fitVsDatCorrelation=0.759904095890801 cont.fitVsDatCorrelation=0.245362740368337 fstatistic=9042.81278988706,52,692 cont.fstatistic=4058.44912958907,52,692 residuals=-0.459655480692207,-0.0801756596830286,-0.00933727990255659,0.0679877510452234,1.18472322414819 cont.residuals=-0.477892521291616,-0.174229835919770,-0.0270775973223452,0.135484493318442,1.51735310403774 predictedValues: Include Exclude Both Lung 56.6235057850606 48.5469497846817 60.9050755143684 cerebhem 55.343560869947 54.5805465541368 67.8427314426564 cortex 51.7084623020018 46.9156324203249 57.4998243208028 heart 52.5207136370557 49.1529877047991 58.2549948036482 kidney 55.9414621704265 46.983552559027 62.4027737487639 liver 56.0017115586007 48.940366902019 61.9898617495002 stomach 52.5011519400792 49.549444289401 59.2068315118433 testicle 55.9063830679548 50.442521991172 61.6099611998666 diffExp=8.07655600037894,0.763014315810231,4.79282988167694,3.36772593225668,8.9579096113995,7.0613446565817,2.95170765067822,5.46386107678276 diffExpScore=0.976434518702002 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 57.7263993477403 55.0195878174133 57.3747942927138 cerebhem 56.0550846280537 55.2444879519257 57.3576327937004 cortex 55.1013197774858 55.7481017694827 47.0620731356533 heart 55.098570351832 56.2276512521407 53.0316151697506 kidney 54.4857968447425 59.1304876882547 58.9508795649732 liver 57.109613376519 52.2754406792145 53.4360263222141 stomach 51.7087469264438 60.361211572279 55.845487741273 testicle 54.9393005091187 52.4198678733754 58.1903645728138 cont.diffExp=2.70681153032698,0.810596676127943,-0.646781991996903,-1.12908090030865,-4.64469084351217,4.83417269730455,-8.65246464583524,2.51943263574334 cont.diffExpScore=4.98731406609616 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=0.209207794489968 cont.tran.correlation=-0.728862529127194 tran.covariance=0.00037528967649244 cont.tran.covariance=-0.00122862226252043 tran.mean=51.978684596043 cont.tran.mean=55.5407292728764 weightedLogRatios: wLogRatio Lung 0.609335237312921 cerebhem 0.0556230433548415 cortex 0.379061854674261 heart 0.260313393238214 kidney 0.687047928178151 liver 0.533457024321345 stomach 0.22751589729441 testicle 0.408522954624175 cont.weightedLogRatios: wLogRatio Lung 0.193623826173145 cerebhem 0.0585426788105081 cortex -0.0468539147742717 heart -0.0815303394666553 kidney -0.330404035722906 liver 0.35384944643782 stomach -0.622435527548875 testicle 0.186963657174760 varWeightedLogRatios=0.0446009796938474 cont.varWeightedLogRatios=0.0994474255737526 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.59758696225255 0.0815261530225018 44.128010814635 2.45582538992831e-203 *** df.mm.trans1 0.277162773228236 0.0723089947353028 3.83303314121333 0.000138101576029704 *** df.mm.trans2 0.306734962896511 0.0658968049509504 4.65477746796413 3.88797662279753e-06 *** df.mm.exp2 -0.0135934560554354 0.0888811352101498 -0.152939721385141 0.878490394984866 df.mm.exp3 -0.0674485587428322 0.0888811352101498 -0.758862480585531 0.448193218992361 df.mm.exp4 -0.0183236160164074 0.0888811352101498 -0.206158663175073 0.836727613842799 df.mm.exp5 -0.0691453539798327 0.0888811352101497 -0.777953092254572 0.43686256229206 df.mm.exp6 -0.0206250917538281 0.0888811352101498 -0.232052523913677 0.816565825058667 df.mm.exp7 -0.0268698247804895 0.0888811352101498 -0.302311899110635 0.762505148939952 df.mm.exp8 0.0140504562936691 0.0888811352101497 0.158081422570136 0.874438756907011 df.mm.trans1:exp2 -0.00927042189020412 0.0841998487830685 -0.110100220180779 0.912361815058896 df.mm.trans2:exp2 0.130739618607043 0.071161860446508 1.83721473534717 0.0666068395169232 . df.mm.trans1:exp3 -0.0233541878004869 0.0841998487830685 -0.277366148966090 0.781581808984883 df.mm.trans2:exp3 0.033268126029004 0.071161860446508 0.467499385489106 0.640289811091473 df.mm.trans1:exp4 -0.0568929422841145 0.0841998487830685 -0.675689364130485 0.499463621736525 df.mm.trans2:exp4 0.0307298817497557 0.071161860446508 0.431830780658906 0.665998918749354 df.mm.trans1:exp5 0.0570269840502105 0.0841998487830685 0.677281311955015 