fitVsDatCorrelation=0.761174901345313 cont.fitVsDatCorrelation=0.219475000917868 fstatistic=12446.4262699186,56,784 cont.fstatistic=5492.24630127679,56,784 residuals=-0.563826195701723,-0.090256848634477,0.000110368797799918,0.0781227231025539,0.844684371426882 cont.residuals=-0.59960595071737,-0.153258799550761,-0.0157854734251463,0.135383001086376,0.802352738527143 predictedValues: Include Exclude Both Lung 67.0204200539797 80.0547318160357 64.8448816953522 cerebhem 65.4820328550519 80.785335321195 66.9030447669891 cortex 60.2520768729843 67.5694409931975 62.5180977205837 heart 65.9952491600712 75.0900861590936 66.4345107681854 kidney 69.3096614257598 87.1663573514692 62.8257362932076 liver 66.5397759454305 83.8755947003604 66.3794705449417 stomach 65.6664079973374 75.5719803830872 67.4740082659455 testicle 65.6074577029592 79.7042137474826 73.623802800067 diffExp=-13.034311762056,-15.3033024661431,-7.31736412021318,-9.09483699902243,-17.8566959257094,-17.3358187549299,-9.90557238574975,-14.0967560445234 diffExpScore=0.990471168188165 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,-1,0,0,-1,-1,0,-1 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 65.800291971176 69.4077100348742 70.4205714352155 cerebhem 68.1220070322373 69.1217994222685 70.6570752942478 cortex 67.4010325342558 71.2012461752988 74.2687029625121 heart 65.0392282588312 68.0708702439907 67.4509712960192 kidney 67.9745266406473 65.6890270223538 68.1089752435925 liver 65.0663614215763 63.8318862311514 74.0779472565847 stomach 68.7969218086808 72.0484959054769 67.2580350953806 testicle 65.7206595617097 73.1265050260016 64.7902758239506 cont.diffExp=-3.60741806369823,-0.999792390031203,-3.800213641043,-3.03164198515948,2.28549961829347,1.23447519042492,-3.25157409679601,-7.40584546429197 cont.diffExpScore=1.30853044596031 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=0.896240514217997 cont.tran.correlation=0.285100331940274 tran.covariance=0.00278794857127712 cont.tran.covariance=0.000300899557856461 tran.mean=72.2306764053435 cont.tran.mean=67.9011605806581 weightedLogRatios: wLogRatio Lung -0.763074519503058 cerebhem -0.90030925140705 cortex -0.476338055690927 heart -0.54923352040956 kidney -0.99790235377401 liver -0.998740864098725 stomach -0.597796380703551 testicle -0.833225368814878 cont.weightedLogRatios: wLogRatio Lung -0.224879925040894 cerebhem -0.0616098057715836 cortex -0.23245891005675 heart -0.191245247339111 kidney 0.143714265301765 liver 0.0797958304275506 stomach -0.196463116964231 testicle -0.452608937246874 varWeightedLogRatios=0.041383116316162 cont.varWeightedLogRatios=0.0363156773492463 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.46885689008100 0.0701733975778713 63.6830628746729 0 *** df.mm.trans1 -0.293021732651802 0.0611624066437315 -4.79087970423795 1.98596211070663e-06 *** df.mm.trans2 -0.095545623673448 0.0545808476909836 -1.75053389083276 0.080417361141676 . df.mm.exp2 -0.0453831413185148 0.0714086979476104 -0.635540804172211 0.525261219007341 df.mm.exp3 -0.239473127100668 0.0714086979476104 -3.35355683528019 0.00083610000997385 *** df.mm.exp4 -0.103655279383215 0.0714086979476104 -1.45157778201280 0.147019065339473 df.mm.exp5 0.150328038194569 0.0714086979476104 2.10517825580377 0.0355933818693679 * df.mm.exp6 