fitVsDatCorrelation=0.84169492847553 cont.fitVsDatCorrelation=0.263837272951890 fstatistic=7471.11285498441,49,623 cont.fstatistic=2332.43926699286,49,623 residuals=-0.546629398298924,-0.107141056691579,0.00483033091238015,0.108000775691351,0.785018771012697 cont.residuals=-0.619905503898323,-0.245549456341333,-0.0208762785329345,0.20472759325287,0.84573042406863 predictedValues: Include Exclude Both Lung 86.530901044077 48.301742394945 58.8347983175858 cerebhem 80.3657164601802 55.3879451292539 60.3164849058521 cortex 96.971307147709 47.8948129907914 86.5390428984439 heart 82.8815444951268 48.6821877831135 66.0344396703973 kidney 88.9067161919364 47.8632268810227 61.7241110763879 liver 81.8452384943665 49.3889992089174 58.4144624887841 stomach 81.1021641060822 46.4038157416716 61.6493115742619 testicle 85.8820587427225 51.0542101698166 71.7811738155261 diffExp=38.2291586491319,24.9777713309263,49.0764941569176,34.1993567120133,41.0434893109137,32.4562392854491,34.6983483644107,34.8278485729059 diffExpScore=0.99655776237328 diffExp1.5=1,0,1,1,1,1,1,1 diffExp1.5Score=0.875 diffExp1.4=1,1,1,1,1,1,1,1 diffExp1.4Score=0.888888888888889 diffExp1.3=1,1,1,1,1,1,1,1 diffExp1.3Score=0.888888888888889 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 62.0658846729202 74.2737186189975 66.3269241250256 cerebhem 62.6788124674768 70.7463244305856 64.1447125685886 cortex 65.7696674366902 71.7280238285222 71.8478496023599 heart 65.7230156893961 76.3423656555933 63.3229090376078 kidney 70.4647241472477 59.443287945837 76.681731830429 liver 69.2338694220024 68.7143727868211 65.5698912312303 stomach 67.5113070625757 63.7114306913273 70.2018165083723 testicle 68.3307461033077 70.856098596735 74.7480111213328 cont.diffExp=-12.2078339460773,-8.0675119631088,-5.95835639183206,-10.6193499661972,11.0214362014108,0.519496635181341,3.79987637124835,-2.52535249342729 cont.diffExpScore=2.18548198260830 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=-0.362859013070453 cont.tran.correlation=-0.675525747947222 tran.covariance=-0.00122638707097841 cont.tran.covariance=-0.0024884393386803 tran.mean=67.4664116863583 cont.tran.mean=67.9746030972522 weightedLogRatios: wLogRatio Lung 2.43065957864824 cerebhem 1.56352468105578 cortex 2.97802850406996 heart 2.20893684919420 kidney 2.58716542187348 liver 2.09732611956521 stomach 2.29838251861227 testicle 2.18068887034584 cont.weightedLogRatios: wLogRatio Lung -0.757382839351586 cerebhem -0.508349766146444 cortex -0.36679569673066 heart -0.63810864360446 kidney 0.709284705422355 liver 0.0318875526555934 stomach 0.242344897646379 testicle -0.153965664810765 varWeightedLogRatios=0.166411332292937 cont.varWeightedLogRatios=0.242959592562423 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.3717122468556 0.0891043482032485 49.0628385147226 3.63690438829141e-216 *** df.mm.trans1 0.261116433963192 0.0772130451533874 3.38176578121575 0.000765297940303114 *** df.mm.trans2 -0.483616420279321 0.0704177370965315 -6.86782109479547 1.58151106597482e-11 *** df.mm.exp2 0.0381084641873889 0.0933460198547162 0.408249481303015 0.683230796874868 df.mm.exp3 -0.280409066794381 0.0933460198547162 -3.00397453721979 0.00277172756425653 ** df.mm.exp4 -0.150686530325265 0.0933460198547162 -1.61427911505808 0.106973230607052 df.mm.exp5 -0.0299751769515329 0.0933460198547162 -0.321118961453164 0.748227935957379 df.mm.exp6 -0.0262413987537624 0.0933460198547162 -0.281119632037922 0.778711982781913 df.mm.exp7 -0.151606460133216 0.0933460198547162 -1.62413416628986 0.104852980811655 df.mm.exp8 -0.150995014907091 0.0933460198547162 -1.6175838578024 0.106258474081979 df.mm.trans1:exp2 -0.112022378776301 0.085660182595463 -1.30775321020894 0.191439388073102 df.mm.trans2:exp2 0.0987858745059603 0.0710256712856207 1.39084746005012 0.164768242883289 df.mm.trans1:exp3 0.394322611291118 0.085660182595463 4.60333610486611 5.04108943215975e-06 *** df.mm.trans2:exp3 0.271948642617832 0.0710256712856207 