fitVsDatCorrelation=0.88014237953839 cont.fitVsDatCorrelation=0.231107291546875 fstatistic=11174.2667110261,60,876 cont.fstatistic=2649.07260701515,60,876 residuals=-0.509108072526773,-0.092396338059447,0.00250716005522922,0.087886445518591,0.813001487505689 cont.residuals=-0.76188597049592,-0.217377000885192,-0.0454593525574121,0.145701252844253,1.10410714369590 predictedValues: Include Exclude Both Lung 62.8585094102867 61.0495650516229 108.564693954701 cerebhem 68.1008123478359 55.8544008007578 85.5778809301991 cortex 61.3394557785137 69.219059533281 95.9709114503896 heart 64.3595756103648 69.0542659530874 105.551493981287 kidney 62.5863781524081 61.8273859691426 105.936023297036 liver 63.3150002197973 62.1161128475878 101.481456032281 stomach 68.760060083461 78.5795678232455 114.897085159651 testicle 63.6877905107479 63.419663567057 107.184185035655 diffExp=1.80894435866372,12.2464115470782,-7.87960375476725,-4.69469034272258,0.758992183265534,1.19888737220951,-9.81950773978448,0.268126943690902 diffExpScore=5.4376792393034 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 77.445128381946 62.5569375611018 73.3808913044189 cerebhem 84.4872270983101 76.6339965022543 73.4676444081425 cortex 78.4392619550685 70.0985432256503 76.4620435538982 heart 80.1785093953411 67.7449441289037 67.7994335987504 kidney 76.5960440940786 72.484838009161 76.0765671979031 liver 76.3224807457221 75.0376473141405 74.2061174939906 stomach 77.884295158278 71.3650995388357 80.0143910491299 testicle 76.5162459173003 74.4929597951541 78.5424826355928 cont.diffExp=14.8881908208442,7.85323059605582,8.34071872941821,12.4335652664374,4.11120608491754,1.28483343158162,6.51919561944223,2.02328612214620 cont.diffExpScore=0.98289259721746 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=1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.219618464026698 cont.tran.correlation=0.212401158646823 tran.covariance=0.000689376722726107 cont.tran.covariance=0.000431807809352042 tran.mean=64.7579752286998 cont.tran.mean=74.8927599263279 weightedLogRatios: wLogRatio Lung 0.120488556215808 cerebhem 0.817122647919724 cortex -0.504784679136327 heart -0.295687225072717 kidney 0.050396608808696 liver 0.079116304041174 stomach -0.573649813466773 testicle 0.0175164150808012 cont.weightedLogRatios: wLogRatio Lung 0.905811067642255 cerebhem 0.428073878032194 cortex 0.484104366343198 heart 0.724574894960277 kidney 0.237827217288348 liver 0.073453136176911 stomach 0.376892874407619 testicle 0.115879331440114 varWeightedLogRatios=0.193164377530548 cont.varWeightedLogRatios=0.0829370979164462 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.73442959554673 0.0747430329792908 49.9635811752707 1.19528869356353e-258 *** df.mm.trans1 0.305272241473148 0.064409396152779 4.739560680697 2.49852337746512e-06 *** df.mm.trans2 0.395570225335329 0.0567714089350204 6.96777185480973 6.32763160294628e-12 *** df.mm.exp2 0.229084470254776 0.0727256072134276 3.14998360319609 0.00168819543484985 ** df.mm.exp3 0.224428256717551 0.0727256072134276 3.08595920084823 0.0020927506290774 ** df.mm.exp4 0.174953316872925 0.0727256072134276 2.40566319865148 0.0163494781627837 * df.mm.exp5 0.0328325585234789 0.0727256072134276 0.451458018454563 0.651771219138395 df.mm.exp6 0.0920254688296527 0.0727256072134276 1.26537917462258 0.206071904149494 df.mm.exp7 0.285471811204529 0.0727256072134276 3.92532729725798 9.3404571546386e-05 *** df.mm.exp8 0.0639919852591743 0.0727256072134276 