fitVsDatCorrelation=0.951873436311998 cont.fitVsDatCorrelation=0.2148638628359 fstatistic=10085.9093565401,54,738 cont.fstatistic=980.975963447455,54,738 residuals=-0.525669208534549,-0.101663811402005,-0.00212616598216062,0.0886228314527627,1.03115003072306 cont.residuals=-0.899319330525445,-0.41370756360994,-0.145699968199754,0.387013820459405,2.01292951336867 predictedValues: Include Exclude Both Lung 55.8064684967275 208.475662726683 105.367813415365 cerebhem 69.4639061071403 227.205871894172 85.2802405884295 cortex 53.6695645514019 198.161166810606 110.356502796638 heart 55.4872188265005 210.094463565452 109.970022880018 kidney 56.9062380212103 181.515549922100 95.3386764611778 liver 58.1389969212419 201.784636677162 77.0301861421658 stomach 57.7598500867188 253.791523448415 86.8752535433613 testicle 59.8521570339403 212.437956392966 87.7341802157683 diffExp=-152.669194229956,-157.741965787032,-144.491602259204,-154.607244738951,-124.609311900889,-143.64563975592,-196.031673361696,-152.585799359025 diffExpScore=0.999185258013784 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 70.8806957631087 92.3295604104379 85.4733346796118 cerebhem 77.0804170724417 78.295143320757 88.8152878300646 cortex 72.7251386775242 87.30062277129 96.127626581932 heart 78.2936681284929 87.49672565723 72.6444229344526 kidney 73.787948977566 94.9492172606328 84.3704652595344 liver 75.836478433151 79.6535454354815 82.6388356550643 stomach 70.0411289778798 65.249048080893 83.5848908790684 testicle 81.03079233319 81.7210207043891 100.640240475606 cont.diffExp=-21.4488646473292,-1.21472624831534,-14.5754840937658,-9.2030575287371,-21.1612682830668,-3.81706700233057,4.79208089698672,-0.690228371199069 cont.diffExpScore=1.12564894295755 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=-1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=-1,0,-1,0,-1,0,0,0 cont.diffExp1.2Score=0.75 tran.correlation=0.371327249880383 cont.tran.correlation=0.0801429598738785 tran.covariance=0.00301604203028335 cont.tran.covariance=0.000812871691876115 tran.mean=135.034451967652 cont.tran.mean=79.1669470002791 weightedLogRatios: wLogRatio Lung -6.1690524217309 cerebhem -5.72773635050178 cortex -6.05565576535205 heart -6.23344303734477 kidney -5.36049873583588 liver -5.82987029657719 stomach -7.09972886618642 testicle -5.9858341586444 cont.weightedLogRatios: wLogRatio Lung -1.16140877220513 cerebhem -0.0680597009083766 cortex -0.799735467849307 heart -0.49077434884011 kidney -1.11632152241231 liver -0.213769847157744 stomach 0.298626091752078 testicle -0.0373130257558565 varWeightedLogRatios=0.254449999069923 cont.varWeightedLogRatios=0.286640442395167 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.13884136624403 0.0829403327648787 61.9582921232273 1.1755183551917e-294 *** df.mm.trans1 -1.27678486394370 0.0730630481593983 -17.4751108269970 1.74079744205018e-57 *** df.mm.trans2 0.408592680971322 0.0659270194861663 6.19765134471244 9.52686558308873e-10 *** df.mm.exp2 0.516466150087402 0.0877951663784345 5.88262624688486 6.12157371460331e-09 *** df.mm.exp3 -0.136044194238266 0.0877951663784345 -1.54956360184862 0.121674997438527 df.mm.exp4 -0.0407527310235793 0.0877951663784345 -0.464179666200729 0.642655919540589 df.mm.exp5 -0.0189441569510379 0.0877951663784345 -0.215776764627116 0.829221382720188 df.mm.exp6 0.32158536236544 0.0877951663784345 3.6629050963839 0.000267198540935501 *** df.mm.exp7 0.424078952399662 0.0877951663784345 4.83032232744683 1.65815238749139e-06 *** df.mm.exp8 0.271961003648915 0.0877951663784345 3.09767627156884 0.00202437505411802 ** df.mm.trans1:exp2 -0.297548654555667 0.0828121074893898 -3.59305738709512 0.000348517014685058 *** df.mm.trans2:exp2 -0.430431927864146 0.0677553606731371 -6.35273613169325 3.69835010328186e-10 *** df.mm.trans1:exp3 0.0970004813998479 0.0828121074893898 1.17133211966953 0.241843341975307 df.mm.trans2:exp3 0.0853025587323278 0.067755360673137 1.2589787418274 0.20843608613395 df.mm.trans1:exp4 0.0350156482912750 0.0828121074893898 0.422832474052919 0.672540666646638 df.mm.trans2:exp4 0.0484876782071835 0.067755360673137 0.715628663554696 0.474447019006256 df.mm.trans1:exp5 0.0384593378293266 0.0828121074893898 0.464416846706312 0.642486093496444 df.mm.trans2:exp5 -0.119536827406385 0.067755360673137 -1.76424162189986 0.0781049600642767 . df.mm.trans1:exp6 -0.280638505749307 0.0828121074893897 -3.38885839592072 0.000739116168907605 *** df.mm.trans2:exp6 -0.354206697669791 0.0677553606731371 -5.22772949846053 2.23641095543588e-07 *** df.mm.trans1:exp7 -0.38967483881915 0.0828121074893897 -4.7055297906659 3.02346509913369e-06 *** df.mm.trans2:exp7 -0.227388105119928 0.067755360673137 -3.35601645184778 0.000831213456169892 *** df.mm.trans1:exp8 -0.20197331717619 0.0828121074893897 -2.43893463527743 0.0149653052724692 * df.mm.trans2:exp8 -0.253133336725818 0.0677553606731371 -3.73598980524914 0.000201405873667128 *** df.mm.trans1:probe2 0.0424573593324116 0.0483518536832103 0.878091657262656 0.380179758386964 df.mm.trans1:probe3 0.0921273116650293 0.0483518536832103 1.90535221810988 0.0571226746471096 . df.mm.trans1:probe4 -0.147403078500787 0.0483518536832104 -3.0485507229265 0.00238162028791937 ** df.mm.trans1:probe5 -0.104966894608927 0.0483518536832103 -2.17089701041546 0.0302570155550208 * df.mm.trans1:probe6 0.169079061756846 0.0483518536832103 3.49684756378961 0.000498961304965313 *** df.mm.trans1:probe7 0.0657723223527984 0.0483518536832103 1.36028543566753 0.174154955360302 df.mm.trans1:probe8 -0.0382281430376785 0.0483518536832103 -0.790624146245562 0.429417339222776 df.mm.trans1:probe9 -0.0170758478939366 0.0483518536832103 -0.353158081711063 0.724070781029205 df.mm.trans1:probe10 0.0469839392858576 0.0483518536832103 0.971709163286375 0.331513622608312 df.mm.trans1:probe11 0.600351489821498 0.0483518536832103 12.4163076302070 2.78489923933077e-32 *** df.mm.trans1:probe12 1.17648455866490 0.0483518536832103 24.3317364081415 1.77955906691627e-96 *** df.mm.trans1:probe13 0.612210874157358 0.0483518536832103 12.6615802192076 2.17193049024587e-33 *** df.mm.trans1:probe14 0.395583144957187 0.0483518536832103 8.1813439366555 1.22674298107351e-15 *** df.mm.trans1:probe15 0.280739666481735 0.0483518536832103 5.80618208189232 9.4939584207119e-09 *** df.mm.trans1:probe16 0.251513382056642 0.0483518536832103 5.20173194815853 2.56028986446599e-07 *** df.mm.trans1:probe17 