fitVsDatCorrelation=0.885540254266038 cont.fitVsDatCorrelation=0.256579646702495 fstatistic=8972.44414129412,69,1083 cont.fstatistic=2060.81391216966,69,1083 residuals=-0.715090874400457,-0.103527803909997,-0.00194588115173269,0.0902620359690128,1.10461243864795 cont.residuals=-0.654619897127602,-0.251548971113122,-0.105161399941341,0.154963015995264,1.85429206538129 predictedValues: Include Exclude Both Lung 59.3038550604253 86.0310451812196 62.7327852437467 cerebhem 62.3238778483428 120.054096792820 74.8606133869371 cortex 58.3461057674292 183.328779267577 118.248933616472 heart 58.2242469944646 100.369071857879 65.5660430012535 kidney 59.4225314782888 100.923070878270 74.8402812096157 liver 61.838710042273 91.6450288195941 64.4137860477822 stomach 64.9722078600068 112.146768037757 59.3212534920725 testicle 59.5140848771044 90.572725172566 57.9261768479813 diffExp=-26.7271901207943,-57.7302189444767,-124.982673500147,-42.144824863414,-41.5005393999814,-29.8063187773212,-47.1745601777499,-31.0586402954615 diffExpScore=0.997513210856441 diffExp1.5=0,-1,-1,-1,-1,0,-1,-1 diffExp1.5Score=0.857142857142857 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 64.686643471355 80.6736153962595 65.6518632295465 cerebhem 68.0578446706324 80.2746219950117 70.9849520751012 cortex 62.9642254947521 73.7848809292084 69.87522407158 heart 62.0704219237302 62.9721019176007 67.5556721776275 kidney 66.958651289086 69.6467434959717 68.5653490514066 liver 73.7073650075465 68.4448967064205 64.86566375879 stomach 65.7013142516908 80.0363589558486 65.0488128712772 testicle 66.6299996242986 60.0259596725893 72.107447359738 cont.diffExp=-15.9869719249044,-12.2167773243793,-10.8206554344564,-0.90167999387046,-2.68809220688571,5.26246830112598,-14.3350447041578,6.60403995170932 cont.diffExpScore=1.49330898421732 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=-1,0,0,0,0,0,-1,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=-0.145094099695074 cont.tran.correlation=-0.0190254894345219 tran.covariance=-0.000787614709100591 cont.tran.covariance=4.6092050404963e-06 tran.mean=85.563512871001 cont.tran.mean=69.1647278001251 weightedLogRatios: wLogRatio Lung -1.58809789534851 cerebhem -2.92406039431712 cortex -5.31095114737396 heart -2.36149320819921 kidney -2.30387373638666 liver -1.69994092411664 stomach -2.42732646544410 testicle -1.80413961346674 cont.weightedLogRatios: wLogRatio Lung -0.945263524462013 cerebhem -0.710390510504761 cortex -0.66953270238385 heart -0.0596428274529947 kidney -0.166249808349282 liver 0.315781200425645 stomach -0.845459712759417 testicle 0.432851153393224 varWeightedLogRatios=1.43972122143700 cont.varWeightedLogRatios=0.286157409857123 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.63572841909433 0.0819858990592221 56.5429966895373 0 *** df.mm.trans1 -0.515425232403149 0.0699219178225029 -7.37144015001932 3.34478190887385e-13 *** df.mm.trans2 -0.179702362151500 0.0609053494581559 -2.95051852998498 0.00324067360426913 ** df.mm.exp2 0.206160842679600 0.0763587489399721 2.69989811962044 0.0070441521598999 ** df.mm.exp3 0.106383423599765 0.0763587489399721 1.39320542932672 