0.498453839699505 df.mm.trans2:exp5 0.0364115823965022 0.071161860446508 0.51167271580642 0.609043318482868 df.mm.trans1:exp6 0.00958315001513055 0.0841998487830685 0.113814337598402 0.909417984428492 df.mm.trans2:exp6 0.0286962802209902 0.071161860446508 0.403253653585420 0.686886195197702 df.mm.trans1:exp7 -0.0487192597000853 0.0841998487830685 -0.578614574779167 0.56303753474262 df.mm.trans2:exp7 0.0473095038497437 0.071161860446508 0.664815444015913 0.506390028593523 df.mm.trans1:exp8 -0.0267960909425563 0.0841998487830685 -0.31824393190531 0.750395929097972 df.mm.trans2:exp8 0.0242526868825420 0.071161860446508 0.340810185826615 0.733349924993683 df.mm.trans1:probe2 0.353226479448756 0.0461181565170105 7.65916303090888 6.3235094952215e-14 *** df.mm.trans1:probe3 0.269126689264076 0.0461181565170105 5.83559078656601 8.23505862867472e-09 *** df.mm.trans1:probe4 -0.0269151619345349 0.0461181565170105 -0.583613135633626 0.55967078665664 df.mm.trans1:probe5 0.381670161131926 0.0461181565170105 8.27591972352902 6.55451045023988e-16 *** df.mm.trans1:probe6 0.611503577018787 0.0461181565170105 13.2594974127649 6.41249837839008e-36 *** df.mm.trans1:probe7 -0.0327580388750799 0.0461181565170105 -0.710306771759128 0.477753221878233 df.mm.trans1:probe8 0.216330707964322 0.0461181565170105 4.69079261406578 3.28004104193084e-06 *** df.mm.trans1:probe9 0.256827280805212 0.0461181565170105 5.56889737581948 3.67095758919706e-08 *** df.mm.trans1:probe10 0.0966890803499804 0.0461181565170105 2.09655128591961 0.0363958026268172 * df.mm.trans1:probe11 0.0531499793235162 0.0461181565170105 1.15247406526131 0.249524283815332 df.mm.trans1:probe12 0.00802963282773517 0.0461181565170105 0.174110012935436 0.86182992609333 df.mm.trans1:probe13 0.0663623587887311 0.0461181565170105 1.43896382250781 0.150612918495720 df.mm.trans1:probe14 -0.0389984660329627 0.0461181565170105 -0.845620661757768 0.398056688065424 df.mm.trans1:probe15 0.0784040100570474 0.0461181565170105 1.70006817224206 0.0895674544326033 . df.mm.trans1:probe16 0.24272067917379 0.0461181565170105 5.26301781130959 1.89261836396637e-07 *** df.mm.trans1:probe17 0.30486588194639 0.0461181565170105 6.61053920995176 7.65327308566043e-11 *** df.mm.trans1:probe18 0.262124423743729 0.0461181565170105 5.68375762476641 1.94270502236898e-08 *** df.mm.trans1:probe19 0.348767031533102 0.0461181565170105 7.56246688664714 1.26124541915512e-13 *** df.mm.trans1:probe20 0.225448426742342 0.0461181565170105 4.88849606681885 1.26409540762378e-06 *** df.mm.trans1:probe21 0.36528676853425 0.0461181565170105 7.92067151252058 9.43250337087713e-15 *** df.mm.trans2:probe2 -0.0456266488318196 0.0461181565170105 -0.98934242558005 0.322841431706045 df.mm.trans2:probe3 -0.0237792745704707 0.0461181565170105 -0.515616329150099 0.606287089336429 df.mm.trans2:probe4 -0.0954725617844337 0.0461181565170105 -2.07017298597395 0.0388065520780379 * df.mm.trans2:probe5 -0.0786874660167871 0.0461181565170105 -1.70621447081831 0.0884168412505077 . df.mm.trans2:probe6 0.0256603646682756 0.0461181565170105 0.55640482200998 0.578114018162322 df.mm.trans3:probe2 0.227351391807929 0.0461181565170105 4.92975888409745 1.03155878637752e-06 *** df.mm.trans3:probe3 -0.124663842609590 0.0461181565170105 -2.70314019519856 0.00703734236133314 ** df.mm.trans3:probe4 -0.391360290617269 0.0461181565170105 -8.48603500603752 1.29612778908726e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00984247751783 0.121584196556498 32.9799644286378 4.43427030382256e-144 *** df.mm.trans1 0.0369457004944329 0.107838168523336 0.342603189578816 0.732001039920009 df.mm.trans2 0.0128389014232091 0.0982753360555381 0.130642152329588 0.896096364050895 df.mm.exp2 -0.0250012724432274 0.132553064420602 -0.188613311600977 0.85045119856413 df.mm.exp3 0.164750816226674 0.132553064420602 1.24290462047641 0.2143239493784 df.mm.exp4 0.0538453946388157 0.132553064420602 0.406217652335518 0.684708270984738 df.mm.exp5 -0.0128166079897517 0.132553064420602 -0.0966903937360778 0.923000264352353 df.mm.exp6 0.00921519195495657 0.132553064420602 0.0695207764168769 0.944595179655832 