fitVsDatCorrelation=0.800061156041238 cont.fitVsDatCorrelation=0.24052456793532 fstatistic=10566.4487429396,58,830 cont.fstatistic=4027.55800519883,58,830 residuals=-0.528827658504816,-0.0902464501206462,-0.00321010110703344,0.0820399391061635,2.29537961065379 cont.residuals=-0.613841031492663,-0.176814257271805,-0.0238221735944907,0.157509471462136,2.29223251656789 predictedValues: Include Exclude Both Lung 76.8536957424744 94.0998131298929 63.9338494643369 cerebhem 72.8604714173963 78.0995771394106 76.5976656875839 cortex 70.9628267366255 79.4327861282897 63.4110196856252 heart 83.60435009712 78.5259655200528 73.201662964461 kidney 81.806053193199 98.6751805563795 68.2958367888364 liver 86.3901606050688 86.2937407917732 69.9824443909063 stomach 74.025758098184 86.5634542747029 66.3311094214575 testicle 75.7723893917508 81.410897328768 64.2654035604875 diffExp=-17.2461173874184,-5.23910572201429,-8.46995939166423,5.07838457706707,-16.8691273631806,0.0964198132955545,-12.5376961765189,-5.6385079370172 diffExpScore=1.15122525633936 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=-1,0,0,0,-1,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 74.0046850180218 81.5652479203972 82.8762195506033 cerebhem 77.189205485477 83.7034402381116 78.3565435895262 cortex 74.7557205142728 89.3400458279877 79.3798008286649 heart 78.7740974808113 76.1973923741986 76.5995611249405 kidney 75.9210266037887 80.9017211509927 75.426184987708 liver 74.7669503993867 80.5376779054805 82.0725402830502 stomach 76.4741997671844 78.8285615269759 73.9716047106159 testicle 79.8629888399274 73.8449654532907 77.0410373900577 cont.diffExp=-7.56056290237538,-6.51423475263465,-14.5843253137149,2.57670510661269,-4.98069454720408,-5.77072750609376,-2.35436175979147,6.01802338663666 cont.diffExpScore=1.47378906980759 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=0.316467148623798 cont.tran.correlation=-0.712571014368441 tran.covariance=0.00202395727418839 cont.tran.covariance=-0.00112176099371751 tran.mean=81.586070009443 cont.tran.mean=78.541745406644 weightedLogRatios: wLogRatio Lung -0.899522744760499 cerebhem -0.300200552513515 cortex -0.486936411765301 heart 0.275403116699898 kidney -0.843312296381893 liver 0.00497869207609673 stomach -0.685728885840118 testicle -0.313199668474817 cont.weightedLogRatios: wLogRatio Lung -0.423414799942896 cerebhem -0.355418096808350 cortex -0.784780873480898 heart 0.144666431617722 kidney -0.277133651341148 liver -0.323534195247752 stomach -0.131964643806836 testicle 0.340105294182968 varWeightedLogRatios=0.167517825750894 cont.varWeightedLogRatios=0.120970746846245 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.49770791570957 0.0774484544559544 58.073565796816 1.41236971166343e-294 *** df.mm.trans1 -0.255628559889519 0.0670508693304095 -3.81245705599798 0.000147773699597601 *** df.mm.trans2 0.126565585491855 0.0594031032397235 2.13062245218225 0.033413662191755 * df.mm.exp2 -0.420446371189834 0.076776599746762 -5.4762306819607 5.76051287299461e-08 *** df.mm.exp3 -0.240980952203077 0.076776599746762 -3.13872915703382 0.00175681106422308 ** df.mm.exp4 -0.232103917543214 0.076776599746762 -3.02310753939064 0.00257889180459505 ** df.mm.exp5 0.0439252090167148 0.076776599746762 0.572117144567441 0.567397531788461 df.mm.exp6 -0.0600242349494735 0.076776599746762 -0.781803767651289 0.434552863728654 df.mm.exp7 -0.157778846889772 