fitVsDatCorrelation=0.87959522543948 cont.fitVsDatCorrelation=0.215881122678088 fstatistic=7559.3115290777,67,1037 cont.fstatistic=1782.58812204928,67,1037 residuals=-0.894519140434797,-0.103203021397514,-0.0075328929482533,0.0842392214323394,2.20900552489201 cont.residuals=-0.750465116483314,-0.307162833478889,-0.0325358708980549,0.213732662065558,1.74529037259296 predictedValues: Include Exclude Both Lung 75.7010572104208 101.261918448542 65.2995974633408 cerebhem 67.1961990385127 66.4258575103692 64.1532334535706 cortex 64.6255041065033 79.6341078549806 64.2532133779273 heart 68.068785369501 85.8021485023018 62.7116362461397 kidney 72.6911676107888 95.0403722782405 64.7508008227507 liver 74.1771516978692 91.9349461600988 65.3205613864961 stomach 71.5072162735092 81.2943985597831 68.8845195502169 testicle 79.8048206581582 89.0505363264794 65.3583823978866 diffExp=-25.5608612381216,0.770341528143462,-15.0086037484773,-17.7333631328008,-22.3492046674517,-17.7577944622296,-9.7871822862739,-9.2457156683212 diffExpScore=1.00459481689245 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=-1,0,0,0,-1,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,-1,-1,-1,-1,0,0 diffExp1.2Score=0.833333333333333 cont.predictedValues: Include Exclude Both Lung 80.4924639129875 82.5841575007218 81.6944531898529 cerebhem 76.2289840787834 81.1047152669439 72.3799194950509 cortex 71.3287039855953 84.9711985153924 77.7518593923304 heart 77.2528603305337 82.6116160802096 70.8173624163193 kidney 77.8438183226338 98.2359136881461 76.0136314216162 liver 73.1472689644367 86.8192342184611 73.1282012691352 stomach 79.9192174520536 73.0772689094451 80.0454293011464 testicle 76.4237943285691 79.5081060175818 85.7186173616202 cont.diffExp=-2.09169358773427,-4.87573118816056,-13.6424945297971,-5.35875574967589,-20.3920953655123,-13.6719652540244,6.84194854260853,-3.08431168901265 cont.diffExpScore=1.22145569970624 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,-1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.655906129303621 cont.tran.correlation=-0.242224131417477 tran.covariance=0.00588652612064795 cont.tran.covariance=-0.000925505280215834 tran.mean=79.0135117253787 cont.tran.mean=80.096832598281 weightedLogRatios: wLogRatio Lung -1.30105967374921 cerebhem 0.0484485373573003 cortex -0.892350331079307 heart -1.00395886628091 kidney -1.18499186166410 liver -0.947305308855673 stomach -0.555952091486297 testicle -0.486098801986464 cont.weightedLogRatios: wLogRatio Lung -0.112904337244172 cerebhem -0.270611096497778 cortex -0.762149904538436 heart -0.293792041067934 kidney -1.04026473094493 liver -0.750211132531559 stomach 0.388091473557291 testicle -0.172347676637310 varWeightedLogRatios=0.192618354852467 cont.varWeightedLogRatios=0.206111844376562 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.81139850806772 0.0915421981945918 52.5593508016937 1.14022596326926e-294 *** df.mm.trans1 -0.692055233892672 0.0781887031277387 -8.85108981488087 3.67735949636131e-18 *** df.mm.trans2 -0.187696930485709 0.0682244550001087 -2.75116789845240 0.00604176660280419 ** df.mm.exp2 -0.523088075775911 0.0858134981873052 -6.09563864456575 1.53525015796594e-09 *** df.mm.exp3 -0.382296777613973 0.0858134981873052 -4.45497253566721 9.30136641532548e-06 *** df.mm.exp4 -0.231500892452473 0.0858134981873052 -2.69772119005306 0.00709489218950147 ** df.mm.exp5 -0.0955410759238878 0.0858134981873052 -1.11335719836698 0.265813050205759 df.mm.exp6 -0.117286134766339 0.0858134981873052 -1.36675624748846 0.171997954447731 df.mm.exp7 -0.330072651947136 0.0858134981873052 -3.84639548462045 0.000127178793483728 *** df.mm.exp8 -0.0766144232012525 0.0858134981873052 -0.892801538448253 0.372170592810902 df.mm.trans1:exp2 0.403912633640995 0.0781887031277387 5.16586946046558 2.86899792000494e-07 *** df.mm.trans2:exp2 