fitVsDatCorrelation=0.919420652282584 cont.fitVsDatCorrelation=0.273493653892098 fstatistic=8973.47018137137,48,600 cont.fstatistic=1489.68245958031,48,600 residuals=-0.570077844923043,-0.0899871718009229,-0.0143793287657964,0.0818629457669812,0.780839225357625 cont.residuals=-0.703343972695675,-0.285412550163410,-0.0331860665564622,0.254589802564442,2.61255238981903 predictedValues: Include Exclude Both Lung 64.2445285905048 99.7247323885569 66.5521327923114 cerebhem 64.2174932066532 83.1883355853349 80.6380732441947 cortex 60.3878089207684 124.828568943746 412.841895673284 heart 61.0308815243629 85.8840433358403 60.6283296473507 kidney 65.1186892937482 112.139606700439 73.7895632697216 liver 68.3452557039121 100.973502174127 50.1850525442589 stomach 63.2893459066436 86.3552474001258 61.2601160753575 testicle 61.4898358306535 89.9234758586221 62.1296648264496 diffExp=-35.480203798052,-18.9708423786816,-64.4407600229775,-24.8531618114774,-47.0209174066904,-32.6282464702150,-23.0659014934821,-28.4336400279687 diffExpScore=0.996375415254573 diffExp1.5=-1,0,-1,0,-1,0,0,0 diffExp1.5Score=0.75 diffExp1.4=-1,0,-1,-1,-1,-1,0,-1 diffExp1.4Score=0.857142857142857 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 85.036409610159 70.171824173378 89.3670087740312 cerebhem 69.318910873535 76.4871674409988 56.7680812074185 cortex 79.9309881913811 65.8295973066042 89.447491674188 heart 77.5247686874857 89.880870952147 74.4904149674097 kidney 82.1926888195377 74.1960151922446 76.1477670071362 liver 73.5783922393025 84.8880148734571 71.4369925936417 stomach 70.5356431327549 74.1362463934549 76.8143440552259 testicle 74.4367740370226 67.2805664194291 63.9090211276448 cont.diffExp=14.8645854367809,-7.16825656746373,14.1013908847769,-12.3561022646613,7.9966736272931,-11.3096226341547,-3.60060326070004,7.15620761759347 cont.diffExpScore=7.35224974817847 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=1,0,1,0,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.0260850406932242 cont.tran.correlation=-0.247209052189391 tran.covariance=2.48283312164996e-05 cont.tran.covariance=-0.00199701636385142 tran.mean=80.6963344602524 cont.tran.mean=75.9640548964308 weightedLogRatios: wLogRatio Lung -1.9270846650137 cerebhem -1.11082486721849 cortex -3.24145336991125 heart -1.46287311717068 kidney -2.41762282792394 liver -1.72495284852403 stomach -1.33719728883022 testicle -1.63776340008337 cont.weightedLogRatios: wLogRatio Lung 0.835203153721869 cerebhem -0.421953780267072 cortex 0.831521625340261 heart -0.6543350438653 kidney 0.446055629268476 liver -0.624806660090468 stomach -0.213136184384174 testicle 0.430535532243558 varWeightedLogRatios=0.469104070586145 cont.varWeightedLogRatios=0.395122907223768 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04394653005826 0.089951081684413 44.9571751037543 2.92987146097512e-194 *** df.mm.trans1 -0.129059219587047 0.0803735488768517 -1.60574245371187 0.108856656330278 df.mm.trans2 0.446557269247099 0.0744740565986797 5.99614536446529 3.49087633607595e-09 *** df.mm.exp2 -0.373712800617412 0.102177562691974 -3.65748399914386 0.000277089266931905 *** df.mm.exp3 -1.66246079143187 0.102177562691974 -16.2703116773646 1.41374694194355e-49 *** df.mm.exp4 -0.107508910092448 0.102177562691974 -1.05217728100000 0.293141778652965 df.mm.exp5 0.0276141908902014 0.102177562691974 0.270256895571562 0.787055556144784 df.mm.exp6 0.356588368377143 0.102177562691974 3.48988915944415 0.000518665052466731 *** df.mm.exp7 -0.07606710463965 0.102177562691974 -0.744459963964528 0.456889721941575 df.mm.exp8 -0.0785173228528065 0.102177562691974 -0.76843996650719 0.442528186493012 df.mm.trans1:exp2 0.373291892027114 0.0969341471613846 3.85098443591425 0.000130289570042127 *** df.mm.trans2:exp2 0.192406227030538 0.0854878823130538 2.25068421189746 0.0247668213749022 * df.mm.trans1:exp3 1.60055147491554 0.0969341471613846 16.5117404112588 