fitVsDatCorrelation=0.90293102696791 cont.fitVsDatCorrelation=0.191377441967839 fstatistic=7987.21219560616,59,853 cont.fstatistic=1519.76676693791,59,853 residuals=-0.832195858816507,-0.126085507230608,-0.00435521509943949,0.108130490615991,0.739529528920961 cont.residuals=-1.1691541321828,-0.335789782457454,-0.0184421691797425,0.296952695951943,1.58543430094885 predictedValues: Include Exclude Both Lung 150.625073459016 217.019554531781 100.461813997952 cerebhem 103.245133736381 138.311642884243 73.548108819544 cortex 116.120609294549 154.367859807044 84.1493907991726 heart 128.307664123579 159.317374817746 93.4850108838756 kidney 145.570400763405 212.446405663761 101.366066319726 liver 132.044158747647 207.372307769617 97.5450436314198 stomach 136.845247243255 162.803327821932 110.680395808126 testicle 138.908734278862 170.611437275955 181.498839962465 diffExp=-66.3944810727653,-35.0665091478612,-38.2472505124946,-31.0097106941667,-66.8760049003569,-75.3281490219695,-25.9580805786771,-31.7027029970933 diffExpScore=0.997308810416723 diffExp1.5=0,0,0,0,0,-1,0,0 diffExp1.5Score=0.5 diffExp1.4=-1,0,0,0,-1,-1,0,0 diffExp1.4Score=0.75 diffExp1.3=-1,-1,-1,0,-1,-1,0,0 diffExp1.3Score=0.833333333333333 diffExp1.2=-1,-1,-1,-1,-1,-1,0,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 122.080344466410 129.638946742289 120.335871800508 cerebhem 115.187657921589 129.987368802022 121.014552063426 cortex 131.088981215956 116.781217826393 124.142591211470 heart 125.383150494734 137.928054220101 111.490780011925 kidney 115.846190923578 121.639263782186 121.240171198114 liver 120.707957978341 118.286461609099 128.069411780576 stomach 137.222948890879 120.202134225648 107.449435811050 testicle 122.266258899062 135.905432057731 120.200743780751 cont.diffExp=-7.55860227587846,-14.7997108804330,14.3077633895636,-12.5449037253676,-5.79307285860818,2.42149636924231,17.0208146652309,-13.6391731586688 cont.diffExpScore=4.08079462759462 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=0.810622883160114 cont.tran.correlation=-0.2897462995895 tran.covariance=0.0171954567708621 cont.tran.covariance=-0.00109838005425152 tran.mean=154.619808263673 cont.tran.mean=125.009523128501 weightedLogRatios: wLogRatio Lung -1.89805416065357 cerebhem -1.39865507445791 cortex -1.39421572692183 heart -1.07425449188465 kidney -1.95429176780549 liver -2.30601866561725 stomach -0.86945043625491 testicle -1.03538223872344 cont.weightedLogRatios: wLogRatio Lung -0.290440093589779 cerebhem -0.581044562024364 cortex 0.556846273067131 heart -0.465256513169824 kidney -0.233084077858444 liver 0.0969311231581892 stomach 0.64301004512453 testicle -0.513887453137658 varWeightedLogRatios=0.261438586677802 cont.varWeightedLogRatios=0.230338466887854 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.6491825847721 0.0973848177363153 58.008863353507 2.66586284792213e-298 *** df.mm.trans1 -0.615205231175799 0.0808670815963353 -7.60761015522635 7.38027837956462e-14 *** df.mm.trans2 -0.279118483073309 0.073856185984324 -3.77921604471361 0.000168265729448088 *** df.mm.exp2 -0.516327772566194 0.0944771505549064 -5.46510737817102 6.07528239344492e-08 *** df.mm.exp3 -0.423629400236223 0.0944771505549064 -4.48393498055413 8.32926468456911e-06 *** df.mm.exp4 -0.397475384877789 0.0944771505549064 -4.20710597793477 2.86047257910434e-05 *** df.mm.exp5 -0.0643923713132894 0.0944771505549064 -0.681565552465166 0.495698702835792 df.mm.exp6 -0.147665648067511 0.0944771505549064 -1.56297736754554 0.118428818848731 df.mm.exp7 -0.480256690724555 0.0944771505549064 -5.08331049257723 4.56000861801489e-07 *** df.mm.exp8 -0.913047017838388 0.0944771505549064 -9.66420994362823 4.88845835673043e-21 *** df.mm.trans1:exp2 0.138640080474625 0.0802901390446852 1.72673857741691 0.0845766690484723 . df.mm.trans2:exp2 0.0658497308772569 0.0629847670366043 1.04548661486654 0.296094296748163 df.mm.trans1:exp3 0.163464994550545 0.0802901390446852 2.03592865195524 0.0420660441924409 * df.mm.trans2:exp3 0.0829803917565255 0.0629847670366043 1.317467630043 0.188035680043926 df.mm.trans1:exp4 0.237112598735053 0.0802901390446852 2.95319701218962 0.00323134839928783 ** df.mm.trans2:exp4 0.0883862031071569 0.0629847670366042 1.40329491186004 0.160892820159551 df.mm.trans1:exp5 0.0302584030097184 0.0802901390446852 0.376863253317798 0.706368961019738 df.mm.trans2:exp5 0.0430946566964842 0.0629847670366042 0.684207606442989 0.494029918458174 df.mm.trans1:exp6 0.0160082587367135 0.0802901390446852 0.199380134686330 0.842012915324808 df.mm.trans2:exp6 0.102193951538646 0.0629847670366042 1.62251852863495 0.105061875062214 df.mm.trans1:exp7 0.384313603988369 0.0802901390446851 4.78656044890496 1.99936657547520e-06 *** df.mm.trans2:exp7 0.192812122720529 0.0629847670366043 3.06125007350545 0.00227323586491248 ** df.mm.trans1:exp8 0.832070355459459 0.0802901390446852 10.3632944887089 8.75892140051362e-24 *** df.mm.trans2:exp8 0.672448229595457 0.0629847670366043 10.6763628927079 4.62593697215056e-25 *** df.mm.trans1:probe2 0.0732564590245073 0.0609847384493805 1.20122609175921 0.229996937999829 df.mm.trans1:probe3 0.0717576572854559 0.0609847384493805 1.17664942262591 0.239663707773221 df.mm.trans1:probe4 0.138284663757773 0.0609847384493805 2.26752901256687 0.0236073512919114 * df.mm.trans1:probe5 0.0465036446004019 0.0609847384493805 0.762545610308742 0.445945241741035 df.mm.trans1:probe6 0.07788454725413 0.0609847384493805 1.27711537729685 0.201909035647996 df.mm.trans1:probe7 -0.189151154718121 0.0609847384493805 -3.10161459288906 0.00198801318989120 ** df.mm.trans1:probe8 -0.224871103324623 0.0609847384493805 -3.68733406164026 0.000240950786574183 *** df.mm.trans1:probe9 -0.227249239351784 0.0609847384493805 -3.72632965443327 0.000207086501714007 *** df.mm.trans1:probe10 -0.240629302235501 0.0609847384493805 -3.94572983919955 8.61006004947157e-05 *** df.mm.trans1:probe11 -0.139041070686315 0.0609847384493805 -2.27993222930233 0.0228577748302059 * df.mm.trans1:probe12 -0.000619074623119018 0.0609847384493805 -0.0101513040616362 0.991902943944933 df.mm.trans2:probe2 -0.00129067134481569 0.0609847384493805 -0.0211638416041908 0.983119907511064 df.mm.trans2:probe3 -0.0852530405630773 0.0609847384493805 -1.39794057875382 0.162494311736282 df.mm.trans2:probe4 0.135475861527657 0.0609847384493805 2.22147155128175 0.0265808326801346 * df.mm.trans2:probe5 0.0643342225734373 0.0609847384493805 1.05492331703344 0.291759098137687 df.mm.trans2:probe6 0.144741009423646 0.0609847384493805 2.37339723189576 0.0178459553737074 * df.mm.trans3:probe2 -0.511050782447219 0.0609847384493805 -8.37997826081372 2.16803438168055e-16 *** df.mm.trans3:probe3 -0.762287952522559 0.0609847384493805 -12.4996510914823 4.83726502274947e-33 *** df.mm.trans3:probe4 -0.114920278573659 0.0609847384493805 -1.88441045244535 0.0598492904397223 . df.mm.trans3:probe5 -0.212941207079869 0.0609847384493805 -3.49171304975945 0.000504579510682261 *** df.mm.trans3:probe6 0.40302577091704 0.0609847384493805 6.60863326078811 6.82717547713508e-11 *** df.mm.trans3:probe7 -0.486283675658194 0.0609847384493805 -7.97385850989303 4.92942565989039e-15 *** df.mm.trans3:probe8 0.0473305432906277 0.0609847384493805 0.776104718886574 0.437902289609001 df.mm.trans3:probe9 -0.116357830424955 0.0609847384493805 -1.90798277378095 0.0567289471252884 . df.mm.trans3:probe10 0.0478095112701456 0.0609847384493805 0.783958617938965 0.433282025414426 df.mm.trans3:probe11 -6.29903177852184e-05 0.0609847384493805 -0.00103288657763946 0.99917611740065 df.mm.trans3:probe12 -0.421458073801612 0.0609847384493805 -6.91087777889606 9.42766342958597e-12 *** df.mm.trans3:probe13 -0.101480690104419 0.0609847384493805 -1.66403419420503 0.0964728777089784 . df.mm.trans3:probe14 1.09078099325817 0.0609847384493805 17.8861305466376 5.30766989429653e-61 *** df.mm.trans3:probe15 -0.196536148960789 0.0609847384493805 -3.22271036915114 0.00131803234197558 ** df.mm.trans3:probe16 -0.549580910188339 0.0609847384493805 -9.01177776870373 1.30714336586506e-18 *** df.mm.trans3:probe17 0.401006388006801 0.0609847384493805 6.57552033841467 8.4417059471198e-11 *** df.mm.trans3:probe18 -0.653633099044835 0.0609847384493805 -10.7179782297070 3.11367213274774e-25 *** df.mm.trans3:probe19 -0.155920975715882 0.0609847384493805 -2.55672123354767 0.0107384775172932 * df.mm.trans3:probe20 -0.609062100830995 0.0609847384493805 -9.98712327571164 2.7467178231683e-22 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.89135337771372 0.222401200584383 21.9933766763001 2.82658693857681e-85 *** df.mm.trans1 -0.0562875064040738 0.184679054218465 -0.304785546158846 0.760603837578841 df.mm.trans2 -0.0238958569278840 0.16866802049137 -0.141673903910591 0.88737104114124 df.mm.exp2 -0.0610567924530287 0.215760856770257 -0.282983639233701 0.777258036655389 df.mm.exp3 -0.0643981225429349 0.215760856770257 -0.298469905556160 0.765417232796187 df.mm.exp4 0.165018680873817 0.215760856770257 0.764822143107863 0.444588995961864 df.mm.exp5 -0.123596161132719 0.215760856770257 -0.572838664912812 0.566904899153242 df.mm.exp6 -0.165234893599160 0.215760856770257 -0.765824237410694 0.443992745727108 df.mm.exp7 0.154615494275055 0.215760856770257 0.716605859790825 0.473813479496458 df.mm.exp8 0.0498513198497738 0.215760856770257 0.231048952048126 0.81733217929042 df.mm.trans1:exp2 0.00294000970094315 0.183361469823506 0.0160339557911105 0.987211051787233 df.mm.trans2:exp2 0.0637408213672165 0.143840571180171 0.443135207572112 0.657780301693878 df.mm.trans1:exp3 0.135595071907241 0.183361469823506 0.739495991375707 0.459809327664758 df.mm.trans2:exp3 -0.040052880033925 0.143840571180171 -0.278453288285095 0.780731903669886 df.mm.trans1:exp4 -0.138323816950704 0.183361469823506 -0.754377771316113 0.450830619178955 df.mm.trans2:exp4 -0.103039731655347 0.143840571180171 -0.716346791520186 0.473973310081449 df.mm.trans1:exp5 0.07118014291134 0.183361469823506 0.388195747884515 0.697968040224647 df.mm.trans2:exp5 0.0599027177322985 0.143840571180171 0.416452168124845 0.677183927308479 df.mm.trans1:exp6 0.153929562235684 0.183361469823506 0.839486956468268 0.401431317563452 df.mm.trans2:exp6 0.0735909631010813 0.143840571180171 0.511614786407536 0.609053047497461 df.mm.trans1:exp7 -0.0376879161792328 0.183361469823506 -0.205538907467905 0.837200143802347 df.mm.trans2:exp7 -0.230193970469933 0.143840571180171 -1.60034104829574 0.109893220929339 