fitVsDatCorrelation=0.864504850720119 cont.fitVsDatCorrelation=0.240189677979525 fstatistic=9992.12385166078,66,1014 cont.fstatistic=2667.62615099497,66,1014 residuals=-0.637776443791067,-0.0961797724022847,-0.00916099973320877,0.0792387176213165,0.851145347529124 cont.residuals=-0.520451389448043,-0.194461995205993,-0.0740955422050329,0.0949886031036977,1.56765307087387 predictedValues: Include Exclude Both Lung 76.7645211307804 45.7131689004571 56.6912915428135 cerebhem 74.0827982222847 46.7417807390397 63.3801074443229 cortex 70.0859657523535 46.2855115396543 54.1103409880887 heart 75.636147232259 47.6327230463174 56.9223777219467 kidney 102.934193480706 45.5449279321686 67.0334991258488 liver 72.4559956719398 46.749534939054 55.2219750518662 stomach 69.8441416037326 47.0019013231205 55.5692617052067 testicle 72.6641332759721 44.5069331397239 57.3874653567789 diffExp=31.0513522303233,27.3410174832450,23.8004542126992,28.0034241859416,57.3892655485373,25.7064607328857,22.8422402806121,28.1572001362482 diffExpScore=0.995923216469795 diffExp1.5=1,1,1,1,1,1,0,1 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 57.9796725564113 56.5471262131237 53.6763049308118 cerebhem 56.0863161914599 60.86168195964 56.7235661525702 cortex 58.0269500094656 50.2235082424076 56.1434643542727 heart 57.7783112390222 54.2170140569543 55.7054433956659 kidney 56.7168859251238 66.1377954465603 55.8044538769069 liver 58.5605373571149 50.865982633299 51.741195196685 stomach 58.8194646157791 47.515182981703 54.6737820673486 testicle 56.9937258592103 53.3309728930982 56.9021128825505 cont.diffExp=1.43254634328763,-4.77536576818009,7.80344176705807,3.56129718206794,-9.42090952143651,7.69455472381593,11.3042816340761,3.66275296611217 cont.diffExpScore=2.23042912362242 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=0,0,0,0,0,0,1,0 cont.diffExp1.2Score=0.5 tran.correlation=-0.291740079420489 cont.tran.correlation=-0.806092881620197 tran.covariance=-0.000772659652271452 cont.tran.covariance=-0.0014427147701826 tran.mean=61.5402736205977 cont.tran.mean=56.2913205112733 weightedLogRatios: wLogRatio Lung 2.11570406790739 cerebhem 1.87667957483278 cortex 1.67711428665971 heart 1.89346048044531 kidney 3.44616249333471 liver 1.78069627894695 stomach 1.60341433991075 testicle 1.98078575603719 cont.weightedLogRatios: wLogRatio Lung 0.101262819440422 cerebhem -0.33238346840725 cortex 0.576064734892975 heart 0.256052787738681 kidney -0.632331037013777 liver 0.563414210436635 stomach 0.84681357027978 testicle 0.266341940690384 varWeightedLogRatios=0.346263506902557 cont.varWeightedLogRatios=0.240729413921978 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.6193763428491 0.0735815434388907 62.7790085252218 0 *** df.mm.trans1 -0.0500073411772189 0.0618759768077486 -0.808186694694032 0.419172675769792 df.mm.trans2 -0.796004845496739 0.0549940277251821 -14.4743871002604 2.65816812649378e-43 *** df.mm.exp2 -0.124836634170977 0.0693669432164143 -1.79965597996016 0.0722121570605051 . df.mm.exp3 -0.0319821307131566 0.0693669432164143 -0.461057230291627 0.644856419565142 df.mm.exp4 0.0222573710284966 0.0693669432164143 0.320864232968389 0.74837939585956 df.mm.exp5 0.122088295779493 0.0693669432164143 1.76003569017876 0.0787032356766561 . df.mm.exp6 -0.00908561811405059 0.0693669432164143 -0.130979075807115 0.895817840640229 df.mm.exp7 -0.0466843101719104 0.0693669432164143 -0.67300515212646 0.501097359824754 df.mm.exp8 -0.093841392117644 0.0693669432164143 -1.35282582403658 0.176412840668659 df.mm.trans1:exp2 0.0892774278023103 0.0608994403466723 1.46598108774227 0.142963461796984 df.mm.trans2:exp2 0.147088645242542 0.0432476227491443 3.40108047315623 0.000697385051379212 *** df.mm.trans1:exp3 -0.0590378675093136 0.0608994403466723 -0.969432020610345 