fitVsDatCorrelation=0.795538559187257 cont.fitVsDatCorrelation=0.268150243086877 fstatistic=11478.6956160849,43,485 cont.fstatistic=4533.70727346604,43,485 residuals=-0.39896762570148,-0.0813540512275055,0.000533131467650685,0.0765996994356317,0.897080862501462 cont.residuals=-0.546501114565286,-0.151438484438974,-0.00811293883637124,0.130932324936990,1.02184681540675 predictedValues: Include Exclude Both Lung 55.3449638637849 44.7527116332141 63.6195532792748 cerebhem 60.1313048975817 47.1791397919011 57.962927225011 cortex 58.0175648533889 42.8205640618932 63.7497248558193 heart 57.8754069497674 43.2972822407233 59.4062707074385 kidney 55.8129774915884 41.7528081723586 62.0378626926379 liver 55.5961637211702 46.1066589603658 61.7210106325943 stomach 58.5490780282725 43.1348158159302 60.8288779087672 testicle 59.0617942644678 46.7483746863494 63.4413740770742 diffExp=10.5922522305708,12.9521651056807,15.1970007914957,14.5781247090441,14.0601693192298,9.4895047608043,15.4142622123423,12.3134195781184 diffExpScore=0.990530024913213 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=0,0,1,1,1,0,1,0 diffExp1.3Score=0.8 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 53.543497455928 54.5511122996054 53.3348162121499 cerebhem 53.3012765366896 54.4009129936181 59.4715736505253 cortex 51.6666260223712 54.5264678173071 58.2963823417055 heart 56.2271265492768 58.3128236012696 58.4490905883929 kidney 52.3580474226049 58.6202018022893 55.3186617606552 liver 52.9838648293284 55.332206573845 50.0789149982408 stomach 53.5324084807457 51.8838490357979 50.314271285739 testicle 51.5401815603965 55.0176774023556 50.2737483361201 cont.diffExp=-1.00761484367739,-1.09963645692849,-2.85984179493599,-2.08569705199285,-6.26215437968441,-2.34834174451662,1.64855944494779,-3.47749584195905 cont.diffExpScore=1.12422081061017 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=0.353356099955887 cont.tran.correlation=0.286455574058628 tran.covariance=0.000477516181097701 cont.tran.covariance=0.000294272902867567 tran.mean=51.0113505895474 cont.tran.mean=54.2373925239643 weightedLogRatios: wLogRatio Lung 0.830056548679549 cerebhem 0.964309023700502 cortex 1.18723441540926 heart 1.13561856220119 kidney 1.12522640529675 liver 0.734503621349397 stomach 1.19681004006723 testicle 0.92626010778806 cont.weightedLogRatios: wLogRatio Lung -0.0743851060611693 cerebhem -0.0814002178639476 cortex -0.213974768485758 heart -0.147424944756091 kidney -0.453543486150099 liver -0.173109628009491 stomach 0.124012936110497 testicle -0.259539462719397 varWeightedLogRatios=0.0303850026059690 cont.varWeightedLogRatios=0.0276414694476396 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.67009918430889 0.0680065639135555 53.9668375095972 3.80925137484860e-207 *** df.mm.trans1 0.358641918047177 0.0591465662553607 6.06361350714375 2.67919585799672e-09 *** df.mm.trans2 0.127398579348529 0.0555271269160609 2.29434848197917 0.0221969211095245 * df.mm.exp2 0.228861965667085 0.0754465086949056 3.03343348321880 0.00254753020565648 ** df.mm.exp3 0.000982553534181382 0.0754465086949056 0.0130231809420722 0.989614653963767 df.mm.exp4 0.0801657815265979 0.0754465086949056 1.06255124210951 0.288514284812521 df.mm.exp5 -0.0357886040707336 0.0754465086949056 -0.474357325339697 0.635458614309809 df.mm.exp6 0.0646303434278176 0.0754465086949056 0.856637961726937 0.392068050932369 df.mm.exp7 0.06431432257832 0.0754465086949056 0.85244928746004 0.394385523546688 df.mm.exp8 0.111430667359483 0.0754465086949056 1.47694928880131 0.140338239222183 