fitVsDatCorrelation=0.83547934302317 cont.fitVsDatCorrelation=0.2379237358933 fstatistic=10658.7650041764,53,715 cont.fstatistic=3402.63501728035,53,715 residuals=-0.402843887197856,-0.0885831915730039,-0.00509854908720609,0.0786522402048232,0.711639474436664 cont.residuals=-0.600312257223449,-0.183259246433181,-0.0470272643076035,0.156250800355431,1.06147741616383 predictedValues: Include Exclude Both Lung 58.794735698943 61.8580992810528 53.8964543071304 cerebhem 58.5476430843323 63.5137739425667 60.8107463452786 cortex 54.8498506303986 59.5629109811127 51.7653629352896 heart 56.4370196432421 57.9032091795984 52.8973827318751 kidney 59.456358900492 54.7260612729194 65.5050921591176 liver 60.2538051019455 57.2112654653889 59.3031571400535 stomach 59.7126409816132 74.459049267471 61.0426180489133 testicle 57.8497152760344 62.0615998623376 60.2631540214387 diffExp=-3.06336358210984,-4.96613085823436,-4.7130603507141,-1.46618953635632,4.73029762757264,3.04253963655658,-14.7464082858578,-4.21188458630319 diffExpScore=1.55109359495016 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,-1,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 56.6532978083692 58.2865915291248 55.9454358949273 cerebhem 58.9880961972936 57.1665825564614 55.109031290562 cortex 53.2683372179166 65.430180538943 54.6596701692004 heart 60.0589582412059 61.0295842858461 55.2811688758428 kidney 58.7310037087612 56.4693824175815 53.8115834463848 liver 57.2428368226681 56.1939731183518 60.6837556449435 stomach 57.7003957486884 58.9574883811697 55.2667079551044 testicle 60.109843915278 58.855435807564 53.6836844108336 cont.diffExp=-1.63329372075559,1.82151364083217,-12.1618433210264,-0.970626044640284,2.26162129117967,1.04886370431633,-1.25709263248129,1.25440810771396 cont.diffExpScore=2.10683683209580 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,-1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.207724337947068 cont.tran.correlation=-0.574387554691768 tran.covariance=0.000551917569497667 cont.tran.covariance=-0.00112444551972797 tran.mean=59.8248586605905 cont.tran.mean=58.4463742684515 weightedLogRatios: wLogRatio Lung -0.208213893920371 cerebhem -0.334664279416175 cortex -0.333511272730730 heart -0.103768435956255 kidney 0.335240584889256 liver 0.211024372810485 stomach -0.926940514521206 testicle -0.287650718287201 cont.weightedLogRatios: wLogRatio Lung -0.115141601379704 cerebhem 0.127398434949292 cortex -0.838639618708048 heart -0.0657848635963747 kidney 0.159171039403535 liver 0.074675779297377 stomach -0.0876338063485256 testicle 0.0861635595087235 varWeightedLogRatios=0.148077507101051 cont.varWeightedLogRatios=0.104104601371647 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.37324871414003 0.0763102667764911 57.3087855523956 1.66928427640785e-269 *** df.mm.trans1 -0.161673084391227 0.0677649937519402 -2.38579058950476 0.0173017933183835 * df.mm.trans2 -0.239196459282415 0.0616410740072463 -3.88047196021108 0.00011388748135035 *** df.mm.exp2 -0.098499562774408 0.0831013278039477 -1.18529469236385 0.236294739753494 df.mm.exp3 -0.066919381196713 0.0831013278039477 -0.805274512034138 0.420928991792189 df.mm.exp4 -0.0882864024088662 0.0831013278039477 -1.06239460598212 0.288415274538748 df.mm.exp5 -0.306375936533288 0.0831013278039477 -3.68677546592382 0.000244310676822571 *** df.mm.exp6 -0.149176661098233 0.0831013278039477 -1.7951176598546 0.0730571733224852 . df.mm.exp7 0.0763900932900623 0.0831013278039477 0.919240345596901 0.358279876448527 df.mm.exp8 -0.124575572373357 0.0831013278039477 -1.49908040780353 0.134294150832199 df.mm.trans1:exp2 0.0942880752898622 0.0789086094694834 1.19490225368027 0.232521421234145 df.mm.trans2:exp2 0.124913316986258 0.0664469955936022 1.87989413020633 0.0605288812807164 . df.mm.trans1:exp3 -0.00253347729078987 0.0789086094694834 -0.0321064749185531 0.974396099962458 df.mm.trans2:exp3 0.0291094219389732 0.0664469955936022 0.438084847613125 0.66145709483572 df.mm.trans1:exp4 0.0473594002780826 0.0789086094694834 0.600180393451212 0.548576188109499 df.mm.trans2:exp4 0.0222161710510122 0.0664469955936022 0.334344252174905 0.738217847892991 df.mm.trans1:exp5 0.317566194144295 0.0789086094694834 4.02448093154028 6.31903731259683e-05 *** df.mm.trans2:exp5 0.183872931943565 0.0664469955936022 2.76721212601024 0.00579995065987122 ** df.mm.trans1:exp6 0.173690064726716 0.0789086094694834 2.20115480293551 0.0280439002193259 * df.mm.trans2:exp6 0.0710844481416967 0.0664469955936022 1.06979175667261 0.285074061633562 df.mm.trans1:exp7 -0.0608986755371436 0.0789086094694834 -0.771762117550622 0.440510323645599 df.mm.trans2:exp7 0.109016166060988 0.0664469955936022 1.64064853628212 0.101310196607845 df.mm.trans1:exp8 0.108371782271259 0.0789086094694834 1.37338350022719 0.170063642791169 df.mm.trans2:exp8 0.127859969698820 0.0664469955936022 1.9242400436105 0.0547210863878889 . df.mm.trans1:probe2 -0.274607439903634 0.0432200253878018 -6.35370843583858 3.74056391567051e-10 *** df.mm.trans1:probe3 -0.386711939276794 0.0432200253878018 -8.94751763347962 3.08817264637204e-18 *** df.mm.trans1:probe4 -0.345194421912659 0.0432200253878018 -7.98690928141112 5.53234560480583e-15 *** df.mm.trans1:probe5 -0.108719119000358 0.0432200253878018 -2.51548022068128 0.0121051366985509 * df.mm.trans1:probe6 -0.258038117563769 0.0432200253878018 -5.97033701966765 3.72829687862679e-09 *** df.mm.trans1:probe7 -0.362402287401298 0.0432200253878018 -8.38505494037911 2.6919646884348e-16 *** df.mm.trans1:probe8 -0.0497247063553103 0.0432200253878018 -1.15050155360030 0.250321983913723 df.mm.trans1:probe9 -0.361627147895882 0.0432200253878018 -8.36712020992809 3.09245014469193e-16 *** df.mm.trans1:probe10 -0.080679710485895 0.0432200253878018 -1.866720571355 0.0623497112643996 . df.mm.trans1:probe11 0.200206166021755 0.0432200253878018 4.63225470659395 4.29748954914201e-06 *** df.mm.trans1:probe12 -0.0249608946212245 0.0432200253878018 -0.577530771841455 0.563762771227792 df.mm.trans1:probe13 0.356580481737781 0.0432200253878018 8.25035336139391 7.584876105672e-16 *** df.mm.trans1:probe14 0.0499499506966937 0.0432200253878018 1.15571312715590 0.248184559870142 df.mm.trans1:probe15 0.233027433093768 0.0432200253878018 5.39165423904485 9.49321284956982e-08 *** df.mm.trans1:probe16 -0.0225693464277866 0.0432200253878018 -0.522196510188919 