fitVsDatCorrelation=0.861374174120877 cont.fitVsDatCorrelation=0.324842279486631 fstatistic=6189.38640513749,40,416 cont.fstatistic=1778.08433440410,40,416 residuals=-0.718774753404258,-0.112808370988062,-0.0154744910932039,0.097350795307564,0.70232929015104 cont.residuals=-0.799245868828485,-0.231095100859503,-0.033528006547914,0.211190092027435,1.03131024953542 predictedValues: Include Exclude Both Lung 74.3124227126565 101.405566102948 78.2663267553276 cerebhem 63.6482186539345 119.772891680267 77.7274086740909 cortex 81.7659091512924 95.1803398700766 74.1179698897076 heart 72.8781134794323 89.9627697523994 74.3821077817513 kidney 72.0407431712012 94.7059760176572 73.3480623490114 liver 70.2094009196476 96.1652160824783 75.127056094948 stomach 99.7179009561097 110.730095372968 81.8825668159453 testicle 69.3902529720069 98.6593174933256 69.7401082134732 diffExp=-27.0931433902913,-56.1246730263326,-13.4144307187842,-17.0846562729671,-22.6652328464559,-25.9558151628307,-11.0121944168585,-29.2690645213187 diffExpScore=0.99508887202611 diffExp1.5=0,-1,0,0,0,0,0,0 diffExp1.5Score=0.5 diffExp1.4=0,-1,0,0,0,0,0,-1 diffExp1.4Score=0.666666666666667 diffExp1.3=-1,-1,0,0,-1,-1,0,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,-1,0,-1,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 80.5808793584198 72.1316758456135 67.8237137601766 cerebhem 83.2920373786533 93.6029099769581 88.2932066547067 cortex 91.4762767026656 83.1716313356212 74.8673455072207 heart 88.5358589307982 85.978819968863 89.9620664068887 kidney 77.0301586244647 72.628947369071 83.4014399123748 liver 86.1370054211076 73.3547458660128 89.3672268445366 stomach 102.577421123357 93.8899258878892 90.618843859727 testicle 92.5474445173445 88.330868152419 85.7207427674846 cont.diffExp=8.44920351280635,-10.3108725983048,8.30464536704433,2.55703896193525,4.40121125539368,12.7822595550948,8.68749523546802,4.21657636492543 cont.diffExpScore=1.48947220396386 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=0.0883304059375644 cont.tran.correlation=0.678930594325318 tran.covariance=0.000563835597875196 cont.tran.covariance=0.00687992474762038 tran.mean=88.1590708992751 cont.tran.mean=85.3291629037037 weightedLogRatios: wLogRatio Lung -1.38754141614403 cerebhem -2.82572440728979 cortex -0.680542462275974 heart -0.925428971612649 kidney -1.20743002934316 liver -1.38693665405504 stomach -0.487584097578937 testicle -1.55400417809552 cont.weightedLogRatios: wLogRatio Lung 0.480055412932762 cerebhem -0.522936696648013 cortex 0.425281436491582 heart 0.130964304252832 kidney 0.253853406911318 liver 0.702864708986658 stomach 0.405869692282293 testicle 0.210048526455886 varWeightedLogRatios=0.514259098939862 cont.varWeightedLogRatios=0.132188280923629 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.5766413799944 0.0981660937516982 46.6214066902834 2.82179315334922e-167 *** df.mm.trans1 -0.332672191788549 0.0796232161762807 -4.17808031079822 3.58459658629615e-05 *** df.mm.trans2 0.0243064675499579 0.0796232161762807 0.305268597743464 0.760314272504007 df.mm.exp2 0.0184721128294843 0.107670239882483 0.171561917663095 0.863865370820365 df.mm.exp3 0.0866871328481287 0.107670239882483 0.80511692871441 0.421212125674998 df.mm.exp4 -0.0883198037842828 0.107670239882483 -0.820280551809669 0.412525949177831 df.mm.exp5 -0.0344958087735172 0.107670239882483 -0.320383875908214 