fitVsDatCorrelation=0.852430836450906 cont.fitVsDatCorrelation=0.240491737522755 fstatistic=10225.5070906347,49,623 cont.fstatistic=2957.82810716813,49,623 residuals=-0.615624763188918,-0.0798865928248648,-0.00410285312110214,0.0729828183119133,0.884935484102682 cont.residuals=-0.57768113172627,-0.199413617420023,-0.0191535032416949,0.140607504320458,1.02494648403066 predictedValues: Include Exclude Both Lung 68.7567588518632 68.1936933118607 59.432567883175 cerebhem 70.200447032787 54.0718810175496 64.5119364380927 cortex 62.9324864503005 61.760052980399 59.9431135763156 heart 64.3202818184912 64.0169072355003 64.9701540703908 kidney 70.0549101524262 69.03138586391 59.8088351474497 liver 72.4043135490773 66.334626757322 58.6827594665483 stomach 68.615403916066 69.4644587143405 67.1028847161952 testicle 68.614801760541 61.6279113888775 59.1025191853409 diffExp=0.563065540002412,16.1285660152374,1.17243346990153,0.303374582990898,1.0235242885161,6.0696867917552,-0.849054798274523,6.98689037166353 diffExpScore=1.02154759919670 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 66.5822799102164 64.8116158043307 63.1669261762198 cerebhem 64.8698428163964 62.2161417465865 66.9474095947917 cortex 61.8963901376178 64.787032789652 66.8319707433526 heart 62.2726109300651 72.8354205915806 58.3822508535072 kidney 63.7298834328003 59.6823184404675 64.4955041894211 liver 62.9629659747804 67.484921477706 66.5424745839639 stomach 62.4946596136976 65.3457957374228 56.896478453305 testicle 62.5307253346886 65.2567272571323 64.8592168921704 cont.diffExp=1.77066410588573,2.6537010698099,-2.89064265203422,-10.5628096615155,4.04756499233284,-4.52195550292558,-2.85113612372530,-2.72600192244368 cont.diffExpScore=1.99149563914990 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=0.128607430441208 cont.tran.correlation=-0.389166932460693 tran.covariance=0.000419932306686541 cont.tran.covariance=-0.000563141695419204 tran.mean=66.275020050082 cont.tran.mean=64.3599582496963 weightedLogRatios: wLogRatio Lung 0.0347540203218452 cerebhem 1.07570423144780 cortex 0.077717741487775 heart 0.0196747187637952 kidney 0.0624329926733895 liver 0.371095993648193 stomach -0.0520786863263619 testicle 0.448347233409212 cont.weightedLogRatios: wLogRatio Lung 0.112799913131901 cerebhem 0.173401309243862 cortex -0.189342648964631 heart -0.659604073618467 kidney 0.27046628679951 liver -0.289721811923662 stomach -0.185469339168284 testicle -0.177383486373645 varWeightedLogRatios=0.141587261031989 cont.varWeightedLogRatios=0.0892972749673303 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19126129453989 0.0761335064325861 55.0514680188955 1.72995712825921e-241 *** df.mm.trans1 0.208856829011968 0.0665398571788164 3.13882292308946 0.00177644385566758 ** df.mm.trans2 0.0434918090351732 0.0607918346698554 0.715421886366251 0.474616420567804 df.mm.exp2 -0.293266069833316 0.081243192017069 -3.60973101317464 0.000331148832293335 *** df.mm.exp3 -0.196161506186937 0.081243192017069 -2.41449777288075 0.0160440462704028 * df.mm.exp4 -0.218990538352507 0.081243192017069 -2.69549402128978 0.00721818207575472 ** df.mm.exp5 0.0246024666302246 0.081243192017069 0.302824963168060 0.762124342028982 df.mm.exp6 0.0367471614530140 0.081243192017069 0.45231065570754 0.651202648761064 df.mm.exp7 -0.104979514031494 0.081243192017069 -1.29216382844040 0.196779506358776 df.mm.exp8 -0.0977351663204789 0.081243192017069 -1.20299515435022 0.229435160070654 df.mm.trans1:exp2 0.314045706546156 0.0754185042074785 4.16404050764793 3.56750825467882e-05 *** df.mm.trans2:exp2 0.0612282740438057 0.0633642176453489 0.96629101280003 0.334273527736362 df.mm.trans1:exp3 0.107648972024764 0.0754185042074785 1.42735490654414 0.153978639269176 df.mm.trans2:exp3 0.0970661826059803 0.0633642176453489 1.53187692065042 0.126060421511495 df.mm.trans1:exp4 0.152290502432943 0.0754185042074784 2.01927237928225 0.0438866628811968 * df.mm.trans2:exp4 0.155785675238294 0.0633642176453489 2.45857490279815 0.0142199225105533 * df.mm.trans1:exp5 -0.00589814362398865 0.0754185042074784 -0.0782055237765348 0.937689679010284 df.mm.trans2:exp5 -0.0123932850013270 0.0633642176453489 -0.195588069447848 0.844996372829819 df.mm.trans1:exp6 0.0149436732839512 0.0754185042074784 0.198143326243129 0.842997564286188 df.mm.trans2:exp6 -0.0643872138729873 0.0633642176453489 -1.01614469910075 0.309954940845062 df.mm.trans1:exp7 0.102921528150816 0.0754185042074784 1.36467209516216 0.172848814512863 df.mm.trans2:exp7 0.123442663254598 0.0633642176453489 1.94814467599852 0.0518463398862144 . df.mm.trans1:exp8 0.0956684045945409 0.0754185042074784 1.26850042439657 0.205093078914615 df.mm.trans2:exp8 -0.00350204594006990 0.0633642176453489 -0.0552685106864405 0.955942267817166 df.mm.trans1:probe2 -0.0363335833577918 0.0440349194218225 -0.825108432917578 0.409625849068722 df.mm.trans1:probe3 0.388464309024359 0.0440349194218225 8.82173316370022 1.12825646347571e-17 *** df.mm.trans1:probe4 0.254382099737008 0.0440349194218225 5.77682673380669 1.20261322725801e-08 *** df.mm.trans1:probe5 0.0540684997224312 0.0440349194218225 1.22785508483607 0.219965073441534 df.mm.trans1:probe6 -0.294179703052035 0.0440349194218225 -6.68060046241955 5.2818798721492e-11 *** df.mm.trans1:probe7 -0.137177562497365 0.0440349194218225 -3.11519958020823 0.00192265937374527 ** df.mm.trans1:probe8 -0.258087384060695 0.0440349194218225 -5.86097096234935 7.4612884166354e-09 *** df.mm.trans1:probe9 -0.215148202604868 0.0440349194218225 -4.88585434990591 1.31110800323602e-06 *** df.mm.trans1:probe10 -0.131606263050390 0.0440349194218225 -2.98867954746771 0.00291220680485142 ** df.mm.trans1:probe11 -0.249885779342004 0.0440349194218225 -5.67471866925154 2.12954247582863e-08 *** df.mm.trans1:probe12 -0.400626364102772 0.0440349194218225 -9.09792431467996 1.22700050918922e-18 *** df.mm.trans1:probe13 -0.520499691349302 0.0440349194218225 -11.8201576881132 3.14057120358164e-29 *** df.mm.trans1:probe14 -0.54412332682936 0.0440349194218225 -12.3566327354219 1.58657751531440e-31 *** df.mm.trans1:probe15 -0.572453236568497 0.0440349194218225 -12.9999837421027 2.33142318464025e-34 *** df.mm.trans1:probe16 -0.469324843039052 0.0440349194218225 -10.6580152570114 1.77136544184797e-24 *** df.mm.trans1:probe17 -0.597416756454965 0.0440349194218225 -13.5668865595539 6.3821542676859e-37 *** df.mm.trans2:probe2 0.0328741632685548 0.0440349194218225 0.746547596775282 0.455618314021113 df.mm.trans2:probe3 -0.0201781679203762 0.0440349194218225 -0.458231062650167 0.64694623685911 df.mm.trans2:probe4 -0.0912183722849855 0.0440349194218225 -2.07150083349034 0.0387230501989694 * df.mm.trans2:probe5 -0.0453621405798732 0.0440349194218225 -1.03014019726792 0.303343953166112 df.mm.trans2:probe6 -0.0125266632650504 0.0440349194218225 -0.284471129492803 0.776143883740674 df.mm.trans3:probe2 -0.443870701687245 0.0440349194218225 -10.0799707939803 3.07796774994691e-22 *** df.mm.trans3:probe3 -0.404665458146751 0.0440349194218225 -9.18964911165953 5.80702836738884e-19 *** df.mm.trans3:probe4 -0.540836368807318 0.0440349194218225 -12.2819883835031 3.33946413171127e-31 *** df.mm.trans3:probe5 -0.404465349398336 0.0440349194218225 -9.1851047920368 6.02707761945963e-19 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.16259526083893 0.141341668229233 29.4505881598051 4.23125556553882e-120 *** df.mm.trans1 0.0264504769491158 0.123531081885956 0.214120013726867 0.830523529268815 df.mm.trans2 0.00216522356684304 0.112859892175876 0.0191850579076297 0.984699620007856 df.mm.exp2 -0.125052529982126 0.150827787002406 -0.829108034185579 0.407360821941442 df.mm.exp3 -0.129756711672486 0.150827787002406 -0.86029712595608 0.389956216361537 df.mm.exp4 0.128569686665176 0.150827787002406 0.8524270575098 0.39430468109952 df.mm.exp5 -0.147048609719977 0.150827787002406 -0.974943759651077 