fitVsDatCorrelation=0.773774675259558 cont.fitVsDatCorrelation=0.287757237650702 fstatistic=12535.7522478844,62,922 cont.fstatistic=5476.02123748996,62,922 residuals=-0.385292025721877,-0.088370421767752,-0.00476481899764354,0.0846182553549809,0.661659164977315 cont.residuals=-0.55882313649656,-0.156775391457998,-0.0158263926989652,0.136289798380842,0.853085632425762 predictedValues: Include Exclude Both Lung 56.3214417255259 67.9548374727919 61.8937024083385 cerebhem 58.2702935795526 65.8942262719761 52.7479437244431 cortex 55.7334739296402 60.6952958515842 56.9139890350071 heart 58.7147581393016 63.8047595084829 62.0144716583902 kidney 57.1877204000318 67.0399276612122 57.2990769710168 liver 61.444098064779 67.5762180629236 61.5841748697362 stomach 60.6527771456668 68.0314643659679 74.6577823477175 testicle 57.9586299109672 58.2236976804738 59.5617811079596 diffExp=-11.6333957472660,-7.62393269242345,-4.96182192194398,-5.09000136918129,-9.85220726118037,-6.13211999814455,-7.37868722030115,-0.265067769506572 diffExpScore=0.98145993173525 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=-1,0,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 60.4019435586409 67.8703280070869 56.191709138703 cerebhem 59.5403245974511 61.1940908474458 60.8707587993407 cortex 61.5718058956269 62.2674220687407 62.2952230829742 heart 60.6691752161612 62.0020605917301 60.0759261425962 kidney 61.0562130766309 63.3239163016922 56.6988226828204 liver 60.502534799314 64.1044703743688 58.7852443468239 stomach 59.4088107227841 60.3500154147346 52.2490488641321 testicle 59.1160303235403 53.3549546248647 59.90082984141 cont.diffExp=-7.46838444844597,-1.65376624999473,-0.695616173113748,-1.33288537556894,-2.26770322506130,-3.60193557505479,-0.941204691950453,5.7610756986756 cont.diffExpScore=1.79710731666545 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=0.389925831339803 cont.tran.correlation=0.59345166390299 tran.covariance=0.000755706975106404 cont.tran.covariance=0.00059264235185988 tran.mean=61.5939762356799 cont.tran.mean=61.0458810263008 weightedLogRatios: wLogRatio Lung -0.774535441519564 cerebhem -0.507396663868667 cortex -0.346533086752438 heart -0.34204585665967 kidney -0.655794718767459 liver -0.396275320617296 stomach -0.477883126805296 testicle -0.0185348335895394 cont.weightedLogRatios: wLogRatio Lung -0.484882644481961 cerebhem -0.112336622945058 cortex -0.0463507113120072 heart -0.0894549436572057 kidney -0.150614380788394 liver -0.238925618833265 stomach -0.0643254973897837 testicle 0.41303616974458 varWeightedLogRatios=0.051762064399129 cont.varWeightedLogRatios=0.0625045373371437 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04404578540784 0.0771996897720134 52.3842232702066 1.33228221434991e-278 *** df.mm.trans1 -0.190168437029157 0.0698477883240121 -2.72261214839041 0.00659906608459825 ** df.mm.trans2 0.164501524184286 0.0635442731345899 2.58877025528112 0.00978362792185346 ** df.mm.exp2 0.163118249834815 0.0868321945289524 1.87854574814905 0.0606218798152582 . df.mm.exp3 -0.0395942362225921 0.0868321945289524 -0.455985667958562 0.648507663406753 df.mm.exp4 -0.0233490757574503 0.0868321945289524 -0.26889883279024 0.788067693230425 df.mm.exp5 0.0788428555737574 0.0868321945289524 0.907991051031987 0.364120210447758 df.mm.exp6 0.0864787644718252 0.0868321945289524 0.995929734828833 0.319545586695508 df.mm.exp7 -0.112279239918328 0.0868321945289524 -1.29306002833881 0.196314197425573 df.mm.exp8 -0.0874923883461735 0.0868321945289524 -1.00760309952780 0.313909397122009 df.mm.trans1:exp2 -0.129101140771930 