df.mm.exp7 0.00958615227054446 0.132553064420602 0.0723193561193484 0.942368657855564 df.mm.exp8 -0.112003935301034 0.132553064420602 -0.84497431870482 0.398417220585639 df.mm.trans1:exp2 -0.00437846279053108 0.125571618246754 -0.0348682516930474 0.97219485185493 df.mm.trans2:exp2 0.0290805783244668 0.106127387434389 0.274015775074508 0.784154270713683 df.mm.trans1:exp3 -0.211291744520845 0.125571618246754 -1.68263933738313 0.0928960276556858 . df.mm.trans2:exp3 -0.151596719407715 0.106127387434389 -1.42844107513187 0.153616125224077 df.mm.trans1:exp4 -0.100436221814939 0.125571618246754 -0.79983218514854 0.424082504472905 df.mm.trans2:exp4 -0.0321260075399988 0.106127387434389 -0.302711753456288 0.76220050980274 df.mm.trans1:exp5 -0.0449579292293029 0.125571618246754 -0.358026199367428 0.720432888023462 df.mm.trans2:exp5 0.084874001554434 0.106127387434389 0.79973702930269 0.424137608426018 df.mm.trans1:exp6 -0.0199573256753016 0.125571618246754 -0.15893181878157 0.873768963297979 df.mm.trans2:exp6 -0.0603777806210431 0.106127387434389 -0.568917996387788 0.569596384766089 df.mm.trans1:exp7 -0.119673795439039 0.125571618246754 -0.95303219875589 0.340906524626935 df.mm.trans2:exp7 0.0830712899006504 0.106127387434389 0.7827507291839 0.43404128413399 df.mm.trans1:exp8 0.062518287411089 0.125571618246754 0.497869568649165 0.618734152691259 df.mm.trans2:exp8 0.0636003486909845 0.106127387434389 0.599283090147716 0.549180267067625 df.mm.trans1:probe2 -0.0816798171678037 0.0687784078961744 -1.18757935326309 0.235406645029513 df.mm.trans1:probe3 0.0853830670649967 0.0687784078961744 1.24142255799070 0.214870319926222 df.mm.trans1:probe4 0.0825622606238828 0.0687784078961744 1.20040959291346 0.230391070389605 df.mm.trans1:probe5 -0.0445205462467227 0.0687784078961744 -0.647304111981327 0.517649808956035 df.mm.trans1:probe6 0.0363659323785027 0.0687784078961744 0.528740537777488 0.597154992000643 df.mm.trans1:probe7 0.0230810901589942 0.0687784078961744 0.335586281581811 0.737284585083276 df.mm.trans1:probe8 0.0452780983089909 0.0687784078961744 0.658318499860322 0.510552445673645 df.mm.trans1:probe9 0.052999065587583 0.0687784078961744 0.770577092560628 0.441220641854801 df.mm.trans1:probe10 0.0726237414957523 0.0687784078961744 1.05590902315422 0.291378275035857 df.mm.trans1:probe11 0.0448775049062550 0.0687784078961744 0.652494093407929 0.514299167651598 df.mm.trans1:probe12 -0.0285439973993953 0.0687784078961744 -0.415013930570832 0.6782603407 df.mm.trans1:probe13 0.00633708579493384 0.0687784078961744 0.092137721543367 0.926615284595127 df.mm.trans1:probe14 -0.0128506635161321 0.0687784078961744 -0.186841538052625 0.851839640892544 df.mm.trans1:probe15 -0.0197291309797577 0.0687784078961744 -0.286850649546004 0.774312544725617 df.mm.trans1:probe16 0.00566125153091968 0.0687784078961744 0.0823114652416165 0.934422839590098 df.mm.trans1:probe17 -0.0186953002616404 0.0687784078961744 -0.271819322858741 0.785842021613231 df.mm.trans1:probe18 -0.00425069707037252 0.0687784078961744 -0.061802783757211 0.950737732140422 df.mm.trans1:probe19 -0.0321112887685121 0.0687784078961744 -0.466880373517606 0.640732425202646 df.mm.trans1:probe20 -0.00519647204072045 0.0687784078961744 -0.0755538285876706 0.939795907413827 df.mm.trans1:probe21 0.0155692795598710 0.0687784078961744 0.226368711287616 0.82098150852624 df.mm.trans2:probe2 0.00786611742305128 0.0687784078961744 0.114368995498205 0.908978465365004 df.mm.trans2:probe3 0.0232941435441718 0.0687784078961744 0.338683959933121 0.734950564302501 df.mm.trans2:probe4 -0.012042609705298 0.0687784078961744 -0.175092882688956 0.861057868287417 df.mm.trans2:probe5 -0.0762368820405366 0.0687784078961744 -1.10844208774971 0.268056029267720 df.mm.trans2:probe6 -0.092801918994754 0.0687784078961744 -1.34928856066056 0.177685588180580 df.mm.trans3:probe2 -0.048815055990619 0.0687784078961744 -0.70974390777275 0.478102038043261 df.mm.trans3:probe3 0.0202135400382914 0.0687784078961744 0.293893689263715 0.768927266435502 df.mm.trans3:probe4 -0.0114864695411388 0.0687784078961744 -0.167006912379804 0.867413398687044