0.601695256769956 df.mm.trans1:probe17 -0.41540428763292 0.0432200253878018 -9.61138462797297 1.18876609908544e-20 *** df.mm.trans1:probe18 -0.370863187597396 0.0432200253878018 -8.58081836532346 5.83401896747742e-17 *** df.mm.trans1:probe19 -0.382486539544337 0.0432200253878018 -8.84975277345135 6.82395629533435e-18 *** df.mm.trans1:probe20 -0.305402403435306 0.0432200253878018 -7.06622452659412 3.78724958783789e-12 *** df.mm.trans1:probe21 -0.370644668609113 0.0432200253878018 -8.5757623991059 6.07116096915657e-17 *** df.mm.trans1:probe22 -0.295333816210817 0.0432200253878018 -6.83326336717447 1.77845847033080e-11 *** df.mm.trans2:probe2 -0.246482215687722 0.0432200253878018 -5.70296323234664 1.72340628172866e-08 *** df.mm.trans2:probe3 0.359141551471914 0.0432200253878018 8.30960991460398 4.81678583705205e-16 *** df.mm.trans2:probe4 -0.205199612501207 0.0432200253878018 -4.74779018892297 2.48504477762126e-06 *** df.mm.trans2:probe5 0.188319233336691 0.0432200253878018 4.3572217194912 1.51019447619546e-05 *** df.mm.trans2:probe6 -0.187871098735286 0.0432200253878018 -4.34685303975572 1.58141082636727e-05 *** df.mm.trans3:probe2 0.0584197200495872 0.0432200253878018 1.35168176153074 0.176904596044810 df.mm.trans3:probe3 -0.091194276149524 0.0432200253878018 -2.11000052247221 0.0352051468850945 * df.mm.trans3:probe4 0.0915166460428194 0.0432200253878018 2.11745933098523 0.0345654932285129 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08029631688748 0.134878946867906 30.2515434145814 4.38555544482397e-130 *** df.mm.trans1 -0.064743904659271 0.119775115169532 -0.540545542933784 0.588989201554894 df.mm.trans2 0.0160717584840677 0.108951042855813 0.147513580988272 0.882768247442247 df.mm.exp2 0.0360461420253173 0.14688219621025 0.245408517542318 0.806210555967161 df.mm.exp3 0.0772541693212601 0.146882196210250 0.525960064014 0.599079044536936 df.mm.exp4 0.116307709341750 0.146882196210250 0.79184347962271 0.428714563929936 df.mm.exp5 0.0432321782984725 0.14688219621025 0.294332324910155 0.768589402510845 df.mm.exp6 -0.107509439111254 0.14688219621025 -0.731943297997555 0.464443013895876 df.mm.exp7 0.0419645606127329 0.14688219621025 0.285702159250560 0.77518899901399 df.mm.exp8 0.110203271346433 0.14688219621025 0.750283384847311 0.453330866210064 df.mm.trans1:exp2 0.00433932263670838 0.139471536316705 0.0311126036989718 0.975188420727706 df.mm.trans2:exp2 -0.0554487113844329 0.117445543919425 -0.472122734792509 0.636983239952422 df.mm.trans1:exp3 -0.138862262630118 0.139471536316705 -0.995631555350454 0.319766018402354 df.mm.trans2:exp3 0.0383573828447817 0.117445543919425 0.326597174866823 0.744068158626812 df.mm.trans1:exp4 -0.0579311917578936 0.139471536316705 -0.415362111064343 0.678001454469711 df.mm.trans2:exp4 -0.070321050425064 0.117445543919425 -0.5987545212725 0.549526234853771 df.mm.trans1:exp5 -0.0072146182073161 0.139471536316705 -0.0517282479124165 0.958759676698238 df.mm.trans2:exp5 -0.0749056671231545 0.117445543919425 -0.637790627242054 0.523814173773785 df.mm.trans1:exp6 0.117861753196258 0.139471536316705 0.845059546261999 0.398360276515465 df.mm.trans2:exp6 0.0709468743929384 0.117445543919425 