0.101464064659732 0.0527148128949007 1.92477330540894 0.0545310441008579 . df.mm.trans1:exp3 0.224113784762182 0.0781887031277387 2.86631924814052 0.00423671520181822 ** df.mm.trans2:exp3 0.142028857197839 0.0527148128949007 2.69428741938260 0.00716788420406265 ** df.mm.trans1:exp4 0.125227509771134 0.0781887031277387 1.60160617533900 0.109547278971712 df.mm.trans2:exp4 0.0658345273212086 0.0527148128949007 1.24888098251368 0.211990456065455 df.mm.trans1:exp5 0.0549688360345585 0.0781887031277387 0.703027852306933 0.482196186876287 df.mm.trans2:exp5 0.032132436468547 0.0527148128949007 0.60955231943269 0.542291932420979 df.mm.trans1:exp6 0.0969501827823433 0.0781887031277387 1.23995128329413 0.215273916257539 df.mm.trans2:exp6 0.0206569426108622 0.0527148128949007 0.391862201086565 0.695240546734003 df.mm.trans1:exp7 0.273078897319109 0.0781887031277387 3.49256205046621 0.000498580605232259 *** df.mm.trans2:exp7 0.110439355592613 0.0527148128949007 2.09503457430077 0.0364100338325047 * df.mm.trans1:exp8 0.129406208945115 0.0781887031277387 1.65504994671291 0.098216951453267 . df.mm.trans2:exp8 -0.0518919564555986 0.0527148128949007 -0.984390413356059 0.325153202157981 df.mm.trans1:probe2 0.267725798975154 0.058641527345804 4.56546428943431 5.58086845142002e-06 *** df.mm.trans1:probe3 0.0282087546886166 0.058641527345804 0.481037175622524 0.630591554236525 df.mm.trans1:probe4 -0.0518689146380793 0.058641527345804 -0.884508248433107 0.376626940894856 df.mm.trans1:probe5 -0.0482643732628419 0.058641527345804 -0.82304086280412 0.410673979181991 df.mm.trans1:probe6 0.346205123561657 0.058641527345804 5.90375352129073 4.80981350505258e-09 *** df.mm.trans1:probe7 -0.263478069837606 0.058641527345804 -4.49302877607363 7.8104918809969e-06 *** df.mm.trans1:probe8 0.215230417708170 0.058641527345804 3.67027305477525 0.00025463181415724 *** df.mm.trans1:probe9 1.51824510838086 0.058641527345804 25.8902722541297 2.10929044329695e-114 *** df.mm.trans1:probe10 0.191480158277883 0.058641527345804 3.26526553697588 0.00112940215799580 ** df.mm.trans1:probe11 0.605477744113542 0.058641527345804 10.3250677722477 7.41264751611033e-24 *** df.mm.trans1:probe12 0.418703922208607 0.058641527345804 7.1400582685977 1.75260978889999e-12 *** df.mm.trans1:probe13 0.273257041632253 0.058641527345804 4.65978725316753 3.57711498287362e-06 *** df.mm.trans1:probe14 0.549680728621235 0.058641527345804 9.37357455544796 4.25339704191166e-20 *** df.mm.trans1:probe15 0.451571111630814 0.058641527345804 7.70053462229825 3.15217720105536e-14 *** df.mm.trans1:probe16 0.314697786891712 0.058641527345804 5.3664664127175 9.90401626171477e-08 *** df.mm.trans1:probe17 0.743142158031155 0.058641527345804 12.672626237187 2.54128791972093e-34 *** df.mm.trans1:probe18 0.694392052513885 0.058641527345804 11.8413022979281 1.97342408947190e-30 *** df.mm.trans1:probe19 0.564343409523392 0.058641527345804 9.62361376086792 4.67271839904714e-21 *** df.mm.trans1:probe20 0.691496137047056 0.058641527345804 11.7919189411518 3.31245464370863e-30 *** df.mm.trans1:probe21 0.408634953796077 0.058641527345804 6.96835454824346 5.69282718267844e-12 *** df.mm.trans1:probe22 0.379073028632073 0.058641527345804 6.46424207876974 1.56430326014608e-10 *** df.mm.trans2:probe2 0.0778161979397751 0.058641527345804 1.32698109107731 0.184807040118183 df.mm.trans2:probe3 0.0639605126879613 0.058641527345804 1.09070339029186 0.275656781649824 df.mm.trans2:probe4 -0.0849994347276842 0.058641527345804 -1.44947511729103 0.147507284450582 df.mm.trans2:probe5 -0.157626849927223 0.058641527345804 -2.68797313203852 0.00730387171642915 ** df.mm.trans2:probe6 -0.0429383967963449 0.058641527345804 -0.732218254533872 0.46420081518088 df.mm.trans3:probe2 0.677525279128718 0.058641527345804 11.5536772283131 3.93856739306954e-29 *** df.mm.trans3:probe3 0.255711887284064 