0.0842396022934237 -1.53254689311381 0.125730571669485 df.mm.trans2:exp2 -0.193910755355677 0.0719332547703942 -2.69570389904678 0.00715192949904605 ** df.mm.trans1:exp3 0.0290998605210599 0.0842396022934237 0.345441570577448 0.729841225673655 df.mm.trans2:exp3 -0.0733828967572754 0.0719332547703942 -1.02015259828724 0.307923696264980 df.mm.trans1:exp4 0.0649648769923046 0.0842396022934237 0.771191639367179 0.440790936244246 df.mm.trans2:exp4 -0.039666465943554 0.0719332547703942 -0.551434327143496 0.581469584955602 df.mm.trans1:exp5 -0.0635789688976665 0.0842396022934237 -0.754739661236861 0.450597936970766 df.mm.trans2:exp5 -0.0923978081554238 0.0719332547703942 -1.28449363858692 0.199291924415438 df.mm.trans1:exp6 0.000573712074473546 0.0842396022934237 0.00681047938088774 0.994567538912625 df.mm.trans2:exp6 -0.0920659768390013 0.0719332547703942 -1.27988059393210 0.200909074650099 df.mm.trans1:exp7 0.186369353624186 0.0842396022934237 2.21237219253509 0.0271853769043331 * df.mm.trans2:exp7 0.113406219625109 0.0719332547703941 1.57654787048207 0.115242624385879 df.mm.trans1:exp8 0.116146556650276 0.0842396022934237 1.37876430429614 0.168301841928025 df.mm.trans2:exp8 -0.0670584928656181 0.0719332547703942 -0.932232151591973 0.351460608240166 df.mm.trans1:probe2 0.139906695379205 0.0421198011467118 3.32163712957433 0.000930074624659027 *** df.mm.trans1:probe3 0.460493843892454 0.0421198011467119 10.9329538923619 3.022485557326e-26 *** df.mm.trans1:probe4 0.345328338005353 0.0421198011467118 8.1987171972276 8.09610137212812e-16 *** df.mm.trans1:probe5 -0.112321375522427 0.0421198011467119 -2.66671191374311 0.00779390209553413 ** df.mm.trans1:probe6 0.0697310433923853 0.0421198011467118 1.65554066006860 0.0981551704242898 . df.mm.trans1:probe7 0.275001423566104 0.0421198011467119 6.52902948445122 1.09186084040808e-10 *** df.mm.trans1:probe8 0.0878724680850966 0.0421198011467118 2.08625078212072 0.0372302438195631 * df.mm.trans1:probe9 -0.0804814336911073 0.0421198011467119 -1.91077430329678 0.0563434938357694 . df.mm.trans1:probe10 0.06699617477868 0.0421198011467118 1.59060994958924 0.112040179409130 df.mm.trans1:probe11 0.472736483432681 0.0421198011467118 11.2236162223569 1.72322541999078e-27 *** df.mm.trans1:probe12 0.436405400044365 0.0421198011467118 10.3610508160824 7.1696878053952e-24 *** df.mm.trans1:probe13 0.366821209488689 0.0421198011467118 8.7089967070589 1.39968337398719e-17 *** df.mm.trans1:probe14 0.383970795715286 0.0421198011467118 9.1161587961405 4.75659978486823e-19 *** df.mm.trans1:probe15 0.466732343186187 0.0421198011467118 11.0810671104658 7.07135090338755e-27 *** df.mm.trans1:probe16 0.200817933018393 0.0421198011467118 4.76777970339659 2.16374626373308e-06 *** df.mm.trans1:probe17 0.221337244756278 0.0421198011467119 5.25494515003323 1.84002750744626e-07 *** df.mm.trans1:probe18 0.237976636719362 0.0421198011467118 5.64999430767587 2.13769925290257e-08 *** df.mm.trans1:probe19 0.165674092396050 0.0421198011467119 3.93340158038671 9.00346276630512e-05 *** df.mm.trans1:probe20 -0.0156923361517767 0.0421198011467118 -0.372564345617804 0.709558336771586 df.mm.trans1:probe21 -0.0334441440581266 0.0421198011467119 -0.794024262878969 0.427385617990837 df.mm.trans1:probe22 0.417409565083706 0.0421198011467119 9.91005545419798 4.5565545659861e-22 *** df.mm.trans1:probe23 0.0288591089178972 0.0421198011467118 0.68516726414199 0.493410594412348 df.mm.trans1:probe24 0.212124974609777 0.0421198011467119 5.03622925167435 5.71085832695783e-07 *** df.mm.trans1:probe25 0.132638246838348 0.0421198011467118 3.14907105986426 