0.879909947968907 0.379149413247989 df.mm.trans1:exp2 -0.148981646659896 0.0670496968887816 -2.22195854079727 0.0265408918660193 * df.mm.trans2:exp2 -0.318022227123014 0.048785820430252 -6.51874303472428 1.19576446100318e-10 *** df.mm.trans1:exp3 -0.248891288521705 0.0670496968887816 -3.71204196395627 0.000218580253328873 *** df.mm.trans2:exp3 -0.0988380813051958 0.048785820430252 -2.02595919128802 0.0430716187144271 * df.mm.trans1:exp4 -0.151353907006169 0.0670496968887816 -2.25733916824749 0.0242324323198487 * df.mm.trans2:exp4 -0.0517467343572786 0.048785820430252 -1.06069210071520 0.289122201798715 df.mm.trans1:exp5 -0.0371712237341433 0.0670496968887816 -0.554383173361707 0.579458139107582 df.mm.trans2:exp5 -0.0201722296273006 0.0487858204302519 -0.413485505612034 0.679352140733709 df.mm.trans1:exp6 -0.0847895156944583 0.0670496968887816 -1.2645771663234 0.206359275332197 df.mm.trans2:exp6 -0.074706123470801 0.048785820430252 -1.53130813035330 0.126054286731774 df.mm.trans1:exp7 -0.195735075114978 0.0670496968887816 -2.91925369088025 0.00359858374941982 ** df.mm.trans2:exp7 -0.0330461729905727 0.048785820430252 -0.677372496744584 0.498348491495845 df.mm.trans1:exp8 -0.0508854310220997 0.0670496968887816 -0.758921119457194 0.448103894601853 df.mm.trans2:exp8 -0.0259040970726838 0.048785820430252 -0.530975944326247 0.595570040208028 df.mm.trans1:probe2 0.255308101409355 0.0467088669540562 5.46594507763336 6.00542527037399e-08 *** df.mm.trans1:probe3 -0.153407534508229 0.0467088669540562 -3.2843343140634 0.00106278070703984 ** df.mm.trans1:probe4 0.00109572129816449 0.0467088669540562 0.0234585287466352 0.981289861011221 df.mm.trans1:probe5 0.218051810641419 0.0467088669540562 4.66831727808553 3.51208098027808e-06 *** df.mm.trans1:probe6 0.362646593053249 0.0467088669540562 7.76397752079826 2.29341931277387e-14 *** df.mm.trans1:probe7 0.366762465104283 0.0467088669540562 7.85209509502849 1.19312823303967e-14 *** df.mm.trans1:probe8 0.0134683868154198 0.0467088669540562 0.288347538566233 0.77314893267358 df.mm.trans1:probe9 0.23596993261028 0.0467088669540562 5.05193013657097 5.32337273476511e-07 *** df.mm.trans1:probe10 0.393192203030497 0.0467088669540562 8.4179349376479 1.55200119642794e-16 *** df.mm.trans1:probe11 0.371885470172303 0.0467088669540562 7.96177459277907 5.24445671175529e-15 *** df.mm.trans1:probe12 0.412292672149506 0.0467088669540562 8.82686091604504 5.7674273964873e-18 *** df.mm.trans1:probe13 0.231821700726213 0.0467088669540562 4.96311976383923 8.33531175201712e-07 *** df.mm.trans1:probe14 0.219146928259902 0.0467088669540562 4.69176288252633 3.14133831534245e-06 *** df.mm.trans1:probe15 0.291931248551157 0.0467088669540562 6.2500177715359 6.40128188097558e-10 *** df.mm.trans1:probe16 0.158104017772809 0.0467088669540562 3.38488231642020 0.000743727439882383 *** df.mm.trans1:probe17 -0.0648122365732586 0.0467088669540562 -1.38757886456568 0.165618182321864 df.mm.trans1:probe18 0.0361934300128176 0.0467088669540562 0.774872789109147 0.438623935188337 df.mm.trans1:probe19 0.102267167997796 0.0467088669540562 2.18945940389409 0.0288266335403443 * df.mm.trans1:probe20 0.0240339517746865 0.0467088669540562 0.514547950784737 0.606998734289647 df.mm.trans1:probe21 -0.0317999049182692 0.0467088669540562 -0.680810882215324 0.496171073488668 df.mm.trans1:probe22 -0.105064245153421 0.0467088669540562 -2.24934261960935 0.0247383461692117 * df.mm.trans2:probe2 -0.0716968412903831 0.0467088669540562 -1.53497282134687 