fitVsDatCorrelation=0.829128965712082 cont.fitVsDatCorrelation=0.223089943063590 fstatistic=12689.9955332529,53,715 cont.fstatistic=4164.87615266783,53,715 residuals=-0.438422356589423,-0.0785383648114152,0.00292860164906122,0.0782219870505436,0.608421281922478 cont.residuals=-0.506832283061913,-0.162215612454789,-0.0300750855439008,0.111129539001886,1.06085254917633 predictedValues: Include Exclude Both Lung 59.2715769224278 69.4174282833781 63.5063961949609 cerebhem 61.6124939481501 68.1382954368223 66.103691074295 cortex 60.7796102847049 71.3414638684527 61.1768296251248 heart 69.2144082145586 70.572543082914 71.6733935815449 kidney 61.6005961113532 75.0448672335342 63.069827853987 liver 63.0162968570463 80.392055638187 63.000767638766 stomach 62.5268836905002 69.7275093791271 63.357055567805 testicle 62.1213306898854 70.8896888321823 63.7081796992654 diffExp=-10.1458513609503,-6.52580148867217,-10.5618535837477,-1.35813486835545,-13.4442711221810,-17.3757587811406,-7.20062568862688,-8.7683581422969 diffExpScore=0.986907679705954 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,-1,-1,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 62.1486218451645 69.3074000848526 63.5816788531089 cerebhem 62.4126989076641 61.0345056865633 58.7304420065782 cortex 65.6755412914864 68.7079015618417 59.5664980125976 heart 65.8134618987995 63.3222773977714 67.2059555110249 kidney 64.5871576963913 67.3915199706045 69.316175088049 liver 62.3023243131109 65.9137163616231 66.505339282532 stomach 63.5122077159212 62.8489890599648 59.697463279632 testicle 64.2984710529445 62.7302455115523 61.5493328574116 cont.diffExp=-7.15877823968808,1.37819322110084,-3.03236027035531,2.4911845010281,-2.80436227421319,-3.61139204851224,0.66321865595642,1.56822554139219 cont.diffExpScore=1.97354204779109 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=0.064647626223039 cont.tran.correlation=0.0595025239898573 tran.covariance=0.000187903303531235 cont.tran.covariance=6.8266611713969e-05 tran.mean=67.2291905295765 cont.tran.mean=64.500440022266 weightedLogRatios: wLogRatio Lung -0.657492793375916 cerebhem -0.41993460741292 cortex -0.67091370429826 heart -0.0825267096165355 kidney -0.83296706009835 liver -1.03865886897338 stomach -0.456713395529063 testicle -0.553903375540419 cont.weightedLogRatios: wLogRatio Lung -0.456156781325088 cerebhem 0.092055240497161 cortex -0.18990734510278 heart 0.160813364127648 kidney -0.178058944698555 liver -0.234416681815920 stomach 0.0435215702181735 testicle 0.102501745790483 varWeightedLogRatios=0.0824205440019611 cont.varWeightedLogRatios=0.046163415698467 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30095183476065 0.0715409778984995 60.1187174274124 8.13259785580308e-282 *** df.mm.trans1 -0.175812860813179 0.0635297729268721 -2.76740892833906 0.00579648782251369 ** df.mm.trans2 0.0219973692133090 0.0577885899168515 0.380652465217782 0.703574207965068 df.mm.exp2 -0.0199477328070985 0.0779076067597985 -0.256043455019746 0.797990929147877 df.mm.exp3 0.0898363410847377 0.0779076067597986 1.153113859109 0.249248993507968 df.mm.exp4 0.0506033969695668 0.0779076067597986 0.649530887601066 