0.163843556019801 df.mm.exp4 0.0915997355216684 0.0763587489399721 1.19959712270401 0.230558214903913 df.mm.exp5 -0.0148225677706152 0.0763587489399721 -0.194117478041288 0.846120300197965 df.mm.exp6 0.0786262637848189 0.0763587489399721 1.02969554735148 0.303382851057094 df.mm.exp7 0.41230206804786 0.0763587489399721 5.39953932943536 8.20848703576706e-08 *** df.mm.exp8 0.134698406056304 0.0763587489399721 1.76402059916139 0.0780104066568443 . df.mm.trans1:exp2 -0.156490531613471 0.0694230630488614 -2.25415769257156 0.0243854807518424 * df.mm.trans2:exp2 0.127073383535087 0.0460793163886159 2.75770982501990 0.00591869796315009 ** df.mm.trans1:exp3 -0.122665119639995 0.0694230630488614 -1.76692174405587 0.0775229524133746 . df.mm.trans2:exp3 0.650189503700022 0.0460793163886159 14.1102246009156 1.25512912141600e-41 *** df.mm.trans1:exp4 -0.109972165860392 0.0694230630488614 -1.58408691623115 0.113465887219629 df.mm.trans2:exp4 0.0625461534633162 0.0460793163886159 1.35735853665504 0.174950101304874 df.mm.trans1:exp5 0.0168217266913724 0.0694230630488614 0.242307468910338 0.80858780382691 df.mm.trans2:exp5 0.174472898326503 0.0460793163886159 3.78636038900976 0.000161264877429568 *** df.mm.trans1:exp6 -0.0367710326655724 0.0694230630488614 -0.529665950344083 0.596452118607632 df.mm.trans2:exp6 -0.0154117534825748 0.0460793163886159 -0.33446141762603 0.738096159944792 df.mm.trans1:exp7 -0.321016774312778 0.0694230630488614 -4.62406526325178 4.21635411098759e-06 *** df.mm.trans2:exp7 -0.147201847298241 0.0460793163886159 -3.19453192527414 0.00144100305624542 ** df.mm.trans1:exp8 -0.131159714236358 0.0694230630488614 -1.88928157986987 0.059121052333721 . df.mm.trans2:exp8 -0.083253506582851 0.0460793163886159 -1.80674352633016 0.0710797375572214 . df.mm.trans1:probe2 -0.124807337145269 0.052730601699835 -2.36688626948968 0.0181135583953138 * df.mm.trans1:probe3 -0.0437449859958263 0.052730601699835 -0.829593909146748 0.406951237741741 df.mm.trans1:probe4 -0.0382552894892662 0.052730601699835 -0.725485548354474 0.468310940205391 df.mm.trans1:probe5 -0.162428199142267 0.052730601699835 -3.08034033191726 0.00211977940051511 ** df.mm.trans1:probe6 -0.113850484457162 0.052730601699835 -2.15909700983969 0.0310617023220178 * df.mm.trans1:probe7 -0.212588288075414 0.052730601699835 -4.03159230546157 5.92792850763984e-05 *** df.mm.trans1:probe8 -0.260320898977827 0.052730601699835 -4.93680880904193 9.18989864240417e-07 *** df.mm.trans1:probe9 0.0278068477058978 0.052730601699835 0.527337955750745 0.598066927737003 df.mm.trans1:probe10 0.306579126078901 0.052730601699835 5.81406462653469 8.01609577515895e-09 *** df.mm.trans1:probe11 -0.156465845765345 0.052730601699835 -2.96726835502494 0.00307059533924453 ** df.mm.trans1:probe12 -0.0502271284544737 0.052730601699835 -0.952523332473766 0.341044174163196 df.mm.trans1:probe13 -0.222247097411875 0.052730601699835 -4.21476505572608 2.70873078861871e-05 *** df.mm.trans1:probe14 -0.220702557822716 0.052730601699835 -4.18547391283431 3.07642557355573e-05 *** df.mm.trans1:probe15 -0.156895626512767 0.052730601699835 -2.97541885461280 0.00299081881362091 ** df.mm.trans1:probe16 -0.178624531594691 0.052730601699835 -3.38749276201127 0.000730704408515114 *** df.mm.trans1:probe17 -0.0206535896699873 0.052730601699835 -0.391681281915884 0.695370776827817 df.mm.trans1:probe18 -0.098539971439456 0.052730601699835 -1.86874354289351 0.0619282890865282 . df.mm.trans1:probe19 0.0762977859751013 0.052730601699835 1.44693562211599 0.148204325379731 df.mm.trans1:probe20 -0.0059371807893661 0.052730601699835 -0.112594595888798 0.910372835546282 df.mm.trans1:probe21 0.0122106436236331 0.052730601699835 0.231566552059111 0.816918432768292 df.mm.trans1:probe22 0.0629819286666361 0.052730601699835 1.19440944416216 0.232579368811638 df.mm.trans2:probe2 -0.216108001461170 0.052730601699835 -4.09834127612177 4.47186723187601e-05 *** df.mm.trans2:probe3 -0.0378926333960461 0.052730601699835 -0.718608022183154 0.472537473854644 df.mm.trans2:probe4 -0.126783926875885 0.052730601699835 -2.40437094948380 0.0163674300061253 * df.mm.trans2:probe5 0.296243596258112 0.052730601699835 5.61805833251166 2.45441073949030e-08 *** df.mm.trans2:probe6 0.0502772463382403 0.052730601699835 0.95347378405503 0.340562829739117 df.mm.trans3:probe2 0.232479221255615 0.052730601699835 4.40881032571911 1.14289230871070e-05 *** df.mm.trans3:probe3 0.0852154432629236 0.052730601699835 1.61605292782370 0.106374112588671 df.mm.trans3:probe4 -0.182069463233993 0.052730601699835 -3.45282354770783 0.000576186363868004 *** df.mm.trans3:probe5 1.12204684837902 0.052730601699835 21.2788553934239 3.17292158169423e-84 *** df.mm.trans3:probe6 0.0904844574299006 0.052730601699835 1.7159761981283 0.0864523876151673 . df.mm.trans3:probe7 -0.0331235756545336 0.052730601699835 -0.62816608547513 0.530027584534468 df.mm.trans3:probe8 0.413903487913114 0.052730601699835 7.84939815914161 9.99708638281562e-15 *** df.mm.trans3:probe9 0.64457843239782 0.052730601699835 12.2239916029602 2.7806881173702e-32 *** df.mm.trans3:probe10 -0.0380131433941703 0.052730601699835 -0.720893412340661 0.471130675109305 df.mm.trans3:probe11 0.258424608040739 0.052730601699835 4.90084694105714 1.09986694543471e-06 *** df.mm.trans3:probe12 -0.0974691406535206 0.052730601699835 -1.8484359652931 0.0648117533028902 . df.mm.trans3:probe13 0.136457766137273 0.052730601699835 2.58782873205295 0.00978756598393585 ** df.mm.trans3:probe14 0.711893544777843 0.052730601699835 13.5005769293179 1.67047491482000e-38 *** df.mm.trans3:probe15 0.153888548927758 0.052730601699835 2.91839167327839 0.00359115299251038 ** df.mm.trans3:probe16 -0.0361255815153921 0.052730601699835 -0.685097085010223 0.493429260501418 df.mm.trans3:probe17 0.351159583377483 0.052730601699835 6.65950268074756 4.36047628465093e-11 *** df.mm.trans3:probe18 -0.0338176770777272 0.052730601699835 -0.64132924691874 0.521444487446641 df.mm.trans3:probe19 0.0745243743875805 0.052730601699835 1.41330407742746 0.15785368161078 df.mm.trans3:probe20 0.111229807029892 0.052730601699835 2.10939764471226 0.0351392903260280 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.26602736983655 0.170571749864466 25.0101636011020 2.46140466399357e-109 *** df.mm.trans1 -0.126735271288624 0.145472624118552 -0.871196708360343 0.383839832776841 df.mm.trans2 0.138326006109360 0.126713644082627 1.09164255444473 0.275233035603178 df.mm.exp2 -0.0322567161922074 0.158864458078873 -0.203045518061646 0.839137595389827 df.mm.exp3 -0.178590913044495 0.158864458078873 -1.12417160643842 0.261189339356658 df.mm.exp4 -0.317590987183306 0.158864458078873 -1.99913178204799 0.0458436229047308 * df.mm.exp5 -0.155876399239817 0.158864458078873 -0.981191143222402 0.326717626395293 df.mm.exp6 -0.0217870353366135 0.158864458078873 -0.137142288464526 0.890943817496024 df.mm.exp7 0.0168616563836088 0.158864458078873 0.106138632816393 0.915492015615674 df.mm.exp8 -0.35982561605108 0.158864458078873 -2.26498500924879 0.0237105291830474 * df.mm.trans1:exp2 0.0830599743363384 0.144434756233407 0.575069162730563 0.565363969422795 df.mm.trans2:exp2 0.0272986722265307 0.0958680665718759 0.284752506258837 0.775888200899933 df.mm.trans1:exp3 0.151602886509415 0.144434756233407 1.04962884601283 0.294122955134694 df.mm.trans2:exp3 0.0893331831506731 0.0958680665718758 0.931834617564721 0.351629680533403 df.mm.trans1:exp4 0.276305822783076 0.144434756233407 1.91301477558881 0.0560096469482386 . df.mm.trans2:exp4 0.0698712137388142 0.0958680665718758 0.728826774517552 0.466265203723809 df.mm.trans1:exp5 0.190396940829948 0.144434756233407 1.31822108331229 0.187708380079303 df.mm.trans2:exp5 0.00890076805556424 0.0958680665718758 0.092843929932508 0.926044722256646 df.mm.trans1:exp6 0.152335019449304 0.144434756233407 1.05469779865955 0.291798791641438 df.mm.trans2:exp6 -0.142595545818815 0.0958680665718758 -1.48741443233244 0.137196468591496 df.mm.trans1:exp7 -0.00129746961170267 0.144434756233407 -0.0089830844426805 0.992834286426273 df.mm.trans2:exp7 -0.0247922130641047 0.0958680665718758 -0.258607625569637 0.7959871387106 df.mm.trans1:exp8 0.389425794637409 0.144434756233407 2.69620557262613 0.00712218576244071 ** df.mm.trans2:exp8 0.0641911708575547 0.0958680665718758 0.669578235516289 0.503269415073973 df.mm.trans1:probe2 -0.0490043102752999 0.109706072709528 -0.446687307867176 0.655190152916648 df.mm.trans1:probe3 0.0374618680561668 0.109706072709528 0.341474880386573 0.732812354377673 df.mm.trans1:probe4 0.0182601795647283 0.109706072709528 0.166446388187427 0.867836739550674 df.mm.trans1:probe5 -0.0661544491603969 0.109706072709528 -0.60301538033866 0.54662471188629 df.mm.trans1:probe6 0.0426479934591230 0.109706072709528 0.388747791309996 0.697539138549615 df.mm.trans1:probe7 0.118879036787545 0.109706072709528 1.08361400468964 0.278777161381572 df.mm.trans1:probe8 -0.0320236070004442 0.109706072709528 -0.291903685999535 0.770416169796521 df.mm.trans1:probe9 -0.0345076954699988 0.109706072709528 -0.31454681238445 0.753166354077995 df.mm.trans1:probe10 0.126100544707429 0.109706072709528 1.14943996802537 0.250628381766088 df.mm.trans1:probe11 0.161618404081554 0.109706072709528 1.47319469278128 0.140989116581893 df.mm.trans1:probe12 0.0141426168940714 0.109706072709528 0.128913710469950 0.897449863688112 df.mm.trans1:probe13 0.0980812722408927 0.109706072709528 0.894036855193833 