fitVsDatCorrelation=0.666621602654385 cont.fitVsDatCorrelation=0.233015153493426 fstatistic=11563.0516566376,57,807 cont.fstatistic=6787.63114425717,57,807 residuals=-0.539222834234727,-0.079485063029377,-0.00777758431873135,0.0641516164677726,1.80477110931773 cont.residuals=-0.448758466393116,-0.110586955969963,-0.0235785636442951,0.0734391303778475,1.75145175296518 predictedValues: Include Exclude Both Lung 46.4797656709246 48.2485792283256 54.5096792055562 cerebhem 52.0293205981065 49.40633919515 51.0646178836805 cortex 45.5915352663854 45.449978107524 54.355470804537 heart 47.0168272846857 51.4521957140301 57.3144926151649 kidney 45.7919648949243 46.8611064425329 55.3848581664186 liver 47.9152277622296 49.3037302078107 56.2016529881402 stomach 47.3484448917507 48.7636749929022 55.5298586748674 testicle 49.5455037780313 48.2371428964909 53.7656136000147 diffExp=-1.76881355740102,2.62298140295651,0.141557158861360,-4.43536842934434,-1.06914154760855,-1.38850244558110,-1.41523010115142,1.30836088154035 diffExpScore=2.02022259871021 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 48.9587899619859 48.2788567684018 48.025601882079 cerebhem 50.0365493882302 53.4944815902896 47.2853992219078 cortex 49.0331444745979 49.4592631114212 50.7466138404231 heart 48.0128465275003 48.1926357039478 49.8882260432983 kidney 48.5399582515648 48.3812618389225 50.5683922653909 liver 53.348441558171 47.9102881817670 50.3690040161641 stomach 49.4065737891482 49.7065579943881 51.8529226176035 testicle 49.604569115904 52.388534277332 49.4769228366736 cont.diffExp=0.679933193584112,-3.45793220205940,-0.426118636823304,-0.179789176447500,0.158696412642314,5.43815337640401,-0.299984205239944,-2.78396516142799 cont.diffExpScore=7.17505422170943 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=0.422696736305196 cont.tran.correlation=-0.00861310204516427 tran.covariance=0.00072175459519387 cont.tran.covariance=-1.01663486454339e-06 tran.mean=48.0900835582378 cont.tran.mean=49.6720470333483 weightedLogRatios: wLogRatio Lung -0.144082125240505 cerebhem 0.203083843706335 cortex 0.0118734699588421 heart -0.351177024198805 kidney -0.0885244918871195 liver -0.110943724305365 stomach -0.114044613443555 testicle 0.10409182362603 cont.weightedLogRatios: wLogRatio Lung 0.0543183500174461 cerebhem -0.263701661007758 cortex -0.0337187256167265 heart -0.0144770326777180 kidney 0.0127084996940866 liver 0.421788791632921 stomach -0.0236270403374519 testicle -0.214672538887941 varWeightedLogRatios=0.0284966867870871 cont.varWeightedLogRatios=0.0425820648460523 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.75124667526547 0.0668265872721234 56.134045271384 6.89224135346734e-281 *** df.mm.trans1 0.081785339849623 0.0580300938242475 1.40936080677935 0.159113732261559 df.mm.trans2 0.103975811643889 0.0515804171766633 2.01580012987818 0.0441518651483092 * df.mm.exp2 0.201789142684705 0.0670387731246303 3.01003633090903 0.00269351026024737 ** df.mm.exp3 -0.0762160255039255 0.0670387731246303 -1.1368946946901 0.255919775338526 df.mm.exp4 0.0256000197008702 0.0670387731246303 0.381868857493526 0.70265917145826 df.mm.exp5 -0.060014723213696 0.0670387731246303 -0.895224068348089 0.370934254721989 df.mm.exp6 0.0214817674798798 0.0670387731246303 0.320437956702214 0.748719308586423 df.mm.exp7 0.0105936104808910 