0.332560855190491 df.mm.trans2:exp3 0.0444247009869347 0.0432476227491443 1.02721717779999 0.30456318288574 df.mm.trans1:exp4 -0.0370656330939276 0.0608994403466723 -0.608636678480625 0.542901602959131 df.mm.trans2:exp4 0.0188761968359257 0.0432476227491443 0.436467848080717 0.662590238767694 df.mm.trans1:exp5 0.171259021049896 0.0608994403466722 2.81216083555116 0.00501592337386083 ** df.mm.trans2:exp5 -0.125775446064932 0.0432476227491443 -2.90826265282803 0.00371368529119292 ** df.mm.trans1:exp6 -0.0486775295191541 0.0608994403466722 -0.799309964788765 0.424297892903921 df.mm.trans2:exp6 0.0315035098340121 0.0432476227491443 0.728444890872885 0.466509482101214 df.mm.trans1:exp7 -0.0477920477137443 0.0608994403466723 -0.784769900046476 0.432771793101506 df.mm.trans2:exp7 0.0744859486096233 0.0432476227491443 1.72231313248535 0.0853177426640677 . df.mm.trans1:exp8 0.0389467334839779 0.0608994403466723 0.639525310286469 0.522625704755251 df.mm.trans2:exp8 0.06709995390928 0.0432476227491443 1.55152930135582 0.121086928555601 df.mm.trans1:probe2 -0.566827481213203 0.0469687337843242 -12.0681873992179 1.97504211086824e-31 *** df.mm.trans1:probe3 -0.152670362601972 0.0469687337843242 -3.25046792410925 0.00119006912772239 ** df.mm.trans1:probe4 -0.572645962505564 0.0469687337843242 -12.192067283208 5.27055188536603e-32 *** df.mm.trans1:probe5 -0.629853544620711 0.0469687337843242 -13.4100601373019 7.22655517071186e-38 *** df.mm.trans1:probe6 -0.656820388528288 0.0469687337843242 -13.9842047167876 9.15822325981389e-41 *** df.mm.trans1:probe7 -0.509605159528608 0.0469687337843242 -10.8498807285005 5.03782775438448e-26 *** df.mm.trans1:probe8 -0.61415676017254 0.0469687337843241 -13.0758636797127 3.2120914436672e-36 *** df.mm.trans1:probe9 -0.693145520491423 0.0469687337843242 -14.7575943536030 8.5675573295385e-45 *** df.mm.trans1:probe10 -0.562561855400173 0.0469687337843242 -11.9773689872800 5.16940638910877e-31 *** df.mm.trans1:probe11 -0.599924811310704 0.0469687337843242 -12.7728546838307 9.45416790554822e-35 *** df.mm.trans1:probe12 -0.702849329290506 0.0469687337843242 -14.9641958098747 6.80810284197e-46 *** df.mm.trans1:probe13 -0.686247659662456 0.0469687337843242 -14.6107336598350 5.1136361775335e-44 *** df.mm.trans1:probe14 -0.671083018875838 0.0469687337843242 -14.2878669447932 2.49339180368062e-42 *** df.mm.trans1:probe15 -0.629516747486944 0.0469687337843242 -13.4028894706343 7.84495090552383e-38 *** df.mm.trans1:probe16 -0.668522273598027 0.0469687337843242 -14.2333467337616 4.7798073156632e-42 *** df.mm.trans2:probe2 0.058111004953432 0.0469687337843242 1.23722741218173 0.216289104108331 df.mm.trans2:probe3 -0.0729983808418658 0.0469687337843242 -1.55419094704718 0.120450915661015 df.mm.trans2:probe4 0.00989581717184805 0.0469687337843241 0.210689460296900 0.833171929943127 df.mm.trans2:probe5 -0.0134805880355290 0.0469687337843241 -0.287011953471656 0.774161805075442 df.mm.trans2:probe6 -0.0100952084778815 0.0469687337843242 -0.214934652576281 0.829861486389552 df.mm.trans3:probe2 0.231105353774544 0.0469687337843242 4.92040843246397 1.00729111917145e-06 *** df.mm.trans3:probe3 0.12533857739375 0.0469687337843242 2.6685534672766 0.00773945685857097 ** df.mm.trans3:probe4 0.0485125428198497 0.0469687337843242 1.03286886639556 0.301911547211812 df.mm.trans3:probe5 0.286030113337166 0.0469687337843242 6.08979826134101 1.60237411261820e-09 *** df.mm.trans3:probe6 0.15975659176268 0.0469687337843242 3.40133912266545 0.000696732982275186 *** df.mm.trans3:probe7 0.0806433375262248 0.0469687337843241 1.71695787875677 0.0862922386851509 . df.mm.trans3:probe8 0.258328343830658 0.0469687337843242 5.50000655791312 4.80730749138208e-08 *** df.mm.trans3:probe9 0.0994114353537588 0.0469687337843242 2.11654492987285 0.0345415967204941 * df.mm.trans3:probe10 0.174977632977806 0.0469687337843242 3.72540664564827 0.000205759161581824 *** df.mm.trans3:probe11 1.31539245211916 0.0469687337843242 28.0057039254947 2.64365215105211e-128 *** df.mm.trans3:probe12 0.094470482688639 0.0469687337843242 2.01134829655912 0.0445529067070403 * df.mm.trans3:probe13 0.141686002262245 0.0469687337843242 3.01660255336782 0.00262000533199107 ** df.mm.trans3:probe14 -0.0169063258227723 0.0469687337843241 -0.359948511714292 0.718960592718239 df.mm.trans3:probe15 0.0211925231155621 0.0469687337843242 0.451204906073817 0.651938350480386 df.mm.trans3:probe16 0.804841694467712 0.0469687337843241 17.1356906950796 5.3061964363566e-58 *** df.mm.trans3:probe17 0.263973429056773 0.0469687337843242 5.62019470801392 2.46424914436707e-08 *** df.mm.trans3:probe18 0.36552138405753 0.0469687337843242 7.78222776317472 1.75027603646404e-14 *** df.mm.trans3:probe19 0.159908819881740 0.0469687337843242 3.40458017488881 0.000688610080648324 *** df.mm.trans3:probe20 0.721612673431296 0.0469687337843242 15.3636816513911 4.77866413230321e-48 *** df.mm.trans3:probe21 0.00166960896689918 0.0469687337843242 0.0355472424393185 0.971650372439976 df.mm.trans3:probe22 0.32187500511325 0.0469687337843242 6.85296321998521 1.25372258683914e-11 *** df.mm.trans3:probe23 0.418556774293126 0.0469687337843242 8.91139148470764 2.29414792219085e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04979034639821 0.142109107338410 28.4977537488451 1.09617604962727e-131 *** df.mm.trans1 0.0396500240924971 0.119501975888070 0.331793878702346 0.740113472033703 df.mm.trans2 -0.0186409003744246 0.106210767316397 -0.175508574558115 0.860715074516772 df.mm.exp2 -0.0148893114674850 0.133969388498430 -0.111139653874433 0.911527598373958 df.mm.exp3 -0.162714715032289 0.133969388498430 -1.21456637860369 0.224814282537832 df.mm.exp4 -0.082664833869217 0.133969388498430 -0.617042705022021 0.537345080134127 df.mm.exp5 0.0957634651851414 0.133969388498430 0.714816020723002 0.474887267526785 df.mm.exp6 -0.0591940517702575 0.133969388498430 -0.441847592451699 0.658693709162571 df.mm.exp7 -0.178057361296213 0.133969388498430 -1.32908990099854 0.184117281399801 df.mm.exp8 -0.134069220775566 0.133969388498430 -1.00074518722714 0.317188763990667 df.mm.trans1:exp2 -0.0183112998635554 0.117615976787192 -0.155687180974459 0.876310588960315 df.mm.trans2:exp2 0.0884187093329746 0.0835247641753173 1.05859274439117 0.290037398863321 df.mm.trans1:exp3 0.163529797044437 0.117615976787192 1.39037060705042 0.164721455629323 df.mm.trans2:exp3 0.0441235410386438 0.0835247641753173 0.528268968781871 0.597428257185001 df.mm.trans1:exp4 0.0791858250979001 0.117615976787192 0.673257386121738 0.500936972177277 df.mm.trans2:exp4 0.0405852227861082 0.0835247641753172 0.485906463631796 0.627138467495847 df.mm.trans1:exp5 -0.117783963008991 0.117615976787192 -1.00142826022780 0.316858718645358 df.mm.trans2:exp5 0.0609025274331671 0.0835247641753172 0.729155335360584 0.466075029959755 df.mm.trans1:exp6 0.0691626216104298 0.117615976787192 0.588037641651089 0.556637965128479 df.mm.trans2:exp6 -0.0466859484723739 0.0835247641753172 -0.558947384447333 0.576321046384212 df.mm.trans1:exp7 0.192437716351735 0.117615976787192 1.63615285617124 0.102117922598236 df.mm.trans2:exp7 0.00403228022391602 0.0835247641753173 0.0482764634384639 0.961505418810559 df.mm.trans1:exp8 0.116917933955222 0.117615976787192 0.994065067935172 0.320428341696494 df.mm.trans2:exp8 0.0755121053490246 0.0835247641753172 0.904068465138384 0.366173830736158 df.mm.trans1:probe2 -0.0418377986739122 0.0907114001549728 -0.46121875092255 0.644740583361316 df.mm.trans1:probe3 -0.0927824935993124 0.0907114001549728 -1.02283167761496 0.306631390725482 df.mm.trans1:probe4 0.0363319256948612 0.0907114001549728 