0.164387361982642 0.0483518536832103 3.39981509415686 0.00071056704664357 *** df.mm.trans1:probe18 0.0295615400743586 0.0483518536832103 0.611383800671607 0.5411337978039 df.mm.trans1:probe19 0.185060304511982 0.0483518536832103 3.82736731717573 0.000140498858605637 *** df.mm.trans1:probe20 0.143542411181158 0.0483518536832103 2.96870544243481 0.00308741556669117 ** df.mm.trans1:probe21 0.0667555172566115 0.0483518536832103 1.38061960755378 0.167813933869684 df.mm.trans1:probe22 0.300562366220361 0.0483518536832103 6.21614981277807 8.51900106349689e-10 *** df.mm.trans2:probe2 -0.446437595266229 0.0483518536832103 -9.23310196525618 2.73397744121569e-19 *** df.mm.trans2:probe3 -0.599852194239046 0.0483518536832103 -12.4059813336037 3.09850561582204e-32 *** df.mm.trans2:probe4 -0.410718103483101 0.0483518536832103 -8.49436106780987 1.0925360615148e-16 *** df.mm.trans2:probe5 -0.386263492990234 0.0483518536832103 -7.98859740768035 5.24421173993493e-15 *** df.mm.trans2:probe6 -0.44045773555184 0.0483518536832103 -9.10942811908748 7.65803117183007e-19 *** df.mm.trans3:probe2 0.232936192257833 0.0483518536832103 4.8175235180015 1.76462701734349e-06 *** df.mm.trans3:probe3 0.403678881877219 0.0483518536832103 8.34877778465384 3.39518264232087e-16 *** df.mm.trans3:probe4 0.459644707999909 0.0483518536832103 9.50624790957116 2.70675936835635e-20 *** df.mm.trans3:probe5 0.837780371031014 0.0483518536832103 17.3267477296723 1.09234936660342e-56 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17968720046799 0.264291873924165 15.8146640621546 9.8871939541392e-49 *** df.mm.trans1 -0.0129594930835006 0.232817608381187 -0.0556637153590303 0.955624771434 df.mm.trans2 0.390318647524034 0.210078437611623 1.85796625280328 0.0635718476543417 . df.mm.exp2 -0.119381991291323 0.279761947777787 -0.426727052194201 0.669702595600119 df.mm.exp3 -0.147790069150955 0.279761947777787 -0.528270804249416 0.597470203888236 df.mm.exp4 0.208333458846323 0.279761947777787 0.744681185204647 0.456701538350696 df.mm.exp5 0.0811621913175115 0.279761947777787 0.2901116179745 0.771812342687418 df.mm.exp6 -0.0463717894066821 0.279761947777787 -0.165754455797237 0.868395585349365 df.mm.exp7 -0.336726713830933 0.279761947777787 -1.20361870692433 0.229122915832631 df.mm.exp8 -0.151569712398515 0.279761947777787 -0.541781016333593 0.588132897584188 df.mm.trans1:exp2 0.203233123406754 0.263883280213316 0.770162941897894 0.441449700796861 df.mm.trans2:exp2 -0.0454967888940108 0.215904501992713 -0.210726448379230 0.83315890440112 df.mm.trans1:exp3 0.173479058086355 0.263883280213316 0.65740829788882 0.511123393802052 df.mm.trans2:exp3 0.091783310959281 0.215904501992713 0.425110686030895 0.670879909091612 df.mm.trans1:exp4 -0.108864848252686 0.263883280213316 -0.412549245881296 0.680056709516371 df.mm.trans2:exp4 -0.262096441946706 0.215904501992713 -1.21394616382549 0.225156615129726 df.mm.trans1:exp5 -0.0409648883513407 0.263883280213316 -0.155238665815529 0.876675614882129 df.mm.trans2:exp5 -0.0531843524972841 0.215904501992713 -0.246332762894769 0.805493147847231 df.mm.trans1:exp6 0.113953089793715 0.263883280213316 0.431831413121734 0.665990076671144 df.mm.trans2:exp6 -0.101306017244936 