0.058641527345804 4.36059391455056 1.42641049000649e-05 *** df.mm.trans3:probe4 1.30684521319542 0.058641527345804 22.2853201876729 3.32865861667237e-90 *** df.mm.trans3:probe5 0.00229611493692688 0.058641527345804 0.0391551011860057 0.9687742667248 df.mm.trans3:probe6 0.379601281425350 0.058641527345804 6.47325024784696 1.47720269880040e-10 *** df.mm.trans3:probe7 0.112096317814541 0.058641527345804 1.91155181128756 0.0562091103920542 . df.mm.trans3:probe8 0.0288133120582534 0.058641527345804 0.491346548468013 0.623285335285533 df.mm.trans3:probe9 0.120945642825089 0.058641527345804 2.06245724317314 0.0394128424982785 * df.mm.trans3:probe10 0.414513500377035 0.058641527345804 7.06860000307776 2.87035348196713e-12 *** df.mm.trans3:probe11 -0.0452896431109946 0.058641527345804 -0.772313497974318 0.440104746625041 df.mm.trans3:probe12 0.756185812698034 0.058641527345804 12.8950565737975 2.14696504336152e-35 *** df.mm.trans3:probe13 0.0420721389645826 0.058641527345804 0.717446166033275 0.473260416829011 df.mm.trans3:probe14 -0.0595038931573767 0.058641527345804 -1.01470571880721 0.310482812543294 df.mm.trans3:probe15 -0.0537248064944223 0.058641527345804 -0.916156330267658 0.359797903744637 df.mm.trans3:probe16 0.0711191344269269 0.058641527345804 1.21277766193816 0.225491039582927 df.mm.trans3:probe17 0.386568589577904 0.058641527345804 6.59206209446664 6.893341743046e-11 *** df.mm.trans3:probe18 0.0563356248136291 0.058641527345804 0.960677993283203 0.336938038180498 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.50681913419678 0.187890070119636 23.9864678922475 1.70018581108501e-101 *** df.mm.trans1 -0.104137418130449 0.160482064042265 -0.648903780942295 0.516544248033564 df.mm.trans2 -0.0674955168591368 0.140030476508720 -0.48200590715646 0.629903460733175 df.mm.exp2 0.0485586325760444 0.176131931607653 0.275694657594584 0.782837526135841 df.mm.exp3 -0.0429066020880382 0.176131931607653 -0.243604902849847 0.807585012558616 df.mm.exp4 0.102134719445351 0.176131931607653 0.579876224107185 0.56212405220035 df.mm.exp5 0.212168352251668 0.176131931607653 1.20459902026334 0.228632982394191 df.mm.exp6 0.0650935776762522 0.176131931607653 0.369572837146039 0.711776247724397 df.mm.exp7 -0.109055958770629 0.176131931607653 -0.61917199099116 0.535939058824842 df.mm.exp8 -0.137912291272415 0.176131931607653 -0.78300561410764 0.43380259067002 df.mm.trans1:exp2 -0.102980437771764 0.160482064042265 -0.641694374921812 0.521213451416974 df.mm.trans2:exp2 -0.0666353960714677 0.108196985505112 -0.61587109622679 0.538114748375524 df.mm.trans1:exp3 -0.0779581351168788 0.160482064042265 -0.485774753597058 0.627229498140156 df.mm.trans2:exp3 0.0714010958440954 0.108196985505112 0.659917607785122 0.509453241964992 df.mm.trans1:exp4 -0.143214341475036 0.160482064042265 -0.89240091925362 0.372385107748587 df.mm.trans2:exp4 -0.101802282614245 0.108196985505112 -0.940897587294005 0.346976464784247 df.mm.trans1:exp5 -0.245627426154739 0.160482064042265 -1.53055998887234 0.126183155517457 df.mm.trans2:exp5 -0.0386143482311869 0.108196985505113 -0.356889316748684 0.721247275856911 df.mm.trans1:exp6 -0.160782349692484 0.160482064042265 -1.00187114773237 0.316639546112010 df.mm.trans2:exp6 -0.0150832524986230 0.108196985505112 -0.139405478149022 0.88915681237436 df.mm.trans1:exp7 0.101908737367482 0.160482064042265 0.635016367565181 0.525557822061111 df.mm.trans2:exp7 -0.0132445459817762 0.108196985505113 -0.122411413958944 0.902596917339858 df.mm.trans1:exp8 0.08604281899293 0.160482064042265 0.536152245463826 0.591968317927824 df.mm.trans2:exp8 0.0999534059008912 0.108196985505112 0.923809526062704 0.355800396824674 df.mm.trans1:probe2 0.0555424286930412 0.120361548031699 0.461463229755181 0.644563074408535 df.mm.trans1:probe3 0.0172301814426730 0.120361548031699 0.143153538023084 0.886196733676675 