fitVsDatCorrelation=0.780062533304448 cont.fitVsDatCorrelation=0.247492206705247 fstatistic=15261.0349609565,56,784 cont.fstatistic=6356.41679032045,56,784 residuals=-0.411642228077270,-0.0721496582379556,-0.00515682612110968,0.0699454219576517,0.456219013268076 cont.residuals=-0.53753191113481,-0.120736311156683,-0.0248450278579372,0.073952556574227,0.74800222754157 predictedValues: Include Exclude Both Lung 44.8093832834673 42.222887752209 58.7413076252666 cerebhem 47.9729112560837 45.2664342646905 55.4315933347053 cortex 45.4006981254137 43.8801909736427 56.7025286336929 heart 48.1007157121972 45.0457926380989 58.3555641249671 kidney 45.2473709161032 44.2720306152522 66.0102038813875 liver 48.4571274742836 49.7043630724272 62.174632412182 stomach 46.524867180188 44.09107068112 61.6228065040707 testicle 46.5959750914279 44.7118082960443 59.0825392492036 diffExp=2.58649553125832,2.70647699139325,1.52050715177105,3.05492307409830,0.975340300851023,-1.24723559814362,2.43379649906802,1.88416679538356 diffExpScore=1.10020276426638 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 50.0872233534404 47.5827884456518 45.8391764043283 cerebhem 50.1888196029446 44.9103548786344 47.9865661604224 cortex 50.0979072470186 52.7650103409725 49.1538127348784 heart 48.7407766635558 45.2730260994704 49.0988368144975 kidney 49.2261248624377 48.6496814534039 48.5204234905161 liver 50.2924183149071 49.0342256184928 48.8074185188751 stomach 49.5739395604628 47.7944926332271 50.487279439192 testicle 49.2573217247197 45.5563286404031 47.6311408545455 cont.diffExp=2.50443490778861,5.27846472431013,-2.6671030939539,3.46775056408541,0.576443409033772,1.25819269641427,1.77944692723574,3.70099308431659 cont.diffExpScore=1.25648279931915 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=0.775079833030167 cont.tran.correlation=0.41093323606213 tran.covariance=0.00110958007449523 cont.tran.covariance=0.000250524025743684 tran.mean=45.7689767082906 cont.tran.mean=48.6894024649839 weightedLogRatios: wLogRatio Lung 0.224305766844526 cerebhem 0.223084507684456 cortex 0.129393820277829 heart 0.252003127767660 kidney 0.0828347338824112 liver -0.098943710601768 stomach 0.204877772544853 testicle 0.157712504615275 cont.weightedLogRatios: wLogRatio Lung 0.199440433905143 cerebhem 0.428963833554337 cortex -0.204359593379177 heart 0.284119114536584 kidney 0.0458274459786514 liver 0.0989410229958795 stomach 0.142022527390551 testicle 0.301342767850679 varWeightedLogRatios=0.0130243836986397 cont.varWeightedLogRatios=0.0370131280712537 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.21276142849554 0.0583022143142164 55.1053071703339 7.6018954544467e-272 *** df.mm.trans1 0.575988788504161 0.0508156062439333 11.3348797953764 1.10018167127823e-27 *** df.mm.trans2 0.53159778005867 0.0453474448917778 11.7227725030007 2.33680344959307e-29 *** df.mm.exp2 0.195815770313138 0.0593285398077062 3.30053244101085 0.00100864063437965 ** df.mm.exp3 0.0869348464340651 0.0593285398077062 1.46531242325929 0.143236606856358 df.mm.exp4 0.142185114433284 0.0593285398077061 2.39657195161266 0.0167825296487517 * df.mm.exp5 -0.0595484694433355 0.0593285398077062 -1.00370697873809 0.315829551340224 df.mm.exp6 0.184588260482311 0.0593285398077061 3.11128945833814 