fitVsDatCorrelation=0.800302100379227 cont.fitVsDatCorrelation=0.239320136853806 fstatistic=12296.8503517474,60,876 cont.fstatistic=4680.47654762319,60,876 residuals=-0.594309613649659,-0.078456938980633,-0.00784123169131184,0.0656098547851901,0.942081308964037 cont.residuals=-0.436231569801355,-0.170462399549623,-0.0364051074319530,0.119729148019592,1.23908026298699 predictedValues: Include Exclude Both Lung 56.193830995207 54.830026050033 55.5862689190649 cerebhem 55.6168695559141 64.1450159317008 52.9588717491649 cortex 56.8194124795395 56.7875477915066 59.0121108608853 heart 56.7670067005016 57.5506227346686 56.5484454034836 kidney 55.0740155790643 58.0876744258063 53.2106224493492 liver 55.6719984901049 57.2329494819324 53.5192878993268 stomach 58.288536646992 61.8804173213 55.6739257098203 testicle 58.0155902389828 59.9451950620708 59.8128121208303 diffExp=1.36380494517400,-8.52814637578674,0.0318646880328828,-0.783616034166975,-3.01365884674201,-1.56095099182755,-3.59188067430808,-1.92960482308804 diffExpScore=1.09422057344446 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 56.9738914615302 60.865411538017 56.0008790723072 cerebhem 56.8750067129007 52.249643465437 54.4481789826826 cortex 55.9430103810464 49.8625685096792 58.5408217421918 heart 58.0923487574168 56.1631011688329 58.06580073944 kidney 61.0464752945806 58.576484992449 57.2500187786381 liver 58.1209411128797 55.5078749674779 56.5738614418578 stomach 57.8153769306756 54.5902851592914 56.1495968825927 testicle 56.0673360676477 58.3842071963248 56.0365244628044 cont.diffExp=-3.89152007648676,4.62536324746372,6.08044187136719,1.92924758858388,2.46999030213163,2.61306614540172,3.22509177138413,-2.31687112867711 cont.diffExpScore=1.72557486316269 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=0.209164174509665 cont.tran.correlation=0.359601037193831 tran.covariance=0.000218626269940332 cont.tran.covariance=0.000670449416031504 tran.mean=57.6816693428328 cont.tran.mean=56.6958727322617 weightedLogRatios: wLogRatio Lung 0.0986820112763422 cerebhem -0.583452521847716 cortex 0.00226606748250536 heart -0.0554667089537934 kidney -0.214983379419154 liver -0.111530813153314 stomach -0.244892346454219 testicle -0.133397521051996 cont.weightedLogRatios: wLogRatio Lung -0.269284720432489 cerebhem 0.33915977301175 cortex 0.456431785164078 heart 0.136620736482019 kidney 0.168966361753340 liver 0.185822732735126 stomach 0.231234395278353 testicle -0.163863124736852 varWeightedLogRatios=0.0423713618092696 cont.varWeightedLogRatios=0.0586802887938075 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99233396124321 0.0669399118381954 59.6405619848041 0 *** df.mm.trans1 -0.0505577714736589 0.057884945111453 -0.873418319328349 0.382674469150267 df.mm.trans2 0.0700782249519847 0.0510237488531066 1.37344327939788 0.169965979160544 df.mm.exp2 0.195008613257544 0.0655751950716844 2.97381674647506 0.0030218515628788 ** df.mm.exp3 -0.0136563204142847 0.0655751950716844 -0.208254362024483 0.835078773244064 df.mm.exp4 0.0414137955892473 0.0655751950716844 0.631546662483813 0.527847910491939 df.mm.exp5 0.0812647312830697 0.0655751950716844 1.23926022933266 0.215581057336049 df.mm.exp6 0.0714562246829489 0.0655751950716844 1.08968375320631 0.276152138606037 df.mm.exp7 0.155988561787247 0.0655751950716844 2.37877388876581 0.0175837587538925 * df.mm.exp8 0.0478138755435314 0.0655751950716844 0.729145761461525 0.466107402532822 df.mm.trans1:exp2 -0.205329030847319 