans2:exp6 -0.0619537326977119 0.0813149534405518 -0.761898397236437 0.446380234221159 df.mm.trans1:exp7 -0.248759223645755 0.0934237688279145 -2.66269737098667 0.0079318704565578 ** df.mm.trans2:exp7 0.0972493387539161 0.0813149534405517 1.19595885675583 0.232122247988474 df.mm.trans1:exp8 -0.302098633413027 0.0934237688279145 -3.23363783331723 0.00128030057974885 ** df.mm.trans2:exp8 -0.0125654464853996 0.0813149534405517 -0.154528115109677 0.877238402075736 df.mm.trans1:probe2 -0.0629682014870135 0.0467118844139573 -1.34801244430633 0.178095472755031 df.mm.trans1:probe3 -0.451045416265603 0.0467118844139572 -9.65590281626132 8.8401877521082e-21 *** df.mm.trans1:probe4 -0.187345298570168 0.0467118844139572 -4.01065598017687 6.71397395408262e-05 *** df.mm.trans1:probe5 0.0306523627873324 0.0467118844139572 0.656200518816441 0.511913243129263 df.mm.trans1:probe6 0.476560794737217 0.0467118844139572 10.2021316569884 7.31947570048037e-23 *** df.mm.trans1:probe7 -0.0117757692442931 0.0467118844139572 -0.252093645804076 0.801043534774152 df.mm.trans1:probe8 0.0384925552244354 0.0467118844139572 0.824042012163698 0.410199608057976 df.mm.trans1:probe9 -0.268835988795416 0.0467118844139572 -5.75519468264246 1.30041294289819e-08 *** df.mm.trans1:probe10 0.160128137541862 0.0467118844139573 3.42799567071236 0.000644068559142996 *** df.mm.trans1:probe11 -0.09389939808095 0.0467118844139572 -2.01018218937220 0.0447994955385158 * df.mm.trans1:probe12 -0.134891182119607 0.0467118844139572 -2.88772726281412 0.00400148023374578 ** df.mm.trans1:probe13 -0.24816552112266 0.0467118844139573 -5.31268486031169 1.45801507042344e-07 *** df.mm.trans1:probe14 -0.196847711198479 0.0467118844139572 -4.21408199793504 2.84030767089701e-05 *** df.mm.trans1:probe15 -0.0712920523990841 0.0467118844139572 -1.52620801523011 0.127414969753245 df.mm.trans1:probe16 -0.207156765202274 0.0467118844139572 -4.43477645574017 1.07180711559027e-05 *** df.mm.trans1:probe17 0.552923473052249 0.0467118844139573 11.8368907610809 1.44331054990871e-29 *** df.mm.trans1:probe18 0.0793014010708384 0.0467118844139573 1.69767077620066 0.090019520124756 . df.mm.trans1:probe19 0.0603637999239145 0.0467118844139573 1.29225786288078 0.196699216070962 df.mm.trans1:probe20 0.158603390655422 0.0467118844139573 3.39535415120252 0.000724543529883791 *** df.mm.trans1:probe21 0.166945774766637 0.0467118844139572 3.57394647767100 0.000376065780002223 *** df.mm.trans1:probe22 0.0744216136474081 0.0467118844139573 1.59320512501464 0.111570815906504 df.mm.trans2:probe2 0.0336733109559189 0.0467118844139573 0.72087245844138 0.471231441919626 df.mm.trans2:probe3 0.0512521205784056 0.0467118844139573 1.09719659614271 0.272937108480992 df.mm.trans2:probe4 -0.0191126018512360 0.0467118844139572 -0.409159298346039 0.682549365614574 df.mm.trans2:probe5 0.435587154598645 0.0467118844139573 9.32497500504378 1.46687029463636e-19 *** df.mm.trans2:probe6 -0.0355940970601165 0.0467118844139573 -0.761992317515694 0.446324213830934 df.mm.trans3:probe2 -0.00545854360367052 0.0467118844139573 -0.116855564106498 0.90700841694757 df.mm.trans3:probe3 0.345771141119902 0.0467118844139573 7.40220921202202 3.89835776351446e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.18637372072727 0.158431310124674 26.4239039457093 6.31207402066032e-107 *** df.mm.trans1 0.0643924276331266 0.142294485331566 0.452529326650173 0.651029529146819 df.mm.trans2 0.00283277164346670 0.130859104625640 0.0216474937037866 0.982735388186862 df.mm.exp2 0.255547082718797 0.179244565986281 1.42568942780868 0.154408925410759 df.mm.exp3 -0.125350337316488 0.179244565986281 -0.699325732006188 0.484583478949504 df.mm.exp4 0.350451483127163 0.179244565986281 1.95515820074560 0.0509665589728847 . df.mm.exp5 0.09184817197495 0.179244565986281 0.512418167153695 0.608521887259878 df.mm.exp6 0.105505689101914 0.179244565986281 0.588613041189704 0.556312953380883 df.mm.exp7 0.0554585293767009 0.179244565986281 0.309401454217281 0.757109302473056 