fitVsDatCorrelation=0.660857460267864 cont.fitVsDatCorrelation=0.259693420835113 fstatistic=12213.1850280828,53,715 cont.fstatistic=7371.44225513394,53,715 residuals=-0.348416919003127,-0.0777500104299945,-0.00704510690262856,0.0670963673617789,1.06847699169386 cont.residuals=-0.3909048369972,-0.0990273022801514,-0.0269606673887354,0.0667012724835174,1.28243364814808 predictedValues: Include Exclude Both Lung 45.0022212811938 46.0113487203939 47.3655373154578 cerebhem 50.6290967421952 54.8790086059075 54.5916578689781 cortex 44.2278040090941 45.7047096253306 46.6650066950067 heart 45.2863522252761 47.8182302515025 49.4145979227721 kidney 43.0058432707881 44.0672131309301 47.6313494578533 liver 48.756264768687 52.2113073695099 50.7742578631229 stomach 45.9970023094821 48.4533895792222 50.2188781765044 testicle 50.2933608750917 47.7090658288943 49.9721486355106 diffExp=-1.00912743920018,-4.24991186371233,-1.4769056162365,-2.53187802622636,-1.06136986014196,-3.45504260082295,-2.45638726974006,2.58429504619748 diffExpScore=1.28442255097352 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=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 46.0531391632199 50.9662416867392 47.8828062114126 cerebhem 47.0749517096045 47.5682239035492 47.8267749174854 cortex 45.248421267942 47.5289013752736 50.3210001646756 heart 45.1166617778198 51.3993971599959 49.2377256154862 kidney 50.6234846966404 51.2954834284312 48.0323297398714 liver 47.7138084251773 47.9396826288563 47.794540165305 stomach 46.7076823234856 53.1895135671201 47.7753044207066 testicle 47.3908558850111 46.6382310684076 50.741442049719 cont.diffExp=-4.91310252351933,-0.493272193944719,-2.28048010733165,-6.28273538217616,-0.671998731790808,-0.22587420367897,-6.4818312436345,0.752624816603472 cont.diffExpScore=1.02339479388624 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=0.81342348441015 cont.tran.correlation=0.0521910744587378 tran.covariance=0.00362196138687169 cont.tran.covariance=8.13447582176142e-05 tran.mean=47.5032636620937 cont.tran.mean=48.2784175042046 weightedLogRatios: wLogRatio Lung -0.0846643987057069 cerebhem -0.319582926596837 cortex -0.125011062355519 heart -0.208911990504007 kidney -0.0919987267544336 liver -0.268457341509557 stomach -0.200539127145829 testicle 0.205282857374389 cont.weightedLogRatios: wLogRatio Lung -0.393354747469882 cerebhem -0.0402045871436843 cortex -0.188653889432253 heart -0.505129310533878 kidney -0.0518386615028353 liver -0.0182656935844339 stomach -0.507969797389529 testicle 0.0616402298235083 varWeightedLogRatios=0.0259844244645096 cont.varWeightedLogRatios=0.0534562659649769 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68208889648943 0.0625362830256127 58.8792412715251 1.96594148435128e-276 *** df.mm.trans1 0.107583968457871 0.0522622529971072 2.05854057734223 0.0399000639626535 * df.mm.trans2 0.151068536081146 0.0479250585119254 3.15218261118148 0.00168823297633724 ** df.mm.exp2 0.152071254831753 0.0618948682953533 2.45692832087615 0.0142495718098767 * df.mm.exp3 -0.00914459766525614 0.0618948682953533 -0.147744036252237 0.882586429921883 df.mm.exp4 0.00246175514723406 