df.mm.trans1:exp2 -0.145917047488384 0.0688729244953491 -2.11864166590222 0.0346285374880196 * df.mm.trans2:exp2 -0.176062162367927 0.0616018163923244 -2.85806771096873 0.00444564161436668 ** df.mm.trans1:exp3 0.0461775855536632 0.0688729244953491 0.670475167012569 0.502874063347619 df.mm.trans2:exp3 -0.0451161356394262 0.0616018163923244 -0.732383203639556 0.46428848210668 df.mm.trans1:exp4 -0.0354589055263723 0.0688729244953491 -0.514845358842962 0.606895632788633 df.mm.trans2:exp4 -0.113227952331839 0.0616018163923244 -1.83806191055671 0.066664687841677 . df.mm.trans1:exp5 0.0442093500403659 0.0688729244953491 0.64189738368597 0.521243074478638 df.mm.trans2:exp5 -0.0335967233216565 0.0616018163923244 -0.545385270909684 0.585739292822693 df.mm.trans1:exp6 -0.0601018102833304 0.0688729244953491 -0.87264786160473 0.38328686224158 df.mm.trans2:exp6 -0.0348249959022866 0.0616018163923244 -0.5653241729188 0.572114704036017 df.mm.trans1:exp7 -0.00803464731120122 0.0688729244953491 -0.116659011797064 0.907178562437641 df.mm.trans2:exp7 -0.101135898044388 0.0616018163923244 -1.64176811606141 0.101286293268683 df.mm.trans1:exp8 -0.0464320790492115 0.0688729244953491 -0.67417028374259 0.500524250775869 df.mm.trans2:exp8 -0.0678032163372075 0.0616018163923244 -1.10066910860855 0.271586670291238 df.mm.trans1:probe2 -0.0995161345438285 0.0377232543474528 -2.63805804311653 0.00860617809628927 ** df.mm.trans1:probe3 -0.156082674897057 0.0377232543474528 -4.13757183989074 4.13803668975409e-05 *** df.mm.trans1:probe4 -0.00750268627022129 0.0377232543474528 -0.198887566833902 0.84243404226699 df.mm.trans1:probe5 -0.0722620537038478 0.0377232543474528 -1.91558376799289 0.056005791082823 . df.mm.trans1:probe6 -0.170606216985344 0.0377232543474528 -4.52257420353936 7.68945987202062e-06 *** df.mm.trans1:probe7 0.270834025384533 0.0377232543474528 7.17949790041962 2.64469486515938e-12 *** df.mm.trans1:probe8 0.0540258793603235 0.0377232543474528 1.43216380174187 0.152741105028690 df.mm.trans1:probe9 0.117987438960436 0.0377232543474528 3.12771103663813 0.00186765668849169 ** df.mm.trans1:probe10 -0.183499855951476 0.0377232543474528 -4.86436971374041 1.55456649464485e-06 *** df.mm.trans1:probe11 0.0125196833246618 0.0377232543474528 0.331882377096853 0.740121449297845 df.mm.trans1:probe12 -0.0083843558006587 0.0377232543474528 -0.222259610038784 0.824205353587574 df.mm.trans2:probe2 0.114492708514665 0.0377232543474528 3.03506975989191 0.00253400877520475 ** df.mm.trans2:probe3 -0.0389698205671312 0.0377232543474528 -1.03304503392514 0.302097663981414 df.mm.trans2:probe4 -0.0411873638398389 0.0377232543474528 -1.09182955055997 0.275450099804902 df.mm.trans2:probe5 -0.0194589418394249 0.0377232543474528 -0.515834123434762 0.606205333997139 df.mm.trans2:probe6 0.0216661615643042 0.0377232543474528 0.574344974713645 0.566000662114226 df.mm.trans3:probe2 0.13854089476813 0.0377232543474528 3.67255946404011 0.000266842797261108 *** df.mm.trans3:probe3 -0.0370355236690935 0.0377232543474528 -0.981769052266146 0.326703120706849 df.mm.trans3:probe4 -0.114380639542798 0.0377232543474528 -3.03209894059739 0.00255860721883219 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.9341855501472 0.108129470665894 36.3840267220333 1.00373292412838e-140 *** df.mm.trans1 0.00431189465003993 0.094042200235654 0.0458506355576012 0.963448172268415 df.mm.trans2 0.0824872484629995 0.0882873430962274 0.934304347261802 0.350611844885914 df.mm.exp2 -0.116200359510115 0.119958877192203 -0.968668282247543 0.333193521400541 df.mm.exp3 -0.125084898082436 