fitVsDatCorrelation=0.72954320530599 cont.fitVsDatCorrelation=0.219272453065088 fstatistic=13240.6940715889,54,738 cont.fstatistic=6499.43464280885,54,738 residuals=-0.419799705462569,-0.0792912999506042,-0.00379510507658601,0.0714664801576428,0.70620937287122 cont.residuals=-0.450471268187864,-0.120515780753773,-0.0263427806061208,0.0904147226666263,0.737951441999563 predictedValues: Include Exclude Both Lung 48.2859536624347 56.2962283349314 58.2945576891599 cerebhem 54.7024189695606 56.6863413085844 57.7326491185489 cortex 47.8193496346827 51.385554819244 51.0624141584751 heart 50.0547762993922 51.617978371037 52.8920792205937 kidney 48.6078241740724 48.4699918685268 57.9187365161025 liver 50.6849291505369 51.2150388809426 56.876362328015 stomach 50.9271797919087 61.0096449577399 52.8286189800241 testicle 50.8526504001451 52.1759920551027 54.6389682400159 diffExp=-8.01027467249674,-1.98392233902379,-3.56620518456128,-1.56320207164486,0.137832305545651,-0.530109730405734,-10.0824651658312,-1.32334165495754 diffExpScore=0.974058324282154 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 52.674777863272 53.5611517329179 47.7979905483897 cerebhem 51.31683736635 55.5769021327018 55.1618409788854 cortex 51.7625645298227 55.4552597854262 51.8375860311882 heart 54.4675243723709 50.7987774671026 53.2369656608103 kidney 52.7590921829166 52.6695348665982 49.4320491934998 liver 51.5859680499262 50.183137230531 52.3567284888931 stomach 56.0374599585837 54.3195911185819 50.6087931538412 testicle 52.023315418852 51.4890483524185 52.3796517250941 cont.diffExp=-0.886373869645873,-4.26006476635182,-3.69269525560351,3.66874690526827,0.089557316318384,1.40283081939521,1.7178688400018,0.534267066433451 cont.diffExpScore=6.69963852573896 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=0.416889057580737 cont.tran.correlation=-0.0522330408999294 tran.covariance=0.00133994668795217 cont.tran.covariance=-6.10560720236487e-05 tran.mean=51.9244907924276 cont.tran.mean=52.9175589017733 weightedLogRatios: wLogRatio Lung -0.606869166627361 cerebhem -0.143203980770746 cortex -0.28075837289415 heart -0.120809432212654 kidney 0.0110244679651330 liver -0.0408987471673576 stomach -0.726282545854726 testicle -0.101265142060902 cont.weightedLogRatios: wLogRatio Lung -0.066289898931146 cerebhem -0.317232112012449 cortex -0.274336601240942 heart 0.276331074694421 kidney 0.00673602045817944 liver 0.108337847634005 stomach 0.124867204576251 testicle 0.0407394817901231 varWeightedLogRatios=0.0738873190459296 cont.varWeightedLogRatios=0.0406303886175517 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.57729267565205 0.0652974657982167 54.7845560608226 2.92748315214143e-262 *** df.mm.trans1 0.247871841709314 0.0575212532824802 4.30922185391276 1.86029617769962e-05 *** df.mm.trans2 0.387883050172746 0.0519031833677332 7.47320347240748 2.21946264821775e-13 *** df.mm.exp2 0.141358856869950 0.0691195909485445 2.04513445363389 0.0411957655054642 * df.mm.exp3 0.0314792628164095 0.0691195909485445 0.455431844783977 0.648932550227924 df.mm.exp4 0.0464748594896043 0.0691195909485445 0.672383312051168 0.501550157515929 df.mm.exp5 -0.136571047916038 0.0691195909485445 -1.97586597434738 0.0485420316905058 * df.mm.exp6 -0.0214775075999568 0.0691195909485445 -0.310729668755499 0.756093879119407 df.mm.exp7 0.232116155242224 0.0691195909485445 3.35818184188939 0.000824826956975485 *** df.mm.exp8 0.0405478719779659 0.0691195909485445 0.586633563965264 0.557629229241094 df.mm.trans1:exp2 -0.0165916298630025 0.0651965162931739 -0.254486448146919 0.79919066496745 df.mm.trans2:exp2 -0.134453109811173 0.0533426042398695 -2.52055766168760 0.0119266604819262 * df.mm.trans1:exp3 -0.0411896050786636 0.0651965162931739 -0.63177616566878 0.527728796614914 df.mm.trans2:exp3 -0.122749705124794 0.0533426042398695 -2.30115696213136 0.0216605879151985 * df.mm.trans1:exp4 -0.0104976316457547 0.0651965162931739 -0.161015223551965 0.872125476250163 df.mm.trans2:exp4 -0.133232370269014 0.0533426042398695 -2.49767277334077 0.0127178123734387 * df.mm.trans1:exp5 0.143214853179385 0.0651965162931739 2.196664198059 0.0283546906004037 * df.mm.trans2:exp5 -0.0131116106165134 0.0533426042398695 -0.245799971774034 0.80590542063208 df.mm.trans1:exp6 0.0699654146730117 0.0651965162931739 1.07314652148580 0.283556151501526 df.mm.trans2:exp6 -0.0731168159676462 0.0533426042398695 -1.37070203094803 0.170884590518139 df.mm.trans1:exp7 -0.178860094002054 0.0651965162931739 -2.74339955831015 0.00622822767963293 ** df.mm.trans2:exp7 -0.151711730133123 0.0533426042398695 -2.84410055142621 0.00457692802380146 ** df.mm.trans1:exp8 0.0112436671669332 0.0651965162931739 0.172458097551915 0.863124683727862 df.mm.trans2:exp8 -0.116552945828931 0.0533426042398695 -2.18498791894035 0.0292035346723455 * df.mm.trans1:probe2 0.301995869864662 0.038066564322934 7.93336291929867 7.91101667169483e-15 *** df.mm.trans1:probe3 0.0646240188963001 0.038066564322934 1.69765830055133 0.089993854820599 . df.mm.trans1:probe4 0.0945872433814848 0.038066564322934 2.48478540324950 0.0131835327066788 * df.mm.trans1:probe5 0.0590588328031982 0.038066564322934 1.55146212571690 0.121219684718203 df.mm.trans1:probe6 -0.0394101105596777 0.038066564322934 -1.03529465452532 0.300870439992047 df.mm.trans1:probe7 0.160012126368542 0.038066564322934 4.20348222159202 2.95079558696844e-05 *** df.mm.trans1:probe8 0.505809648877063 0.038066564322934 13.2875046086659 2.80796243168300e-36 *** df.mm.trans1:probe9 0.0594695950570885 0.038066564322934 1.56225275684414 0.118657145012247 df.mm.trans1:probe10 -0.0332002953693971 0.038066564322934 -0.87216421970067 0.383402450456 df.mm.trans1:probe11 0.0184191514311167 0.0380665643229340 0.483866925180312 0.6286238569766 df.mm.trans1:probe12 -0.0360776335815767 0.038066564322934 -0.947751241102705 0.343566333362782 df.mm.trans1:probe13 0.0898285052733855 0.038066564322934 2.35977443384000 0.0185454526606939 * df.mm.trans1:probe14 -0.00574984903140465 0.038066564322934 -0.151047228287438 0.879979737130217 df.mm.trans1:probe15 0.0877469880603771 0.038066564322934 2.30509344935845 0.0214379317246939 * df.mm.trans1:probe16 -0.0367677819008608 0.038066564322934 -0.965881280720393 0.334419990441641 df.mm.trans1:probe17 -0.040277995084826 0.038066564322934 -1.05809378390788 0.290358891917071 df.mm.trans1:probe18 -0.0215903576300168 