0.400522157444281 0.688856336894913 df.mm.trans1:probe5 -0.0615354958855559 0.0907114001549728 -0.678365627478219 0.497694674115128 df.mm.trans1:probe6 -0.0355539778542027 0.0907114001549728 -0.391946081677295 0.695180412540405 df.mm.trans1:probe7 -0.0150992852783349 0.0907114001549728 -0.166454108883106 0.867832779549996 df.mm.trans1:probe8 -0.15130319153913 0.0907114001549728 -1.66796225480636 0.0956319687820078 . df.mm.trans1:probe9 -0.0822662263241995 0.0907114001549728 -0.906900634139199 0.364674805166602 df.mm.trans1:probe10 -0.047553457253268 0.0907114001549728 -0.524228015134006 0.600234526491564 df.mm.trans1:probe11 -0.117290682683473 0.0907114001549728 -1.29300928530584 0.196302360374808 df.mm.trans1:probe12 -0.103635994134296 0.0907114001549728 -1.14248037134519 0.253524089094683 df.mm.trans1:probe13 -0.186840965475910 0.0907114001549728 -2.05972970494014 0.0396791959426801 * df.mm.trans1:probe14 -0.0780036745176067 0.0907114001549728 -0.859910379338693 0.390041701953134 df.mm.trans1:probe15 -0.133346877135956 0.0907114001549728 -1.47001233481286 0.141868616191707 df.mm.trans1:probe16 -0.0338496923871717 0.0907114001549728 -0.373158085194831 0.709108782914556 df.mm.trans2:probe2 0.0566679640001875 0.0907114001549728 0.624706088797826 0.532304514508221 df.mm.trans2:probe3 0.174279774502465 0.0907114001549728 1.92125547841531 0.05497960077755 . df.mm.trans2:probe4 -0.0330850707114583 0.0907114001549728 -0.364728916706558 0.715389822270496 df.mm.trans2:probe5 -0.00329026564269334 0.0907114001549728 -0.0362717986611628 0.971072775635724 df.mm.trans2:probe6 -0.0807492368438585 0.0907114001549728 -0.890177383503123 0.373581795302458 df.mm.trans3:probe2 -0.0674229700647277 0.0907114001549728 -0.743268982173588 0.457491016816987 df.mm.trans3:probe3 -0.163823205479055 0.0907114001549728 -1.8059825468373 0.0712174990276528 . df.mm.trans3:probe4 -0.041793938572176 0.0907114001549728 -0.460735238357853 0.645087365193913 df.mm.trans3:probe5 -0.0995897174284465 0.0907114001549728 -1.09787432735363 0.272519997879406 df.mm.trans3:probe6 -0.00294296851922714 0.0907114001549728 -0.0324432046490223 0.974124992980293 df.mm.trans3:probe7 -0.105985017067206 0.0907114001549728 -1.16837593605809 0.242929776751289 df.mm.trans3:probe8 -0.131849241833150 0.0907114001549728 -1.45350244410181 0.146393712603686 df.mm.trans3:probe9 -0.154755505498653 0.0907114001549728 -1.70602046969032 0.0883104913295676 . df.mm.trans3:probe10 -0.0619410266526959 0.0907114001549728 -0.682836187589155 0.494866330946637 df.mm.trans3:probe11 -0.0114344206179297 0.0907114001549728 -0.126052740872647 0.89971516457903 df.mm.trans3:probe12 -0.159075410469080 0.0907114001549728 -1.75364298420389 0.079793811026153 . df.mm.trans3:probe13 -0.185802973433696 0.0907114001549728 -2.04828690899123 0.0407889796632401 * df.mm.trans3:probe14 -0.00321105402202476 0.0907114001549728 -0.0353985719164178 0.971768890448154 df.mm.trans3:probe15 -0.0243825875035641 0.0907114001549728 -0.268792979293765 0.78814367403935 df.mm.trans3:probe16 -0.0282935072130081 0.0907114001549728 -0.311906851450546 0.755175436881676 df.mm.trans3:probe17 -0.165865632607157 0.0907114001549728 -1.82849820776429 0.0677684532424784 . df.mm.trans3:probe18 -0.150345447990069 0.0907114001549728 -1.65740411605615 0.0977471259577938 . df.mm.trans3:probe19 -0.104984162279423 0.0907114001549728 -1.15734254018863 0.247404989265319 df.mm.trans3:probe20 -0.0794881969165749 0.0907114001549728 -0.876275713755669 0.381087701308124 df.mm.trans3:probe21 -0.126990895826321 0.0907114001549728 -1.39994417029577 0.16183591246542 df.mm.trans3:probe22 -0.0945247437621038 0.0907114001549728 -1.04203819586751 0.29764234665087 df.mm.trans3:probe23 -0.0555371198890516 0.0907114001549728 -0.612239694174835 0.540516443280526