fitVsDatCorrelation=0.70410775566195 cont.fitVsDatCorrelation=0.24307535149236 fstatistic=19522.6742322356,53,715 cont.fstatistic=10455.8624057802,53,715 residuals=-0.357150065778793,-0.0665555498240851,-0.00314351122545208,0.065270882942681,0.389791835784321 cont.residuals=-0.410767681150653,-0.0945840872094043,-0.0166202659906213,0.0790657437161104,0.570455387679147 predictedValues: Include Exclude Both Lung 48.8468728406813 45.0865414433236 60.3426580257176 cerebhem 51.0331450331605 51.6508132728553 63.7815920759287 cortex 48.2121783542052 45.6534967091531 60.8579751461223 heart 48.2820780688316 46.6868267122118 59.2465040845482 kidney 47.6985109949422 45.5372540869638 57.8775674335956 liver 48.7501181482047 49.5315755304597 52.2147064052524 stomach 48.0720660505635 48.6607352975098 59.0383166124603 testicle 49.4621491723878 51.3161344622067 55.0692694517696 diffExp=3.76033139735770,-0.61766823969483,2.55868164505217,1.59525135661976,2.16125690797846,-0.781457382254942,-0.58866924694621,-1.85398528981887 diffExpScore=1.92394242210431 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 51.4088899674694 47.9661151382464 52.2287947576702 cerebhem 49.2135320556986 49.0479063612373 50.579358667119 cortex 49.7543172367752 48.0801476356062 48.2426409570157 heart 49.5456675128733 46.9835767671895 51.0617137961324 kidney 48.7607557444784 51.2274363306649 49.7977837344838 liver 50.6768502464533 48.748854530659 46.3725110598593 stomach 48.6883256570999 49.3371969636346 46.8971249856104 testicle 49.4259683711012 50.7302439065166 50.484198303025 cont.diffExp=3.44277482922301,0.165625694461347,1.67416960116893,2.56209074568385,-2.46668058618654,1.92799571579434,-0.648871306534716,-1.30427553541544 cont.diffExpScore=2.23404150513959 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=0.739439669121449 cont.tran.correlation=-0.503661061964648 tran.covariance=0.000855147581944981 cont.tran.covariance=-0.000271692376409876 tran.mean=48.4050310111038 cont.tran.mean=49.3497365266065 weightedLogRatios: wLogRatio Lung 0.308301133655195 cerebhem -0.0473824725414765 cortex 0.209855882575530 heart 0.129698584604812 kidney 0.178138115460701 liver -0.0619357287167015 stomach -0.0472094406874487 testicle -0.144231721908867 cont.weightedLogRatios: wLogRatio Lung 0.270690704367864 cerebhem 0.0131287936217338 cortex 0.133145872331801 heart 0.205821244592089 kidney -0.193035337034581 liver 0.15150691459306 stomach -0.0515270327833605 testicle -0.101932163658052 varWeightedLogRatios=0.026038964994362 cont.varWeightedLogRatios=0.0262745315258577 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.82249861940421 0.0542053918731613 70.5187895025043 0 *** df.mm.trans1 0.124461260592032 0.0481354370358178 2.58564725400581 0.00991635941242989 ** df.mm.trans2 -0.0357516553404486 0.0437854395376674 -0.816519274853743 0.414475158376678 df.mm.exp2 0.124281778851739 0.0590292791399431 2.10542599642965 0.0356024374048594 * df.mm.exp3 -0.00908588221175867 0.0590292791399431 -0.153921618968417 0.87771497087698 df.mm.exp4 0.041580840064123 0.0590292791399431 0.704410432753983 0.481406574548763 df.mm.exp5 0.027866206920146 