df.mm.exp8 0.0151923707330006 0.179244565986281 0.0847577757763846 0.93247847725984 df.mm.trans1:exp2 -0.183650823394432 0.171613606393285 -1.07014139061655 0.284928785582498 df.mm.trans2:exp2 -0.0644764953285064 0.149370471655235 -0.431654895469073 0.666126704593777 df.mm.trans1:exp3 0.153096261813498 0.171613606393286 0.892098622195772 0.372650169066087 df.mm.trans2:exp3 0.132069929423673 0.149370471655235 0.884176959208558 0.376907781824613 df.mm.trans1:exp4 -0.335027844210862 0.171613606393286 -1.95222192023097 0.051314762999017 . df.mm.trans2:exp4 -0.108575061956946 0.149370471655235 -0.726884375163186 0.467542605861137 df.mm.trans1:exp5 -0.0453784831272935 0.171613606393286 -0.264422408461600 0.791533179515688 df.mm.trans2:exp5 -0.0471761714723997 0.149370471655235 -0.31583331664968 0.752224259954492 df.mm.trans1:exp6 0.0316591480322058 0.171613606393286 0.184479242045953 0.853691558811497 df.mm.trans2:exp6 -0.0733970870576211 0.149370471655235 -0.491376148473512 0.623316239020579 df.mm.trans1:exp7 0.023718415453605 0.171613606393286 0.138208245558628 0.890116045275795 df.mm.trans2:exp7 0.0109939791959074 0.149370471655235 0.0736020919936763 0.941348275886569 df.mm.trans1:exp8 0.0403849259819401 0.171613606393286 0.235324732290692 0.814026321815611 df.mm.trans2:exp8 0.0760008370708523 0.149370471655235 0.508807639345691 0.611049244978607 df.mm.trans1:probe2 0.0323194748980970 0.0858068031966428 0.376653991222942 0.706546220242099 df.mm.trans1:probe3 0.052216837022556 0.0858068031966428 0.608539592168364 0.543029348258019 df.mm.trans1:probe4 0.212820572196199 0.0858068031966428 2.48022958865487 0.0133665530092254 * df.mm.trans1:probe5 -0.0385972344340667 0.0858068031966428 -0.449815550704223 0.652984309350601 df.mm.trans1:probe6 -0.0448197497796188 0.0858068031966428 -0.522333289551712 0.601605461210925 df.mm.trans1:probe7 0.0554507286853119 0.0858068031966428 0.646227648852456 0.518346186107537 df.mm.trans1:probe8 0.0980664542809444 0.0858068031966428 1.14287504752049 0.253485717948333 df.mm.trans1:probe9 -0.0308781374032264 0.0858068031966428 -0.359856517815531 0.719064230041026 df.mm.trans1:probe10 0.0735236727325092 0.0858068031966428 0.856851321730464 0.391823684128467 df.mm.trans1:probe11 0.0989136939244934 0.0858068031966428 1.15274885253345 0.249411523343113 df.mm.trans1:probe12 0.031553428448274 0.0858068031966428 0.367726418801120 0.713189681507167 df.mm.trans1:probe13 0.114900072353351 0.0858068031966428 1.33905550693965 0.180992280068967 df.mm.trans1:probe14 0.185593188910726 0.0858068031966428 2.16291927908564 0.0308891701940743 * df.mm.trans1:probe15 0.0837514318697346 0.0858068031966428 0.976046522532742 0.329382484972354 df.mm.trans1:probe16 0.0970333630100835 0.0858068031966428 1.13083531136468 0.258516178706550 df.mm.trans1:probe17 0.100527305315442 0.0858068031966428 1.17155402101468 0.241779373356297 df.mm.trans1:probe18 0.0126306029166895 0.0858068031966428 0.147198152665634 0.883018493370114 df.mm.trans1:probe19 0.0847446347042722 0.0858068031966428 0.987621395357937 0.323683294103953 df.mm.trans1:probe20 -0.0183029566741016 0.0858068031966428 -0.213304260178029 0.831152476363414 df.mm.trans1:probe21 0.0307879162943108 0.0858068031966428 0.358805073110047 0.719850359252358 df.mm.trans1:probe22 -0.091072600182573 0.0858068031966428 -1.06136805928852 0.288892943291164 df.mm.trans2:probe2 0.109457281708276 0.0858068031966428 1.27562474804513 0.202516237481961 df.mm.trans2:probe3 0.169767161559250 0.0858068031966428 1.97848137017988 0.0482705619569927 * df.mm.trans2:probe4 0.0862772723485308 0.0858068031966428 1.00548288870301 0.315015837933924 df.mm.trans2:probe5 0.0327670769085170 0.0858068031966428 0.381870384256420 0.702674742698866 df.mm.trans2:probe6 0.0957550903017162 0.0858068031966428 1.11593820926151 0.264835939026026 df.mm.trans3:probe2 0.108497378557065 0.0858068031966428 1.26443795264604 0.206498576116193 df.mm.trans3:probe3 0.108756698172094 0.0858068031966428 1.26746008615257 0.205417164733805