9.0978122812874e-51 *** df.mm.trans2:exp3 1.88698842469416 0.0854878823130538 22.0731684261879 1.72055617891083e-79 *** df.mm.trans1:exp4 0.0561923386352052 0.0969341471613846 0.579696013022648 0.562337232124245 df.mm.trans2:exp4 -0.0419067510713249 0.0854878823130538 -0.490206915149259 0.624166690345628 df.mm.trans1:exp5 -0.0140991589762169 0.0969341471613846 -0.145450900318371 0.884403727783666 df.mm.trans2:exp5 0.0897166782861422 0.0854878823130538 1.04946661279552 0.294385940252624 df.mm.trans1:exp6 -0.294712781434559 0.0969341471613846 -3.04034016974323 0.00246612775500386 ** df.mm.trans2:exp6 -0.344143954962344 0.0854878823130537 -4.02564604071136 6.40984118087544e-05 *** df.mm.trans1:exp7 0.0610875464876691 0.0969341471613846 0.630196357801189 0.528806049397833 df.mm.trans2:exp7 -0.0678770379428743 0.0854878823130537 -0.793996015649456 0.427511658364706 df.mm.trans1:exp8 0.0346926509354971 0.0969341471613846 0.357899171256313 0.720544639936814 df.mm.trans2:exp8 -0.0249373510313606 0.0854878823130537 -0.291706267094567 0.770612041798722 df.mm.trans1:probe2 -0.119588358643009 0.0484670735806923 -2.46741446941103 0.0138873124554804 * df.mm.trans1:probe3 0.183887467676686 0.0484670735806923 3.79406995494649 0.000163223698199793 *** df.mm.trans1:probe4 0.467299656231724 0.0484670735806923 9.6415900880362 1.50392899261019e-20 *** df.mm.trans1:probe5 -0.0638279560094713 0.0484670735806923 -1.31693439058591 0.188363366666042 df.mm.trans1:probe6 -0.0741344222856248 0.0484670735806923 -1.52958321616425 0.126646943276699 df.mm.trans1:probe7 0.224112155516568 0.0484670735806923 4.62400840321927 4.61241805125998e-06 *** df.mm.trans1:probe8 0.24524145357942 0.0484670735806923 5.05995999884581 5.58287827130361e-07 *** df.mm.trans1:probe9 0.0494876410540714 0.0484670735806923 1.02105692376248 0.307638980801842 df.mm.trans1:probe10 0.150038313007007 0.0484670735806923 3.09567510316483 0.00205520170381875 ** df.mm.trans1:probe11 0.139609369448989 0.0484670735806923 2.88049925722368 0.0041122503582118 ** df.mm.trans1:probe12 0.171473499517506 0.0484670735806923 3.53793796177979 0.000434438844554442 *** df.mm.trans1:probe13 0.63115185318997 0.0484670735806923 13.0222810366129 2.55790070504103e-34 *** df.mm.trans1:probe14 0.507210340957093 0.0484670735806923 10.4650498469367 1.17771792110767e-23 *** df.mm.trans1:probe15 0.562070555069923 0.0484670735806923 11.5969567284507 3.37198380310182e-28 *** df.mm.trans1:probe16 0.718519176663685 0.0484670735806923 14.8248929341160 1.35886669221048e-42 *** df.mm.trans1:probe17 0.686436392791168 0.0484670735806923 14.1629428409439 1.71238182797543e-39 *** df.mm.trans1:probe18 0.725007144858207 0.0484670735806923 14.9587563534479 3.14953684862188e-43 *** df.mm.trans2:probe2 -0.0689499358295046 0.0484670735806923 -1.42261396728875 0.155367771790262 df.mm.trans2:probe3 0.261823447004283 0.0484670735806923 5.40208904027135 9.50907671901591e-08 *** df.mm.trans2:probe4 0.38781283105237 0.0484670735806923 8.00157307634233 6.36936326611333e-15 *** df.mm.trans2:probe5 0.303162628427455 0.0484670735806923 6.25502234878537 7.55758132316248e-10 *** df.mm.trans2:probe6 0.123340264241645 0.0484670735806923 2.54482590198679 0.0111825322007145 * df.mm.trans3:probe2 -0.243299335804411 0.0484670735806923 -5.0198891294591 6.82475092749592e-07 *** df.mm.trans3:probe3 -0.247077626461890 0.0484670735806923 -5.09784495345139 4.61152652319112e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.29099348927542 0.220002458219192 19.5042979247087 5.48489943299877e-66 *** df.mm.trans1 0.290288068652328 0.196577717550359 1.47670891833386 0.140278322713875 df.mm.trans2 -0.0334154248591906 0.182148732605002 -0.183451316851343 0.854505890068493 df.mm.exp2 0.335591722996233 0.249905999418071 1.34287181491316 0.179821253116522 df.mm.exp3 -0.126693402691586 0.249905999418071 -0.506964230497078 0.612366178201134 df.mm.exp4 0.337137365906955 0.249905999418071 