0.00193023754677969 ** df.mm.exp7 0.0329753764786196 0.0593285398077062 0.555809675840639 0.578499544298325 df.mm.exp8 0.0905795469884964 0.0593285398077061 1.52674492381036 0.127227760184064 df.mm.trans1:exp2 -0.127596833322966 0.0553999986741208 -2.30319199235957 0.0215289379678556 * df.mm.trans2:exp2 -0.126212415746409 0.0432117009551111 -2.92079258526574 0.0035916216657999 ** df.mm.trans1:exp3 -0.0738249300746247 0.0553999986741208 -1.33257999713835 0.183056836657500 df.mm.trans2:exp3 -0.0484342966691676 0.0432117009551111 -1.12086068353296 0.262690380700785 df.mm.trans1:exp4 -0.0713056235421987 0.0553999986741208 -1.28710514889431 0.198437506218546 df.mm.trans2:exp4 -0.0774679657136083 0.0432117009551111 -1.79275436979634 0.0733976125870503 . df.mm.trans1:exp5 0.0692754708183494 0.0553999986741208 1.25045979199112 0.211504714389452 df.mm.trans2:exp5 0.106939146201660 0.0432117009551111 2.47477289340566 0.0135425686282543 * df.mm.trans1:exp6 -0.106326388854191 0.0553999986741208 -1.91924894221811 0.0553155519130276 . df.mm.trans2:exp6 -0.0214579808675781 0.0432117009551111 -0.496578019223751 0.619625858098638 df.mm.trans1:exp7 0.00459400545902902 0.0553999986741208 0.0829242882486751 0.933932908307846 df.mm.trans2:exp7 0.0103194687740929 0.0432117009551111 0.23881190848777 0.811313845318795 df.mm.trans1:exp8 -0.0514829467970981 0.0553999986741208 -0.929295090780345 0.353022207909483 df.mm.trans2:exp8 -0.0333043503706768 0.0432117009551111 -0.770725281221255 0.441101979365553 df.mm.trans1:probe2 0.106940313717001 0.0352060847327627 3.03755201774773 0.00246417049968376 ** df.mm.trans1:probe3 0.0655606233026346 0.0352060847327627 1.86219580507980 0.0629494068989201 . df.mm.trans1:probe4 -0.0459442674519292 0.0352060847327627 -1.30500928463578 0.192272576173174 df.mm.trans1:probe5 0.0234026632920738 0.0352060847327627 0.664733482002198 0.506416447026307 df.mm.trans1:probe6 0.00811480918074533 0.0352060847327627 0.230494508047176 0.817767661360099 df.mm.trans1:probe7 -0.133778231182359 0.0352060847327627 -3.79986108077123 0.000155990340977242 *** df.mm.trans1:probe8 -0.00161884431039133 0.0352060847327627 -0.0459819466628972 0.963336348709765 df.mm.trans1:probe9 -0.00188629552463012 0.0352060847327627 -0.0535786793376299 0.957284492759829 df.mm.trans1:probe10 0.00909249428938548 0.0352060847327627 0.258264852749275 0.796270291872681 df.mm.trans1:probe11 -0.0239744435602094 0.0352060847327627 -0.680974432181005 0.496088742232865 df.mm.trans1:probe12 0.0879591291979912 0.0352060847327627 2.49840701872017 0.0126789851049392 * df.mm.trans1:probe13 -0.0575356245046699 0.0352060847327627 -1.63425228739302 0.102607337362262 df.mm.trans1:probe14 0.0875136006182262 0.0352060847327627 2.48575214433845 0.0131351164938938 * df.mm.trans1:probe15 -0.0207483899053874 0.0352060847327627 -0.589341020533276 0.555802313413816 df.mm.trans1:probe16 -0.0272042858214184 0.0352060847327627 -0.772715456089956 0.439923683257137 df.mm.trans1:probe17 0.0386897500475440 0.0352060847327627 1.0989506598426 0.272126943244888 df.mm.trans1:probe18 0.00173706649219448 0.0352060847327627 0.0493399509028042 0.96066094873718 df.mm.trans1:probe19 0.0324570477830643 0.0352060847327627 0.921915857143291 0.356856076187513 df.mm.trans1:probe20 0.121399642574001 