0.0618948682953533 0.0397731704587676 0.968285068935735 df.mm.exp5 -0.0941441348258793 0.0618948682953533 -1.52103296151528 0.128693657303988 df.mm.exp6 0.137038269473500 0.0618948682953533 2.21404897122606 0.0271401676296367 * df.mm.exp7 0.0150824838061896 0.0618948682953533 0.243679067773732 0.807549285138544 df.mm.exp8 0.093823669718227 0.0618948682953533 1.51585538998991 0.129997877435134 df.mm.trans1:exp2 -0.0342566597329146 0.0532706209163567 -0.643068527147506 0.520385808242221 df.mm.trans2:exp2 0.0241715862877699 0.0429481476239571 0.562808587215683 0.573741656075856 df.mm.trans1:exp3 -0.00821361139696353 0.0532706209163566 -0.154186515112339 0.877506183978704 df.mm.trans2:exp3 0.00245786814068705 0.0429481476239571 0.0572287345709879 0.954378977767667 df.mm.trans1:exp4 0.00383210621261975 0.0532706209163567 0.0719365786750781 0.942672496972035 df.mm.trans2:exp4 0.0360571202819371 0.0429481476239571 0.839550068553455 0.401441324389659 df.mm.trans1:exp5 0.0487682809491855 0.0532706209163567 0.915481744163625 0.360247427150787 df.mm.trans2:exp5 0.0509720969778687 0.0429481476239571 1.18682876440137 0.235689347244562 df.mm.trans1:exp6 -0.0569164225668406 0.0532706209163567 -1.06843925578056 0.285683003830263 df.mm.trans2:exp6 -0.0106272591424636 0.0429481476239571 -0.247443946488987 0.804635703970178 df.mm.trans1:exp7 0.00678189301788913 0.0532706209163566 0.127310192770942 0.898730688369073 df.mm.trans2:exp7 0.0366317350651619 0.0429481476239571 0.852929336694563 0.393984116311142 df.mm.trans1:exp8 0.0173375577647383 0.0532706209163567 0.325461905014418 0.744926727239853 df.mm.trans2:exp8 -0.0575903079507297 0.0429481476239571 -1.34092646916872 0.180370156418456 df.mm.trans1:probe2 -0.0134335682429734 0.0385982318601182 -0.348035845052626 0.727915703260316 df.mm.trans1:probe3 0.0504601114089725 0.0385982318601182 1.30731665615778 0.191525428133467 df.mm.trans1:probe4 -0.00354014163899971 0.0385982318601182 -0.0917177152525886 0.92694801186641 df.mm.trans1:probe5 0.0114214434837238 0.0385982318601182 0.2959058727124 0.76738788442575 df.mm.trans1:probe6 -0.00866530255594745 0.0385982318601182 -0.224499987132854 0.822432415895734 df.mm.trans1:probe7 0.0295718670190988 0.0385982318601182 0.766145639164732 0.443842504945592 df.mm.trans1:probe8 0.103533928435865 0.0385982318601182 2.68234899492486 0.00747955091788515 ** df.mm.trans1:probe9 0.0761660720593309 0.0385982318601182 1.97330469269578 0.0488455558647149 * df.mm.trans1:probe10 0.182714992121519 0.0385982318601182 4.73376585703943 2.65753965306922e-06 *** df.mm.trans1:probe11 0.0305617022445562 0.0385982318601182 0.791790213482142 0.428745606888979 df.mm.trans1:probe12 -0.0157774836993312 0.0385982318601182 -0.408761825062597 0.682836856588718 df.mm.trans2:probe2 -0.0639266634707126 0.0385982318601182 -1.65620704343105 0.0981186508056002 . df.mm.trans2:probe3 -0.0635597396007262 0.0385982318601182 -1.6467008082409 0.100058972850123 df.mm.trans2:probe4 -0.0493357934702887 0.0385982318601182 -1.27818791412736 0.201597955762231 df.mm.trans2:probe5 -0.0284167698638736 0.0385982318601182 -0.736219471577281 0.461838608791439 df.mm.trans2:probe6 