fitVsDatCorrelation=0.808770991566673 cont.fitVsDatCorrelation=0.227296890225331 fstatistic=8359.8154991244,44,508 cont.fstatistic=3041.76617368727,44,508 residuals=-0.618191023038417,-0.0960760944359724,-0.00242519055645221,0.0843163567121202,0.891167011420102 cont.residuals=-0.671511515785684,-0.181420282794838,-0.0381767325790537,0.146987354160095,1.50726012618895 predictedValues: Include Exclude Both Lung 78.6155886055274 79.3475166216466 66.3299455158785 cerebhem 77.3173445811905 102.831596891314 73.1126047122491 cortex 65.7193700071392 87.7927153215507 89.6100956669132 heart 69.5290005184661 70.0480276232348 67.3092847679496 kidney 85.3142921079072 82.4856165098689 66.3769459041649 liver 78.0724178807251 69.5688124975097 63.6600469317591 stomach 70.8979676497396 67.6314311609612 64.9612251648604 testicle 75.9205563706236 88.206306351191 75.239681535026 diffExp=-0.731928016119156,-25.5142523101236,-22.0733453144115,-0.519027104768654,2.82867559803837,8.50360538321536,3.26653648877833,-12.2857499805674 diffExpScore=1.59331608689948 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,-1,0,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,-1,-1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 73.5383179848015 72.4280127420156 69.5961142951605 cerebhem 76.4263691488919 80.6365119930627 72.7541683577266 cortex 74.673020753863 75.844974035362 74.2733272330269 heart 77.4057806960019 82.4742717941748 72.5816055284781 kidney 75.4017150155665 74.5767863401673 71.9705000165927 liver 82.1328622418933 83.2826531115576 78.4244407784987 stomach 72.8997171768734 77.4310785800351 67.9101001864594 testicle 72.6295209540169 78.4960520359027 70.7404905202073 cont.diffExp=1.11030524278583,-4.21014284417089,-1.17195328149897,-5.0684910981729,0.824928675399235,-1.14979086966432,-4.53136140316174,-5.86653108188585 cont.diffExpScore=1.13627986707971 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=0.160038003654992 cont.tran.correlation=0.7061295881796 tran.covariance=0.00200365069325587 cont.tran.covariance=0.00137653707191812 tran.mean=78.0811600436622 cont.tran.mean=76.8923527877616 weightedLogRatios: wLogRatio Lung -0.0404900294859748 cerebhem -1.28057702022523 cortex -1.25395617215513 heart -0.031574254158935 kidney 0.149353566732714 liver 0.495875278326656 stomach 0.199885782230368 testicle -0.660662708410136 cont.weightedLogRatios: wLogRatio Lung 0.0652687953596262 cerebhem -0.233967983884081 cortex -0.0672876530392017 heart -0.277850252692404 kidney 0.0474938063165912 liver -0.0613816788815077 stomach -0.260464959853822 testicle -0.33589107749465 varWeightedLogRatios=0.460885663528751 cont.varWeightedLogRatios=0.0242104063863330 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.57285537388352 0.0848393281749478 53.9001837031737 2.81175031281881e-212 *** df.mm.trans1 -0.138934406864744 0.0731070599992228 -1.90042393807411 0.0579433428453901 . df.mm.trans2 -0.266842556946773 0.0682783453876941 -3.90815792959837 0.000105601700988378 *** df.mm.exp2 0.145244500165214 0.0917964552491227 1.58224519422936 0.114215962104993 df.mm.exp3 -0.378861449109769 0.0917964552491227 -4.12719040274041 4.29147770030077e-05 *** df.mm.exp4 -0.262138829082022 0.0917964552491227 -2.85565306819979 0.00447059507894115 ** df.mm.exp5 0.119850432474352 0.0917964552491227 1.30561068125228 0.192275947010625 df.mm.exp6 -0.0973696540617833 0.0917964552491227 -1.06071257106318 0.289324578622703 df.mm.exp7 -0.242241650805033 0.0917964552491227 -2.63889983711924 0.00857295218380228 ** df.mm.exp8 -0.0550785240038111 0.0917964552491226 -0.60000709019031 0.548769053187421 df.mm.trans1:exp2 -0.161896197733564 