0.0608053252709258 -3.37682645282218 0.000765562146233833 *** df.mm.trans2:exp2 -0.0381001837074747 0.0446015859625533 -0.854233832390241 0.393208981082226 df.mm.trans1:exp3 0.0247273746515912 0.0608053252709258 0.406664622570725 0.684353660926106 df.mm.trans2:exp3 0.0487354285631085 0.0446015859625533 1.09268375801762 0.274833076031446 df.mm.trans1:exp4 -0.0312654886862555 0.0608053252709258 -0.514189975087677 0.607248861497931 df.mm.trans2:exp4 0.00701319575757796 0.0446015859625533 0.157240950208948 0.875091190135666 df.mm.trans1:exp5 -0.101393695049037 0.0608053252709258 -1.66751340605883 0.0957697739830741 . df.mm.trans2:exp5 -0.0235491985864125 0.0446015859625533 -0.527990161744113 0.597639879893345 df.mm.trans1:exp6 -0.0807859063677216 0.0608053252709258 -1.32859919764872 0.184326225340815 df.mm.trans2:exp6 -0.0285644167718818 0.0446015859625533 -0.640435001478737 0.522057314706693 df.mm.trans1:exp7 -0.119390096851742 0.0608053252709258 -1.96348093394426 0.0499062903647961 * df.mm.trans2:exp7 -0.0350227565847443 0.0446015859625533 -0.785235677810847 0.4325277166346 df.mm.trans1:exp8 -0.0159090859988254 0.0608053252709258 -0.261639682510380 0.793660751513585 df.mm.trans2:exp8 0.0413788891068383 0.0446015859625533 0.927744792339431 0.353795511179849 df.mm.trans1:probe2 0.203738621155701 0.0416305603341562 4.89396778521237 1.17612149710643e-06 *** df.mm.trans1:probe3 0.124150188228992 0.0416305603341562 2.98218873905312 0.00294124323844622 ** df.mm.trans1:probe4 0.0195052198459319 0.0416305603341562 0.46853128301347 0.639521268293099 df.mm.trans1:probe5 0.371908022334768 0.0416305603341562 8.93353390753264 2.39276336110930e-18 *** df.mm.trans1:probe6 0.0124285841001768 0.0416305603341562 0.298544722925088 0.765358249237391 df.mm.trans1:probe7 0.410473790606417 0.0416305603341562 9.85991510351206 8.07648548805081e-22 *** df.mm.trans1:probe8 0.49436455635241 0.0416305603341562 11.8750396916182 2.90867367448745e-30 *** df.mm.trans1:probe9 0.424105414656827 0.0416305603341562 10.1873578268622 4.13079781903489e-23 *** df.mm.trans1:probe10 0.257400005861421 0.0416305603341562 6.18295799516863 9.63686627935707e-10 *** df.mm.trans1:probe11 -0.0151936775899661 0.0416305603341562 -0.364964522889216 0.715225981620648 df.mm.trans1:probe12 -0.0962838736346053 0.0416305603341562 -2.31281714350619 0.0209635961023836 * df.mm.trans1:probe13 -0.144588213938080 0.0416305603341562 -3.47312677940228 0.000539629442899533 *** df.mm.trans1:probe14 -0.154038422367611 0.0416305603341562 -3.70012849049328 0.000228934450240239 *** df.mm.trans1:probe15 -0.0361360323342544 0.0416305603341562 -0.86801695783581 0.385622775279175 df.mm.trans1:probe16 -0.0912144544405933 0.0416305603341562 -2.19104556144433 0.0287112567084889 * df.mm.trans1:probe17 0.176055871230627 0.0416305603341562 4.22900556267988 2.59404860508545e-05 *** df.mm.trans1:probe18 0.148495514597032 0.0416305603341562 3.56698332679413 0.000380691880100973 *** df.mm.trans1:probe19 0.271200471982199 0.0416305603341562 6.51445644270346 1.22878727386760e-10 *** df.mm.trans1:probe20 0.455848757889128 0.0416305603341562 10.9498588111753 3.05592196594320e-26 *** df.mm.trans1:probe21 0.070630159360905 0.0416305603341562 1.69659401156212 0.0901287023640384 . df.mm.trans1:probe22 -0.003046995853212 0.0416305603341562 -0.0731913245643264 0.941670578093299 df.mm.trans1:probe23 -0.0277873609396449 0.0416305603341562 -0.667475064390293 0.504644525019345 df.mm.trans2:probe2 -0.118290633370394 