fitVsDatCorrelation=0.90770825036319 cont.fitVsDatCorrelation=0.292409339496626 fstatistic=10724.9261292812,62,922 cont.fstatistic=2052.83477484443,62,922 residuals=-0.850686254893063,-0.0981970658810034,-0.00235485733777582,0.0931653350694976,0.910778074017932 cont.residuals=-0.966060390179417,-0.285983998506312,-0.0148486283984346,0.254441470699809,1.15267350470483 predictedValues: Include Exclude Both Lung 88.884162496135 184.987668403229 59.4662759261227 cerebhem 66.0705855577682 118.391988732842 63.798347436894 cortex 73.8399418238145 113.470649398746 62.9993359434312 heart 84.4531151059075 146.091517587846 60.8674859421523 kidney 96.6231150641147 173.873211681911 62.7374707554355 liver 87.3788255888423 177.619576044071 62.2521313237226 stomach 81.271344941921 139.316660293076 73.2986452441975 testicle 98.926623551018 156.619795631309 75.42906500319 diffExp=-96.1035059070941,-52.3214031750741,-39.6307075749312,-61.6384024819382,-77.2500966177958,-90.2407504552284,-58.0453153511551,-57.6931720802914 diffExpScore=0.998127071997926 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 83.8233832479714 81.4370785672214 82.4115444126717 cerebhem 83.3742483730139 98.833919856838 88.1811053165317 cortex 87.861387767146 88.0894458635486 76.962594045416 heart 78.7882759881528 73.4315423098148 80.7105959861799 kidney 82.0917575137327 80.9456971133113 81.927326947431 liver 91.3779167275336 87.0578172387418 85.080653460253 stomach 79.6280410386599 85.1344968116397 85.6386680356555 testicle 78.7685036888189 70.7167123374085 89.018620074998 cont.diffExp=2.38630468074997,-15.4596714838241,-0.228058096402478,5.35673367833799,1.14606040042135,4.32009948879181,-5.50645577297986,8.05179135141037 cont.diffExpScore=39.7965935100145 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=0.784957225154158 cont.tran.correlation=0.538183861976417 tran.covariance=0.0201947109606701 cont.tran.covariance=0.00322615574998029 tran.mean=117.988673868909 cont.tran.mean=83.210014027722 weightedLogRatios: wLogRatio Lung -3.55762637555189 cerebhem -2.6144606983324 cortex -1.94058434090664 heart -2.58137076126089 kidney -2.85797685464053 liver -3.42277537540775 stomach -2.51545387662436 testicle -2.21639784526417 cont.weightedLogRatios: wLogRatio Lung 0.127490005225273 cerebhem -0.76688336829062 cortex -0.0116058509309057 heart 0.304987785374072 kidney 0.0618713650240527 liver 0.217495040853131 stomach -0.294932578371641 testicle 0.465032954286076 varWeightedLogRatios=0.306784654681607 cont.varWeightedLogRatios=0.150078743385217 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.60134712641027 0.078087388917943 71.7317764626034 0 *** df.mm.trans1 -1.09278321929268 0.0670637030316405 -16.2947044361255 1.18860480183843e-52 *** df.mm.trans2 -0.368913665845813 0.0588862531572191 -6.26485208459194 5.71851270963051e-10 *** df.mm.exp2 -0.813216363520014 0.0749252322481015 -10.8537049418438 6.53552717637604e-26 *** df.mm.exp3 -0.731893957424504 0.0749252322481015 -9.76832417417097 1.63026091174154e-21 *** df.mm.exp4 -0.310483133755179 0.0749252322481015 -4.14390619073518 3.72855269647897e-05 *** df.mm.exp5 -0.0320283066245401 0.0749252322481015 -0.427470234840034 0.669136674793447 df.mm.exp6 -0.103509521644129 0.0749252322481015 -1.38150418141349 0.167458669568767 df.mm.exp7 -0.582212940833152 0.0749252322481015 -7.7705857341257 2.07937076911099e-14 *** df.mm.exp8 -0.297206876574034 0.0749252322481015 -3.96671278361723 7.85196161773402e-05 *** df.mm.trans1:exp2 0.516606035210561 0.0687820951281278 7.51076329164186 1.38730346962732e-13 *** df.mm.trans2:exp2 0.366928255374533 0.0487525017918996 7.52634720041177 1.23995689662761e-13 *** df.mm.trans1:exp3 0.546459782751073 0.0687820951281278 7.94479699597872 5.64947712994483e-15 *** df.mm.trans2:exp3 0.243148999721800 0.0487525017918996 4.9874158409282 7.31080205901809e-07 *** df.mm.trans1:exp4 0.259345686314417 0.0687820951281279 3.77054065932865 0.000173266322077094 *** df.mm.trans2:exp4 0.0744272263025762 0.0487525017918996 1.52663398937493 0.127195003880552 df.mm.trans1:exp5 0.115512328597043 0.0687820951281279 1.67939531911416 0.0934138251812689 . df.mm.trans2:exp5 -0.0299344938404069 0.0487525017918996 -0.61400938906033 0.53936058800168 df.mm.trans1:exp6 0.0864285278033698 0.0687820951281279 1.25655561439892 0.209232857139953 df.mm.trans2:exp6 0.0628644060244017 0.0487525017918996 1.28946010386788 0.197561550746522 df.mm.trans1:exp7 0.492672457519502 0.0687820951281278 7.1628009673411 1.61609240954104e-12 *** df.mm.trans2:exp7 0.298673248984474 0.0487525017918996 6.12631635314558 1.33050205590641e-09 *** df.mm.trans1:exp8 0.404251298649817 0.0687820951281278 5.87727515273815 5.82368916632418e-09 *** df.mm.trans2:exp8 0.130738895449500 0.0487525017918996 2.68168587547691 0.0074561456496212 ** df.mm.trans1:probe2 0.100053674279669 0.0492720813173858 2.03063624682654 0.0425784588829313 * df.mm.trans1:probe3 -0.257893727747205 0.0492720813173858 -5.23407416232297 2.05378647773892e-07 *** df.mm.trans1:probe4 0.123570432605065 0.0492720813173858 2.50791988690485 0.0123152460251661 * df.mm.trans1:probe5 -0.0132509986492618 0.0492720813173858 -0.268935232589538 0.788039690422861 df.mm.trans1:probe6 -0.328870312721571 0.0492720813173858 -6.67457724391942 4.27517491972611e-11 *** df.mm.trans1:probe7 -0.325441686034611 0.0492720813173858 -6.60499165720808 6.70830881431457e-11 *** df.mm.trans1:probe8 -0.545498807934486 0.0492720813173858 -11.0711541576793 7.7969338652473e-27 *** df.mm.trans1:probe9 -0.108200120626062 0.0492720813173858 -2.19597219628479 0.0283423694625115 * df.mm.trans1:probe10 0.212572410285259 0.0492720813173858 4.31425676776216 1.77425157933001e-05 *** df.mm.trans1:probe11 -0.0761043191003925 0.0492720813173858 -1.54457285070154 0.122792760929882 df.mm.trans1:probe12 0.165869557801453 0.0492720813173858 3.36640047196312 0.000793032120965156 *** df.mm.trans1:probe13 0.102818631127201 0.0492720813173858 2.08675234287132 0.0371847450405532 * df.mm.trans1:probe14 -0.195113289556433 0.0492720813173858 -3.95991572386828 8.07495817587523e-05 *** df.mm.trans1:probe15 0.0621380670794505 0.0492720813173858 1.26112121546457 0.207584181038368 df.mm.trans1:probe16 -0.0223567834283986 0.0492720813173858 -0.453741405490616 0.650121740402985 df.mm.trans1:probe17 0.0743973269953703 0.0492720813173858 1.50992864531417 0.131404244176781 df.mm.trans1:probe18 0.220917831444807 0.0492720813173858 4.48363100437682 8.26242608769389e-06 *** df.mm.trans1:probe19 0.388883389290729 0.0492720813173858 7.89257078031144 8.37097670819159e-15 *** df.mm.trans1:probe20 0.109380699406529 0.0492720813173858 2.21993259635114 0.0266659002418461 * df.mm.trans1:probe21 0.142944107009859 0.0492720813173858 2.90111769561926 0.00380676736390072 ** df.mm.trans1:probe22 -0.573863635658424 0.0492720813173858 -11.6468316400496 2.40898934618950e-29 *** df.mm.trans2:probe2 -0.0577500030523354 0.0492720813173858 -1.17206339793806 0.241474367050408 df.mm.trans2:probe3 -0.36871524070644 0.0492720813173858 -7.48324874549875 1.69067119533913e-13 *** df.mm.trans2:probe4 0.126270794901476 0.0492720813173859 