fitVsDatCorrelation=0.793847738210624 cont.fitVsDatCorrelation=0.264276190284218 fstatistic=10205.3919920511,52,692 cont.fstatistic=4049.37166245408,52,692 residuals=-0.491199283476829,-0.0881871749709849,-0.00541227522280322,0.0731800167602257,0.834622179275777 cont.residuals=-0.516429721571397,-0.170527232825422,-0.0228789194926499,0.138797402290263,0.926235893789864 predictedValues: Include Exclude Both Lung 78.7210703042648 74.6855972109022 65.3302254063717 cerebhem 70.6099038944427 66.1917581474868 49.7635177922372 cortex 60.4821059467054 62.0287331665994 48.7075176208912 heart 67.3158361217927 67.988124020965 55.7104709811932 kidney 71.6409889165275 76.53761543742 50.1278369826331 liver 73.7924284957183 70.3744847922901 54.1467696784183 stomach 66.858226704335 66.7194364455577 56.0772514777846 testicle 66.6200267682585 69.8042752198598 51.4199705510454 diffExp=4.03547309336261,4.41814574695591,-1.54662721989401,-0.672287899172261,-4.89662652089257,3.41794370342824,0.138790258777291,-3.18424845160126 diffExpScore=8.2308159866001 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 65.3243125445198 57.9462388233723 59.0450258151392 cerebhem 59.776811377773 56.2733234894416 62.4271588584795 cortex 57.4562675737742 58.7288675242219 61.0376779406664 heart 62.2610485667032 60.5896415870637 56.3981870693071 kidney 60.6625059112195 55.2664703387371 59.341675070274 liver 61.77151839935 54.4293219783404 61.263964970257 stomach 58.6275235519913 53.0916858490647 59.3682664530798 testicle 59.0749145349335 51.5135796306373 57.8510262417957 cont.diffExp=7.37807372114752,3.50348788833139,-1.27259995044771,1.67140697963951,5.3960355724824,7.34219642100956,5.53583770292659,7.56133490429617 cont.diffExpScore=1.04053964460306 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=0.778734153208177 cont.tran.correlation=0.355823079555166 tran.covariance=0.00423820475269019 cont.tran.covariance=0.000774737750081582 tran.mean=69.3981632245704 cont.tran.mean=58.2996269800715 weightedLogRatios: wLogRatio Lung 0.228365254938057 cerebhem 0.272987255638783 cortex -0.103903734062822 heart -0.0418803621284632 kidney -0.284606642511973 liver 0.202864063164627 stomach 0.00873098649085803 testicle -0.197141629637110 cont.weightedLogRatios: wLogRatio Lung 0.493709212239797 cerebhem 0.245238036900536 cortex -0.0889869328953453 heart 0.112051911299899 kidney 0.378110394786716 liver 0.513771717490721 stomach 0.398879316917053 testicle 0.549257932090431 varWeightedLogRatios=0.0427723620084662 cont.varWeightedLogRatios=0.0493945101649188 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.63003164410746 0.0720409123906903 64.269475364193 5.99424900881514e-294 *** df.mm.trans1 -0.213643138218969 0.0602998385602346 -3.5430134361895 0.00042215839872585 *** df.mm.trans2 -0.279209401175354 0.0553498834608992 -5.04444424661879 5.81773158836695e-07 *** df.mm.exp2 0.0427009162441427 0.0716468843907677 0.595991250802317 0.551375957488624 df.mm.exp3 -0.155631431436534 0.0716468843907677 -2.1722009653303 0.0301793183965117 * df.mm.exp4 -0.0911828745997324 0.0716468843907677 -1.27267047792914 0.203562424552627 df.mm.exp5 0.195129908932653 0.0716468843907677 2.72349468636207 0.00662240878067965 ** df.mm.exp6 0.0636452653668719 0.0716468843907677 0.888318674399652 0.374678019182931 df.mm.exp7 -0.123402876214085 0.0716468843907677 -1.72237602881706 0.0854482360653898 . df.mm.exp8 0.0049305479045612 0.0716468843907677 0.0688173386252165 0.945154896585714 df.mm.trans1:exp2 -0.151441348788939 0.061844918257854 -2.44872744689426 0.0145832783803625 * df.mm.trans2:exp2 -0.163432225461717 0.0501628526475246 -3.25803292348809 0.00117669060854161 ** df.mm.trans1:exp3 -0.107931865747427 0.061844918257854 -1.74520184984997 0.08139360798606 . df.mm.trans2:exp3 -0.0300581178288987 0.0501628526475246 -0.599210695613859 0.549228508436502 df.mm.trans1:exp4 -0.0653324590102545 0.061844918257854 -1.05639171092214 0.291157943302462 df.mm.trans2:exp4 -0.00277134735437635 0.0501628526475246 -0.0552470046679674 0.955957628257948 df.mm.trans1:exp5 -0.289373376860182 0.061844918257854 -4.67901623951832 