fitVsDatCorrelation=0.904793336301737 cont.fitVsDatCorrelation=0.253605772466079 fstatistic=8595.03123751412,57,807 cont.fstatistic=1654.42659828563,57,807 residuals=-0.880934183983195,-0.0952805769529655,-0.00627820709519206,0.0948247048365881,0.82655067000907 cont.residuals=-0.818744733435743,-0.268369642137253,-0.0404947448297338,0.184279817946151,2.36245896181472 predictedValues: Include Exclude Both Lung 58.7917687805867 61.9783440308901 82.5380964714982 cerebhem 55.8189514832712 50.1628099173074 67.5918457061769 cortex 54.8099517298398 55.6199344627533 67.7289482562717 heart 58.1764802044467 64.3855779141633 75.4662370624489 kidney 58.0258647961643 73.3540886846295 101.73666842485 liver 61.2021653777742 150.649120493434 208.535252435773 stomach 61.2241015746818 64.5410923105103 81.8702740593248 testicle 61.6244652205812 80.9154761973714 109.184043642001 diffExp=-3.1865752503034,5.65614156596379,-0.809982732913546,-6.20909770971657,-15.3282238884652,-89.4469551156596,-3.31699073582846,-19.2910109767902 diffExpScore=1.07757522063366 diffExp1.5=0,0,0,0,0,-1,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,-1,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,0,0,0,-1,0,-1 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,0,-1,-1,0,-1 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 78.3310044238979 63.521127752194 67.5486566355151 cerebhem 67.7251175530201 63.0051863871447 76.3919141207908 cortex 73.7676096659556 71.3887505780795 74.7370484062597 heart 76.1385634965815 80.5260837630933 75.0064206233912 kidney 74.222693514381 71.1699559196318 73.7653749438162 liver 73.3074247675797 84.6166455089511 69.048233338912 stomach 77.5271248647818 79.7376769261037 92.5263112423237 testicle 75.4111019041024 77.0345586005016 70.9904123778624 cont.diffExp=14.8098766717039,4.71993116587546,2.37885908787611,-4.38752026651176,3.05273759474925,-11.3092207413714,-2.21055206132186,-1.62345669639920 cont.diffExpScore=6.91875959504422 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=1,0,0,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.584284965097964 cont.tran.correlation=0.302690333597674 tran.covariance=0.00974994791307386 cont.tran.covariance=0.00161557542211231 tran.mean=66.9550120736503 cont.tran.mean=74.214414101625 weightedLogRatios: wLogRatio Lung -0.216431788223374 cerebhem 0.424013368170656 cortex -0.0588439699406404 heart -0.417213410984610 kidney -0.97938435742235 liver -4.11162982662408 stomach -0.218479915853723 testicle -1.15944076051658 cont.weightedLogRatios: wLogRatio Lung 0.891966968229756 cerebhem 0.301915633264958 cortex 0.140444494120780 heart -0.244306359758809 kidney 0.180011457351246 liver -0.626442995282259 stomach -0.122710217597425 testicle -0.0923041680378876 varWeightedLogRatios=1.99811454249107 cont.varWeightedLogRatios=0.198803091029985 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.97344928888511 0.0841586523476246 47.2137941619173 2.09777160017496e-234 *** df.mm.trans1 -0.0378319532107366 0.0721701226581507 -0.524205194855159 0.600279771425581 df.mm.trans2 0.101459265558548 0.0641503794029957 1.58158480904962 0.114136205740036 df.mm.exp2 -0.0636269226604724 0.0824477055663026 -0.771724600744712 0.440503473432331 df.mm.exp3 0.0193728338296290 0.0824477055663026 0.234971169865361 0.814290717503243 df.mm.exp4 0.117158520031156 0.0824477055663025 1.42100400764870 0.155701872643044 