0.076776599746762 -2.05503822011116 0.0401877950597947 * df.mm.exp8 -0.164189004245452 0.076776599746762 -2.13852924962826 0.0327651499431613 * df.mm.trans1:exp2 0.367089073764148 0.0711738128607857 5.15764238291077 3.12940060942362e-07 *** df.mm.trans2:exp2 0.234074952950557 0.0534342519961608 4.38061625654225 1.33493372773525e-05 *** df.mm.trans1:exp3 0.161233565879213 0.0711738128607857 2.26534956325273 0.0237481871505714 * df.mm.trans2:exp3 0.0715360980346814 0.0534342519961607 1.33876858685738 0.181012577856817 df.mm.trans1:exp4 0.316295911768368 0.0711738128607857 4.44399279812415 1.00281707179091e-05 *** df.mm.trans2:exp4 0.0511771978728871 0.0534342519961608 0.957760162462164 0.338462601576298 df.mm.trans1:exp5 0.018522472604695 0.0711738128607857 0.260242803640779 0.794741034237841 df.mm.trans2:exp5 0.00355218161715934 0.0534342519961608 0.0664776147220057 0.947013588250031 df.mm.trans1:exp6 0.176994463222381 0.0711738128607858 2.4867919268085 0.0130855919463768 * df.mm.trans2:exp6 -0.0265747587982528 0.0534342519961608 -0.497335656540344 0.619084089788019 df.mm.trans1:exp7 0.120288402768915 0.0711738128607857 1.69006545994939 0.0913909326753062 . df.mm.trans2:exp7 0.0743005066006317 0.0534342519961608 1.39050335365357 0.164748797960648 df.mm.trans1:exp8 0.150019415324636 0.0711738128607857 2.10778949861897 0.0353485043774414 * df.mm.trans2:exp8 0.019342081389312 0.0534342519961608 0.361979080210606 0.717459800423674 df.mm.trans1:probe2 -0.178475517791921 0.0477448451323697 -3.73811072791436 0.000198121449238707 *** df.mm.trans1:probe3 0.209012314464965 0.0477448451323697 4.3776938407799 1.35254294262986e-05 *** df.mm.trans1:probe4 0.0292532858047037 0.0477448451323697 0.612700401972207 0.540242287530116 df.mm.trans1:probe5 0.671775500696441 0.0477448451323697 14.0701158174036 1.75246355537836e-40 *** df.mm.trans1:probe6 0.353185457908055 0.0477448451323697 7.39735267606103 3.40271039579241e-13 *** df.mm.trans1:probe7 -0.0816952451075885 0.0477448451323697 -1.71107990571743 0.0874399959719895 . df.mm.trans1:probe8 -0.0852516421039761 0.0477448451323697 -1.78556746529643 0.0745343478765118 . df.mm.trans1:probe9 -0.202297858159662 0.0477448451323697 -4.23706177282183 2.51836185181598e-05 *** df.mm.trans1:probe10 -0.120903174679537 0.0477448451323697 -2.53227703104576 0.0115157593403013 * df.mm.trans1:probe11 0.137315653946768 0.0477448451323697 2.87603098441451 0.00413022414066554 ** df.mm.trans1:probe12 0.190799687043767 0.0477448451323697 3.99623637933658 7.00795461285099e-05 *** df.mm.trans1:probe13 0.0252905816146441 0.0477448451323697 0.529702872520111 0.596459610781883 df.mm.trans1:probe14 0.184647361981607 0.0477448451323697 3.86737796446262 0.000118617632255780 *** df.mm.trans1:probe15 0.256316100350656 0.0477448451323697 5.36845600064332 1.03135143213594e-07 *** df.mm.trans1:probe16 0.0573180607608264 0.0477448451323697 1.20050783706420 0.230284683068314 df.mm.trans1:probe17 0.319661366323303 0.0477448451323697 6.6952016586725 3.96510261336552e-11 *** df.mm.trans1:probe18 0.45456274807822 0.0477448451323697 9.52066651002789 1.82185111747866e-20 *** df.mm.trans1:probe19 0.180852543577822 0.0477448451323697 3.78789674731207 0.000162892252768159 *** df.mm.trans1:probe20 0.334402184839091 0.0477448451323697 7.00394323014309 5.14298459412982e-12 *** df.mm.trans1:probe21 0.239462648996086 0.0477448451323697 5.01546603266155 6.47172359845215e-07 *** df.mm.trans1:probe22 0.119318245188991 0.0477448451323697 2.49908120673947 0.0126437722198004 * df.mm.trans2:probe2 -0.228459082074138 0.0477448451323697 -4.78499996053499 2.02356533461924e-06 *** df.mm.trans2:probe3 -0.368561548690124 0.0477448451323697 -7.71939981516978 3.35792066578435e-14 *** df.mm.trans2:probe4 -0.270188311495433 0.0477448451323697 -5.65900487783241 2.09644620229948e-08 *** df.mm.trans2:probe5 -0.283145074899454 0.0477448451323697 -5.93038000467802 4.43218689648482e-09 *** df.mm.trans2:probe6 -0.0484075900304203 0.0477448451323697 -1.01388097282991 0.310935010802192 df.mm.trans3:probe2 -0.204454960340175 0.0477448451323697 -4.28224156499693 2.06644505401041e-05 *** df.mm.trans3:probe3 -0.385253082664707 0.0477448451323697 -8.06899847714693 2.47698915253517e-15 *** df.mm.trans3:probe4 -0.197844225879223 0.0477448451323697 -4.14378191678519 3.76662203008825e-05 *** df.mm.trans3:probe5 -0.402695132729665 0.0477448451323697 -8.43431645056587 1.46826212021056e-16 *** df.mm.trans3:probe6 0.0095004404251879 0.0477448451323697 0.198983584486419 0.842324317927402 df.mm.trans3:probe7 -0.313385334948064 0.0477448451323697 -6.56375225596025 9.2365086933254e-11 *** df.mm.trans3:probe8 -0.395171517174208 0.0477448451323697 -8.27673680956801 5.03024248590633e-16 *** df.mm.trans3:probe9 -0.00786013815222693 0.0477448451323697 -0.164627995555021 0.869276878264308 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.35815501576857 0.125308371704320 34.7794401642388 3.24160335541524e-164 *** df.mm.trans1 -0.0389591934463112 0.108485512282637 -0.35911885952855 0.719597509936026 df.mm.trans2 0.0717021228633537 0.0961117454627405 0.746028724357632 0.455861297347841 df.mm.exp2 0.124086691623933 0.124221338783880 0.998916070610212 0.318126477668385 df.mm.exp3 0.144248102340915 0.12422133878388 1.16121838448286 0.245886945527512 df.mm.exp4 0.0731365984174847 0.12422133878388 0.588760346116761 0.556182238615558 df.mm.exp5 0.111590753298597 0.12422133878388 0.898321933985453 0.369274457339028 df.mm.exp6 0.00731405115671935 0.124221338783880 0.058879184754596 0.953062510002498 df.mm.exp7 0.112363987442114 0.12422133878388 0.904546582271223 0.365968117767788 df.mm.exp8 0.0497586017363979 0.124221338783880 0.400564043372353 0.688844205432684 df.mm.trans1:exp2 -0.0819554719417888 0.115156263094251 -0.711689227660255 0.476857214943530 df.mm.trans2:exp2 -0.098209900892201 0.0864543928979903 -1.13597351852417 0.256295541898666 df.mm.trans1:exp3 -0.134150766633709 0.115156263094251 -1.16494546652588 0.244375766980618 df.mm.trans2:exp3 -0.053201561383027 0.0864543928979903 -0.615371406815624 0.538478157471496 df.mm.trans1:exp4 -0.0106807700182437 0.115156263094251 -0.0927502311316053 0.926124372514457 df.mm.trans2:exp4 -0.141212645049006 0.0864543928979903 -1.63337732549491 0.102768909594877 df.mm.trans1:exp5 -0.0860254791643473 0.115156263094251 -0.747032569943144 0.455255464084842 df.mm.trans2:exp5 -0.119758942344822 0.0864543928979903 -1.38522680375685 0.166355290121458 df.mm.trans1:exp6 0.00293349434899381 0.115156263094251 0.0254740321557053 0.979682984253438 df.mm.trans2:exp6 -0.01999221564499 0.0864543928979903 -0.231245804577904 0.81718084005572 df.mm.trans1:exp7 -0.0795389636936358 0.115156263094251 -0.690704626534608 0.489944290496976 df.mm.trans2:exp7 -0.146491888251007 0.0864543928979903 -1.69444123474276 0.0905566023237444 . df.mm.trans1:exp8 