0.371500805924373 df.mm.trans1:probe14 0.093288223547955 0.109706072709528 0.850346942916795 0.395320178255498 df.mm.trans1:probe15 0.405718795070674 0.109706072709528 3.69823461044774 0.000227996311393374 *** df.mm.trans1:probe16 0.0947641029334072 0.109706072709528 0.863799975634135 0.387889044772384 df.mm.trans1:probe17 0.0883521662158132 0.109706072709528 0.805353468898168 0.420792512337371 df.mm.trans1:probe18 -0.0213593282176263 0.109706072709528 -0.194695951555754 0.845667497515001 df.mm.trans1:probe19 -0.0399304782668114 0.109706072709528 -0.363976918329183 0.715946290257058 df.mm.trans1:probe20 0.0718236844905572 0.109706072709528 0.654691966603588 0.512805112783316 df.mm.trans1:probe21 0.0829437174099587 0.109706072709528 0.756054021089343 0.44978122401088 df.mm.trans1:probe22 0.0599283025967613 0.109706072709528 0.546262400217672 0.584998090441919 df.mm.trans2:probe2 -0.0494076547321703 0.109706072709528 -0.450363899754103 0.652538210324291 df.mm.trans2:probe3 -0.156731638258494 0.109706072709528 -1.42865052396395 0.153393042021421 df.mm.trans2:probe4 -0.0436733958805539 0.109706072709528 -0.398094606815332 0.690638911043366 df.mm.trans2:probe5 -0.033626156202802 0.109706072709528 -0.306511347752233 0.759274258094667 df.mm.trans2:probe6 -0.0790479781005926 0.109706072709528 -0.72054332224516 0.471346026994918 df.mm.trans3:probe2 -0.192227866778736 0.109706072709528 -1.75220807773972 0.0800210269987675 . df.mm.trans3:probe3 -0.0458550837952361 0.109706072709528 -0.417981271799309 0.676043669823947 df.mm.trans3:probe4 -0.162343483317690 0.109706072709528 -1.47980398266130 0.139216385793254 df.mm.trans3:probe5 -0.100537909161320 0.109706072709528 -0.916429753414994 0.359645553456894 df.mm.trans3:probe6 -0.120247543658479 0.109706072709528 -1.09608830840988 0.273283804880426 df.mm.trans3:probe7 -0.085525921713696 0.109706072709528 -0.779591499370734 0.435801688254758 df.mm.trans3:probe8 -0.173803893219145 0.109706072709528 -1.58426866377151 0.113424536861228 df.mm.trans3:probe9 -0.097486934129372 0.109706072709528 -0.88861930540063 0.374405033048238 df.mm.trans3:probe10 -0.099406595594473 0.109706072709528 -0.90611752968019 0.365075194707573 df.mm.trans3:probe11 -0.0576518368082166 0.109706072709528 -0.525511809732383 0.599335020071309 df.mm.trans3:probe12 -0.195620142122931 0.109706072709528 -1.78312956877856 0.0748452155184781 . df.mm.trans3:probe13 -0.0287183074019225 0.109706072709528 -0.261775001990644 0.79354469006742 df.mm.trans3:probe14 -0.0267519123328307 0.109706072709528 -0.243850788494293 0.807392568395434 df.mm.trans3:probe15 -0.0718460962107678 0.109706072709528 -0.654896255387764 0.512673616411653 df.mm.trans3:probe16 -0.125415050697081 0.109706072709528 -1.1431915079956 0.253211727732184 df.mm.trans3:probe17 -0.0671605686698329 0.109706072709528 -0.612186426977983 0.540542924520428 df.mm.trans3:probe18 -0.0992973287253512 0.109706072709528 -0.905121533137581 0.365602312959303 df.mm.trans3:probe19 -0.163757130428027 0.109706072709528 -1.49268975165678 0.135809662677335 df.mm.trans3:probe20 0.0486111436882345 0.109706072709528 0.443103489967629 0.657779375769795