0.0352060847327627 3.44825741048754 0.00059431558245247 *** df.mm.trans1:probe21 -0.0782019981713637 0.0352060847327627 -2.22126370384461 0.0266180328799577 * df.mm.trans1:probe22 0.204378354606555 0.0352060847327627 5.80519975901669 9.33427563436582e-09 *** df.mm.trans2:probe2 0.00398544053288972 0.0352060847327627 0.113203173915584 0.909898459533417 df.mm.trans2:probe3 -0.0123732741684065 0.0352060847327627 -0.351452717969856 0.725343169021111 df.mm.trans2:probe4 0.0387049676040105 0.0352060847327627 1.09938290206953 0.271938558353796 df.mm.trans2:probe5 -0.0470530427340099 0.0352060847327627 -1.33650313834024 0.181772608709884 df.mm.trans2:probe6 -0.00142211087922298 0.0352060847327627 -0.0403938946922879 0.96778938165504 df.mm.trans3:probe2 -0.102124848406735 0.0352060847327627 -2.90077266989300 0.00382660634302123 ** df.mm.trans3:probe3 -0.351533639204648 0.0352060847327627 -9.98502508509592 3.50315233418890e-22 *** df.mm.trans3:probe4 -0.32803313729332 0.0352060847327627 -9.3175125772521 1.17812749017671e-19 *** df.mm.trans3:probe5 -0.503611674600227 0.0352060847327627 -14.3046771154183 2.06210991776185e-41 *** df.mm.trans3:probe6 -0.389892730448505 0.0352060847327627 -11.074583652458 1.38610115988297e-26 *** df.mm.trans3:probe7 -0.0552376421128124 0.0352060847327627 -1.56897997979901 0.117056094149107 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98684214241146 0.090280141753841 44.1607873554522 7.30757333400622e-215 *** df.mm.trans1 -0.0465258306635968 0.0786872366508216 -0.591275442420954 0.554506313147042 df.mm.trans2 -0.120874936865705 0.0702198673096694 -1.72137803013279 0.0855767366513543 . df.mm.exp2 -0.101558381950543 0.0918693920443782 -1.10546483100143 0.269297343872042 df.mm.exp3 0.0337751510198739 0.0918693920443782 0.36764313193189 0.713238574109416 df.mm.exp4 -0.145705912449239 0.0918693920443782 -1.5860115018379 0.113139927891594 df.mm.exp5 -0.0520130313571876 0.0918693920443782 -0.566162790454326 0.571445139286478 df.mm.exp6 -0.0286074031761046 0.0918693920443782 -0.311392102848418 0.755585338331742 df.mm.exp7 -0.102443668466341 0.0918693920443782 -1.11510119079546 0.265148754255820 df.mm.exp8 -0.0985771493149584 0.0918693920443782 -1.07301405964829 0.283594967196865 df.mm.trans1:exp2 0.103584714088745 0.0857861025055897 1.20747663156740 0.22761278470226 df.mm.trans2:exp2 0.0437556624421269 0.066912698489065 0.653921653589826 0.513354036895247 df.mm.trans1:exp3 -0.0335618679996372 0.0857861025055897 -0.391227331926525 0.695735506300538 df.mm.trans2:exp3 0.0696020285121138 0.0669126984890651 1.04019162406801 0.2985715554222 df.mm.trans1:exp4 0.118455942666611 0.0857861025055897 1.38082905280482 0.167725018301365 df.mm.trans2:exp4 0.0959462086717398 0.066912698489065 1.43390134964321 0.151999119637251 df.mm.trans1:exp5 0.0346715543859232 0.0857861025055897 0.404162834926136 0.686203206337153 df.mm.trans2:exp5 0.0741871836746841 0.066912698489065 1.10871606361546 0.267892681594374 df.mm.trans1:exp6 0.032695786913556 0.0857861025055897 0.381131511498912 0.703208923791994 df.mm.trans2:exp6 0.0586548308990513 0.066912698489065 0.876587437414988 0.380979273834888 df.mm.trans1:exp7 0.092142999350948 0.0857861025055897 1.07410170948079 0.283107564970868 df.mm.trans2:exp7 0.106882975874972 0.0669126984890651 