fitVsDatCorrelation=0.855892421944596 cont.fitVsDatCorrelation=0.303900744226840 fstatistic=12034.3520060383,45,531 cont.fstatistic=3537.74146708575,45,531 residuals=-0.403851721068323,-0.0769775303078648,-0.00374871976014716,0.0757764965140704,0.526916247811239 cont.residuals=-0.565311185347322,-0.174037957934293,-0.0197932241049107,0.128294814438157,1.02392641997932 predictedValues: Include Exclude Both Lung 54.7780770710239 76.007533516045 77.885300264635 cerebhem 46.0767527659003 57.4500242617761 54.8273905437866 cortex 49.305386289826 71.8069231990951 50.2240356514355 heart 54.1302494923587 77.1298109816916 61.761842607474 kidney 51.3095612246477 68.9488517129034 56.1126412439406 liver 56.7429983958493 72.2298036853807 57.6924574163189 stomach 52.4535709718699 93.255631635221 62.7924951488836 testicle 50.0003455790188 71.5179567180961 56.7651194466816 diffExp=-21.2294564450211,-11.3732714958757,-22.5015369092691,-22.9995614893329,-17.6392904882557,-15.4868052895315,-40.802060663351,-21.5176111390773 diffExpScore=0.994270969198244 diffExp1.5=0,0,0,0,0,0,-1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,-1,-1,0,0,-1,-1 diffExp1.4Score=0.8 diffExp1.3=-1,0,-1,-1,-1,0,-1,-1 diffExp1.3Score=0.857142857142857 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 62.5930850147291 54.7761621073611 54.3280151008965 cerebhem 55.2968873671876 59.6797327988868 54.5352458981799 cortex 56.5823363298265 58.8146619686997 57.9020049124895 heart 60.4569667992644 61.7389559713288 55.8413688269321 kidney 54.8146133799722 54.1921006089667 58.401380364337 liver 53.8414874845084 59.6482006299021 55.4227161599167 stomach 58.3292792424197 55.9382130253969 53.4641207543663 testicle 55.5101488401636 57.2765817260097 55.5069387952574 cont.diffExp=7.81692290736799,-4.38284543169922,-2.23232563887318,-1.28198917206443,0.622512771005482,-5.80671314539372,2.39106621702277,-1.76643288584607 cont.diffExpScore=4.66342560916262 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=0.521931248227075 cont.tran.correlation=-0.115074286704411 tran.covariance=0.00527231369954187 cont.tran.covariance=-0.000287120280784319 tran.mean=62.696467343794 cont.tran.mean=57.468088330914 weightedLogRatios: wLogRatio Lung -1.36488924672779 cerebhem -0.86932612404907 cortex -1.53612442186219 heart -1.47603134014092 kidney -1.20725132040108 liver -1.00369834072742 stomach -2.44415810869045 testicle -1.46424132144016 cont.weightedLogRatios: wLogRatio Lung 0.54293080139486 cerebhem -0.308982406482444 cortex -0.156907168816985 heart -0.086292171977173 kidney 0.0456666595444724 liver -0.413494236698347 stomach 0.169316670514688 testicle -0.126313513538794 varWeightedLogRatios=0.227475059056408 cont.varWeightedLogRatios=0.0894350790867214 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.21258449223349 0.0655614756216226 64.2539609167087 1.35548410498928e-252 *** df.mm.trans1 -0.164364865633859 0.0561075386533828 -2.92946134474479 0.00354145923774585 ** df.mm.trans2 0.165406707590401 0.0521946274899457 3.16903703589538 0.00161755626765933 ** df.mm.exp2 -0.101851211548741 0.0695928366532609 -1.46353010520614 0.143914070117975 df.mm.exp3 0.276635229250063 0.0695928366532609 3.97505321745067 8.01015177977849e-05 *** df.mm.exp4 0.234712002945252 0.0695928366532609 3.37264601117900 0.000798787344673855 *** df.mm.exp5 0.164995656341863 0.0695928366532609 2.37087126026974 0.0181022216425670 * df.mm.exp6 0.284373296287224 0.0695928366532609 4.08624378546436 5.06184666885624e-05 *** df.mm.exp7 0.376552017068969 0.0695928366532609 5.41078701742107 9.50965895213685e-08 *** df.mm.exp8 0.164171149172587 0.0695928366532609 2.35902367352193 0.0186842630496681 * df.mm.trans1:exp2 -0.0711303044994953 0.0622457254014921 -1.14273396350828 