0.0160368428465978 0.0714086979476104 0.224578283983884 0.822365876593677 df.mm.exp7 -0.117779291397826 0.0714086979476104 -1.64936898141226 0.0994726482273282 . df.mm.exp8 -0.152666468538629 0.0714086979476104 -2.13792539181479 0.032831415982942 * df.mm.trans1:exp2 0.0221615888930768 0.0666802484005255 0.332356123809854 0.73970920544477 df.mm.trans2:exp2 0.0544680483720745 0.0520102350623704 1.04725633919471 0.295304116150984 df.mm.trans1:exp3 0.13301281991724 0.0666802484005255 1.99478590898878 0.0464120200276242 * df.mm.trans2:exp3 0.0699184031757047 0.0520102350623704 1.34432007645916 0.179233745656262 df.mm.trans1:exp4 0.0882406865146326 0.0666802484005254 1.32334069880185 0.186107886888748 df.mm.trans2:exp4 0.0396332724533945 0.0520102350623704 0.762028327806373 0.446272276004652 df.mm.trans1:exp5 -0.116741077083867 0.0666802484005255 -1.75075948101818 0.080378450628185 . df.mm.trans2:exp5 -0.0652201403650843 0.0520102350623704 -1.25398664872160 0.210220625314226 df.mm.trans1:exp6 -0.0232342893591738 0.0666802484005254 -0.348443353414243 0.727600793751236 df.mm.trans2:exp6 0.0305872942853996 0.0520102350623704 0.588101442893298 0.556633568468998 df.mm.trans1:exp7 0.097369442452122 0.0666802484005255 1.46024414707122 0.144623583265159 df.mm.trans2:exp7 0.0601543275777748 0.0520102350623704 1.15658634316184 0.247793698142450 df.mm.trans1:exp8 0.131358492642425 0.0666802484005254 1.96997605427922 0.0491927027565942 * df.mm.trans2:exp8 0.14827837459168 0.0520102350623704 2.85094605732632 0.00447342300439803 ** df.mm.trans1:probe2 0.043744138886673 0.0423745583280525 1.03232082203707 0.302240189651131 df.mm.trans1:probe3 -0.137537100001124 0.0423745583280525 -3.2457471045798 0.00122115636081152 ** df.mm.trans1:probe4 -0.0507173900174712 0.0423745583280525 -1.19688303592053 0.231713935793611 df.mm.trans1:probe5 -0.166149433710156 0.0423745583280525 -3.92097145706798 9.59115171296506e-05 *** df.mm.trans1:probe6 0.0847417188492656 0.0423745583280525 1.99982541866791 0.0458636985338473 * df.mm.trans1:probe7 -0.0837653032442297 0.0423745583280525 -1.97678292233139 0.048416190967878 * df.mm.trans1:probe8 0.124772377872313 0.0423745583280525 2.9445115842001 0.0033302789150291 ** df.mm.trans1:probe9 -0.0179069852183123 0.0423745583280525 -0.422588126575415 0.67271166389446 df.mm.trans1:probe10 0.274465431756685 0.0423745583280525 6.47712784713523 1.6494183256778e-10 *** df.mm.trans1:probe11 -0.00108179215618072 0.0423745583280525 -0.0255292845250627 0.979639286851127 df.mm.trans1:probe12 0.0439310842850692 0.0423745583280525 1.03673255883794 0.300180164141555 df.mm.trans1:probe13 -0.0526232074012875 0.0423745583280525 -1.24185854620342 0.214660131739049 df.mm.trans1:probe14 -0.088707587850814 0.0423745583280525 -2.09341622310405 0.0366327666814223 * df.mm.trans1:probe15 -0.0493492054056556 0.0423745583280525 -1.16459515692428 0.244537150911911 df.mm.trans1:probe16 -0.0769933468755149 0.0423745583280525 -1.81697107682994 0.069603221249355 . df.mm.trans1:probe17 0.171881555236559 0.0423745583280525 4.05624417146482 5.48546507216957e-05 *** df.mm.trans1:probe18 0.0639036812363081 0.0423745583280525 1.50806719309221 0.131940177647922 df.mm.trans1:probe19 0.217957304990726 0.0423745583280525 5.14358883232148 3.40823071855205e-07 *** df.mm.trans1:probe20 0.0186801367222925 0.0423745583280525 0.440833779969479 0.659454866694504 df.mm.trans1:probe21 0.291910335263312 0.0423745583280525 6.88881127688506 1.15527368702651e-11 *** df.mm.trans1:probe22 0.234547167883463 0.0423745583280525 5.53509410216531 4.24384726768121e-08 *** df.mm.trans2:probe2 0.177369885579681 0.0423745583280525 4.18576364162974 3.16396380784583e-05 *** df.mm.trans2:probe3 0.0178838781417495 0.0423745583280525 0.422042821149834 0.673109466947908 df.mm.trans2:probe4 0.107648460432969 0.0423745583280525 2.54040312584696 0.0112643088393382 * df.mm.trans2:probe5 0.0866682085310412 0.0423745583280525 2.04528877587535 0.0411597231540309 * df.mm.trans2:probe6 -0.267379766107889 0.0423745583280525 -6.30991275561873 4.66572589233913e-10 *** df.mm.trans3:probe2 -0.0875886241979887 0.0423745583280525 -2.06700972597522 0.0390609659972795 * df.mm.trans3:probe3 0.0800908614531859 0.0423745583280525 1.89006952787906 0.0591172887907516 . df.mm.trans3:probe4 -0.171466108563722 0.0423745583280525 -4.04644001800037 5.71529958903895e-05 *** df.mm.trans3:probe5 0.243034582257006 0.0423745583280525 5.73538915439537 1.38887543146013e-08 *** df.mm.trans3:probe6 0.417677048508389 0.0423745583280525 9.85678824720355 1.09705767988548e-21 *** df.mm.trans3:probe7 0.160211380800846 0.0423745583280525 3.78083895436814 0.000168172970730306 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.10836568117298 0.105562857350517 38.9186668899204 2.62145117588541e-185 *** df.mm.trans1 0.0234408057445412 0.0920074933037384 0.254770615988391 0.798967084116757 df.mm.trans2 0.134228407073005 0.0821067589392384 1.63480338046638 0.102491692628281 df.mm.exp2 0.0271954220351651 0.107421137570897 0.253166393971729 0.800206002560202 df.mm.exp3 -0.00365578383996748 0.107421137570897 -0.0340322577347004 0.972860090006673 df.mm.exp4 0.0120021845131303 0.107421137570897 0.111730193745239 0.911065936478045 df.mm.exp5 0.0108191182223857 0.107421137570897 0.100716846488850 0.919800987787354 df.mm.exp6 -0.145594140566652 0.107421137570897 -1.35535839462286 0.175693734108357 df.mm.exp7 0.127825152838914 0.107421137570897 1.18994413696792 0.234428562080117 df.mm.exp8 0.134311884142155 0.107421137570897 1.25033012291002 0.211552033488559 df.mm.trans1:exp2 0.00748062093805314 0.100308062498907 0.0745764672519189 0.94057072218027 df.mm.trans2:exp2 -0.0313232215154159 0.0782397491665307 -0.400349206753532 0.689008394125095 df.mm.trans1:exp3 0.0276918455668893 0.100308062498907 0.276067993708792 0.782568596821918 df.mm.trans2:exp3 0.0291681476213186 0.0782397491665307 0.37280471796037 0.709394555164197 df.mm.trans1:exp4 -0.0236358605564401 0.100308062498907 -0.235632709551117 0.813779226764453 df.mm.trans2:exp4 -0.0314507695352811 0.0782397491665307 -0.401979426957762 0.687808727140908 df.mm.trans1:exp5 0.0216896329714150 0.100308062498907 0.216230205539573 0.82886455703513 df.mm.trans2:exp5 -0.0658851800286163 0.0782397491665307 -0.84209344649587 0.39999244742343 df.mm.trans1:exp6 0.134377559026431 0.100308062498907 1.33964863520214 0.180747787132073 df.mm.trans2:exp6 0.0618490333087285 0.0782397491665307 0.790506538781009 0.429471053163957 df.mm.trans1:exp7 -0.083290425626767 0.100308062498907 -0.830346270796277 0.406595810921453 df.mm.trans2:exp7 -0.0904836632614505 0.0782397491665308 -1.15649224627317 0.247832139762302 df.mm.trans1:exp8 -0.135522830660853 0.100308062498907 -1.35106617837754 0.177063997521585 df.mm.trans2:exp8 -0.082118954195246 0.0782397491665307 -1.04958100032323 0.294234224259895 df.mm.trans1:probe2 0.098574325341644 0.0637446612316513 1.54639343024226 0.122413121071430 df.mm.trans1:probe3 0.0274518866958426 0.0637446612316513 0.430653895799697 0.666838412424524 df.mm.trans1:probe4 0.0997508932674067 0.0637446612316513 1.56485094343677 0.118021388371426 df.mm.trans1:probe5 0.103209850063166 0.0637446612316513 1.61911363350252 0.105824986682957 df.mm.trans1:probe6 0.0913405379167506 0.0637446612316513 1.43291275146658 0.152281395564884 df.mm.trans1:probe7 0.0700021490738903 0.0637446612316513 1.09816489289195 0.27246963463623 df.mm.trans1:probe8 0.119731615149516 0.0637446612316513 1.87830028171936 0.060711000670668 . df.mm.trans1:probe9 0.062866508215983 0.0637446612316513 0.986223897049557 0.324327514492089 df.mm.trans1:probe10 0.0535123684596243 0.0637446612316513 0.83948000390429 0.401455910358972 df.mm.trans1:probe11 -0.00269093989482404 0.0637446612316513 -0.0422143571372201 0.966338565753508 df.mm.trans1:probe12 0.0806313683654551 0.0637446612316513 1.26491170880078 0.206278829282794 df.mm.trans1:probe13 0.0529287154760665 0.0637446612316513 0.830323896203964 0.406608450077476 df.mm.trans1:probe14 0.0972637623195301 0.0637446612316513 1.5258338571456 0.127454537790529 df.mm.trans1:probe15 0.0298817796785455 0.0637446612316513 0.468773056459641 0.639362176129515 df.mm.trans1:probe16 0.103104314129101 0.0637446612316513 1.61745802922091 0.106181688970642 df.mm.trans1:probe17 0.0958667785170323 0.0637446612316513 1.50391855042805 0.13300505261559 df.mm.trans1:probe18 0.132852585890636 0.0637446612316513 2.08413666844731 0.0374709609400548 * df.mm.trans1:probe19 0.0437627104466273 0.0637446612316513 0.686531383194451 0.492581074885861 df.mm.trans1:probe20 0.0285134940776628 0.0637446612316513 0.447307955313203 0.654776276665166 df.mm.trans1:probe21 0.151132737800276 0.0637446612316513 2.37090816517248 0.0179852970377358 * df.mm.trans1:probe22 0.0500284300289845 0.0637446612316513 0.78482541223615 0.432793020320903 df.mm.trans2:probe2 -0.0166727348921528 0.0637446612316513 -0.261555000371925 0.793733237854875 df.mm.trans2:probe3 -0.0210866586836433 0.0637446612316513 -0.33079881948095 0.740884839876963 df.mm.trans2:probe4 0.0174743064225667 0.0637446612316513 0.274129724512366 0.78405714148376 df.mm.trans2:probe5 -0.0480919459758569 0.0637446612316513 -0.754446647713577 0.450807617386867 df.mm.trans2:probe6 0.0346273262224972 0.0637446612316513 0.543219236771213 0.587133271845157 df.mm.trans3:probe2 0.00108936175117196 0.0637446612316513 0.0170894586326714 0.986369596446143 df.mm.trans3:probe3 -0.0405835543751813 0.0637446612316513 -0.636658091690198 0.524533414951404 df.mm.trans3:probe4 -0.0313524704771076 0.0637446612316513 -0.491844648184279 0.622966849400858 df.mm.trans3:probe5 -0.0170833594693053 0.0637446612316513 -0.267996709673042 0.788772344597083 df.mm.trans3:probe6 0.00624475191016095 0.0637446612316513 0.0979650968332423 0.92198504843814 df.mm.trans3:probe7 0.000839011289814834 0.0637446612316513 0.013162063670961 0.989501844367806