0.125151580146303 df.mm.trans2:probe3 -0.148215894285015 0.0467088669540562 -3.17318539177590 0.00156033703810194 ** df.mm.trans2:probe4 -0.0474615619180466 0.0467088669540562 -1.01611460549302 0.309855370624545 df.mm.trans2:probe5 -0.107873669151394 0.0467088669540562 -2.30949017148073 0.0211481787692613 * df.mm.trans2:probe6 0.0639143048363687 0.0467088669540562 1.36835485431998 0.171551868676123 df.mm.trans3:probe2 -0.131113474715070 0.0467088669540562 -2.8070360782683 0.00511095934055236 ** df.mm.trans3:probe3 0.493024024676453 0.0467088669540562 10.5552554970216 1.33895051540809e-24 *** df.mm.trans3:probe4 0.560678578189169 0.0467088669540562 12.0036861253917 7.69191559192231e-31 *** df.mm.trans3:probe5 0.124916295646619 0.0467088669540562 2.67435936242019 0.00762671239266671 ** df.mm.trans3:probe6 0.546441460474874 0.0467088669540562 11.6988806646148 1.76816186535763e-29 *** df.mm.trans3:probe7 -0.0442666661399902 0.0467088669540562 -0.947714406849812 0.343536221521218 df.mm.trans3:probe8 0.587247936826907 0.0467088669540562 12.5725151373193 1.90539552361655e-33 *** df.mm.trans3:probe9 0.610345139517442 0.0467088669540562 13.0670080290706 8.87124741544354e-36 *** df.mm.trans3:probe10 0.0684664810410437 0.0467088669540562 1.46581335634600 0.143057982170936 df.mm.trans3:probe11 -0.0429236791224582 0.0467088669540562 -0.918962114081654 0.358368350513424 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22211563924392 0.153187505751333 27.5617493641918 6.56060092572649e-121 *** df.mm.trans1 0.184533501416385 0.132008487618204 1.39789118674016 0.162499602296283 df.mm.trans2 -0.0947178201605348 0.116354263214798 -0.814046838882727 0.415839340900367 df.mm.exp2 0.288812821578789 0.149052746847494 1.93765514347946 0.0529864117825136 . df.mm.exp3 0.085448907235756 0.149052746847494 0.573279654639205 0.566602490049835 df.mm.exp4 0.193468264521810 0.149052746847494 1.29798523417862 0.194634057427738 df.mm.exp5 0.100199302096385 0.149052746847494 0.672240560577564 0.50160782607841 df.mm.exp6 0.156127647687659 0.149052746847494 1.04746575282778 0.295173737193585 df.mm.exp7 0.050843528766927 0.149052746847494 0.34111098146315 0.733101831991795 df.mm.exp8 0.0945848038185742 0.149052746847494 0.634572698719537 0.525872825424177 df.mm.trans1:exp2 -0.201782121957186 0.137419842604209 -1.46836234224449 0.142364799201416 df.mm.trans2:exp2 -0.0858491677337282 0.0999876222552191 -0.858597952400524 0.390797236196069 df.mm.trans1:exp3 -0.0726939797057903 0.137419842604209 -0.528991871393424 0.596945097853351 df.mm.trans2:exp3 0.0283759622039494 0.0999876222552191 0.283794749429279 0.776634738906615 df.mm.trans1:exp4 -0.158782412925388 0.137419842604209 -1.15545477215184 0.248219300394978 df.mm.trans2:exp4 -0.113795575960693 0.0999876222552191 -1.13809663030319 0.255391301242027 df.mm.trans1:exp5 -0.111223535079889 0.137419842604209 -0.809370269766862 0.41852197774757 df.mm.trans2:exp5 0.0471009639747599 0.099987622255219 0.471067947335865 0.637709496769288 df.mm.trans1:exp6 -0.170729781262018 0.137419842604209 -1.24239540685363 0.214423189610253 df.mm.trans2:exp6 0.0257851610848695 0.099987622255219 0.257883531013996 0.796557365160031 df.mm.trans1:exp7 -0.0451888635202908 0.137419842604209 -0.328837980483225 0.742356750043578 df.mm.trans2:exp7 0.0808882760884479 0.0999876222552191 0.808982894722509 0.418744645758514 df.mm.trans1:exp8 -0.106651385316327 0.137419842604209 -0.77609887549864 0.437900090605137 df.mm.trans2:exp8 0.0800426746972425 