fitVsDatCorrelation=0.918766710116562 cont.fitVsDatCorrelation=0.270150339014786 fstatistic=4670.58596306055,43,485 cont.fstatistic=775.923617434629,43,485 residuals=-0.764214163055622,-0.137115940777951,-0.0049034608163852,0.114743348069269,0.761811200724188 cont.residuals=-1.01226271384019,-0.425556908002769,-0.138898118020594,0.413440879831746,1.70278511571957 predictedValues: Include Exclude Both Lung 120.526837140812 49.3287956543681 70.7029390046961 cerebhem 82.5814631530526 52.9857865930332 60.0081552704851 cortex 124.896766622313 58.826767944966 72.4198334380454 heart 120.259448821291 53.035753464675 68.5643002639456 kidney 152.085022878074 77.8006454540688 112.595149173657 liver 226.493204364904 101.531981336264 134.772011827426 stomach 120.620837257628 51.2616318082155 70.2489579633394 testicle 101.237687870746 50.9825681206201 68.268213037299 diffExp=71.1980414864442,29.5956765600194,66.0699986773469,67.223695356616,74.2843774240054,124.961223028639,69.3592054494122,50.2551197501261 diffExpScore=0.998194774246785 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 75.6046496685582 106.432445872109 85.0413498508722 cerebhem 93.4509482948187 80.1478910604943 91.9451512929945 cortex 81.7395962431946 91.6814647449256 93.409929789705 heart 95.131569300579 103.498232297095 80.837345642843 kidney 87.7556906055825 84.6358682782394 90.3517193676977 liver 93.2703104202417 82.0869582841874 87.6760792412446 stomach 90.5810243550147 68.7129292090995 79.0676462094374 testicle 91.6546031095855 92.3615421344368 67.6949659645843 cont.diffExp=-30.8277962035507,13.3030572343244,-9.94186850173108,-8.36666299651591,3.1198223273431,11.1833521360542,21.8680951459152,-0.70693902485128 cont.diffExpScore=72.5507341869256 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=-1,0,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=-1,0,0,0,0,0,1,0 cont.diffExp1.3Score=2 cont.diffExp1.2=-1,0,0,0,0,0,1,0 cont.diffExp1.2Score=2 tran.correlation=0.93454069668271 cont.tran.correlation=-0.427669557206835 tran.covariance=0.0667171933058935 cont.tran.covariance=-0.00485115844803954 tran.mean=96.5284499053145 cont.tran.mean=88.6716077423851 weightedLogRatios: wLogRatio Lung 3.88183843847892 cerebhem 1.86020597529193 cortex 3.35114761239364 heart 3.58609406369866 kidney 3.14318735296804 liver 4.02899147752349 stomach 3.73499849450882 testicle 2.93223733961396 cont.weightedLogRatios: wLogRatio Lung -1.53777495347262 cerebhem 0.684992096504243 cortex -0.51203301792122 heart -0.387532321107083 kidney 0.161317336220239 liver 0.571129947016335 stomach 1.20693574177902 testicle -0.0347437051355581 varWeightedLogRatios=0.482105615020695 cont.varWeightedLogRatios=0.721934246716821 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.66694852710209 0.120582735621753 38.7032895135295 2.06072099673138e-150 *** df.mm.trans1 0.418898936114638 0.104873035061297 3.99434359718679 7.4940317852537e-05 *** df.mm.trans2 -0.73581387392393 0.0984553913540732 -7.47357624406505 3.67133619173459e-13 *** df.mm.exp2 -0.142564837518266 0.133774534221523 -1.06570984042588 0.287085065161455 df.mm.exp3 0.187711011386484 0.133774534221523 1.40318942225316 0.161200230243963 df.mm.exp4 0.100952465443254 0.133774534221523 0.754646360988874 0.450827465112026 df.mm.exp5 0.222897516599754 0.133774534221523 1.66621784853797 0.0963156955647106 . df.mm.exp6 0.707610926288063 0.133774534221523 5.28957869601924 1.85901336005091e-07 *** df.mm.exp7 0.0456558270561871 0.133774534221523 0.341289374109005 0.733033553620677 df.mm.exp8 -0.106382687860256 