fitVsDatCorrelation=0.735779929293455 cont.fitVsDatCorrelation=0.254526536919237 fstatistic=11832.6545746715,50,646 cont.fstatistic=5796.12254645232,50,646 residuals=-0.500763045940789,-0.0742467578546414,-0.00353131169647314,0.0630205674325349,0.948835248301179 cont.residuals=-0.395426319783095,-0.112495437265804,-0.0269922194103293,0.0637116168434938,1.12068493103364 predictedValues: Include Exclude Both Lung 44.7458099847623 50.1618262496393 48.1168754041917 cerebhem 47.5827250285836 53.8190838354287 56.6333661607602 cortex 43.8846728008425 48.3540144681008 50.2253297236655 heart 46.1093744046752 50.7074703868989 50.1090548321932 kidney 44.4017920399441 49.8482239719725 48.5524692382224 liver 48.1808456605628 50.9893545034761 52.3696781060714 stomach 45.3895717143918 50.7575019082615 51.9559421107274 testicle 46.9922378500472 50.9226794238131 53.6101087565395 diffExp=-5.41601626487703,-6.2363588068451,-4.46934166725828,-4.59809598222373,-5.44643193202842,-2.80850884291329,-5.3679301938697,-3.93044157376586 diffExpScore=0.97453729507689 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 49.2291643223583 53.1540565023117 49.9708676257612 cerebhem 46.2986041775121 52.8458902556681 52.0941944138876 cortex 52.2120262655862 47.6336033314215 49.1969420568712 heart 45.3970431194008 49.6229242633329 48.4874609064831 kidney 46.7972870035906 54.5370629716649 49.7831895741231 liver 46.8766356477156 54.232871369676 49.8372734963641 stomach 49.2692432879033 54.1250581058893 49.039798146226 testicle 50.8613652917531 54.2893655846847 50.8630324273657 cont.diffExp=-3.92489217995344,-6.54728607815599,4.57842293416474,-4.22588114393213,-7.73977596807431,-7.35623572196038,-4.85581481798599,-3.42800029293161 cont.diffExpScore=1.23643399332822 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=0.760187781782085 cont.tran.correlation=-0.237397994739813 tran.covariance=0.000782398829577953 cont.tran.covariance=-0.000565916621139203 tran.mean=48.3029490144625 cont.tran.mean=50.4613875937793 weightedLogRatios: wLogRatio Lung -0.440815962157186 cerebhem -0.483279380557303 cortex -0.371454754637183 heart -0.368682697461492 kidney -0.445587598548668 liver -0.221142125025019 stomach -0.432708052533005 testicle -0.312479651806342 cont.weightedLogRatios: wLogRatio Lung -0.301834537963305 cerebhem -0.51601051119627 cortex 0.358785163346988 heart -0.343558458572365 kidney -0.600336872263468 liver -0.571468680452226 stomach -0.37075319332657 testicle -0.258401967932324 varWeightedLogRatios=0.00732537902543313 cont.varWeightedLogRatios=0.0925732107222995 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.90179808501106 0.0656764787959434 59.409367806303 5.62600846993752e-264 *** df.mm.trans1 -0.10332588251064 0.0518331359133646 -1.99343297853600 0.0466341512046306 * df.mm.trans2 0.0190894181113194 0.0518331359133646 0.368285996495099 0.71278064236783 df.mm.exp2 -0.0311195787322851 0.0686358536106039 -0.453401204985921 0.65041212831279 df.mm.exp3 -0.0990242649166551 0.0686358536106039 -1.44274835537787 0.149576051833509 df.mm.exp4 0.000268639117099913 0.0686358536106039 0.00391397648558434 0.996878314891613 df.mm.exp5 -0.0230015121991748 0.0686358536106039 -0.335123860040711 0.737640447220652 df.mm.exp6 0.00563150583770592 0.0686358536106039 0.0820490391167201 0.934633132213782 df.mm.exp7 -0.0506734410281844 0.0686358536106039 -0.738294030925488 0.460603914890040 df.mm.exp8 -0.0440659752598254 0.0686358536106039 -0.642025602388916 0.521084314652955 df.mm.trans1:exp2 0.092591545819117 0.0520457128925365 1.77904270444521 0.0757028925055311 . df.mm.trans2:exp2 0.101493396985541 0.0520457128925365 1.95008179050412 0.0515985533776641 . df.mm.trans1:exp3 0.079591576389894 0.0520457128925365 1.5292628723183 0.126688783202673 df.mm.trans2:exp3 0.0623192085515916 0.0520457128925365 1.19739369658107 0.231592321464276 df.mm.trans1:exp4 0.0297498308432968 0.0520457128925366 0.571609632953322 0.56778529263611 df.mm.trans2:exp4 0.0105503013875666 