2.56272500623839 0.0105431890726728 * df.mm.trans2:probe5 0.0551913842912414 0.0492720813173858 1.12013503013454 0.262947919593617 df.mm.trans2:probe6 0.0142614598004041 0.0492720813173859 0.289443015579938 0.77230745379555 df.mm.trans3:probe2 0.0710384915100875 0.0492720813173858 1.44175950377443 0.149709794331595 df.mm.trans3:probe3 -0.257840795980074 0.0492720813173858 -5.23299988728291 2.06541621985404e-07 *** df.mm.trans3:probe4 -0.137071129841316 0.0492720813173858 -2.78192286943133 0.00551407333992585 ** df.mm.trans3:probe5 -0.145475983582357 0.0492720813173858 -2.95250331816256 0.00323203082082335 ** df.mm.trans3:probe6 -0.0931251462542231 0.0492720813173858 -1.89001852092178 0.0590689301003861 . df.mm.trans3:probe7 0.142082486682411 0.0492720813173858 2.88363070695527 0.004022637012205 ** df.mm.trans3:probe8 -0.160707390619346 0.0492720813173859 -3.26163186783505 0.00114837776223341 ** df.mm.trans3:probe9 -0.364997543314257 0.0492720813173858 -7.40779633324453 2.89871976146905e-13 *** df.mm.trans3:probe10 0.477370784959576 0.0492720813173858 9.68846397789845 3.32213007748742e-21 *** df.mm.trans3:probe11 0.0113152081073871 0.0492720813173858 0.229647455614879 0.818416663144184 df.mm.trans3:probe12 -0.105717519564991 0.0492720813173858 -2.14558664335716 0.0321665419202006 * df.mm.trans3:probe13 -0.142002909442998 0.0492720813173858 -2.88201564955794 0.00404312540277669 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37679457309293 0.177964989145167 24.5935708709692 4.28023737583568e-103 *** df.mm.trans1 0.0310574491634317 0.152841468352877 0.20320041084483 0.839023226889614 df.mm.trans2 0.0411645360332733 0.134204659025499 0.306729560152245 0.759118484601369 df.mm.exp2 0.120570483086509 0.170758279006414 0.706088652263704 0.480311382259606 df.mm.exp3 0.193976494274569 0.170758279006414 1.13597124194068 0.256263785368441 df.mm.exp4 -0.144569244316488 0.170758279006414 -0.846630951996522 0.397420544659117 df.mm.exp5 -0.0210336136874807 0.170758279006414 -0.123177709507664 0.90199322158707 df.mm.exp6 0.121159475332875 0.170758279006414 0.709537927167349 0.478170114902813 df.mm.exp7 -0.045355439674433 0.170758279006414 -0.265611951223339 0.790597457871545 df.mm.exp8 -0.280467571474141 0.170758279006414 -1.6424830064234 0.100830928351063 df.mm.trans1:exp2 -0.125942998844285 0.156757768219426 -0.803424291343526 0.4219366125087 df.mm.trans2:exp2 0.073039701187253 0.111109342653427 0.65736777342909 0.511108534123632 df.mm.trans1:exp3 -0.146928065578333 0.156757768219426 -0.937293680863495 0.348852960830403 df.mm.trans2:exp3 -0.115454445693078 0.111109342653427 -1.03910654978136 0.299027602648419 df.mm.trans1:exp4 0.082621443298364 0.156757768219426 0.527064427089267 0.598275636453689 df.mm.trans2:exp4 0.0410921394212271 0.111109342653427 0.369835141131219 0.711590260929632 df.mm.trans1:exp5 0.000159224477833378 0.156757768219426 0.00101573580462372 0.99918977994058 df.mm.trans2:exp5 0.0149814575799848 0.111109342653427 0.134835264273996 0.892771552182482 df.mm.trans1:exp6 -0.0348676422982165 0.156757768219426 -0.222430075997315 0.824028362720318 df.mm.trans2:exp6 -0.0544176913907514 0.111109342653427 -0.489767017706976 0.624415280264472 df.mm.trans1:exp7 -0.00599026009085172 0.156757768219426 -0.0382134815957999 0.96952574423267 df.mm.trans2:exp7 0.0897570811049011 0.111109342653427 0.807826587408333 0.419398781965215 df.mm.trans1:exp8 0.218268784119811 0.156757768219426 1.39239532814911 0.164138428636445 df.mm.trans2:exp8 0.139318820332450 0.111109342653427 1.25388934004419 0.210200053933358 