1.34905671209178 0.177827635090580 df.mm.exp5 0.181826250282416 0.249905999418071 0.72757857236647 0.467155412041098 df.mm.exp6 0.269593682137221 0.249905999418072 1.07878035247251 0.281119140719949 df.mm.exp7 0.0193565381106157 0.249905999418072 0.0774552758064599 0.938287190710245 df.mm.exp8 0.160086207914765 0.249905999418071 0.640585693370867 0.522036464015815 df.mm.trans1:exp2 -0.539953482978656 0.237081647730546 -2.27750012768739 0.023107371221522 * df.mm.trans2:exp2 -0.24941560751561 0.209086360104148 -1.19288320573075 0.233386621992463 df.mm.trans1:exp3 0.064777504333002 0.237081647730546 0.273228674395855 0.784771458569257 df.mm.trans2:exp3 0.0628160815695642 0.209086360104148 0.300431274131296 0.763952277060124 df.mm.trans1:exp4 -0.42961939777017 0.237081647730546 -1.81211579168056 0.0704680636385041 . df.mm.trans2:exp4 -0.0895990938821352 0.209086360104148 -0.428526728560891 0.668421473395689 df.mm.trans1:exp5 -0.215839409107421 0.237081647730546 -0.91040116843094 0.362976566263977 df.mm.trans2:exp5 -0.126062670571394 0.209086360104148 -0.602921541647195 0.546788703301758 df.mm.trans1:exp6 -0.414321796341814 0.237081647730546 -1.7475911792747 0.0810463958400157 . df.mm.trans2:exp6 -0.0792076317648883 0.209086360104148 -0.378827350217556 0.704950088527773 df.mm.trans1:exp7 -0.206317892832084 0.237081647730546 -0.870239830062992 0.38451737444649 df.mm.trans2:exp7 0.0356011642897606 0.209086360104148 0.170270142308792 0.86485508861446 df.mm.trans1:exp8 -0.293215626628214 0.237081647730546 -1.23677066291300 0.216656104593057 df.mm.trans2:exp8 -0.202161638859043 0.209086360104148 -0.966881047421476 0.333993060278227 df.mm.trans1:probe2 -0.196194314595205 0.118540823865273 -1.65507804145337 0.0984316018481998 . df.mm.trans1:probe3 -0.111761130721248 0.118540823865273 -0.942807102878492 0.346159036772813 df.mm.trans1:probe4 -0.233943639081977 0.118540823865273 -1.97352803408778 0.0488938476503828 * df.mm.trans1:probe5 -0.2048506152261 0.118540823865273 -1.72810183484908 0.0844844778876494 . df.mm.trans1:probe6 -0.185987866500156 0.118540823865273 -1.56897733992080 0.117180413921099 df.mm.trans1:probe7 -0.101869179123658 0.118540823865273 -0.859359466232804 0.390485430346511 df.mm.trans1:probe8 -0.221568951669848 0.118540823865273 -1.86913625572294 0.0620908012549908 . df.mm.trans1:probe9 -0.180419634017305 0.118540823865273 -1.52200421875218 0.128534850617953 df.mm.trans1:probe10 -0.268988930093087 0.118540823865273 -2.26916703733058 0.0236123657727908 * df.mm.trans1:probe11 -0.217027633776669 0.118540823865273 -1.83082609602351 0.0676222919282202 . df.mm.trans1:probe12 -0.175994260143846 0.118540823865273 -1.48467215264061 0.138155848680262 df.mm.trans1:probe13 -0.133989002629799 0.118540823865273 -1.13031948202152 0.258793262405024 df.mm.trans1:probe14 -0.070655832416987 0.118540823865273 -0.596046409271548 0.551369007885919 df.mm.trans1:probe15 -0.0838815080938278 0.118540823865273 -0.7076170500482 0.479457864998292 df.mm.trans1:probe16 -0.264884147612406 0.118540823865273 -2.23453945210857 0.0258150312940257 * df.mm.trans1:probe17 -0.106839241674246 0.118540823865273 -0.901286478282569 0.367797652340524 df.mm.trans1:probe18 -0.143387053372887 0.118540823865273 -1.20960061434914 0.226908556850595 df.mm.trans2:probe2 -0.168217200962838 0.118540823865273 -1.41906556305045 0.156399167058870 df.mm.trans2:probe3 0.0555662212602157 0.118540823865273 0.468751772160528 0.63941733606247 df.mm.trans2:probe4 -0.0320906709726007 0.118540823865273 -0.270714087570989 0.78670404006022 df.mm.trans2:probe5 0.0173934023211252 0.118540823865273 0.146729217445743 0.88339504264986 df.mm.trans2:probe6 0.0676674573130034 0.118540823865273 0.570836738826031 0.568324042928253 df.mm.trans3:probe2 -0.122605072972452 0.118540823865273 -1.0342856492359 0.301419329839767 df.mm.trans3:probe3 -0.0427385721388486 0.118540823865273 -0.360538848518743 0.71857110038685