fitVsDatCorrelation=0.900703222528557 cont.fitVsDatCorrelation=0.243970500769710 fstatistic=8606.85663931392,61,899 cont.fstatistic=1715.43010333561,61,899 residuals=-0.765960006072456,-0.0988449003393226,-0.00997530381277926,0.0797468966397253,1.88225186061648 cont.residuals=-0.859956044332088,-0.268425581787403,-0.0492557885264582,0.184601897505182,2.26411883564385 predictedValues: Include Exclude Both Lung 80.7750375956982 84.722927146438 53.7009690881007 cerebhem 79.8778910183826 59.098372163251 51.1307844509298 cortex 69.2439153596045 67.588589259462 50.1643335381897 heart 74.4536531840072 76.4110914298928 50.1937527026533 kidney 75.8312184840244 78.1939116637562 50.4712748696896 liver 79.5430744126414 86.1336217366222 56.9497226580381 stomach 76.8754416330634 88.2214510200742 52.9619123434651 testicle 71.5419142473009 73.4837991480225 50.2388785565812 diffExp=-3.94788955073973,20.7795188551316,1.65532610014250,-1.95743824588561,-2.36269317973181,-6.59054732398084,-11.3460093870107,-1.94188490072169 diffExpScore=7.53638099050504 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=0,1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 68.3125852225004 60.8825340103668 62.989867770072 cerebhem 67.7493382677921 78.2119220339718 65.8811209958247 cortex 71.6904968089221 53.1169576268188 70.3060792464175 heart 69.809123287096 63.8358513717978 70.0417463907633 kidney 68.829630879417 67.1576817673747 68.9189353278083 liver 66.2949717074921 69.6826618322825 67.0667117806438 stomach 65.9416184080343 65.8141519019414 76.0676944846979 testicle 70.6219720208587 76.6805953786851 63.3852919254366 cont.diffExp=7.43005121213358,-10.4625837661797,18.5735391821033,5.97327191529826,1.67194911204226,-3.38769012479041,0.127466506092972,-6.05862335782639 cont.diffExpScore=3.61093701278209 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=0,0,1,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.312371953152636 cont.tran.correlation=-0.329146770738861 tran.covariance=0.00204436905873962 cont.tran.covariance=-0.00132654227302112 tran.mean=76.3747443438901 cont.tran.mean=67.7895057828345 weightedLogRatios: wLogRatio Lung -0.210701334374067 cerebhem 1.27443610148428 cortex 0.102241544456146 heart -0.112190275779370 kidney -0.133276849712790 liver -0.351526536135174 stomach -0.607236974506835 testicle -0.114723096967911 cont.weightedLogRatios: wLogRatio Lung 0.479765122414869 cerebhem -0.615733725875775 cortex 1.23615913673333 heart 0.3757819257253 kidney 0.103757845615884 liver -0.210265961677608 stomach 0.00810294224195274 testicle -0.353797810119372 varWeightedLogRatios=0.315989845070131 cont.varWeightedLogRatios=0.332543868846472 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.89143194854247 0.0826474612082483 59.184297702956 0 *** df.mm.trans1 -0.622177751439954 0.0702180477983509 -8.86065293678766 4.19222284691651e-18 *** df.mm.trans2 -0.438744576455341 0.0621816549826341 -7.0558523503093 3.42536798430909e-12 *** df.mm.exp2 -0.322307445657599 0.078943611728805 -4.08275525529312 4.84689706840839e-05 *** df.mm.exp3 -0.311853020657209 0.078943611728805 -3.95032623701735 8.41542094667981e-05 *** df.mm.exp4 -0.11720906348445 0.078943611728805 -1.48471878746944 0.137968868902676 df.mm.exp5 -0.0813256912041392 0.078943611728805 -1.03017444253143 0.303205195841863 df.mm.exp6 -0.0575934779390891 0.078943611728805 -0.72955210279636 0.46585404425919 df.mm.exp7 0.00484041204580281 0.078943611728805 0.0613148035642336 0.951122129642137 df.mm.exp8 -0.197063927815790 