fitVsDatCorrelation=0.88258557766628 cont.fitVsDatCorrelation=0.202089219527119 fstatistic=9602.46612677785,50,646 cont.fstatistic=2202.98872154630,50,646 residuals=-0.509530684038677,-0.100600806372830,-0.0064665814710901,0.0887176508403318,0.685220098852508 cont.residuals=-0.764534117956761,-0.245455829156305,-0.0232949211497443,0.202523461715599,0.99587353640844 predictedValues: Include Exclude Both Lung 76.764193652833 49.1839037723686 67.4037303637881 cerebhem 57.5297482263458 63.8469931292408 65.2195042056682 cortex 68.9155807330163 55.3240369468089 93.0411044979168 heart 98.2311194432107 46.6377895802648 74.1154066019707 kidney 70.1347728399951 47.6475691702525 58.8573896950186 liver 69.5720609499832 48.9450879830016 61.0791332870794 stomach 70.4283747789536 47.3564832903395 66.271777105148 testicle 66.634327528662 50.328179914378 62.3850343606182 diffExp=27.5802898804643,-6.31724490289507,13.5915437862074,51.5933298629459,22.4872036697426,20.6269729669816,23.0718914886141,16.3061476142841 diffExpScore=1.06846228437545 diffExp1.5=1,0,0,1,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=1,0,0,1,1,1,1,0 diffExp1.4Score=0.833333333333333 diffExp1.3=1,0,0,1,1,1,1,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 70.12590804663 71.3657750270113 61.7133822672695 cerebhem 69.1675866839769 64.062895190171 64.542982924967 cortex 66.0882507060806 73.30932550598 67.5428801865884 heart 68.7441086466413 70.1830725955714 74.0972799320014 kidney 69.2153770680315 69.2986873627164 64.0199971499079 liver 68.3838652439918 63.1655812376822 62.5557107081819 stomach 71.6467669025252 64.7214392253785 68.182102356078 testicle 68.6269897888264 63.0040098866425 69.1863907284458 cont.diffExp=-1.23986698038138,5.10469149380589,-7.22107479989944,-1.43896394893018,-0.0833102946848499,5.21828400630961,6.92532767714667,5.62297990218394 cont.diffExpScore=2.36566391650679 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=-0.632755400522085 cont.tran.correlation=-0.363488080977346 tran.covariance=-0.0111885273856866 cont.tran.covariance=-0.000495319743537929 tran.mean=61.7175138712284 cont.tran.mean=68.1943524448661 weightedLogRatios: wLogRatio Lung 1.83328567662077 cerebhem -0.427625977954953 cortex 0.905729098562165 heart 3.13970473169327 kidney 1.56843226332485 liver 1.43005276404459 stomach 1.60985550638717 testicle 1.13914732617952 cont.weightedLogRatios: wLogRatio Lung -0.0746446316258263 cerebhem 0.321863370623562 cortex -0.439969055013614 heart -0.0878518263397882 kidney -0.0050977415549406 liver 0.332229815967412 stomach 0.429079843022085 testicle 0.357845816708717 varWeightedLogRatios=0.991455037963312 cont.varWeightedLogRatios=0.0923133304308226 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.48615732231614 0.0825828427242146 42.2140629616997 6.78883505467728e-188 *** df.mm.trans1 0.575036000759448 0.0730414757941869 7.87273250584037 1.46945231885837e-14 *** df.mm.trans2 0.427620527832684 0.0668497102653759 6.39674467002389 3.04716812632463e-10 *** df.mm.exp2 0.00542874386255183 0.0902648393100349 0.0601423976827299 0.952060823171008 df.mm.exp3 -0.312555963653185 0.0902648393100349 -3.46265462878232 0.000570182031025471 *** df.mm.exp4 0.0985064336214895 0.090264839310035 1.09130459184829 0.27554573124713 df.mm.exp5 0.0135285868725798 0.0902648393100349 0.149876596202790 0.880908757241947 df.mm.exp6 -0.00471256133175202 0.0902648393100349 -0.0522081617579318 0.958378969228713 df.mm.exp7 -0.107068506904108 0.0902648393100349 -1.1861596134499 0.235995346511755 df.mm.exp8 -0.0411447332291491 0.0902648393100349 -0.455822372738384 0.648670897768067 df.mm.trans1:exp2 -0.293864872342439 0.0850527827039488 -3.45508827577484 0.000586124070495119 *** df.mm.trans2:exp2 0.255494333906052 0.0724855218467272 3.52476366861651 0.000453810120039642 *** df.mm.trans1:exp3 0.204699948608852 0.0850527827039488 2.40674016888278 0.0163752534581899 * df.mm.trans2:exp3 0.430197031369633 0.0724855218467272 5.93493735589429 4.79642736839589e-09 *** df.mm.trans1:exp4 