fitVsDatCorrelation=0.91193227723993 cont.fitVsDatCorrelation=0.218800531992552 fstatistic=10160.6956127813,58,830 cont.fstatistic=1785.09650674057,58,830 residuals=-0.866090835516983,-0.0815790923720088,-0.000641363287757243,0.0751196833895458,1.25533299295693 cont.residuals=-0.759971749667511,-0.208272061512492,-0.0739137649317061,0.0942180931527572,1.95759719310183 predictedValues: Include Exclude Both Lung 52.9470997617942 56.8875237043345 53.7390083075126 cerebhem 52.0243828658834 60.3467247118063 59.5648608658423 cortex 53.7232214227026 56.9136999611045 56.3526795046432 heart 53.9575729745525 60.4282216587089 55.7965621935195 kidney 57.9982780442667 59.4169952334935 59.62245316383 liver 59.8661409148848 56.3092830888485 60.0492905974655 stomach 52.5744425801678 59.9140739724965 61.9608472453211 testicle 57.5585438560062 60.6272539813137 56.9449624785747 diffExp=-3.94042394254031,-8.32234184592282,-3.19047853840194,-6.47064868415639,-1.41871718922680,3.55685782603634,-7.33963139232875,-3.06871012530749 diffExpScore=1.19598952523735 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 62.5111015960972 67.7488813011232 52.5896010023021 cerebhem 60.4968062130403 56.887673833652 54.7567470812155 cortex 62.185337107325 57.4927555342145 60.5239876780303 heart 60.7832754412163 57.92479089991 55.6141883590552 kidney 56.4910475186758 53.6572596344202 58.7280506237052 liver 57.8567891493596 53.4350849706421 64.519127519843 stomach 59.1676637295856 60.2701547152228 56.3813583975195 testicle 59.0525164252736 50.9648584669542 58.3588167425034 cont.diffExp=-5.23777970502599,3.60913237938831,4.6925815731105,2.85848454130628,2.83378788425556,4.42170417871749,-1.10249098563721,8.0876579583194 cont.diffExpScore=1.55193018133876 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=-0.219962353061254 cont.tran.correlation=0.68620018692109 tran.covariance=-0.00035922729695485 cont.tran.covariance=0.00208967014424529 tran.mean=56.9683411707728 cont.tran.mean=58.5578747835445 weightedLogRatios: wLogRatio Lung -0.287503180677353 cerebhem -0.597421490572659 cortex -0.231495128522260 heart -0.458109800416429 kidney -0.098419946020168 liver 0.24877278422932 stomach -0.526328250136189 testicle -0.211859426749172 cont.weightedLogRatios: wLogRatio Lung -0.335982637216739 cerebhem 0.250466222341111 cortex 0.320972323926356 heart 0.196685989839721 kidney 0.206291308906988 liver 0.319461493618811 stomach -0.0755017461863628 testicle 0.589867553392424 varWeightedLogRatios=0.0731468331140081 cont.varWeightedLogRatios=0.07780548442469 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.80812048274844 0.0741362149215233 51.3665350568479 5.74135121879939e-260 *** df.mm.trans1 -0.0208280708734507 0.0641833035180994 -0.324509175000274 0.745634343276383 df.mm.trans2 0.278349428604001 0.0568626095862265 4.89512230672269 1.18055641579289e-06 *** df.mm.exp2 -0.0614768308827728 0.0734930934355414 -0.836498070892775 0.403115523690838 df.mm.exp3 -0.0324785668698205 0.0734930934355413 -0.441926790009275 0.658657301806706 df.mm.exp4 0.0417118379228893 0.0734930934355413 0.567561330908918 0.570486350741233 df.mm.exp5 0.0307311896999715 0.0734930934355413 0.418150716800687 0.675945087621539 df.mm.exp6 0.00157455993096406 0.0734930934355414 0.0214245972969563 0.982912101568535 df.mm.exp7 -0.0975913329768006 0.0734930934355413 -1.32789801619107 