0.604083152287261 0.545979976200551 df.mm.trans1:exp7 -0.023650727912395 0.139471536316705 -0.169573868166836 0.865393239929692 df.mm.trans2:exp7 -0.0305199883105297 0.117445543919425 -0.259865017368970 0.795042706242262 df.mm.trans1:exp8 -0.0509798504698693 0.139471536316705 -0.365521538058539 0.714830238985784 df.mm.trans2:exp8 -0.100491150722741 0.117445543919425 -0.855640387613892 0.392483358834431 df.mm.trans1:probe2 -0.0416715519525794 0.0763917065705601 -0.54549837702721 0.585580927432728 df.mm.trans1:probe3 0.146969692476068 0.0763917065705601 1.92389591847013 0.0547642949613526 . df.mm.trans1:probe4 -0.0380706670042565 0.0763917065705601 -0.498361258222345 0.618382699955249 df.mm.trans1:probe5 0.0118876610515151 0.0763917065705601 0.155614550128356 0.876380777107415 df.mm.trans1:probe6 0.0603604859985477 0.0763917065705601 0.790144489608896 0.42970536314886 df.mm.trans1:probe7 -0.00322622812096245 0.0763917065705601 -0.042232701242019 0.966324984516366 df.mm.trans1:probe8 0.0237598617843816 0.0763917065705601 0.311026718095838 0.755870964011653 df.mm.trans1:probe9 -0.00725927951154796 0.0763917065705601 -0.0950270629815403 0.924319955939011 df.mm.trans1:probe10 0.00439251974090846 0.0763917065705601 0.0574999556640519 0.95416300643226 df.mm.trans1:probe11 0.0433715393466855 0.0763917065705601 0.567751936614021 0.57038164987277 df.mm.trans1:probe12 0.059612767389601 0.0763917065705601 0.780356534312255 0.435439302099288 df.mm.trans1:probe13 0.0216018608937635 0.07639170657056 0.28277756661727 0.777429195338533 df.mm.trans1:probe14 -0.00575557833383939 0.0763917065705601 -0.0753429736318717 0.93996290554069 df.mm.trans1:probe15 0.113043698093453 0.0763917065705601 1.47979019147895 0.139369703651932 df.mm.trans1:probe16 0.0438833971418038 0.07639170657056 0.574452373324982 0.565842404590744 df.mm.trans1:probe17 0.00207375517764142 0.0763917065705601 0.0271463391870422 0.97835058980138 df.mm.trans1:probe18 0.0871651033171202 0.0763917065705601 1.14102835543554 0.254240146197709 df.mm.trans1:probe19 -0.0568180995111608 0.0763917065705601 -0.743773140591905 0.457258072560997 df.mm.trans1:probe20 -0.00801232297822403 0.0763917065705601 -0.104884722935511 0.916496696667048 df.mm.trans1:probe21 0.0797093380602561 0.0763917065705601 1.04342921029826 0.29710220452892 df.mm.trans1:probe22 0.019324516517787 0.0763917065705601 0.252966158046721 0.80036707564882 df.mm.trans2:probe2 -0.0556817798996105 0.0763917065705601 -0.728898232534959 0.466302593598580 df.mm.trans2:probe3 -0.145748429765309 0.0763917065705601 -1.90790906903862 0.0568032701771338 . df.mm.trans2:probe4 -0.0289821126066073 0.0763917065705601 -0.379388207276632 0.704512253173989 df.mm.trans2:probe5 -0.054479796632035 0.0763917065705601 -0.713163759232348 0.475977120145351 df.mm.trans2:probe6 -0.025067875008157 0.0763917065705601 -0.328149168719025 0.742894950663034 df.mm.trans3:probe2 0.0388852692869188 0.0763917065705601 0.509024749316236 0.610891918569926 df.mm.trans3:probe3 -0.0144399596100893 0.0763917065705601 -0.189025226144826 0.850126687434654 df.mm.trans3:probe4 -0.0534345656472286 0.0763917065705601 -0.699481240124844 0.484478824318184