fitVsDatCorrelation=0.855849037538512 cont.fitVsDatCorrelation=0.275861719364079 fstatistic=11697.9276848359,54,738 cont.fstatistic=3377.51534820962,54,738 residuals=-0.577661199304706,-0.0843998000545273,-0.00415523903372555,0.0766652066509318,1.21275029311579 cont.residuals=-0.604573956348609,-0.179243161157418,-0.0285718240918331,0.148977621831737,1.09990965906065 predictedValues: Include Exclude Both Lung 57.5383349421369 66.4733791513148 98.428858878748 cerebhem 55.1424937986801 60.9382745577421 84.1287647978126 cortex 51.6914676180763 61.6205048666321 79.1123142824876 heart 58.64922171354 71.4991142175217 88.69136928576 kidney 56.9728114803192 67.9576224318667 95.5180638719129 liver 68.3567009839185 70.4834910419425 102.04149084063 stomach 61.8392435910715 59.5258439367184 86.0645824358114 testicle 56.5702427624125 60.8742533665685 78.8359738972677 diffExp=-8.93504420917785,-5.79578075906194,-9.92903724855584,-12.8498925039816,-10.9848109515475,-2.12679005802394,2.31339965435314,-4.30401060415603 diffExpScore=1.0676490629479 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,-1,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 62.957494375192 63.726085828813 61.4952362747237 cerebhem 61.2945199641533 65.8874497098134 64.1262128823967 cortex 66.1835079802252 66.5627584440702 64.335206811099 heart 63.4273508332838 61.4179625671886 62.2943104621591 kidney 65.4885484317459 66.4728442811767 62.6404808284553 liver 62.7788812526389 60.9848749632066 70.6298002099136 stomach 64.8767104248497 60.5679104118137 68.6740206732827 testicle 60.9627468586249 70.8859171933122 73.3248362744728 cont.diffExp=-0.768591453621006,-4.59292974566016,-0.379250463844983,2.00938826609514,-0.984295849430822,1.79400628943229,4.30880001303603,-9.92317033468726 cont.diffExpScore=2.59651007154753 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=0.455770187497756 cont.tran.correlation=-0.254688877102039 tran.covariance=0.00269722936626521 cont.tran.covariance=-0.000417228642546101 tran.mean=61.6333125287789 cont.tran.mean=64.0297227200068 weightedLogRatios: wLogRatio Lung -0.595390384858004 cerebhem -0.405748873160075 cortex -0.708631233922851 heart -0.826246599759014 kidney -0.728289486870942 liver -0.129910809609411 stomach 0.156531894365086 testicle -0.298599179620821 cont.weightedLogRatios: wLogRatio Lung -0.0503389853095832 cerebhem -0.300000230037704 cortex -0.0239715872289907 heart 0.133078669234153 kidney -0.0624973843224059 liver 0.119599048660516 stomach 0.284386503858181 testicle -0.631235534171433 varWeightedLogRatios=0.114535137924704 cont.varWeightedLogRatios=0.0818743079631711 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.53455522902495 0.0730128098664834 48.410069897166 3.05330866476201e-231 *** df.mm.trans1 0.482526891666705 0.0643177844324589 7.50223124015433 1.80720936776269e-13 *** df.mm.trans2 0.627503057495889 0.0580359009705553 10.8123255950529 2.14301092325553e-25 *** df.mm.exp2 0.0275146192637309 0.0772865453549196 0.356007881286151 0.72193640595356 df.mm.exp3 0.0354999433080083 0.0772865453549196 0.45932889282324 0.64613326682677 df.mm.exp4 0.196177851813226 0.0772865453549196 2.53831829217268 0.0113431149614861 * df.mm.exp5 