fitVsDatCorrelation=0.824468883444622 cont.fitVsDatCorrelation=0.250482790516133 fstatistic=5993.00248089543,44,508 cont.fstatistic=2040.14370683298,44,508 residuals=-0.584820532229804,-0.0956801194358205,-0.0091800984684385,0.086296389784735,0.882494193555208 cont.residuals=-0.597271976848394,-0.210008985813181,-0.074416973673751,0.116489832584098,1.42458590041670 predictedValues: Include Exclude Both Lung 63.0999128120415 57.2019469925416 96.9808778918459 cerebhem 96.571051197918 69.5653888068282 98.0176440827697 cortex 73.3262164958974 56.7928033811552 131.044706521883 heart 61.379023676396 54.7542493835555 103.120891569190 kidney 59.9178512950989 55.662707130843 108.798501825701 liver 58.7632449327144 57.0086047838649 93.6763765963125 stomach 62.8557789560204 56.683072765844 89.9369871309507 testicle 65.2666517599887 57.6012706502874 109.013677397067 diffExp=5.89796581949989,27.0056623910898,16.5334131147422,6.62477429284043,4.25514416425597,1.75464014884945,6.17270619017636,7.66538110970126 diffExpScore=0.98699773674811 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=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,1,0,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 79.4128485828506 69.5110136133438 76.2842734884593 cerebhem 69.2685603019078 71.253535500935 70.0700309846554 cortex 67.7792842099729 71.1252602055789 70.0799650159874 heart 68.5953538912044 72.4012000310422 71.6782081232144 kidney 69.377490045687 75.397557601139 74.5223549450221 liver 66.750517748491 75.0927477832447 65.4791167918385 stomach 73.4573812699522 67.9086200112963 69.013234082991 testicle 76.5712685277701 73.472733353398 64.5801052248698 cont.diffExp=9.90183496950682,-1.98497519902728,-3.34597599560603,-3.80584613983785,-6.02006755545199,-8.34223003475365,5.54876125865593,3.09853517437215 cont.diffExpScore=7.06697212020469 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=0.937345192091121 cont.tran.correlation=-0.459113676172112 tran.covariance=0.0112798977004304 cont.tran.covariance=-0.00105996188123926 tran.mean=62.9031109388122 cont.tran.mean=71.7109607923634 weightedLogRatios: wLogRatio Lung 0.401912439370334 cerebhem 1.44530984386323 cortex 1.06474616904489 heart 0.463701050986425 kidney 0.298792029945225 liver 0.123026550339918 stomach 0.422685160314027 testicle 0.514240600832822 cont.weightedLogRatios: wLogRatio Lung 0.573727437957031 cerebhem -0.120136406257664 cortex -0.204325252547568 heart -0.229773872049093 kidney -0.356246396964542 liver -0.501647347733395 stomach 0.334389155346550 testicle 0.178348201998558 varWeightedLogRatios=0.192120180829286 cont.varWeightedLogRatios=0.135295620896271 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.50974614898672 0.0989534066341238 35.4686742818655 1.49338858767049e-139 *** df.mm.trans1 0.618809156627457 0.0852693295851036 7.25711295771184 1.48968969355573e-12 *** df.mm.trans2 0.532836705531576 0.0796372981822921 6.69079335554425 5.87696698767215e-11 *** df.mm.exp2 0.610605171526932 0.107067938410666 5.70296935376598 2.00029493527904e-08 *** df.mm.exp3 -0.158004214292491 0.107067938410666 -1.47573789724479 0.1406338060622 df.mm.exp4 -0.132772381694998 0.107067938410666 -1.24007600842879 0.215519766996365 df.mm.exp5 -0.194006193512129 0.107067938410666 -1.81199149242984 0.0705778242515464 . df.mm.exp6 -0.0399207491893202 0.107067938410666 -0.372854374352494 0.70941234877527 df.mm.exp7 0.0624157229379888 0.107067938410666 0.582954373311918 0.560182696839712 df.mm.exp8 -0.0762410146801307 0.107067938410666 -0.712080720072365 0.476741684981864 df.mm.trans1:exp2 -0.185045540064389 