0.119958877192203 -1.04273148440711 0.297592143174268 df.mm.exp4 0.0240220857829312 0.119958877192203 0.200252672792544 0.841366914399934 df.mm.exp5 0.0130315786521968 0.119958877192203 0.108633716463660 0.913537945685057 df.mm.exp6 0.0666993726898992 0.119958877192203 0.556018647815706 0.578454520147658 df.mm.exp7 0.00796289158179556 0.119958877192203 0.0663801776756969 0.947102505059034 df.mm.exp8 0.0294900973325749 0.119958877192203 0.245835056336219 0.805913916732442 df.mm.trans1:exp2 0.111666280300640 0.109506971685270 1.01971845794053 0.308370386426464 df.mm.trans2:exp2 0.113443193670675 0.097946013079363 1.15822165807561 0.247343601147007 df.mm.trans1:exp3 0.0894025795433034 0.109506971685270 0.816409934157 0.414666521353904 df.mm.trans2:exp3 0.124633027355361 0.097946013079363 1.27246657048076 0.203817088279105 df.mm.trans1:exp4 0.0248828733513443 0.109506971685270 0.227226385392698 0.820343460299983 df.mm.trans2:exp4 0.0426618397100851 0.097946013079363 0.43556484198614 0.663346263054083 df.mm.trans1:exp5 -0.0354202898261156 0.109506971685270 -0.323452372766877 0.746492090574633 df.mm.trans2:exp5 0.0589096966433416 0.097946013079363 0.601450684833988 0.547820863271997 df.mm.trans1:exp6 -0.077206302796844 0.109506971685270 -0.705035502385549 0.481126584131541 df.mm.trans2:exp6 -0.0524823388635049 0.097946013079363 -0.535829251375244 0.592322199994228 df.mm.trans1:exp7 -0.00817001521292382 0.109506971685269 -0.0746072609550833 0.940557958409751 df.mm.trans2:exp7 -0.0580934462875148 0.097946013079363 -0.593117008657038 0.55337931681373 df.mm.trans1:exp8 -0.0676227295322293 0.109506971685270 -0.617519857334578 0.537181706283399 df.mm.trans2:exp8 -0.0209736589562396 0.097946013079363 -0.214134892241558 0.830531862838758 df.mm.trans1:probe2 0.0266159748043014 0.0599794385961016 0.443751649353239 0.657420050824182 df.mm.trans1:probe3 0.066870135493558 0.0599794385961016 1.11488431800534 0.265452134476057 df.mm.trans1:probe4 0.109617589821934 0.0599794385961016 1.82758612597382 0.0682259384519058 . df.mm.trans1:probe5 0.101963924943656 0.0599794385961016 1.69998131576849 0.089775573817036 . df.mm.trans1:probe6 0.0906024210015099 0.0599794385961016 1.51055800324544 0.131552322879117 df.mm.trans1:probe7 0.0827550056092323 0.0599794385961016 1.37972291082116 0.168307719615714 df.mm.trans1:probe8 0.049953629425327 0.0599794385961016 0.832845898437164 0.405341572523632 df.mm.trans1:probe9 0.0571336644187187 0.0599794385961016 0.952554171162785 0.341290481844978 df.mm.trans1:probe10 -0.0257236309534146 0.0599794385961016 -0.428874153468427 0.668205235749793 df.mm.trans1:probe11 0.102624026074463 0.0599794385961016 1.71098677274270 0.0877229680366037 . df.mm.trans1:probe12 0.00953790462364037 0.0599794385961016 0.159019571487958 0.873719649824149 df.mm.trans2:probe2 -0.0649389620820814 0.0599794385961016 -1.08268706080057 0.279485329484111 df.mm.trans2:probe3 -0.0773844937846819 0.0599794385961016 -1.29018369621271 0.197601670827182 df.mm.trans2:probe4 -0.0210948230657917 0.0599794385961016 -0.351700908837161 0.725215327141246 df.mm.trans2:probe5 0.0168738189176115 0.0599794385961016 0.281326723166565 0.778579799643595 df.mm.trans2:probe6 -0.0288025007728466 0.0599794385961016 -0.480206241455528 0.631297256367205 df.mm.trans3:probe2 -0.0424806480022562 0.0599794385961016 -0.708253511479469 0.47912808487046 df.mm.trans3:probe3 -0.0705856111001833 0.0599794385961016 -1.17683013966675 0.239840546070160 df.mm.trans3:probe4 -0.0645151933105177 0.0599794385961016 -1.07562182675566 0.282631202186919