fitVsDatCorrelation=0.88836154995685 cont.fitVsDatCorrelation=0.248951795006113 fstatistic=9675.8374671911,51,669 cont.fstatistic=2164.40360408843,51,669 residuals=-0.500869063379187,-0.0878310707711553,-0.00553435162410619,0.0845002587259285,1.06604329755976 cont.residuals=-0.843300286854269,-0.238590343524940,-0.0631409324253879,0.198285902526388,1.33358008058557 predictedValues: Include Exclude Both Lung 67.8877879907325 91.0822951792918 50.6152828302162 cerebhem 76.447832332331 91.8445597132607 61.8257068143083 cortex 71.9768245565287 95.7370059151236 55.1345700954568 heart 73.0228075716866 96.464603715509 51.7740139831403 kidney 66.4440617374083 84.205696083047 50.8202740805596 liver 65.8702079772894 90.728912464635 53.6523021146055 stomach 90.3505387723997 117.496724315257 52.5235162649913 testicle 66.422091535975 89.684994708478 55.9829098791638 diffExp=-23.1945071885593,-15.3967273809296,-23.760181358595,-23.4417961438223,-17.7616343456386,-24.8587044873457,-27.1461855428577,-23.2629031725029 diffExpScore=0.994438964959519 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,-1,0,-1,-1,-1 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 67.8360575286613 59.7055465220021 64.7007306517255 cerebhem 71.1161402273392 69.8371618287149 68.522982366809 cortex 69.4677123970193 76.2948101842265 68.3167876837942 heart 75.2072594901492 73.9664361543348 70.7160711585015 kidney 68.6206149829521 64.7745446247396 69.3994771239745 liver 69.99478269957 65.6183668589463 66.1425170029355 stomach 68.6359531106498 58.8756968572205 75.1279234448516 testicle 77.2719760212565 72.2353415866586 66.5404627676804 cont.diffExp=8.13051100665916,1.27897839862429,-6.8270977872073,1.24082333581433,3.84607035821251,4.37641584062369,9.76025625342931,5.03663443459797 cont.diffExpScore=1.45449057497199 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=0.928576976745003 cont.tran.correlation=0.627792662260968 tran.covariance=0.00961060461920084 cont.tran.covariance=0.00291581790632887 tran.mean=83.4791840355596 cont.tran.mean=69.3411500671525 weightedLogRatios: wLogRatio Lung -1.28284935486170 cerebhem -0.812554086360226 cortex -1.26055988051503 heart -1.23332387357987 kidney -1.02218812638619 liver -1.39211525476953 stomach -1.21769106821031 testicle -1.30503981352160 cont.weightedLogRatios: wLogRatio Lung 0.530242278548655 cerebhem 0.0772242964089147 cortex -0.401944326339471 heart 0.0717347148696352 kidney 0.242243017751644 liver 0.272215243783463 stomach 0.636886200481109 testicle 0.290747025401012 varWeightedLogRatios=0.034421347881096 cont.varWeightedLogRatios=0.101035196213842 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.7253060160238 0.0765958207777826 61.6914339195178 2.78163397202605e-278 *** df.mm.trans1 -0.506882323948258 0.0642275482693172 -7.89197684804677 1.21790131692662e-14 *** df.mm.trans2 -0.285155313550899 0.0590210437714331 -4.83141766613279 1.68227554280067e-06 *** df.mm.exp2 -0.072979131236045 0.0765958207777826 -0.952782155670997 0.34104456717886 df.mm.exp3 0.0228061274677937 0.0765958207777826 0.297746368355502 0.765989189034947 df.mm.exp4 0.107693559092807 0.0765958207777826 1.40599784687005 0.160189038063565 df.mm.exp5 -0.104038428939852 0.0765958207777826 -1.35827813950431 0.174833300996958 df.mm.exp6 -0.0923280844770715 0.0765958207777826 -1.20539323868506 0.228477709042186 df.mm.exp7 0.503480393959044 0.0765958207777826 6.57320972406218 9.91564341890541e-11 *** df.mm.exp8 -0.138079329519775 0.0765958207777826 -1.80270056665842 0.0718851681971995 . df.mm.trans1:exp2 0.191731543623883 0.0663339266172797 2.89039942909 0.00397220380866414 ** df.mm.trans2:exp2 0.0813132705456394 0.0541614242825195 1.50131337243809 0.133746339835514 df.mm.trans1:exp3 0.0356818930459753 0.0663339266172796 0.537913174533532 0.590815888813116 df.mm.trans2:exp3 0.0270353424504953 0.0541614242825195 0.499162324636668 0.617829134107107 df.mm.trans1:exp4 -0.0347778997805273 0.0663339266172796 -0.524285257243732 0.600253808657648 df.mm.trans2:exp4 -0.0502808593863195 