1.59734965542360 0.110590734321822 df.mm.trans1:exp8 0.0818692176633713 0.0857861025055897 0.95434126591818 0.340205077516789 df.mm.trans2:exp8 0.0550555931155812 0.066912698489065 0.822797381644658 0.410873411223521 df.mm.trans1:probe2 -0.00743132285343626 0.0545161167145676 -0.136314236986922 0.891607863633246 df.mm.trans1:probe3 -0.0229189653383962 0.0545161167145676 -0.420407151492357 0.674303245610882 df.mm.trans1:probe4 -0.0638705758881807 0.0545161167145676 -1.17159071000216 0.241717344642489 df.mm.trans1:probe5 -0.0220173003155523 0.0545161167145676 -0.40386773017655 0.686420122379072 df.mm.trans1:probe6 -0.035804704431474 0.0545161167145676 -0.656772833232754 0.511519728784407 df.mm.trans1:probe7 0.0297759935636552 0.0545161167145676 0.546186987594049 0.585092798085281 df.mm.trans1:probe8 -0.0424454681870723 0.0545161167145676 -0.778585687041979 0.436458733653053 df.mm.trans1:probe9 0.0453945971750101 0.0545161167145676 0.832682148156014 0.405277592801622 df.mm.trans1:probe10 -0.0595318952314986 0.0545161167145676 -1.09200542553668 0.275166172118434 df.mm.trans1:probe11 -0.102136253124657 0.0545161167145676 -1.87350565814173 0.0613704033919791 . df.mm.trans1:probe12 -0.12428842750398 0.0545161167145676 -2.27984741016537 0.0228845254493114 * df.mm.trans1:probe13 -0.0797682543435178 0.0545161167145676 -1.46320499607784 0.143812075085978 df.mm.trans1:probe14 -0.0749773819862296 0.0545161167145676 -1.37532506907621 0.169423531643445 df.mm.trans1:probe15 -0.0427520296130562 0.0545161167145676 -0.784209004410473 0.433154350911295 df.mm.trans1:probe16 0.0149829258630067 0.0545161167145676 0.274834796862979 0.783515570751352 df.mm.trans1:probe17 -0.00119099021080326 0.0545161167145676 -0.0218465709331238 0.98257590361208 df.mm.trans1:probe18 -0.067245164368777 0.0545161167145676 -1.23349145943126 0.217762136774903 df.mm.trans1:probe19 -0.0564129467223556 0.0545161167145676 -1.03479393108132 0.301084233098837 df.mm.trans1:probe20 -0.0182708854123771 0.0545161167145676 -0.335146494531861 0.737604234009183 df.mm.trans1:probe21 -0.0515851571012511 0.0545161167145676 -0.946236823347815 0.344319347873739 df.mm.trans1:probe22 0.012533793107461 0.0545161167145676 0.229909866344383 0.818221780469266 df.mm.trans2:probe2 0.0414254644486317 0.0545161167145676 0.75987555506796 0.447557406323714 df.mm.trans2:probe3 -0.00721632893864954 0.0545161167145676 -0.132370560735872 0.894725163293584 df.mm.trans2:probe4 -0.0423603005341456 0.0545161167145675 -0.777023439800992 0.437379320489473 df.mm.trans2:probe5 -0.0141285289494595 0.0545161167145676 -0.259162423901777 0.795577953714262 df.mm.trans2:probe6 -0.023169566675925 0.0545161167145676 -0.425003981799271 0.670950390385858 df.mm.trans3:probe2 0.0113123883637714 0.0545161167145676 0.207505395569537 0.835669065884946 df.mm.trans3:probe3 0.0166302559913539 0.0545161167145675 0.30505210190274 0.760407435991532 df.mm.trans3:probe4 0.0203419848666184 0.0545161167145676 0.373137084820692 0.709147282176352 df.mm.trans3:probe5 0.0413168467196911 0.0545161167145676 0.757883158406449 0.448748673491209 df.mm.trans3:probe6 -0.0418108299099408 0.0545161167145676 -0.766944390570803 0.443345463672065 df.mm.trans3:probe7 -0.00790653171838278 0.0545161167145676 -0.145031087958435 0.88472360247836