0.148078327187609 0.0850527827039489 1.74101684248285 0.0821567080949791 . df.mm.trans2:exp4 -0.151661696793219 0.0724855218467272 -2.09230330318808 0.0368010999157157 * df.mm.trans1:exp5 -0.103848172559274 0.0850527827039488 -1.22098500787150 0.222537139732955 df.mm.trans2:exp5 -0.0452633832109172 0.0724855218467272 -0.624447228325513 0.53255438549206 df.mm.trans1:exp6 -0.0936626780087323 0.0850527827039489 -1.10123002482767 0.2712067010227 df.mm.trans2:exp6 -0.00015483323915645 0.0724855218467272 -0.00213605745274000 0.998296333471078 df.mm.trans1:exp7 0.0209264367457454 0.0850527827039488 0.246040588919777 0.805728965763954 df.mm.trans2:exp7 0.0692058290017308 0.0724855218467272 0.954753821708956 0.340059330816416 df.mm.trans1:exp8 -0.100373696610387 0.0850527827039488 -1.18013418749351 0.238381224275607 df.mm.trans2:exp8 0.0641434794552688 0.0724855218467271 0.884914363876723 0.376532067666798 df.mm.trans1:probe2 0.337862666778065 0.046585327665538 7.25255533681697 1.17426050482321e-12 *** df.mm.trans1:probe3 0.34846830777798 0.046585327665538 7.48021587998325 2.43196385145336e-13 *** df.mm.trans1:probe4 0.445665932698235 0.046585327665538 9.56665875354401 2.28642361773633e-20 *** df.mm.trans1:probe5 0.163675677054220 0.046585327665538 3.51345982214269 0.000473181857071922 *** df.mm.trans1:probe6 0.486756503348785 0.046585327665538 10.4487083753812 1.01946147322973e-23 *** df.mm.trans1:probe7 0.347829757123209 0.046585327665538 7.46650876045076 2.67677713634819e-13 *** df.mm.trans1:probe8 0.0679319289379258 0.046585327665538 1.45822584796756 0.145264289636039 df.mm.trans1:probe9 0.486469300548009 0.046585327665538 10.4425432840280 1.07773543647167e-23 *** df.mm.trans1:probe10 0.88417587969626 0.046585327665538 18.9797072169214 3.56868666583011e-64 *** df.mm.trans1:probe11 0.164360048068144 0.046585327665538 3.52815052087163 0.000448151998071733 *** df.mm.trans1:probe12 0.792997742789953 0.046585327665538 17.0224785898969 5.86132544977737e-54 *** df.mm.trans1:probe13 0.219167293325305 0.046585327665538 4.70464209029137 3.11216623068030e-06 *** df.mm.trans1:probe14 0.169538390547936 0.046585327665538 3.63930874899382 0.000295217519346929 *** df.mm.trans1:probe15 0.0912094657948008 0.046585327665538 1.95790113251203 0.0506716807993737 . df.mm.trans1:probe16 0.273194560321071 0.046585327665538 5.86439065712893 7.19608555767899e-09 *** df.mm.trans1:probe17 0.469370168397925 0.046585327665538 10.0754935495527 2.83008195756666e-22 *** df.mm.trans1:probe18 0.394992347779003 0.046585327665538 8.47890027982357 1.54374987668161e-16 *** df.mm.trans1:probe19 0.285868566276331 0.046585327665538 6.13645069384809 1.47189847502146e-09 *** df.mm.trans2:probe2 -0.0629173435424956 0.046585327665538 -1.35058282715567 0.177302018136626 df.mm.trans2:probe3 -0.111902999456633 0.046585327665538 -2.40210824017482 0.0165821729479707 * df.mm.trans2:probe4 -0.049272958246829 0.046585327665538 -1.05769264092306 0.290590939546587 df.mm.trans2:probe5 -0.0443745996897424 0.046585327665538 -0.95254454381715 0.341177150792513 df.mm.trans2:probe6 0.0863535081659259 0.046585327665538 1.85366321314526 0.064243040008666 . df.mm.trans3:probe2 -0.649790304063277 0.046585327665538 -13.9483896888841 7.62130753100676e-39 *** df.mm.trans3:probe3 -0.258528560282858 0.046585327665538 -5.54957050294844 4.18247945118369e-08 *** df.mm.trans3:probe4 -0.204135087165235 0.046585327665538 -4.38196096055897 1.37238228484238e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37065127223717 0.172027254999821 25.4067372768444 2.93727946005357e-99 *** df.mm.trans1 -0.126851790663548 0.152151756557607 -0.83371887077439 0.404747581926567 df.mm.trans2 -0.132994980909884 0.139253769610355 -0.955054798746322 0.339907228913612 df.mm.exp2 -0.166543639189929 0.188029523049491 -0.885731328191974 0.376091902489936 df.mm.exp3 -0.122693778719613 0.188029523049491 -0.652524011813395 0.514295311350729 df.mm.exp4 -0.219490558161026 0.188029523049491 -1.16731965598431 0.243511989933166 df.mm.exp5 -0.0791564013804923 