fitVsDatCorrelation=0.91773117205437 cont.fitVsDatCorrelation=0.283549221913537 fstatistic=6814.70098545234,55,761 cont.fstatistic=1157.68939081589,55,761 residuals=-0.820540252874027,-0.125878050323381,0.00098422438774584,0.133824156111102,0.789553528346978 cont.residuals=-1.06259212111572,-0.398782145056378,-0.0275491442004364,0.355507747165083,1.40432065506919 predictedValues: Include Exclude Both Lung 85.2673554709658 243.81363190503 73.6127966895614 cerebhem 129.568320037043 155.158252390700 106.256553300913 cortex 95.3884444720143 175.953313241468 83.8954969431673 heart 178.323383640891 236.662624087467 172.280206044246 kidney 118.460752031496 243.925522286332 101.798570706134 liver 94.069962828472 217.000723273631 77.4416910957268 stomach 87.4343235263221 238.23690544772 73.582837628859 testicle 84.2030357493596 218.526779392720 71.7536873602719 diffExp=-158.546276434064,-25.5899323536578,-80.5648687694533,-58.3392404465764,-125.464770254837,-122.930760445159,-150.802581921398,-134.323743643361 diffExpScore=0.998833903791462 diffExp1.5=-1,0,-1,0,-1,-1,-1,-1 diffExp1.5Score=0.857142857142857 diffExp1.4=-1,0,-1,0,-1,-1,-1,-1 diffExp1.4Score=0.857142857142857 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 120.464120263054 142.941623097440 139.105577726007 cerebhem 101.079647849087 134.970573759653 104.998337285840 cortex 116.163973377250 116.891110790680 115.425463920898 heart 127.098440861683 164.596254357209 130.719621237648 kidney 122.048525301216 167.965757443644 137.421802655273 liver 131.408478145724 107.789142292743 110.110891783012 stomach 110.207432033075 136.488548458130 111.561057540869 testicle 113.214532833090 129.962595293466 117.046277994182 cont.diffExp=-22.4775028343859,-33.8909259105662,-0.72713741342993,-37.4978134955258,-45.9172321424287,23.6193358529802,-26.2811164250543,-16.7480624603763 cont.diffExpScore=1.28733868391782 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,-1,0,0,-1,0,0,0 cont.diffExp1.3Score=0.666666666666667 cont.diffExp1.2=0,-1,0,-1,-1,1,-1,0 cont.diffExp1.2Score=1.25 tran.correlation=-0.0299738216884815 cont.tran.correlation=0.0774706939855983 tran.covariance=-0.00465437762165592 cont.tran.covariance=0.000370155943827755 tran.mean=162.624583111352 cont.tran.mean=127.705672259821 weightedLogRatios: wLogRatio Lung -5.2226973538115 cerebhem -0.892954359993866 cortex -2.97809264218892 heart -1.50720483632184 kidney -3.70943590118192 liver -4.14752143249021 stomach -4.9838986413984 testicle -4.68262351835058 cont.weightedLogRatios: wLogRatio Lung -0.834360337984369 cerebhem -1.37648400127528 cortex -0.0296910092105264 heart -1.28600536232677 kidney -1.58523936498276 liver 0.946929220034792 stomach -1.02859622075297 testicle -0.661978997099285 varWeightedLogRatios=2.58139409293275 cont.varWeightedLogRatios=0.695098583486239 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.27853615597726 0.107333715346957 49.1787332518433 1.72653071209757e-238 *** df.mm.trans1 -1.13129472821027 0.0934220078630716 -12.1095098905218 5.42893831910079e-31 *** df.mm.trans2 0.390046030392812 0.0835591840705392 4.66790137710705 3.59573672246211e-06 *** df.mm.exp2 -0.400579524687248 0.109404653146284 -3.66144869680851 0.000268141821433273 *** df.mm.exp3 -0.344772731925848 0.109404653146284 -3.15135345719578 