fitVsDatCorrelation=0.848475829447386 cont.fitVsDatCorrelation=0.273591052716417 fstatistic=12627.2698784900,55,761 cont.fstatistic=3813.26168371826,55,761 residuals=-0.376685457081059,-0.0872365664032841,-0.00263511705815294,0.0667075238731314,1.18644544811517 cont.residuals=-0.519415995813249,-0.187135051150079,-0.0170478636592436,0.156137718848547,0.825503178675424 predictedValues: Include Exclude Both Lung 55.2869301323854 68.246907093881 71.3313418030285 cerebhem 58.1596066555296 62.4733593836549 66.1443954840202 cortex 55.5108066073397 76.2051241480228 68.94732390621 heart 54.5108905153184 72.7862464536873 70.9758192310465 kidney 57.3409466417347 73.5887124224663 73.3309262260315 liver 58.0314768449225 72.9459573786341 77.1899701786612 stomach 57.5245839706881 67.8273115883146 71.9316588920082 testicle 57.6648587319899 74.3293390874792 75.2790316231405 diffExp=-12.9599769614956,-4.31375272812532,-20.6943175406831,-18.2753559383689,-16.2477657807317,-14.9144805337116,-10.3027276176264,-16.6644803554893 diffExpScore=0.991332450092264 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,-1,-1,0,0,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,0,-1,-1,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 58.9088736550616 60.3443014247518 56.6388244421061 cerebhem 60.3925639596288 62.963543439234 67.0742705204665 cortex 60.4540365050688 57.6227079172512 54.4575111479453 heart 59.2654336155215 60.048692000851 61.3990149105874 kidney 59.156344427453 53.3412592257368 57.9401326593226 liver 57.1123325508166 63.7864785937251 62.4870461156715 stomach 58.2302902378142 64.7637360218251 59.2851134559434 testicle 60.3356139309235 58.9184414372721 66.5125218233013 cont.diffExp=-1.43542776969022,-2.57097947960521,2.83132858781762,-0.783258385329447,5.81508520171624,-6.67414604290847,-6.53344578401096,1.41717249365136 cont.diffExpScore=3.14102043655962 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=-0.308836577478481 cont.tran.correlation=-0.427291341912679 tran.covariance=-0.000511758370725495 cont.tran.covariance=-0.000519547554346296 tran.mean=63.902066103503 cont.tran.mean=59.7277905589334 weightedLogRatios: wLogRatio Lung -0.86719770793535 cerebhem -0.293277491047375 cortex -1.32285390492333 heart -1.19784064235115 kidney -1.04125412120563 liver -0.955042046528793 stomach -0.681183968451122 testicle -1.06152587148269 cont.weightedLogRatios: wLogRatio Lung -0.098418410250501 cerebhem -0.171833713424042 cortex 0.195603099502872 heart -0.0536813084046208 kidney 0.416838389913681 liver -0.453167768723582 stomach -0.437863610879564 testicle 0.0971661418706517 varWeightedLogRatios=0.103942344645305 cont.varWeightedLogRatios=0.0898161571078058 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15083089115510 0.0672359272026921 61.7353112219578 1.54163068755319e-298 *** df.mm.trans1 0.0241802279597286 0.0585213630172629 0.413186342782137 0.679586490265876 df.mm.trans2 0.0780695625834818 0.0523430983370168 1.49149677920902 0.136245511385246 df.mm.exp2 0.0377582792415923 0.0685332029251133 0.550948702672644 0.58183054579582 df.mm.exp3 0.148330786310355 0.0685332029251133 2.16436384087342 0.0307467713721468 * df.mm.exp4 0.0552554336599056 0.0685332029251133 0.806257861905033 0.420346059141922 df.mm.exp5 