0.516203890450914 df.mm.exp5 0.123388003973727 0.0779076067597985 1.58377351205451 0.113687451275877 df.mm.exp6 0.216034606111261 0.0779076067597985 2.77295908700327 0.00569959590220826 ** df.mm.exp7 0.0602780340038118 0.0779076067597985 0.773711791579718 0.439356977817956 df.mm.exp8 0.0647742288143438 0.0779076067597985 0.831423676176488 0.40601189682538 df.mm.trans1:exp2 0.0586825254247076 0.0739769276733384 0.793254427702558 0.42789275492673 df.mm.trans2:exp2 0.00134916513621435 0.0622941478779898 0.0216579756232776 0.982726829010224 df.mm.trans1:exp3 -0.0647118467063602 0.0739769276733385 -0.87475715390763 0.38199979057352 df.mm.trans2:exp3 -0.0624966057498386 0.0622941478779898 -1.00325003035992 0.31607963899994 df.mm.trans1:exp4 0.104475773984660 0.0739769276733385 1.41227511428963 0.158304053842332 df.mm.trans2:exp4 -0.034100200276757 0.0622941478779898 -0.54740615994212 0.584270546301782 df.mm.trans1:exp5 -0.0848463375382172 0.0739769276733384 -1.14692972804812 0.251794317088386 df.mm.trans2:exp5 -0.0454398035130296 0.0622941478779898 -0.729439362458711 0.465971830649874 df.mm.trans1:exp6 -0.154771114090622 0.0739769276733385 -2.09215385064447 0.0367768610324064 * df.mm.trans2:exp6 -0.0692572092666952 0.0622941478779898 -1.11177713518681 0.266607671793598 df.mm.trans1:exp7 -0.00681131188623119 0.0739769276733385 -0.092073462638352 0.926665462975636 df.mm.trans2:exp7 -0.0558210754646385 0.0622941478779898 -0.896088595255696 0.370507033389799 df.mm.trans1:exp8 -0.0178146906732427 0.0739769276733384 -0.240814146160643 0.809768199663416 df.mm.trans2:exp8 -0.043787202390888 0.0622941478779898 -0.702910367706613 0.48234040348747 df.mm.trans1:probe2 -0.288525808951285 0.0405188320216156 -7.12078296821005 2.61998256510589e-12 *** df.mm.trans1:probe3 0.138202872472085 0.0405188320216156 3.41083060830474 0.000684055503947587 *** df.mm.trans1:probe4 -0.175968315371811 0.0405188320216156 -4.34287728920561 1.60955971497961e-05 *** df.mm.trans1:probe5 -0.157046814599935 0.0405188320216156 -3.87589687965722 0.000116003819623629 *** df.mm.trans1:probe6 -0.198222499322470 0.0405188320216156 -4.8921079269196 1.23334087120086e-06 *** df.mm.trans1:probe7 0.202969844645933 0.0405188320216156 5.00927185012773 6.89112653300764e-07 *** df.mm.trans1:probe8 0.600788837948639 0.0405188320216156 14.8273977302242 1.44835960879469e-43 *** df.mm.trans1:probe9 -0.00703511919809313 0.0405188320216156 -0.173625912867876 0.862208609011906 df.mm.trans1:probe10 0.406307800214741 0.0405188320216156 10.0276286344579 3.12712979822992e-22 *** df.mm.trans1:probe11 -0.137263148723146 0.0405188320216156 -3.38763833690765 0.00074358288069588 *** df.mm.trans1:probe12 -0.0437258289342860 0.0405188320216156 -1.07914830592747 0.280885522386179 df.mm.trans1:probe13 0.0864655501080178 0.0405188320216156 2.13395958851654 0.0331857277213140 * df.mm.trans1:probe14 0.0242821462467651 0.0405188320216157 0.599280508229142 0.54917567952334 df.mm.trans1:probe15 -0.148698418364261 0.0405188320216156 -3.66985944424396 0.000260736398644344 *** df.mm.trans1:probe16 0.0106554004814016 0.0405188320216156 0.262974028365804 0.79264635551214 df.mm.trans1:probe17 -0.240616216117094 0.0405188320216156 -5.93837986220167 4.49107892255825e-09 *** df.mm.trans1:probe18 -0.211327933043078 0.0405188320216156 -5.21554848694407 2.40260774724503e-07 *** df.mm.trans1:probe19 -0.198646457835201 0.0405188320216156 -4.90257117305921 1.17143505050960e-06 *** df.mm.trans1:probe20 -0.221639057825951 0.0405188320216156 -5.47002583163583 6.22640636127159e-08 *** df.mm.trans1:probe21 -0.239614633730873 0.0405188320216156 -5.91366092692517 5.18353373429495e-09 *** df.mm.trans1:probe22 -0.319578611807406 0.0405188320216156 -7.88716248377841 1.15811432437617e-14 *** df.mm.trans2:probe2 -0.273606274733236 0.0405188320216156 -6.75257062166242 3.00821842561301e-11 *** df.mm.trans2:probe3 0.0126612430954523 0.0405188320216156 0.312477987734145 0.754768400116717 df.mm.trans2:probe4 -0.13298309186671 0.0405188320216156 -3.28200703800562 0.00108079340826458 ** df.mm.trans2:probe5 -0.186686728085094 0.0405188320216156 -4.60740645202955 4.82739573764222e-06 *** df.mm.trans2:probe6 -0.247497548270334 0.0405188320216156 -6.10821032892314 1.65383704589455e-09 *** df.mm.trans3:probe2 0.043309197799943 0.0405188320216156 1.06886589862311 0.285490819567117 df.mm.trans3:probe3 -0.0228770506699012 0.0405188320216156 -0.564602914953149 0.57252096105854 df.mm.trans3:probe4 -0.00441500655047116 0.0405188320216156 -0.108961841449820 0.913263327883741 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.2369522967073 0.124742100799342 33.9656961808171 2.63396547413455e-151 *** df.mm.trans1 -0.0455704499314251 0.110773399679366 -0.411384412352866 0.680913960501425 df.mm.trans2 0.0174833008690911 0.100762812030431 0.173509457673839 0.862300103796912 df.mm.exp2 -0.0435050279239363 0.135843244262811 -0.320259046815523 0.748865533038529 df.mm.exp3 0.111742517054057 0.135843244262811 0.822584278375095 0.411018683569722 df.mm.exp4 -0.0884552272769666 0.135843244262811 -0.651156616267462 0.515154606055339 df.mm.exp5 -0.0758984647980605 0.135843244262811 -0.558720937577303 0.576527121055051 df.mm.exp6 -0.0926919084163418 0.135843244262811 -0.682344631264944 0.495242049555255 df.mm.exp7 -0.0130775098913962 0.135843244262811 -0.0962691222693092 0.923333810070714 df.mm.exp8 -0.0332144114122634 0.135843244262811 -0.244505434131156 0.806909542324925 df.mm.trans1:exp2 0.0477451476745688 0.128989533547412 0.37014745585555 0.71138224122582 df.mm.trans2:exp2 -0.0836072846653073 0.108618907681555 -0.769730486614945 0.441714000549032 df.mm.trans1:exp3 -0.056544582600215 0.128989533547412 -0.438365664602012 0.661253644872201 df.mm.trans2:exp3 -0.120429992809442 0.108618907681555 -1.10873875810383 0.267915693258249 df.mm.trans1:exp4 0.145750989755173 0.128989533547412 1.12994431212203 0.258878604991192 df.mm.trans2:exp4 -0.00185925569905897 0.108618907681555 -0.0171172380457910 0.986347862195332 df.mm.trans1:exp5 0.114385415498914 0.128989533547412 0.886780596480489 0.375495206822447 df.mm.trans2:exp5 0.0478659745147633 0.108618907681555 0.440678106017185 0.659579251551129 df.mm.trans1:exp6 0.0951619988550304 0.128989533547412 0.737749771147532 0.46090856841717 df.mm.trans2:exp6 0.0424867834638647 0.108618907681555 0.391154582298192 