0.038066564322934 -0.567173791857262 0.570768583802634 df.mm.trans1:probe19 0.0552805027663855 0.038066564322934 1.45220625369336 0.146869261637049 df.mm.trans1:probe20 0.161336062107992 0.038066564322934 4.23826171280688 2.53812543014325e-05 *** df.mm.trans1:probe21 0.0100206800781762 0.038066564322934 0.263240989997592 0.7924383158844 df.mm.trans1:probe22 -0.0517581646855127 0.038066564322934 -1.35967523221763 0.17434797548353 df.mm.trans2:probe2 0.266982108969849 0.0380665643229340 7.01355937207602 5.25897037938859e-12 *** df.mm.trans2:probe3 0.155635274171788 0.0380665643229340 4.08850330834879 4.81856524066317e-05 *** df.mm.trans2:probe4 0.149622673717418 0.038066564322934 3.93055365984985 9.27207489967945e-05 *** df.mm.trans2:probe5 0.137296114957429 0.038066564322934 3.60673776053679 0.000330957841441081 *** df.mm.trans2:probe6 0.0104337910056251 0.0380665643229340 0.274093320245847 0.784089606130395 df.mm.trans3:probe2 -0.25979016708523 0.038066564322934 -6.82462869202813 1.83958801937359e-11 *** df.mm.trans3:probe3 -0.0650505705131553 0.038066564322934 -1.70886371465796 0.0878966407856272 . df.mm.trans3:probe4 -0.267176023390863 0.038066564322934 -7.0186534598789 5.08235966375276e-12 *** df.mm.trans3:probe5 -0.145677382186915 0.038066564322934 -3.82691174730335 0.000140753938409848 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.1052365338364 0.0931497734575432 44.0713528488346 7.10468465939741e-209 *** df.mm.trans1 -0.122463102951272 0.0820566563611312 -1.49242131451606 0.136016078246576 df.mm.trans2 -0.117859554285088 0.0740422268050977 -1.59178835335857 0.111860483115378 df.mm.exp2 -0.132461996729763 0.0986022069865812 -1.34339788913437 0.179556101242805 df.mm.exp3 -0.0638488908971474 0.0986022069865812 -0.647540180371791 0.51748377545877 df.mm.exp4 -0.127253235512105 0.0986022069865812 -1.29057187867431 0.197256344625848 df.mm.exp5 -0.0488028262837379 0.0986022069865812 -0.494946591716547 0.620785139118093 df.mm.exp6 -0.177128867841989 0.0986022069865812 -1.79639861272167 0.0728399993622304 . df.mm.exp7 0.018802845065654 0.0986022069865812 0.190693957471083 0.848817817691319 df.mm.exp8 -0.143434233694972 0.0986022069865812 -1.45467569214239 0.146184239902549 df.mm.trans1:exp2 0.106344166262484 0.0930057644457011 1.14341478612941 0.253237177988314 df.mm.trans2:exp2 0.169405658210028 0.0760956254555736 2.22622072157001 0.0263005865293377 * df.mm.trans1:exp3 0.0463793438573749 0.0930057644457011 0.498671712810363 0.618159268277358 df.mm.trans2:exp3 0.098601432366032 0.0760956254555736 1.29575690817599 0.195464360766923 df.mm.trans1:exp4 0.160721133934005 0.0930057644457011 1.72807712394900 0.0843927021943212 . df.mm.trans2:exp4 0.0743015000050685 0.0760956254555736 0.976422751771026 0.329174959617092 df.mm.trans1:exp5 0.0504022048815397 0.093005764445701 0.541925601944444 0.588033336686051 df.mm.trans2:exp5 0.0320160044584266 0.0760956254555736 0.420733836758045 0.674071936957953 df.mm.trans1:exp6 0.156241823768344 0.093005764445701 1.67991548372856 0.093396979669326 . df.mm.trans2:exp6 0.111983902172600 0.0760956254555736 1.4716207600919 0.141549592076557 df.mm.trans1:exp7 0.0430808073061939 0.093005764445701 