0.0828619919837021 -1.95380528343329 0.0512726423114279 . df.mm.trans2:exp2 0.114011018416488 0.0728397572395052 1.56523062043723 0.118151533862133 df.mm.trans1:exp3 0.199685148133276 0.0828619919837021 2.40985213308113 0.0163134655799718 * df.mm.trans2:exp3 0.480002827336801 0.0728397572395051 6.58984661025845 1.10362890768360e-10 *** df.mm.trans1:exp4 0.139312760180027 0.0828619919837021 1.68126250461645 0.0933265788588421 . df.mm.trans2:exp4 0.137482794800546 0.0728397572395051 1.887469151613 0.0596670328897371 . df.mm.trans1:exp5 -0.0380784490079511 0.0828619919837021 -0.459540593900284 0.646042599077198 df.mm.trans2:exp5 -0.0810636496376355 0.0728397572395052 -1.11290389630335 0.266276046552101 df.mm.trans1:exp6 0.0904364763057394 0.0828619919837021 1.09141084037090 0.275609508927598 df.mm.trans2:exp6 -0.0341511252922208 0.0728397572395051 -0.468852815913816 0.639375979429114 df.mm.trans1:exp7 0.138913410822137 0.0828619919837021 1.67644305304004 0.0942666814660598 . df.mm.trans2:exp7 0.0824773338277776 0.0728397572395051 1.13231203608468 0.258037447148464 df.mm.trans1:exp8 0.0201959985735154 0.0828619919837021 0.243730546297846 0.807537909962505 df.mm.trans2:exp8 0.160919835071826 0.0728397572395051 2.20923079881642 0.0276040288060359 * df.mm.trans1:probe2 0.0238040591204298 0.0483809799528245 0.492012752607342 0.622922869649451 df.mm.trans1:probe3 -0.0632668633586762 0.0483809799528245 -1.30768048560337 0.191573184949522 df.mm.trans1:probe4 -0.267663066806278 0.0483809799528245 -5.53240275553063 5.0649666800878e-08 *** df.mm.trans1:probe5 0.27034143775567 0.0483809799528245 5.58776275344723 3.75606135832649e-08 *** df.mm.trans1:probe6 -0.142042229980620 0.0483809799528245 -2.93591056070223 0.00347642543940949 ** df.mm.trans1:probe7 -0.051339517098491 0.0483809799528245 -1.06115083135049 0.289125588055398 df.mm.trans1:probe8 -0.290604654704956 0.0483809799528245 -6.00658884934368 3.6137608026309e-09 *** df.mm.trans1:probe9 -0.187847334371708 0.0483809799528245 -3.8826690686066 0.000116953159386616 *** df.mm.trans1:probe10 -0.229343502688276 0.0483809799528245 -4.74036497218339 2.77422802886692e-06 *** df.mm.trans1:probe11 -0.0395951986447457 0.0483809799528245 -0.818404229996051 0.413510131619904 df.mm.trans1:probe12 -0.201409431091513 0.0483809799528245 -4.16298783711913 3.68968979570548e-05 *** df.mm.trans2:probe2 0.00239204723730833 0.0483809799528245 0.049441893067085 0.96058657585025 df.mm.trans2:probe3 0.427527504932937 0.0483809799528245 8.83668551876817 1.61481369328172e-17 *** df.mm.trans2:probe4 -0.196992252823861 0.0483809799528245 -4.07168794464158 5.41250732112576e-05 *** df.mm.trans2:probe5 0.0504104973010176 0.0483809799528245 1.04194866144034 0.297931090048723 df.mm.trans2:probe6 0.462729866708255 0.0483809799528245 9.56429297545141 4.87327420855588e-20 *** df.mm.trans3:probe2 -0.0499498571650842 0.0483809799528245 -1.03242756169448 0.302363124003672 df.mm.trans3:probe3 0.0151999594674675 0.0483809799528245 0.314172211523799 0.753519225001587 df.mm.trans3:probe4 -0.171951049154228 0.0483809799528245 -3.55410430549142 0.000414666804217349 *** df.mm.trans3:probe5 0.344516207127736 0.0483809799528245 7.1209017978484 3.68360111959326e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36972338062981 0.140478444266350 31.1060063588454 1.02933452322140e-119 *** df.mm.trans1 -0.0645811279632752 0.121051949308224 -0.533499281360914 0.593921345538171 df.mm.trans2 -0.0802670275782986 0.113056479152744 -0.7099728222551 0.478046754618293 df.mm.exp2 0.101502122766781 0.151998177024347 0.667785132386966 0.504574015911442 df.mm.exp3 -0.00363260687029245 0.151998177024347 -0.0238990160369527 