3.82887817454375 0.000141740353725527 *** df.mm.trans1:exp4 0.107597356332963 0.085660182595463 1.25609534176573 0.209552234454204 df.mm.trans2:exp4 0.158532105131984 0.0710256712856207 2.23203951842239 0.0259671077366401 * df.mm.trans1:exp5 0.0570612766861381 0.085660182595463 0.666135361345358 0.505571164532241 df.mm.trans2:exp5 0.0208550460609243 0.0710256712856207 0.293626877204136 0.769140824454002 df.mm.trans1:exp6 -0.0294300603256446 0.085660182595463 -0.343567564694911 0.731287434335141 df.mm.trans2:exp6 0.0485014756330083 0.0710256712856207 0.682872470686912 0.494941276688318 df.mm.trans1:exp7 0.0868145176663893 0.085660182595463 1.01347574842769 0.311226408864942 df.mm.trans2:exp7 0.111520517358770 0.0710256712856207 1.57014379927934 0.116889382712650 df.mm.trans1:exp8 0.143468372301464 0.085660182595463 1.67485485034517 0.0944645548891662 . df.mm.trans2:exp8 0.206415393174755 0.0710256712856207 2.90620826862279 0.00378833393832633 ** df.mm.trans1:probe2 0.0250510828880234 0.0524559346581302 0.47756432234576 0.633127829890887 df.mm.trans1:probe3 -0.0105150221103613 0.0524559346581302 -0.200454384787739 0.841190648235717 df.mm.trans1:probe4 0.0443529316318806 0.0524559346581302 0.845527430231506 0.398141038051679 df.mm.trans1:probe5 -0.180455034570078 0.0524559346581302 -3.44012618869826 0.000620290386874226 *** df.mm.trans1:probe6 -0.043504028684195 0.0524559346581302 -0.829344267101955 0.407227274651101 df.mm.trans1:probe7 -0.296411439002165 0.0524559346581302 -5.65067500815609 2.43318733254629e-08 *** df.mm.trans1:probe8 -0.495360490603869 0.0524559346581302 -9.44336410803221 7.12559300240313e-20 *** df.mm.trans1:probe9 -0.450996480669435 0.0524559346581302 -8.59762548525165 6.57308938283076e-17 *** df.mm.trans1:probe10 -0.0370419583171391 0.0524559346581302 -0.706153813835399 0.480356473370404 df.mm.trans1:probe11 -0.279441570355606 0.0524559346581302 -5.32716788246751 1.39542746634006e-07 *** df.mm.trans1:probe12 -0.309991432290152 0.0524559346581302 -5.90955883848895 5.64874713408252e-09 *** df.mm.trans1:probe13 -0.377273077893170 0.0524559346581302 -7.19219055673993 1.83539454982943e-12 *** df.mm.trans1:probe14 -0.221791954502572 0.0524559346581302 -4.22815751826846 2.70943043012615e-05 *** df.mm.trans1:probe15 -0.443002902895 0.0524559346581302 -8.44523895689158 2.13601718628678e-16 *** df.mm.trans1:probe16 -0.714814672394749 0.0524559346581302 -13.6269552159044 3.3882164212428e-37 *** df.mm.trans2:probe2 0.0426794748165478 0.0524559346581302 0.813625285579252 0.416170494823332 df.mm.trans2:probe3 -0.0960990904283747 0.0524559346581302 -1.83199653298867 0.0674291708333105 . df.mm.trans2:probe4 -0.0980438354422616 0.0524559346581302 -1.86907041274243 0.062081992656577 . df.mm.trans2:probe5 0.0614997967012325 0.0524559346581302 1.17240874844846 0.241481099801965 df.mm.trans2:probe6 -0.0375746513140871 0.0524559346581302 -0.716308870654417 0.474069065776393 df.mm.trans3:probe2 0.0494532691986701 0.0524559346581302 0.942758326983795 0.346169967145409 df.mm.trans3:probe3 -0.0802631540116963 0.0524559346581302 -1.53010626032676 0.126498005757586 df.mm.trans3:probe4 -0.123902633791395 0.0524559346581302 -2.3620327156289 0.0184816146259881 * df.mm.trans3:probe5 -0.0509069555918731 0.0524559346581302 -0.970470851842556 0.332188463367776 df.mm.trans3:probe6 -0.241253161175973 0.0524559346581302 -4.59915856515162 5.13983047959705e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.18714858067556 0.159175099312784 26.3052989993596 3.83867861731674e-103 *** df.mm.trans1 -0.141303149053897 0.137932596762824 -1.02443622733260 0.306026883153127 df.mm.trans2 0.112042837996307 0.125793527720495 0.89068841638068 0.373440184574882 df.mm.exp2 -0.00537534574784493 0.166752378311948 -0.0322354967422962 0.974294572963655 df.mm.exp3 -0.0568681486652127 0.166752378311948 -0.3410335087325 0.733193335492505 df.mm.exp4 0.131072302466142 0.166752378311948 0.78602958346382 0.432149010228396 df.mm.exp5 -0.240886306981192 0.166752378311948 -1.44457494051785 0.149080053149860 df.mm.exp6 0.04297422212749 0.166752378311948 0.257712798836950 0.796713669855043 df.mm.exp7 -0.126072834848354 0.166752378311948 -0.756048196281234 0.449906073456541 df.mm.exp8 -0.0704694730138956 0.166752378311948 -0.422599507888677 0.672733300754085 df.mm.trans1:exp2 0.0152023402594612 0.153022476980388 0.0993471061209499 0.92089463625024 df.mm.trans2:exp2 -0.0432812399414524 0.126879535158686 -0.341120732254586 0.733127705901383 df.mm.trans1:exp3 0.114830422665958 0.153022476980388 0.75041539603803 0.45328787435804 df.mm.trans2:exp3 0.0219924989329820 0.126879535158686 0.173333697238695 0.86244543920022 df.mm.trans1:exp4 -0.0738196002551321 0.153022476980388 -0.482410177327041 0.629684039048974 df.mm.trans2:exp4 -0.103601436566251 0.126879535158686 -0.816533859748765 0.414506972962339 df.mm.trans1:exp5 0.367802048027806 0.153022476980388 2.40358184814212 0.0165264919718926 * df.mm.trans2:exp5 0.0181518518830042 0.126879535158686 0.143063669490135 0.88628615535254 df.mm.trans1:exp6 0.0663194864319917 0.153022476980388 0.433397025983911 0.66487637891868 df.mm.trans2:exp6 -0.120773003261931 0.126879535158686 -0.951871419696503 0.341531334792733 df.mm.trans1:exp7 0.210171453895713 0.153022476980388 1.37346785938284 0.170100991170990 df.mm.trans2:exp7 -0.0273203423202182 0.126879535158686 -0.215325050537497 0.829584371326419 df.mm.trans1:exp8 0.166632824023891 0.153022476980388 1.08894345008706 0.276599871886927 df.mm.trans2:exp8 0.0233633436768729 0.126879535158686 0.184137998674513 0.85396511649816 df.mm.trans1:probe2 0.0257488992392351 0.0937067469446839 0.274781700131314 0.783575055300095 df.mm.trans1:probe3 0.104124526670518 0.0937067469446839 1.11117427576462 0.266921985825372 df.mm.trans1:probe4 0.161260994692489 0.0937067469446839 1.72091124652618 0.0857634253074555 . df.mm.trans1:probe5 0.041587004975032 0.0937067469446839 0.443799473687645 0.657341663035115 df.mm.trans1:probe6 0.0289814554513266 0.0937067469446839 0.309278215243505 0.757213307811801 df.mm.trans1:probe7 0.193660494715757 0.0937067469446839 2.06666543264037 0.0391783127877096 * df.mm.trans1:probe8 0.139693274852023 0.0937067469446839 1.49074937938551 0.136533423893516 df.mm.trans1:probe9 0.0556541018980356 0.0937067469446839 0.593917766998025 0.552782699667812 df.mm.trans1:probe10 0.130582409622041 0.0937067469446839 1.39352195951402 0.163958928661736 df.mm.trans1:probe11 0.138529508635239 0.0937067469446839 1.47833014326081 0.139824818429977 df.mm.trans1:probe12 0.137048762699348 0.0937067469446839 1.46252822948009 0.144100779537581 df.mm.trans1:probe13 0.0814813320921514 0.0937067469446839 0.86953538297782 0.384889241271091 df.mm.trans1:probe14 0.153882994891293 0.0937067469446839 1.64217625633864 0.101058111869105 df.mm.trans1:probe15 0.138684442554256 0.0937067469446839 1.47998353454872 0.139383131039212 df.mm.trans1:probe16 0.280802791830211 0.0937067469446839 2.99661231432964 0.00283855381085666 ** df.mm.trans2:probe2 -0.0071144883072247 0.0937067469446839 -0.0759229035175499 0.939504808788324 df.mm.trans2:probe3 -0.0231115958097743 0.0937067469446839 -0.246637478765721 0.805270041486755 df.mm.trans2:probe4 -0.0246720626548374 0.0937067469446839 -0.263290141417476 0.792414011976087 df.mm.trans2:probe5 0.0842871763577917 0.0937067469446839 0.899478203074826 0.368745528375941 df.mm.trans2:probe6 0.0733999785751589 0.0937067469446839 0.783294490187433 0.433751860804244 df.mm.trans3:probe2 0.0359026914929127 0.0937067469446839 0.383138809781823 0.701747540275214 df.mm.trans3:probe3 0.0478258602337797 0.0937067469446839 0.510377980168406 0.609967460023896 df.mm.trans3:probe4 0.00480374613367175 0.0937067469446839 0.0512636100419478 0.959131896653706 df.mm.trans3:probe5 0.157918999090078 0.0937067469446839 1.68524683909153 0.092441735045893 . df.mm.trans3:probe6 -0.0262054537133776 0.0937067469446839 -0.279653862371799 0.779835898516426