0.0416305603341562 -2.84143745414211 0.00459528637477746 ** df.mm.trans2:probe3 -0.241919475898254 0.0416305603341562 -5.81110304440867 8.68458777810542e-09 *** df.mm.trans2:probe4 -0.224093510289539 0.0416305603341562 -5.38290881724403 9.41555577391e-08 *** df.mm.trans2:probe5 -0.265808062274820 0.0416305603341562 -6.38492636518119 2.77843623116375e-10 *** df.mm.trans2:probe6 -0.0806758649490996 0.0416305603341562 -1.93790004990416 0.0529564721752505 . df.mm.trans3:probe2 -0.150281202078967 0.0416305603341562 -3.60987699595452 0.000323726679897957 *** df.mm.trans3:probe3 -0.164790955489764 0.0416305603341562 -3.95841310246692 8.15636229120753e-05 *** df.mm.trans3:probe4 0.0380824701327418 0.0416305603341562 0.91477198065712 0.360563071861704 df.mm.trans3:probe5 0.483802202335571 0.0416305603341562 11.6213233368043 3.89008908261897e-29 *** df.mm.trans3:probe6 -0.0437552224286873 0.0416305603341562 -1.05103611571588 0.293531853105703 df.mm.trans3:probe7 -0.0122348220768770 0.0416305603341562 -0.293890401154144 0.768911270606051 df.mm.trans3:probe8 -0.00751000774068325 0.0416305603341562 -0.180396508728267 0.85688301862613 df.mm.trans3:probe9 -0.124335357600627 0.0416305603341562 -2.98663665832561 0.00289922132856814 ** df.mm.trans3:probe10 0.0418408364229718 0.0416305603341562 1.00505100308830 0.315149825826480 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10418041668063 0.108397285434755 37.8623911126534 1.33275980356937e-186 *** df.mm.trans1 -0.0587534565150653 0.0937343767764135 -0.626807992282395 0.530948418583846 df.mm.trans2 -0.00131523929770496 0.0826238893434815 -0.0159183900462164 0.987303123391367 df.mm.exp2 -0.126251130843087 0.106187369275399 -1.18894678062559 0.234782799213257 df.mm.exp3 -0.262011014812302 0.106187369275399 -2.46744049316045 0.0137984915227249 * df.mm.exp4 -0.0971737086623289 0.106187369275399 -0.915115510681 0.360382819292965 df.mm.exp5 0.00865004140696648 0.106187369275399 0.0814601723914301 0.93509460500243 df.mm.exp6 -0.0823869217752882 0.106187369275399 -0.775863667566864 0.438038896925951 df.mm.exp7 -0.09679957363108 0.106187369275399 -0.911592162906194 0.36223423339249 df.mm.exp8 -0.0582956623246704 0.106187369275399 -0.548988667131203 0.583153128008311 df.mm.trans1:exp2 0.124514007777667 0.0984634132067206 1.26457131357262 0.206361373528421 df.mm.trans2:exp2 -0.0263808602907956 0.0722243383904044 -0.365262747693104 0.715003455200192 df.mm.trans1:exp3 0.243751397235373 0.0984634132067206 2.47555299270019 0.0134911145433494 * df.mm.trans2:exp3 0.0626165476873698 0.0722243383904044 0.866972949601833 0.386194240905467 df.mm.trans1:exp4 0.116614554560374 0.0984634132067206 1.18434401939272 0.236598149688444 df.mm.trans2:exp4 0.0167686287705333 0.0722243383904044 0.232174210858000 0.816456935006579 df.mm.trans1:exp5 0.0603923043849799 0.0984634132067206 0.613347663036912 0.539805700289676 df.mm.trans2:exp5 -0.0469817636782185 0.0722243383904044 -0.650497667756559 0.515541375562072 df.mm.trans1:exp6 0.102319834562960 0.0984634132067206 1.03916603366311 0.299014258543332 df.mm.trans2:exp6 -0.00975323455955284 0.0722243383904044 -0.135040829406180 0.89261061456206 df.mm.trans1:exp7 0.111461232425229 0.0984634132067206 1.13200658798228 0.257941435677352 df.mm.trans2:exp7 -0.0120095452620585 0.0722243383904044 -0.166281139152035 0.86797407861841 df.mm.trans1:exp8 0.0422559421339887 0.0984634132067206 0.42915374104769 0.667916865955049 df.mm.trans2:exp8 0.0166760325395075 