0.748838212371618 df.mm.exp6 -0.0689194903800887 0.107670239882483 -0.640097862281266 0.522461358427446 df.mm.exp7 0.336866106949311 0.107670239882483 3.12868353703850 0.00187947689585050 ** df.mm.exp8 0.0193548353434484 0.107670239882483 0.179760306697312 0.857428273479 df.mm.trans1:exp2 -0.173378909220528 0.0868065225753715 -1.99730278413110 0.0464431797788774 * df.mm.trans2:exp2 0.147997285264747 0.0868065225753715 1.70490973343905 0.0889578465279066 . df.mm.trans1:exp3 0.00889513093201333 0.0868065225753715 0.102470766805455 0.918432390859498 df.mm.trans2:exp3 -0.150041708532083 0.0868065225753715 -1.72846122711351 0.0846475686917748 . df.mm.trans1:exp4 0.068830036683353 0.0868065225753715 0.792913189485155 0.428280378143391 df.mm.trans2:exp4 -0.0314122630725780 0.0868065225753715 -0.361865239392623 0.717636473606572 df.mm.trans1:exp5 0.0034495107002155 0.0868065225753715 0.039737920583334 0.96832113281113 df.mm.trans2:exp5 -0.0338550704829067 0.0868065225753715 -0.390006067268866 0.696731666127079 df.mm.trans1:exp6 0.0121235742612486 0.0868065225753715 0.139662019645149 0.888994626768305 df.mm.trans2:exp6 0.0158592212974723 0.0868065225753715 0.182696194098804 0.855125402877825 df.mm.trans1:exp7 -0.0427990323115785 0.0868065225753715 -0.493039359737252 0.622244926505494 df.mm.trans2:exp7 -0.2488984221132 0.0868065225753715 -2.86727788107269 0.00435066613629045 ** df.mm.trans1:exp8 -0.0878865592024676 0.0868065225753715 -1.01244188334072 0.311915315714883 df.mm.trans2:exp8 -0.0468101395111216 0.0868065225753715 -0.539246799921949 0.590005053059865 df.mm.trans1:probe2 -0.0854913173153415 0.0551645823517433 -1.54975010542502 0.121961906961763 df.mm.trans1:probe3 0.277077752583038 0.0551645823517433 5.02274721879195 7.57375528248871e-07 *** df.mm.trans1:probe4 -0.099252792518523 0.0551645823517433 -1.79921225335600 0.0727097075724318 . df.mm.trans1:probe5 0.569524789970368 0.0551645823517433 10.3241022716158 2.12717334800848e-22 *** df.mm.trans1:probe6 0.174157868050354 0.0551645823517433 3.15705948682579 0.00170972129552287 ** df.mm.trans2:probe2 -0.0815884652418055 0.0551645823517433 -1.4790008691007 0.139896882504844 df.mm.trans2:probe3 0.180432854251362 0.0551645823517433 3.27080975798704 0.00116170035327071 ** df.mm.trans2:probe4 -0.320101716433571 0.0551645823517433 -5.80266726923666 1.29489188932521e-08 *** df.mm.trans2:probe5 0.430964470778411 0.0551645823517433 7.81233995447428 4.62611032825984e-14 *** df.mm.trans2:probe6 0.0266346069679865 0.0551645823517433 0.482820785230594 0.629476969523534 df.mm.trans3:probe2 -0.290310792186411 0.0551645823517433 -5.26263011175026 2.27948097083875e-07 *** df.mm.trans3:probe3 0.441523773688978 0.0551645823517433 8.00375449004057 1.21593500943135e-14 *** df.mm.trans3:probe4 0.0227198914110870 0.0551645823517433 0.411856492744188 0.680656738227373 df.mm.trans3:probe5 0.168321985134413 0.0551645823517433 3.05126909257011 0.00242471857496801 ** df.mm.trans3:probe6 -0.292147264359616 0.0551645823517433 -5.29592089534569 1.92265752137407e-07 *** df.mm.trans3:probe7 0.592785010026697 0.0551645823517433 10.7457536113833 6.18861729929971e-24 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.43483060087693 0.182771006430640 24.2644098070330 1.12141769458035e-81 *** df.mm.trans1 -0.0382663789872475 0.148246862023391 -0.258126063951422 0.796437268447333 df.mm.trans2 -0.182703040941150 