fitVsDatCorrelation=0.854039589228706 cont.fitVsDatCorrelation=0.231711471779790 fstatistic=10167.3193525694,57,807 cont.fstatistic=2897.44096426628,57,807 residuals=-0.457466768634315,-0.0978441818226027,-0.00732702616237666,0.0895089310081764,0.933904319013024 cont.residuals=-0.680667461570408,-0.223097118244779,-0.0410548085964927,0.184124162287954,1.15487053017002 predictedValues: Include Exclude Both Lung 57.2263615845234 47.9953237131166 53.6248456941611 cerebhem 67.3192058812449 61.8277080286649 61.779396490217 cortex 59.2398698815649 47.2394918470365 56.6598223292402 heart 72.8670761322425 46.7554832287412 71.0022501322824 kidney 73.3405903428431 46.6980773492223 67.3518979945872 liver 68.2276026586643 50.1883922348877 65.3878771400562 stomach 65.5471601037018 49.5843435278409 61.8436690665183 testicle 84.803473892563 53.1851561516578 77.6678058959121 diffExp=9.23103787140678,5.49149785258003,12.0003780345284,26.1115929035013,26.6425129936208,18.0392104237766,15.9628165758608,31.6183177409052 diffExpScore=0.993155249554754 diffExp1.5=0,0,0,1,1,0,0,1 diffExp1.5Score=0.75 diffExp1.4=0,0,0,1,1,0,0,1 diffExp1.4Score=0.75 diffExp1.3=0,0,0,1,1,1,1,1 diffExp1.3Score=0.833333333333333 diffExp1.2=0,0,1,1,1,1,1,1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 61.5003812897937 56.3162740574702 68.0304588609213 cerebhem 60.7585998298632 56.1598408336255 65.0842172300886 cortex 61.7847402946943 66.1437006731909 58.6917257879108 heart 59.0968827819393 56.2671614968375 56.7420902908895 kidney 60.2948927796246 62.2327030681341 62.0978080960937 liver 58.8823900431617 59.895262824217 62.6754077785381 stomach 61.9246924964417 58.6808310788992 61.9152658584664 testicle 61.2890723162644 57.7463063511633 72.4180971895505 cont.diffExp=5.18410723232355,4.59875899623763,-4.35896037849655,2.82972128510180,-1.93781028850951,-1.01287278105531,3.24386141754244,3.54276596510110 cont.diffExpScore=2.04046850960492 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=0.183082353479173 cont.tran.correlation=0.198937812425156 tran.covariance=0.00240778645923471 cont.tran.covariance=0.000215623069954696 tran.mean=59.5028322849072 cont.tran.mean=59.9358582634576 weightedLogRatios: wLogRatio Lung 0.696442348245169 cerebhem 0.354577898536707 cortex 0.898307946606469 heart 1.80445399486746 kidney 1.83697669569033 liver 1.24954647718151 stomach 1.12844219880040 testicle 1.96283303828649 cont.weightedLogRatios: wLogRatio Lung 0.358844958706688 cerebhem 0.320143884153690 cortex -0.283446834333691 heart 0.198949614685146 kidney -0.130172771032295 liver -0.0696552465975764 stomach 0.220551221929466 testicle 0.243279226371749 varWeightedLogRatios=0.343739611586292 cont.varWeightedLogRatios=0.0555261128762417 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.50765388065306 0.0752997419353229 59.8628064957355 4.5625867541293e-299 *** df.mm.trans1 -0.325256120030592 0.0653879132216468 -4.97425447617611 8.00600092704985e-07 *** df.mm.trans2 -0.603182093213159 0.0581204616435537 -10.3781366519834 9.02370820122743e-24 *** df.mm.exp2 0.274121525432367 0.0755388315041353 3.62888225795974 0.00030257017341693 *** df.mm.exp3 -0.0363460741470431 0.0755388315041353 -0.481157484479402 0.63053500530592 df.mm.exp4 -0.065248867785354 0.0755388315041353 -0.86377915154515 0.387965874865172 df.mm.exp5 -0.00721962079261875 0.0755388315041353 -0.0955749599095072 