0.00169062173373558 ** df.mm.trans1:probe26 0.457707480032621 0.0421198011467119 10.8668005919195 5.75524460074463e-26 *** df.mm.trans1:probe27 0.0648722146283622 0.0421198011467118 1.54018330719082 0.123858791739534 df.mm.trans1:probe28 0.342285014600679 0.0421198011467118 8.12646321402209 1.41458588994766e-15 *** df.mm.trans1:probe29 0.283168517776852 0.0421198011467118 6.72293102216979 3.11850885175124e-11 *** df.mm.trans1:probe30 0.138522289393049 0.0421198011467118 3.28876883607659 0.00104435699571701 ** df.mm.trans1:probe31 -0.0653930016769026 0.0421198011467118 -1.55254773043979 0.120874390504959 df.mm.trans1:probe32 0.0338714220733255 0.0421198011467119 0.804168613126744 0.421506894392736 df.mm.trans2:probe2 0.0339580669976323 0.0421198011467118 0.806225719806925 0.420320606373242 df.mm.trans2:probe3 0.00833988592283859 0.0421198011467118 0.198003924419991 0.843085639875188 df.mm.trans2:probe4 0.0096188070945435 0.0421198011467118 0.228367818286682 0.819410943753032 df.mm.trans2:probe5 -0.0784269965817376 0.0421198011467119 -1.86199826320548 0.0629213006237053 . df.mm.trans2:probe6 0.119174417679025 0.0421198011467119 2.82941548712247 0.00476452535963993 ** df.mm.trans3:probe2 0.298242288437276 0.0421198011467118 7.08080950806101 2.84052398840865e-12 *** df.mm.trans3:probe3 0.0228764029598871 0.0421198011467119 0.543127040894706 0.587173611407278 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.32009536994687 0.116715018184535 37.0140487243595 1.6196704080986e-184 *** df.mm.trans1 -0.218028951245548 0.105599982441147 -2.06466844222309 0.0392335430048932 * df.mm.trans2 -0.0638139255804138 0.0960699585235298 -0.664244333620528 0.506700020602521 df.mm.exp2 -0.197899475253603 0.131278003750784 -1.5074838860994 0.132029219465909 df.mm.exp3 -0.170093313103708 0.131278003750784 -1.29567260503603 0.195412579990564 df.mm.exp4 -0.152856835157722 0.131278003750784 -1.16437507267328 0.244573239319384 df.mm.exp5 -0.0675464027362968 0.131278003750784 -0.514529477950668 0.607005166589587 df.mm.exp6 -0.100542517465261 0.131278003750784 -0.765874819791815 0.443946808750555 df.mm.exp7 -0.0612689444494595 0.131278003750784 -0.466711426887413 0.640816611378101 df.mm.exp8 -0.326072391202058 0.131278003750784 -2.48383112087132 0.0131744225209829 * df.mm.trans1:exp2 0.183532000034414 0.127358370772874 1.44106742980969 0.149905153007656 df.mm.trans2:exp2 0.0943511617529378 0.108752912911872 0.867573651377919 0.385853516671439 df.mm.trans1:exp3 0.189276099822346 0.127358370772874 1.48616929278951 0.137576198188347 df.mm.trans2:exp3 0.083932738304774 0.108752912911872 0.771774622467251 0.440445683780943 df.mm.trans1:exp4 0.157271299882217 0.127358370772874 1.23487210874178 0.217192706595772 df.mm.trans2:exp4 0.0624255114240829 0.108752912911872 0.574012316108437 0.566099467416912 df.mm.trans1:exp5 0.07832008586549 0.127358370772874 0.61495828966879 0.538734005618283 df.mm.trans2:exp5 -0.00178945837699404 0.108752912911872 -0.0164543489372476 0.986875481255607 df.mm.trans1:exp6 0.102206496587444 0.127358370772874 0.802511024341819 0.422464218816429 df.mm.trans2:exp6 0.0434576760129629 0.108752912911872 0.399600110464892 0.689543624075086 df.mm.trans1:exp7 0.0446902059385900 0.127358370772874 0.350901206315594 0.725742627239266 df.mm.trans2:exp7 -0.0561687961616894 0.108752912911872 -0.516480843204685 0.60564246539002 df.mm.trans1:exp8 0.304553236957793 0.127358370772874 2.39130914685555 0.0169880265979406 * df.mm.trans2:exp8 0.0854402912549939 0.108752912911872 0.785636807027232 0.432282138175854 df.mm.trans1:probe2 0.00358511852086974 