0.0264255229384660 0.115156263094251 0.22947534270748 0.818556018719302 df.mm.trans2:exp8 -0.149194055613099 0.0864543928979903 -1.72569664319009 0.084774187543821 . df.mm.trans1:probe2 -0.0392198186373014 0.0772491696940788 -0.50770537460298 0.611794862118994 df.mm.trans1:probe3 -0.0167680936194562 0.0772491696940788 -0.217065033654872 0.828211007683012 df.mm.trans1:probe4 0.0598774467717911 0.0772491696940788 0.775120910799651 0.43848898624417 df.mm.trans1:probe5 -0.0387047450828711 0.0772491696940788 -0.501037684109087 0.616477448269582 df.mm.trans1:probe6 -0.090853792967624 0.0772491696940788 -1.17611352105689 0.239886734118737 df.mm.trans1:probe7 -0.00839908181490586 0.0772491696940788 -0.108727146818119 0.913445188569492 df.mm.trans1:probe8 -0.067021726502699 0.0772491696940788 -0.867604490353976 0.385861653124872 df.mm.trans1:probe9 0.149269605773328 0.0772491696940788 1.93231340044771 0.0536608282593579 . df.mm.trans1:probe10 -0.0240174540542921 0.0772491696940788 -0.310908895841932 0.755947944979413 df.mm.trans1:probe11 -0.0161001704923245 0.0772491696940788 -0.208418686648467 0.83495320032427 df.mm.trans1:probe12 -0.0317996479560951 0.0772491696940788 -0.411650352774375 0.680701997023184 df.mm.trans1:probe13 0.000784697096880717 0.0772491696940788 0.0101580004029592 0.991897668688306 df.mm.trans1:probe14 -0.115689932873343 0.0772491696940788 -1.49762040590853 0.134612087526898 df.mm.trans1:probe15 -0.103785909682718 0.0772491696940788 -1.34352136202538 0.179470404548522 df.mm.trans1:probe16 -0.0109985161526723 0.0772491696940788 -0.142377143938614 0.886816668257974 df.mm.trans1:probe17 -0.00622347937402254 0.0772491696940788 -0.0805637057157856 0.935808352180743 df.mm.trans1:probe18 -0.0471697473277775 0.0772491696940788 -0.610618178998927 0.541619548422792 df.mm.trans1:probe19 -0.0181347831258916 0.0772491696940788 -0.234756997359438 0.81445524678709 df.mm.trans1:probe20 0.0216256022376363 0.0772491696940788 0.279946079980894 0.77958862533765 df.mm.trans1:probe21 -0.0114628016762511 0.0772491696940788 -0.148387377128401 0.88207310347406 df.mm.trans1:probe22 -0.0522976726413214 0.0772491696940788 -0.676999802696003 0.498594715422698 df.mm.trans2:probe2 -0.0273159332749907 0.0772491696940788 -0.353608114924302 0.723722390152273 df.mm.trans2:probe3 -0.0200800741386668 0.0772491696940788 -0.259939028706557 0.794975270788342 df.mm.trans2:probe4 -0.0915208277968924 0.0772491696940788 -1.18474836893823 0.236456077679842 df.mm.trans2:probe5 -0.149932751818638 0.0772491696940788 -1.94089790754257 0.0526089010115151 . df.mm.trans2:probe6 -0.1379581735367 0.0772491696940788 -1.78588551932714 0.074482782168407 . df.mm.trans3:probe2 -0.0138404385116363 0.0772491696940788 -0.179166178309063 0.85785094605071 df.mm.trans3:probe3 0.0842272704483707 0.0772491696940788 1.09033237226920 0.275883195006657 df.mm.trans3:probe4 -0.0425800755972826 0.0772491696940788 -0.551204314116354 0.581641968478655 df.mm.trans3:probe5 0.0696299204057404 0.0772491696940788 0.901367881124003 0.367654226469644 df.mm.trans3:probe6 -0.0123779713731971 0.0772491696940788 -0.160234361381697 0.872735450119746 df.mm.trans3:probe7 0.0114820861174402 0.0772491696940788 0.148637016590746 0.881876166432822 df.mm.trans3:probe8 0.0604469598993968 0.0772491696940788 0.782493328262013 0.434147887595367 df.mm.trans3:probe9 0.081064217404943 0.0772491696940788 1.04938626170317 0.294305833644481