0.133774534221523 -0.795238708767187 0.426863660224579 df.mm.trans1:exp2 -0.235522367168283 0.122118883354791 -1.92863184380766 0.0543598546228213 . df.mm.trans2:exp2 0.214080536727872 0.109226449807072 1.95996974273177 0.0505715157378384 . df.mm.trans1:exp3 -0.152095925316678 0.122118883354791 -1.24547425540073 0.213558838574641 df.mm.trans2:exp3 -0.0116220237981798 0.109226449807072 -0.106403017022964 0.915306595107278 df.mm.trans1:exp4 -0.103173426062869 0.122118883354791 -0.844860542682164 0.398605275340853 df.mm.trans2:exp4 -0.0284941865406123 0.109226449807072 -0.260872587097189 0.7943014951027 df.mm.trans1:exp5 0.0096697659074625 0.122118883354791 0.079183215910753 0.936919554101707 df.mm.trans2:exp5 0.232744209988406 0.109226449807072 2.13084111402966 0.0336046048507083 * df.mm.trans1:exp6 -0.0767684276139738 0.122118883354791 -0.628636829170301 0.529882780094649 df.mm.trans2:exp6 0.0142549087345052 0.109226449807072 0.130507846402440 0.896218759209081 df.mm.trans1:exp7 -0.044876220768787 0.122118883354791 -0.367479783109451 0.713421469713241 df.mm.trans2:exp7 -0.0072212735647134 0.109226449807072 -0.0661128653130117 0.947315211336978 df.mm.trans1:exp8 -0.0680186578186597 0.122118883354791 -0.556987223843552 0.577793091161944 df.mm.trans2:exp8 0.139358459714085 0.109226449807072 1.27586733762963 0.202613113370858 df.mm.trans1:probe2 -0.453732253416372 0.0668872671107613 -6.78353703201864 3.42535833384997e-11 *** df.mm.trans1:probe3 0.0191365160920401 0.0668872671107613 0.286101031162646 0.77492290279695 df.mm.trans1:probe4 -0.08479183749003 0.0668872671107613 -1.26768279154865 0.205519520268942 df.mm.trans1:probe5 0.285386780689685 0.0668872671107613 4.26668322712455 2.38656918581411e-05 *** df.mm.trans1:probe6 0.30822947469637 0.0668872671107613 4.60819357720148 5.19949718563756e-06 *** df.mm.trans1:probe7 -0.758839537326756 0.0668872671107613 -11.3450522065756 1.27638647701134e-26 *** df.mm.trans1:probe8 -0.814849036808233 0.0668872671107613 -12.1824238305161 5.68363098748593e-30 *** df.mm.trans1:probe9 -0.724719359045997 0.0668872671107613 -10.8349375053088 1.20598070046430e-24 *** df.mm.trans1:probe10 -0.859470305851766 0.0668872671107613 -12.8495353895762 9.92752219168708e-33 *** df.mm.trans1:probe11 -0.85296114063773 0.0668872671107613 -12.7522199288734 2.53310806490992e-32 *** df.mm.trans1:probe12 -0.766989624449739 0.0668872671107613 -11.4669003172106 4.23130366363825e-27 *** df.mm.trans2:probe2 -0.0851240660712694 0.0668872671107613 -1.27264978445463 0.203752092142433 df.mm.trans2:probe3 -0.0447497781966283 0.0668872671107613 -0.669032838829031 0.503792858532746 df.mm.trans2:probe4 -0.0559629728615931 0.0668872671107613 -0.836676026379158 0.403186745469823 df.mm.trans2:probe5 -0.0319937012888694 0.0668872671107613 -0.478322746179623 0.632636045438316 df.mm.trans2:probe6 -0.108436004497641 0.0668872671107613 -1.62117558664906 0.105629889764602 df.mm.trans3:probe2 -0.0823827858617277 0.0668872671107613 -1.23166619627779 0.218670647154104 df.mm.trans3:probe3 -0.375060907052731 0.0668872671107613 -5.60735881810887 3.45410918475353e-08 *** df.mm.trans3:probe4 0.0912579042195834 0.0668872671107613 1.36435390712653 0.173088935811755 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.53704682934941 0.294070387140972 15.4284383186616 5.5499936888288e-44 *** df.mm.trans1 -0.260247616982494 0.25575845382928 -1.01755235491144 0.309398061949589 df.mm.trans2 0.0807060271099061 0.240107465652696 0.336124605249233 0.736922272876291 df.mm.exp2 -0.149772620534890 0.326241802902252 -0.459084700987151 