fitVsDatCorrelation=0.84590619888074 cont.fitVsDatCorrelation=0.272103513468496 fstatistic=11945.6687271975,42,462 cont.fstatistic=3661.93225499218,42,462 residuals=-0.392130172185167,-0.0771853626623177,0.00087022562834174,0.0720547513489195,0.454861658895781 cont.residuals=-0.491842045284903,-0.138375310457193,-0.0404416125221935,0.0845793293705085,1.18045428298845 predictedValues: Include Exclude Both Lung 46.6139173288937 44.2835060648389 70.5840381455341 cerebhem 54.4717920224079 47.6492812907014 66.326562526428 cortex 51.7567012896807 43.8343529178487 80.7879508885772 heart 48.9882373615587 46.9289836003665 68.5071433524965 kidney 47.2846535187314 44.3067724773017 68.4317409723322 liver 60.7559882101819 52.7992279085374 117.601254761118 stomach 46.6405042541784 46.2430948776074 65.6156017975391 testicle 48.9768652734519 47.9719579058208 66.7859932088842 diffExp=2.33041126405481,6.8225107317065,7.922348371832,2.05925376119220,2.97788104142971,7.9567603016445,0.39740937657097,1.00490736763113 diffExpScore=0.969203746433683 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 53.3013678093708 54.4627022866247 51.9007341665087 cerebhem 50.2287252747856 52.6199420791725 48.8059831367566 cortex 50.2089135033236 47.280661064264 57.1957806673258 heart 51.3666980971806 54.4679856536385 48.7786264281 kidney 51.4327814158305 52.6617325026357 48.4240381235261 liver 51.8568456236984 54.296448642706 50.9710725148781 stomach 48.2601958003621 55.3196103300192 52.4173239934266 testicle 54.4648146548338 58.1157131443104 51.9509274876162 cont.diffExp=-1.16133447725382,-2.39121680438694,2.92825243905956,-3.10128755645784,-1.22895108680522,-2.43960301900755,-7.05941452965711,-3.65089848947657 cont.diffExpScore=1.25420799773319 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=0.792566247537686 cont.tran.correlation=0.456551912512959 tran.covariance=0.00431252424544577 cont.tran.covariance=0.00096143843978128 tran.mean=48.7191147688817 cont.tran.mean=52.5215711176723 weightedLogRatios: wLogRatio Lung 0.195723801505937 cerebhem 0.525998434729236 cortex 0.641864452166118 heart 0.166200789265085 kidney 0.24872249999075 liver 0.566625217700963 stomach 0.0328441687541733 testicle 0.0804582727493218 cont.weightedLogRatios: wLogRatio Lung -0.085930621447631 cerebhem -0.183234506221239 cortex 0.233523296015732 heart -0.232633919884898 kidney -0.0933217602271092 liver -0.182576030756923 stomach -0.538554645238418 testicle -0.261470889659046 varWeightedLogRatios=0.0556549539627804 cont.varWeightedLogRatios=0.0463146887792074 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.58801683889711 0.0681286569914336 52.6653099788575 2.15940181513757e-197 *** df.mm.trans1 0.271853500479787 0.0600333160080225 4.52837721713487 7.57392650626341e-06 *** df.mm.trans2 0.199736048345895 0.056760520694035 3.51892558249357 0.000476205149851077 *** df.mm.exp2 0.291252673172663 0.0782040737006575 3.72426472676467 0.000220092038688432 *** df.mm.exp3 -0.0405634967472984 0.0782040737006575 -0.518687771976734 0.604226831855726 df.mm.exp4 0.137570259438413 0.0782040737006574 1.75911884034318 0.0792192419014954 . df.mm.exp5 0.0457791641444383 0.0782040737006574 0.585380811742208 0.558577498278873 df.mm.exp6 -0.069644894211242 0.0782040737006575 -0.890553278309036 0.373632392033647 df.mm.exp7 0.116860601074009 0.0782040737006575 1.49430324462787 0.135779042010552 df.mm.exp8 0.184763897731973 0.0782040737006575 2.36258661459499 0.018561430224966 * df.mm.trans1:exp2 -0.135468834732976 0.0724029033161627 -1.87104147110542 0.0619711851698699 . df.mm.trans2:exp2 -0.217997410406113 0.0660945056252131 -3.29826826517563 0.00104795395436623 ** df.mm.trans1:exp3 0.145218262360106 0.0724029033161627 2.0056966738748 0.0454712094568719 * df.mm.trans2:exp3 0.0303690357810785 0.0660945056252131 0.459478976259921 0.646106371668316 df.mm.trans1:exp4 -0.0878891956605735 0.0724029033161626 -1.21389048829695 0.225409712811237 df.mm.trans2:exp4 -0.0795470717756564 0.0660945056252131 -1.20353531694035 0.229385553174376 df.mm.trans1:exp5 -0.0314925228824349 0.0724029033161626 -0.434962155383689 0.663793002669918 df.mm.trans2:exp5 -0.0452539053151251 0.0660945056252131 -0.68468482950362 0.493886158061959 df.mm.trans1:exp6 0.334611391265572 0.0724029033161626 4.62151897147578 4.94895567525817e-06 *** df.mm.trans2:exp6 0.245529177743524 0.0660945056252131 3.71481979358148 0.000228227633976008 *** df.mm.trans1:exp7 -0.116290399134975 0.0724029033161626 -1.60615657395904 0.108922752169969 df.mm.trans2:exp7 -0.0735607323341942 0.0660945056252132 -1.11296289515074 0.266303108441764 df.mm.trans1:exp8 -0.135315000059246 0.0724029033161627 -1.86891676799705 0.0622669183884974 . df.mm.trans2:exp8 -0.10475955196546 0.0660945056252132 -1.58499637714965 0.113651369343541 df.mm.trans1:probe2 -0.0410506810916184 0.0362014516580813 -1.13395124260037 0.257403046070441 df.mm.trans1:probe3 -0.0193057183655663 0.0362014516580813 -0.53328575185069 0.59409223202172 df.mm.trans1:probe4 -0.00995103868789973 0.0362014516580813 -0.274879548529882 0.783531660417275 df.mm.trans1:probe5 -0.0482567989247146 0.0362014516580813 -1.33300728878208 0.183186372390324 df.mm.trans1:probe6 -0.0128160777095625 0.0362014516580813 -0.354021099225769 0.723484582001358 df.mm.trans1:probe7 -0.104178983516420 0.0362014516580813 -2.87775707174338 0.00419072093210381 ** df.mm.trans1:probe8 -0.0522750941531501 0.0362014516580813 -1.44400546825808 0.149415213904756 df.mm.trans1:probe9 -0.0465383380625767 0.0362014516580813 -1.2855378978204 0.199248385440170 df.mm.trans1:probe10 -0.0386134633764366 0.0362014516580813 -1.06662748613333 0.286697147356073 df.mm.trans1:probe11 0.0878854599092747 0.0362014516580813 2.42767778318237 0.0155769054929799 * df.mm.trans1:probe12 0.0155329183678645 0.0362014516580813 0.42906893664296 0.66807306190374 df.mm.trans2:probe2 -0.0342238700981011 0.0362014516580813 -0.945372865744219 0.344962704006672 df.mm.trans2:probe3 -0.0302978745486925 0.0362014516580813 -0.836924298916313 0.403067815805930 df.mm.trans2:probe4 0.0031069114475551 0.0362014516580813 0.0858228414954055 0.93164442865436 df.mm.trans2:probe5 0.0801585117669974 0.0362014516580813 2.21423473633283 0.0273000794107103 * df.mm.trans2:probe6 0.00699089340305453 0.0362014516580813 0.193110858345757 0.846957013654565 df.mm.trans3:probe2 0.303603580139717 0.0362014516580813 8.38650292278936 6.1207098300608e-16 *** df.mm.trans3:probe3 0.287989347733359 0.0362014516580813 7.95518783206227 1.39122050269475e-14 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01481489099635 0.122921674327605 32.6615701661878 3.9431044454971e-122 *** df.mm.trans1 -0.0644439827169328 0.108315590604879 -0.594964975559391 0.552158167016296 df.mm.trans2 -0.00253174693275899 0.102410623481023 -0.0247215264071518 0.980287758759698 df.mm.exp2 -0.0323158191769125 0.141100325516955 -0.229027247517082 0.818949086550113 df.mm.exp3 -0.298331368562874 0.141100325516955 -2.11432090939455 0.0350229614011184 * df.mm.exp4 0.0251657784102100 0.141100325516955 0.178353794139094 0.858523313747505 df.mm.exp5 2.32842252783578e-05 0.141100325516955 0.000165018933819256 0.99986840519518 df.mm.exp6 -0.0124576536392823 0.141100325516955 -0.0882893330943124 0.929684973497029 df.mm.exp7 -0.0936477301355596 0.141100325516955 -0.663696060178875 0.507215912514375 df.mm.exp8 0.0855462338236352 0.141100325516955 0.606279493050184 0.544626944831262 df.mm.trans1:exp2 -0.027059094456984 0.130633517448046 -0.207137455881072 0.835993775645822 df.mm.trans2:exp2 -0.00210511162294768 0.119251540454233 -0.0176526996207281 0.985923535768198 df.mm.trans1:exp3 0.238561946018574 0.130633517448046 1.82619247095946 0.0684665019423768 . df.mm.trans2:exp3 0.156916617790132 0.119251540454233 1.31584562507479 0.188878409116120 df.mm.trans1:exp4 -0.0621377061742925 0.130633517448046 -0.47566434241507 0.634538354130308 df.mm.trans2:exp4 -0.0250687742120763 0.119251540454233 -0.210217613261754 0.833590509802782 df.mm.trans1:exp5 -0.0357095376247507 0.130633517448046 -0.273356626402964 0.784701246313643 df.mm.trans2:exp5 -0.0336503365876235 0.119251540454233 -0.282179470885225 0.777932262132097 df.mm.trans1:exp6 -0.0150173862492848 0.130633517448046 -0.114958140473078 0.908528245955934 df.mm.trans2:exp6 0.00940037023838117 0.119251540454233 0.078828082241746 0.937203497569346 df.mm.trans1:exp7 -0.00570714578532888 0.130633517448046 -0.0436882195076669 0.965171805207835 df.mm.trans2:exp7 0.109259087307892 0.119251540454233 0.916206926063348 0.360036358702028 df.mm.trans1:exp8 -0.0639533366164433 0.130633517448046 -0.489562999341864 0.62467552636595 df.mm.trans2:exp8 -0.0206262622161613 0.119251540454233 -0.172964325136557 0.862755218896428 df.mm.trans1:probe2 -0.00599054663961085 0.0653167587240232 -0.09171530793379 0.926963987939405 df.mm.trans1:probe3 0.0171042536226242 0.0653167587240232 0.261866233976693 0.79354128265951 df.mm.trans1:probe4 0.0408097252014698 0.0653167587240232 0.624797157708013 0.532412639528685 df.mm.trans1:probe5 0.0885658014292731 0.0653167587240232 1.35594299471414 0.175779675808616 df.mm.trans1:probe6 0.00558104280478735 0.0653167587240232 0.0854458015647777 0.931943997448927 df.mm.trans1:probe7 0.0127980996232730 0.0653167587240232 0.195938988297744 0.844744071877726 df.mm.trans1:probe8 -0.0463035491189202 0.0653167587240232 -0.708907637541573 0.478739355488445 df.mm.trans1:probe9 0.0162072362650858 0.0653167587240232 0.248132892410732 0.804141836631384 df.mm.trans1:probe10 0.101477060520232 0.0653167587240232 1.55361445519661 0.120961139391602 df.mm.trans1:probe11 0.0789146478650754 0.0653167587240232 1.20818377100594 0.227594641951144 df.mm.trans1:probe12 0.0747025038444545 0.0653167587240232 1.14369581871152 0.253342055692862 df.mm.trans2:probe2 0.0174366002876600 0.0653167587240232 0.266954463575471 0.789623338611027 df.mm.trans2:probe3 -0.0208818664874621 0.0653167587240232 -0.319701511455771 0.749339132840929 df.mm.trans2:probe4 0.0247823215778048 0.0653167587240232 0.379417504204629 0.704552184511475 df.mm.trans2:probe5 -0.0765366232657954 0.0653167587240232 -1.17177619895651 0.241890845449876 df.mm.trans2:probe6 -0.077703777622878 0.0653167587240232 -1.18964533973880 0.234796825407537 df.mm.trans3:probe2 0.025890188087629 0.0653167587240232 0.39637894766059 0.692008504118497 df.mm.trans3:probe3 -0.0214088645417790 0.0653167587240232 -0.327769855087818 0.743234161356904