0.463205776146762 0.643353440677369 df.mm.trans2:exp7 -0.00474191330927485 0.0760956254555736 -0.0623151893539963 0.950328705295368 df.mm.trans1:exp8 0.130989482663874 0.093005764445701 1.40840176353099 0.159433268833423 df.mm.trans2:exp8 0.103979341205960 0.0760956254555736 1.36642994368534 0.172220223952042 df.mm.trans1:probe2 -0.0669336698142414 0.0543036670664352 -1.23258102868734 0.218124628937299 df.mm.trans1:probe3 -0.0452693357373737 0.0543036670664352 -0.833633126138445 0.404757477475334 df.mm.trans1:probe4 -0.0773917320974707 0.0543036670664352 -1.42516585487292 0.154532034756173 df.mm.trans1:probe5 -0.0393679255880906 0.0543036670664352 -0.724958878742532 0.468707046424628 df.mm.trans1:probe6 0.0140719297365037 0.0543036670664352 0.259134060307348 0.795604068599547 df.mm.trans1:probe7 0.013225277639025 0.0543036670664352 0.243542993566994 0.807652464474027 df.mm.trans1:probe8 0.0125575410272553 0.0543036670664352 0.231246648810888 0.817187288009122 df.mm.trans1:probe9 0.0230712863682234 0.0543036670664352 0.424856876424164 0.671064850190304 df.mm.trans1:probe10 -0.0438673845351826 0.0543036670664352 -0.807816247133277 0.419456666661205 df.mm.trans1:probe11 -0.0581338894712842 0.0543036670664352 -1.07053340247102 0.284729266861359 df.mm.trans1:probe12 0.00434943916595552 0.0543036670664352 0.0800947597265284 0.936183604397097 df.mm.trans1:probe13 -0.0351533011173354 0.0543036670664352 -0.647346726590836 0.517608872853135 df.mm.trans1:probe14 -0.0157110855571024 0.0543036670664352 -0.289319053497463 0.772418489224198 df.mm.trans1:probe15 -0.0127156358249108 0.0543036670664352 -0.234157958602584 0.814927294895636 df.mm.trans1:probe16 -0.0527527276499721 0.0543036670664352 -0.971439508595881 0.331647737418722 df.mm.trans1:probe17 -0.00207958126382153 0.0543036670664352 -0.0382954112707962 0.969462505919368 df.mm.trans1:probe18 -0.0170666953204339 0.0543036670664352 -0.314282556637555 0.753395268070982 df.mm.trans1:probe19 -0.0352643140727817 0.0543036670664352 -0.649391026017437 0.516287714414142 df.mm.trans1:probe20 -0.0288898909697878 0.0543036670664352 -0.532006262752824 0.594881770070819 df.mm.trans1:probe21 -0.00564548657985686 0.0543036670664352 -0.103961424427381 0.917228206604567 df.mm.trans1:probe22 -0.0342234030440752 0.0543036670664352 -0.63022268831691 0.528743980299799 df.mm.trans2:probe2 -0.00303572331629697 0.0543036670664352 -0.0559027314413787 0.95543442557212 df.mm.trans2:probe3 -0.0190377348192779 0.0543036670664352 -0.350579175361898 0.726004124002008 df.mm.trans2:probe4 -0.0410210300873806 0.0543036670664352 -0.755400736329564 0.450249587602492 df.mm.trans2:probe5 0.0032542205077241 0.0543036670664352 0.059926349057475 0.952230517594377 df.mm.trans2:probe6 -0.0122422412187515 0.0543036670664352 -0.225440414618303 0.821699087050588 df.mm.trans3:probe2 -0.0042312043964748 0.0543036670664352 -0.0779174708643218 0.937914821970103 df.mm.trans3:probe3 0.0191026330129955 0.0543036670664352 0.35177427317432 0.725107970055598 df.mm.trans3:probe4 -0.00123613005199087 0.0543036670664352 -0.0227632887200526 0.981845244937884 df.mm.trans3:probe5 -0.00328668217901253 0.0543036670664352 -0.0605241295213377 0.951754584419351