fitVsDatCorrelation=0.630899575374194 cont.fitVsDatCorrelation=0.274710794399905 fstatistic=12005.4961689323,43,485 cont.fstatistic=7812.86475740845,43,485 residuals=-0.364124617273153,-0.0795489879102073,-0.00702365662074018,0.0662414203962664,0.533738536264537 cont.residuals=-0.445010600339443,-0.0931739842268463,-0.0178325359412492,0.0706138046806144,0.582441253907387 predictedValues: Include Exclude Both Lung 44.3968213833495 43.3461269258754 51.0763820492492 cerebhem 51.9101416192712 53.8396270298784 51.2731725848491 cortex 44.7814884268744 42.9666403495954 58.0864119868008 heart 45.0677401170744 43.4112435625925 48.5361663852065 kidney 43.299093673025 41.9679776425473 53.6889956966468 liver 49.2916188751253 47.9523240641208 48.9222139045861 stomach 45.5958351447223 46.2115779719133 48.3986145531085 testicle 47.4171643263044 46.8779819164727 49.7271008986124 diffExp=1.05069445747413,-1.92948541060727,1.81484807727898,1.65649655448184,1.33111603047776,1.33929481100453,-0.615742827191028,0.539182409831668 diffExpScore=1.66120098326229 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=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 48.6054444638757 45.684590717466 45.7641739985804 cerebhem 47.7528380437991 46.4118282383782 47.1957016985516 cortex 46.8019023688907 43.5304223801969 47.2327155319468 heart 47.0049915408229 47.8419671879563 49.1153766987892 kidney 45.6576609356012 47.7082022746748 47.8709320229668 liver 47.8107635525362 43.9418386688177 48.0302496491133 stomach 47.6445580976681 46.6259554876864 45.7526548574646 testicle 48.9415701446475 47.4809603838776 44.413754767297 cont.diffExp=2.92085374640971,1.34100980542089,3.27147998869385,-0.836975647133436,-2.05054133907364,3.86892488371846,1.01860260998173,1.46060976076993 cont.diffExpScore=1.39811975828339 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=0.968468392436858 cont.tran.correlation=-0.0847835601748966 tran.covariance=0.00479591119876453 cont.tran.covariance=-6.65371219987372e-05 tran.mean=46.1458376892964 cont.tran.mean=46.840343405431 weightedLogRatios: wLogRatio Lung 0.0905615348323783 cerebhem -0.144805774037424 cortex 0.156427675078274 heart 0.141907872039297 kidney 0.117171713521537 liver 0.106991220824334 stomach -0.0513289628958355 testicle 0.0440666582613423 cont.weightedLogRatios: wLogRatio Lung 0.238772122889171 cerebhem 0.109715166177217 cortex 0.276064726507867 heart -0.0681105356320767 kidney -0.168836429209268 liver 0.322773314823022 stomach 0.0832665624894122 testicle 0.117420564238853 varWeightedLogRatios=0.0109951763184211 cont.varWeightedLogRatios=0.0285077918807597 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.49048709165018 0.0641669937576049 54.3969241388451 1.39779096714109e-208 *** df.mm.trans1 0.310437482674112 0.0558072210870074 5.5626758800643 4.39783284689741e-08 *** df.mm.trans2 0.251380267106032 0.0523921310114951 4.79805387283976 2.136114880972e-06 *** df.mm.exp2 0.369293240284617 0.071186888057053 5.18765815396574 3.13484183798401e-07 *** df.mm.exp3 -0.128775937422072 0.071186888057053 -1.80898394264495 0.0710725718369071 . df.mm.exp4 0.0675129191015344 0.071186888057053 0.948389808070076 0.343403352771590 df.mm.exp5 -0.107232496642151 0.071186888057053 -1.50635179551898 0.132627900376740 df.mm.exp6 0.248666735475618 0.071186888057053 3.49315361666496 0.00052105602727941 *** df.mm.exp7 0.144512529744984 0.071186888057053 2.03004420742713 0.0428973166345597 * df.mm.exp8 0.170919270204531 0.071186888057053 2.40099370641890 0.0167261766154063 * df.mm.trans1:exp2 -0.212946938690206 0.0649844406457384 -3.27689115385455 0.00112480930116681 ** df.mm.trans2:exp2 -0.152500837316462 0.0581238507054685 -2.62372219778126 0.00897152490757088 ** df.mm.trans1:exp3 0.137402910282776 0.0649844406457384 2.11439706054908 0.0349910128474268 * df.mm.trans2:exp3 0.119982591234548 0.0581238507054685 2.06425743955845 0.0395245076562107 * df.mm.trans1:exp4 -0.0525141016889593 0.0649844406457384 -0.808102696078266 0.419427645056462 df.mm.trans2:exp4 -0.0660117982497123 0.0581238507054685 -1.13570930777134 0.256639145384232 df.mm.trans1:exp5 0.0821963236822589 0.0649844406457384 1.26486160172326 0.206528360636365 df.mm.trans2:exp5 0.0749220320756868 0.0581238507054685 1.28900668428422 0.198010219241705 df.mm.trans1:exp6 -0.144080547822356 0.0649844406457384 -2.21715454331304 0.0270754452235897 * df.mm.trans2:exp6 -0.147676821794853 0.0581238507054685 -2.54072674130242 0.0113728312711966 * df.mm.trans1:exp7 -0.117864028351821 0.0649844406457384 -1.81372690417318 0.0703376522594952 . df.mm.trans2:exp7 -0.0804995125655738 0.0581238507054685 -1.38496523524378 0.16669981345988 df.mm.trans1:exp8 -0.105102866873548 0.0649844406457384 -1.61735433634821 0.106451982221061 df.mm.trans2:exp8 -0.0925885286838207 0.0581238507054685 -1.59295242073680 0.111822346875527 df.mm.trans1:probe2 -0.0344311866319673 0.0355934440285265 -0.967346306931477 0.333853061910980 df.mm.trans1:probe3 -0.0816042386239862 0.0355934440285265 -2.29267610514409 0.0222938872881691 * df.mm.trans1:probe4 -0.0793808294431679 0.0355934440285265 -2.23020928740551 0.0261903514312869 * df.mm.trans1:probe5 0.0667597389394182 0.0355934440285265 1.87561897314329 0.0613081049902868 . df.mm.trans1:probe6 -0.0294368728978095 0.0355934440285265 -0.827030755276652 0.408626334103745 df.mm.trans1:probe7 0.00728269054199413 0.0355934440285265 0.204607638871849 0.83796452182051 df.mm.trans1:probe8 0.0075567335014737 0.0355934440285265 0.212306892679936 0.831956814523478 df.mm.trans1:probe9 -0.00184547859084978 0.0355934440285265 -0.0518488345598341 0.958670491004273 df.mm.trans1:probe10 0.0249615483982252 0.0355934440285265 0.701296238099905 0.483454508644893 df.mm.trans1:probe11 -0.0363563982470267 0.0355934440285265 -1.02143524571235 0.307557492472958 df.mm.trans1:probe12 0.0323871264307773 0.0355934440285265 0.909918309810663 0.363317562140486 df.mm.trans2:probe2 0.0819889475578454 0.0355934440285265 2.30348452631150 0.0216736723475391 * df.mm.trans2:probe3 0.105238034698587 0.0355934440285265 2.95666906001689 0.00326142217446934 ** df.mm.trans2:probe4 0.0613963470523474 0.0355934440285265 1.72493414807348 0.0851764048588641 . df.mm.trans2:probe5 -0.0110806090214527 0.0355934440285265 -0.311310392233246 0.755698431611115 df.mm.trans2:probe6 0.0359572417970532 0.0355934440285265 1.01022092069076 0.312893178252757 df.mm.trans3:probe2 -0.00855096850115749 0.0355934440285265 -0.240239986170045 0.810245725251624 df.mm.trans3:probe3 -0.232348876140236 0.0355934440285265 -6.52785597128559 1.68461131167969e-10 *** df.mm.trans3:probe4 -0.235030722881822 0.0355934440285265 -6.60320262050099 1.05882770674574e-10 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.87381546532991 0.0795219918100646 48.7137630378069 6.24015795352597e-189 *** df.mm.trans1 0.0238466634747481 0.0691617468474203 0.344795563468877 0.730397536021515 df.mm.trans2 -0.0856195649410713 0.0649294344214804 -1.31865564060336 0.187906589775509 df.mm.exp2 -0.0327050267647280 0.0882217289225278 -0.37071396314901 0.71101244706447 df.mm.exp3 -0.117697918785278 0.0882217289225278 -1.33411485155358 0.182792302638243 df.mm.exp4 -0.0580101791515415 0.0882217289225278 -0.657549788017455 0.511139409119579 df.mm.exp5 -0.0642287729505498 0.0882217289225278 -0.728038021188097 0.466941648642717 df.mm.exp6 -0.103708359633829 0.0882217289225278 -1.17554213571240 0.240354634731181 df.mm.exp7 0.000680918603423255 0.0882217289225278 0.00771826410272696 0.99384495102533 df.mm.exp8 0.0754116733585117 0.0882217289225278 0.854797046935395 0.393085553661697 df.mm.trans1:exp2 0.0150079767598044 0.0805350516549574 0.186353351135903 0.8522455913582 df.mm.trans2:exp2 0.0484983148253974 0.0720327400287766 0.673281549556808 0.501088884055066 df.mm.trans1:exp3 0.0798862191529795 0.0805350516549574 0.99194347692533 0.321719718304056 df.mm.trans2:exp3 0.0693969198843367 0.0720327400287766 0.9634080260811 0.335822893740484 df.mm.trans1:exp4 0.0245284275810156 0.0805350516549574 0.304568347284417 0.760825573196086 df.mm.trans2:exp4 0.104152350482302 0.0720327400287766 1.44590293859005 0.1488503025556 df.mm.trans1:exp5 0.00166463423551399 0.0805350516549574 0.0206696860721703 0.983517651522271 df.mm.trans2:exp5 0.107571053890324 0.0720327400287766 1.49336334904586 0.135992343812537 df.mm.trans1:exp6 0.0872236020679447 0.0805350516549574 1.08305142016477 0.279323744512296 df.mm.trans2:exp6 0.0648142130880452 0.0720327400287766 0.899788249928467 0.368679479976109 df.mm.trans1:exp7 -0.0206480513781125 0.0805350516549573 -0.256385896001862 0.797761592336503 df.mm.trans2:exp7 0.019715394568271 0.0720327400287766 0.273700466765457 0.784431339188643 df.mm.trans1:exp8 -0.0685200834426244 0.0805350516549574 -0.850810697138313 0.395294365901238 df.mm.trans2:exp8 -0.0368439343127709 0.0720327400287766 -0.511488724405763 0.609241665680389 df.mm.trans1:probe2 -0.00431721927938514 0.0441108644612639 -0.097872017066823 0.922074354190106 df.mm.trans1:probe3 -0.025758588055531 0.0441108644612639 -0.583951105246441 0.559524693462863 df.mm.trans1:probe4 -0.0418942990523752 0.0441108644612639 -0.949750125372509 0.34271224933951 df.mm.trans1:probe5 0.00926154585370188 0.0441108644612639 0.209960651798945 0.833786554233676 df.mm.trans1:probe6 -0.0349349141306508 0.0441108644612639 -0.79197981171575 0.428759628024270 df.mm.trans1:probe7 0.0173355615334269 0.0441108644612639 0.392999814108158 0.694492297873923 df.mm.trans1:probe8 0.0158102066821327 0.0441108644612639 0.358419787851052 0.720185140037091 df.mm.trans1:probe9 0.00580725727998813 0.0441108644612639 0.131651404952351 0.895314618635755 df.mm.trans1:probe10 -0.0559759484761307 0.0441108644612639 -1.26898325752120 0.205055693044424 df.mm.trans1:probe11 -0.0300796060832846 0.0441108644612639 -0.681909240515998 0.495621850574044 df.mm.trans1:probe12 -0.0780792469435842 0.0441108644612639 -1.77006839238323 0.07734394873724 . df.mm.trans2:probe2 0.0417116310078487 0.0441108644612639 0.945609013046613 0.344818900360594 df.mm.trans2:probe3 0.0814648266401144 0.0441108644612639 1.84681999854374 0.0653821908870225 . df.mm.trans2:probe4 0.115851427928929 0.0441108644612639 2.62636947481872 0.0089030312041755 ** df.mm.trans2:probe5 0.0279073150903157 0.0441108644612639 0.632663073624925 0.527251846448729 df.mm.trans2:probe6 0.068716371602363 0.0441108644612639 1.55781058570517 0.119930420607946 df.mm.trans3:probe2 -0.0333198970572449 0.0441108644612639 -0.755367129259162 0.450395401291145 df.mm.trans3:probe3 0.039045154275743 0.0441108644612639 0.885159580357593 0.376509194305134 df.mm.trans3:probe4 0.0401107189897561 0.0441108644612639 0.909316094337245 0.363634944421817