0.184576859131247 df.mm.exp8 0.0892316827351208 0.0734930934355413 1.21415058972015 0.225035568355179 df.mm.trans1:exp2 0.0438960434323939 0.0681299210435803 0.644299050402762 0.519559542418197 df.mm.trans2:exp2 0.120507455370451 0.051149028318068 2.35600673821369 0.0187042264269273 * df.mm.trans1:exp3 0.0470306062434994 0.0681299210435802 0.690307658120074 0.490193712460092 df.mm.trans2:exp3 0.0329386015933233 0.051149028318068 0.643973163839909 0.51977074189687 df.mm.trans1:exp4 -0.0228070832267391 0.0681299210435802 -0.334758691590883 0.737891701691968 df.mm.trans2:exp4 0.0186683538669675 0.051149028318068 0.36497963853544 0.715219583843507 df.mm.trans1:exp5 0.0603888341148206 0.0681299210435803 0.886377573756354 0.37567084129964 df.mm.trans2:exp5 0.0127730607569007 0.051149028318068 0.249722451763345 0.802863784672982 df.mm.trans1:exp6 0.121243227786159 0.0681299210435803 1.77958855564509 0.0755091504657423 . df.mm.trans2:exp6 -0.0117912022829503 0.051149028318068 -0.230526418011838 0.81773954461939 df.mm.trans1:exp7 0.0905281547360438 0.0681299210435802 1.32875766402454 0.18429311369976 df.mm.trans2:exp7 0.149426718273957 0.051149028318068 2.9213989627477 0.00357912945377672 ** df.mm.trans1:exp8 -0.00572239628051819 0.0681299210435803 -0.083992410278265 0.933082719539267 df.mm.trans2:exp8 -0.0255632051259847 0.051149028318068 -0.499778900334548 0.617363231709741 df.mm.trans1:probe2 -0.006668308922545 0.0457029404265416 -0.145905468232683 0.884031441631042 df.mm.trans1:probe3 1.74474654914813 0.0457029404265416 38.1758051640563 6.77497793911048e-185 *** df.mm.trans1:probe4 -0.0454934218344543 0.0457029404265417 -0.995415643060777 0.31982425783197 df.mm.trans1:probe5 0.0193584243385060 0.0457029404265416 0.423570653394191 0.671988699761503 df.mm.trans1:probe6 1.24552756535515 0.0457029404265417 27.2526790121323 2.58817365773698e-117 *** df.mm.trans1:probe7 -0.0644724614721356 0.0457029404265416 -1.41068519597250 0.158712132103976 df.mm.trans1:probe8 0.0187143567949547 0.0457029404265417 0.409478178434367 0.682294427515058 df.mm.trans1:probe9 0.190959374269369 0.0457029404265416 4.17827326835344 3.24861918593882e-05 *** df.mm.trans1:probe10 0.074790319182721 0.0457029404265416 1.63644436188808 0.102125714461689 df.mm.trans1:probe11 0.0580734322107631 0.0457029404265416 1.27067168258254 0.20420151064108 df.mm.trans1:probe12 0.103439768741922 0.0457029404265417 2.26330664452937 0.0238743102656455 * df.mm.trans1:probe13 -0.00418665011995638 0.0457029404265417 -0.0916057059104454 0.927033421587472 df.mm.trans1:probe14 0.0961671742702345 0.0457029404265417 2.10417914849054 0.0356630482503474 * df.mm.trans1:probe15 0.0428702547812508 0.0457029404265417 0.93801961933185 0.34850719751369 df.mm.trans1:probe16 0.087418881754896 0.0457029404265417 1.91276274434474 0.0561223107610719 . df.mm.trans1:probe17 0.541116486211009 0.0457029404265416 11.8398615310266 5.40229152822993e-30 *** df.mm.trans1:probe18 0.357726235072023 0.0457029404265416 7.82720393334422 1.51845479469812e-14 *** df.mm.trans1:probe19 0.243081433186211 0.0457029404265417 5.31872634271564 1.34474540301535e-07 *** df.mm.trans1:probe20 0.217794022926233 0.0457029404265417 4.76542692644236 2.22446849236250e-06 *** df.mm.trans1:probe21 0.363028922076783 0.0457029404265416 7.94322900646358 6.39749643174986e-15 *** df.mm.trans1:probe22 