0.329966555169964 df.mm.exp6 -0.0675320341214884 0.150827787002406 -0.447742657129957 0.654494577967181 df.mm.exp7 0.0493982871617991 0.150827787002406 0.327514499440419 0.743388802074856 df.mm.exp8 -0.0823743191615618 0.150827787002406 -0.546148165392414 0.585159611682143 df.mm.trans1:exp2 0.098996899418796 0.140014268349472 0.707048650011168 0.479800619862272 df.mm.trans2:exp2 0.084182166136722 0.117635514869712 0.715618631243793 0.474494979999138 df.mm.trans1:exp3 0.0567800972850043 0.140014268349472 0.405530793070907 0.685226616793848 df.mm.trans2:exp3 0.129377340201561 0.117635514869712 1.09981530955897 0.271837396501035 df.mm.trans1:exp4 -0.195486464209355 0.140014268349472 -1.39618959205947 0.163154686982036 df.mm.trans2:exp4 -0.0118521467337232 0.117635514869712 -0.100753133497568 0.91977884640052 df.mm.trans1:exp5 0.103263715235625 0.140014268349472 0.737522800018359 0.46108216357486 df.mm.trans2:exp5 0.0645995691905649 0.117635514869712 0.549150222720687 0.583099128422887 df.mm.trans1:exp6 0.0116402712662518 0.140014268349472 0.0831363217725637 0.933769849773383 df.mm.trans2:exp6 0.107951377996825 0.117635514869712 0.91767675872705 0.359143236214238 df.mm.trans1:exp7 -0.112755654980977 0.140014268349472 -0.805315460418227 0.420944904660625 df.mm.trans2:exp7 -0.0411900272404839 0.117635514869712 -0.350149589484980 0.726344782409586 df.mm.trans1:exp8 0.0195938857306495 0.140014268349472 0.139942064202654 0.888750995386117 df.mm.trans2:exp8 0.0892186162426977 0.117635514869712 0.758432658211362 0.448478828339736 df.mm.trans1:probe2 -0.0120949273951848 0.0817507200582094 -0.14794887906275 0.88243098296209 df.mm.trans1:probe3 -0.0145941701487367 0.0817507200582094 -0.178520386589196 0.858372347826249 df.mm.trans1:probe4 0.112929610997037 0.0817507200582094 1.38138980203021 0.167654359411338 df.mm.trans1:probe5 -0.0624075174408127 0.0817507200582094 -0.763387984795441 0.445521022481896 df.mm.trans1:probe6 0.0670906920826715 0.0817507200582094 0.820674020178668 0.412145872026789 df.mm.trans1:probe7 -0.027658248735821 0.0817507200582094 -0.338324221684254 0.735232855508381 df.mm.trans1:probe8 -0.0393427304551357 0.0817507200582094 -0.481252402757092 0.630506101958634 df.mm.trans1:probe9 -0.0699849038148321 0.0817507200582094 -0.856076909964835 0.392284375204871 df.mm.trans1:probe10 0.0914591162206154 0.0817507200582094 1.11875609359151 0.263675380703193 df.mm.trans1:probe11 0.125385110746554 0.0817507200582094 1.53374931324490 0.12559898272075 df.mm.trans1:probe12 -0.0302033831054863 0.0817507200582094 -0.369457089600929 0.711912593194343 df.mm.trans1:probe13 0.0387732902214532 0.0817507200582094 0.474286834340361 0.635461566192315 df.mm.trans1:probe14 -0.00186722785020132 0.0817507200582094 -0.0228405064673655 0.98178481042788 df.mm.trans1:probe15 0.0379136852531836 0.0817507200582094 0.463771881473188 0.642973182913464 df.mm.trans1:probe16 -0.00519844055949434 0.0817507200582094 -0.0635889268717495 0.949317945700544 df.mm.trans1:probe17 -0.00355974329369455 0.0817507200582094 -0.0435438769366176 0.96528194113593 df.mm.trans2:probe2 0.0637446672147935 0.0817507200582094 0.779744412885967 0.435837446789472 df.mm.trans2:probe3 0.0278844184842634 0.0817507200582094 0.341090799743522 0.733150227799079 df.mm.trans2:probe4 -0.0159742694231202 0.0817507200582094 -0.195402186203937 0.845141816208294 df.mm.trans2:probe5 0.00686209205111502 0.0817507200582094 0.0839392245870002 0.933131717138094 df.mm.trans2:probe6 -0.00854895792294341 0.0817507200582094 -0.104573487754680 0.916747909970364 df.mm.trans3:probe2 -0.0848743406347048 0.0817507200582094 -1.03820908946455 0.299575494675218 df.mm.trans3:probe3 0.0435176172391755 0.0817507200582094 0.532320904429826 0.594693545902596 df.mm.trans3:probe4 -0.141022767462512 0.0817507200582094 -1.72503394908447 0.085017292194005 . df.mm.trans3:probe5 -0.0447710950334635 0.0817507200582094 -0.547653831080447 0.584125763477629