0.215904501992713 -0.469216789413476 0.639053271649467 df.mm.trans1:exp7 0.324811217843621 0.263883280213316 1.23088972359693 0.218756251200337 df.mm.trans2:exp7 -0.0104261835747941 0.215904501992713 -0.0482907187138969 0.96149761660106 df.mm.trans1:exp8 0.2854008247734 0.263883280213316 1.08154190194502 0.279809408837352 df.mm.trans2:exp8 0.0295166178305784 0.215904501992713 0.136711451397037 0.89129616290877 df.mm.trans1:probe2 0.0888579264867949 0.154074641270959 0.57671999593059 0.564304482629691 df.mm.trans1:probe3 0.164241444687446 0.154074641270959 1.06598622156521 0.286778480454301 df.mm.trans1:probe4 0.179849583417878 0.154074641270959 1.16728867212866 0.243470819760345 df.mm.trans1:probe5 0.128833480310264 0.154074641270959 0.83617576031668 0.403326682562174 df.mm.trans1:probe6 0.139067869068827 0.154074641270959 0.902600635131508 0.367032384092559 df.mm.trans1:probe7 0.0262175703005021 0.154074641270959 0.170161488511242 0.864929773403126 df.mm.trans1:probe8 0.0112981438413462 0.154074641270959 0.0733290290222195 0.941564161562508 df.mm.trans1:probe9 0.142919362890511 0.154074641270959 0.927598219353757 0.353919283500451 df.mm.trans1:probe10 -0.0168573044386256 0.154074641270959 -0.109409986611489 0.912907055453784 df.mm.trans1:probe11 0.187581772356216 0.154074641270959 1.21747336751108 0.223813301967909 df.mm.trans1:probe12 -0.0319822564764736 0.154074641270959 -0.207576381243873 0.83561701281949 df.mm.trans1:probe13 0.111257434154248 0.154074641270959 0.722100880693197 0.470461191550725 df.mm.trans1:probe14 0.248229042067013 0.154074641270959 1.61109602475382 0.107586370472572 df.mm.trans1:probe15 0.0904235231570399 0.154074641270959 0.586881283066036 0.557462923128619 df.mm.trans1:probe16 0.113745512491969 0.154074641270959 0.738249406610227 0.460597594660381 df.mm.trans1:probe17 0.140972790149639 0.154074641270959 0.914964260093396 0.360509240672911 df.mm.trans1:probe18 0.122671249427285 0.154074641270959 0.796180659032344 0.426183065798212 df.mm.trans1:probe19 0.183700169005964 0.154074641270959 1.19228036158724 0.233534555745688 df.mm.trans1:probe20 0.137227103400696 0.154074641270959 0.8906534019402 0.373405400315586 df.mm.trans1:probe21 0.199518117632298 0.154074641270959 1.29494455405819 0.195744323441164 df.mm.trans1:probe22 0.177528669317068 0.154074641270959 1.15222510240905 0.249601686007584 df.mm.trans2:probe2 -0.170170991934030 0.154074641270959 -1.10447112211518 0.269748787692529 df.mm.trans2:probe3 -0.0225730682696886 0.154074641270959 -0.146507355678285 0.883560893776371 df.mm.trans2:probe4 -0.0249594003435956 0.154074641270959 -0.161995511641020 0.871353729140027 df.mm.trans2:probe5 -0.202203621946442 0.154074641270959 -1.31237444577815 0.189801833570215 df.mm.trans2:probe6 -0.0711493436795879 0.154074641270959 -0.461784905631959 0.644371663907825 df.mm.trans3:probe2 -0.151213167464705 0.154074641270959 -0.981428002800134 0.326703344176808 df.mm.trans3:probe3 -0.00339772011477455 0.154074641270959 -0.0220524291781361 0.98241209415568 df.mm.trans3:probe4 -0.200184595654194 0.154074641270959 -1.29927023683375 0.194256944453629 df.mm.trans3:probe5 -0.189414030272422 0.154074641270959 -1.22936538232346 0.219326647368284