0.0841913425766162 0.0685332029251133 1.22847523511505 0.219648396154800 df.mm.exp6 0.0361020518267993 0.0685332029251133 0.526781914253274 0.598498554192299 df.mm.exp7 0.0251280129565566 0.0685332029251133 0.366654583239223 0.713978608125182 df.mm.exp8 0.0736192252327896 0.0685332029251133 1.07421252897276 0.283067921351607 df.mm.trans1:exp2 0.0128962550252400 0.0638009397743074 0.202132681287453 0.83986702204126 df.mm.trans2:exp2 -0.126150178963490 0.0500495749068848 -2.52050450374828 0.0119220851758080 * df.mm.trans1:exp3 -0.144289606657745 0.0638009397743073 -2.26155926806348 0.0240060814747962 * df.mm.trans2:exp3 -0.0380341955740595 0.0500495749068848 -0.759930441863304 0.44753151737608 df.mm.trans1:exp4 -0.0693914617754101 0.0638009397743074 -1.08762444598589 0.277105279792455 df.mm.trans2:exp4 0.00913946562276376 0.0500495749068848 0.182608256708820 0.855154040193681 df.mm.trans1:exp5 -0.0477129087756031 0.0638009397743074 -0.747840219037292 0.454787569128251 df.mm.trans2:exp5 -0.00883180816084191 0.0500495749068848 -0.176461202263418 0.859978575527898 df.mm.trans1:exp6 0.0123469799404466 0.0638009397743074 0.193523480753159 0.84660065920549 df.mm.trans2:exp6 0.0304846895427505 0.0500495749068848 0.609089879373922 0.54264664851613 df.mm.trans1:exp7 0.0145478549994556 0.0638009397743074 0.228019446906549 0.819692373638835 df.mm.trans2:exp7 -0.0312951890688176 0.0500495749068848 -0.625283813639598 0.53197225485711 df.mm.trans1:exp8 -0.0315078071542695 0.0638009397743074 -0.493845502366059 0.62155779663634 df.mm.trans2:exp8 0.0117544061338871 0.0500495749068848 0.234855264120738 0.814384249835173 df.mm.trans1:probe2 -0.357776208479007 0.0405447896296326 -8.82422160152292 7.47369749199038e-18 *** df.mm.trans1:probe3 -0.383733410374072 0.0405447896296326 -9.46443214724725 3.59980139238237e-20 *** df.mm.trans1:probe4 -0.237334707628756 0.0405447896296326 -5.85364259616966 7.1477509754246e-09 *** df.mm.trans1:probe5 -0.347298487084425 0.0405447896296326 -8.56579822603391 5.9239500668876e-17 *** df.mm.trans1:probe6 -0.346306068745872 0.0405447896296326 -8.54132113914755 7.18894096837727e-17 *** df.mm.trans1:probe7 -0.333349981691442 0.0405447896296326 -8.22177115078209 8.63444887256084e-16 *** df.mm.trans1:probe8 -0.166872901596888 0.0405447896296326 -4.11576686230792 4.28050341182556e-05 *** df.mm.trans1:probe9 -0.206854177041198 0.0405447896296326 -5.1018683024567 4.25065507698856e-07 *** df.mm.trans1:probe10 0.0523180160874609 0.0405447896296326 1.29037581808597 0.197312130119658 df.mm.trans1:probe11 -0.117695130320401 0.0405447896296326 -2.90284229849308 0.00380483951005905 ** df.mm.trans1:probe12 -0.173257048544702 0.0405447896296326 -4.27322598359408 2.17109652018709e-05 *** df.mm.trans1:probe13 0.0335499626146235 0.0405447896296326 0.827479015703243 0.408225012098609 df.mm.trans1:probe14 -0.106233565708742 0.0405447896296326 -2.62015333361355 0.00896424576506265 ** df.mm.trans1:probe15 0.0206753916412621 0.0405447896296326 0.509939546613191 0.610241672169274 df.mm.trans1:probe16 -0.298858666178895 0.0405447896296326 -7.37107453038727 4.42124263876018e-13 *** df.mm.trans1:probe17 -0.0499943273961681 0.0405447896296326 -1.23306417058406 0.217932577760140 df.mm.trans1:probe18 -0.366457662283795 0.0405447896296326 -9.03834168659652 1.29607951713600e-18 *** df.mm.trans1:probe19 -0.389872737542440 0.0405447896296326 -9.61585301351512 9.77043849718723e-21 *** df.mm.trans1:probe20 -0.354662472783678 0.0405447896296326 -8.7474241702433 1.38974304734209e-17 *** df.mm.trans1:probe21 -0.419274150001607 0.0405447896296326 -10.3410118496502 1.52762425788568e-23 *** df.mm.trans2:probe2 -0.0336701490619655 0.0405447896296326 -0.830443304048056 0.406548639243561 df.mm.trans2:probe3 -0.0671848256229814 0.0405447896296326 -1.65705202164567 0.0979211350352222 . df.mm.trans2:probe4 0.10064919015747 0.0405447896296326 2.48241983931542 0.0132639326772609 * df.mm.trans2:probe5 -0.0458540574259608 0.0405447896296326 -1.13094821418059 0.258433173768792 df.mm.trans2:probe6 -0.0289285521263948 0.0405447896296326 -0.713496170300807 0.475757530032466 df.mm.trans3:probe2 -0.211955277479094 0.0405447896296326 -5.22768225992186 2.21953395040425e-07 *** df.mm.trans3:probe3 -0.256028672102654 0.0405447896296326 -6.31471206143667 4.59870490197768e-10 *** df.mm.trans3:probe4 -0.00887893752953696 0.0405447896296326 -0.218990839776060 0.826715902181878 df.mm.trans3:probe5 -0.142558773663817 0.0405447896296326 -3.51608122686191 0.000463904369821713 *** df.mm.trans3:probe6 0.433956004809355 0.0405447896296326 10.7031263147113 5.32953113099505e-25 *** df.mm.trans3:probe7 0.285251535281731 0.0405447896296326 7.03546714355748 4.43226156629477e-12 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20113246669604 0.122196606300761 34.3801075486159 4.70247732229454e-157 *** df.mm.trans1 -0.166092428301876 0.106358493952889 -1.56162824546431 0.118791329420469 df.mm.trans2 -0.0751533542781854 0.0951299289852638 -0.79000746746933 0.429769500385874 df.mm.exp2 -0.101742212210697 0.124554314408784 -0.816850164473478 0.414269697961828 df.mm.exp3 0.0190157291704838 0.124554314408784 0.152670176547033 0.878698849644964 df.mm.exp4 -0.0795753546858243 0.124554314408784 -0.638880757070043 0.523092743637531 df.mm.exp5 -0.141879870258939 0.124554314408784 -1.13910040717893 0.255019712868460 df.mm.exp6 -0.0737615234979069 0.124554314408784 -0.592203681165337 0.553890108716877 df.mm.exp7 0.0134296919646615 0.124554314408784 0.107821973316681 0.914165332441528 df.mm.exp8 -0.160677106102803 0.124554314408784 -1.29001638253545 0.197436832319378 df.mm.trans1:exp2 0.126616460728326 0.115953756326089 1.09195652422205 0.27519778796796 df.mm.trans2:exp2 0.144231577891085 0.0909616101817092 1.58563131856352 0.113238387687840 df.mm.trans1:exp3 0.0068758843455131 0.115953756326089 0.0592985045363819 0.952729920069307 df.mm.trans2:exp3 -0.0651655221303122 0.0909616101817091 -0.716406866590582 0.473959944282053 df.mm.trans1:exp4 0.0856098481507541 0.115953756326089 0.738310261463194 0.460553552744909 df.mm.trans2:exp4 0.0746646034941967 0.0909616101817092 0.820836431380702 0.411996494209608 df.mm.trans1:exp5 0.146071979312339 0.115953756326089 1.25974340064975 0.208148142468479 df.mm.trans2:exp5 0.0185234786196738 0.0909616101817092 0.203640619187264 0.83868878988179 df.mm.trans1:exp6 0.0427898628718051 0.115953756326089 0.369025240988917 0.712211558285562 df.mm.trans2:exp6 0.129236239473675 0.0909616101817092 1.42077783380821 0.155790956245766 df.mm.trans1:exp7 -0.0250157573861776 