fitVsDatCorrelation=0.907201002729006 cont.fitVsDatCorrelation=0.235353292791826 fstatistic=9286.63449627925,54,738 cont.fstatistic=1728.88138254032,54,738 residuals=-0.54588330462817,-0.0983123165906221,-0.000177537462254562,0.0859776602174288,0.900955041105579 cont.residuals=-0.718390678901248,-0.268258934386016,-0.109030331038859,0.146437217360593,1.25298035934008 predictedValues: Include Exclude Both Lung 85.3365916346489 63.566473233592 66.1035564799021 cerebhem 83.8336071477337 84.766400835246 76.5739401678693 cortex 78.4190966169646 57.2700854506394 65.0358282564835 heart 75.2845608506377 61.6679717672381 64.7284646135548 kidney 85.0307333436233 63.0407461951594 64.1615028235254 liver 84.8483521981925 65.3542886073578 62.0118387556263 stomach 77.963815280119 59.2906868327163 65.8946381629009 testicle 82.0034678452994 65.1724696084283 67.5865199418561 diffExp=21.7701184010569,-0.932793687512316,21.1490111663252,13.6165890833995,21.9899871484639,19.4940635908347,18.6731284474027,16.8309982368711 diffExpScore=1.00647937893736 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=1,0,1,0,1,0,1,0 diffExp1.3Score=0.8 diffExp1.2=1,0,1,1,1,1,1,1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 71.6091234140146 74.6569352939942 82.4840194847617 cerebhem 74.6208549795379 81.1201487957552 79.5415777672601 cortex 72.4302704839696 81.4856943227937 69.3047062952583 heart 73.3852399927934 71.5084083194094 73.8841836284868 kidney 73.8998376339764 78.8308903414097 63.821866640524 liver 77.8728819288953 88.5874723190394 71.467816889074 stomach 74.300186705412 86.159296439101 86.1012439953875 testicle 72.3101322487317 75.3215666410994 77.4393477994305 cont.diffExp=-3.04781187997958,-6.49929381621736,-9.05542383882411,1.87683167338403,-4.93105270743335,-10.7145903901441,-11.8591097336889,-3.01143439236769 cont.diffExpScore=1.05708034298205 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=0.43567909866173 cont.tran.correlation=0.741461736263887 tran.covariance=0.00264910571122466 cont.tran.covariance=0.00138571817903968 tran.mean=73.3030842154748 cont.tran.mean=76.7561837412458 weightedLogRatios: wLogRatio Lung 1.26623079712585 cerebhem -0.0490675111297835 cortex 1.32156127440358 heart 0.842237031463325 kidney 1.28471953436511 liver 1.12518309173823 stomach 1.15522627884146 testicle 0.985954011259877 cont.weightedLogRatios: wLogRatio Lung -0.178897201268947 cerebhem -0.363623084737071 cortex -0.511445480103731 heart 0.110957051269983 kidney -0.28001678702671 liver -0.569733957390365 stomach -0.648928903377504 testicle -0.175505352444346 varWeightedLogRatios=0.202880507317467 cont.varWeightedLogRatios=0.0624025812193568 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.51814260807278 0.0851659611127928 53.0510376333217 4.87350423252237e-254 *** df.mm.trans1 -0.0100531146790822 0.0750236285639832 -0.133999312903247 0.893439650439974 df.mm.trans2 -0.332729259263904 0.0676961110556183 -4.91504244594698 1.09432334370038e-06 *** df.mm.exp2 0.123009433290660 0.090151069768111 1.36448112714656 0.172832098453324 df.mm.exp3 -0.172559395811626 0.090151069768111 -1.91411367891127 0.0559920986892011 . df.mm.exp4 -0.134628243130984 0.090151069768111 -1.49336267974721 0.135769665131164 df.mm.exp5 0.0179236865844987 0.090151069768111 0.198818346034079 0.842459603644903 df.mm.exp6 0.0858963506621337 0.090151069768111 0.952804563307774 0.341001056995763 df.mm.exp7 -0.156826989925180 0.090151069768111 -1.73960209599924 0.082345819995755 . df.mm.exp8 -0.0370768516532442 0.090151069768111 -0.41127467204343 0.680990541071464 df.mm.trans1:exp2 -0.140778804549594 0.0850342950287306 -1.65555326238700 0.098237448874557 . df.mm.trans2:exp2 0.164803633488951 0.0695735141144159 2.3687697191481 0.0181039392337237 * df.mm.trans1:exp3 0.0880235345043508 0.0850342950287306 1.03515333989198 0.300936375526654 df.mm.trans2:exp3 0.0682516333443932 0.0695735141144159 0.981000229946 0.326914107019064 df.mm.trans1:exp4 0.00929998342911664 0.0850342950287306 0.109367443170717 0.912940786024845 df.mm.trans2:exp4 0.104306762172072 0.0695735141144159 1.49923089985848 0.134241372685301 df.mm.trans1:exp5 -0.0215142649969347 0.0850342950287306 -0.253006919027972 0.800333323709088 df.mm.trans2:exp5 -0.0262285851910102 0.0695735141144159 -0.376990950146294 0.706288703840919 df.mm.trans1:exp6 -0.0916341176460208 0.0850342950287306 -1.07761365711400 0.281558312813378 df.mm.trans2:exp6 -0.0581594684751409 0.0695735141144159 -0.8359426602988 0.403457726421771 df.mm.trans1:exp7 0.0664684640106085 0.0850342950287306 0.781666549809707 0.434661258018181 df.mm.trans2:exp7 0.0871930509318664 0.0695735141144159 1.25325063771358 0.210511410634495 df.mm.trans1:exp8 -0.00276494948143276 0.0850342950287306 -0.0325156982897143 0.974069588914942 df.mm.trans2:exp8 0.0620278052074833 0.0695735141144159 0.891543369585682 0.372928311542967 df.mm.trans1:probe2 -0.0413508556735382 0.0496493316730396 -0.83285825367902 0.405194118833309 df.mm.trans1:probe3 -0.0573455033919229 0.0496493316730396 -1.15501058039543 0.248459926108963 df.mm.trans1:probe4 -0.45358842079772 0.0496493316730396 -9.1358414204803 6.15139203950495e-19 *** df.mm.trans1:probe5 -0.278061149607675 0.0496493316730396 -5.60050136100153 3.01684780949484e-08 *** df.mm.trans1:probe6 -0.483254503711699 0.0496493316730396 -9.7333536510445 3.80600792541095e-21 *** df.mm.trans1:probe7 -0.36318700271519 0.0496493316730396 -7.31504313304596 6.72109550067324e-13 *** df.mm.trans1:probe8 -0.445612038423915 0.0496493316730396 -8.9751870449827 2.31397698454039e-18 *** df.mm.trans1:probe9 -0.245783160104767 0.0496493316730396 -4.95038204589229 9.18455487808685e-07 *** df.mm.trans1:probe10 -0.32026805467283 0.0496493316730396 -6.45060152635933 2.01481990158539e-10 *** df.mm.trans1:probe11 0.59359039412605 0.0496493316730396 11.9556572893081 3.07395408933016e-30 *** df.mm.trans1:probe12 0.440425041771305 0.0496493316730396 8.8707144070272 5.4231886349941e-18 *** df.mm.trans1:probe13 0.76309557329672 0.0496493316730396 15.3697048395738 1.86499124290358e-46 *** df.mm.trans1:probe14 0.404487415050223 0.0496493316730396 8.1468853944285 1.59382405051966e-15 *** df.mm.trans1:probe15 0.470602187081364 0.0496493316730396 9.47852007717776 3.43102233823193e-20 *** df.mm.trans1:probe16 0.611012412376392 0.0496493316730396 12.3065586542060 8.6312694315993e-32 *** df.mm.trans1:probe17 -0.416788528750443 0.0496493316730396 -8.39464529946061 2.37931803613857e-16 *** df.mm.trans1:probe18 -0.41502020688035 0.0496493316730396 -8.35902907240366 3.13625554844262e-16 *** df.mm.trans1:probe19 -0.209156960543304 0.0496493316730396 -4.21268431004641 2.83578793140674e-05 *** df.mm.trans1:probe20 -0.391988416580149 0.0496493316730396 -7.89513984118753 1.05003277070012e-14 *** df.mm.trans1:probe21 -0.420742342986769 0.0496493316730396 -8.47428009217772 1.27868080109091e-16 *** df.mm.trans1:probe22 -0.401192066363756 0.0496493316730396 -8.08051292625173 2.63224505274998e-15 *** df.mm.trans2:probe2 -0.116845727522842 0.0496493316730396 -2.35341994716701 0.0188630123904776 * df.mm.trans2:probe3 -0.059355132087079 0.0496493316730396 -1.1954870304792 0.232280796805167 df.mm.trans2:probe4 0.0275946182221996 0.0496493316730396 0.555790325717192 0.578522648827524 df.mm.trans2:probe5 -0.112682092259295 0.0496493316730396 -2.26955909500154 0.0235220719794382 * df.mm.trans2:probe6 -0.105310512930133 0.0496493316730396 -2.12108621368047 0.0342473529238222 * df.mm.trans3:probe2 -0.0856529622518326 0.0496493316730396 -1.72515841332751 0.0849175720135243 . df.mm.trans3:probe3 -0.0357056608225513 0.0496493316730396 -0.719156927583378 0.472271884447198 df.mm.trans3:probe4 -0.00358001445187216 0.0496493316730396 -0.0721059948087108 0.942537091868233 df.mm.trans3:probe5 0.204250216613905 0.0496493316730396 4.11385631450134 4.32905954595562e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30665879273639 0.196753368695287 21.8886152816328 3.12757366480751e-82 *** df.mm.trans1 -0.0687036037374848 0.173322198902425 -0.396392407738623 0.6919301508155 df.mm.trans2 0.0700682772114083 0.156393912823024 0.448024324902579 0.654267132053526 df.mm.exp2 0.160550183605335 0.208270140283726 0.770874708139234 0.441027919440754 df.mm.exp3 0.273017631599898 0.208270140283726 1.31088225718755 0.190305302250546 df.mm.exp4 0.0915177456864105 0.208270140283726 0.439418466620976 0.660486983340528 df.mm.exp5 0.34239835108325 0.208270140283726 1.64401075745568 0.100599872678645 df.mm.exp6 0.398299654851031 0.208270140283726 1.91241843073821 0.0562093840465866 . df.mm.exp7 0.137266131775012 0.208270140283726 0.659077348236355 0.510051686553523 df.mm.exp8 0.0817144032731446 0.208270140283726 0.392348145354995 0.694914278212728 df.mm.trans1:exp2 -0.119352645930592 0.196449189123500 -0.607549700068042 0.543673062783838 df.mm.trans2:exp2 -0.0775222333060607 0.160731154737409 -0.482309938186601 0.629728801907795 df.mm.trans1:exp3 -0.26161580681317 0.196449189123500 -1.33172250789339 0.183362543964307 df.mm.trans2:exp3 -0.185493580351025 0.160731154737409 -1.15406114423847 0.248848685475931 df.mm.trans1:exp4 -0.0670174081362396 0.196449189123500 -0.341143724925779 0.733092485995172 df.mm.trans2:exp4 -0.134606127836910 0.160731154737409 -0.837461337578395 0.402604417524684 df.mm.trans1:exp5 -0.310910208068575 0.196449189123500 -1.58264948537465 0.113929804919147 df.mm.trans2:exp5 -0.287996845435393 0.160731154737409 -1.79179230004228 0.073575821957974 . df.mm.trans1:exp6 -0.314444364290692 0.196449189123500 -1.60063966511469 0.109884705894659 df.mm.trans2:exp6 -0.227212626994098 0.160731154737409 -1.41361907942055 