fitVsDatCorrelation=0.892313568400547 cont.fitVsDatCorrelation=0.239406215677111 fstatistic=10202.7080049086,51,669 cont.fstatistic=2195.19784935544,51,669 residuals=-0.519875369510566,-0.0864975069467043,-0.00517905727197021,0.0821424417451888,0.748760524166723 cont.residuals=-0.562791917613385,-0.225385532868595,-0.0612568786288826,0.15351201929668,1.27258422503931 predictedValues: Include Exclude Both Lung 56.3403792261867 54.4520297582949 102.414374958211 cerebhem 54.7511666747671 51.3528976344704 85.2270792487916 cortex 70.3189226622158 52.5313400105282 167.704340357181 heart 74.5751917366955 50.6419078386137 191.215187510210 kidney 54.8060529092206 50.0773866673886 85.3194114184869 liver 53.1521405373918 47.6033156444786 77.4515782362665 stomach 54.6305992823404 47.5720166893225 82.4873846244732 testicle 57.1588521542443 49.786056049525 92.2257503594261 diffExp=1.88834946789176,3.39826904029673,17.7875826516876,23.9332838980818,4.72866624183199,5.54882489291321,7.05858259301787,7.37279610471928 diffExpScore=0.986247935536556 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,1,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,1,1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,1,1,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 64.6322084026178 55.6268803363676 59.0443630756117 cerebhem 62.7175488697139 63.512417191966 55.4442179370704 cortex 64.009913636892 59.7535606318027 55.7239696778001 heart 63.96862159839 66.9722773851214 59.9613520709545 kidney 63.1460283347267 65.4290952059614 54.8639575127488 liver 64.5148359775721 61.6935742733402 48.678096831114 stomach 63.8915685302896 62.9572618067568 64.2649888071821 testicle 58.5143088857356 70.592335559826 59.6972655433005 cont.diffExp=9.00532806625024,-0.794868322252093,4.25635300508935,-3.00365578673145,-2.28306687123475,2.82126170423187,0.934306723532828,-12.0780266740904 cont.diffExpScore=16.4196182005217 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,-1 cont.diffExp1.2Score=0.5 tran.correlation=0.328293216707641 cont.tran.correlation=-0.752892037747634 tran.covariance=0.00209799224341348 cont.tran.covariance=-0.00171480152589339 tran.mean=54.9843909672302 cont.tran.mean=63.2457772891925 weightedLogRatios: wLogRatio Lung 0.13685521929717 cerebhem 0.254435635522658 cortex 1.19779421534739 heart 1.59389681742598 kidney 0.357196510953940 liver 0.431986211612388 stomach 0.543909709676478 testicle 0.549191273176797 cont.weightedLogRatios: wLogRatio Lung 0.614243095719101 cerebhem -0.0522019272274367 cortex 0.283812684741464 heart -0.191865137899264 kidney -0.147865034655988 liver 0.185324783355736 stomach 0.0611322386847173 testicle -0.781206116320388 varWeightedLogRatios=0.251888672985416 cont.varWeightedLogRatios=0.166786936685825 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.24570845594433 0.0821330511213863 39.5176900362246 4.17903097255952e-177 *** df.mm.trans1 0.552021844393446 0.0736856634390834 7.4915773113695 2.16013147672465e-13 *** df.mm.trans2 0.744667195357092 0.067874250015572 10.9712769597638 7.2242164362976e-26 *** df.mm.exp2 0.0964962901820599 0.0930038479559683 1.03755158848637 0.299853727368342 df.mm.exp3 -0.30745620974649 0.0930038479559683 -3.30584396778994 0.000997493229547766 *** df.mm.exp4 -0.416516613332622 0.0930038479559682 -4.47848796030265 8.83842581903504e-06 *** df.mm.exp5 0.0712636532056745 0.0930038479559683 0.766244136902954 0.443801327023781 df.mm.exp6 0.0867033121547625 0.0930038479559682 0.932255106216803 0.351541016157848 df.mm.exp7 0.050488951463346 0.0930038479559683 0.542869489521009 0.587400473819373 df.mm.exp8 0.0296253013639647 0.0930038479559682 0.318538447763909 0.750175953341813 df.mm.trans1:exp2 -0.125109106039121 0.0888682538384904 -1.40780425669773 0.159653522217022 df.mm.trans2:exp2 -0.155095052451107 0.077570571446368 -1.99940582567887 0.0459684340348288 * df.mm.trans1:exp3 0.529085649048062 0.0888682538384904 5.95359564518535 4.23518354301423e-09 *** df.mm.trans2:exp3 0.27154602766508 0.077570571446368 3.50063203869558 0.000494980004387254 *** df.mm.trans1:exp4 0.696913021333933 0.0888682538384903 7.84209198709481 