0.0999876222552191 0.800525834017065 0.423623250298494 df.mm.trans1:probe2 -0.0762810157468697 0.0957308599872477 -0.796827854226224 0.425766920658726 df.mm.trans1:probe3 -0.0945719174242066 0.0957308599872477 -0.987893741232498 0.323477472106352 df.mm.trans1:probe4 -0.128978595418785 0.0957308599872477 -1.34730425942027 0.178230644037114 df.mm.trans1:probe5 -0.123365505938740 0.0957308599872477 -1.28867019428399 0.197852994967651 df.mm.trans1:probe6 -0.133799785462198 0.0957308599872477 -1.39766618079083 0.162567170082630 df.mm.trans1:probe7 -0.0396006420638178 0.0957308599872477 -0.413666419262221 0.679219674033296 df.mm.trans1:probe8 -0.0737736079952166 0.0957308599872477 -0.770635592378925 0.44113074596542 df.mm.trans1:probe9 -0.138002126470371 0.0957308599872477 -1.44156363463939 0.149782890230199 df.mm.trans1:probe10 -0.190616390148995 0.0957308599872477 -1.99116972493914 0.0467724514506622 * df.mm.trans1:probe11 -0.130415693463636 0.0957308599872477 -1.36231611709128 0.173448304895137 df.mm.trans1:probe12 -0.162946639290846 0.0957308599872477 -1.70213282647364 0.089085268128405 . df.mm.trans1:probe13 -0.129704405589045 0.0957308599872477 -1.35488603785993 0.17580316613076 df.mm.trans1:probe14 -0.148948669622001 0.0957308599872477 -1.55591070258685 0.120090514093105 df.mm.trans1:probe15 -0.00133678653408266 0.0957308599872477 -0.0139640084112974 0.988861874917217 df.mm.trans1:probe16 0.0080464634614017 0.0957308599872477 0.08405297374821 0.933033517708612 df.mm.trans1:probe17 -0.0967939291307428 0.0957308599872477 -1.01110476959715 0.312245479894991 df.mm.trans1:probe18 -0.085745227593868 0.0957308599872477 -0.89569056002725 0.370664106505650 df.mm.trans1:probe19 -0.103218823732629 0.0957308599872477 -1.07821891233797 0.281232881376214 df.mm.trans1:probe20 0.117368749557718 0.0957308599872477 1.22602836298925 0.220517479045388 df.mm.trans1:probe21 -0.125760747712594 0.0957308599872477 -1.31369077567408 0.189294225894320 df.mm.trans1:probe22 -0.0251774098415553 0.0957308599872477 -0.263002023014409 0.79261086368534 df.mm.trans2:probe2 0.0216164525668117 0.0957308599872477 0.225804433070916 0.821406199815507 df.mm.trans2:probe3 0.0658763027255114 0.0957308599872477 0.688140717990905 0.491546348527422 df.mm.trans2:probe4 0.0980441548155717 0.0957308599872477 1.02416456750344 0.306040283309028 df.mm.trans2:probe5 -0.0755899944022221 0.0957308599872477 -0.789609478200566 0.429969541994481 df.mm.trans2:probe6 0.0376015910543152 0.0957308599872477 0.392784427710396 0.69457426564175 df.mm.trans3:probe2 0.0234824127298051 0.0957308599872477 0.245296163984458 0.80628457459374 df.mm.trans3:probe3 -0.0257865647215625 0.0957308599872477 -0.269365225852954 0.787712076488056 df.mm.trans3:probe4 0.00103709264189577 0.0957308599872477 0.0108334202997228 0.991358816912246 df.mm.trans3:probe5 0.00413432445960903 0.0957308599872477 0.0431869562245629 0.965562344525217 df.mm.trans3:probe6 -0.0892616455715047 0.0957308599872477 -0.932422894596322 0.351374930167432 df.mm.trans3:probe7 -0.050247897956175 0.0957308599872477 -0.524887146766137 0.599794451922664 df.mm.trans3:probe8 0.0194536299113842 0.0957308599872477 0.203211690712646 0.839016767499932 df.mm.trans3:probe9 -0.0759804040566236 0.0957308599872477 -0.793687678839874 0.427592208232855 df.mm.trans3:probe10 -0.039815888218495 0.0957308599872477 -0.415914870333337 0.677574165845319 df.mm.trans3:probe11 0.0540396333091191 0.0957308599872477 0.564495433513475 0.572561514716968