0.0966471159503831 -1.91465144349872 0.0560983379489065 . df.mm.trans2:exp2 -0.414925951270699 0.0849575576834895 -4.88392042549659 1.39556016801805e-06 *** df.mm.trans1:exp3 0.308203031680478 0.0966471159503831 3.18895218599905 0.00151599170244513 ** df.mm.trans2:exp3 0.150825894818733 0.0849575576834895 1.77530874157937 0.0764455796002163 . df.mm.trans1:exp4 0.105121136739402 0.0966471159503832 1.08768001720164 0.277252167972788 df.mm.trans2:exp4 0.0890394255536802 0.0849575576834895 1.04804596532068 0.295115797954381 df.mm.trans1:exp5 0.142261284713418 0.0966471159503832 1.47196616592731 0.141649237444674 df.mm.trans2:exp5 0.166728649207296 0.0849575576834895 1.96249343499781 0.0502504491551662 . df.mm.trans1:exp6 -0.0312820653358496 0.0966471159503832 -0.323673034919213 0.746318794772662 df.mm.trans2:exp6 0.0365350306132144 0.0849575576834895 0.430038617038947 0.667349912654746 df.mm.trans1:exp7 -0.0662922315469648 0.0966471159503832 -0.685920432235122 0.493076199997558 df.mm.trans2:exp7 -0.0715280331988516 0.0849575576834895 -0.841926664904025 0.400225173867453 df.mm.trans1:exp8 0.110002839908302 0.0966471159503831 1.13819061051729 0.255577394886828 df.mm.trans2:exp8 0.0831977059018745 0.0849575576834894 0.979285518209324 0.327905051809704 df.mm.trans1:probe2 -0.0550686433138559 0.0564297582927222 -0.97587948238577 0.329588662978232 df.mm.trans1:probe3 0.117265072273714 0.0564297582927222 2.07807149669889 0.0382043159167509 * df.mm.trans1:probe4 -0.08761722418222 0.0564297582927222 -1.55267764443925 0.121122911937715 df.mm.trans1:probe5 -0.0793125302956526 0.0564297582927222 -1.40550894944878 0.160481261545029 df.mm.trans1:probe6 0.0904850901689231 0.0564297582927222 1.60349951703750 0.109445939365174 df.mm.trans1:probe7 0.117098196150307 0.0564297582927222 2.07511426050906 0.0384786872769846 * df.mm.trans1:probe8 -0.0196973421665729 0.0564297582927222 -0.349059481424596 0.727189289280415 df.mm.trans1:probe9 -0.141914349947087 0.0564297582927222 -2.51488495149890 0.0122147955466171 * df.mm.trans1:probe10 0.131103320829044 0.0564297582927222 2.32330112330027 0.0205568594545244 * df.mm.trans1:probe11 0.146898475361391 0.0564297582927222 2.60320936693321 0.00950557397505713 ** df.mm.trans1:probe12 0.0555493323422207 0.0564297582927222 0.984397842961964 0.325388541814437 df.mm.trans2:probe2 0.048011542068248 0.0564297582927222 0.850819559055955 0.395270446462874 df.mm.trans2:probe3 -0.064170508332882 0.0564297582927222 -1.13717496360707 0.256001248116161 df.mm.trans2:probe4 0.00556466795923224 0.0564297582927222 0.0986122947818815 0.92148500609661 df.mm.trans2:probe5 0.0271985456044607 0.0564297582927222 0.481989404657232 0.630021059230697 df.mm.trans2:probe6 0.0274516505133258 0.0564297582927222 0.486474713765809 0.626840429312394 df.mm.trans3:probe2 -0.427441412111555 0.0564297582927222 -7.57475178068736 1.71348033459998e-13 *** df.mm.trans3:probe3 0.00725850564113154 0.0564297582927222 0.128629040079863 0.897702093939908 df.mm.trans3:probe4 -0.24446092586362 0.0564297582927222 -4.33212782155667 1.78036763378396e-05 *** df.mm.trans3:probe5 0.230252896275699 0.0564297582927222 4.08034525119337 5.22098733594612e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.31176978959293 0.169283693522868 25.4706741084337 7.93887002775026e-93 *** df.mm.trans1 0.0701346187887508 0.145873775824180 0.480789767677525 0.630872918355964 df.mm.trans2 -0.069090687380425 0.136238826302635 -0.507129202852571 0.612284232044375 df.mm.exp2 -0.0269382001433583 0.183165559312701 -0.147070225671461 0.883134939176828 df.mm.exp3 -0.0506163734334172 0.183165559312701 -0.276342198955670 0.782397637076596 df.mm.exp4 -0.043417673405342 0.183165559312701 -0.237040596323128 0.812720854395853 df.mm.exp5 -0.0304403833520389 0.183165559312701 -0.166190540766842 0.868073125648622 df.mm.exp6 0.0562761937173435 0.183165559312701 0.307242223529962 0.758784918635301 df.mm.exp7 -0.00110849756832973 0.183165559312701 -0.00605188864374494 0.995173696753366 df.mm.exp8 0.185551307290974 0.183165559312701 1.01302509045491 0.311530420408548 df.mm.trans1:exp2 -0.109730848003901 0.165338226473668 -0.663675003320402 0.507199414905496 df.mm.trans2:exp2 0.0516974298542235 0.145340414711742 0.355698929005787 0.722213701503809 df.mm.trans1:exp3 -0.107787197037857 0.165338226473668 -0.651919397811026 0.514748008462948 df.mm.trans2:exp3 0.0735737150270719 0.145340414711742 0.506216493003635 0.612924288030106 df.mm.trans1:exp4 -0.103017697794092 0.165338226473668 -0.623072473869185 0.533516513755776 df.mm.trans2:exp4 0.0841553384014782 0.145340414711742 0.579022280680745 0.562830785351698 df.mm.trans1:exp5 -0.104657328779238 0.165338226473668 -0.632989303268629 0.527025463401156 df.mm.trans2:exp5 0.111730056010895 0.145340414711742 0.768747331789941 0.442400560730311 df.mm.trans1:exp6 -0.229974316039562 0.165338226473668 -1.39093252023112 0.164854814787100 df.mm.trans2:exp6 0.0209625836504605 0.145340414711742 0.144230933233789 0.885375314413067 df.mm.trans1:exp7 -0.0768462871604853 0.165338226473668 -0.464782336181185 0.64228648395527 df.mm.trans2:exp7 -0.0222137336317699 0.145340414711742 -0.152839343934907 0.878585657638627 df.mm.trans1:exp8 -0.221989561504271 0.165338226473668 -1.34263906320312 0.179988505401705 df.mm.trans2:exp8 -0.130122153956617 0.145340414711742 -0.895292298530264 0.371054866081273 df.mm.trans1:probe2 -0.0365509331273367 0.096536726054467 -0.37862205008604 0.705126726432737 df.mm.trans1:probe3 -0.0114163098824884 0.096536726054467 -0.118258722344149 0.90590940773275 df.mm.trans1:probe4 -0.0661339390696975 0.096536726054467 -0.68506507080408 0.493615319235107 df.mm.trans1:probe5 -0.0651748258084011 0.096536726054467 -0.675129854431036 0.499900432554177 df.mm.trans1:probe6 -0.0431830307460576 0.096536726054467 -0.447322304277165 0.654833032284502 df.mm.trans1:probe7 -0.0737523868831992 0.096536726054467 -0.7639826820063 0.445232252691061 df.mm.trans1:probe8 0.0164605782611290 0.096536726054467 0.170511047286209 0.864676135071309 df.mm.trans1:probe9 0.049110184301819 0.096536726054467 0.508720217776088 0.611169210529688 df.mm.trans1:probe10 0.115279291488989 0.096536726054467 1.19414958638588 0.232976752964486 df.mm.trans1:probe11 0.0463974146853253 0.096536726054467 0.480619310200632 0.630993999731055 df.mm.trans1:probe12 -0.0541879919373517 0.096536726054467 -0.561319967561136 0.574826957320592 df.mm.trans2:probe2 -0.00768126839484786 0.096536726054467 -0.0795683540222196 0.93661189917779 df.mm.trans2:probe3 0.0475365792568443 0.096536726054467 0.492419633435918 0.622635465636264 df.mm.trans2:probe4 -0.0803219290997182 0.096536726054467 -0.832034940302406 0.405780169572599 df.mm.trans2:probe5 -0.0948711206612002 0.096536726054467 -0.982746406871857 0.326200069676287 df.mm.trans2:probe6 0.122204916559548 0.096536726054467 1.26589041864335 0.206132544156483 df.mm.trans3:probe2 0.0665285859817552 0.096536726054467 0.689153120276931 0.491041554221954 df.mm.trans3:probe3 -0.104772485857731 0.096536726054467 -1.08531219298464 0.278298170041616 df.mm.trans3:probe4 0.0869015510909538 0.096536726054467 0.90019161248459 0.368444819925029 df.mm.trans3:probe5 0.0596077581343314 0.096536726054467 0.617461981264002 0.537206721667747