fitVsDatCorrelation=0.952540355424125 cont.fitVsDatCorrelation=0.244954806923746 fstatistic=6914.91730312257,56,784 cont.fstatistic=669.06706352847,56,784 residuals=-1.11447095659393,-0.107111932039816,0.00239801758995220,0.111241242965529,0.91461161047261 cont.residuals=-1.29305756110632,-0.57048739474378,-0.164132483476290,0.516888370133342,1.82178687940700 predictedValues: Include Exclude Both Lung 83.7433951505668 48.7816431598571 73.9175908571289 cerebhem 92.0581938670684 45.5231996890103 80.930886523505 cortex 105.843301681854 46.1977045321954 97.5278400825531 heart 130.898363226594 46.6632957599048 135.115020582771 kidney 100.027357789692 48.0333102407623 99.66682957209 liver 113.901401891587 50.1781642579849 107.016265357221 stomach 112.153724188532 45.8540853325083 103.660477925922 testicle 121.968302433045 47.3735533117318 100.996611487144 diffExp=34.9617519907098,46.5349941780581,59.6455971496589,84.2350674666888,51.9940475489301,63.723237633602,66.2996388560234,74.5947491213136 diffExpScore=0.997929559832218 diffExp1.5=1,1,1,1,1,1,1,1 diffExp1.5Score=0.888888888888889 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 92.4196398520795 109.844395848644 95.5469681746904 cerebhem 79.5719819187562 170.141089119937 85.9751973018205 cortex 92.8529130709175 92.9106066255285 79.9497077575443 heart 83.3047039189535 120.059875486829 94.033228382042 kidney 80.1308664098473 122.989113710247 105.129339906881 liver 86.7772299123152 122.788560532329 89.0008204821596 stomach 90.8151374026678 107.549426440180 81.2767378649036 testicle 90.6001794068056 93.1940432203587 80.987468500732 cont.diffExp=-17.424755996564,-90.5691072011808,-0.057693554610978,-36.7551715678751,-42.8582473003995,-36.011330620014,-16.7342890375125,-2.5938638135531 cont.diffExpScore=0.995901714240295 cont.diffExp1.5=0,-1,0,0,-1,0,0,0 cont.diffExp1.5Score=0.666666666666667 cont.diffExp1.4=0,-1,0,-1,-1,-1,0,0 cont.diffExp1.4Score=0.8 cont.diffExp1.3=0,-1,0,-1,-1,-1,0,0 cont.diffExp1.3Score=0.8 cont.diffExp1.2=0,-1,0,-1,-1,-1,0,0 cont.diffExp1.2Score=0.8 tran.correlation=-0.0782595294570355 cont.tran.correlation=-0.80531155827174 tran.covariance=-0.000410843089730500 cont.tran.covariance=-0.010077768532322 tran.mean=77.4499372820559 cont.tran.mean=102.246860179775 weightedLogRatios: wLogRatio Lung 2.24675650904517 cerebhem 2.93673556143909 cortex 3.52125756965656 heart 4.49582816418949 kidney 3.10927154489187 liver 3.54580695522104 stomach 3.82150007230126 testicle 4.09573140849595 cont.weightedLogRatios: wLogRatio Lung -0.796730235196933 cerebhem -3.61488842552566 cortex -0.00281463641745841 heart -1.68315158703702 kidney -1.96989076708526 liver -1.60956026800927 stomach -0.776854673231003 testicle -0.127604897997375 varWeightedLogRatios=0.498084201114738 cont.varWeightedLogRatios=1.3757341165402 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.75610827134085 0.101024475513922 47.0787722197619 1.08960282434918e-230 *** df.mm.trans1 -0.216538509525473 0.0880518866924676 -2.4592148749948 0.0141390968677164 * df.mm.trans2 -0.872646023073121 0.0785768069013333 -11.1056437323659 1.02671838394528e-26 *** df.mm.exp2 -0.0651130847232478 0.102802864137853 -0.633378119075893 0.526671467318915 df.mm.exp3 -0.0974085962733958 0.102802864137853 -0.947528039129108 0.343661732134839 df.mm.exp4 -0.200907899022521 0.102802864137853 -1.95430254504500 0.0510205835061361 . df.mm.exp5 -0.13665500465049 0.102802864137853 -1.32929180326380 0.184138396189748 df.mm.exp6 -0.0342282454687317 0.102802864137853 -0.332950309855507 0.739260805086381 df.mm.exp7 -0.107946704253246 0.102802864137853 -1.05003596114302 0.294025140125069 df.mm.exp8 0.034577808715236 0.102802864137853 0.336350635803971 0.736696473773705 df.mm.trans1:exp2 0.159776700113823 0.0959955959710491 1.66441698181664 0.0964287041940466 . df.mm.trans2:exp2 -0.00401891332556695 0.0748760484726577 -0.0536742176910487 0.95720839812067 df.mm.trans1:exp3 0.331611006900971 0.0959955959710492 3.45443979535249 0.000581056065338252 *** df.mm.trans2:exp3 0.0429846303420454 0.0748760484726578 0.574077174461764 0.566080238379336 df.mm.trans1:exp4 0.64757176414025 0.0959955959710491 6.74584867763671 2.95468874395013e-11 *** df.mm.trans2:exp4 0.156511719304044 0.0748760484726577 2.09027749856747 0.0369144691797602 * df.mm.trans1:exp5 0.314341427398469 0.0959955959710491 3.27454008924815 0.00110478332813585 ** df.mm.trans2:exp5 0.121195660892010 0.0748760484726577 1.61861721290309 0.105931841104359 df.mm.trans1:exp6 0.341804120215889 0.0959955959710491 3.56062293023288 0.000392279826825814 *** df.mm.trans2:exp6 0.0624541252624698 0.0748760484726577 0.834100176711061 0.404478599026592 df.mm.trans1:exp7 0.400059868156065 0.0959955959710491 4.16748147776193 3.42261708770566e-05 *** df.mm.trans2:exp7 0.046056923778677 0.0748760484726578 0.615108899550107 0.538661298774586 df.mm.trans1:exp8 0.341426082415958 0.095995595971049 3.55668485582326 0.000398109261821986 *** df.mm.trans2:exp8 -0.0638677600688022 0.0748760484726577 -0.852979842975081 0.393930993334493 df.mm.trans1:probe2 -0.587926227690323 0.0610041365814608 -9.63748133547054 7.52928877185296e-21 *** df.mm.trans1:probe3 0.179538208775786 0.0610041365814608 2.94304974771740 0.00334586989050640 ** df.mm.trans1:probe4 0.0228409772734207 0.0610041365814608 0.374416860124238 0.708195446865582 df.mm.trans1:probe5 -0.846564230024932 0.0610041365814608 -13.8771610822569 2.58327507188518e-39 *** df.mm.trans1:probe6 -0.638639007527028 0.0610041365814608 -10.4687820091386 4.27667173468992e-24 *** df.mm.trans1:probe7 0.0798564949874049 0.0610041365814608 1.30903409936423 0.190906343312867 df.mm.trans1:probe8 -0.417957217854558 0.0610041365814607 -6.85129306430632 1.48052205703255e-11 *** df.mm.trans1:probe9 0.438705854941186 0.0610041365814607 7.19141159149705 1.49847511337369e-12 *** df.mm.trans1:probe10 0.0176444897862920 0.0610041365814608 0.289234317130785 0.772478537833188 df.mm.trans1:probe11 0.681832528086526 0.0610041365814608 11.1768244957627 5.14945952969554e-27 *** df.mm.trans1:probe12 0.642021501009461 0.0610041365814607 10.5242289619516 2.55575505444107e-24 *** df.mm.trans1:probe13 0.589468733131385 0.0610041365814607 9.66276659524964 6.04005016877538e-21 *** df.mm.trans1:probe14 0.768502834247378 0.0610041365814607 12.5975528433416 2.86178884604859e-33 *** df.mm.trans1:probe15 0.947251848876954 0.0610041365814607 15.5276658593808 1.31002808286984e-47 *** df.mm.trans1:probe16 0.484600165717807 0.0610041365814608 7.94372632535673 6.80636256337611e-15 *** df.mm.trans1:probe17 -0.864124522932466 0.0610041365814607 -14.1650152162808 1.00864166714297e-40 *** df.mm.trans1:probe18 -0.950276630668313 0.0610041365814608 -15.5772490837466 7.24926267926415e-48 *** df.mm.trans1:probe19 -0.942482819211346 0.0610041365814607 -15.4494903464918 3.32345593940056e-47 *** df.mm.trans1:probe20 -0.960797023255617 0.0610041365814608 -15.7497028414235 9.18718217036963e-49 *** df.mm.trans1:probe21 -0.947323033051363 0.0610041365814607 -15.5288327339306 1.29192609534368e-47 *** df.mm.trans1:probe22 -0.938734209342239 0.0610041365814607 -15.3880418926792 6.89663917688967e-47 *** df.mm.trans2:probe2 -0.0239893926115151 0.0610041365814607 -0.393242064486582 0.694247612596556 df.mm.trans2:probe3 -0.0536188718353776 0.0610041365814607 -0.87893829566424 0.379704033821859 df.mm.trans2:probe4 0.0980592563088703 0.0610041365814607 1.60741978829466 0.108364938008015 df.mm.trans2:probe5 -0.0135050347981708 0.0610041365814607 -0.221379000752468 0.82485498730254 df.mm.trans2:probe6 0.0436478208023996 0.0610041365814607 0.715489526585058 0.47451963380598 df.mm.trans3:probe2 0.930445353429738 0.0610041365814607 15.2521682228431 3.44596136647744e-46 *** df.mm.trans3:probe3 0.895053821414946 0.0610041365814608 14.6720185150027 3.03769250601187e-43 *** df.mm.trans3:probe4 0.965720259028595 0.0610041365814608 15.8304061518687 3.48035561351359e-49 *** df.mm.trans3:probe5 0.79657004934187 0.0610041365814608 13.0576399237810 2.1161376381059e-35 *** df.mm.trans3:probe6 -0.00936092664821059 0.0610041365814608 -0.153447408205026 0.878084947591232 df.mm.trans3:probe7 0.873761121324051 0.0610041365814608 14.3229815269542 1.67364932236988e-41 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.91300456466632 0.321756716267142 15.2693147222053 2.81399622148832e-46 *** df.mm.trans1 -0.402674937754635 0.280439821926029 -1.43586932479528 0.151438389106094 df.mm.trans2 -0.071312690355478 0.250262277875881 -0.284951815194641 0.775756312905864 df.mm.exp2 0.393444872826422 0.327420774219157 1.20164908217788 0.229862372825588 df.mm.exp3 0.0154703393300303 0.327420774219157 0.0472491074120889 0.962326722087089 df.mm.exp4 0.00106103492331412 0.327420774219157 0.00324058522506555 0.997415215967997 df.mm.exp5 -0.125220777192189 0.327420774219157 -0.382446035963415 0.702234201646144 df.mm.exp6 0.119376176791960 0.327420774219157 0.364595609660541 0.715511611233727 df.mm.exp7 0.123130307676245 0.327420774219157 0.376061378420137 0.706973003618751 df.mm.exp8 -0.0189407836477359 0.327420774219157 -0.0578484480494755 0.9538840872418 df.mm.trans1:exp2 -0.54312233645619 0.305740045455574 -1.77641870775187 0.0760516672974522 . df.mm.trans2:exp2 0.0441183748861105 0.238475590801772 0.185001637852249 0.853275597608916 df.mm.trans1:exp3 -0.0107931866503656 0.305740045455574 -0.035301841583372 0.971848040790186 df.mm.trans2:exp3 -0.182897308655333 0.238475590801772 -0.766943518371919 0.443345981965195 df.mm.trans1:exp4 -0.104895526302815 0.305740045455574 -0.343087298709965 0.731624759194195 df.mm.trans2:exp4 0.0878647646777196 0.238475590801772 0.368443430131830 0.712642082188994 df.mm.trans1:exp5 -0.0174576030968314 0.305740045455574 -0.0570994979438114 0.954480485332206 df.mm.trans2:exp5 0.238251841096015 0.238475590801772 0.999061750072597 0.318073027032841 df.mm.trans1:exp6 -0.182371426312086 0.305740045455574 -0.596491787787694 0.551018928854317 df.mm.trans2:exp6 -0.00797710182324124 0.238475590801772 -0.0334503912808085 0.973323939787677 df.mm.trans1:exp7 -0.140643833053922 0.305740045455574 -0.460011160279489 0.645635754437317 df.mm.trans2:exp7 -0.144244566067371 0.238475590801772 -0.604860923427889 0.545446428117974 df.mm.trans1:exp8 -0.000942531670960719 0.305740045455574 -0.00308278776355999 0.997541079363493 df.mm.trans2:exp8 -0.14544019177886 0.238475590801772 -0.609874542253485 0.542121634927348 df.mm.trans1:probe2 0.00815579729642356 0.194294408016581 0.0419764901094197 0.96652812742947 df.mm.trans1:probe3 0.229369802881461 0.194294408016581 1.18052704255846 0.238148695700297 df.mm.trans1:probe4 0.101443019760844 0.194294408016581 0.522109827021823 0.601741345591972 df.mm.trans1:probe5 -0.0998068886478282 0.194294408016581 -0.513688940750734 0.607614223039797 df.mm.trans1:probe6 0.0089548037946657 0.194294408016581 0.0460888395403614 0.963251178138057 df.mm.trans1:probe7 0.0450573891803251 0.194294408016581 0.23190265556423 0.816674137757983 df.mm.trans1:probe8 -0.0040762525390696 0.194294408016581 -0.0209797728132337 0.983267129233153 df.mm.trans1:probe9 -0.245651613952219 0.194294408016581 -1.26432673209646 0.206488518493273 df.mm.trans1:probe10 -0.110159385677138 0.194294408016581 -0.566971467690091 0.570895853624369 df.mm.trans1:probe11 -0.0355195145265862 0.194294408016581 -0.182812850298579 0.85499212827074 df.mm.trans1:probe12 0.0920899094496659 0.194294408016581 0.473970972143507 0.635652573074877 df.mm.trans1:probe13 0.111637458384999 0.194294408016581 0.574578854454071 0.56574098398771 df.mm.trans1:probe14 -0.0748677781526366 0.194294408016581 -0.385331615649214 0.70009626128741 df.mm.trans1:probe15 0.0479624275185486 0.194294408016581 0.246854389728270 0.805085539150119 df.mm.trans1:probe16 0.0540055851005019 0.194294408016581 0.277957485507731 0.78111827939418 df.mm.trans1:probe17 0.120368401127637 0.194294408016581 0.619515519547865 0.535756792666326 df.mm.trans1:probe18 -0.105345933949812 0.194294408016581 -0.542197457071549 0.587836558525977 df.mm.trans1:probe19 -0.0299846447212839 0.194294408016581 -0.154325824543160 0.877392553742117 df.mm.trans1:probe20 0.165751786350720 0.194294408016581 0.853096020841607 0.393866608369435 df.mm.trans1:probe21 0.139458263664163 0.194294408016581 0.71776776844891 0.473114303965769 df.mm.trans1:probe22 0.0454439358416832 0.194294408016581 0.233892144944311 0.815129771906735 df.mm.trans2:probe2 -0.314626577665743 0.194294408016581 -1.61932904234122 0.105778646635607 df.mm.trans2:probe3 -0.498083938154573 0.194294408016581 -2.56355261707824 0.0105461017066917 * df.mm.trans2:probe4 -0.319308124694739 0.194294408016581 -1.64342416209678 0.100696145545101 df.mm.trans2:probe5 -0.345284994244567 0.194294408016581 -1.77712265509516 0.0759357013593644 . df.mm.trans2:probe6 -0.376848576579473 0.194294408016581 -1.9395750007757 0.0527897428666556 . df.mm.trans3:probe2 0.0318414080273838 0.194294408016581 0.163882266877524 0.869866065338954 df.mm.trans3:probe3 0.166118813187999 0.194294408016581 0.854985045034454 0.392820620158089 df.mm.trans3:probe4 -0.102926249764157 0.194294408016581 -0.529743757501108 0.59643958270463 df.mm.trans3:probe5 0.0783170895831398 0.194294408016581 0.40308463008599 0.686995863657459 df.mm.trans3:probe6 0.373537667208262 0.194294408016581 1.92253431800459 0.0549005865045782 . df.mm.trans3:probe7 0.297318295399724 0.194294408016581 1.53024628158290 0.126359148247984