0.00168882330295316 ** df.mm.exp4 -0.142264922038515 0.109404653146284 -1.30035531348282 0.193872885534067 df.mm.exp5 0.00507164782055922 0.109404653146284 0.0463567835069864 0.963038043160497 df.mm.exp6 -0.0689627325699287 0.109404653146284 -0.63034551627086 0.528657781012402 df.mm.exp7 0.00236475896420432 0.109404653146284 0.0216147933035574 0.982760898937902 df.mm.exp8 -0.0964766330686858 0.109404653146284 -0.881832996076389 0.378145566190573 df.mm.trans1:exp2 0.818996155884951 0.101850189229333 8.0411844306037 3.40083381454936e-15 *** df.mm.trans2:exp2 -0.0513790258551726 0.0798978618989978 -0.64305883329047 0.520379635100384 df.mm.trans1:exp3 0.456938497235367 0.101850189229333 4.48637848091272 8.36314629591678e-06 *** df.mm.trans2:exp3 0.0185872963227054 0.0798978618989977 0.23263821935814 0.816104898539424 df.mm.trans1:exp4 0.880071907361535 0.101850189229333 8.64084705213365 3.26362586983003e-17 *** df.mm.trans2:exp4 0.112496392160665 0.0798978618989978 1.40800253582351 0.159538686391459 df.mm.trans1:exp5 0.323718372727871 0.101850189229333 3.17837772494428 0.00154091586808070 ** df.mm.trans2:exp5 -0.00461283543158557 0.0798978618989978 -0.0577341536049719 0.953975544184758 df.mm.trans1:exp6 0.167209844725676 0.101850189229333 1.64172345668573 0.101060486955911 df.mm.trans2:exp6 -0.0475407106156125 0.0798978618989978 -0.595018558515504 0.552007947236665 df.mm.trans1:exp7 0.0227314856158420 0.101850189229333 0.223185502038276 0.823451028928991 df.mm.trans2:exp7 -0.0255033091136484 0.0798978618989977 -0.319198893530945 0.749663293544035 df.mm.trans1:exp8 0.0839159290169282 0.101850189229333 0.823915298065647 0.410245827733677 df.mm.trans2:exp8 -0.0130189295344543 0.0798978618989978 -0.162944654900929 0.87060527024637 df.mm.trans1:probe2 0.309787409660887 0.0647246656655757 4.78623421960215 2.04188726794278e-06 *** df.mm.trans1:probe3 0.360348626145561 0.0647246656655757 5.56740807295069 3.58568665451661e-08 *** df.mm.trans1:probe4 -0.129719851590759 0.0647246656655757 -2.00417955437584 0.0454047502939358 * df.mm.trans1:probe5 -0.453749567465414 0.0647246656655757 -7.01045826655762 5.24391731833455e-12 *** df.mm.trans1:probe6 -0.072385752803461 0.0647246656655757 -1.11836425973166 0.263764357254152 df.mm.trans1:probe7 -0.131139323799852 0.0647246656655757 -2.02611048587617 0.0431018449512804 * df.mm.trans1:probe8 -0.0781459990166078 0.0647246656655757 -1.20736041218627 0.227668495319944 df.mm.trans1:probe9 -0.107320338650347 0.0647246656655757 -1.65810572440586 0.097708232581603 . df.mm.trans1:probe10 0.448540588294709 0.0647246656655757 6.92997922325721 8.9780473559765e-12 *** df.mm.trans1:probe11 0.588415296192894 0.0647246656655757 9.0910519218927 8.37771379765898e-19 *** df.mm.trans1:probe12 0.466813725059601 0.0647246656655757 7.21230029169358 1.33050581976199e-12 *** df.mm.trans1:probe13 0.434333542761997 0.0647246656655757 6.71047951033296 3.78884742614529e-11 *** df.mm.trans1:probe14 0.666135438292708 0.0647246656655757 10.2918328189526 2.39387338052662e-23 *** df.mm.trans1:probe15 0.685841815205658 0.0647246656655757 10.5962975343792 1.44681869081721e-24 *** df.mm.trans1:probe16 0.832996901660115 0.0647246656655757 12.8698525221298 1.95365591102842e-34 *** df.mm.trans1:probe17 0.801013984255355 0.0647246656655757 12.3757145134452 