0.0541614242825195 -0.928351867632616 0.353559895237106 df.mm.trans1:exp5 0.0825426802046823 0.0663339266172796 1.24435088368763 0.213806223193342 df.mm.trans2:exp5 0.0255375568673738 0.0541614242825195 0.471508222054196 0.637431577906712 df.mm.trans1:exp6 0.0621581789867389 0.0663339266172797 0.937049593722483 0.349071192857941 df.mm.trans2:exp6 0.088440720609345 0.0541614242825195 1.63290980215026 0.102958497581705 df.mm.trans1:exp7 -0.217639579296798 0.0663339266172797 -3.28096933794515 0.00108822202053683 ** df.mm.trans2:exp7 -0.248833379406459 0.0541614242825195 -4.594291651351 5.18990888849482e-06 *** df.mm.trans1:exp8 0.116252868995452 0.0663339266172796 1.75254013931943 0.0801390486745867 . df.mm.trans2:exp8 0.122619361044556 0.0541614242825195 2.26396116182143 0.0238958885940845 * df.mm.trans1:probe2 -0.0161968919512735 0.0469051693338093 -0.345311448211718 0.729968785063511 df.mm.trans1:probe3 -0.132695402655913 0.0469051693338093 -2.82901446771383 0.00480902730393663 ** df.mm.trans1:probe4 0.0884159118359823 0.0469051693338093 1.88499291425118 0.0598639690828571 . df.mm.trans1:probe5 0.11300847948915 0.0469051693338093 2.40929690893779 0.0162524042088372 * df.mm.trans1:probe6 -0.135456687481843 0.0469051693338093 -2.88788398817710 0.00400370712236916 ** df.mm.trans1:probe7 0.0646550169845241 0.0469051693338093 1.37841986081310 0.168534471558261 df.mm.trans1:probe8 0.314227167316870 0.0469051693338093 6.69920121342307 4.44871411198267e-11 *** df.mm.trans1:probe9 -0.19571249379188 0.0469051693338093 -4.17251438533471 3.41003910889763e-05 *** df.mm.trans1:probe10 -0.186766510800054 0.0469051693338093 -3.98178950108667 7.58904623487406e-05 *** df.mm.trans1:probe11 -0.00389175656217953 0.0469051693338093 -0.0829707389921808 0.933899625131195 df.mm.trans1:probe12 0.0767925304780015 0.0469051693338093 1.63718693629466 0.102061679774273 df.mm.trans2:probe2 0.140268934168221 0.0469051693338093 2.99047921925133 0.0028877140440646 ** df.mm.trans2:probe3 0.0276586666642008 0.0469051693338093 0.589672035236945 0.555609636033577 df.mm.trans2:probe4 0.491166474958138 0.0469051693338093 10.4714785584220 7.27040602016072e-24 *** df.mm.trans2:probe5 0.405820305705223 0.0469051693338093 8.65193136426239 3.75074588345552e-17 *** df.mm.trans2:probe6 0.224114902541959 0.0469051693338093 4.77804271309638 2.17707672538562e-06 *** df.mm.trans3:probe2 0.0126636942189126 0.0469051693338093 0.269985044266424 0.787255019273 df.mm.trans3:probe3 -0.137845953126019 0.0469051693338092 -2.938822204116 0.00340810369042364 ** df.mm.trans3:probe4 -0.041434428469273 0.0469051693338093 -0.88336592869748 0.377355915566405 df.mm.trans3:probe5 0.172185504648559 0.0469051693338092 3.67092811078389 0.000260937851493663 *** df.mm.trans3:probe6 -0.008458394211913 0.0469051693338093 -0.180329680759007 0.856948366028445 df.mm.trans3:probe7 0.0860774513135775 0.0469051693338093 1.83513784378416 0.0669290596329152 . df.mm.trans3:probe8 -0.0186009027677709 0.0469051693338092 -0.396564025499922 0.69181544663763 df.mm.trans3:probe9 0.086061766425015 0.0469051693338093 1.83480344804942 0.0669786756547352 . df.mm.trans3:probe10 -0.0155424598499077 0.0469051693338093 -0.331359209883604 0.740476940550868 df.mm.trans3:probe11 -0.216953288974944 0.0469051693338093 -4.62535989223184 4.49018863812047e-06 *** df.mm.trans3:probe12 -0.0263307765989821 0.0469051693338092 -0.561361934578987 0.574738815584751 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20495406939168 0.16157060277722 26.0254897680220 1.10122174694885e-103 *** df.mm.trans1 0.0132446129097181 0.135481069115805 0.0977598788978922 0.922152247890007 df.mm.trans2 -0.128286240719606 0.124498510778504 -1.03042389758252 0.303183437939135 df.mm.exp2 0.146565089150860 0.16157060277722 0.907127204030731 0.36466615376678 df.mm.exp3 0.214565158699292 0.16157060277722 1.32799627538150 0.184632233788830 df.mm.exp4 0.228440021174319 0.16157060277722 1.41387119468324 0.157864890104515 df.mm.exp5 0.0228800587858601 0.16157060277722 0.141610283012981 0.887430476024017 