1.75410330190400e-14 *** df.mm.trans2:exp4 0.343975938841897 0.077570571446368 4.43436128454617 1.07929581528934e-05 *** df.mm.trans1:exp5 -0.0988745042583396 0.0888682538384904 -1.11259645585064 0.266281409906462 df.mm.trans2:exp5 -0.155014237089756 0.077570571446368 -1.99836399551255 0.0460814765115998 * df.mm.trans1:exp6 -0.144956428116254 0.0888682538384904 -1.63113847583524 0.103331738831829 df.mm.trans2:exp6 -0.221121023101837 0.077570571446368 -2.85057875659352 0.00449837969403819 ** df.mm.trans1:exp7 -0.081306292731033 0.0888682538384904 -0.914908183959589 0.36056947133091 df.mm.trans2:exp7 -0.185564373931186 0.077570571446368 -2.39220068218119 0.0170226900119994 * df.mm.trans1:exp8 -0.0152025232350717 0.0888682538384903 -0.171068098881529 0.864221945884597 df.mm.trans2:exp8 -0.119210481701082 0.077570571446368 -1.53680035454558 0.124815002052564 df.mm.trans1:probe2 0.112715917050784 0.0444341269192452 2.53669701343822 0.0114165299707510 * df.mm.trans1:probe3 0.0880147396647991 0.0444341269192452 1.98079147194132 0.0480237946901148 * df.mm.trans1:probe4 0.148413006875258 0.0444341269192452 3.34006803250539 0.00088410019393418 *** df.mm.trans1:probe5 0.143302897930927 0.0444341269192452 3.22506388369837 0.00132074094445459 ** df.mm.trans1:probe6 0.718246501225086 0.0444341269192451 16.1642987276521 7.34889425267948e-50 *** df.mm.trans1:probe7 0.153202668312645 0.0444341269192452 3.44786043824100 0.00060045486434525 *** df.mm.trans1:probe8 0.179431122668874 0.0444341269192452 4.03813769076578 6.01163192779793e-05 *** df.mm.trans1:probe9 0.769607826354636 0.0444341269192452 17.3201968782536 8.38665646221462e-56 *** df.mm.trans1:probe10 0.144850075366014 0.0444341269192452 3.25988345915438 0.00117106052217652 ** df.mm.trans1:probe11 0.0807637970787346 0.0444341269192452 1.81760738149565 0.0695713435014728 . df.mm.trans1:probe12 0.152748881826379 0.0444341269192452 3.43764787151069 0.000623148431983311 *** df.mm.trans1:probe13 0.451686505005614 0.0444341269192452 10.1653061806866 1.13780673691570e-22 *** df.mm.trans1:probe14 0.190144753129387 0.0444341269192452 4.27925034906069 2.14909941293252e-05 *** df.mm.trans1:probe15 0.0883287277447134 0.0444341269192451 1.98785784415754 0.0472346037373668 * df.mm.trans1:probe16 0.427370068092385 0.0444341269192452 9.61805930988786 1.34077996293038e-20 *** df.mm.trans1:probe17 0.432452522204331 0.0444341269192452 9.73244108048467 5.02728587713013e-21 *** df.mm.trans1:probe18 0.282336111317832 0.0444341269192452 6.3540375583603 3.87811906803110e-10 *** df.mm.trans1:probe19 0.413391348618069 0.0444341269192452 9.30346509045556 1.90467511216824e-19 *** df.mm.trans1:probe20 0.345410111539872 0.0444341269192452 7.77353209094492 2.88734115883616e-14 *** df.mm.trans1:probe21 0.285931052802238 0.0444341269192452 6.43494252338728 2.35474803422572e-10 *** df.mm.trans2:probe2 -0.0493825676077367 0.0444341269192452 -1.11136576842131 0.266810183056086 df.mm.trans2:probe3 0.145998249469839 0.0444341269192452 3.28572337508008 0.00107031152313690 ** df.mm.trans2:probe4 0.0156000355908372 0.0444341269192452 0.35108230255517 0.725637136387609 df.mm.trans2:probe5 -0.0374578662123598 0.0444341269192452 -0.84299768690034 0.399531026291695 df.mm.trans2:probe6 -0.0122575775569261 0.0444341269192452 -0.275859534254000 0.782741144269768 df.mm.trans3:probe2 0.116707891507921 0.0444341269192452 2.62653729463451 0.00882330196043258 ** df.mm.trans3:probe3 0.143180871020299 0.0444341269192452 3.22231764068452 0.00133326903365688 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19186784707368 0.176654267266424 23.729219293365 8.81344283625243e-91 *** df.mm.trans1 0.0687798545020069 0.158485368620163 0.433982361279353 0.66444116805751 df.mm.trans2 -0.186789927977432 0.145986003673944 -1.27950572847122 0.201162588371092 df.mm.exp2 0.165408960323150 0.200035508109128 0.826897993694762 0.408589708948408 df.mm.exp3 0.119766029003589 0.200035508109128 0.598723847259416 0.549559717106295 df.mm.exp4 0.15988088202554 0.200035508109127 0.799262508625811 0.424421887617125 df.mm.exp5 0.212470038862474 0.200035508109128 1.06216161755923 