fitVsDatCorrelation=0.885714443221636 cont.fitVsDatCorrelation=0.266232705314575 fstatistic=8229.25021042212,55,761 cont.fstatistic=1898.15206581493,55,761 residuals=-0.671931423196985,-0.105314380079332,-0.00228034242402671,0.0900575199355879,0.834319853202069 cont.residuals=-0.799745782192852,-0.293471657851022,-0.0485851963691777,0.254612266298038,1.34262789659810 predictedValues: Include Exclude Both Lung 64.8477776967942 80.2572864778455 56.5819344869118 cerebhem 65.2234006832396 80.4845553297341 63.9958561807489 cortex 63.9689166779711 120.877353221684 80.92567954103 heart 65.2726123575892 90.6029924965687 55.3275301450301 kidney 63.963920391678 82.3041983446948 55.3760278806098 liver 67.40536810593 94.0560371424896 58.2232540422823 stomach 72.7299072494716 138.787004685568 57.6793099415232 testicle 66.9874903527997 91.4672971922963 56.5804197871961 diffExp=-15.4095087810513,-15.2611546464945,-56.9084365437134,-25.3303801389795,-18.3402779530168,-26.6506690365595,-66.0570974360965,-24.4798068394966 diffExpScore=0.995990976994157 diffExp1.5=0,0,-1,0,0,0,-1,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,-1,0,0,0,-1,0 diffExp1.4Score=0.666666666666667 diffExp1.3=0,0,-1,-1,0,-1,-1,-1 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 68.584130658009 77.6390769920679 71.6699888580676 cerebhem 80.7863418260808 68.2290244513987 76.3933772388161 cortex 68.2979691036055 66.6552732635125 77.1272606402246 heart 69.4299904724949 66.4366080155297 73.999435579561 kidney 68.8716870871315 85.253646971728 65.575588819183 liver 71.5720002326465 64.4889556853008 70.3922755335562 stomach 72.3559147995732 79.2660900690557 61.9977946132725 testicle 73.7874607824991 66.0788350366753 66.9847268709828 cont.diffExp=-9.0549463340588,12.5573173746821,1.64269584009303,2.99338245696524,-16.3819598845966,7.08304454734574,-6.9101752694825,7.70862574582385 cont.diffExpScore=47.2330501054676 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,-1,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.673029734950751 cont.tran.correlation=-0.286223436693387 tran.covariance=0.00546878221851036 cont.tran.covariance=-0.00167248747605521 tran.mean=81.8272574003972 cont.tran.mean=71.7333128404568 weightedLogRatios: wLogRatio Lung -0.912184304858871 cerebhem -0.900475649473842 cortex -2.84880646317418 heart -1.42397941426238 kidney -1.08010243069099 liver -1.45837046665127 stomach -2.97882631632524 testicle -1.35810956424780 cont.weightedLogRatios: wLogRatio Lung -0.532009772507668 cerebhem 0.727672637292154 cortex 0.102537669105579 heart 0.185902639983937 kidney -0.925867454836195 liver 0.439620028074106 stomach -0.394698189341797 testicle 0.468507012909908 varWeightedLogRatios=0.685673814519693 cont.varWeightedLogRatios=0.326107921247690 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42737005001201 0.0883186748133326 50.129489141108 2.50919751547247e-243 *** df.mm.trans1 -0.466313528849071 0.077335900117829 -6.02971618793594 2.56042062318472e-09 *** df.mm.trans2 -0.0144861120998048 0.0693524880394768 -0.208876602834479 0.834600465912648 df.mm.exp2 -0.114525157952694 0.0914593527892917 -1.25219733641176 0.210882667715030 df.mm.exp3 0.0380520719848781 0.0914593527892917 0.416054463806935 0.67748745320113 df.mm.exp4 0.150198719335401 