0.923881880541375 df.mm.exp6 0.0221903643081731 0.0755388315041353 0.293761021534446 0.769016055061114 df.mm.exp7 0.0257295373890639 0.0755388315041353 0.340613388858886 0.733483297455977 df.mm.exp8 0.125569269549680 0.0755388315041353 1.66231416411050 0.0968381327960265 . df.mm.trans1:exp2 -0.111690611439645 0.0702620184453878 -1.58962998659736 0.112309892719928 df.mm.trans2:exp2 -0.0208734949877372 0.0537132605481458 -0.388609717129856 0.697667379627258 df.mm.trans1:exp3 0.0709262080002523 0.0702620184453878 1.00945303834931 0.313059979229908 df.mm.trans2:exp3 0.0204727250631046 0.0537132605481458 0.381148432513307 0.703193431182492 df.mm.trans1:exp4 0.306871115143546 0.0702620184453878 4.36752490084051 1.42030299014694e-05 *** df.mm.trans2:exp4 0.0390768214390406 0.0537132605481458 0.727507901033379 0.467125882434007 df.mm.trans1:exp5 0.255319173719785 0.0702620184453878 3.63381495961770 0.000296932949914977 *** df.mm.trans2:exp5 -0.0201809691508390 0.0537132605481458 -0.375716702819593 0.707226257531781 df.mm.trans1:exp6 0.153644190643643 0.0702620184453878 2.18673180821108 0.0290485627021827 * df.mm.trans2:exp6 0.0224898217523284 0.0537132605481458 0.418701481213744 0.675545721366024 df.mm.trans1:exp7 0.110025688677051 0.0702620184453878 1.56593407236898 0.117756070826501 df.mm.trans2:exp7 0.00684200831317439 0.0537132605481458 0.127380245461762 0.898671183425516 df.mm.trans1:exp8 0.267752579041758 0.0702620184453878 3.81077266161764 0.000149065766876442 *** df.mm.trans2:exp8 -0.0228935153595945 0.0537132605481458 -0.426217197130939 0.670063246800648 df.mm.trans1:probe2 -0.224531969825235 0.0459972882866247 -4.88141753979279 1.26961627195605e-06 *** df.mm.trans1:probe3 -0.210578980412280 0.0459972882866247 -4.57807380078781 5.43244929981778e-06 *** df.mm.trans1:probe4 -0.242499892049164 0.0459972882866247 -5.27204757241481 1.73341782049326e-07 *** df.mm.trans1:probe5 -0.37116230658134 0.0459972882866247 -8.06922147819892 2.55801306394688e-15 *** df.mm.trans1:probe6 -0.287409765789047 0.0459972882866247 -6.2484067321121 6.70717097196853e-10 *** df.mm.trans1:probe7 0.242205340737613 0.0459972882866247 5.26564390553525 1.79286177046922e-07 *** df.mm.trans1:probe8 -0.127876581023465 0.0459972882866247 -2.78008956151116 0.00556088846978905 ** df.mm.trans1:probe9 -0.321633650018351 0.0459972882866247 -6.99244807681144 5.66814847986639e-12 *** df.mm.trans1:probe10 0.190058819703833 0.0459972882866247 4.13195705189211 3.97240332768355e-05 *** df.mm.trans1:probe11 -0.0223888584487395 0.0459972882866247 -0.486743007744869 0.626572628842606 df.mm.trans1:probe12 -0.0438934881927222 0.0459972882866247 -0.954262519112148 0.340236521077466 df.mm.trans1:probe13 -0.196490431877071 0.0459972882866247 -4.27178295060944 2.1702703833818e-05 *** df.mm.trans1:probe14 0.0226040497004074 0.0459972882866247 0.491421354223186 0.623262087428982 df.mm.trans1:probe15 0.00958435863712892 0.0459972882866247 0.20836790589492 0.834994290979777 df.mm.trans1:probe16 0.036987237064903 0.0459972882866247 0.804117773952737 0.421565766920448 df.mm.trans1:probe17 -0.210161327624083 0.0459972882866247 -4.56899385708341 5.66693312082645e-06 *** df.mm.trans1:probe18 -0.367490904758909 0.0459972882866247 -7.98940368982078 4.67215140518644e-15 *** df.mm.trans1:probe19 -0.493918814610305 0.0459972882866247 -10.737998543143 3.11072667434928e-25 *** df.mm.trans1:probe20 -0.500409899369664 0.0459972882866247 -10.8791174003876 8.11610086899874e-26 *** df.mm.trans1:probe21 -0.460664251501772 0.0459972882866247 -10.0150306390067 2.47281617764629e-22 *** df.mm.trans1:probe22 -0.4818217299897 0.0459972882866247 -10.4750029390277 3.67574176882452e-24 *** df.mm.trans2:probe2 -0.158660827572152 0.0459972882866247 -3.44935176577111 0.000591079145423142 *** df.mm.trans2:probe3 -0.0637381673999948 0.0459972882866247 -1.385694021848 0.166223172083954 df.mm.trans2:probe4 -0.171730677178489 0.0459972882866247 -3.73349568149272 0.000202103025348256 *** df.mm.trans2:probe5 -0.0341305822588926 0.0459972882866247 -0.742012921418614 0.458295404860946 df.mm.trans2:probe6 -0.0388946004887599 0.0459972882866247 -0.845584640694349 0.398035136307508 df.mm.trans3:probe2 0.668571338645519 0.0459972882866247 14.5350163792140 1.09445044580967e-42 *** df.mm.trans3:probe3 0.278852325383989 0.0459972882866247 6.06236445171214 2.05822628538040e-09 *** df.mm.trans3:probe4 0.187057053653462 0.0459972882866247 4.06669742111417 5.23589811552146e-05 *** df.mm.trans3:probe5 0.425398393715365 0.0459972882866247 9.24833635984285 1.99303502806573e-19 *** df.mm.trans3:probe6 0.475360458378565 0.0459972882866247 10.3345322319097 1.34920734211407e-23 *** df.mm.trans3:probe7 0.680059687251194 0.0459972882866247 14.7847778115421 5.99365698124667e-44 *** df.mm.trans3:probe8 0.507075401664495 0.0459972882866247 11.0240281667201 2.01554571128200e-26 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85823685274149 0.14080987568935 27.4003285199498 2.25821673095517e-117 *** df.mm.trans1 0.244355333196893 0.122274840466711 1.99841056642735 0.0460072499638255 * df.mm.trans2 0.151263255249774 0.108684767951341 1.39176131210489 0.164378157795471 df.mm.exp2 0.0293570794567697 0.141256971145427 0.207827473707799 0.83541611551712 df.mm.exp3 0.313115913676388 0.141256971145427 2.21664043294563 0.0269257689176691 * df.mm.exp4 0.140701599205951 0.141256971145427 0.996068357299657 0.319515499399639 df.mm.exp5 0.171345947382787 0.141256971145427 1.21300878812120 0.225481403627767 df.mm.exp6 0.100098934031166 0.141256971145427 0.708630046499521 0.478758728303315 df.mm.exp7 0.142193956784742 0.141256971145428 1.00663319928012 0.314412802384472 df.mm.exp8 -0.0408666781834931 0.141256971145427 -0.289307337203343 0.7724205046775 df.mm.trans1:exp2 -0.0414918208968183 0.131389375696344 -0.315792815643715 0.7522414232504 df.mm.trans2:exp2 -0.0321387069136266 0.100443339462577 -0.319968522408604 0.749075013632983 df.mm.trans1:exp3 -0.308502875127826 0.131389375696344 -2.34800472635483 0.0191148422336901 * df.mm.trans2:exp3 -0.152269808579701 0.100443339462577 -1.51597716079953 0.129916713552288 df.mm.trans1:exp4 -0.180566795618693 0.131389375696344 -1.37428764435265 0.169733931103097 df.mm.trans2:exp4 -0.141574064387107 0.100443339462577 -1.40949181045354 0.159075030924038 df.mm.trans1:exp5 -0.191141918719239 0.131389375696344 -1.45477454098717 0.146120526457923 df.mm.trans2:exp5 -0.0714488660194662 0.100443339462577 -0.711335031289821 0.477082202195852 df.mm.trans1:exp6 -0.143600243296011 0.131389375696344 -1.09293649151578 0.274747831506926 df.mm.trans2:exp6 -0.0384850697860434 0.100443339462577 -0.383152033693405 0.701707945766678 df.mm.trans1:exp7 -0.135318321790703 0.131389375696344 -1.02990307301132 0.303364077148478 df.mm.trans2:exp7 -0.101064393722801 0.100443339462577 -1.00618313034540 0.314629079669204 df.mm.trans1:exp8 0.0374248649670949 0.131389375696344 0.284839354542548 0.775840299609835 df.mm.trans2:exp8 0.0659425132413921 0.100443339462577 0.656514544361211 0.511680268476441 df.mm.trans1:probe2 0.0471756255318239 0.0860145370916409 0.548461075615188 0.583527039978741 df.mm.trans1:probe3 -0.0264080501176687 0.0860145370916409 -0.307018453049782 0.758908531274757 df.mm.trans1:probe4 0.0327138384096694 0.0860145370916409 0.380329180575787 0.703801158426333 df.mm.trans1:probe5 -0.0483842468024067 0.0860145370916409 -0.562512436134576 0.573923152377283 df.mm.trans1:probe6 0.0360866571675325 0.0860145370916409 0.419541375071116 0.674932182444145 df.mm.trans1:probe7 0.0316421740953881 0.0860145370916409 0.367870073655994 0.713066578756356 df.mm.trans1:probe8 0.00928423632124215 0.0860145370916409 0.107937990892756 0.9140717103426 df.mm.trans1:probe9 0.100416285298725 0.0860145370916409 1.16743388610859 0.243380010989238 df.mm.trans1:probe10 0.042625225275827 0.0860145370916409 0.495558387187663 0.620340933986651 df.mm.trans1:probe11 0.0971302065350877 0.0860145370916409 1.12923012573565 0.259136320589219 df.mm.trans1:probe12 -0.0380871396796637 0.0860145370916409 -0.442798868278338 0.658029884730722 df.mm.trans1:probe13 -0.0182416029030080 0.0860145370916409 -0.212075813226468 0.832101439822607 df.mm.trans1:probe14 -0.0164024774701418 0.0860145370916409 -0.190694248027707 0.848813106715019 df.mm.trans1:probe15 0.015538402316331 0.0860145370916408 0.180648560600590 0.856688792767826 df.mm.trans1:probe16 -0.0631316276413313 0.0860145370916409 -0.733964627096349 0.463183480143593 df.mm.trans1:probe17 0.0539170617495566 0.0860145370916409 0.626836620559996 0.530943620784607 df.mm.trans1:probe18 -0.0135030129145444 0.0860145370916409 -0.156985241926701 0.875295745288139 df.mm.trans1:probe19 -0.0257931736540653 0.0860145370916409 -0.299869935085333 0.764353589073287 df.mm.trans1:probe20 0.171621072363184 0.0860145370916409 1.99525659459559 0.0463507167835295 * df.mm.trans1:probe21 0.0487109158954236 0.0860145370916409 0.566310271989564 0.571340327151968 df.mm.trans1:probe22 0.0566252908906042 0.0860145370916409 0.658322334866198 0.510518788771669 df.mm.trans2:probe2 0.090660446122758 0.0860145370916409 1.05401306788604 0.29219239972771 df.mm.trans2:probe3 0.0190060991820862 0.0860145370916409 0.22096380245396 0.825176510957902 df.mm.trans2:probe4 0.0473542391584902 0.0860145370916409 0.550537627238969 0.582103034301332 df.mm.trans2:probe5 0.0577071904769908 0.0860145370916409 0.670900436463535 0.502475925142089 df.mm.trans2:probe6 0.0860402553403202 0.0860145370916409 1.00029899886169 0.317465668045830 df.mm.trans3:probe2 -0.0850267756454327 0.0860145370916409 -0.98851634293915 0.323196120500207 df.mm.trans3:probe3 0.0167548155659757 0.0860145370916409 0.194790510214860 0.84560597920766 df.mm.trans3:probe4 0.0437108453347556 0.0860145370916409 0.508179742782149 0.611466172143584 df.mm.trans3:probe5 -0.0986326189484699 0.0860145370916409 -1.14669708497513 0.251846708296473 df.mm.trans3:probe6 0.00422445636945048 0.0860145370916409 0.0491132837807369 0.960841169614601 df.mm.trans3:probe7 -0.0998263063726642 0.0860145370916409 -1.16057482546593 0.246158052780739 df.mm.trans3:probe8 -0.0524037113030703 0.0860145370916409 -0.609242496384521 0.542535189745203