df.mm.trans1:probe2 0.134758392630401 0.112293489874822 1.20005525503411 0.230426140963773 df.mm.trans1:probe3 0.0722276840633084 0.112293489874822 0.643204553922255 0.52025131199487 df.mm.trans1:probe4 -0.0737618595148928 0.112293489874822 -0.656866747993297 0.511430520502812 df.mm.trans1:probe5 -0.0351420107076918 0.112293489874822 -0.312947889916556 0.754391025797807 df.mm.trans1:probe6 0.00431553837261998 0.112293489874822 0.0384308865761556 0.969352454614899 df.mm.trans1:probe7 -0.0570947498938231 0.112293489874822 -0.50844220762458 0.611264888731476 df.mm.trans1:probe8 0.00593656318934174 0.112293489874822 0.0528664947180771 0.95784973224364 df.mm.trans1:probe9 0.0926406648238736 0.112293489874822 0.82498695985977 0.409592381406588 df.mm.trans1:probe10 0.347611659803292 0.112293489874822 3.0955637783703 0.00202371867321242 ** df.mm.trans1:probe11 -0.101744316862420 0.112293489874822 -0.906057127406385 0.365142331050682 df.mm.trans1:probe12 0.0520061797455828 0.112293489874822 0.463127290847902 0.643382423350518 df.mm.trans1:probe13 0.142827654843945 0.112293489874822 1.27191393733653 0.203724414113427 df.mm.trans1:probe14 0.0956690161051863 0.112293489874822 0.851955141939501 0.394460221616213 df.mm.trans1:probe15 0.0391876803802372 0.112293489874822 0.348975532098264 0.727187357656943 df.mm.trans1:probe16 -0.104292480828291 0.112293489874822 -0.928749128240202 0.353262190905564 df.mm.trans1:probe17 0.0564855747700827 0.112293489874822 0.503017359537487 0.6150722037767 df.mm.trans1:probe18 -0.141770417401853 0.112293489874822 -1.26249898867592 0.207088514735632 df.mm.trans1:probe19 0.0276664812009854 0.112293489874822 0.246376537338239 0.8054456072112 df.mm.trans1:probe20 0.14323227802128 0.112293489874822 1.27551720211872 0.202447512808948 df.mm.trans1:probe21 0.042902702466398 0.112293489874822 0.382058679574596 0.70250588180439 df.mm.trans1:probe22 -0.0135628413989806 0.112293489874822 -0.120780300034308 0.903891369413469 df.mm.trans2:probe2 0.079513684436635 0.112293489874822 0.708088104887217 0.479069509443599 df.mm.trans2:probe3 -0.0506642061712747 0.112293489874822 -0.451176699804699 0.651968295452359 df.mm.trans2:probe4 -0.156061017057296 0.112293489874822 -1.38976014754963 0.164937189619873 df.mm.trans2:probe5 -0.121354601154274 0.112293489874822 -1.08069133205810 0.280117032625024 df.mm.trans2:probe6 -0.09587401442368 0.112293489874822 -0.853780700293082 0.393448270177494 df.mm.trans3:probe2 -0.0882226183921035 0.112293489874822 -0.785643214851087 0.432278384968591 df.mm.trans3:probe3 0.00229826421966283 0.112293489874822 0.0204665846811315 0.983675596243506 df.mm.trans3:probe4 -0.115744744031312 0.112293489874822 -1.03073423188055 0.302935770546079 df.mm.trans3:probe5 -0.159294822155759 0.112293489874822 -1.41855794430586 0.156365857395536 df.mm.trans3:probe6 0.160380268869368 0.112293489874822 1.42822410317953 0.153566008677421 df.mm.trans3:probe7 -0.0336265314302475 0.112293489874822 -0.299452189683767 0.76466253594806 df.mm.trans3:probe8 -0.177500463965986 0.112293489874822 -1.58068347652078 0.114293399955684 df.mm.trans3:probe9 0.0953087822619688 0.112293489874822 0.848747174642206 0.396242292399937 df.mm.trans3:probe10 0.0114546492071703 0.112293489874822 0.102006351569794 0.918773813131124 df.mm.trans3:probe11 -0.0798030767553504 0.112293489874822 -0.710665211708263 0.477471445374856 df.mm.trans3:probe12 -0.0078106138379271 0.112293489874822 -0.0695553575423996 0.944562652603666 df.mm.trans3:probe13 -0.0922224721862053 0.112293489874822 -0.821262855834376 0.411708859472793