0.288545589798356 df.mm.exp6 0.294755487019025 0.200035508109127 1.47351582629132 0.141082348854407 df.mm.exp7 0.0275381971218834 0.200035508109128 0.137666544216041 0.890545384130898 df.mm.exp8 0.127816347299321 0.200035508109128 0.638968293717096 0.523062258930521 df.mm.trans1:exp2 -0.195480534187477 0.191140546354271 -1.02270574148701 0.306816662255315 df.mm.trans2:exp2 -0.032840070925957 0.166841147055936 -0.196834363137931 0.844016940704518 df.mm.trans1:exp3 -0.129440925685009 0.191140546354271 -0.677202865398821 0.498511352088117 df.mm.trans2:exp3 -0.0482037915183849 0.166841147055936 -0.28892028356904 0.772731884364793 df.mm.trans1:exp4 -0.170201075042927 0.191140546354271 -0.890449872040578 0.373544479975307 df.mm.trans2:exp4 0.0257313376525883 0.166841147055936 0.154226568844923 0.877477606923378 df.mm.trans1:exp5 -0.235732953227199 0.191140546354271 -1.2332964288502 0.217898339530256 df.mm.trans2:exp5 -0.050169542219291 0.166841147055936 -0.300702453229184 0.7637348229968 df.mm.trans1:exp6 -0.296573143132246 0.191140546354271 -1.55159723454258 0.121231538628650 df.mm.trans2:exp6 -0.191242249924492 0.166841147055936 -1.14625350699834 0.25210009675656 df.mm.trans1:exp7 -0.0390636609487473 0.191140546354271 -0.204371399443132 0.838125435633934 df.mm.trans2:exp7 0.0962513713284234 0.166841147055936 0.576904277073532 0.56419818730707 df.mm.trans1:exp8 -0.227257895254631 0.191140546354271 -1.18895702449975 0.234878421444539 df.mm.trans2:exp8 0.110438686116164 0.166841147055936 0.661939144299563 0.508238161282042 df.mm.trans1:probe2 -0.067992640183221 0.0955702731771356 -0.711441308294678 0.477058794884886 df.mm.trans1:probe3 -0.0949428758618761 0.0955702731771356 -0.993435225259882 0.320857076742957 df.mm.trans1:probe4 -0.00574823666890434 0.0955702731771356 -0.0601467012472615 0.952056757556297 df.mm.trans1:probe5 -0.199619154208315 0.0955702731771356 -2.08871595290231 0.0371107163811844 * df.mm.trans1:probe6 -0.0720447806987966 0.0955702731771355 -0.753840899515528 0.451209994091498 df.mm.trans1:probe7 -0.132066010609618 0.0955702731771356 -1.38187331917362 0.167471847955593 df.mm.trans1:probe8 -0.0719379521730774 0.0955702731771356 -0.752723098737443 0.451881103940761 df.mm.trans1:probe9 -0.0959990688779308 0.0955702731771356 -1.00448670581908 0.315507249033735 df.mm.trans1:probe10 -0.17226358053974 0.0955702731771356 -1.80248077998539 0.0719197502883846 . df.mm.trans1:probe11 0.000698566833054481 0.0955702731771356 0.00730945732214992 0.994170127849842 df.mm.trans1:probe12 -0.0416927169615518 0.0955702731771356 -0.43625193876213 0.662794692611427 df.mm.trans1:probe13 -0.165255227617973 0.0955702731771356 -1.72914884643763 0.0842437666668795 . df.mm.trans1:probe14 -0.107786628553107 0.0955702731771356 -1.12782589156493 0.259797772025443 df.mm.trans1:probe15 -0.203609710238547 0.0955702731771355 -2.13047115457297 0.0334968844913789 * df.mm.trans1:probe16 -0.148490620156412 0.0955702731771356 -1.55373229792062 0.120721201785097 df.mm.trans1:probe17 -0.136215656445167 0.0955702731771356 -1.42529315776567 0.154538849888063 df.mm.trans1:probe18 -0.0610893023966172 0.0955702731771356 -0.639208201104445 0.522906294468698 df.mm.trans1:probe19 -0.136996654595087 0.0955702731771356 -1.43346513555705 0.152192130066817 df.mm.trans1:probe20 -0.220393960315566 0.0955702731771356 -2.30609323368863 0.0214102579997634 * df.mm.trans1:probe21 -0.0729897806279176 0.0955702731771356 -0.76372891068998 0.445298062101597 df.mm.trans2:probe2 0.0116398623009954 0.0955702731771356 0.121793753580900 0.903098934887278 df.mm.trans2:probe3 0.00438068584196189 0.0955702731771356 0.0458373267788245 0.963453586369023 df.mm.trans2:probe4 0.0448671381132639 0.0955702731771356 0.469467509317511 0.638888472859122 df.mm.trans2:probe5 0.0850319556767581 0.0955702731771356 0.889732265588013 0.373929489394007 df.mm.trans2:probe6 -0.0236220203648232 0.0955702731771356 -0.247169120475786 0.804853173891258 df.mm.trans3:probe2 -0.00962543596117814 0.0955702731771356 -0.100715794160573 0.919806248400299 df.mm.trans3:probe3 0.0229193488481024 0.0955702731771356 0.239816713776912 0.810545808352377