fitVsDatCorrelation=0.82249865428761 cont.fitVsDatCorrelation=0.21561816232643 fstatistic=9947.67725206267,43,485 cont.fstatistic=3367.48591430413,43,485 residuals=-0.486831119674876,-0.0862365354470821,-0.00543382844834294,0.0768056292749745,0.713698552853116 cont.residuals=-0.484793111004724,-0.167657395536270,-0.0551057046355752,0.122414400901900,0.902455111635315 predictedValues: Include Exclude Both Lung 50.467509129582 53.0333269024273 83.7617770312819 cerebhem 64.4163761928152 48.7502964798213 75.8273311377257 cortex 65.5302521866828 50.6380390313081 99.0290565900185 heart 52.8301837751953 53.448276340318 77.6382229669387 kidney 51.2725835328103 53.8469012733594 88.6350655416575 liver 53.1685623046993 52.7769557862737 84.9447588584662 stomach 52.1529220885816 50.9127902358441 74.5606234300228 testicle 51.8773725507918 54.298073026003 90.6417475285671 diffExp=-2.56581777284522,15.6660797129940,14.8922131553747,-0.618092565122723,-2.57431774054905,0.391606518425576,1.24013185273743,-2.42070047521128 diffExpScore=1.61404158386723 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=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 57.1186220089352 58.1551839740905 57.7242057854958 cerebhem 61.7602244416447 56.6527641444384 59.7563799769811 cortex 58.3712047119733 60.8909048410882 67.1320360381494 heart 60.0987719554985 59.4362197814964 59.0188693189116 kidney 59.5442044729701 56.7626388187593 60.4190221478719 liver 59.041259709755 56.7655468669277 57.9856932669621 stomach 58.1914911139523 61.8014311416681 62.7998414038448 testicle 60.2255652126093 60.9230363718747 55.923354531028 cont.diffExp=-1.03656196515531,5.10746029720628,-2.51970012911491,0.662552174002158,2.78156565421086,2.27571284282724,-3.60994002771577,-0.697471159265369 cont.diffExpScore=4.71563246647724 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=-0.802329421876344 cont.tran.correlation=-0.310370971809909 tran.covariance=-0.00313878582320494 cont.tran.covariance=-0.000269030556163604 tran.mean=53.7137763022821 cont.tran.mean=59.1086918479801 weightedLogRatios: wLogRatio Lung -0.195691422522788 cerebhem 1.12188254401185 cortex 1.04505587433751 heart -0.0462115603253574 kidney -0.194076041397280 liver 0.0293470700114273 stomach 0.0948718665856001 testicle -0.181132799849652 cont.weightedLogRatios: wLogRatio Lung -0.0729127061196103 cerebhem 0.352189187074627 cortex -0.172761481281624 heart 0.0453450827284811 kidney 0.194366835075109 liver 0.159530497126836 stomach -0.246397066493987 testicle -0.0472535629650847 varWeightedLogRatios=0.303032261699375 cont.varWeightedLogRatios=0.0402579129700825 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.21918715802031 0.0748092121683155 43.0319617693254 1.03048027877924e-167 *** df.mm.trans1 0.639392776444493 0.0650629552413344 9.82729379679772 6.60237829136325e-21 *** df.mm.trans2 0.719913318265674 0.0610814659573264 11.7861172285654 2.27428101526499e-28 *** df.mm.exp2 0.259346882822534 0.0829933693384388 3.1249108801083 0.00188516769872061 ** df.mm.exp3 0.0475281578799012 0.0829933693384388 0.572674157691875 0.56713071677599 df.mm.exp4 0.129463719758204 0.0829933693384388 1.55992847127659 0.119429040411386 df.mm.exp5 -0.0255000083617610 0.0829933693384388 -0.307253562122228 0.758782263402411 df.mm.exp6 0.0332673011478080 0.0829933693384388 0.400842879533511 0.688712331296544 df.mm.exp7 0.108408365122841 0.0829933693384388 1.30622923237111 0.192093748242932 df.mm.exp8 -0.0278169332078217 0.0829933693384388 -0.335170549521697 0.737641355307077 df.mm.trans1:exp2 -0.0153087389784606 0.0757622341833676 -0.202062929419542 0.839952255318976 df.mm.trans2:exp2 -0.34355612917385 0.0677638023045073 -5.06990631414139 5.6730622106001e-07 *** df.mm.trans1:exp3 0.213653997931177 0.0757622341833676 2.82005936379951 0.00499811666960829 ** df.mm.trans2:exp3 -0.0937456299181463 0.0677638023045073 -1.38341749916697 0.167173319609322 df.mm.trans1:exp4 -0.0837107756223488 0.0757622341833676 -1.10491429568647 0.269744557101522 df.mm.trans2:exp4 -0.121669856297540 0.0677638023045073 -1.79549925121967 0.073196578891542 . df.mm.trans1:exp5 0.0413264379707701 0.0757622341833676 0.545475439263678 0.585677340594792 df.mm.trans2:exp5 0.0407243412242123 0.0677638023045073 0.600974854409897 0.548137489230255 df.mm.trans1:exp6 0.0188702407585590 0.0757622341833676 0.249071862280187 0.803410636413338 df.mm.trans2:exp6 -0.0381131745605770 0.0677638023045073 -0.562441499213836 0.574075114076266 df.mm.trans1:exp7 -0.0755578985151476 0.0757622341833676 -0.997302935025314 0.319114794738573 df.mm.trans2:exp7 -0.149214716899116 0.0677638023045073 -2.20198264891625 0.0281365117476978 * df.mm.trans1:exp8 0.0553699009830532 0.0757622341833676 0.730837752870926 0.465231168028622 df.mm.trans2:exp8 0.0513851465533859 0.0677638023045073 0.758297864138123 0.448641000538975 df.mm.trans1:probe2 0.0683036576623666 0.0414966846692194 1.64600276399988 0.10041101450079 df.mm.trans1:probe3 0.0370634016214092 0.0414966846692194 0.893165367711927 0.372211580967392 df.mm.trans1:probe4 0.129140876849460 0.0414966846692194 3.11207697383237 0.00196737766585040 ** df.mm.trans1:probe5 0.155081382688478 0.0414966846692194 3.73719934314443 0.000208245395062494 *** df.mm.trans1:probe6 0.0899692440334887 0.0414966846692194 2.16810679577553 0.0306361328462577 * df.mm.trans1:probe7 0.00950029694028963 0.0414966846692194 0.228941107368430 0.819011195047677 df.mm.trans1:probe8 0.162277448722802 0.0414966846692194 3.91061237822627 0.000105186176167776 *** df.mm.trans1:probe9 0.174016077394945 0.0414966846692194 4.19349349910893 3.26606922978875e-05 *** df.mm.trans1:probe10 0.0440708925263844 0.0414966846692194 1.06203406073725 0.288748759433119 df.mm.trans1:probe11 0.0686360688306668 0.0414966846692194 1.65401331161230 0.0987718261397495 . df.mm.trans1:probe12 0.0659376316713172 0.0414966846692194 1.58898553455349 0.112715294709657 df.mm.trans2:probe2 0.0987792393081756 0.0414966846692194 2.38041279913251 0.0176786028106387 * df.mm.trans2:probe3 0.0706422007760954 0.0414966846692194 1.70235770252979 0.0893291063316917 . df.mm.trans2:probe4 0.104319330136096 0.0414966846692194 2.51391962918608 0.0122625434529705 * df.mm.trans2:probe5 0.0119490924557637 0.0414966846692194 0.28795294253054 0.773505767188265 df.mm.trans2:probe6 0.0325106286044259 0.0414966846692194 0.7834512290217 0.433744593004865 df.mm.trans3:probe2 -0.154451430282359 0.0414966846692194 -3.72201855433828 0.000220803123250455 *** df.mm.trans3:probe3 -0.373077688283889 0.0414966846692194 -8.9905420458956 5.52406717337977e-18 *** df.mm.trans3:probe4 -0.0745367640037745 0.0414966846692194 -1.79621009721442 0.073083325693406 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.09873390571285 0.128434885412814 31.9129330986573 3.10683946997248e-121 *** df.mm.trans1 -0.0630749360430258 0.111702194941428 -0.564670515884667 0.572558954490869 df.mm.trans2 -0.0479347836606641 0.104866644811407 -0.457102291647363 0.647802192362277 df.mm.exp2 0.0173557365013052 0.142485712281306 0.121806855041299 0.903102436001142 df.mm.exp3 -0.0833234712565882 0.142485712281305 -0.584784747344247 0.558964398952387 df.mm.exp4 0.0504673774238069 0.142485712281305 0.354192547559931 0.723348523909534 df.mm.exp5 -0.0282753005091238 0.142485712281306 -0.198443058299773 0.842781585334661 df.mm.exp6 0.00440115676129644 0.142485712281306 0.030888407622284 0.975371241466385 df.mm.exp7 -0.00485550295703772 0.142485712281305 -0.0340771216937993 0.972829671897216 df.mm.exp8 0.131157598061359 0.142485712281306 0.920496490219443 0.357770979277149 df.mm.trans1:exp2 0.0607736100557031 0.130071064564437 0.467233894480784 0.640542386896809 df.mm.trans2:exp2 -0.0435299801460153 0.116339096908738 -0.374164672948789 0.708445328425887 df.mm.trans1:exp3 0.105015976244430 0.130071064564437 0.807373850564633 0.419846900954742 df.mm.trans2:exp3 0.129292265725446 0.116339096908738 1.11133977451166 0.266972738655817 df.mm.trans1:exp4 0.000391837308924217 0.130071064564437 0.00301248636840442 0.99759762593497 df.mm.trans2:exp4 -0.0286785995512933 0.116339096908738 -0.246508700113000 0.805392768904033 df.mm.trans1:exp5 0.0698640762950326 0.130071064564437 0.537122353299587 0.591429424399883 df.mm.trans2:exp5 0.00403861923820027 0.116339096908738 0.0347142048160161 0.972321916132146 df.mm.trans1:exp6 0.0287051666037439 0.130071064564437 0.220688334487518 0.825427985870076 df.mm.trans2:exp6 -0.0285866076148293 0.116339096908738 -0.245717977656764 0.806004500581258 df.mm.trans1:exp7 0.0234644529679581 0.130071064564436 0.180397177854526 0.856916121726755 df.mm.trans2:exp7 0.0656670014858578 0.116339096908738 0.564444827497418 0.572712379145367 df.mm.trans1:exp8 -0.0781908578350163 0.130071064564437 -0.601139523973688 0.548027904935139 df.mm.trans2:exp8 -0.0846612526207205 0.116339096908738 -0.727711103749869 0.467141604942182 df.mm.trans1:probe2 0.044171439641215 0.0712428561406528 0.620012195384315 0.535540946302102 df.mm.trans1:probe3 -0.0370954052018726 0.0712428561406528 -0.520689472761118 0.602820732345373 df.mm.trans1:probe4 0.053191539140926 0.0712428561406528 0.746622777670668 0.455653059862163 df.mm.trans1:probe5 0.0621271017583487 0.0712428561406527 0.872046758424352 0.383614359325814 df.mm.trans1:probe6 -0.0351583605603557 0.0712428561406527 -0.49350015517266 0.621882620208589 df.mm.trans1:probe7 -0.0274499942901753 0.0712428561406528 -0.385301709914346 0.700182818489843 df.mm.trans1:probe8 0.0181641050551746 0.0712428561406528 0.254960371315177 0.798861782997022 df.mm.trans1:probe9 -0.0276538570148686 0.0712428561406528 -0.388163228047348 0.698065561123569 df.mm.trans1:probe10 -0.0280409888812466 0.0712428561406528 -0.393597202586685 0.694051418274511 df.mm.trans1:probe11 0.0394404545741086 0.0712428561406528 0.553605746746626 0.58010381213902 df.mm.trans1:probe12 0.0898435430119112 0.0712428561406528 1.26108844983047 0.207883247827538 df.mm.trans2:probe2 0.021538901798417 0.0712428561406527 0.302330689211749 0.762529595392432 df.mm.trans2:probe3 9.12059922135825e-05 0.0712428561406527 0.00128021246135215 0.998979064911092 df.mm.trans2:probe4 0.0112693855294448 0.0712428561406527 0.158182674585589 0.874378694469521 df.mm.trans2:probe5 0.0220602392398616 0.0712428561406528 0.309648439645775 0.756961309221494 df.mm.trans2:probe6 0.0681992799067985 0.0712428561406528 0.95727885715523 0.338903450880863 df.mm.trans3:probe2 0.0917572776807891 0.0712428561406528 1.28795057710257 0.198377328115251 df.mm.trans3:probe3 0.137264977297171 0.0712428561406528 1.92671917905948 0.0545985635811094 . df.mm.trans3:probe4 0.0427873872162078 0.0712428561406528 0.600584950324476 0.548397005881202