0.0914593527892917 1.64224559604566 0.100952244665202 df.mm.exp5 0.0330040678708249 0.0914593527892917 0.360860501023456 0.718303880230845 df.mm.exp6 0.168740134420148 0.0914593527892917 1.84497407070985 0.0654296971702322 . df.mm.exp7 0.64320412570915 0.0914593527892917 7.03267742546784 4.51629919773062e-12 *** df.mm.exp8 0.163233964915105 0.0914593527892918 1.78477061051560 0.0746967792647072 . df.mm.trans1:exp2 0.120300826864182 0.085796477941229 1.40216509757649 0.161273718763290 df.mm.trans2:exp2 0.117352909591883 0.0684419125907234 1.71463515775258 0.0868192760090426 . df.mm.trans1:exp3 -0.0516974253254961 0.085796477941229 -0.602558829523388 0.546981720822769 df.mm.trans2:exp3 0.371486794703949 0.0684419125907234 5.42776758629477 7.67743816500146e-08 *** df.mm.trans1:exp4 -0.143668825439780 0.085796477941229 -1.67453057383304 0.0944372417369367 . df.mm.trans2:exp4 -0.0289490321885121 0.0684419125907234 -0.422972285441886 0.67243497813331 df.mm.trans1:exp5 -0.0467275291688681 0.085796477941229 -0.54463225402885 0.586166021527894 df.mm.trans2:exp5 -0.0078195039151888 0.0684419125907235 -0.114250225033142 0.90906957419168 df.mm.trans1:exp6 -0.130058115999201 0.085796477941229 -1.51589108457682 0.129962142857996 df.mm.trans2:exp6 -0.0100869450772026 0.0684419125907234 -0.147379649331567 0.882871404552542 df.mm.trans1:exp7 -0.528494088629814 0.085796477941229 -6.15985762249862 1.17887937743563e-09 *** df.mm.trans2:exp7 -0.0955012633243446 0.0684419125907234 -1.39536228181456 0.16331365841141 df.mm.trans1:exp8 -0.130770715923437 0.085796477941229 -1.52419678594517 0.127875004806553 df.mm.trans2:exp8 -0.032490019373489 0.0684419125907235 -0.474709401646567 0.635130311325906 df.mm.trans1:probe2 0.505966021811561 0.0525393981709909 9.63022111834781 8.62612687529476e-21 *** df.mm.trans1:probe3 -0.16174611792626 0.0525393981709909 -3.07856815184393 0.00215467743199756 ** df.mm.trans1:probe4 -0.160837518770394 0.0525393981709909 -3.06127447914314 0.00228148317458059 ** df.mm.trans1:probe5 0.170933869680312 0.0525393981709909 3.25344171480616 0.00119048827325716 ** df.mm.trans1:probe6 0.0212314357342606 0.0525393981709909 0.404105042565626 0.686249014648922 df.mm.trans1:probe7 0.149508160491056 0.0525393981709909 2.84563900036459 0.00455138087829234 ** df.mm.trans1:probe8 0.648186670501469 0.0525393981709909 12.3371544605808 5.22318941417848e-32 *** df.mm.trans1:probe9 0.304004890867471 0.0525393981709909 5.78622712574816 1.05162586173213e-08 *** df.mm.trans1:probe10 -0.0787028046668163 0.0525393981709909 -1.49797689746418 0.134553914782683 df.mm.trans1:probe11 0.183808294149989 0.0525393981709909 3.49848495697989 0.000495094740284024 *** df.mm.trans1:probe12 -0.0443705469256359 0.0525393981709909 -0.84451951240916 0.398644575345895 df.mm.trans1:probe13 -0.0210044498845333 0.0525393981709909 -0.399784744700990 0.689427250936829 df.mm.trans1:probe14 0.0336028166808695 0.0525393981709909 0.639573688520532 0.522642273445287 df.mm.trans1:probe15 -0.131517403417827 0.0525393981709909 -2.50321488247353 0.0125155424415729 * df.mm.trans1:probe16 0.087405681657333 0.0525393981709909 1.66362167630602 0.0965997666041302 . df.mm.trans1:probe17 0.863821274226314 0.0525393981709909 16.4414002500559 3.31887067739928e-52 *** df.mm.trans1:probe18 0.806982108401695 0.0525393981709909 15.3595613291068 1.41089697225731e-46 *** df.mm.trans1:probe19 0.741150918875222 0.0525393981709909 14.1065742029082 2.59484436337049e-40 *** df.mm.trans1:probe20 0.72095852452913 0.0525393981709909 13.7222455838331 1.88340686118354e-38 *** df.mm.trans1:probe21 0.497699767044579 0.0525393981709909 9.47288671683679 3.34839988692927e-20 *** df.mm.trans1:probe22 0.77052978518002 0.0525393981709909 14.6657520261711 4.51325700751391e-43 *** df.mm.trans2:probe2 -0.0340899098711746 0.0525393981709909 -0.648844696702236 0.516634516895946 df.mm.trans2:probe3 -0.0235670928053318 0.0525393981709909 -0.448560387552063 0.65387650506319 df.mm.trans2:probe4 -0.0491493615901211 0.0525393981709909 -0.93547629590585 0.349839644073703 df.mm.trans2:probe5 -0.124986072944158 0.0525393981709909 -2.37890187735663 0.0176100189376031 * df.mm.trans2:probe6 -0.0999641562953303 0.0525393981709909 -1.90265133928627 0.0574632489523998 . df.mm.trans3:probe2 0.0847620496181365 0.0525393981709909 1.61330454038084 0.107092928771773 df.mm.trans3:probe3 -0.0902967739872726 0.0525393981709909 -1.71864880700383 0.0860850471541422 . df.mm.trans3:probe4 -0.0162168086239082 0.0525393981709909 -0.308659961637362 0.757664679389577 df.mm.trans3:probe5 0.0625525256398015 0.0525393981709909 1.19058321597485 0.234188490243057 df.mm.trans3:probe6 0.493915086480331 0.0525393981709909 9.40085162134654 6.19487616367606e-20 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.44734380646813 0.183381214509297 24.2519050730939 1.04471554238094e-96 *** df.mm.trans1 -0.251762156280108 0.160577038986960 -1.56785900318259 0.11732973461419 df.mm.trans2 -0.0951507955845642 0.144000614963947 -0.660766591923145 0.508961983035755 df.mm.exp2 -0.0292779916801127 0.189902387328423 -0.154173900033591 0.877513497436643 df.mm.exp3 -0.230102551590869 0.189902387328424 -1.21168856710012 0.226007712614625 df.mm.exp4 -0.17555027407438 0.189902387328423 -0.924423734446147 0.355558768802823 df.mm.exp5 0.186612604050234 0.189902387328424 0.982676451178573 0.326079012088373 df.mm.exp6 -0.124945567710667 0.189902387328423 -0.657946271599957 0.510771629435818 df.mm.exp7 0.219248858238635 0.189902387328423 1.15453450229385 0.248643518694963 df.mm.exp8 -0.0204872795504996 0.189902387328424 -0.107883212205585 0.9141167705611 df.mm.trans1:exp2 0.193024729658227 0.178144230070666 1.08353062898337 0.278916137906892 df.mm.trans2:exp2 -0.0999228254630644 0.142109934062680 -0.703137511970075 0.48218510539223 df.mm.trans1:exp3 0.225921406330958 0.178144230070666 1.26819379017406 0.205116589885881 df.mm.trans2:exp3 0.0775658438922168 0.142109934062680 0.545815775679727 0.585352535340599 df.mm.trans1:exp4 0.187808011160261 0.178144230070666 1.05424694970902 0.292104467700147 df.mm.trans2:exp4 0.0197276342417472 0.142109934062680 0.138819529907360 0.889629487194128 df.mm.trans1:exp5 -0.182428614557016 0.178144230070666 -1.02405008842919 0.306137025014829 df.mm.trans2:exp5 -0.093052579000639 0.142109934062680 -0.654792922214687 0.512798944982637 df.mm.trans1:exp6 0.167588330536132 0.178144230070666 0.940745206676935 0.347133961788946 df.mm.trans2:exp6 -0.0606313227603618 0.142109934062680 -0.426650840141964 0.669754328115385 df.mm.trans1:exp7 -0.165712832371708 0.178144230070666 -0.930217230757194 0.352553604236232 df.mm.trans2:exp7 -0.198509306752384 0.142109934062680 -1.39687142958512 0.162859440796976 df.mm.trans1:exp8 0.0936149121700182 0.178144230070666 0.525500669501802 0.599388266730153 df.mm.trans2:exp8 -0.140735090908466 0.142109934062680 -0.990325495798147 0.322329819128012 df.mm.trans1:probe2 -0.0098344317510671 0.109090616073526 -0.090149199858206 0.92819237367581 df.mm.trans1:probe3 0.0599224910719428 0.109090616073526 0.549290976884352 0.582966919317043 df.mm.trans1:probe4 -0.0525212733331371 0.109090616073526 -0.481446298714991 0.630337745590839 df.mm.trans1:probe5 0.0706217517021423 0.109090616073526 0.647367796094799 0.517589164479034 df.mm.trans1:probe6 0.0477770268921133 0.109090616073526 0.437957256194356 0.661541548267671 df.mm.trans1:probe7 -0.0548059678920629 0.109090616073526 -0.502389388424796 0.615538980882088 df.mm.trans1:probe8 0.0318111894919975 0.109090616073526 0.291603353587784 0.770669378591388 df.mm.trans1:probe9 0.0197244137914434 0.109090616073526 0.180807612069487 0.856566728534074 df.mm.trans1:probe10 0.205689033842631 0.109090616073526 1.88548787463074 0.0597446919369565 . df.mm.trans1:probe11 -0.0241839923117140 0.109090616073526 -0.221687191640886 0.824616875446226 df.mm.trans1:probe12 0.0381142714817011 0.109090616073526 0.349381760352445 0.726899380849633 df.mm.trans1:probe13 0.0868503636742414 0.109090616073526 0.796130472081166 0.426204478841761 df.mm.trans1:probe14 0.213208641007359 0.109090616073526 1.95441779211934 0.0510176757607667 . df.mm.trans1:probe15 -0.00288692240911688 0.109090616073526 -0.0264635264977432 0.9788945628713 df.mm.trans1:probe16 -0.00829798851286307 0.109090616073526 -0.0760650990115439 0.939387298714819 df.mm.trans1:probe17 0.0301260897088947 0.109090616073526 0.276156564086045 0.782502795119229 df.mm.trans1:probe18 0.123639116949592 0.109090616073526 1.13336161623893 0.257419351217551 df.mm.trans1:probe19 -0.0301201849899781 0.109090616073526 -0.276102437350591 0.782544350891941 df.mm.trans1:probe20 0.0328715700454953 0.109090616073526 0.301323534769849 0.763250111301931 df.mm.trans1:probe21 -0.00726231002593193 0.109090616073526 -0.066571354047879 0.94694043644243 df.mm.trans1:probe22 0.138983847032516 0.109090616073526 1.27402201981188 0.203044552050917 df.mm.trans2:probe2 0.0793888981696103 0.109090616073526 0.727733521241672 0.467000563650615 df.mm.trans2:probe3 -0.093875411419241 0.109090616073526 -0.860526916045372 0.389769677832284 df.mm.trans2:probe4 -0.076610118174506 0.109090616073526 -0.702261302868357 0.482730957125882 df.mm.trans2:probe5 -0.0270612343365180 0.109090616073526 -0.248061981044081 0.80415337064744 df.mm.trans2:probe6 0.116692174270051 0.109090616073526 1.06968113729784 0.285102020982995 df.mm.trans3:probe2 0.237008801918585 0.109090616073526 2.17258651980518 0.0301193905982479 * df.mm.trans3:probe3 0.223812542232365 0.109090616073526 2.05162048110094 0.0405483020002377 * df.mm.trans3:probe4 0.197207505683101 0.109090616073526 1.80774032433904 0.0710416423651726 . df.mm.trans3:probe5 0.145430056976253 0.109090616073526 1.33311243634589 0.182893850087983 df.mm.trans3:probe6 0.226388532834272 0.109090616073526 2.07523379171026 0.0383002956643640 *