0.0422241617883322 0.0772865453549196 0.546332632600255 0.585002431389515 df.mm.exp6 0.194819674927995 0.0772865453549196 2.52074502791311 0.0119203676925395 * df.mm.exp7 0.0959318376790472 0.0772865453549196 1.24124887764751 0.214908207825107 df.mm.exp8 0.117005070822279 0.0772865453549196 1.51391254823155 0.130476151428012 df.mm.trans1:exp2 -0.0700454083260461 0.0728999324840658 -0.960843253748643 0.336945679887812 df.mm.trans2:exp2 -0.114454713896193 0.0596453993051447 -1.91891940081824 0.0553799409912704 . df.mm.trans1:exp3 -0.142658632121494 0.0728999324840657 -1.95691034628429 0.0507346722597836 . df.mm.trans2:exp3 -0.111306810947205 0.0596453993051447 -1.86614243921415 0.0624178922662879 . df.mm.trans1:exp4 -0.177054967125174 0.0728999324840658 -2.42873979566269 0.0153892368449344 * df.mm.trans2:exp4 -0.123294344757034 0.0596453993051447 -2.06712246364991 0.0390707584100958 * df.mm.trans1:exp5 -0.0521014196308159 0.0728999324840657 -0.714697776190726 0.475021818414074 df.mm.trans2:exp5 -0.0201414044140423 0.0596453993051447 -0.33768580055939 0.735696003008365 df.mm.trans1:exp6 -0.0225314976033097 0.0728999324840657 -0.309074327445153 0.757352221703012 df.mm.trans2:exp6 -0.136242716150496 0.0596453993051447 -2.28421165316507 0.0226422359503022 * df.mm.trans1:exp7 -0.0238450854914161 0.0728999324840657 -0.327093382379032 0.743690009551966 df.mm.trans2:exp7 -0.206322821554214 0.0596453993051447 -3.45915735258424 0.000572979988753009 *** df.mm.trans1:exp8 -0.133973390420306 0.0728999324840657 -1.83777111795803 0.0664979540075502 . df.mm.trans2:exp8 -0.20499630852121 0.0596453993051447 -3.43691736344077 0.000621338283520522 *** df.mm.trans1:probe2 0.459226160306741 0.0425643903512186 10.7889753974496 2.66930294264473e-25 *** df.mm.trans1:probe3 0.153176594167918 0.0425643903512186 3.59870288060011 0.000341167596032519 *** df.mm.trans1:probe4 0.096424050924404 0.0425643903512186 2.26536901218986 0.023779076586987 * df.mm.trans1:probe5 0.35378972269475 0.0425643903512186 8.31187102118616 4.51456513972103e-16 *** df.mm.trans1:probe6 0.0181840423398455 0.0425643903512186 0.42721256406589 0.669349121654883 df.mm.trans1:probe7 0.0363555874520021 0.0425643903512186 0.854131520550753 0.393309281002322 df.mm.trans1:probe8 0.384211046157122 0.0425643903512186 9.02658402920418 1.51755192388339e-18 *** df.mm.trans1:probe9 -0.0228491075986515 0.0425643903512186 -0.536812753809296 0.591558750356661 df.mm.trans1:probe10 0.0344842195089192 0.0425643903512186 0.810165944452013 0.418105964144191 df.mm.trans1:probe11 0.0827852174046729 0.0425643903512186 1.94494075262381 0.052161593831689 . df.mm.trans1:probe12 -0.0865111688367891 0.0425643903512186 -2.03247757392847 0.0424628894088875 * df.mm.trans1:probe13 0.176708685372713 0.0425643903512186 4.1515615263042 3.68760222524515e-05 *** df.mm.trans1:probe14 -0.0784840795982367 0.0425643903512186 -1.84389060786794 0.0655997780854114 . df.mm.trans1:probe15 0.182992085392983 0.0425643903512186 4.29918257686836 1.94441685181025e-05 *** df.mm.trans1:probe16 0.0232566263820893 0.0425643903512186 0.546386925554155 0.58496513718937 df.mm.trans1:probe17 -0.204057219878672 0.0425643903512186 -4.79408299272938 1.97693888520053e-06 *** df.mm.trans1:probe18 -0.174554287200372 0.0425643903512186 -4.10094648977805 4.57209013431764e-05 *** df.mm.trans1:probe19 -0.197913622333228 0.0425643903512186 -4.64974643593272 3.93734793437021e-06 *** df.mm.trans1:probe20 -0.0506060898024622 0.0425643903512186 -1.18893021572464 0.234849538900763 df.mm.trans1:probe21 -0.091198898046428 0.0425643903512186 -2.14261022638650 0.0324709024525810 * df.mm.trans1:probe22 -0.14044847519457 0.0425643903512186 -3.29967078197677 0.00101446245638403 ** df.mm.trans2:probe2 0.0304307772097825 0.0425643903512186 0.714935112630159 0.47487523279772 df.mm.trans2:probe3 0.162074769909624 0.0425643903512186 3.80775499360545 0.000151884411412367 *** df.mm.trans2:probe4 0.00609124860166837 0.0425643903512186 0.143106680288538 0.886244992186555 df.mm.trans2:probe5 0.159228113444719 0.0425643903512186 3.74087616739846 0.000197601663622500 *** df.mm.trans2:probe6 0.0243510331148565 0.0425643903512186 0.572098717118343 0.567429316393945 df.mm.trans3:probe2 -0.0923196750138255 0.0425643903512186 -2.16894155541881 0.0304057663504247 * df.mm.trans3:probe3 0.265322523940377 0.0425643903512186 6.23343883821846 7.67168037065733e-10 *** df.mm.trans3:probe4 -0.131383234335480 0.0425643903512186 -3.08669367166722 0.00209967236810689 ** df.mm.trans3:probe5 -0.317875293790064 0.0425643903512186 -7.46810399883862 2.30088815783211e-13 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10725407751796 0.135684901285953 30.2705314931248 1.73468763434244e-131 *** df.mm.trans1 0.00747069906049508 0.119526316650573 0.0625025456304754 0.95017955837659 df.mm.trans2 0.0847729021934622 0.107852245498169 0.78600961715629 0.432114143648004 df.mm.exp2 -0.035308879569811 0.143627088128661 -0.245837188721541 0.805876620469324 df.mm.exp3 0.0483754423658134 0.143627088128661 0.3368128045768 0.736353775822119 df.mm.exp4 -0.0423666676528218 0.143627088128661 -0.294976861292836 0.768094516439188 df.mm.exp5 0.0631629913564814 0.143627088128661 0.439770743662924 0.660231910835069 df.mm.exp6 -0.185301624364083 0.143627088128661 -1.29015791365268 0.197399931684052 df.mm.exp7 -0.131211126606022 0.143627088128661 -0.913554179198313 0.361249505345946 df.mm.exp8 -0.10165870971707 0.143627088128661 -0.707796217563114 0.479295279596267 df.mm.trans1:exp2 0.008539515293079 0.135475133212121 0.0630338209722092 0.949756640625936 df.mm.trans2:exp2 0.0686628690693685 0.110843290809917 0.619458954778934 0.535805240820727 df.mm.trans1:exp3 0.00159605914790673 0.135475133212121 0.0117811963720877 0.990603366725002 df.mm.trans2:exp3 -0.00482419314971047 0.110843290809917 -0.0435226445774097 0.965296686555393 df.mm.trans1:exp4 0.0498020309982934 0.135475133212121 0.367610127537692 0.713269360252616 df.mm.trans2:exp4 0.00547502056781876 0.110843290809917 0.0493942441424603 0.960618479636306 df.mm.trans1:exp5 -0.0237475033303594 0.135475133212121 -0.175290496250531 0.860899460560071 df.mm.trans2:exp5 -0.0209634731769181 0.110843290809917 -0.189127127350161 0.850045179925604 df.mm.trans1:exp6 0.182460549268607 0.135475133212120 1.34681948592675 0.178451812165505 df.mm.trans2:exp6 0.141333516961597 0.110843290809917 1.27507507156177 0.202683931825849 df.mm.trans1:exp7 