0.078943611728805 -2.49626186970978 0.0127290965587121 * df.mm.trans1:exp2 0.311138574835892 0.0708242580190684 4.39310743999779 1.25052742265328e-05 *** df.mm.trans2:exp2 -0.0378754256064093 0.0508334427938743 -0.745088735382163 0.456412818699574 df.mm.trans1:exp3 0.157820319873302 0.0708242580190684 2.22833707387109 0.026104687521986 * df.mm.trans2:exp3 0.0859059399934942 0.0508334427938743 1.68994927889965 0.0913843494757611 . df.mm.trans1:exp4 0.0357179132219982 0.0708242580190684 0.504317506755689 0.61416185327911 df.mm.trans2:exp4 0.013950673376173 0.0508334427938743 0.274438885297262 0.78381041331567 df.mm.trans1:exp5 0.0181677752676413 0.0708242580190684 0.256519104834813 0.797608720354199 df.mm.trans2:exp5 0.00113122823457004 0.0508334427938743 0.022253622269046 0.982250581971225 df.mm.trans1:exp6 0.0422241922332621 0.0708242580190684 0.596182627453631 0.551203307039177 df.mm.trans2:exp6 0.0741070579815127 0.0508334427938743 1.45784062437028 0.145233709113978 df.mm.trans1:exp7 -0.0543219182630423 0.0708242580190684 -0.76699593871378 0.443285302151763 df.mm.trans2:exp7 0.0356234787131397 0.0508334427938743 0.700788236153712 0.483616515562498 df.mm.trans1:exp8 0.0756794418610913 0.0708242580190684 1.06855255498357 0.285558171093880 df.mm.trans2:exp8 0.0547426384560756 0.0508334427938743 1.07690204415335 0.281812923368294 df.mm.trans1:probe2 -0.375781605435585 0.0523071811024326 -7.18413031472095 1.41938100662094e-12 *** df.mm.trans1:probe3 -0.127040977839539 0.0523071811024326 -2.42874831260274 0.0153461890504944 * df.mm.trans1:probe4 0.176963586696614 0.0523071811024326 3.38316045649772 0.00074752155834968 *** df.mm.trans1:probe5 -0.0220358556166405 0.0523071811024326 -0.421277827483915 0.673652919729034 df.mm.trans1:probe6 0.35563808691891 0.0523071811024326 6.7990298735936 1.91814027948074e-11 *** df.mm.trans1:probe7 0.0819923943556148 0.0523071811024326 1.56751697620734 0.117345797963997 df.mm.trans1:probe8 0.0606261820410687 0.0523071811024326 1.15904127814392 0.246747126328117 df.mm.trans1:probe9 0.0735236220056086 0.0523071811024326 1.40561239309815 0.160184746354464 df.mm.trans1:probe10 -0.0739519966552032 0.0523071811024326 -1.41380198849530 0.15776623258428 df.mm.trans1:probe11 -0.0169510942969710 0.0523071811024326 -0.324068205162421 0.745961777745688 df.mm.trans1:probe12 0.111555867198329 0.0523071811024326 2.13270653946865 0.0332191689654146 * df.mm.trans1:probe13 0.226028257653357 0.0523071811024326 4.32117068611914 1.72510762838681e-05 *** df.mm.trans1:probe14 1.1758318538785 0.0523071811024326 22.4793580746758 3.75663918305187e-89 *** df.mm.trans1:probe15 0.534350511269283 0.0523071811024326 10.2156243178708 2.96961892353677e-23 *** df.mm.trans1:probe16 0.41244082207497 0.0523071811024326 7.88497512927129 9.08954556048911e-15 *** df.mm.trans1:probe17 0.936988972717551 0.0523071811024326 17.9131995448705 1.34459117549257e-61 *** df.mm.trans1:probe18 0.63188989457229 0.0523071811024326 12.0803660463917 3.04642150263678e-31 *** df.mm.trans2:probe2 -0.0465342436719356 0.0523071811024326 -0.889633941098987 0.373900468305953 df.mm.trans2:probe3 0.00733315130160526 0.0523071811024326 0.140193968534547 0.888538140425893 df.mm.trans2:probe4 -0.14882669578328 0.0523071811024326 -2.84524404960447 0.00453856549098869 ** df.mm.trans2:probe5 -0.123073696119662 0.0523071811024326 -2.35290247965471 0.0188421514297618 * df.mm.trans2:probe6 0.0184768319658899 0.0523071811024326 0.353237004489056 0.723993579730218 df.mm.trans3:probe2 -0.0829203543054605 