0.188029523049491 -0.420978578771683 0.673910742578365 df.mm.exp6 -0.160770996249633 0.188029523049491 -0.855030601802445 0.392851196169229 df.mm.exp7 -0.175951487516118 0.188029523049491 -0.935765217411129 0.34974375581126 df.mm.exp8 -0.260529835540534 0.188029523049491 -1.38557940963324 0.166353543041371 df.mm.trans1:exp2 0.152783678828624 0.177172355128511 0.862344911077142 0.388817749715628 df.mm.trans2:exp2 0.0585905642594525 0.150993656057153 0.388033284241262 0.698119234092742 df.mm.trans1:exp3 0.0633924467610032 0.177172355128511 0.357801005213379 0.720609111137753 df.mm.trans2:exp3 0.149563190273242 0.150993656057153 0.990526318646331 0.322287791504466 df.mm.trans1:exp4 0.199589285892839 0.177172355128511 1.12652612055683 0.260361186046299 df.mm.trans2:exp4 0.202779295932434 0.150993656057153 1.34296566642296 0.179754733616525 df.mm.trans1:exp5 0.0660871385538954 0.177172355128511 0.37301044232301 0.709263031429899 df.mm.trans2:exp5 0.0497639529556572 0.150993656057153 0.329576448806703 0.74182685404726 df.mm.trans1:exp6 0.135615592075823 0.177172355128511 0.765444428265655 0.444286505214391 df.mm.trans2:exp6 0.0387121353176925 0.150993656057153 0.256382528435761 0.797737102185508 df.mm.trans1:exp7 0.197407204559777 0.177172355128511 1.11420997037934 0.265603488468075 df.mm.trans2:exp7 0.0782255846749055 0.150993656057153 0.518071995324732 0.604585418845228 df.mm.trans1:exp8 0.238923417320602 0.177172355128511 1.34853666728819 0.177958411170954 df.mm.trans2:exp8 0.135909795834523 0.150993656057153 0.90010268897045 0.368401016345425 df.mm.trans1:probe2 -0.0511791772223665 0.0970412954702017 -0.527395857344897 0.598099795067807 df.mm.trans1:probe3 -0.0775631343346461 0.0970412954702018 -0.799279667061568 0.424422044275994 df.mm.trans1:probe4 -0.0586377406863038 0.0970412954702018 -0.604255542984889 0.545885906693165 df.mm.trans1:probe5 0.097545679046966 0.0970412954702018 1.00519761792462 0.31517806518326 df.mm.trans1:probe6 -0.0384701202561663 0.0970412954702017 -0.396430406970187 0.691918451773776 df.mm.trans1:probe7 0.0309162897811378 0.0970412954702017 0.31858900513783 0.750141166794169 df.mm.trans1:probe8 -0.0703845823267547 0.0970412954702018 -0.725305469034752 0.46852730118749 df.mm.trans1:probe9 0.00846255265431393 0.0970412954702018 0.0872056850983868 0.93053504433028 df.mm.trans1:probe10 0.00562173814029178 0.0970412954702018 0.0579314003698357 0.9538211856876 df.mm.trans1:probe11 0.0578592130755509 0.0970412954702018 0.596232900593517 0.55122850788091 df.mm.trans1:probe12 -0.0479956336217097 0.0970412954702017 -0.494589786638283 0.621057867080579 df.mm.trans1:probe13 0.0483187474572271 0.0970412954702018 0.497919439586049 0.618710280176377 df.mm.trans1:probe14 0.0285739428222816 0.0970412954702018 0.294451374374487 0.768507606821375 df.mm.trans1:probe15 0.0710399511787691 0.0970412954702018 0.732058973806498 0.464398068034073 df.mm.trans1:probe16 0.0543359949545375 0.0970412954702018 0.55992652088226 0.575723657874619 df.mm.trans1:probe17 0.0164708689806594 0.0970412954702017 0.169730514219249 0.865275212780387 df.mm.trans1:probe18 0.0688301940702914 0.0970412954702018 0.709287667036833 0.478401886775523 df.mm.trans1:probe19 0.00559033289588463 0.0970412954702018 0.0576077727404334 0.95407887224887 df.mm.trans2:probe2 0.0567765361072769 0.0970412954702018 0.585076032138412 0.558700893994343 df.mm.trans2:probe3 0.087618341181515 0.0970412954702018 0.902897480469228 0.366916847886192 df.mm.trans2:probe4 -0.0543781975104459 0.0970412954702018 -0.560361413632856 0.575427220565652 df.mm.trans2:probe5 0.0380000292634414 0.0970412954702018 0.391586170395983 0.695493141578398 df.mm.trans2:probe6 0.173604508348985 0.0970412954702018 1.78897558516511 0.0740871627096191 . df.mm.trans3:probe2 0.00564339219080111 0.0970412954702018 0.0581545430062196 0.9536435125163 df.mm.trans3:probe3 -0.0180760014094769 0.0970412954702018 -0.186271229396639 0.852290545316401 df.mm.trans3:probe4 0.059217806139258 0.0970412954702018 0.61023305441591 0.541922041922587