fitVsDatCorrelation=0.741766086086812 cont.fitVsDatCorrelation=0.266638163616413 fstatistic=9353.50059225653,55,761 cont.fstatistic=4521.90597352157,55,761 residuals=-0.411432855519919,-0.0885911818153592,-0.00465020583491677,0.0756005244167353,1.72718820646732 cont.residuals=-0.461034186995146,-0.148232284804094,-0.0269965249352323,0.120366653005781,1.53915653078176 predictedValues: Include Exclude Both Lung 67.2526898015632 51.8506849873736 51.5620867630997 cerebhem 71.1637969134014 76.1966765806121 53.9916153464299 cortex 61.3528721679444 49.8867392607746 48.1426476177172 heart 63.1013468312342 50.1904162430868 48.361429679596 kidney 63.6875064941178 47.9721308242099 47.6674031329528 liver 64.5432560386583 50.7028757967301 50.3932580897828 stomach 64.9271403846973 53.7952171413436 54.0445256849146 testicle 62.2642001181634 55.6011231592341 52.7057532509906 diffExp=15.4020048141896,-5.03287966721072,11.4661329071698,12.9109305881474,15.7153756699079,13.8403802419281,11.1319232433538,6.6630769589293 diffExpScore=1.1090985879324 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=0,0,0,0,1,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,0,1,1,1,1,1,0 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 55.6942121564623 58.3427194997033 55.9190051821724 cerebhem 59.0601228153828 55.5389348557323 53.2333738146697 cortex 54.4124257788194 60.7379915979256 57.6021978147622 heart 58.9675287349 53.029868637907 53.4990459549022 kidney 55.446588792043 56.9771300196947 50.0556003191864 liver 59.5311230345035 52.9182459533603 54.2527028833531 stomach 55.3788327735737 63.3961848373019 56.6862372397435 testicle 56.4235442536662 54.2372871722724 57.9200578736752 cont.diffExp=-2.64850734324104,3.5211879596505,-6.32556581910625,5.93766009699296,-1.53054122765170,6.61287708114327,-8.01735206372819,2.18625708139383 cont.diffExpScore=29.0984235939468 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=0.802932157507079 cont.tran.correlation=-0.785658954770865 tran.covariance=0.00544893100533315 cont.tran.covariance=-0.00181848397394244 tran.mean=59.6555420464465 cont.tran.mean=56.880796307078 weightedLogRatios: wLogRatio Lung 1.06074971609933 cerebhem -0.293776589678773 cortex 0.830277676418113 heart 0.922604415350298 kidney 1.13695936775812 liver 0.976673339813506 stomach 0.767225403138597 testicle 0.461198428205248 cont.weightedLogRatios: wLogRatio Lung -0.187835995857143 cerebhem 0.248826190729787 cortex -0.44558011515696 heart 0.427065259320453 kidney -0.109709683630899 liver 0.474256956823509 stomach -0.551884917556157 testicle 0.158590356457476 varWeightedLogRatios=0.214958516722273 cont.varWeightedLogRatios=0.149254770145166 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22084241562348 0.0750591031607127 56.2335844405978 3.11738214187748e-273 *** df.mm.trans1 0.116143472700326 0.0643816974080019 1.80398276802641 0.0716293228039409 . df.mm.trans2 -0.254598254752176 0.0575433575461905 -4.42445949643811 1.10795847650660e-05 *** df.mm.exp2 0.395434792462607 0.0743415806587898 5.31916040738437 1.37194975205143e-07 *** df.mm.exp3 -0.0618097322960695 0.0743415806587898 -0.831428814780809 0.40599222345089 df.mm.exp4 -0.0321748883751093 0.0743415806587898 -0.432798012767369 0.665284144730191 df.mm.exp5 -0.0536776548925938 