0.358035093271744 0.0457029404265417 7.83396188363885 1.444318331714e-14 *** df.mm.trans2:probe2 -0.236831363722402 0.0457029404265417 -5.1819721337856 2.75860568422699e-07 *** df.mm.trans2:probe3 -0.0110015959283284 0.0457029404265417 -0.24071965229483 0.809831852893725 df.mm.trans2:probe4 -0.182036688172343 0.0457029404265417 -3.98304105760834 7.40104679592302e-05 *** df.mm.trans2:probe5 -0.0755835777147694 0.0457029404265417 -1.65380119986492 0.0985460300694386 . df.mm.trans2:probe6 -0.175454694289742 0.0457029404265417 -3.83902419958624 0.000132914539586157 *** df.mm.trans3:probe2 0.00985721323726302 0.0457029404265416 0.215680066649246 0.829290129524641 df.mm.trans3:probe3 -0.0697417293931545 0.0457029404265416 -1.52597904516123 0.127396031053974 df.mm.trans3:probe4 -0.215945948106374 0.0457029404265417 -4.72499025425868 2.70201990653601e-06 *** df.mm.trans3:probe5 -0.177696169535419 0.0457029404265416 -3.88806864234546 0.000109115329540281 *** df.mm.trans3:probe6 -0.0379438256409833 0.0457029404265417 -0.83022723017068 0.406649054942025 df.mm.trans3:probe7 -0.272149811668031 0.0457029404265416 -5.95475497042597 3.84289056080718e-09 *** df.mm.trans3:probe8 0.092941010715334 0.0457029404265416 2.03358930186818 0.0423106562566949 * df.mm.trans3:probe9 -0.0628449584998882 0.0457029404265416 -1.37507473071451 0.169479327586514 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.4165459291423 0.176292184877079 25.0524204020829 1.34618840508883e-103 *** df.mm.trans1 -0.246069625541130 0.152624662883207 -1.61225335992670 0.107286965539912 df.mm.trans2 -0.195321744761324 0.135216421453126 -1.44451201017056 0.148972389295601 df.mm.exp2 -0.247865045952147 0.174762874377141 -1.41829348387376 0.156480444738151 df.mm.exp3 -0.309895307061184 0.174762874377141 -1.77323306317580 0.076556756108229 . df.mm.exp4 -0.24061193174065 0.174762874377141 -1.37679088077600 0.168948153673493 df.mm.exp5 -0.44485224092019 0.174762874377141 -2.54546191521314 0.0110931593960961 * df.mm.exp6 -0.519157087172729 0.174762874377141 -2.97063715061346 0.00305752277974267 ** df.mm.exp7 -0.241560085948676 0.174762874377141 -1.38221625622491 0.167277149624244 df.mm.exp8 -0.445680659200652 0.174762874377141 -2.55020215700313 0.0109446329166399 * df.mm.trans1:exp2 0.215111453222042 0.162009520569530 1.32777044500741 0.184618994356666 df.mm.trans2:exp2 0.0731357875715653 0.121629812987848 0.60129819963525 0.547805571201684 df.mm.trans1:exp3 0.30467037468507 0.162009520569530 1.8805708060491 0.0603802572351606 . df.mm.trans2:exp3 0.145746308754740 0.121629812987848 1.19827783316004 0.231150913808308 df.mm.trans1:exp4 0.212582441386350 0.162009520569530 1.31216017823542 0.189828860690444 df.mm.trans2:exp4 0.0839494447477691 0.121629812987848 0.690204504023667 0.490258537119474 df.mm.trans1:exp5 0.343590249014649 0.162009520569530 2.12080282570300 0.0342343525461935 * df.mm.trans2:exp5 0.211661068094012 0.121629812987848 1.74020713256509 0.0821932834718004 . df.mm.trans1:exp6 0.441783725231044 0.162009520569530 2.72689977526007 0.00652813514973665 ** df.mm.trans2:exp6 0.281816691853664 0.121629812987848 2.31700341331463 0.0207460969018863 * df.mm.trans1:exp7 0.186591091315329 0.162009520569530 1.15172917405955 0.249764022398878 df.mm.trans2:exp7 0.124589173012782 0.121629812987848 1.02433087704599 0.305977462312942 df.mm.trans1:exp8 