0.0523071811024326 -1.58525756039268 0.113259495898762 df.mm.trans3:probe3 0.0861164587639631 0.0523071811024326 1.64636015455167 0.100039137713958 df.mm.trans3:probe4 0.230095294243966 0.0523071811024326 4.39892361611635 1.21817136190621e-05 *** df.mm.trans3:probe5 0.0460766041238715 0.0523071811024326 0.880884864233845 0.378615572346393 df.mm.trans3:probe6 -0.00272666121170002 0.0523071811024326 -0.0521278561419785 0.958438397397743 df.mm.trans3:probe7 0.122549524727683 0.0523071811024326 2.34288145804868 0.0193526822337512 * df.mm.trans3:probe8 0.00925179962140925 0.0523071811024326 0.176874368421643 0.859646911221173 df.mm.trans3:probe9 0.234097788796697 0.0523071811024326 4.47544264215396 8.60400892942958e-06 *** df.mm.trans3:probe10 0.175951135553186 0.0523071811024326 3.36380458370759 0.00080125702208047 *** df.mm.trans3:probe11 0.0808112025958429 0.0523071811024326 1.54493514834208 0.122713873502791 df.mm.trans3:probe12 0.133165844443740 0.0523071811024326 2.54584249499057 0.0110673326355967 * df.mm.trans3:probe13 0.301426692218421 0.0523071811024326 5.76262543431924 1.13731686905326e-08 *** df.mm.trans3:probe14 0.420851437150421 0.0523071811024326 8.04576787126556 2.69803647704145e-15 *** df.mm.trans3:probe15 0.614406425348045 0.0523071811024326 11.7461199857981 9.72006414336572e-30 *** df.mm.trans3:probe16 0.0775896405404176 0.0523071811024326 1.48334586007368 0.138333023479454 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22799261850907 0.184492704033322 22.9168553881970 6.72980952467903e-92 *** df.mm.trans1 -0.000352945686784134 0.156746708499812 -0.00225169440661370 0.99820390885699 df.mm.trans2 -0.126960903064386 0.138807187798629 -0.914656546810607 0.360617215947141 df.mm.exp2 0.197318398381603 0.176224655677026 1.11969802195691 0.263141523234872 df.mm.exp3 -0.198070307892621 0.176224655677026 -1.12396478876164 0.261327993937746 df.mm.exp4 -0.0370782324862777 0.176224655677026 -0.210403205747966 0.833400667936437 df.mm.exp5 0.0156802187830475 0.176224655677026 0.0889785752328849 0.929118754642134 df.mm.exp6 0.0423112984048871 0.176224655677026 0.240098630026168 0.81030853183616 df.mm.exp7 -0.146085454831380 0.176224655677026 -0.828972848720535 0.407339809760451 df.mm.exp8 0.257691686354213 0.176224655677026 1.46229076382192 0.144011024980887 df.mm.trans1:exp2 -0.205597719409422 0.158099942600385 -1.30042880489269 0.193787308986216 df.mm.trans2:exp2 0.0531573575047159 0.113474741743538 0.468451011105676 0.639575655391232 df.mm.trans1:exp3 0.246334492219772 0.158099942600385 1.55809349559607 0.119563030012881 df.mm.trans2:exp3 0.0616202021069392 0.113474741743538 0.54303011542609 0.587243673424306 df.mm.trans1:exp4 0.05874892635729 0.158099942600385 0.371593597005815 0.710283018061154 df.mm.trans2:exp4 0.0844468630164753 0.113474741743538 0.744190836823687 0.456955499122145 df.mm.trans1:exp5 -0.00813989864835176 0.158099942600385 -0.051485778643995 0.95894987105859 df.mm.trans2:exp5 0.0824167590526143 0.113474741743538 0.726300476972071 0.467843638200253 df.mm.trans1:exp6 -0.0722912590800079 0.158099942600385 -0.45725038156865 0.647601562358717 df.mm.trans2:exp6 0.0926938984953061 0.113474741743538 0.816868115944266 0.414220211360485 df.mm.trans1:exp7 0.110761222345166 0.158099942600385 0.70057724578134 0.483748153984214 df.mm.trans2:exp7 0.223974008812886 0.113474741743538 1.9737785287856 0.0487127606841916 * df.mm.trans1:exp8 -0.224444385322533 0.158099942600385 -1.41963609619923 0.156060302545718 df.mm.trans2:exp8 -0.0269893394704668 