0.0743415806587898 -0.722040807000883 0.470491191035774 df.mm.exp6 -0.0405776424590514 0.0743415806587898 -0.545827006897972 0.585344818150491 df.mm.exp7 -0.0453964804844278 0.0743415806587898 -0.610647232438962 0.541615473459177 df.mm.exp8 -0.0291731044907274 0.0743415806587898 -0.392419749919295 0.694857978327013 df.mm.trans1:exp2 -0.338907588408296 0.0676298482247902 -5.01121320399573 6.7310653334135e-07 *** df.mm.trans2:exp2 -0.0104850906796763 0.0516531497880793 -0.202990344687480 0.839196839697969 df.mm.trans1:exp3 -0.0300052962707922 0.0676298482247902 -0.443669430856323 0.657407713942415 df.mm.trans2:exp3 0.0231968079288112 0.0516531497880793 0.449087965089879 0.653496062433495 df.mm.trans1:exp4 -0.0315400126432974 0.0676298482247902 -0.466362315918021 0.641089646881405 df.mm.trans2:exp4 -0.000369160176290011 0.0516531497880793 -0.0071469054221202 0.994299516116921 df.mm.trans1:exp5 -0.000790946887339152 0.0676298482247901 -0.0116952338072707 0.99067183145491 df.mm.trans2:exp5 -0.0240702561773409 0.0516531497880793 -0.46599783897198 0.641350394833961 df.mm.trans1:exp6 -0.000543736988315371 0.0676298482247902 -0.00803989662239195 0.993587266840113 df.mm.trans2:exp6 0.0181921277456938 0.0516531497880793 0.352197839247594 0.724787407629736 df.mm.trans1:exp7 0.0102051897927129 0.0676298482247902 0.150897718397838 0.88009638919572 df.mm.trans2:exp7 0.0822128974508732 0.0516531497880793 1.59163376847633 0.111882297345790 df.mm.trans1:exp8 -0.0478972865619258 0.0676298482247902 -0.708227030211916 0.479021148631456 df.mm.trans2:exp8 0.0990083607180108 0.0516531497880793 1.91679231807196 0.0556385311016364 . df.mm.trans1:probe2 -0.413105283133025 0.0463029917917103 -8.9217838231996 3.37765603588082e-18 *** df.mm.trans1:probe3 -0.0786555578282769 0.0463029917917103 -1.69871437642953 0.0897817540815854 . df.mm.trans1:probe4 -0.34353624620866 0.0463029917917103 -7.41930991746765 3.15112848755365e-13 *** df.mm.trans1:probe5 -0.128125827669961 0.0463029917917103 -2.76711768963705 0.0057927273614394 ** df.mm.trans1:probe6 -0.319828505491836 0.0463029917917103 -6.90729676670904 1.04373143710602e-11 *** df.mm.trans1:probe7 -0.368374743183694 0.0463029917917103 -7.95574387160107 6.44922977982143e-15 *** df.mm.trans1:probe8 -0.282421418164077 0.0463029917917103 -6.09942051767462 1.69303465798105e-09 *** df.mm.trans1:probe9 -0.360887499903808 0.0463029917917104 -7.7940428024009 2.13223597119905e-14 *** df.mm.trans1:probe10 -0.371595720561332 0.0463029917917103 -8.02530692256172 3.83190886594981e-15 *** df.mm.trans1:probe11 -0.346733342736122 0.0463029917917103 -7.48835721665394 1.93437799783455e-13 *** df.mm.trans1:probe12 -0.294975008515232 0.0463029917917104 -6.3705388593927 3.25924495607785e-10 *** df.mm.trans1:probe13 -0.0227342129625902 0.0463029917917103 -0.490987991982417 0.623576496195946 df.mm.trans1:probe14 -0.0980445753976099 0.0463029917917103 -2.11745659629629 0.0345448738270746 * df.mm.trans1:probe15 -0.00102170560070785 0.0463029917917103 -0.0220656497814202 0.98240137154442 df.mm.trans1:probe16 0.0699603166477656 0.0463029917917104 1.51092432563484 0.131222839979606 df.mm.trans1:probe17 -0.133404723951576 0.0463029917917103 -2.88112536122254 0.00407403345346854 ** df.mm.trans1:probe18 -0.105324405927788 0.0463029917917103 -2.27467819793546 0.0232027028188427 * df.mm.trans2:probe2 -0.136332243021092 0.0463029917917103 -2.94435062931505 0.00333490591996003 ** df.mm.trans2:probe3 0.0093999453473616 0.0463029917917103 0.203009459726628 0.839181904442686 df.mm.trans2:probe4 -0.120950383428343 0.0463029917917104 -2.61215050579079 0.00917488371399356 ** df.mm.trans2:probe5 -0.0313049639859532 0.0463029917917104 -0.676089444215088 0.499189269845603 df.mm.trans2:probe6 -0.00682860035884786 0.0463029917917104 -0.147476439310135 0.882795038217756 df.mm.trans3:probe2 -0.240676018215823 0.0463029917917103 -5.19785026631715 2.59246688153846e-07 *** df.mm.trans3:probe3 -0.0406257118075812 0.0463029917917103 -0.877388484751312 0.38055256527133 df.mm.trans3:probe4 -0.154816974812573 0.0463029917917103 -3.34356310082515 0.000867563967780233 *** df.mm.trans3:probe5 0.0402094174817225 0.0463029917917104 0.86839782756589 0.38545035623631 df.mm.trans3:probe6 -0.26968890659034 0.0463029917917103 -5.82443803639104 8.45317318335422e-09 *** df.mm.trans3:probe7 -0.264783926246257 0.0463029917917104 -5.71850578116773 1.54391694406706e-08 *** df.mm.trans3:probe8 0.030724142103703 0.0463029917917104 0.663545505696751 0.50718220279802 df.mm.trans3:probe9 -0.269741387379531 0.0463029917917104 -5.82557145751914 8.39843429225468e-09 *** df.mm.trans3:probe10 -0.226610472535728 0.0463029917917103 -4.89407841193316 1.20617276775513e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.02894293320914 0.107866665752256 37.3511399940985 2.88791966710693e-174 *** df.mm.trans1 0.00680817500923466 0.0925222756792379 0.0735841715873665 0.941360602719211 df.mm.trans2 0.0410605373548201 0.082694967743051 0.496530060721506 0.619663875550458 df.mm.exp2 0.0586482317493488 0.106835521538890 0.548958163956733 0.583195188045317 df.mm.exp3 -0.0127052879284406 0.106835521538890 -0.118923816212342 0.905367097906106 df.mm.exp4 0.00587192407935254 0.106835521538890 0.0549622821583277 0.956182938713637 df.mm.exp5 0.0826292580903672 0.106835521538889 0.773424951740317 0.439511103762588 df.mm.exp6 -0.000711869252576944 0.106835521538890 -0.00666322625960893 0.994685300278284 df.mm.exp7 0.063763171804293 0.106835521538890 0.596834937348832 0.550795102395103 df.mm.exp8 -0.0951150982454974 0.106835521538890 -0.890294696702297 0.37358903384919 df.mm.trans1:exp2 3.14935471011410e-05 0.0971901598360367 0.000324040490871419 0.999741538018042 df.mm.trans2:exp2 -0.107898506041913 0.0742302107089159 -1.45356594049049 0.146478936982737 df.mm.trans1:exp3 -0.0105783997596568 0.0971901598360367 -0.108842292033503 0.913356271031 df.mm.trans2:exp3 0.0529401034193552 0.0742302107089159 0.713188106483396 0.47594800283045 df.mm.trans1:exp4 0.0512387773779783 0.0971901598360368 0.527201287295132 0.598207466728075 df.mm.trans2:exp4 -0.101351188081959 0.0742302107089159 -1.36536306598124 0.172542423001756 df.mm.trans1:exp5 -0.0870852953340356 0.0971901598360367 -0.8960299631254 0.370520101775336 df.mm.trans2:exp5 -0.106313876514521 0.0742302107089159 -1.43221843908563 0.152491924872881 df.mm.trans1:exp6 0.067334890738556 0.0971901598360367 0.692815927581068 0.488636442006863 df.mm.trans2:exp6 -0.0968745153208363 0.0742302107089158 -1.30505510351731 0.19226855089361 df.mm.trans1:exp7 -0.0694419614806441 0.0971901598360367 -0.714495804902422 0.475139754426996 df.mm.trans2:exp7 0.0193059337397727 0.0742302107089159 0.260081893280330 0.79487095512543 df.mm.trans1:exp8 0.108125390428020 0.0971901598360368 1.11251376281746 0.266268624514492 df.mm.trans2:exp8 0.0221491473456994 0.0742302107089159 0.298384540932456 0.765491138781653 df.mm.trans1:probe2 -0.0229932722254760 0.0665415536371624 -0.3455475709337 0.729778249147391 df.mm.trans1:probe3 0.034662451188587 0.0665415536371624 0.520914365444401 0.602577963038994 df.mm.trans1:probe4 -0.0568793525545818 0.0665415536371624 -0.854794477218452 0.392933954457193 df.mm.trans1:probe5 -0.0293691237015443 0.0665415536371624 -0.441365163514038 0.659074031099924 df.mm.trans1:probe6 -0.0296081221944420 0.0665415536371624 -0.444956881468218 0.656477443474834 df.mm.trans1:probe7 0.00339972772403277 0.0665415536371624 0.0510917996079683 0.95926577769582 df.mm.trans1:probe8 -0.0885471062498568 0.0665415536371624 -1.33070391972941 0.183685029675185 df.mm.trans1:probe9 0.0755161202302227 0.0665415536371624 1.13487161183517 0.256786439829299 df.mm.trans1:probe10 0.0160329387127080 0.0665415536371624 0.240946263445131 0.809661664856796 df.mm.trans1:probe11 -0.0185741126275261 0.0665415536371624 -0.279135541812069 0.780216648667278 df.mm.trans1:probe12 -0.115206382154547 0.0665415536371624 -1.73134493947563 0.0837955467567305 . df.mm.trans1:probe13 -0.0273203488837043 0.0665415536371624 -0.41057575891114 0.68149921502553 df.mm.trans1:probe14 -0.0367102543826211 0.0665415536371624 -0.551689168287153 0.581323290861823 df.mm.trans1:probe15 -0.101706945096173 0.0665415536371624 -1.52847265410664 0.126810754615254 df.mm.trans1:probe16 -0.00712264061218951 0.0665415536371624 -0.107040491585571 0.91478506845715 df.mm.trans1:probe17 0.0118325156568426 0.0665415536371624 0.177821451560373 0.858910521604734 df.mm.trans1:probe18 -0.0519026684101738 0.0665415536371624 -0.780003855834034 0.435631130552071 df.mm.trans2:probe2 0.042761348514936 0.0665415536371624 0.642626241462665 0.520660208713418 df.mm.trans2:probe3 -0.0259196365413315 0.0665415536371624 -0.389525568980039 0.696996381621677 df.mm.trans2:probe4 0.0095513650957388 0.0665415536371624 0.143539857031599 0.885901835336833 df.mm.trans2:probe5 -0.0908261803993665 0.0665415536371624 -1.36495430952249 0.172670814824571 df.mm.trans2:probe6 0.00573082263732023 0.0665415536371624 0.0861239680180785 0.931390524765138 df.mm.trans3:probe2 -0.0285644630604252 0.0665415536371624 -0.429272559762903 0.667846385621619 df.mm.trans3:probe3 -0.0653264229892635 0.0665415536371624 -0.981738769513488 0.326540565041263 df.mm.trans3:probe4 -0.0229521999135552 0.0665415536371624 -0.344930327877658 0.730242058871995 df.mm.trans3:probe5 -0.0506423374023569 0.0665415536371624 -0.761063345146692 0.446854991146218 df.mm.trans3:probe6 -0.116483318023377 0.0665415536371624 -1.75053499139104 0.0804289928735596 . df.mm.trans3:probe7 -0.0482212255911167 0.0665415536371624 -0.724678384488244 0.468872103809128 df.mm.trans3:probe8 -0.0834934692148364 0.0665415536371624 -1.25475683465567 0.209952259875124 df.mm.trans3:probe9 -0.0934149539252177 0.0665415536371624 -1.40385892452392 0.160768807002665 df.mm.trans3:probe10 -0.0199749881224815 0.0665415536371624 -0.300188183633359 0.76411559933991