0.38876364959094 0.162009520569530 2.39963459075908 0.0166309210114013 * df.mm.trans2:exp8 0.161009057290644 0.121629812987848 1.32376309175720 0.185946207924895 df.mm.trans1:probe2 0.0366217596168969 0.108679290298686 0.336970912454879 0.736224014939671 df.mm.trans1:probe3 -0.0798964163069785 0.108679290298686 -0.735157692761861 0.46245112995378 df.mm.trans1:probe4 0.0915844065692634 0.108679290298686 0.842703391948546 0.399637116266195 df.mm.trans1:probe5 -0.0324937429220259 0.108679290298686 -0.298987441238552 0.765024472131046 df.mm.trans1:probe6 -0.116877998629498 0.108679290298686 -1.07543947249085 0.282490531073445 df.mm.trans1:probe7 -0.0923679331354203 0.108679290298686 -0.849912921602297 0.395618591170584 df.mm.trans1:probe8 -0.0700296922486329 0.108679290298686 -0.644370165246466 0.519513460444496 df.mm.trans1:probe9 -0.044132258626434 0.108679290298686 -0.406077905966667 0.68479002783081 df.mm.trans1:probe10 -0.158858242096416 0.108679290298686 -1.46171585828194 0.144197577899969 df.mm.trans1:probe11 -0.084765614076829 0.108679290298686 -0.779961056461315 0.435636152332057 df.mm.trans1:probe12 -0.0664063778495269 0.108679290298686 -0.611030654203028 0.541346582352687 df.mm.trans1:probe13 -0.0574147246293964 0.108679290298686 -0.52829499044024 0.59743582716309 df.mm.trans1:probe14 0.0575471490831196 0.108679290298686 0.52951347883264 0.596590892775875 df.mm.trans1:probe15 -0.0269360224813226 0.108679290298686 -0.247848715309914 0.804312762820658 df.mm.trans1:probe16 -0.129864877228180 0.108679290298686 -1.19493674343354 0.232453074779104 df.mm.trans1:probe17 0.0589043846471632 0.108679290298686 0.542001925898437 0.587962652617131 df.mm.trans1:probe18 -0.0615152387967723 0.108679290298686 -0.566025400310477 0.571529510206075 df.mm.trans1:probe19 -0.00112700958219257 0.108679290298686 -0.0103700491519146 0.991728538139556 df.mm.trans1:probe20 -0.0114554144429225 0.108679290298686 -0.105405679512990 0.916079377051406 df.mm.trans1:probe21 -0.130473055748254 0.108679290298686 -1.20053282819267 0.230274988543449 df.mm.trans1:probe22 -0.169139331054633 0.108679290298686 -1.5563161168037 0.120014139952697 df.mm.trans2:probe2 -0.0695475289219916 0.108679290298686 -0.639933594807736 0.522392371018478 df.mm.trans2:probe3 -0.0450513227486024 0.108679290298686 -0.414534568865758 0.678589768439878 df.mm.trans2:probe4 0.0208996485911527 0.108679290298686 0.192305714674007 0.847549782300716 df.mm.trans2:probe5 0.0123221048452138 0.108679290298686 0.113380431647544 0.909756370575223 df.mm.trans2:probe6 0.000133544463671115 0.108679290298686 0.00122879403522135 0.999019859714094 df.mm.trans3:probe2 -0.0160318876537605 0.108679290298686 -0.147515571823295 0.882760915563882 df.mm.trans3:probe3 0.0328785120269343 0.108679290298686 0.302527849938783 0.762325503975638 df.mm.trans3:probe4 -0.102415009255334 0.108679290298686 -0.94235993788572 0.346282591881596 df.mm.trans3:probe5 -0.0270278138975435 0.108679290298686 -0.248693323477383 0.803659535626918 df.mm.trans3:probe6 0.0332203388422378 0.108679290298686 0.305673130096245 0.759930180342525 df.mm.trans3:probe7 0.0401801241227187 0.108679290298686 0.369712794519459 0.711690806603029 df.mm.trans3:probe8 -0.116989465187338 0.108679290298686 -1.07646511921280 0.282032078974266 df.mm.trans3:probe9 0.0424597770461534 0.108679290298686 0.39068875891129 0.696127564044472