fitVsDatCorrelation=0.86118230542691 cont.fitVsDatCorrelation=0.258573415481774 fstatistic=4537.04195271812,43,485 cont.fstatistic=1248.04685603551,43,485 residuals=-0.776680859487298,-0.102588656418668,0.00078129282365022,0.0859083890261111,2.07618391464183 cont.residuals=-0.645648380863253,-0.240675539221755,-0.0868567673980317,0.104729566297763,2.03065307031312 predictedValues: Include Exclude Both Lung 53.2828462876695 65.2703070947207 57.1305113506942 cerebhem 73.4480635618164 68.8124966392413 106.363328946236 cortex 140.875194056839 65.1389402161147 184.075795252685 heart 58.5110821643293 70.2127527175792 65.2723809057066 kidney 54.494633560784 63.2471941172617 55.8390525127871 liver 54.737946808273 65.4478799513936 55.4043099202446 stomach 54.466288606343 70.9572197278519 61.2997844870888 testicle 54.226351995042 75.3406792772106 58.855117392741 diffExp=-11.9874608070512,4.63556692257508,75.736253840724,-11.7016705532499,-8.75256055647771,-10.7099331431206,-16.4909311215089,-21.1143272821686 diffExpScore=116.333148091101 diffExp1.5=0,0,1,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,0,1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,1,0,0,0,-1,-1 diffExp1.3Score=1.5 diffExp1.2=-1,0,1,0,0,0,-1,-1 diffExp1.2Score=1.33333333333333 cont.predictedValues: Include Exclude Both Lung 74.7836759147858 59.2407206657167 59.5219164417711 cerebhem 62.3446938332139 66.8229100895693 72.621544768978 cortex 66.2891549414617 76.2287537717181 63.5967320569705 heart 63.5226545399102 59.820976352881 82.1684964489737 kidney 64.2206955884408 61.4736306052615 66.4857699283415 liver 70.714004097917 64.2752208198947 66.8286668780349 stomach 67.9743728212415 69.0801088930372 70.0330449647785 testicle 61.9608829067197 75.6748672529728 72.5679718347126 cont.diffExp=15.5429552490691,-4.47821625635536,-9.93959883025641,3.70167818702914,2.74706498317926,6.4387832780224,-1.10573607179573,-13.7139843462531 cont.diffExpScore=31.9127283133799 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=1,0,0,0,0,0,0,-1 cont.diffExp1.2Score=2 tran.correlation=-0.268670238957287 cont.tran.correlation=-0.378496569700867 tran.covariance=-0.00483715348280203 cont.tran.covariance=-0.0024540826881039 tran.mean=68.0293672989043 cont.tran.mean=66.5267076934214 weightedLogRatios: wLogRatio Lung -0.827331527444013 cerebhem 0.277982511340712 cortex 3.51906006762265 heart -0.758493348932165 kidney -0.606604302133878 liver -0.731210875194948 stomach -1.0923201096588 testicle -1.36723808118560 cont.weightedLogRatios: wLogRatio Lung 0.978118315757134 cerebhem -0.289079175847999 cortex -0.59571738566843 heart 0.247448401069928 kidney 0.181009652234110 liver 0.402013125441583 stomach -0.0682103576919135 testicle -0.845053332953268 varWeightedLogRatios=2.48164324919582 cont.varWeightedLogRatios=0.338503585978757 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36845376362808 0.110125320427527 39.6680231818525 2.34710745340175e-154 *** df.mm.trans1 -0.424171710779011 0.0933296363814665 -4.54487692468116 6.94844771699524e-06 *** df.mm.trans2 -0.139864396641489 0.0881610780657604 -1.58646422786666 0.113285766238078 df.mm.exp2 -0.247710301256208 0.118053689664300 -2.09828512739079 0.0363966428645812 * df.mm.exp3 -0.199764063649652 0.118053689664300 -1.69214587208333 0.0912604500904774 . df.mm.exp4 0.0333638020030486 0.118053689664300 0.282615495525152 0.777592171801124 df.mm.exp5 0.0138661640722255 0.118053689664300 0.117456422680694 0.906546999274545 df.mm.exp6 0.060340567268893 0.118053689664300 0.511128177700153 0.609493900957008 df.mm.exp7 0.0350694413718099 0.118053689664300 