0.119851862469561 0.0385982318601182 3.10511276536993 0.00197717368589107 ** df.mm.trans3:probe2 -0.139553656040353 0.0385982318601182 -3.61554530648197 0.000320765943109106 *** df.mm.trans3:probe3 -0.25425247950237 0.0385982318601182 -6.58715353656076 8.69168123226938e-11 *** df.mm.trans3:probe4 -0.112791784672953 0.0385982318601182 -2.92220081691087 0.00358531777646039 ** df.mm.trans3:probe5 0.0176684450270888 0.0385982318601182 0.457752704608855 0.647269188077472 df.mm.trans3:probe6 -0.0954412559305641 0.0385982318601182 -2.47268466277025 0.0136417537188747 * df.mm.trans3:probe7 -0.201404623509248 0.0385982318601182 -5.21797537874657 2.37248846462818e-07 *** df.mm.trans3:probe8 0.213363199433757 0.0385982318601182 5.52779723711168 4.5472846098361e-08 *** df.mm.trans3:probe9 -0.135537376463231 0.0385982318601182 -3.5114918464251 0.000473579500782716 *** df.mm.trans3:probe10 -0.270691143708795 0.0385982318601182 -7.01304517496534 5.41149332456891e-12 *** df.mm.trans3:probe11 -0.0662559961961681 0.0385982318601182 -1.71655521517885 0.086493584170744 . df.mm.trans3:probe12 0.132100672318616 0.0385982318601182 3.42245398176153 0.000655920756013261 *** df.mm.trans3:probe13 -0.128869274181260 0.0385982318601182 -3.33873516922454 0.000885233498301122 *** df.mm.trans3:probe14 -0.118185693249815 0.0385982318601182 -3.06194578233857 0.00228146556056022 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93013702733372 0.08046610593788 48.842142682632 5.70419078872283e-230 *** df.mm.trans1 -0.115738911992268 0.0672464013330494 -1.72111681365745 0.0856622164313938 . df.mm.trans2 0.00489585643468184 0.0616656866817662 0.0793935282022812 0.936741830965645 df.mm.exp2 -0.045882531972231 0.0796407907266445 -0.576118488447913 0.564716390771937 df.mm.exp3 -0.137119658404052 0.0796407907266445 -1.72172648152999 0.085551594205198 . df.mm.exp4 -0.0399849740245206 0.0796407907266445 -0.50206651214405 0.615775308353235 df.mm.exp5 0.0979410520294635 0.0796407907266445 1.22978502769557 0.219182114562976 df.mm.exp6 -0.0239498874434564 0.0796407907266445 -0.300723878114933 0.763712481379859 df.mm.exp7 0.0590581297364299 0.0796407907266445 0.741556295430797 0.458599707952370 df.mm.exp8 -0.118095990464064 0.0796407907266445 -1.48285808549304 0.138552740913922 df.mm.trans1:exp2 0.0678276517362905 0.068543879147352 0.989550818833556 0.322728468795019 df.mm.trans2:exp2 -0.0231159806051060 0.0552618420754179 -0.418299132583361 0.675854028585027 df.mm.trans1:exp3 0.119491509874742 0.068543879147352 1.74328490539418 0.0817137029659279 . df.mm.trans2:exp3 0.0672941485772935 0.0552618420754178 1.21773263521427 0.223727303491918 df.mm.trans1:exp4 0.0194406640962549 0.068543879147352 0.283623634058738 0.776780928162182 df.mm.trans2:exp4 0.0484479321696525 0.0552618420754178 0.876697742061039 0.380945286834032 df.mm.trans1:exp5 -0.00332138792929477 0.068543879147352 -0.0484563752535018 0.961366062935917 df.mm.trans2:exp5 -0.0915018319019292 0.0552618420754179 -1.65578685880672 0.0982037739531249 . df.mm.trans1:exp6 0.0593747992893148 0.068543879147352 0.866230508513736 0.386654341961126 df.mm.trans2:exp6 -0.0372699895887247 0.0552618420754178 -0.674425393526713 0.500258896987528 df.mm.trans1:exp7 -0.0449454038362878 0.068543879147352 -0.655717248503933 0.512216999734171 df.mm.trans2:exp7 -0.0163603520606901 0.0552618420754178 -0.296051514865584 0.767276704333839 df.mm.trans1:exp8 0.146729357776148 0.068543879147352 2.14066317228292 0.0326388122225356 * df.mm.trans2:exp8 0.0293531185948295 0.0552618420754179 0.53116431686751 0.595469876481533 df.mm.trans1:probe2 0.0419552806011629 0.0496647588184778 0.84476964349122 0.398522041303987 df.mm.trans1:probe3 0.040958403013082 0.0496647588184778 0.824697511625554 0.409818382356841 df.mm.trans1:probe4 0.0211306812553013 0.0496647588184778 0.425466301619079 0.670624808776317 df.mm.trans1:probe5 0.0417186914464728 0.0496647588184778 0.840005920474768 0.401185857110047 df.mm.trans1:probe6 0.0119400937879653 0.0496647588184778 0.240413807939865 0.810078388690673 df.mm.trans1:probe7 0.0069038919502838 0.0496647588184778 0.139009875705169 0.889481488544251 df.mm.trans1:probe8 0.0521707277547074 0.0496647588184778 1.05045768862764 0.293862643381101 df.mm.trans1:probe9 0.0267378520068998 0.0496647588184778 0.538366693868892 0.590491468598925 df.mm.trans1:probe10 -0.00242194551738729 0.0496647588184778 -0.0487658769518921 0.96111949454557 df.mm.trans1:probe11 0.0918538188378398 0.0496647588184778 1.84947679245883 0.0648015082918479 . df.mm.trans1:probe12 0.0673956589550936 0.0496647588184778 1.35701170323652 0.175205721351791 df.mm.trans2:probe2 -0.0043566546182046 0.0496647588184778 -0.0877212478596333 0.930122793191907 df.mm.trans2:probe3 0.0342401118064909 0.0496647588184778 0.68942470719805 0.490779714807385 df.mm.trans2:probe4 -0.0146203548548354 0.0496647588184778 -0.294380868902878 0.768552327299735 df.mm.trans2:probe5 -0.0511354346585941 0.0496647588184778 -1.02961206044494 0.303540315085893 df.mm.trans2:probe6 -0.0415156261936341 0.0496647588184778 -0.835917201276898 0.403480741022926 df.mm.trans3:probe2 0.0690358541086987 0.0496647588184778 1.39003703533568 0.164950218701281 df.mm.trans3:probe3 0.0454873422812300 0.0496647588184778 0.915887711193444 0.360034584895041 df.mm.trans3:probe4 0.0446492982290248 0.0496647588184778 0.89901369283229 0.368948022693996 df.mm.trans3:probe5 0.0697786267367762 0.0496647588184778 1.40499276341628 0.160457735062062 df.mm.trans3:probe6 0.0673963363541562 0.0496647588184778 1.35702534266775 0.175201389600648 df.mm.trans3:probe7 0.0600213347101929 0.0496647588184778 1.20852967251020 0.227243116171194 df.mm.trans3:probe8 0.0206213319573481 0.0496647588184778 0.415210552672127 0.678112338891656 df.mm.trans3:probe9 0.0583742276843893 0.0496647588184778 1.17536516985302 0.240239894234088 df.mm.trans3:probe10 0.0168015569950837 0.0496647588184778 0.338299377562519 0.735236904214135 df.mm.trans3:probe11 0.0861366217262876 0.0496647588184778 1.73436101927148 0.0832850324322479 . df.mm.trans3:probe12 0.0131489898246170 0.0496647588184778 0.264754931614104 0.791274560199171 df.mm.trans3:probe13 0.0295527099479027 0.0496647588184778 0.595043863112601 0.552002419304472 df.mm.trans3:probe14 0.111470999843710 0.0496647588184778 2.2444687640814 0.0251073174284009 *