df.mm.trans1:probe4 0.155771226694115 0.120361548031699 1.29419427750373 0.195886457857752 df.mm.trans1:probe5 -0.176026111697506 0.120361548031699 -1.46247796390212 0.143913266621337 df.mm.trans1:probe6 0.0328109515925452 0.120361548031699 0.272603270139928 0.785212469287977 df.mm.trans1:probe7 -0.0615505441354717 0.120361548031699 -0.511380462797483 0.609193536695589 df.mm.trans1:probe8 0.0866319226218224 0.120361548031699 0.719764111034918 0.471832427847281 df.mm.trans1:probe9 -0.121674546162168 0.120361548031699 -1.01090878400902 0.312295900756275 df.mm.trans1:probe10 0.108900089704200 0.120361548031699 0.904774751447365 0.365794878312544 df.mm.trans1:probe11 -0.0207454180803004 0.120361548031699 -0.172359182974589 0.86318879058797 df.mm.trans1:probe12 -0.0267389535395271 0.120361548031699 -0.222155281124210 0.824236747728608 df.mm.trans1:probe13 -0.0705155295943107 0.120361548031699 -0.585864262694091 0.558094216240059 df.mm.trans1:probe14 -0.0202648162750202 0.120361548031699 -0.168366198394882 0.866328029463325 df.mm.trans1:probe15 -0.0372349109749942 0.120361548031699 -0.309358857408413 0.757110710177243 df.mm.trans1:probe16 -0.0620787790088193 0.120361548031699 -0.51576919725617 0.606125561060598 df.mm.trans1:probe17 -0.140169399470604 0.120361548031699 -1.16456959687564 0.244461093517905 df.mm.trans1:probe18 -0.0903617174491472 0.120361548031699 -0.750752370062149 0.452972015835015 df.mm.trans1:probe19 -0.0774162771681424 0.120361548031699 -0.643197752389773 0.520237989975057 df.mm.trans1:probe20 0.00520455960075305 0.120361548031699 0.0432410490382057 0.96551770762253 df.mm.trans1:probe21 -0.0829146685832141 0.120361548031699 -0.68888004465826 0.491052849895336 df.mm.trans1:probe22 -0.05512576868186 0.120361548031699 -0.458001492863334 0.647047321365502 df.mm.trans2:probe2 -0.113758545458220 0.120361548031699 -0.945140265463018 0.344807431013871 df.mm.trans2:probe3 -0.158784528385473 0.120361548031699 -1.31922969571357 0.187383543012392 df.mm.trans2:probe4 -0.0696547414191403 0.120361548031699 -0.578712575222077 0.562908796421904 df.mm.trans2:probe5 -0.109213791818063 0.120361548031699 -0.907381083112194 0.364416111647411 df.mm.trans2:probe6 -0.160726465359481 0.120361548031699 -1.33536389310273 0.182050312213067 df.mm.trans3:probe2 0.187464427871336 0.120361548031699 1.55751094047049 0.119654341935783 df.mm.trans3:probe3 0.0298701364574214 0.120361548031699 0.248170092075873 0.804051939259118 df.mm.trans3:probe4 -0.0101980532704817 0.120361548031699 -0.0847284987377855 0.93249359278371 df.mm.trans3:probe5 0.0892292660167246 0.120361548031699 0.741343622410246 0.45865296515456 df.mm.trans3:probe6 0.0155237944603025 0.120361548031699 0.128976360923957 0.897401363684171 df.mm.trans3:probe7 0.0717630532740784 0.120361548031699 0.596229065242485 0.551152331739134 df.mm.trans3:probe8 0.0321353782034094 0.120361548031699 0.266990402906301 0.78952963476433 df.mm.trans3:probe9 0.0245468649268834 0.120361548031699 0.203942748563009 0.838438253421097 df.mm.trans3:probe10 0.0707647872233767 0.120361548031699 0.587935170165307 0.556703816541457 df.mm.trans3:probe11 0.0934232289597035 0.120361548031699 0.776188329973117 0.437814785064532 df.mm.trans3:probe12 0.0956095815334103 0.120361548031699 0.794353205794845 0.427171582251401 df.mm.trans3:probe13 0.0996543498660796 0.120361548031699 0.827958359590342 0.407884582013584 df.mm.trans3:probe14 0.078834355552154 0.120361548031699 0.654979574800682 0.5126261591125 df.mm.trans3:probe15 -0.065899419921634 0.120361548031699 -0.547512232929061 0.584144683290707 df.mm.trans3:probe16 0.137947383735772 0.120361548031699 1.14610842076775 0.252014692725090 df.mm.trans3:probe17 0.104164183200695 0.120361548031699 0.86542741352298 0.387004402923613 df.mm.trans3:probe18 0.165565024847114 0.120361548031699 1.37556410294349 0.169253671744732