0.0636791853864369 0.0562996919497865 0.955115262054753 df.mm.trans1:probe3 0.0611339144779867 0.0636791853864369 0.960029782212128 0.337291912623924 df.mm.trans1:probe4 0.0148423038500831 0.0636791853864369 0.233079361176068 0.815751514233202 df.mm.trans1:probe5 0.0624846144706808 0.0636791853864369 0.98124079464103 0.326731415251464 df.mm.trans1:probe6 -0.0284632869657849 0.0636791853864369 -0.446979445372229 0.654994876548231 df.mm.trans1:probe7 -0.0856480261005067 0.0636791853864369 -1.34499248978692 0.178958395471326 df.mm.trans1:probe8 0.0652275137874591 0.0636791853864369 1.02431451331587 0.305955413207966 df.mm.trans1:probe9 0.0299572492282725 0.0636791853864369 0.470440208154 0.638151806458637 df.mm.trans1:probe10 0.0767748679013793 0.0636791853864369 1.2056509114473 0.228261436198056 df.mm.trans1:probe11 0.102389204226972 0.0636791853864369 1.60789123801793 0.108201358388113 df.mm.trans1:probe12 -0.00462808523083866 0.0636791853864369 -0.0726781475415104 0.942078001139973 df.mm.trans1:probe13 -0.115193944092205 0.0636791853864369 -1.80897326800195 0.0707807768305324 . df.mm.trans1:probe14 -0.0399220335262814 0.0636791853864369 -0.626924375429972 0.530864010219292 df.mm.trans1:probe15 0.0340532367926274 0.0636791853864369 0.534762443740061 0.592943070126405 df.mm.trans1:probe16 0.0165913944596246 0.0636791853864369 0.260546587694860 0.794500361615751 df.mm.trans1:probe17 0.0610737162155488 0.0636791853864369 0.959084445646142 0.337767634902462 df.mm.trans1:probe18 -0.0230201655399208 0.0636791853864369 -0.361502198877436 0.717806885883921 df.mm.trans1:probe19 0.00645739527345608 0.0636791853864369 0.101405117453519 0.919250921797519 df.mm.trans1:probe20 0.00305131560751365 0.0636791853864369 0.0479170012775249 0.961792767021596 df.mm.trans1:probe21 -0.0803111991938161 0.0636791853864369 -1.26118446249662 0.207561408489498 df.mm.trans1:probe22 0.0912714292759018 0.0636791853864369 1.43330083012246 0.152110885665687 df.mm.trans1:probe23 -0.0699773760605418 0.0636791853864369 -1.09890501324545 0.272096365464973 df.mm.trans1:probe24 0.0177445643256059 0.0636791853864369 0.278655642623614 0.780571590864147 df.mm.trans1:probe25 -0.0991860640447723 0.0636791853864369 -1.55759002636202 0.119673631212074 df.mm.trans1:probe26 -0.0492399375293268 0.0636791853864369 -0.773250116667078 0.439572565492034 df.mm.trans1:probe27 -0.0327913696450188 0.0636791853864369 -0.514946437301679 0.606713875226406 df.mm.trans1:probe28 -0.0936929621402522 0.0636791853864369 -1.47132790050119 0.141543672879771 df.mm.trans1:probe29 -0.0295017463904166 0.0636791853864369 -0.463287119823933 0.643267913881135 df.mm.trans1:probe30 0.0858105458777776 0.0636791853864369 1.34754465461574 0.178135916398653 df.mm.trans1:probe31 0.0455269193893186 0.0636791853864369 0.714941925105334 0.474825913877728 df.mm.trans1:probe32 -0.0629788726486104 0.0636791853864369 -0.989002485921 0.322921394828987 df.mm.trans2:probe2 -0.0408063515982828 0.0636791853864369 -0.640811457474677 0.521804424382293 df.mm.trans2:probe3 -0.0597536480298017 0.0636791853864369 -0.938354466489904 0.348308019332394 df.mm.trans2:probe4 -0.0383789219896324 0.0636791853864369 -0.60269178628354 0.546861950504402 df.mm.trans2:probe5 -0.145885201521402 0.0636791853864369 -2.29094013430131 0.0221917391103387 * df.mm.trans2:probe6 -0.0633183839665745 0.0636791853864369 -0.994334076077247 0.320321130647361 df.mm.trans3:probe2 -0.000899992516175813 0.0636791853864369 -0.0141332291032652 0.98872674766951 df.mm.trans3:probe3 -0.0275498214630148 0.0636791853864369 -0.432634640280475 0.665381394180744