0.0670387731246303 0.158022141324046 0.874478883329783 df.mm.exp8 0.0773815626203234 0.0670387731246303 1.15428070970907 0.248726807186721 df.mm.trans1:exp2 -0.0889987963829828 0.0623557370434183 -1.42727518914600 0.153887398554469 df.mm.trans2:exp2 -0.178076784241546 0.0476691393813019 -3.73568280344068 0.000200384145261544 *** df.mm.trans1:exp3 0.0569210236421317 0.0623557370434183 0.912843410101906 0.361597525934034 df.mm.trans2:exp3 0.0164619831382717 0.0476691393813019 0.345338375140224 0.729929993453754 df.mm.trans1:exp4 -0.0141115257611682 0.0623557370434183 -0.226306775130288 0.821020135859569 df.mm.trans2:exp4 0.0386867370197126 0.0476691393813019 0.811567767361192 0.417278995257053 df.mm.trans1:exp5 0.0451062889395635 0.0623557370434183 0.723370311670858 0.469662016033173 df.mm.trans2:exp5 0.0308363863713087 0.0476691393813019 0.646883639426563 0.517891199227289 df.mm.trans1:exp6 0.00893452274098674 0.0623557370434183 0.143283090933007 0.886102378030692 df.mm.trans2:exp6 0.000151592844480111 0.0476691393813018 0.00318010449627653 0.997463433930728 df.mm.trans1:exp7 0.00792329474900209 0.0623557370434183 0.127066010678137 0.898919806121287 df.mm.trans2:exp7 2.56789951711353e-05 0.0476691393813019 0.000538692233684586 0.999570318931016 df.mm.trans1:exp8 -0.0135071180985119 0.0623557370434183 -0.216613879314856 0.828564013905536 df.mm.trans2:exp8 -0.0776186201172541 0.0476691393813019 -1.62827819265602 0.10385621466782 df.mm.trans1:probe2 -0.0254029768471407 0.0408214121451752 -0.622295396268969 0.533923429544143 df.mm.trans1:probe3 -0.0652260622929115 0.0408214121451752 -1.59783943928605 0.110470165237176 df.mm.trans1:probe4 0.0626578340193028 0.0408214121451752 1.53492568548265 0.125194024529132 df.mm.trans1:probe5 -0.0187263664990927 0.0408214121451752 -0.458738821491407 0.646545252725197 df.mm.trans1:probe6 -0.00352365876255297 0.0408214121451752 -0.0863188845604266 0.931234338468643 df.mm.trans1:probe7 -0.0252724696750680 0.0408214121451752 -0.619098368894989 0.536026282643306 df.mm.trans1:probe8 -0.084518275044317 0.0408214121451752 -2.07043976684933 0.0387286911500331 * df.mm.trans1:probe9 0.246095141244472 0.0408214121451752 6.0285798141738 2.51510752857920e-09 *** df.mm.trans1:probe10 -0.0808071071109463 0.0408214121451752 -1.97952747993058 0.0480960664118492 * df.mm.trans1:probe11 0.0881694969776063 0.0408214121451752 2.15988355973686 0.0310755360489253 * df.mm.trans1:probe12 0.0568165179557957 0.0408214121451752 1.39183127114114 0.164356974305405 df.mm.trans1:probe13 0.0072415651244479 0.0408214121451752 0.177396242410585 0.859241673483303 df.mm.trans1:probe14 0.0461547442148057 0.0408214121451752 1.13065035699067 0.258538192197196 df.mm.trans1:probe15 0.0616576652147765 0.0408214121451752 1.51042460254683 0.131326552285941 df.mm.trans1:probe16 0.0339951357027128 0.0408214121451752 0.832777062729095 0.405216857220111 df.mm.trans1:probe17 -0.0650839507361911 0.0408214121451752 -1.5943581399078 0.111247386619357 df.mm.trans1:probe18 -0.0203822394470554 0.0408214121451752 -0.499302654561998 0.617702273209882 df.mm.trans1:probe19 0.0633657581125179 0.0408214121451752 1.55226766499814 0.120990252811196 df.mm.trans1:probe20 -0.0144739786566263 0.0408214121451752 -0.354568298743605 0.723005661232842 df.mm.trans1:probe21 -0.0151287795801068 