0.695799508479394 df.mm.trans1:exp7 0.0347810017522624 0.128989533547412 0.269642046108167 0.78751344894438 df.mm.trans2:exp7 -0.084739323992602 0.108618907681555 -0.780152606957137 0.435559234492565 df.mm.trans1:exp8 0.0672216209809194 0.128989533547412 0.521140119916868 0.60243052740165 df.mm.trans2:exp8 -0.0664935565307543 0.108618907681555 -0.612172944379974 0.540617961902036 df.mm.trans1:probe2 -0.0464827099915193 0.0706504772059871 -0.65792492605528 0.510798135884199 df.mm.trans1:probe3 -0.162237426075701 0.0706504772059871 -2.29633871548644 0.0219448650774336 * df.mm.trans1:probe4 -0.12174676514781 0.0706504772059872 -1.72322636679222 0.0852799374236277 . df.mm.trans1:probe5 -0.0731236816582446 0.0706504772059871 -1.03500619599563 0.301015942920912 df.mm.trans1:probe6 -0.111710599726907 0.0706504772059871 -1.58117261403919 0.114280810759931 df.mm.trans1:probe7 0.00455932758638574 0.0706504772059872 0.064533570991922 0.948563414844642 df.mm.trans1:probe8 -0.0037904060667415 0.0706504772059871 -0.0536501127330006 0.957228915992347 df.mm.trans1:probe9 -0.0823793173030673 0.0706504772059871 -1.16601218506824 0.243998312961897 df.mm.trans1:probe10 -0.116275052637809 0.0706504772059871 -1.64577872982796 0.100248798617106 df.mm.trans1:probe11 -0.0646728922641432 0.0706504772059871 -0.915392150509956 0.360294410382077 df.mm.trans1:probe12 -0.0514207405535017 0.0706504772059871 -0.72781872942741 0.466962824467154 df.mm.trans1:probe13 -0.0647533712630049 0.0706504772059871 -0.916531265234221 0.359697341604811 df.mm.trans1:probe14 -0.0901363820476985 0.0706504772059872 -1.27580712278699 0.202438017473139 df.mm.trans1:probe15 -0.137428661041081 0.0706504772059871 -1.94519083912763 0.0521436512518972 . df.mm.trans1:probe16 -0.0974374675440626 0.0706504772059871 -1.37914804538370 0.168280348886561 df.mm.trans1:probe17 -0.0132795948774577 0.0706504772059871 -0.187961856771894 0.850959882210638 df.mm.trans1:probe18 -0.100649898602379 0.0706504772059871 -1.42461739230615 0.154704143613678 df.mm.trans1:probe19 -0.0594869499230529 0.0706504772059872 -0.841989357688469 0.400075443929873 df.mm.trans1:probe20 -0.0854001559697518 0.0706504772059872 -1.20876969763079 0.227150920775956 df.mm.trans1:probe21 -0.0548919048366136 0.0706504772059871 -0.776950234554989 0.437445095112110 df.mm.trans1:probe22 -0.0754386467878208 0.0706504772059871 -1.06777264317513 0.285983459539974 df.mm.trans2:probe2 -0.0812644111181388 0.0706504772059871 -1.15023159548103 0.250433051756422 df.mm.trans2:probe3 0.0250680632070152 0.0706504772059871 0.354818030937389 0.722830549346562 df.mm.trans2:probe4 -0.0416723924704711 0.0706504772059872 -0.589838796827541 0.555485074577412 df.mm.trans2:probe5 -0.0341427680353536 0.0706504772059871 -0.483263091568477 0.629056889878644 df.mm.trans2:probe6 -0.0268276290856930 0.0706504772059872 -0.379723253778949 0.704263613793761 df.mm.trans3:probe2 -0.0538701747981894 0.0706504772059871 -0.762488477482277 0.446019981672212 df.mm.trans3:probe3 -0.121804527809756 0.0706504772059871 -1.72404395025705 0.0851321525998946 . df.mm.trans3:probe4 -0.0504844459756285 0.0706504772059872 -0.714566241759939 0.475110335082169