df.mm.trans1:exp8 -0.0483295923858556 0.183361469823507 -0.263575507070133 0.792170691780704 df.mm.trans2:exp8 -0.00264528225948182 0.143840571180171 -0.0183903764965477 0.98533173021143 df.mm.trans1:probe2 -0.169269014443023 0.139273034172442 -1.21537536285338 0.224559238107494 df.mm.trans1:probe3 -0.100288355561724 0.139273034172442 -0.720084517133097 0.47167022076678 df.mm.trans1:probe4 -0.0821199044428448 0.139273034172442 -0.589632479329542 0.55559321173029 df.mm.trans1:probe5 -0.0464811696258498 0.139273034172442 -0.333741344130544 0.738656773928042 df.mm.trans1:probe6 -0.00093156882155111 0.139273034172442 -0.00668879533706204 0.99466471722372 df.mm.trans1:probe7 -0.0940390850698142 0.139273034172442 -0.675213874879607 0.499722876884111 df.mm.trans1:probe8 -0.0879303401877152 0.139273034172442 -0.631352226295607 0.527979387867981 df.mm.trans1:probe9 -0.138924481125374 0.139273034172442 -0.997497340033273 0.318806040916752 df.mm.trans1:probe10 -0.119563594137045 0.139273034172442 -0.858483444749304 0.390866731687126 df.mm.trans1:probe11 0.0152006010223800 0.139273034172442 0.109142456130878 0.91311517033534 df.mm.trans1:probe12 -0.148020515849849 0.139273034172442 -1.06280815040316 0.28816975225022 df.mm.trans2:probe2 0.0290095449903002 0.139273034172442 0.208292618615473 0.835050218396205 df.mm.trans2:probe3 0.0152251939451045 0.139273034172442 0.109319036779606 0.912975159384521 df.mm.trans2:probe4 -0.14942540199474 0.139273034172442 -1.07289543078187 0.283621466056039 df.mm.trans2:probe5 0.0237358246154680 0.139273034172442 0.17042656359507 0.864715096878479 df.mm.trans2:probe6 0.0111438957560618 0.139273034172442 0.0800147409890123 0.936244299966622 df.mm.trans3:probe2 0.0480307362531499 0.139273034172442 0.344867450749155 0.730279035843781 df.mm.trans3:probe3 0.132221093287537 0.139273034172442 0.949366071280008 0.342703352221048 df.mm.trans3:probe4 -0.0822885669029808 0.139273034172442 -0.590843499547047 0.554781800562126 df.mm.trans3:probe5 -0.0550698403204133 0.139273034172442 -0.3954092093106 0.692639778833816 df.mm.trans3:probe6 -0.118735715254277 0.139273034172442 -0.852539157775965 0.394154220655339 df.mm.trans3:probe7 -0.101384641708467 0.139273034172442 -0.727956006063149 0.466840289528792 df.mm.trans3:probe8 -0.0849227299979154 0.139273034172442 -0.609757161553384 0.542185071562979 df.mm.trans3:probe9 0.00583660503368653 0.139273034172442 0.0419076461453400 0.966582128125636 df.mm.trans3:probe10 -0.0112619170688932 0.139273034172442 -0.0808621506367789 0.935570548832538 df.mm.trans3:probe11 -0.048299460586406 0.139273034172442 -0.346796929307965 0.728829418882777 df.mm.trans3:probe12 0.0430914493804387 0.139273034172442 0.309402675374221 0.757090797356765 df.mm.trans3:probe13 0.115845217486769 0.139273034172442 0.831784976719431 0.405763113319579 df.mm.trans3:probe14 0.0145764728593593 0.139273034172442 0.104661128020744 0.916669311804987 df.mm.trans3:probe15 -0.00158769997522858 0.139273034172442 -0.0113999094272819 0.990907050892105 df.mm.trans3:probe16 0.0125497859765477 0.139273034172442 0.0901092307719032 0.9282215676104 df.mm.trans3:probe17 -0.0645035518730375 0.139273034172442 -0.463144586863614 0.643378870932934 df.mm.trans3:probe18 -0.157840291882544 0.139273034172442 -1.13331552529482 0.257400204507758 df.mm.trans3:probe19 0.0609449654749023 0.139273034172442 0.437593435348317 0.661791804249169 df.mm.trans3:probe20 -0.124866903070497 0.139273034172442 -0.896561949787729 0.370205662574302