0.253664033577848 df.mm.trans2:exp2 -0.178065822430296 0.0539063794746825 -3.30324210539729 0.00102013252414968 ** df.mm.trans1:exp3 -0.381891959260398 0.0622457254014921 -6.13523188616007 1.66342401361655e-09 *** df.mm.trans2:exp3 -0.333486795043466 0.0539063794746825 -6.18640684633792 1.23031097728647e-09 *** df.mm.trans1:exp4 -0.246608893476954 0.0622457254014921 -3.96186070427001 8.45245114322831e-05 *** df.mm.trans2:exp4 -0.220054604232989 0.0539063794746825 -4.08216256364126 5.14881705046634e-05 *** df.mm.trans1:exp5 -0.230408603346148 0.0622457254014921 -3.70159720784015 0.000236623717904988 *** df.mm.trans2:exp5 -0.262463166736131 0.0539063794746825 -4.86887023936376 1.48275253862768e-06 *** df.mm.trans1:exp6 -0.249131084284557 0.0622457254014921 -4.00238060810816 7.16277510221112e-05 *** df.mm.trans2:exp6 -0.335353002722755 0.0539063794746825 -6.22102626796251 1.00206839360369e-09 *** df.mm.trans1:exp7 -0.419913661653903 0.0622457254014921 -6.74606423084335 3.98111847691077e-11 *** df.mm.trans2:exp7 -0.172040028082488 0.0539063794746825 -3.19145952221273 0.0014992973690181 ** df.mm.trans1:exp8 -0.255431292650720 0.0622457254014921 -4.10359572489771 4.70742609653702e-05 *** df.mm.trans2:exp8 -0.225055048678162 0.0539063794746825 -4.17492420881022 3.48343041073216e-05 *** df.mm.trans1:probe2 -0.0309004846602714 0.0381175664757633 -0.810662576791706 0.417922645211959 df.mm.trans1:probe3 0.015826139611323 0.0381175664757633 0.41519281199092 0.678168468713988 df.mm.trans1:probe4 -0.00182024576860132 0.0381175664757633 -0.0477534621670747 0.961930680873302 df.mm.trans1:probe5 0.0508573590722755 0.0381175664757633 1.33422366049031 0.182702463584045 df.mm.trans1:probe6 -0.0358618435256677 0.0381175664757633 -0.940821957993308 0.347223900683768 df.mm.trans1:probe7 -0.168251942020576 0.0381175664757633 -4.41402632897767 1.22962060613274e-05 *** df.mm.trans1:probe8 -0.04014903517614 0.0381175664757633 -1.05329481622780 0.292684942786814 df.mm.trans1:probe9 -0.157820621812235 0.0381175664757633 -4.14036457213458 4.03281219179606e-05 *** df.mm.trans1:probe10 -0.191975509408697 0.0381175664757633 -5.03640518422819 6.51019061897461e-07 *** df.mm.trans1:probe11 -0.146473925398689 0.0381175664757633 -3.84268826531260 0.000136416786735568 *** df.mm.trans1:probe12 -0.102162081376203 0.0381175664757633 -2.6801837268694 0.00758672350256208 ** df.mm.trans2:probe2 -0.193363185387358 0.0381175664757633 -5.07281034087803 5.42710842253973e-07 *** df.mm.trans2:probe3 -0.181055178652891 0.0381175664757633 -4.74991441985188 2.62228416248212e-06 *** df.mm.trans2:probe4 0.00552868663354621 0.0381175664757633 0.145043011522301 0.884731949175387 df.mm.trans2:probe5 -0.0985025647550606 0.0381175664757633 -2.58417768662363 0.0100270096684479 * df.mm.trans2:probe6 -0.0985126287065084 0.0381175664757633 -2.58444171059941 0.0100194333628045 * df.mm.trans3:probe2 0.0456857086114578 0.0381175664757633 1.19854735848645 0.231238628986342 df.mm.trans3:probe3 0.051805216679279 0.0381175664757633 1.35909034781165 0.174694908201271 df.mm.trans3:probe4 0.0965535115574866 0.0381175664757633 2.53304501007112 0.0115943734958627 * df.mm.trans3:probe5 0.634783606830344 0.0381175664757633 16.6533088421047 2.07657780417309e-50 *** df.mm.trans3:probe6 0.0208373611916459 0.0381175664757633 0.54666032273847 0.584841750658053 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.03100028178743 0.120777702049139 33.3753682459317 1.77802768599105e-132 *** df.mm.trans1 0.092817648320106 0.103361608657171 0.897989587487585 0.369598081481407 df.mm.trans2 -0.0238111791791602 0.0961532227237935 -0.247637869066119 0.804510333233523 df.mm.exp2 -0.0420079915622524 0.128204296965058 -0.327664458654624 0.743294524683288 df.mm.exp3 -0.093533861069452 