0.646379021232432 df.mm.exp3 -0.165029536738888 0.326241802902252 -0.505850370095991 0.613191524765117 df.mm.exp4 0.252485698830815 0.326241802902252 0.773921970099168 0.439354010553684 df.mm.exp5 -0.140685835446350 0.326241802902252 -0.43123178634622 0.666491449143512 df.mm.exp6 -0.0802587629865617 0.326241802902252 -0.246010052275884 0.805778527133049 df.mm.exp7 -0.184012291315600 0.326241802902252 -0.564036520392617 0.5729899991049 df.mm.exp8 0.278835409428683 0.326241802902252 0.854689395865761 0.393145103739206 df.mm.trans1:exp2 0.361691517155503 0.297816657751197 1.21447712121486 0.225156559646286 df.mm.trans2:exp2 -0.133864285992879 0.266375316625386 -0.502540129050836 0.615515731338401 df.mm.trans1:exp3 0.24305029049085 0.297816657751197 0.816107105378572 0.414839516573293 df.mm.trans2:exp3 0.0158392934602611 0.266375316625386 0.059462316782664 0.952608371673035 df.mm.trans1:exp4 -0.0227426102156211 0.297816657751197 -0.0763644666062328 0.939160628805833 df.mm.trans2:exp4 -0.280441638355462 0.266375316625386 -1.05280640078922 0.292953961741568 df.mm.trans1:exp5 0.289724761049131 0.297816657751197 0.972829267633426 0.331123093977635 df.mm.trans2:exp5 -0.088466485702453 0.266375316625386 -0.332112174743529 0.739948036675662 df.mm.trans1:exp6 0.290242819062791 0.297816657751197 0.974568787570195 0.330260021206006 df.mm.trans2:exp6 -0.179472557595735 0.266375316625386 -0.673758214047042 0.500786006051113 df.mm.trans1:exp7 0.364739253376319 0.297816657751197 1.22471072011369 0.221278736336217 df.mm.trans2:exp7 -0.253560801897626 0.266375316625386 -0.951893009870048 0.341625376321866 df.mm.trans1:exp8 -0.0863259963780466 0.297816657751197 -0.289862887556026 0.772045015496404 df.mm.trans2:exp8 -0.420635200916111 0.266375316625386 -1.57910727707427 0.114963400642713 df.mm.trans1:probe2 0.240994209983046 0.163120901451126 1.47739626154072 0.140218493135504 df.mm.trans1:probe3 -0.0487879446382738 0.163120901451126 -0.29909070023679 0.764998958462495 df.mm.trans1:probe4 0.235701207766334 0.163120901451126 1.44494792310202 0.149118275963669 df.mm.trans1:probe5 -0.0143678307225885 0.163120901451126 -0.08808086882044 0.929848757967292 df.mm.trans1:probe6 0.0331235294156493 0.163120901451126 0.203061220977703 0.839172343085584 df.mm.trans1:probe7 0.0394646715953469 0.163120901451126 0.241935099942855 0.808932714013549 df.mm.trans1:probe8 0.338786477886035 0.163120901451126 2.07690415435536 0.038336261126884 * df.mm.trans1:probe9 -0.137293386844001 0.163120901451126 -0.841666430375489 0.400389499118469 df.mm.trans1:probe10 0.087034829538113 0.163120901451126 0.533560253553344 0.593890244805266 df.mm.trans1:probe11 -0.0773315908731236 0.163120901451126 -0.474075303564292 0.635659559299529 df.mm.trans1:probe12 0.082172986568094 0.163120901451126 0.503755103344095 0.61466221500529 df.mm.trans2:probe2 0.177880785627685 0.163120901451126 1.09048432202897 0.276041329286019 df.mm.trans2:probe3 -0.0153085329725084 0.163120901451126 -0.0938477708026592 0.92526884004919 df.mm.trans2:probe4 0.0334494961221761 0.163120901451126 0.205059534520768 0.83761164335081 df.mm.trans2:probe5 0.158140534251614 0.163120901451126 0.969468246219786 0.332794825081382 df.mm.trans2:probe6 0.143413880668850 0.163120901451126 0.87918764176165 0.379734925055918 df.mm.trans3:probe2 0.0540907140234283 0.163120901451126 0.331598915542009 0.740335376666108 df.mm.trans3:probe3 0.192397038762651 0.163120901451126 1.17947508290528 0.238787293120822 df.mm.trans3:probe4 0.0960417174526536 0.163120901451126 0.58877627942382 0.55628546631367