0.0722243383904044 0.230892146763134 0.817452505808936 df.mm.trans1:probe2 -0.0422366892391509 0.0674132906278412 -0.62653356401664 0.531128259906868 df.mm.trans1:probe3 -0.0956497923854233 0.0674132906278412 -1.41885660074752 0.156296514709157 df.mm.trans1:probe4 0.0345059476287072 0.0674132906278412 0.511856746753385 0.608880278609727 df.mm.trans1:probe5 0.0680004145028016 0.0674132906278412 1.00870931932698 0.313392598452841 df.mm.trans1:probe6 0.00750770567783979 0.0674132906278412 0.111368331198762 0.911349783305473 df.mm.trans1:probe7 0.0179513019183812 0.0674132906278412 0.266287281798516 0.790080630928978 df.mm.trans1:probe8 -0.0275390988957859 0.0674132906278412 -0.408511417248819 0.682998088613449 df.mm.trans1:probe9 -0.047403268257999 0.0674132906278412 -0.703173926335852 0.482134200798434 df.mm.trans1:probe10 0.0228843045779094 0.0674132906278412 0.339462802731934 0.734342480447686 df.mm.trans1:probe11 -0.0603165761219804 0.0674132906278412 -0.894728258481868 0.371178134193962 df.mm.trans1:probe12 -0.0541065392359055 0.0674132906278412 -0.80260937764637 0.422418238745358 df.mm.trans1:probe13 0.0410195273506931 0.0674132906278412 0.608478342603741 0.543028003677401 df.mm.trans1:probe14 0.0221860051408642 0.0674132906278412 0.329104319552405 0.742155504853333 df.mm.trans1:probe15 -0.0194893560700286 0.0674132906278412 -0.289102577377816 0.772571284078878 df.mm.trans1:probe16 0.0126038410446854 0.0674132906278412 0.186963741530814 0.851732353010528 df.mm.trans1:probe17 0.00601274795635916 0.0674132906278412 0.0891923224687677 0.928949453913634 df.mm.trans1:probe18 -0.0665805498141626 0.0674132906278412 -0.987647230895821 0.323598158670761 df.mm.trans1:probe19 -0.00251971448127632 0.0674132906278412 -0.0373771174468629 0.9701928332954 df.mm.trans1:probe20 -0.00156242580548481 0.0674132906278412 -0.023176821527824 0.981514505564225 df.mm.trans1:probe21 0.00960191816024043 0.0674132906278412 0.142433607242945 0.886770270610917 df.mm.trans1:probe22 0.0068429008264718 0.0674132906278412 0.101506702354116 0.91917146591403 df.mm.trans1:probe23 0.0747706161217342 0.0674132906278412 1.10913761107598 0.267675218424737 df.mm.trans2:probe2 0.0611270857218668 0.0674132906278412 0.90675125264723 0.36478770171436 df.mm.trans2:probe3 -0.0223297245916381 0.0674132906278412 -0.331236235224158 0.74054527165895 df.mm.trans2:probe4 0.00138432844714657 0.0674132906278412 0.0205349484390079 0.983621309390466 df.mm.trans2:probe5 0.0679813324425269 0.0674132906278412 1.00842625852255 0.313528332210529 df.mm.trans2:probe6 -0.0153649269063805 0.0674132906278412 -0.22792133069432 0.819760587224041 df.mm.trans3:probe2 -0.119709261099375 0.0674132906278412 -1.77575163568615 0.0761209585293208 . df.mm.trans3:probe3 -0.0317133868779560 0.0674132906278412 -0.470432263172429 0.638163320924018 df.mm.trans3:probe4 -0.0495545538449298 0.0674132906278412 -0.735085817402068 0.462483974739732 df.mm.trans3:probe5 -0.0657712923829542 0.0674132906278412 -0.975642811238044 0.329510840844386 df.mm.trans3:probe6 0.00104474251479917 0.0674132906278412 0.0154975748115713 0.987638748102573 df.mm.trans3:probe7 0.0589450699597034 0.0674132906278412 0.874383514151725 0.382149083557908 df.mm.trans3:probe8 -0.0534608182001131 0.0674132906278412 -0.793030835644066 0.427974587514278 df.mm.trans3:probe9 0.0471014580718329 0.0674132906278412 0.698696913222336 0.484926910170102 df.mm.trans3:probe10 0.0256747164975209 0.0674132906278412 0.380855410830775 0.703402881136087