0.161240026447892 0.135475133212120 1.19018171545497 0.234357692845447 df.mm.trans2:exp7 0.0803823588640448 0.110843290809917 0.725189213318207 0.468565832750826 df.mm.trans1:exp8 0.0694618738702225 0.135475133212120 0.512727850663652 0.608295130721188 df.mm.trans2:exp8 0.208136504968517 0.110843290809917 1.87775465206502 0.0608088749040125 . df.mm.trans1:probe2 -0.0324380630325796 0.0791004361243331 -0.410087031398818 0.681861121206165 df.mm.trans1:probe3 0.117868737401931 0.0791004361243331 1.49011488655587 0.136621274461687 df.mm.trans1:probe4 0.206262321569935 0.0791004361243331 2.60760030761050 0.0093022026458902 ** df.mm.trans1:probe5 0.0565428184450068 0.0791004361243331 0.71482309346728 0.47494441586174 df.mm.trans1:probe6 0.00162636140734381 0.0791004361243331 0.02056071353118 0.983601637402726 df.mm.trans1:probe7 -0.0455585862135698 0.0791004361243331 -0.575958723438124 0.564818671209433 df.mm.trans1:probe8 0.0631238853050034 0.0791004361243331 0.798021962935613 0.425114449161858 df.mm.trans1:probe9 0.00145900907595749 0.0791004361243331 0.0184450193632834 0.985288823718112 df.mm.trans1:probe10 0.0963947439177856 0.0791004361243331 1.21863732541586 0.223371278722414 df.mm.trans1:probe11 0.0792649868102735 0.0791004361243331 1.00208027533100 0.316633267031910 df.mm.trans1:probe12 -0.0133924336423114 0.0791004361243331 -0.169309226326649 0.865599814995949 df.mm.trans1:probe13 -0.0541078118451896 0.0791004361243331 -0.684039361807574 0.494165038899074 df.mm.trans1:probe14 0.172112515224047 0.0791004361243331 2.17587315136309 0.0298813051879628 * df.mm.trans1:probe15 -0.0374549743575283 0.0791004361243331 -0.473511603635853 0.635988258975814 df.mm.trans1:probe16 0.0430647046151638 0.0791004361243331 0.54443068490132 0.586309590495906 df.mm.trans1:probe17 0.069581512154366 0.0791004361243331 0.879660284615816 0.379329709784494 df.mm.trans1:probe18 -0.0396479211053012 0.0791004361243331 -0.501235177047331 0.616355091570908 df.mm.trans1:probe19 -0.0232528080858502 0.0791004361243331 -0.293965611634562 0.768866836563255 df.mm.trans1:probe20 0.0127630411266869 0.0791004361243331 0.161352348381816 0.871860055663732 df.mm.trans1:probe21 0.0852387254827367 0.0791004361243331 1.07760120751743 0.281563867318964 df.mm.trans1:probe22 -0.0106049637465146 0.0791004361243331 -0.134069598931734 0.893384091288207 df.mm.trans2:probe2 -0.108939509893644 0.0791004361243331 -1.37723020543665 0.168858647683645 df.mm.trans2:probe3 -0.109832850579957 0.0791004361243331 -1.38852395715388 0.165396487841001 df.mm.trans2:probe4 -0.0214876185703351 0.0791004361243331 -0.271649811595982 0.785967266710385 df.mm.trans2:probe5 -0.0682511086294287 0.0791004361243331 -0.862841116604578 0.388505132060891 df.mm.trans2:probe6 -0.103251805665203 0.0791004361243331 -1.30532536512072 0.19218888251749 df.mm.trans3:probe2 -0.0522639595825228 0.0791004361243331 -0.660729095100971 0.508992250862971 df.mm.trans3:probe3 -0.0862396462649211 0.0791004361243331 -1.09025500351687 0.275956675615848 df.mm.trans3:probe4 -0.129018086901776 0.0791004361243331 -1.63106669473959 0.103302890277993 df.mm.trans3:probe5 -0.135168136786417 0.0791004361243331 -1.70881658065646 0.0879053789812601 .