3.5032949214334e-32 *** df.mm.trans1:probe18 0.763700934509139 0.0647246656655757 11.7992256376431 1.25964954876978e-29 *** df.mm.trans1:probe19 1.01148552735239 0.0647246656655757 15.6275125866020 5.93246076333988e-48 *** df.mm.trans1:probe20 1.11534805548048 0.0647246656655757 17.2321949292616 1.96631371430111e-56 *** df.mm.trans1:probe21 0.84710601403353 0.0647246656655757 13.0878391618185 1.90083564826982e-35 *** df.mm.trans2:probe2 -0.721653126630638 0.0647246656655757 -11.1495844622718 7.59359864715541e-27 *** df.mm.trans2:probe3 0.211383423262178 0.0647246656655757 3.26588667687168 0.00114008172016281 ** df.mm.trans2:probe4 -0.723552892112062 0.0647246656655757 -11.1789359538846 5.71741371169329e-27 *** df.mm.trans2:probe5 -0.208959489527469 0.0647246656655757 -3.22843675403651 0.00129806109958456 ** df.mm.trans2:probe6 -0.795532650680417 0.0647246656655757 -12.2910275781235 8.41333564577818e-32 *** df.mm.trans3:probe2 -0.332312041720483 0.0647246656655757 -5.1342411475325 3.60094056161312e-07 *** df.mm.trans3:probe3 -0.255469333569722 0.0647246656655757 -3.9470166580651 8.64601293147304e-05 *** df.mm.trans3:probe4 0.0596400483849565 0.0647246656655757 0.92144235542458 0.357111541489355 df.mm.trans3:probe5 -0.572333917105792 0.0647246656655757 -8.84259364216681 6.43890463114688e-18 *** df.mm.trans3:probe6 -0.348178540064338 0.0647246656655757 -5.37937950677618 9.9561368368141e-08 *** df.mm.trans3:probe7 -0.220626272239640 0.0647246656655757 -3.40868925271222 0.000687112198467865 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.05668214776097 0.259133813938655 19.5137873784316 4.50847553388867e-69 *** df.mm.trans1 -0.183977366018611 0.225547034546505 -0.815694014281873 0.414930390899334 df.mm.trans2 -0.0912823663455089 0.201735400547792 -0.452485612825716 0.651048142147893 df.mm.exp2 0.0484659950399443 0.264133640960712 0.183490428798327 0.854462105156768 df.mm.exp3 -0.0509344780768184 0.264133640960712 -0.192836012450207 0.847138849695688 df.mm.exp4 0.256847637993485 0.264133640960712 0.97241546763704 0.331152877165957 df.mm.exp5 0.186568729081736 0.264133640960712 0.7063421698317 0.480191567460383 df.mm.exp6 0.0384445216098087 0.264133640960712 0.145549508460859 0.884315572092348 df.mm.exp7 0.0854779390426604 0.264133640960712 0.323616252484002 0.746317494235426 df.mm.exp8 0.0154067535237787 0.264133640960712 0.0583293876075042 0.953501571982334 df.mm.trans1:exp2 -0.223909147848378 0.245895037734004 -0.91058831406997 0.362800657730569 df.mm.trans2:exp2 -0.105845529335101 0.192895937800203 -0.548718291023488 0.58335973725878 df.mm.trans1:exp3 0.0145852832004947 0.245895037734004 0.059315077420441 0.952716724421658 df.mm.trans2:exp3 -0.150259014300379 0.192895937800203 -0.778964119275612 0.436243005107677 df.mm.trans1:exp4 -0.203237678333072 0.245895037734004 -0.826522081152868 0.408767060109745 df.mm.trans2:exp4 -0.115788422864461 0.192895937800203 -0.600263666435486 0.548509234745253 df.mm.trans1:exp5 -0.173501966497940 0.245895037734004 -0.705593606511187 0.480656826722287 df.mm.trans2:exp5 -0.0252449120172603 0.192895937800203 -0.130873217472357 0.895910190162267 df.mm.trans1:exp6 0.0485141525770559 0.245895037734004 0.19729618386824 0.843648448734064 df.mm.trans2:exp6 -0.320703905877619 0.192895937800203 -1.66257469978345 0.096809384714278 . df.mm.trans1:exp7 -0.174465554720603 0.245895037734004 -0.709512303820178 0.478223943708802 df.mm.trans2:exp7 -0.131673538840073 0.192895937800203 -0.682614368875189 0.495058284814595 df.mm.trans1:exp8 -0.0774741655163626 0.245895037734004 -0.315070064976951 0.752794828621402 df.mm.trans2:exp8 -0.110596389805273 0.192895937800203 -0.573347427978631 0.566578862391892 df.mm.trans1:probe2 -0.101023459261160 0.156263569332416 -0.646493995323085 0.518154407259257 df.mm.trans1:probe3 -0.0638490406134951 0.156263569332416 -0.408598375720386 0.682949373141032 df.mm.trans1:probe4 -0.0560536299684946 0.156263569332416 -0.358712079904261 0.719909982909476 df.mm.trans1:probe5 -0.218427004269732 0.156263569332416 -1.39781143617088 0.162577004060087 df.mm.trans1:probe6 -0.0292384595399928 0.156263569332416 -0.187109891735510 0.85162434514476 df.mm.trans1:probe7 0.148893227191353 0.156263569332416 0.95283390637658 0.340976771564434 df.mm.trans1:probe8 0.067059206523255 0.156263569332416 0.429141653488034 0.667941601097839 df.mm.trans1:probe9 -0.123027909979281 0.156263569332416 -0.78731025091054 0.431345401876787 df.mm.trans1:probe10 -0.168657277192176 0.156263569332416 -1.07931284247958 0.28079025054973 df.mm.trans1:probe11 -0.163023118168906 0.156263569332416 -1.04325735592351 0.297160407836404 df.mm.trans1:probe12 0.0211193964643101 0.156263569332416 0.135152400233373 0.892527119745467 df.mm.trans1:probe13 -0.194290928296381 0.156263569332416 -1.24335396360409 0.214120330182203 df.mm.trans1:probe14 -0.179692191201870 0.156263569332416 -1.1499301594706 0.250533879172547 df.mm.trans1:probe15 -0.057836929321425 0.156263569332416 -0.37012420469156 0.711392932819874 df.mm.trans1:probe16 -0.167651328403800 0.156263569332416 -1.07287532929162 0.283667150474191 df.mm.trans1:probe17 -0.252089690986287 0.156263569332416 -1.61323392306509 0.107108267023495 df.mm.trans1:probe18 -0.148171663783699 0.156263569332416 -0.948216301577606 0.343320356721565 df.mm.trans1:probe19 -0.26964417030902 0.156263569332416 -1.72557283480075 0.0848302348805188 . df.mm.trans1:probe20 -0.1151510850919 0.156263569332416 -0.736902949189274 0.461408487516648 df.mm.trans1:probe21 -0.207123192280602 0.156263569332416 -1.32547332155196 0.185411984897432 df.mm.trans2:probe2 -0.080298158599374 0.156263569332416 -0.5138635892065 0.607496535506473 df.mm.trans2:probe3 0.000144229889798817 0.156263569332416 0.000922991138721528 0.999263801605078 df.mm.trans2:probe4 0.174742882799469 0.156263569332416 1.11825733628125 0.263809978613089 df.mm.trans2:probe5 -0.0620420444012987 0.156263569332416 -0.397034604203351 0.691453265774412 df.mm.trans2:probe6 -0.0710719493842276 0.156263569332416 -0.454820977710024 0.649367751736632 df.mm.trans3:probe2 0.0357469198205728 0.156263569332416 0.228760420444058 0.819116590747173 df.mm.trans3:probe3 0.170375789014826 0.156263569332416 1.09031036307886 0.275921561741835 df.mm.trans3:probe4 0.203200604739541 0.156263569332416 1.30037094127344 0.193867534515429 df.mm.trans3:probe5 0.121450954347223 0.156263569332416 0.777218611260971 0.437271334319267 df.mm.trans3:probe6 0.311635730128338 0.156263569332416 1.99429548076815 0.0464761354541181 * df.mm.trans3:probe7 0.234265471790677 0.156263569332416 1.49916882605138 0.134244547857488