0.128204296965058 -0.729568846627227 0.46597546696361 df.mm.exp4 0.0574620722040285 0.128204296965058 0.448207069219292 0.65418658125184 df.mm.exp5 -0.215717431892255 0.128204296965058 -1.68260687823162 0.0930390772717917 . df.mm.exp6 -0.0853511377369477 0.128204296965058 -0.665743190809042 0.505864350107009 df.mm.exp7 -0.0335287463872762 0.128204296965058 -0.261525917469166 0.793788373629589 df.mm.exp8 -0.096920210440615 0.128204296965058 -0.755982542980058 0.449994896256172 df.mm.trans1:exp2 -0.0819301967822964 0.114669409208576 -0.714490441241141 0.475237936996078 df.mm.trans2:exp2 0.127745368943267 0.09930662141158 1.28637312525034 0.198873449683555 df.mm.trans1:exp3 -0.00742409038758592 0.114669409208576 -0.0647434258083775 0.94840265257139 df.mm.trans2:exp3 0.164669936825848 0.09930662141158 1.65819695086964 0.0978682877869262 . df.mm.trans1:exp4 -0.092185061811026 0.114669409208576 -0.8039202647617 0.421802768286419 df.mm.trans2:exp4 0.0621979353676106 0.09930662141158 0.626322137270474 0.531372812496237 df.mm.trans1:exp5 0.0830194488334366 0.114669409208576 0.723989505190785 0.46939101560829 df.mm.trans2:exp5 0.204997483219951 0.09930662141158 2.06428816433430 0.0394755130030428 * df.mm.trans1:exp6 -0.06525935841952 0.114669409208576 -0.569108700131328 0.569523012882547 df.mm.trans2:exp6 0.170560019085013 0.09930662141158 1.71750903072334 0.0864692474767277 . df.mm.trans1:exp7 -0.0370218784549098 0.114669409208576 -0.322857497133952 0.746930256691885 df.mm.trans2:exp7 0.0545213878898294 0.09930662141158 0.549020670674752 0.583222048992685 df.mm.trans1:exp8 -0.0231687325203473 0.114669409208576 -0.202048067398734 0.839956529933345 df.mm.trans2:exp8 0.141556953467209 0.09930662141158 1.42545332280031 0.15461374795294 df.mm.trans1:probe2 0.0947665321926056 0.0702203854168534 1.34955870193588 0.177732658201464 df.mm.trans1:probe3 0.00774041721771146 0.0702203854168534 0.110230343678144 0.912268345733853 df.mm.trans1:probe4 -0.0317518718450958 0.0702203854168534 -0.452174559518653 0.651327890647808 df.mm.trans1:probe5 0.0160570618107746 0.0702203854168534 0.228666671586238 0.819216037130871 df.mm.trans1:probe6 0.0861919339306867 0.0702203854168534 1.22744888708628 0.220197796200870 df.mm.trans1:probe7 -0.0919430565184127 0.0702203854168534 -1.30934992698496 0.190982115480353 df.mm.trans1:probe8 -0.0138057860435565 0.0702203854168534 -0.19660652617613 0.844210686973924 df.mm.trans1:probe9 -0.0524189885977161 0.0702203854168534 -0.746492464923657 0.455700352050413 df.mm.trans1:probe10 0.0848934097061321 0.0702203854168534 1.20895676094875 0.227217494674668 df.mm.trans1:probe11 0.108657136702539 0.0702203854168534 1.54737311761408 0.122369056934709 df.mm.trans1:probe12 0.0226770311679492 0.0702203854168534 0.322940853049015 0.746867162266319 df.mm.trans2:probe2 -0.000945003722439195 0.0702203854168534 -0.0134576835035768 0.989267700882985 df.mm.trans2:probe3 -0.0527298832293232 0.0702203854168534 -0.750919877700751 0.453033516222182 df.mm.trans2:probe4 -0.0892570257484002 0.0702203854168534 -1.27109848826005 0.204250145756071 df.mm.trans2:probe5 0.0176408342339916 0.0702203854168534 0.251220982756920 0.801740449828075 df.mm.trans2:probe6 0.0780830624552098 0.0702203854168534 1.11197143096952 0.266653620935040 df.mm.trans3:probe2 -0.148012002992022 0.0702203854168534 -2.10782099974772 0.0355145395183772 * df.mm.trans3:probe3 -0.178931033462509 0.0702203854168534 -2.54813516616734 0.0111105579536641 * df.mm.trans3:probe4 -0.0559588184971215 0.0702203854168534 -0.796902753593987 0.425863676420495 df.mm.trans3:probe5 -0.0978978907577989 0.0702203854168534 -1.39415199983084 0.163854784232223 df.mm.trans3:probe6 -0.149000605244711 0.0702203854168534 -2.12189956463767 0.03430858124416 *