0.297063492649270 0.766545219530279 df.mm.exp8 0.131295080967702 0.118053689664300 1.11216414616989 0.266618549191883 df.mm.trans1:exp2 0.56867439337572 0.103539935228857 5.49231938496737 6.4122709983607e-08 *** df.mm.trans2:exp2 0.300558449273325 0.0926089334230602 3.24545849049250 0.00125366162321948 ** df.mm.trans1:exp3 1.17202396739909 0.103539935228857 11.3195354508242 1.60705942769747e-26 *** df.mm.trans2:exp3 0.197749376413135 0.0926089334230602 2.13531642255037 0.0332355480662114 * df.mm.trans1:exp4 0.0602379268796158 0.103539935228857 0.581784475202443 0.560982172063917 df.mm.trans2:exp4 0.0396289375472181 0.0926089334230602 0.42791700630201 0.66890149154769 df.mm.trans1:exp5 0.00862161985381171 0.103539935228857 0.0832685459456304 0.933672366048433 df.mm.trans2:exp5 -0.0453526168574494 0.0926089334230602 -0.489721835476364 0.624552176932693 df.mm.trans1:exp6 -0.0333978185497364 0.103539935228857 -0.322559778272183 0.747167660817302 df.mm.trans2:exp6 -0.0576236849449527 0.0926089334230602 -0.622225986360448 0.534085686953955 df.mm.trans1:exp7 -0.0131019348905054 0.103539935228857 -0.126539917777095 0.899356994575157 df.mm.trans2:exp7 0.0484704973991473 0.0926089334230602 0.523389003712225 0.600942622423447 df.mm.trans1:exp8 -0.113742537628488 0.103539935228857 -1.09853784800115 0.272514738859466 df.mm.trans2:exp8 0.0121879197853641 0.0926089334230602 0.131606307673221 0.895350271672077 df.mm.trans1:probe2 0.146426661640066 0.0634050023278802 2.3093865825107 0.0213413984006489 * df.mm.trans1:probe3 0.0147205692340578 0.0634050023278802 0.232167316356756 0.81650599254561 df.mm.trans1:probe4 0.204467065059197 0.0634050023278803 3.22477813346423 0.00134575364415335 ** df.mm.trans1:probe5 -0.0273912662406007 0.0634050023278802 -0.432004814051655 0.665929907048548 df.mm.trans1:probe6 0.00831520738063864 0.0634050023278802 0.131144343117267 0.89571550411004 df.mm.trans1:probe7 -0.0571295227515304 0.0634050023278802 -0.9010254814928 0.368021968784693 df.mm.trans1:probe8 -0.141989006155768 0.0634050023278802 -2.23939753872279 0.0255825415019321 * df.mm.trans1:probe9 0.567008150816611 0.0634050023278802 8.94264064346999 8.0205541403552e-18 *** df.mm.trans1:probe10 -0.213109566641582 0.0634050023278803 -3.36108443840991 0.000837673170320425 *** df.mm.trans2:probe2 -0.172101624804750 0.0634050023278802 -2.71432250589279 0.0068777443353771 ** df.mm.trans2:probe3 -0.133436165447512 0.0634050023278802 -2.10450533157441 0.0358483636255043 * df.mm.trans2:probe4 -0.0358596744065216 0.0634050023278802 -0.565565382697786 0.571950810105445 df.mm.trans2:probe5 -0.114757113730506 0.0634050023278802 -1.80990630892297 0.070929159107155 . df.mm.trans2:probe6 -0.144471213932766 0.0634050023278802 -2.27854599209185 0.0231280200481575 * df.mm.trans3:probe2 0.647648793823284 0.0634050023278802 10.2144747266810 2.57158502078066e-22 *** df.mm.trans3:probe3 0.421072734939623 0.0634050023278803 6.64100180553846 8.3745087217634e-11 *** df.mm.trans3:probe4 0.115858366903266 0.0634050023278802 1.82727486238607 0.0682727848228945 . df.mm.trans3:probe5 0.0346822271413870 0.0634050023278803 0.546995124486205 0.584633665517443 df.mm.trans3:probe6 0.226261880031128 0.0634050023278802 3.56851780970028 0.000394706560179942 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.39322838125509 0.209287681955465 20.9913375704067 4.46307991671248e-70 *** df.mm.trans1 -0.0752651217747797 0.177368321655673 -0.424343654335823 0.671503368592485 df.mm.trans2 -0.321554125155807 0.167545734218502 -1.91920210117947 0.0555452409618198 . df.mm.exp2 -0.260401275918767 