0.115953756326089 -0.215739085811308 0.82924894220621 df.mm.trans2:exp7 0.0572496078037239 0.0909616101817091 0.629382084259056 0.529287837148724 df.mm.trans1:exp8 0.184607912353274 0.115953756326089 1.59208220761830 0.111781502236367 df.mm.trans2:exp8 0.136764727516474 0.0909616101817092 1.50354338762546 0.133113845048659 df.mm.trans1:probe2 -0.0149323183881852 0.0736873261371642 -0.202644323942351 0.839467207956985 df.mm.trans1:probe3 0.0690753436133462 0.0736873261371642 0.93741145505493 0.348844350247883 df.mm.trans1:probe4 0.0706488404756674 0.0736873261371642 0.958765152424709 0.337981568480702 df.mm.trans1:probe5 0.105363830022237 0.0736873261371642 1.42987723324509 0.153162666746464 df.mm.trans1:probe6 0.0594435037140991 0.0736873261371642 0.80669915479697 0.420091865363325 df.mm.trans1:probe7 -0.00744162335673059 0.0736873261371642 -0.100989189686141 0.919585642228884 df.mm.trans1:probe8 0.0493592466553349 0.0736873261371642 0.669847166980328 0.503158416071757 df.mm.trans1:probe9 -0.0260242046315011 0.0736873261371642 -0.353170701065455 0.724058277354896 df.mm.trans1:probe10 0.0880353798874041 0.0736873261371642 1.19471535340463 0.232570476625279 df.mm.trans1:probe11 0.105739219662074 0.0736873261371642 1.43497159152237 0.151706031127741 df.mm.trans1:probe12 0.0218467584828537 0.0736873261371642 0.296479186151868 0.766945053631816 df.mm.trans1:probe13 0.0473925545864106 0.0736873261371642 0.643157474572934 0.520315668540081 df.mm.trans1:probe14 0.080729007829809 0.0736873261371642 1.09556163945394 0.273617252362731 df.mm.trans1:probe15 0.0852966167711587 0.0736873261371642 1.15754799695655 0.247411722099515 df.mm.trans1:probe16 0.0543357691619933 0.0736873261371642 0.737382831083472 0.461116862032703 df.mm.trans1:probe17 0.185591195845077 0.0736873261371642 2.51863116188543 0.0119851561944752 * df.mm.trans1:probe18 0.0536176017448035 0.0736873261371642 0.72763668537786 0.467059820360799 df.mm.trans1:probe19 0.0148960977114869 0.0736873261371642 0.202152778399894 0.839851316727117 df.mm.trans1:probe20 -0.0230835835507828 0.0736873261371642 -0.313263959501179 0.75416596622797 df.mm.trans1:probe21 0.126758285327964 0.0736873261371642 1.72021827867674 0.0857993094997254 . df.mm.trans2:probe2 -0.114704797150996 0.0736873261371642 -1.55664214138373 0.119971227111809 df.mm.trans2:probe3 -0.0338755826592590 0.0736873261371642 -0.459720611875668 0.645848076967774 df.mm.trans2:probe4 -0.0561438569542335 0.0736873261371642 -0.761920127889094 0.446343740882031 df.mm.trans2:probe5 -0.00936809193093683 0.0736873261371642 -0.127133014889138 0.89886870556668 df.mm.trans2:probe6 -0.122771403337408 0.0736873261371642 -1.66611288227337 0.0961024577755992 . df.mm.trans3:probe2 0.0605206741312258 0.0736873261371642 0.821317278069916 0.411722789045216 df.mm.trans3:probe3 0.0267799753355317 0.0736873261371642 0.363427155514946 0.716386751521182 df.mm.trans3:probe4 0.187975187218707 0.0736873261371642 2.55098396254470 0.0109365389911741 * df.mm.trans3:probe5 0.0618851694141552 0.0736873261371642 0.83983464536303 0.401264867071016 df.mm.trans3:probe6 0.0438784694669821 0.0736873261371642 0.595468335834376 0.551707496486717 df.mm.trans3:probe7 0.156616595467825 0.0736873261371642 2.12542106869631 0.0338730047596017 *