3.46800138539990e-06 *** df.mm.trans2:exp5 -0.170634849010352 0.0501628526475246 -3.40161773113938 0.00070840992798261 *** df.mm.trans1:exp6 -0.128299982929023 0.061844918257854 -2.07454365763883 0.0383979548608871 * df.mm.trans2:exp6 -0.123101764952078 0.0501628526475246 -2.45404235315458 0.014371375575862 * df.mm.trans1:exp7 -0.0399336146617552 0.061844918257854 -0.645705674559344 0.518684032291941 df.mm.trans2:exp7 0.0106119225423357 0.0501628526475246 0.211549423173790 0.832520858036269 df.mm.trans1:exp8 -0.171836162330362 0.061844918257854 -2.77850092086652 0.00560910429222636 ** df.mm.trans2:exp8 -0.0725225555709012 0.0501628526475246 -1.44574225234936 0.148702230595305 df.mm.trans1:probe2 -0.239429980639925 0.0443026318955778 -5.40441888879799 8.9555281884987e-08 *** df.mm.trans1:probe3 0.207424392394731 0.0443026318955778 4.68198803365981 3.41961565909935e-06 *** df.mm.trans1:probe4 -0.0437012477400117 0.0443026318955778 -0.986425543363121 0.324269102653333 df.mm.trans1:probe5 -0.0909447258927719 0.0443026318955778 -2.05280639098667 0.0404668605983476 * df.mm.trans1:probe6 -0.161079916599805 0.0443026318955778 -3.63589948740457 0.000297569117871058 *** df.mm.trans1:probe7 -0.0735547474466885 0.0443026318955778 -1.66027940778007 0.0973112783349265 . df.mm.trans1:probe8 -0.132930714544797 0.0443026318955778 -3.00051506777559 0.00279210663003429 ** df.mm.trans1:probe9 -0.243073023080685 0.0443026318955778 -5.48664972441396 5.75045450417749e-08 *** df.mm.trans1:probe10 -0.200964837327383 0.0443026318955778 -4.53618281191648 6.75164153068482e-06 *** df.mm.trans1:probe11 -0.163831089135926 0.0443026318955778 -3.6979990155456 0.000234522193011412 *** df.mm.trans1:probe12 -0.119855532289103 0.0443026318955778 -2.70538176087608 0.0069905277923168 ** df.mm.trans2:probe2 -0.206536109606304 0.0443026318955778 -4.66193769465240 3.75908087277551e-06 *** df.mm.trans2:probe3 -0.0890355886664561 0.0443026318955778 -2.00971330272916 0.0448492562788739 * df.mm.trans2:probe4 -0.00468197546763756 0.0443026318955778 -0.105681655181865 0.91586556204849 df.mm.trans2:probe5 -0.20214345799104 0.0443026318955778 -4.56278666395026 5.97181414836477e-06 *** df.mm.trans2:probe6 -0.210767446562112 0.0443026318955778 -4.75744752724613 2.38735428543360e-06 *** df.mm.trans3:probe2 0.0456857086114578 0.0443026318955778 1.03121883862656 0.302798482969302 df.mm.trans3:probe3 0.051805216679279 0.0443026318955778 1.16934851187588 0.242665864637768 df.mm.trans3:probe4 0.0965535115574866 0.0443026318955778 2.17940802670742 0.0296378631291478 * df.mm.trans3:probe5 0.634783606830344 0.0443026318955778 14.3283497993199 5.75340783131935e-41 *** df.mm.trans3:probe6 0.0208373611916459 0.0443026318955778 0.470341383797694 0.638259335604133 df.mm.trans3:probe7 -0.140114813449897 0.0443026318955778 -3.16267470926219 0.00163160016382930 ** df.mm.trans3:probe8 0.0837705422339357 0.0443026318955778 1.89087055666093 0.059059099705515 . df.mm.trans3:probe9 -0.0261844248339578 0.0443026318955778 -0.591035424163401 0.554689679433834 df.mm.trans3:probe10 0.0911295792705275 0.0443026318955778 2.05697890557206 0.0400625409847658 * df.mm.trans3:probe11 -0.119747679155222 0.0443026318955778 -2.70294729752106 0.00704138413839089 ** df.mm.trans3:probe12 -0.141095566735225 0.0443026318955778 -3.18481229439799 0.00151346693099901 ** df.mm.trans3:probe13 -0.0478390149076596 0.0443026318955778 -1.07982331660153 0.280597073146097 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.13148088334445 0.114253825137174 36.1605476086614 1.33392936039010e-161 *** df.mm.trans1 0.0307161633554175 0.0956329810663416 0.321187973154465 0.748164926587056 df.mm.trans2 -0.0854956290050925 0.0877825626639587 -0.973947745549183 0.33042280900012 df.mm.exp2 -0.17374165701564 0.113628913476448 -1.52902682688814 0.126714752473066 df.mm.exp3 -0.148115512879067 0.113628913476448 -1.30350197275950 0.192836938349273 df.mm.exp4 0.0424432910216073 0.113628913476448 0.373525449844284 0.708871809062067 df.mm.exp5 -0.126399278308394 0.113628913476448 -1.11238657874339 0.266358258734389 df.mm.exp6 -0.155426065761241 0.113628913476448 -1.36783905615235 0.171806480936307 df.mm.exp7 -0.201114885995009 0.113628913476448 -1.76992703566329 0.0771795903346803 . df.mm.exp8 -0.197799011096238 0.113628913476448 -1.74074542336653 0.0821726829402395 . df.mm.trans1:exp2 0.0849951854364479 0.098083411797111 0.866560245806534 0.386483360602484 df.mm.trans2:exp2 0.144446588118181 0.0795561522554057 1.81565578554443 0.0698558595520145 . df.mm.trans1:exp3 0.0197753196412804 0.098083411797111 0.201617371163499 0.840275115275367 df.mm.trans2:exp3 0.161531235624832 0.0795561522554057 2.03040533064313 0.0426972875953753 * df.mm.trans1:exp4 -0.0904715721416816 0.098083411797111 -0.922394220225794 0.356644462980719 df.mm.trans2:exp4 0.00216499268181606 0.0795561522554057 0.0272133910507085 0.978297380974618 df.mm.trans1:exp5 0.052360802752855 0.098083411797111 0.533839533041175 0.593623994434318 df.mm.trans2:exp5 0.0790500161051348 0.0795561522554057 0.993638001136028 0.320746448574188 df.mm.trans1:exp6 0.0995041689372202 0.098083411797111 1.01448519289937 0.31070585826919 df.mm.trans2:exp6 0.0928134175109018 0.0795561522554057 1.16664035250140 0.243757522959974 df.mm.trans1:exp7 0.0929548697667327 0.098083411797111 0.947712442538328 0.343606690624809 df.mm.trans2:exp7 0.113619562781570 0.0795561522554057 1.42816814992269 0.153694621162602 df.mm.trans1:exp8 0.0972410997155198 0.098083411797111 0.991412288111128 0.321830830964203 df.mm.trans2:exp8 0.0801288023012456 0.0795561522554057 1.00719806111288 0.314191653240180 df.mm.trans1:probe2 0.0486939205445126 0.0702620912164912 0.69303261120534 0.488521607655455 df.mm.trans1:probe3 0.0405237128157031 0.0702620912164912 0.576750735910231 0.564295412620278 df.mm.trans1:probe4 -0.0168362392503649 0.0702620912164912 -0.239620525931817 0.810695407786558 df.mm.trans1:probe5 0.0125939679967759 0.0702620912164912 0.179242715079053 0.857799592721701 df.mm.trans1:probe6 0.115067689532082 0.0702620912164912 1.63769235358418 0.101940491405017 df.mm.trans1:probe7 0.0550902891329861 0.0702620912164912 0.784068452549216 0.433268240414345 df.mm.trans1:probe8 0.0480753671207821 0.0702620912164912 0.684229095496923 0.4940595847749 df.mm.trans1:probe9 0.0228172986533781 0.0702620912164912 0.324745510108340 0.745471834110795 df.mm.trans1:probe10 0.0986402196579981 0.0702620912164912 1.40388960747081 0.160800334484957 df.mm.trans1:probe11 0.0845119841163236 0.0702620912164912 1.20281054339709 0.229461015110712 df.mm.trans1:probe12 -0.0799971845356773 0.0702620912164912 -1.13855399335027 0.255283230555494 df.mm.trans2:probe2 0.0330546801917520 0.0702620912164912 0.470448283269908 0.638183013817389 df.mm.trans2:probe3 0.0478836590526157 0.0702620912164912 0.681500624640915 0.495782776174128 df.mm.trans2:probe4 0.0553978551682339 0.0702620912164912 0.788445863325394 0.430705964674736 df.mm.trans2:probe5 0.047367597651005 0.0702620912164912 0.674155819032716 0.500437387092814 df.mm.trans2:probe6 0.073373988732718 0.0702620912164912 1.04428985050613 0.296715964167696 df.mm.trans3:probe2 0.0524497995491744 0.0702620912164912 0.746487880464109 0.455626277143534 df.mm.trans3:probe3 -0.00078681685389444 0.0702620912164912 -0.0111983124935764 0.99106845366204 df.mm.trans3:probe4 -0.038066097858342 0.0702620912164912 -0.541772913377328 0.58814934504157 df.mm.trans3:probe5 0.0170276844533113 0.0702620912164912 0.242345255578086 0.808584455475197 df.mm.trans3:probe6 0.0107121118715983 0.0702620912164912 0.152459337405604 0.878869100115762 df.mm.trans3:probe7 0.0805049547930247 0.0702620912164912 1.14578079586293 0.252281935431224 df.mm.trans3:probe8 -0.054193080704349 0.0702620912164912 -0.771299000158842 0.440793006836562 df.mm.trans3:probe9 -0.0140262903344729 0.0702620912164912 -0.199628136476257 0.841830066164112 df.mm.trans3:probe10 0.00273604380428891 0.0702620912164912 0.0389405404382091 0.968949025667572 df.mm.trans3:probe11 -0.0944573812468037 0.0702620912164912 -1.34435767013769 0.179273276804598 df.mm.trans3:probe12 -0.0129810285967839 0.0702620912164912 -0.184751526349918 0.853478060590042 df.mm.trans3:probe13 0.0718656005285974 0.0702620912164912 1.02282182730892 0.3067495196473