0.0520457128925365 0.202712208195721 0.839423807885097 df.mm.trans1:exp5 0.0152835333711348 0.0520457128925366 0.293655952079897 0.769115134042154 df.mm.trans2:exp5 0.0167300763819461 0.0520457128925365 0.321449653624501 0.747973636730748 df.mm.trans1:exp6 0.068332234591243 0.0520457128925366 1.31292724786640 0.189673662730983 df.mm.trans2:exp6 0.0107310657249066 0.0520457128925365 0.206185392196741 0.836711061385995 df.mm.trans1:exp7 0.0649580130582656 0.0520457128925365 1.24809536555663 0.212448316598963 df.mm.trans2:exp7 0.0624785647305724 0.0520457128925365 1.20045554683010 0.230402482243116 df.mm.trans1:exp8 0.0930506024923879 0.0520457128925366 1.78786296355509 0.0742667301484486 . df.mm.trans2:exp8 0.0591200636805233 0.0520457128925365 1.13592571596807 0.256408889884423 df.mm.trans1:probe2 -0.0149058879932098 0.0387503951950549 -0.384664154215181 0.70061285602302 df.mm.trans1:probe3 -0.0759064822365924 0.0387503951950549 -1.95885698338063 0.0505593430814751 . df.mm.trans1:probe4 0.0147400720481496 0.0387503951950549 0.38038507669286 0.703784629713668 df.mm.trans1:probe5 -0.0121034186479824 0.0387503951950549 -0.31234310223311 0.754880579825696 df.mm.trans1:probe6 0.146264659689776 0.0387503951950549 3.77453336807367 0.000175067752279675 *** df.mm.trans2:probe2 -0.013774512646129 0.0387503951950549 -0.355467668827461 0.722355297908628 df.mm.trans2:probe3 -0.0492237262183994 0.0387503951950549 -1.27027675384021 0.204443382403299 df.mm.trans2:probe4 -0.00963217425629877 0.0387503951950549 -0.248569703813704 0.803772646087557 df.mm.trans2:probe5 0.00712944150644888 0.0387503951950549 0.183983710890224 0.854083969755784 df.mm.trans2:probe6 -0.0640626048071926 0.0387503951950549 -1.65321165073867 0.0987736451924895 . df.mm.trans3:probe2 0.153377797963842 0.0387503951950549 3.95809635467707 8.39307425315364e-05 *** df.mm.trans3:probe3 -0.0246972278159857 0.0387503951950549 -0.637341314628385 0.524128354968158 df.mm.trans3:probe4 -0.00206801412374408 0.0387503951950549 -0.0533675621457401 0.957455549010437 df.mm.trans3:probe5 0.282351048291031 0.0387503951950549 7.28640435458227 9.31497068395825e-13 *** df.mm.trans3:probe6 -0.0114951260619776 0.0387503951950549 -0.296645389140303 0.766832602934371 df.mm.trans3:probe7 -0.0249228729965963 0.0387503951950549 -0.643164356676724 0.520345686856375 df.mm.trans3:probe8 0.351839474570929 0.0387503951950549 9.07963577661343 1.30807811852984e-18 *** df.mm.trans3:probe9 0.0607337314219338 0.0387503951950549 1.56730611691115 0.117532838556960 df.mm.trans3:probe10 -0.11180876045059 0.0387503951950549 -2.88535793990712 0.0040400906591514 ** df.mm.trans3:probe11 -0.0371229960352115 0.0387503951950549 -0.95800303063616 0.338419620903145 df.mm.trans3:probe12 -0.0509683854854811 0.0387503951950549 -1.31529975962633 0.188875832068931 df.mm.trans3:probe13 -0.0960924533907683 0.0387503951950549 -2.47977995855462 0.0134001970971380 * df.mm.trans3:probe14 0.0214505586310699 0.0387503951950549 0.553557157884349 0.580073486857575 df.mm.trans3:probe15 0.478760900336246 0.0387503951950549 12.3549940052571 1.25278926096174e-31 *** df.mm.trans3:probe16 -0.0544759974665948 0.0387503951950549 -1.40581785533756 0.160258994596332 df.mm.trans3:probe17 0.0183510171699346 0.0387503951950549 0.473569806902937 0.635966705791859 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.8972290125379 0.093785489013575 41.5547122857546 1.15217983217915e-184 *** df.mm.trans1 -0.0152768277397347 0.074017305553876 -0.206395350728013 0.836547134322286 df.mm.trans2 0.0950106621029685 0.074017305553876 1.28362767858135 0.199732499773056 df.mm.exp2 -0.108802204977332 0.0980114526957896 -1.11009685077350 0.267370334164411 df.mm.exp3 -0.0352206013274772 0.0980114526957896 -0.359351895709533 0.719449286062618 df.mm.exp4 -0.119645797592955 0.0980114526957896 -1.22073282562513 0.222632573412268 df.mm.exp5 -0.0212120661652700 0.0980114526957896 -0.216424362478419 0.828725302095335 df.mm.exp6 -0.0261970370797744 0.0980114526957896 -0.267285468781749 0.789334682024014 df.mm.exp7 0.0377246481490721 0.0980114526957896 0.384900408181509 0.700437889579997 df.mm.exp8 0.0360551305055129 0.0980114526957896 0.36786650451373 0.713093274825704 df.mm.trans1:exp2 0.0474277996024291 0.0743208637882723 0.638149197748063 0.523602708530908 df.mm.trans2:exp2 0.102987728191653 0.0743208637882723 1.38571758914224 0.166311347930622 df.mm.trans1:exp3 0.094047239251697 0.0743208637882723 1.26542177334782 0.206176396500212 df.mm.trans2:exp3 -0.0744353578115449 0.0743208637882723 -1.00154053676769 0.316940551434172 df.mm.trans1:exp4 0.0386065523319179 0.0743208637882723 0.519457799116834 0.603619462678639 df.mm.trans2:exp4 0.0509042836287401 0.0743208637882723 0.684925888021941 0.493636360601928 df.mm.trans1:exp5 -0.0294489212589044 0.0743208637882723 -0.396240298589633 0.692058608757802 df.mm.trans2:exp5 0.0468981675780896 0.0743208637882723 0.631022907802775 0.528248736046394 df.mm.trans1:exp6 -0.0227698040791948 0.0743208637882723 -0.306371628619148 0.759420423263171 df.mm.trans2:exp6 0.046289820874292 0.0743208637882723 0.622837498312237 0.533611117077685 df.mm.trans1:exp7 -0.0369108488455229 0.0743208637882723 -0.496641817170959 0.61961061437462 df.mm.trans2:exp7 -0.0196218119271194 0.0743208637882723 -0.264014852989581 0.791852674189303 df.mm.trans1:exp8 -0.0034377454142752 0.0743208637882723 -0.0462554555887396 0.963120937756912 df.mm.trans2:exp8 -0.0149211919818342 0.0743208637882723 -0.200767203464456 0.840943800681466 df.mm.trans1:probe2 0.0719789696017736 0.0553352559312601 1.30077955528369 0.193797726501343 df.mm.trans1:probe3 0.0811781770258308 0.0553352559312601 1.46702451555793 0.142856031900866 df.mm.trans1:probe4 0.0296006130268385 0.0553352559312601 0.534932251214482 0.592880780519589 df.mm.trans1:probe5 0.0408048224543732 0.0553352559312601 0.737410928487667 0.461140241400184 df.mm.trans1:probe6 0.110720196683438 0.0553352559312601 2.00089788725255 0.0458213259692643 * df.mm.trans2:probe2 -0.0924469047662028 0.0553352559312601 -1.67066914592470 0.0952714119452863 . df.mm.trans2:probe3 -0.0703605200816814 0.0553352559312601 -1.27153148381723 0.203997231130301 df.mm.trans2:probe4 -0.109319981325875 0.0553352559312601 -1.97559366964303 0.0486259881817172 * df.mm.trans2:probe5 -0.0891674375188868 0.0553352559312601 -1.61140372477277 0.107580199092423 df.mm.trans2:probe6 -0.0767459290964356 0.0553352559312601 -1.38692643243166 0.165942554066763 df.mm.trans3:probe2 -0.103451450446097 0.0553352559312601 -1.86953956758795 0.061999949566531 . df.mm.trans3:probe3 -0.0793914110186836 0.0553352559312601 -1.43473468555575 0.151846642109772 df.mm.trans3:probe4 -0.0595431047623046 0.0553352559312601 -1.07604281863757 0.282309804018789 df.mm.trans3:probe5 -0.0187034155757057 0.0553352559312601 -0.338001790376463 0.735471649912546 df.mm.trans3:probe6 -0.0451935497643492 0.0553352559312601 -0.81672252172269 0.414388095065912 df.mm.trans3:probe7 -0.0137656356785978 0.0553352559312601 -0.248767904782045 0.80361938564844 df.mm.trans3:probe8 -0.0923093704650138 0.0553352559312601 -1.66818367262428 0.095763862004433 . df.mm.trans3:probe9 -0.076481425402186 0.0553352559312601 -1.38214641127159 0.167404450792326 df.mm.trans3:probe10 -0.0493097386197467 0.0553352559312601 -0.891108892330802 0.373202521973467 df.mm.trans3:probe11 -0.0818666940569598 0.0553352559312601 -1.47946716210472 0.139502968821188 df.mm.trans3:probe12 -0.114271990170119 0.0553352559312601 -2.06508469594996 0.0393134033277844 * df.mm.trans3:probe13 -0.0762581968608347 0.0553352559312601 -1.37811230069245 0.168645750280435 df.mm.trans3:probe14 -0.0938372087201591 0.0553352559312601 -1.69579424800579 0.0904066776402945 . df.mm.trans3:probe15 -0.114193044625642 0.0553352559312601 -2.06365801881350 0.0394490184309659 * df.mm.trans3:probe16 -0.0631794538933445 0.0553352559312601 -1.14175768829603 0.253977788542922 df.mm.trans3:probe17 -0.0321295089074257 0.0553352559312601 -0.580633600887984 0.561689911741577