0.157895439051938 df.mm.trans1:exp7 -0.100375155028165 0.196449189123500 -0.510947158784468 0.609540869498927 df.mm.trans2:exp7 0.00602831148206946 0.160731154737409 0.0375055569775386 0.970092052068342 df.mm.trans1:exp8 -0.0719726300070567 0.196449189123500 -0.366367661420127 0.714195752563799 df.mm.trans2:exp8 -0.0728513237074519 0.160731154737409 -0.453249550944067 0.6505022873112 df.mm.trans1:probe2 -0.0916765860784684 0.114701614735524 -0.7992615124892 0.424395949902068 df.mm.trans1:probe3 0.0596109664520921 0.114701614735524 0.519704684101802 0.603425259556756 df.mm.trans1:probe4 0.0402253696564982 0.114701614735524 0.350695757415873 0.72591668741957 df.mm.trans1:probe5 0.145405895757757 0.114701614735524 1.26768830668191 0.205309100992126 df.mm.trans1:probe6 0.0219509699809432 0.114701614735524 0.191374550668334 0.848284795357236 df.mm.trans1:probe7 0.0525219671628283 0.114701614735524 0.457900852432916 0.647158460638215 df.mm.trans1:probe8 -0.0552561515776886 0.114701614735524 -0.481738218813195 0.630134741935739 df.mm.trans1:probe9 0.0140119906671254 0.114701614735524 0.122160361032701 0.902805274756371 df.mm.trans1:probe10 0.137062625908247 0.114701614735524 1.19494940175239 0.232490666814190 df.mm.trans1:probe11 0.218908573525258 0.114701614735524 1.90850472358224 0.0567137042304592 . df.mm.trans1:probe12 0.063249643131391 0.114701614735524 0.551427661042353 0.581507455952643 df.mm.trans1:probe13 0.0450926635897971 0.114701614735524 0.393130155087796 0.694336887832441 df.mm.trans1:probe14 0.0371233764424083 0.114701614735524 0.323651733482623 0.746293410284112 df.mm.trans1:probe15 0.0966993244640796 0.114701614735524 0.843051117345178 0.399473003145227 df.mm.trans1:probe16 0.0230720378126827 0.114701614735524 0.201148326166825 0.84063802011005 df.mm.trans1:probe17 -0.0761801884469178 0.114701614735524 -0.664159686178544 0.506795556008583 df.mm.trans1:probe18 0.0714947363190715 0.114701614735524 0.623310635023947 0.533272990352727 df.mm.trans1:probe19 -0.0332039709683081 0.114701614735524 -0.28948128624753 0.772294403663573 df.mm.trans1:probe20 0.153145820647271 0.114701614735524 1.33516708548865 0.182233354474316 df.mm.trans1:probe21 -0.00629889480624643 0.114701614735524 -0.0549154850240798 0.956220657925194 df.mm.trans1:probe22 -0.0187431011312507 0.114701614735524 -0.163407474031364 0.870242355536319 df.mm.trans2:probe2 -0.152785711798414 0.114701614735524 -1.33202755820573 0.183262334270131 df.mm.trans2:probe3 -0.257768751658383 0.114701614735524 -2.24729836849062 0.0249156243917018 * df.mm.trans2:probe4 -0.209321823834284 0.114701614735524 -1.82492482182516 0.0684164770105505 . df.mm.trans2:probe5 -0.0317990622086016 0.114701614735524 -0.277232908027695 0.781678907907896 df.mm.trans2:probe6 -0.0503847580258137 0.114701614735524 -0.439268079547002 0.660595885837935 df.mm.trans3:probe2 0.123095438183092 0.114701614735524 1.07317964500258 0.283541302344736 df.mm.trans3:probe3 0.0795031923679682 0.114701614735524 0.693130541808716 0.488445700287635 df.mm.trans3:probe4 0.197053726807784 0.114701614735524 1.71796820177419 0.086221874365724 . df.mm.trans3:probe5 0.123253168403270 0.114701614735524 1.07455478013508 0.282925299776934