df.mm.exp5 -0.0537276222439797 0.0824477055663025 -0.651656973046669 0.514808020271086 df.mm.exp6 0.00150096456664309 0.0824477055663025 0.0182050495684934 0.985479774233492 df.mm.exp7 0.0891801383090589 0.0824477055663025 1.08165700544986 0.279728076183365 df.mm.exp8 0.0339021979121914 0.0824477055663025 0.411196378108158 0.681037714976519 df.mm.trans1:exp2 0.0117385083216157 0.0751285548024256 0.156245629274858 0.875878488269298 df.mm.trans2:exp2 -0.147884197703635 0.0560841350062562 -2.63682764630566 0.00852944731154925 ** df.mm.trans1:exp3 -0.0895029138344336 0.0751285548024256 -1.19133016826705 0.233874252824253 df.mm.trans2:exp3 -0.127616197532952 0.0560841350062562 -2.27544202150423 0.0231408282500787 * df.mm.trans1:exp4 -0.127679225551658 0.0751285548024256 -1.69947666220163 0.0896147823655257 . df.mm.trans2:exp4 -0.0790538916374578 0.0560841350062562 -1.40955889983218 0.159055214024294 df.mm.trans1:exp5 0.0406146197672285 0.0751285548024256 0.540601637739972 0.588931359180221 df.mm.trans2:exp5 0.222240833196334 0.0560841350062562 3.96263280465221 8.06912262854714e-05 *** df.mm.trans1:exp6 0.038679747934673 0.0751285548024256 0.514847490896021 0.606800575572303 df.mm.trans2:exp6 0.886667428709019 0.0560841350062562 15.809594435398 3.00867831374295e-49 *** df.mm.trans1:exp7 -0.0486410681172182 0.0751285548024256 -0.647437825007221 0.517532750777486 df.mm.trans2:exp7 -0.0486630614824763 0.0560841350062562 -0.867679629489657 0.385827675905472 df.mm.trans1:exp8 0.0131548980700265 0.0751285548024256 0.175098510874081 0.861046149901824 df.mm.trans2:exp8 0.232717873990175 0.0560841350062562 4.14944215443841 3.68669807823700e-05 *** df.mm.trans1:probe2 0.194854845762133 0.0523368461536498 3.72309109322485 0.000210471821772957 *** df.mm.trans1:probe3 0.255616972613307 0.0523368461536498 4.88407291228191 1.25311339216088e-06 *** df.mm.trans1:probe4 -0.0779279887719383 0.0523368461536498 -1.48896990359638 0.136885968828392 df.mm.trans1:probe5 0.409391384654733 0.0523368461536498 7.82224025217047 1.62351085260134e-14 *** df.mm.trans1:probe6 0.560595435168676 0.0523368461536498 10.7112957002202 4.00541875502701e-25 *** df.mm.trans1:probe7 0.307867810411059 0.0523368461536498 5.88242955082209 5.92010754090119e-09 *** df.mm.trans1:probe8 -0.117674681034848 0.0523368461536498 -2.24840986194278 0.0248191781125001 * df.mm.trans1:probe9 -0.09265593275138 0.0523368461536498 -1.77037669559533 0.0770418974229785 . df.mm.trans1:probe10 -0.0525160226889968 0.0523368461536498 -1.00342352565191 0.315957329033389 df.mm.trans1:probe11 0.0852777095101057 0.0523368461536498 1.62940100096495 0.103618406886672 df.mm.trans1:probe12 -0.047928821957658 0.0523368461536498 -0.91577589174841 0.360058014001334 df.mm.trans1:probe13 0.00644545048429836 0.0523368461536498 0.123153207691879 0.902016442223025 df.mm.trans1:probe14 0.552665834163047 0.0523368461536498 10.5597848319048 1.66630932789552e-24 *** df.mm.trans1:probe15 0.318948235843084 0.0523368461536498 6.09414321426094 1.70300123623505e-09 *** df.mm.trans1:probe16 0.439844267820737 0.0523368461536498 8.40410342131523 1.93690004064302e-16 *** df.mm.trans1:probe17 0.48855286463834 0.0523368461536498 9.33477846953278 9.56856437302518e-20 *** df.mm.trans1:probe18 0.458423281540398 0.0523368461536498 8.75909259405058 1.14485803588317e-17 *** df.mm.trans1:probe19 0.461755035788152 0.0523368461536498 