0.980942543692442 df.mm.exp4 0.139145321418886 0.151998177024347 0.915440725296316 0.360394639777037 df.mm.exp5 0.0207119832802842 0.151998177024347 0.136264682154488 0.891666024999947 df.mm.exp6 0.130751857718541 0.151998177024347 0.860219907095313 0.390073616786744 df.mm.exp7 0.082597209346617 0.151998177024347 0.543409210318269 0.587086455529019 df.mm.exp8 0.0517106693043723 0.151998177024347 0.340205851916824 0.733842139356116 df.mm.trans1:exp2 -0.0629809437139957 0.137204336397828 -0.459030270963015 0.646408771018697 df.mm.trans2:exp2 0.00585628568147873 0.120609344720929 0.0485558204053694 0.961292362250834 df.mm.trans1:exp3 0.0189448623201296 0.137204336397828 0.138077722741928 0.890233705166365 df.mm.trans2:exp3 0.0497309076906713 0.120609344720929 0.412330469133555 0.680271163883076 df.mm.trans1:exp4 -0.0878904611156476 0.137204336397828 -0.64058078208845 0.522083929260894 df.mm.trans2:exp4 -0.00925207464110943 0.120609344720929 -0.0767110928470531 0.93888358184148 df.mm.trans1:exp5 0.00431143354105332 0.137204336397828 0.0314234495369898 0.974944182120307 df.mm.trans2:exp5 0.00852415955460887 0.120609344720929 0.0706757803413361 0.943683610809103 df.mm.trans1:exp6 -0.0202202539334083 0.137204336397828 -0.147373286182290 0.882895860955215 df.mm.trans2:exp6 0.00889528292332921 0.120609344720929 0.0737528501121657 0.941236075978169 df.mm.trans1:exp7 -0.0913190534419012 0.137204336397828 -0.665569732265013 0.505988238439105 df.mm.trans2:exp7 -0.0158021181865891 0.120609344720929 -0.131019020318472 0.895812129910995 df.mm.trans1:exp8 -0.0641458089105081 0.137204336397828 -0.467520273736214 0.640328173519795 df.mm.trans2:exp8 0.0287445207979744 0.120609344720929 0.238327476734781 0.811723215025733 df.mm.trans1:probe2 0.0177746473356801 0.0801100732650687 0.221877806513386 0.82449817385448 df.mm.trans1:probe3 -0.00419600677663788 0.0801100732650687 -0.0523780169661574 0.958248079790895 df.mm.trans1:probe4 -0.0104134084740764 0.0801100732650687 -0.129988752346043 0.896626779803096 df.mm.trans1:probe5 0.0287168422355028 0.0801100732650686 0.358467307107364 0.72014254694998 df.mm.trans1:probe6 -0.0210952062153280 0.0801100732650687 -0.263327760861334 0.792404736910385 df.mm.trans1:probe7 -0.137578779535716 0.0801100732650687 -1.71737178519978 0.0865207236765151 . df.mm.trans1:probe8 -0.0191279056239264 0.0801100732650687 -0.238770292477899 0.81137999796746 df.mm.trans1:probe9 0.0562482175661736 0.0801100732650687 0.702136638672882 0.482915529441769 df.mm.trans1:probe10 -0.0222565653166225 0.0801100732650687 -0.277824802918103 0.781259883103873 df.mm.trans1:probe11 -0.0202135890115224 0.0801100732650687 -0.252322687867724 0.800893709512999 df.mm.trans1:probe12 0.00743571795892252 0.0801100732650687 0.0928187636818054 0.926084137194245 df.mm.trans2:probe2 -0.00936870997004957 0.0801100732650687 -0.116947964072512 0.90694750912719 df.mm.trans2:probe3 0.0222811571045275 0.0801100732650687 0.278131777895190 0.781024368322224 df.mm.trans2:probe4 -0.00366265995188743 0.0801100732650687 -0.0457203420569645 0.963551116977089 df.mm.trans2:probe5 -0.0901709844277843 0.0801100732650687 -1.12558859020670 0.260871190692103 df.mm.trans2:probe6 0.00542586341399166 0.0801100732650687 0.0677301017568482 0.94602713566593 df.mm.trans3:probe2 0.0637231440037436 0.0801100732650687 0.795444834919775 0.426726294584195 df.mm.trans3:probe3 0.0439365511606799 0.0801100732650687 0.548452265363712 0.58362233741334 df.mm.trans3:probe4 0.00335186337052679 0.0801100732650687 0.0418407228194154 0.966642110774286 df.mm.trans3:probe5 -0.0218440005293546 0.0801100732650687 -0.272674829007796 0.785213987700455