0.224355152477317 -1.16066545850822 0.246348947988274 df.mm.exp3 0.065338768641042 0.224355152477317 0.291229186936761 0.771000548014936 df.mm.exp4 -0.475883183414375 0.224355152477317 -2.12111546429712 0.0344187707889419 * df.mm.exp5 -0.225918297520655 0.224355152477317 -1.00696727944991 0.314452610729553 df.mm.exp6 -0.0901785045748836 0.224355152477317 -0.401945324540745 0.687901331697336 df.mm.exp7 -0.104433799062363 0.224355152477317 -0.465484290907568 0.641793629478931 df.mm.exp8 -0.141438490339411 0.224355152477317 -0.630422295978742 0.528715248485961 df.mm.trans1:exp2 0.078480216777728 0.196772485653082 0.398837350238548 0.69018859353696 df.mm.trans2:exp2 0.38083810856163 0.175998661608757 2.16386934468983 0.0309618796598266 * df.mm.trans1:exp3 -0.185912085102779 0.196772485653082 -0.944807321438982 0.345227689176592 df.mm.trans2:exp3 0.186790814446711 0.175998661608757 1.06131951651964 0.289072924377007 df.mm.trans1:exp4 0.312680165365715 0.196772485653082 1.58904414063754 0.112702061543389 df.mm.trans2:exp4 0.48563040348228 0.175998661608758 2.75928463911748 0.00601188960105765 ** df.mm.trans1:exp5 0.0736441927299805 0.196772485653082 0.374260621273129 0.708373995989612 df.mm.trans2:exp5 0.262917455225297 0.175998661608757 1.49386053747249 0.135862349995605 df.mm.trans1:exp6 0.0342225108965817 0.196772485653082 0.173919187852906 0.862001540631725 df.mm.trans2:exp6 0.171743538756409 0.175998661608758 0.975822981757622 0.32963865266944 df.mm.trans1:exp7 0.00896493819910815 0.196772485653082 0.0455599174313105 0.963679769710954 df.mm.trans2:exp7 0.258091474158669 0.175998661608757 1.46643998198351 0.143176524877654 df.mm.trans1:exp8 -0.0466578693192285 0.196772485653082 -0.237115820153272 0.812667061481834 df.mm.trans2:exp8 0.38627543655959 0.175998661608757 2.19476348870354 0.0286538906911582 * df.mm.trans1:probe2 -0.127328065203306 0.120498046317294 -1.05668157364168 0.291182982771467 df.mm.trans1:probe3 0.0713204687848107 0.120498046317294 0.591880706488889 0.554206269505471 df.mm.trans1:probe4 -0.0443368959108567 0.120498046317294 -0.367947010477742 0.713073271233716 df.mm.trans1:probe5 -0.0201881201711509 0.120498046317294 -0.167538983312574 0.867015810740428 df.mm.trans1:probe6 0.074318199774164 0.120498046317294 0.616758545432931 0.537683399168122 df.mm.trans1:probe7 -0.081569770043988 0.120498046317294 -0.676938527527655 0.498767692684151 df.mm.trans1:probe8 0.171834189267347 0.120498046317294 1.42603298990322 0.154502143794742 df.mm.trans1:probe9 -0.0145896460075735 0.120498046317294 -0.121077863529473 0.903679507419908 df.mm.trans1:probe10 -0.0832785164936597 0.120498046317294 -0.69111922590323 0.489821273137672 df.mm.trans2:probe2 0.0101101390619841 0.120498046317294 0.0839029293085982 0.933168230010994 df.mm.trans2:probe3 -0.0153669451170270 0.120498046317294 -0.127528583132070 0.898574909365614 df.mm.trans2:probe4 0.0889996367767136 0.120498046317294 0.738598172308629 0.460508296105919 df.mm.trans2:probe5 0.101092009748897 0.120498046317294 0.838951442272374 0.401909864132131 df.mm.trans2:probe6 -0.0656160595029192 0.120498046317294 -0.544540442839546 0.586319899419733 df.mm.trans3:probe2 0.112864778134165 0.120498046317294 0.936652349009634 0.349403573361702 df.mm.trans3:probe3 0.0723921875294883 0.120498046317294 0.600774782180835 0.548270647902094 df.mm.trans3:probe4 0.131241930339925 0.120498046317294 1.08916230885886 0.276623200861243 df.mm.trans3:probe5 0.145145868302212 0.120498046317294 1.20454955692822 0.228964711745339 df.mm.trans3:probe6 0.0779679594807236 0.120498046317294 0.647047498806906 0.517907315914676