0.113474741743538 -0.23784446702214 0.812055924092388 df.mm.trans1:probe2 0.0219108823017549 0.116764545950570 0.187650130640087 0.851193269518476 df.mm.trans1:probe3 -0.0942997383363214 0.116764545950570 -0.807605918120396 0.41953110660665 df.mm.trans1:probe4 -0.0690593921275006 0.116764545950570 -0.591441447960886 0.554373359133549 df.mm.trans1:probe5 0.0922311942202096 0.116764545950570 0.78989040268485 0.429800067051578 df.mm.trans1:probe6 0.000679742255450452 0.116764545950570 0.00582147817144946 0.995356450196823 df.mm.trans1:probe7 0.0298367904122852 0.116764545950570 0.255529537406980 0.798372583727142 df.mm.trans1:probe8 -0.00332355478463179 0.116764545950570 -0.0284637323562132 0.977298610930472 df.mm.trans1:probe9 -0.11721769759759 0.116764545950570 -1.00388090103319 0.315706146005164 df.mm.trans1:probe10 -0.0432721164129296 0.116764545950570 -0.370592940354069 0.711028041605548 df.mm.trans1:probe11 0.0136767714432533 0.116764545950570 0.117131200501932 0.906782247461452 df.mm.trans1:probe12 0.067054833026136 0.116764545950570 0.574273915769965 0.56592611525602 df.mm.trans1:probe13 -0.0618628687790151 0.116764545950570 -0.529808669878299 0.596375408116424 df.mm.trans1:probe14 -0.092164502657634 0.116764545950570 -0.789319239905664 0.430133562201773 df.mm.trans1:probe15 -0.0135569607015341 0.116764545950570 -0.116105112139717 0.907595167095142 df.mm.trans1:probe16 -0.0169827147180117 0.116764545950570 -0.145444103599743 0.884392847854416 df.mm.trans1:probe17 0.0142337466268108 0.116764545950570 0.121901271579785 0.903004451056707 df.mm.trans1:probe18 0.151563168017199 0.116764545950570 1.29802387174408 0.194612048011024 df.mm.trans2:probe2 -0.0885930127620509 0.116764545950570 -0.758732130894894 0.448211683234869 df.mm.trans2:probe3 0.0812817129750624 0.116764545950570 0.69611637944853 0.486535873668131 df.mm.trans2:probe4 0.0711171116115522 0.116764545950570 0.609064258612012 0.542635723389585 df.mm.trans2:probe5 0.108308702786739 0.116764545950570 0.927582100414187 0.353873347169162 df.mm.trans2:probe6 0.00200713149303305 0.116764545950570 0.017189562779465 0.98628920272879 df.mm.trans3:probe2 -0.105752044167964 0.116764545950570 -0.905686253537371 0.36534460271432 df.mm.trans3:probe3 -0.0288817981965942 0.116764545950570 -0.247350751561358 0.804693262066524 df.mm.trans3:probe4 0.0999977038610061 0.116764545950570 0.85640468214845 0.392002186402376 df.mm.trans3:probe5 0.0595910102742655 0.116764545950570 0.510351920517827 0.609930215179213 df.mm.trans3:probe6 0.0899603714431114 0.116764545950570 0.770442523548152 0.441239851604355 df.mm.trans3:probe7 -0.078161107678674 0.116764545950570 -0.669390755921424 0.503418118799719 df.mm.trans3:probe8 0.232696423522565 0.116764545950570 1.99286882527743 0.0465777406885775 * df.mm.trans3:probe9 0.0666420205526641 0.116764545950570 0.570738489240353 0.568319551501842 df.mm.trans3:probe10 0.183203051418696 0.116764545950570 1.56899553650688 0.117000863652345 df.mm.trans3:probe11 -0.064566154133215 0.116764545950570 -0.552960263816277 0.580428088934204 df.mm.trans3:probe12 0.00803185602043375 0.116764545950570 0.068786770462276 0.945174645115459 df.mm.trans3:probe13 0.152028281471161 0.116764545950570 1.30200721660425 0.193247413127489 df.mm.trans3:probe14 -0.0110299105632239 0.116764545950570 -0.0944628394983284 0.924762549656037 df.mm.trans3:probe15 0.0432296874370594 0.116764545950570 0.370229568274601 0.71129865319937 df.mm.trans3:probe16 0.0297324431831783 0.116764545950570 0.254635882331654 0.799062576969579