8.82275241485775 6.82582225431423e-18 *** df.mm.trans2:probe2 0.194394423866908 0.0523368461536498 3.71429381312368 0.000217801108714502 *** df.mm.trans2:probe3 0.157756294376703 0.0523368461536498 3.01424915658014 0.00265680156326076 ** df.mm.trans2:probe4 0.180369763725647 0.0523368461536498 3.44632466381562 0.000597642558363803 *** df.mm.trans2:probe5 0.112037540617149 0.0523368461536498 2.14070103284846 0.032597218478942 * df.mm.trans2:probe6 0.237342133353730 0.0523368461536498 4.53489560026113 6.63718735626314e-06 *** df.mm.trans3:probe2 0.283609533748271 0.0523368461536498 5.41892671399522 7.92090026148437e-08 *** df.mm.trans3:probe3 0.410975018472318 0.0523368461536498 7.85249873990846 1.29789831536048e-14 *** df.mm.trans3:probe4 1.16396733728912 0.0523368461536498 22.2399212568515 7.71775591227893e-86 *** df.mm.trans3:probe5 0.161343869609636 0.0523368461536498 3.08279694836721 0.00212041303150964 ** df.mm.trans3:probe6 0.197969403020415 0.0523368461536498 3.78260093164994 0.000166663132500181 *** df.mm.trans3:probe7 0.302333466715207 0.0523368461536498 5.77668485845748 1.08738233264184e-08 *** df.mm.trans3:probe8 0.219260988992781 0.0523368461536498 4.18941921622632 3.10512030966713e-05 *** df.mm.trans3:probe9 0.484685207620945 0.0523368461536498 9.26087915572927 1.7923401161123e-19 *** df.mm.trans3:probe10 0.520454078621346 0.0523368461536498 9.9443148922922 4.66329506970063e-22 *** df.mm.trans3:probe11 0.393418066187946 0.0523368461536498 7.51703809268434 1.49116557447147e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39375313278819 0.191163861745535 22.9842245949021 2.53892354718576e-90 *** df.mm.trans1 -0.0121027859011155 0.163932275115269 -0.0738279627523341 0.94116557186769 df.mm.trans2 -0.236647915349912 0.145715668169965 -1.62403891305554 0.104758002021729 df.mm.exp2 -0.276670572235761 0.187277497303676 -1.47732950418027 0.139977467906907 df.mm.exp3 -0.0443837760359214 0.187277497303676 -0.236994709321386 0.812721041288103 df.mm.exp4 0.104094431730106 0.187277497303676 0.555829895362781 0.578481208504894 df.mm.exp5 -0.0282166275363975 0.187277497303676 -0.150667474430435 0.880275682946882 df.mm.exp6 0.198519716936554 0.187277497303676 1.06002974086443 0.289448223435271 df.mm.exp7 -0.097590856230605 0.187277497303676 -0.521102949557034 0.602438015489206 df.mm.exp8 0.105195919353597 0.187277497303676 0.561711475581171 0.574468574921104 df.mm.trans1:exp2 0.131184201670504 0.170652265248614 0.768722298994326 0.442283089086193 df.mm.trans2:exp2 0.268515047777918 0.127393435272333 2.10776204601049 0.0353594256613655 * df.mm.trans1:exp3 -0.0156399763901239 0.170652265248614 -0.0916482202409609 0.927000282664716 df.mm.trans2:exp3 0.161151506973075 0.127393435272333 1.26499066948447 0.206239828285895 df.mm.trans1:exp4 -0.132483041835556 0.170652265248614 -0.776333332830647 0.437779665312064 df.mm.trans2:exp4 0.133114151045988 0.127393435272333 1.04490589143331 0.296379351040778 df.mm.trans1:exp5 -0.0256569208140111 0.170652265248614 -0.150346207105032 0.880529048264218 df.mm.trans2:exp5 0.141914818413677 0.127393435272333 1.11398847287776 0.265615802796814 df.mm.trans1:exp6 -0.264801314576208 0.170652265248614 -1.55170114027161 0.121125810242075 df.mm.trans2:exp6 0.0882387146803235 0.127393435272333 0.692647266255853 0.488730204763638 df.mm.trans1:exp7 0.0872752353347237 0.170652265248614 0.511421487476753 