0.0590292791399431 0.47207432186462 0.637017777563312 df.mm.exp6 0.23671892618603 0.0590292791399431 4.01019510376928 6.70480604922805e-05 *** df.mm.exp7 0.08215217855244 0.0590292791399431 1.39171915614417 0.164440247617468 df.mm.exp8 0.233386227762898 0.0590292791399431 3.9537367076701 8.45909636359484e-05 *** df.mm.trans1:exp2 -0.0804968155074954 0.0560510699168109 -1.43613343379468 0.151401738605719 df.mm.trans2:exp2 0.0116403763512687 0.0471992247844563 0.246622193572598 0.80527141537362 df.mm.trans1:exp3 -0.0039928265911052 0.0560510699168109 -0.0712355107053481 0.943230239794407 df.mm.trans2:exp3 0.0215822984653023 0.0471992247844563 0.457259596187475 0.64762336660003 df.mm.trans1:exp4 -0.0532107636347046 0.0560510699168109 -0.94932645734824 0.342775359790089 df.mm.trans2:exp4 -0.00670258451543879 0.0471992247844563 -0.142006241544164 0.887115078942385 df.mm.trans1:exp5 -0.051656386447854 0.0560510699168109 -0.921595011915395 0.357050714853773 df.mm.trans2:exp5 -0.0179192309143413 0.0471992247844563 -0.379650958170873 0.704317261981616 df.mm.trans1:exp6 -0.23870166608802 0.0560510699168109 -4.25864602481795 2.33142178422408e-05 *** df.mm.trans2:exp6 -0.142692356555104 0.0471992247844563 -3.02319280044818 0.00259066587952442 ** df.mm.trans1:exp7 -0.0981412784251653 0.0560510699168109 -1.75092604959768 0.0803874720460598 . df.mm.trans2:exp7 -0.00586351647122448 0.0471992247844563 -0.124229084227575 0.901168810394662 df.mm.trans1:exp8 -0.220868874772178 0.0560510699168109 -3.94049346604773 8.92940745831067e-05 *** df.mm.trans2:exp8 -0.103964799183753 0.0471992247844563 -2.20268022745134 0.0279356472998785 * df.mm.trans1:probe2 -0.181949124603132 0.0307004353657365 -5.92659753633974 4.80908040871471e-09 *** df.mm.trans1:probe3 -0.0310417004437903 0.0307004353657365 -1.01111596868215 0.312302969025122 df.mm.trans1:probe4 -0.0560565792526757 0.0307004353657365 -1.82592131299995 0.0682790390428444 . df.mm.trans1:probe5 -0.0555826082343408 0.0307004353657365 -1.81048273655344 0.070640604844203 . df.mm.trans1:probe6 -0.05508636503442 0.0307004353657365 -1.79431869216746 0.0731846638711793 . df.mm.trans1:probe7 0.00546191707807386 0.0307004353657365 0.177910085411026 0.858843981648694 df.mm.trans1:probe8 -0.183697345268819 0.0307004353657365 -5.98354202734973 3.45142275729844e-09 *** df.mm.trans1:probe9 0.0149073034496861 0.0307004353657365 0.485573030873806 0.627418678825209 df.mm.trans1:probe10 0.0748519186982954 0.0307004353657365 2.43813867153931 0.0150056076695285 * df.mm.trans1:probe11 -0.0393325807728062 0.0307004353657365 -1.28117338742055 0.200548135879669 df.mm.trans1:probe12 -0.0647777638036014 0.0307004353657365 -2.10999495713657 0.0352056279126063 * df.mm.trans1:probe13 -0.0818567699118726 0.0307004353657365 -2.66630648512658 0.00784229481655004 ** df.mm.trans1:probe14 -0.0696357078278923 0.0307004353657366 -2.26823193216373 0.0236124984907400 * df.mm.trans1:probe15 -0.0983809640220801 0.0307004353657365 -3.20454621734384 0.00141284643189818 ** df.mm.trans1:probe16 -0.0153410113976016 0.0307004353657365 -0.499700125253697 0.617439979569317 df.mm.trans1:probe17 -0.119544162019307 0.0307004353657365 -3.89389142515957 0.000107888441471137 *** df.mm.trans1:probe18 -0.131369611275262 0.0307004353657365 -4.27907974952949 2.13223242974347e-05 *** df.mm.trans1:probe19 -0.112060256850519 0.0307004353657365 -3.65011946949732 0.000281215010903445 *** df.mm.trans1:probe20 -0.121877799528761 0.0307004353657365 -3.96990459831665 7.9166994542499e-05 *** df.mm.trans1:probe21 -0.124170860257516 0.0307004353657365 -4.04459607097619 5.81140436169712e-05 *** df.mm.trans1:probe22 -0.0684674253326115 0.0307004353657365 -2.23017766741592 0.0260452792019235 * df.mm.trans2:probe2 0.0373882239151858 0.0307004353657365 1.21784018597056 0.223686447563111 df.mm.trans2:probe3 -0.0373037037085132 0.0307004353657365 -1.215087123818 0.224733953343465 df.mm.trans2:probe4 0.0170701920384289 0.0307004353657365 0.556024428809249 0.578368106612139 df.mm.trans2:probe5 0.121819580286714 0.0307004353657365 3.96800823296048 7.97856925607113e-05 *** df.mm.trans2:probe6 0.0793939276597376 0.0307004353657366 2.58608474811226 0.00990390028755629 ** df.mm.trans3:probe2 0.153436746258051 0.0307004353657365 4.99786874127833 7.29660514189339e-07 *** df.mm.trans3:probe3 0.257373223906334 0.0307004353657365 8.3833737482947 2.72721862086564e-16 *** df.mm.trans3:probe4 0.344514265484794 0.0307004353657365 11.2218039054030 4.9722116626997e-27 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81425465164546 0.0740461056631473 51.5118873232494 7.64359592905615e-243 *** df.mm.trans1 0.126558919408404 0.0657543748643333 1.92472241838699 0.0546605670639806 . df.mm.trans2 0.0557110219093193 0.0598121546671956 0.931433121232706 0.351943953893624 df.mm.exp2 0.0107505926504448 0.0806356727508468 0.133323035372480 0.893975424702095 df.mm.exp3 0.0490513276273811 0.0806356727508468 0.608308034819068 0.543176377822922 df.mm.exp4 -0.0350140197279926 0.0806356727508468 -0.434224934616483 0.664256108893893 df.mm.exp5 0.060558603841768 0.0806356727508468 0.751015050483747 0.452890695527616 df.mm.exp6 0.120772079870161 0.0806356727508468 1.49775001249546 0.134639535139173 df.mm.exp7 0.0814892334575893 0.0806356727508468 1.01058539821922 0.312556770634628 df.mm.exp8 0.0506658641190793 0.0806356727508468 0.628330643133962 0.529988010903579 df.mm.trans1:exp2 -0.0543930792581569 0.0765673543197389 -0.710395177440974 0.477690751258532 df.mm.trans2:exp2 0.0115520816842727 0.064475482324461 0.179170147594073 0.857854862239892 df.mm.trans1:exp3 -0.0817652032305894 0.076567354319739 -1.06788596729025 0.285932366992033 df.mm.trans2:exp3 -0.0466767937799055 0.064475482324461 -0.723946407178672 0.469335430726012 df.mm.trans1:exp4 -0.00190227406281569 0.0765673543197389 -0.0248444533537354 0.980185965383793 df.mm.trans2:exp4 0.0143173028888106 0.064475482324461 0.222058096700407 0.824332067512605 df.mm.trans1:exp5 -0.113443914148718 0.0765673543197389 -1.48162248985365 0.138881327255222 df.mm.trans2:exp5 0.00522182351331744 0.064475482324461 0.0809892896502825 0.935473134336396 df.mm.trans1:exp6 -0.135113990253216 0.076567354319739 -1.76464227416023 0.0780508072845399 . df.mm.trans2:exp6 -0.104585206508207 0.064475482324461 -1.62209265813478 0.105224342590384 