df.mm.exp6 0.103718279437981 0.16157060277722 0.641937813285205 0.521133452774767 df.mm.exp7 -0.151693726584561 0.16157060277722 -0.938869596183423 0.348136539370357 df.mm.exp8 0.292704247235475 0.16157060277722 1.81161821645901 0.0704935041590356 . df.mm.trans1:exp2 -0.0993446462229115 0.139924246509837 -0.709988788225686 0.477958484832295 df.mm.trans2:exp2 0.0101762607141586 0.114247668864170 0.089071933067249 0.929051400557529 df.mm.trans1:exp3 -0.190796959437431 0.139924246509837 -1.36357324907243 0.173160570278219 df.mm.trans2:exp3 0.030614836073191 0.114247668864170 0.267969021841392 0.78880576330472 df.mm.trans1:exp4 -0.125286134640371 0.139924246509837 -0.895385451524032 0.370903115331995 df.mm.trans2:exp4 -0.0142535189932554 0.114247668864170 -0.124759823416629 0.900751172901828 df.mm.trans1:exp5 -0.0113809347783516 0.139924246509837 -0.0813364021049167 0.935198751950558 df.mm.trans2:exp5 0.0586077148801668 0.114247668864170 0.512988277685087 0.608128865888086 df.mm.trans1:exp6 -0.0723914485654473 0.139924246509837 -0.517361718009023 0.6050746903434 df.mm.trans2:exp6 -0.0092875627190714 0.114247668864170 -0.0812932361019413 0.935233066766234 df.mm.trans1:exp7 0.163416346312159 0.139924246509837 1.16789155838456 0.243266556047820 df.mm.trans2:exp7 0.137697192417947 0.114247668864170 1.20525165884702 0.228532307225003 df.mm.trans1:exp8 -0.162466768210428 0.139924246509837 -1.16110518557629 0.246013249480179 df.mm.trans2:exp8 -0.102199748089731 0.114247668864170 -0.894545587719932 0.371351761408465 df.mm.trans1:probe2 0.065959079199102 0.098941383559524 0.666648037718419 0.505226814461948 df.mm.trans1:probe3 0.137325855866710 0.098941383559524 1.38795164294518 0.165613821854629 df.mm.trans1:probe4 -0.0482682704029122 0.098941383559524 -0.487847133993973 0.625817959662432 df.mm.trans1:probe5 0.0301871214031338 0.098941383559524 0.305101064055497 0.760384077582993 df.mm.trans1:probe6 -0.0552983850948157 0.098941383559524 -0.55890046313682 0.576416681940359 df.mm.trans1:probe7 0.044175025357062 0.098941383559524 0.446476729633419 0.655397400032474 df.mm.trans1:probe8 -0.0278532268782899 0.098941383559524 -0.281512405388319 0.77840446641492 df.mm.trans1:probe9 -0.118540669932048 0.098941383559524 -1.19808987571650 0.23130630247294 df.mm.trans1:probe10 0.08080247728783 0.098941383559524 0.816670177643297 0.414407647043501 df.mm.trans1:probe11 -0.061267202464219 0.098941383559524 -0.619227266287015 0.535977460582272 df.mm.trans1:probe12 -0.0737371667152485 0.098941383559524 -0.745261123934937 0.456375619493119 df.mm.trans2:probe2 0.136158275760136 0.098941383559524 1.37615091745935 0.169235377389108 df.mm.trans2:probe3 -0.0420811754539709 0.098941383559524 -0.425314200590843 0.670744415617555 df.mm.trans2:probe4 -0.0142257366034607 0.098941383559524 -0.143779438811894 0.885717937395774 df.mm.trans2:probe5 0.0704962646640627 0.098941383559524 0.712505345365942 0.476400321108656 df.mm.trans2:probe6 0.0792800632215967 0.098941383559524 0.801283147348563 0.423252237091635 df.mm.trans3:probe2 0.0476315594667008 0.098941383559524 0.481411900188815 0.630381204826754 df.mm.trans3:probe3 0.130232905869120 0.098941383559524 1.31626323772570 0.188536498343133 df.mm.trans3:probe4 0.0156208992124222 0.098941383559524 0.157880339352891 0.874598731490645 df.mm.trans3:probe5 0.106171509831836 0.098941383559524 1.07307484504663 0.283624447432595 df.mm.trans3:probe6 0.135064374384220 0.098941383559524 1.36509486248456 0.172682115849712 df.mm.trans3:probe7 0.113601458939612 0.098941383559524 1.14816929835298 0.251309065230107 df.mm.trans3:probe8 0.064998045484956 0.098941383559524 0.656934875444234 0.511448713125721 df.mm.trans3:probe9 0.167903051623583 0.098941383559524 1.69699518627179 0.0901627034707277 . df.mm.trans3:probe10 0.0292789223616631 0.098941383559524 0.295921901517060 0.76738155499659 df.mm.trans3:probe11 0.0599986631652126 0.098941383559524 0.606406146818403 0.544450797351456 df.mm.trans3:probe12 0.0878192429994125 0.098941383559524 0.887588588718085 0.375081077271938