0.609195849871555 df.mm.trans2:exp7 0.324960493586693 0.127393435272333 2.55084175171205 0.0109298307552349 * df.mm.trans1:exp8 -0.143184909346320 0.170652265248614 -0.83904487958435 0.401692594269504 df.mm.trans2:exp8 0.0876856437407427 0.127393435272333 0.688305826381825 0.491458019058299 df.mm.trans1:probe2 0.0543241445372894 0.118881580719562 0.456960146462374 0.6478226672696 df.mm.trans1:probe3 -0.0699959216611036 0.118881580719562 -0.588786936020157 0.556168972363341 df.mm.trans1:probe4 0.0331591008626319 0.118881580719562 0.278925470724126 0.780373481841011 df.mm.trans1:probe5 -0.0520240494018335 0.118881580719562 -0.437612362545521 0.66178440022171 df.mm.trans1:probe6 0.132220339574841 0.118881580719562 1.11220206506797 0.266382489639497 df.mm.trans1:probe7 0.00489741296406074 0.118881580719562 0.0411957254809187 0.967150051000787 df.mm.trans1:probe8 0.120417913566895 0.118881580719562 1.01292322021658 0.31140043060394 df.mm.trans1:probe9 -0.0736155578325876 0.118881580719562 -0.61923434553116 0.535936758665947 df.mm.trans1:probe10 -0.0926355194511446 0.118881580719562 -0.779225165836827 0.436075530323729 df.mm.trans1:probe11 -0.087331713435001 0.118881580719562 -0.734610970904013 0.462789855724258 df.mm.trans1:probe12 -0.146394061629267 0.118881580719562 -1.23142761681985 0.218521703387750 df.mm.trans1:probe13 -0.0785809399472603 0.118881580719562 -0.661001809293153 0.50879980818747 df.mm.trans1:probe14 -0.0643824537183822 0.118881580719562 -0.541567947941899 0.588265656901694 df.mm.trans1:probe15 -0.00357455241342291 0.118881580719562 -0.0300681770194087 0.976020114648223 df.mm.trans1:probe16 -0.120791710448777 0.118881580719562 -1.01606749941962 0.309901767072915 df.mm.trans1:probe17 -0.102380615899398 0.118881580719562 -0.861198305740153 0.389384634791328 df.mm.trans1:probe18 -0.0303341995649141 0.118881580719562 -0.255163158004028 0.798662099437325 df.mm.trans1:probe19 -0.0441831944549735 0.118881580719562 -0.371657191867262 0.71024566116508 df.mm.trans2:probe2 -0.107179487082065 0.118881580719562 -0.901565124162492 0.36755691190326 df.mm.trans2:probe3 -0.0940395105057887 0.118881580719562 -0.791035162357281 0.429155887442658 df.mm.trans2:probe4 -0.0291869606549657 0.118881580719562 -0.2455128917222 0.806121749859845 df.mm.trans2:probe5 0.130779952847179 0.118881580719562 1.10008591789913 0.271622732344926 df.mm.trans2:probe6 0.00217101707443104 0.118881580719562 0.0182620138569019 0.985434344946604 df.mm.trans3:probe2 0.0177851703225597 0.118881580719562 0.149604086814041 0.881114363245375 df.mm.trans3:probe3 0.138111474796409 0.118881580719562 1.16175671588865 0.245677784792146 df.mm.trans3:probe4 0.0346153067161625 0.118881580719562 0.291174684140675 0.770992543775513 df.mm.trans3:probe5 -0.0596010602851462 0.118881580719562 -0.501348147664216 0.616262854733611 df.mm.trans3:probe6 0.144122599951263 0.118881580719562 1.21232068987411 0.225744445027450 df.mm.trans3:probe7 0.123973528526926 0.118881580719562 1.04283210045277 0.297338353906328 df.mm.trans3:probe8 -0.0255309530322918 0.118881580719562 -0.214759535310341 0.830009059954131 df.mm.trans3:probe9 0.122444844046018 0.118881580719562 1.02997321624501 0.303331168239994 df.mm.trans3:probe10 0.0782115215287918 0.118881580719562 0.657894360551031 0.510793631367726 df.mm.trans3:probe11 0.172170770198697 0.118881580719562 1.44825438185284 0.147934321880807