df.mm.trans1:exp7 -0.135861065744520 0.0765673543197389 -1.77439937622992 0.0764225762633925 . df.mm.trans2:exp7 -0.0533057615966074 0.064475482324461 -0.826760183481157 0.408648816162724 df.mm.trans1:exp8 -0.0900010165872262 0.0765673543197389 -1.17544895454255 0.240206411840412 df.mm.trans2:exp8 0.00536156838359868 0.064475482324461 0.083156700660533 0.933750216117731 df.mm.trans1:probe2 0.0117790518736527 0.0419376671294116 0.280870460374079 0.778891012326464 df.mm.trans1:probe3 -0.0132070257645302 0.0419376671294116 -0.314920372746912 0.752913993456105 df.mm.trans1:probe4 -0.0370522256287321 0.0419376671294116 -0.883507075259956 0.377259327228662 df.mm.trans1:probe5 -0.0178417678450170 0.0419376671294116 -0.425435391767517 0.67064732676823 df.mm.trans1:probe6 0.00606340246904323 0.0419376671294116 0.144581300870474 0.885082204982195 df.mm.trans1:probe7 -0.0139980196717779 0.0419376671294116 -0.333781553193759 0.738642271109391 df.mm.trans1:probe8 0.0120553807028741 0.0419376671294116 0.287459497107302 0.773843803178665 df.mm.trans1:probe9 -0.061163312394119 0.0419376671294116 -1.45843382764665 0.145160119992503 df.mm.trans1:probe10 0.0223866346898277 0.0419376671294116 0.533807343664273 0.593640742089762 df.mm.trans1:probe11 0.0365959343832401 0.0419376671294116 0.87262685047101 0.383159447950266 df.mm.trans1:probe12 0.0247596963432978 0.0419376671294116 0.590392790969848 0.555113892134363 df.mm.trans1:probe13 0.0265989818759153 0.0419376671294116 0.634250393419259 0.526120289972804 df.mm.trans1:probe14 -0.00196306211976522 0.0419376671294116 -0.0468090443301861 0.96267849141001 df.mm.trans1:probe15 -0.0184514799987537 0.0419376671294116 -0.439973924677688 0.660088954938324 df.mm.trans1:probe16 0.0118907446468328 0.0419376671294116 0.283533764769039 0.77684977972094 df.mm.trans1:probe17 -0.000535108930201832 0.0419376671294116 -0.0127596255783753 0.989823127346324 df.mm.trans1:probe18 -0.0130754244345350 0.0419376671294116 -0.311782350558193 0.75529682981572 df.mm.trans1:probe19 -0.0071678126297825 0.0419376671294116 -0.170915864434328 0.864338277082609 df.mm.trans1:probe20 0.0214980041497714 0.0419376671294116 0.512618026258654 0.608376861994878 df.mm.trans1:probe21 -0.0120953035445502 0.0419376671294116 -0.288411453770816 0.773115389238778 df.mm.trans1:probe22 -0.00314116971658539 0.0419376671294116 -0.0749009168033201 0.940314497693145 df.mm.trans2:probe2 0.0033854004692142 0.0419376671294116 0.0807245777111898 0.935683580050166 df.mm.trans2:probe3 -0.047551416361164 0.0419376671294116 -1.13385935880576 0.257233566694121 df.mm.trans2:probe4 0.00541259594855329 0.0419376671294116 0.129062876383921 0.897344192387868 df.mm.trans2:probe5 -0.000535293685465257 0.0419376671294116 -0.0127640310514517 0.989819613804752 df.mm.trans2:probe6 0.0445802480695641 0.0419376671294116 1.06301211109330 0.288135346243062 df.mm.trans3:probe2 -0.00824112559745136 0.0419376671294116 -0.196508918152763 0.844267693368684 df.mm.trans3:probe3 -0.0557706550939972 0.0419376671294116 -1.32984638658845 0.183992960222829 df.mm.trans3:probe4 -0.0995507251200216 0.0419376671294116 -2.37377832230932 0.0178703461958790 *