0.148246862023391 -1.23242433902124 0.218486685239391 df.mm.exp2 0.0299084793608161 0.200466345902753 0.149194515548884 0.881472446822289 df.mm.exp3 0.170425388667509 0.200466345902753 0.850144635998818 0.395733698984814 df.mm.exp4 -0.0127222332420162 0.200466345902752 -0.063463187223395 0.949428154815644 df.mm.exp5 -0.244947770506467 0.200466345902753 -1.22188973617194 0.222441251952348 df.mm.exp6 -0.192350484013872 0.200466345902753 -0.95951509041414 0.337856656595641 df.mm.exp7 0.215235977144325 0.200466345902753 1.07367636285812 0.283590151554767 df.mm.exp8 0.106873442107375 0.200466345902753 0.533124109316684 0.594232359505549 df.mm.trans1:exp2 0.00318308298530395 0.161621135052756 0.0196947199032164 0.984296345431616 df.mm.trans2:exp2 0.230659713207380 0.161621135052756 1.42716305718363 0.154283052411236 df.mm.trans1:exp3 -0.0436071135773467 0.161621135052756 -0.269810712337297 0.787439669287062 df.mm.trans2:exp3 -0.0280123484369519 0.161621135052756 -0.173321072320202 0.862483362558125 df.mm.trans1:exp4 0.10686849628344 0.161621135052756 0.661228472678131 0.508831845865497 df.mm.trans2:exp4 0.188329939802440 0.161621135052756 1.16525564395378 0.244583243327491 df.mm.trans1:exp5 0.199883393559241 0.161621135052756 1.23674043926245 0.21688119741814 df.mm.trans2:exp5 0.251818057030279 0.161621135052756 1.55807628097699 0.119975575435607 df.mm.trans1:exp6 0.259028206377809 0.161621135052756 1.60268770723122 0.109762600298725 df.mm.trans2:exp6 0.209164408230104 0.161621135052756 1.29416495040595 0.196326427965053 df.mm.trans1:exp7 0.0261204717845789 0.161621135052756 0.161615445752516 0.871687155829052 df.mm.trans2:exp7 0.0483938378459026 0.161621135052756 0.299427657342623 0.764763302835823 df.mm.trans1:exp8 0.0315865920511008 0.161621135052756 0.195436024136264 0.845147037667966 df.mm.trans2:exp8 0.0957229071833492 0.161621135052756 0.592267262274227 0.55399340135035 df.mm.trans1:probe2 -0.0828362730577842 0.102708438834867 -0.806518665822264 0.420404678760126 df.mm.trans1:probe3 -0.0022067845612932 0.102708438834867 -0.0214859128064564 0.982868342543635 df.mm.trans1:probe4 -0.090579599023541 0.102708438834867 -0.881909997377857 0.378334761899635 df.mm.trans1:probe5 -0.0432863702477662 0.102708438834867 -0.421449013720883 0.673644913406367 df.mm.trans1:probe6 0.123972245678056 0.102708438834867 1.20703076674524 0.228106176457394 df.mm.trans2:probe2 0.0754617947208297 0.102708438834867 0.734718544813599 0.46292487684834 df.mm.trans2:probe3 0.0518199977781588 0.102708438834867 0.504534957068857 0.61415263773107 df.mm.trans2:probe4 0.0470726041248644 0.102708438834867 0.458312916239990 0.64696696888609 df.mm.trans2:probe5 0.1036711646044 0.102708438834867 1.00937338528805 0.313382329051821 df.mm.trans2:probe6 0.0647287986901166 0.102708438834867 0.630218893641123 0.52889739811119 df.mm.trans3:probe2 -0.0946953313451602 0.102708438834867 -0.921981995047264 0.357072359835016 df.mm.trans3:probe3 0.0951056432824332 0.102708438834867 0.925976914470903 0.354994840960264 df.mm.trans3:probe4 0.082765987604784 0.102708438834867 0.805834345684624 0.420798755744172 df.mm.trans3:probe5 -0.0954971357594368 0.102708438834867 -0.929788602015224 0.353019763682876 df.mm.trans3:probe6 0.0828630041847393 0.102708438834867 0.806778928048605 0.420254859603280 df.mm.trans3:probe7 -0.0491871747241096 0.102708438834867 -0.478901006403107 0.632260675439924