0.0408214121451752 -0.370608922746317 0.711026091216393 df.mm.trans1:probe22 -0.0680563165391252 0.0408214121451752 -1.66717202964692 0.095868196884819 . df.mm.trans2:probe2 0.0894132914330497 0.0408214121451752 2.19035272751136 0.0287841144272642 * df.mm.trans2:probe3 -0.00310714922185263 0.0408214121451752 -0.0761156721086112 0.939345937075138 df.mm.trans2:probe4 -0.00329231036656073 0.0408214121451752 -0.0806515549940342 0.935739061298491 df.mm.trans2:probe5 0.114146393720656 0.0408214121451752 2.79623824170296 0.00529303378457903 ** df.mm.trans2:probe6 0.0988542959930928 0.0408214121451752 2.42162852283337 0.0156706066923801 * df.mm.trans3:probe2 0.0912960301594753 0.0408214121451752 2.23647407970099 0.0255932314449117 * df.mm.trans3:probe3 -0.0434925664674153 0.0408214121451752 -1.06543512783783 0.28699772739849 df.mm.trans3:probe4 -0.039847828960013 0.0408214121451752 -0.976150183592381 0.329282562687469 df.mm.trans3:probe5 -0.159275325622254 0.0408214121451752 -3.90175932806575 0.000103454933787335 *** df.mm.trans3:probe6 0.468145862860324 0.0408214121451752 11.4681447372627 2.59823502027232e-28 *** df.mm.trans3:probe7 0.0425441940134244 0.0408214121451752 1.04220289739420 0.29762973272291 df.mm.trans3:probe8 0.131593307961531 0.0408214121451752 3.22363438808876 0.00131661142564943 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.88331170227185 0.0871845742531209 44.5412704660062 1.22000771846471e-219 *** df.mm.trans1 0.00744681391723328 0.0757083255401577 0.0983618890538438 0.921669361673944 df.mm.trans2 -0.000831894413166667 0.0672938256301123 -0.01236212097287 0.990139761139886 df.mm.exp2 0.139892265005408 0.0874614001987287 1.59947433596474 0.110106650367044 df.mm.exp3 -0.0294374413065165 0.0874614001987287 -0.336576378146578 0.736523768792431 df.mm.exp4 -0.0593485881967462 0.0874614001987287 -0.678568923683992 0.497605575958454 df.mm.exp5 -0.0580651940036156 0.0874614001987287 -0.663895088252425 0.506947082401535 df.mm.exp6 0.0305606468353105 0.0874614001987287 0.349418678021058 0.726866171016108 df.mm.exp7 -0.0384293344417197 0.0874614001987287 -0.439386224716287 0.660499335022019 df.mm.exp8 0.0650259505319936 0.0874614001987287 0.743481700318569 0.457406490197394 df.mm.trans1:exp2 -0.118117461924022 0.0813517285303269 -1.45193549120459 0.146908194133562 df.mm.trans2:exp2 -0.0373074810212639 0.0621910199461159 -0.599885338005201 0.548751051681987 df.mm.trans1:exp3 0.030955005528702 0.0813517285303269 0.380508270542308 0.70366829193887 df.mm.trans2:exp3 0.0535930878588523 0.0621910199461159 0.861749620850196 0.389081296817343 df.mm.trans1:exp4 0.0398382762161696 0.0813517285303269 0.489704114907877 0.624476377588813 df.mm.trans2:exp4 0.057561094602491 0.0621910199461159 0.925553153049485 0.354954937382987 df.mm.trans1:exp5 0.0494736111486338 0.0813517285303269 0.608144559954749 0.543262712773451 df.mm.trans2:exp5 0.0601840639224475 0.0621910199461159 0.96772916692141 0.333469573992890 df.mm.trans1:exp6 0.0553051957601955 0.0813517285303269 0.679828158040655 0.496808234657472 df.mm.trans2:exp6 -0.0382240976459692 0.0621910199461159 -0.614624067575796 0.538976263870859 df.mm.trans1:exp7 0.0475338993671028 0.0813517285303269 0.584301037308418 0.559181091270433 df.mm.trans2:exp7 0.0675724936841463 0.0621910199461159 1.08653136325298 0.277568402499701 df.mm.trans1:exp8 -0.0519219248415085 0.0813517285303269 -0.638239970797335 0.523498549817938 df.mm.trans2:exp8 0.0166680885983192 0.0621910199461159 0.268014395209484 0.78875672739524 df.mm.trans1:probe2 -0.0493534134404343 0.0532572077008174 -0.926699231354499 0.354359770515327 df.mm.trans1:probe3 -0.0308343951194795 0.0532572077008174 -0.578971306432316 0.562770094050231 df.mm.trans1:probe4 -0.0251599039819402 0.0532572077008174 -0.472422514587712 0.636752964349648 df.mm.trans1:probe5 0.0151077329354505 0.0532572077008174 0.283674897495962 0.77673229406278 df.mm.trans1:probe6 -0.00645307686150519 0.0532572077008174 -0.121168141179248 0.90358802040387 df.mm.trans1:probe7 -0.0232380612979345 0.0532572077008174 -0.436336456625342 0.66270934146773 df.mm.trans1:probe8 -0.00061116291892319 0.0532572077008174 -0.0114756846126165 0.990846765648867 df.mm.trans1:probe9 -0.0202262182341115 0.0532572077008174 -0.379783678253209 0.704205921317822 df.mm.trans1:probe10 0.0312782669554277 0.0532572077008174 0.587305799641983 0.557162624574886 df.mm.trans1:probe11 0.0189130096434556 0.0532572077008174 0.355125821648463 0.722588125086607 df.mm.trans1:probe12 0.0236152725700022 0.0532572077008174 0.443419277680977 0.657581345606572 df.mm.trans1:probe13 -0.0272444526162225 0.0532572077008174 -0.511563669828006 0.609096359979174 df.mm.trans1:probe14 0.0214104644121951 0.0532572077008174 0.402020033278359 0.687775732952659 df.mm.trans1:probe15 -0.0097971753325103 0.0532572077008174 -0.183959613270523 0.85409135121362 df.mm.trans1:probe16 -0.0393019225686348 0.0532572077008173 -0.737964385767669 0.460750623672221 df.mm.trans1:probe17 0.0230439997925761 0.0532572077008174 0.432692602323993 0.665353711973945 df.mm.trans1:probe18 0.0440060276496739 0.0532572077008174 0.826292431568818 0.40888244884258 df.mm.trans1:probe19 0.0950585371909853 0.0532572077008174 1.78489525258243 0.0746538360652698 . df.mm.trans1:probe20 -0.00193897058393782 0.0532572077008174 -0.0364076651338981 0.97096630600639 df.mm.trans1:probe21 0.0151361734472257 0.0532572077008174 0.284208919330809 0.776323187308682 df.mm.trans1:probe22 -0.0467985252159296 0.0532572077008174 -0.87872660314505 0.379811089059229 df.mm.trans2:probe2 -0.0105453883357467 0.0532572077008174 -0.198008660066961 0.84308815474242 df.mm.trans2:probe3 0.0253044095194006 0.0532572077008174 0.475135866332928 0.634818701133643 df.mm.trans2:probe4 -0.0197284155884373 0.0532572077008174 -0.370436536952247 0.7111544604886 df.mm.trans2:probe5 -0.0495678450230937 0.0532572077008174 -0.930725570547196 0.352273873121336 df.mm.trans2:probe6 -0.0222680360565547 0.0532572077008174 -0.418122485535661 0.675968801076483 df.mm.trans3:probe2 -0.0188257478057816 0.0532572077008174 -0.353487323472512 0.723815452992731 df.mm.trans3:probe3 -0.0541059053594149 0.0532572077008174 -1.01593582719104 0.309964430347669 df.mm.trans3:probe4 -0.00526563304989218 0.0532572077008174 -0.0988717448250928 0.921264656798987 df.mm.trans3:probe5 -0.0392410315310238 0.0532572077008174 -0.73682104686126 0.461445328967984 df.mm.trans3:probe6 -0.00072075968879386 0.0532572077008173 -0.0135335613696247 0.9892054549097 df.mm.trans3:probe7 -0.0302239037507508 0.0532572077008174 -0.56750823138418 0.570526772133212 df.mm.trans3:probe8 0.0028439370473059 0.0532572077008173 0.0534000404843278 0.95742638889861