fitVsDatCorrelation=0.942235394941218 cont.fitVsDatCorrelation=0.259146967437731 fstatistic=8703.97214618252,43,485 cont.fstatistic=1036.89906520406,43,485 residuals=-0.626623767196378,-0.0882771560257897,-0.0103568860813198,0.0951347582263518,0.78781867243066 cont.residuals=-1.08744780376477,-0.437861372005577,0.0695771594094665,0.357693403999427,1.32520796953581 predictedValues: Include Exclude Both Lung 79.6119622899354 133.502408156756 64.8365151606126 cerebhem 80.2832079667304 138.768365129257 65.8433373319621 cortex 69.6246409459697 129.764120114537 62.3098305465779 heart 73.95157359762 143.015249945835 64.3831123279057 kidney 78.4427707138363 137.204400755607 65.3069334570889 liver 78.3938198829629 154.607374187864 82.2901109691577 stomach 88.6357600437794 181.547199332047 66.1585267760198 testicle 74.7561137847626 138.885917639027 66.1821354214152 diffExp=-53.8904458668203,-58.4851571625266,-60.1394791685672,-69.063676348215,-58.7616300417712,-76.2135543049012,-92.9114392882674,-64.1298038542645 diffExpScore=0.998129425729745 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 96.8520202530083 113.384629344939 93.100809540706 cerebhem 96.3447304623123 91.7901721017867 89.055069188852 cortex 101.323568744139 93.048491635967 110.006138556106 heart 104.639379580389 91.9726935700892 119.620123578361 kidney 103.666243070156 105.390659488756 85.33313456587 liver 82.6244001732424 86.5642100666055 101.273776070221 stomach 88.5482724007278 108.681695875324 102.275786933725 testicle 90.1684200633948 89.99189745748 90.6581537160378 cont.diffExp=-16.5326090919307,4.55455836052559,8.27507710817237,12.6666860102995,-1.72441641859908,-3.93980989336310,-20.1334234745958,0.176522605914826 cont.diffExpScore=3.85124910743674 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,-1,0 cont.diffExp1.2Score=0.5 tran.correlation=0.787725950834146 cont.tran.correlation=0.196769920592674 tran.covariance=0.00582820000920606 cont.tran.covariance=0.00191803413055397 tran.mean=111.312180280408 cont.tran.mean=96.5619677680198 weightedLogRatios: wLogRatio Lung -2.39641899577932 cerebhem -2.54971766116036 cortex -2.83558001262415 heart -3.05577181339450 kidney -2.59530923418174 liver -3.1928699736993 stomach -3.47235381449279 testicle -2.86417071514816 cont.weightedLogRatios: wLogRatio Lung -0.733160235693776 cerebhem 0.220040802936703 cortex 0.389843440062278 heart 0.591724237718314 kidney -0.0767037780106468 liver -0.206709479907298 stomach -0.939556255730024 testicle 0.00881964974265692 varWeightedLogRatios=0.129302740103368 cont.varWeightedLogRatios=0.278572075274255 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.97762530486003 0.0899912694059914 55.3123134912533 1.31030110255114e-211 *** df.mm.trans1 -0.999497733171327 0.0782670711770005 -12.7703479655061 2.12806835654751e-32 *** df.mm.trans2 -0.15622155836782 0.0734775637833378 -2.12611238484265 0.0339983748623793 * df.mm.exp2 0.0316734167379891 0.0998363495961218 0.31725335377466 0.751187895112947 df.mm.exp3 -0.12269725177074 0.0998363495961217 -1.22898375458538 0.21967384381689 df.mm.exp4 0.00209544022901372 0.0998363495961218 0.0209887504650422 0.98326326146603 df.mm.exp5 0.00532797683890089 0.0998363495961218 0.0533671038700303 0.957461389513991 df.mm.exp6 -0.107031940065032 0.0998363495961218 -1.072073853842 0.284220022909475 df.mm.exp7 0.394582349178201 0.0998363495961218 3.95229143267403 8.8918484041321e-05 *** df.mm.exp8 -0.0439416380962182 0.0998363495961218 -0.440136666394352 0.660034205829504 df.mm.trans1:exp2 -0.0232772954668689 0.0911377012212795 -0.255407972276505 0.798516290517369 df.mm.trans2:exp2 0.00701317211651975 0.0815160380975386 0.0860342612349252 0.931474686899087 df.mm.trans1:exp3 -0.0113485685487635 0.0911377012212795 -0.124521119105359 0.900954271985476 df.mm.trans2:exp3 0.0942960771362379 0.0815160380975386 1.15677944287978 0.247931920108565 df.mm.trans1:exp4 -0.0758493336717967 0.0911377012212795 -0.8322498006356 0.405677556449142 df.mm.trans2:exp4 0.0667363110178088 0.0815160380975386 0.818689334949707 0.413365755133079 df.mm.trans1:exp5 -0.0201230150024142 0.0911377012212795 -0.220797921527077 0.825342700790141 df.mm.trans2:exp5 0.0220242971116715 0.0815160380975386 0.270183605897506 0.787133932636115 df.mm.trans1:exp6 0.0916126747071297 0.0911377012212795 1.00521160265714 0.315296211928151 df.mm.trans2:exp6 0.253801257274295 0.0815160380975386 3.11351315885357 0.00195801657186206 ** df.mm.trans1:exp7 -0.287211322348140 0.0911377012212794 -3.15139967872132 0.00172540569163801 ** df.mm.trans2:exp7 -0.0871861941695634 0.0815160380975386 -1.06955877891465 0.285349966800408 df.mm.trans1:exp8 -0.0189917247986159 0.0911377012212795 -0.208384944365719 0.835015893290415 df.mm.trans2:exp8 0.0834749815235126 0.0815160380975386 1.02403138660432 0.306330932443044 df.mm.trans1:probe2 0.162288048156291 0.0499181747980609 3.25108137091974 0.00122965134282179 ** df.mm.trans1:probe3 0.0218006307102462 0.0499181747980609 0.436727320228325 0.662503476745994 df.mm.trans1:probe4 0.314649907423321 0.0499181747980609 6.30331354654305 6.55436507130818e-10 *** df.mm.trans1:probe5 0.157238226886704 0.0499181747980609 3.14991939354346 0.00173399343799152 ** df.mm.trans1:probe6 0.0259947107131198 0.0499181747980609 0.520746417878435 0.60278108730037 df.mm.trans1:probe7 0.708784889678082 0.0499181747980609 14.1989344070652 1.63317083865423e-38 *** df.mm.trans1:probe8 0.82565377507682 0.0499181747980609 16.5401435131978 4.69116668370997e-49 *** df.mm.trans1:probe9 1.28179681434317 0.0499181747980609 25.6779583694428 1.8580714214513e-92 *** df.mm.trans1:probe10 1.24892745989476 0.0499181747980609 25.0194937003841 2.46494242062515e-89 *** df.mm.trans1:probe11 0.993459915071586 0.0499181747980609 19.9017676245282 7.20252421985612e-65 *** df.mm.trans1:probe12 0.643994260366535 0.0499181747980609 12.9009977422402 6.04100702604339e-33 *** df.mm.trans2:probe2 0.10339329738245 0.0499181747980609 2.07125556574769 0.0388631672998695 * df.mm.trans2:probe3 0.112607460989805 0.0499181747980609 2.25584091256035 0.0245253161031588 * df.mm.trans2:probe4 0.341484035074307 0.0499181747980609 6.8408758223983 2.38061506997932e-11 *** df.mm.trans2:probe5 0.0972024439795768 0.0499181747980609 1.94723553841461 0.0520832719316741 . df.mm.trans2:probe6 0.0724704607017247 0.0499181747980609 1.45178506615871 0.147207926107916 df.mm.trans3:probe2 0.414218339054571 0.0499181747980609 8.297946403895 1.05718581653926e-15 *** df.mm.trans3:probe3 0.339370596071736 0.0499181747980609 6.7985377559301 3.11506703797182e-11 *** df.mm.trans3:probe4 0.64626281244456 0.0499181747980609 12.9464431554029 3.89277195970675e-33 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.76501056421812 0.259491289966238 18.3628921218823 1.45207857035629e-57 *** df.mm.trans1 -0.205613319463817 0.225684373558210 -0.9110658226888 0.36271327627462 df.mm.trans2 -0.0896481659184832 0.211873751037959 -0.423120681440251 0.672394765843283 df.mm.exp2 -0.172104205409265 0.287879738926021 -0.597833685869407 0.5502299511081 df.mm.exp3 -0.319383557589817 0.287879738926020 -1.10943395593357 0.267792811702181 df.mm.exp4 -0.38259661015085 0.287879738926020 -1.32901541309640 0.184467755658993 df.mm.exp5 0.0820005153688963 0.287879738926020 0.284842954474017 0.775886048599954 df.mm.exp6 -0.512923221838613 0.287879738926021 -1.78172741073113 0.0754193013660136 . df.mm.exp7 -0.225988923196669 0.287879738926020 -0.785011560868281 0.432830073611036 df.mm.exp8 -0.275984194771232 0.287879738926021 -0.958678772604261 0.338198246991789 df.mm.trans1:exp2 0.166852657909781 0.262797044764132 0.634910708602283 0.5257860512749 df.mm.trans2:exp2 -0.0391763988214698 0.235052822551462 -0.166670616401097 0.867698689338485 df.mm.trans1:exp3 0.364518355364158 0.262797044764132 1.38707174462834 0.16605698494972 df.mm.trans2:exp3 0.121718491882431 0.235052822551462 0.517834632067785 0.604809772136208 df.mm.trans1:exp4 0.45993231951249 0.262797044764132 1.7501426620885 0.080726047342888 . df.mm.trans2:exp4 0.173302495711254 0.235052822551462 0.737291702478116 0.461301505654747 df.mm.trans1:exp5 -0.0140082272492628 0.262797044764132 -0.05330435607385 0.957511357941759 df.mm.trans2:exp5 -0.155112341251215 0.235052822551462 -0.659904184801929 0.509628567528801 df.mm.trans1:exp6 0.35404401111301 0.262797044764132 1.34721458314257 0.178540209752725 df.mm.trans2:exp6 0.243023834895870 0.235052822551462 1.03391157892887 0.301692757633717 df.mm.trans1:exp7 0.136352528118814 0.262797044764132 0.518851070951707 0.604101254969656 df.mm.trans2:exp7 0.183626473521714 0.235052822551462 0.781213650312626 0.435058004283522 df.mm.trans1:exp8 0.204479201131845 0.262797044764133 0.778087901693762 0.436896602068492 df.mm.trans2:exp8 0.0449179943871374 0.235052822551462 0.191097447371869 0.84852921712915 df.mm.trans1:probe2 -0.118471401050313 0.143939869463010 -0.823061751356928 0.410877379738397 df.mm.trans1:probe3 0.164187063272865 0.143939869463010 1.14066425018579 0.254572608414472 df.mm.trans1:probe4 -0.0275371649238805 0.143939869463010 -0.191310197977892 0.848362633518758 df.mm.trans1:probe5 0.0711467946549103 0.143939869463010 0.494281361518072 0.621331282403948 df.mm.trans1:probe6 -0.0588144538769747 0.143939869463010 -0.40860432968566 0.683010401569258 df.mm.trans1:probe7 -0.0533858571546125 0.143939869463010 -0.370889992840598 0.710881411754532 df.mm.trans1:probe8 -0.00712374250367937 0.143939869463010 -0.0494911002091053 0.960548303856652 df.mm.trans1:probe9 0.0291338451147422 0.143939869463010 0.202402886868179 0.839686646860323 df.mm.trans1:probe10 0.136631841099350 0.143939869463010 0.949228602256457 0.342977101460106 df.mm.trans1:probe11 0.144524408859837 0.143939869463010 1.00406099713031 0.315849885171495 df.mm.trans1:probe12 -0.0596992616703217 0.143939869463010 -0.414751395100183 0.678507295229366 df.mm.trans2:probe2 0.222778100988724 0.143939869463010 1.54771643061670 0.122342829097603 df.mm.trans2:probe3 0.0747691964961407 0.143939869463010 0.519447438538597 0.603685726102877 df.mm.trans2:probe4 -0.0379737222762531 0.143939869463010 -0.263816567417491 0.792033322428958 df.mm.trans2:probe5 0.154234168729532 0.143939869463010 1.07151805337135 0.284469464688127 df.mm.trans2:probe6 0.140426657179001 0.143939869463010 0.97559250055516 0.329752783539001 df.mm.trans3:probe2 0.087438641029369 0.143939869463010 0.607466446618107 0.543825708229467 df.mm.trans3:probe3 -0.0392595581232134 0.143939869463010 -0.272749713263443 0.785161706754037 df.mm.trans3:probe4 0.207556120287000 0.143939869463010 1.44196407195116 0.149957912427712