fitVsDatCorrelation=0.81398189197313 cont.fitVsDatCorrelation=0.296909848475202 fstatistic=11113.6755597639,51,669 cont.fstatistic=4104.41815587968,51,669 residuals=-0.393999319359154,-0.0783063728595248,-0.00699894147118461,0.0753848618513268,1.00173754225854 cont.residuals=-0.473900313562177,-0.154809079314561,-0.0470981361558922,0.135200624875479,1.48164482608574 predictedValues: Include Exclude Both Lung 43.0949072100863 44.7978363550226 63.3351196931471 cerebhem 54.2967169342326 53.6656772006216 61.580443161602 cortex 43.8137933706998 48.9488750525105 61.2626166879338 heart 44.9688340844652 46.1116479309734 57.238893548169 kidney 42.2871271231194 45.4359141023795 68.341863473467 liver 48.0188473551295 49.4620239287869 63.1309920975018 stomach 45.3622232150118 49.4077185495728 57.8644042304838 testicle 47.7551823983366 51.0471073412278 69.7896709806088 diffExp=-1.7029291449363,0.631039733611026,-5.13508168181066,-1.14281384650826,-3.1487869792601,-1.44317657365738,-4.04549533456093,-3.29192494289124 diffExpScore=1.01292358035945 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 51.5910024534109 54.7079359049156 51.6541733781883 cerebhem 54.2451230846602 60.3590078976062 52.4436010145748 cortex 55.2347681891673 56.1903077383797 48.0627776321258 heart 54.2636692119014 55.589035955272 51.8296960513505 kidney 51.1081594895118 55.6527616590419 60.266062044682 liver 50.4234121695277 58.8108379318523 53.2923867954268 stomach 52.8531189802062 54.0758940925838 55.2454860981011 testicle 55.58810571861 54.7231793514359 54.4649365341327 cont.diffExp=-3.11693345150464,-6.11388481294602,-0.955539549212439,-1.32536674337059,-4.54460216953004,-8.38742576232461,-1.22277511237768,0.864926367174085 cont.diffExpScore=1.02828711007997 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=0.8846970386105 cont.tran.correlation=-0.0930535373865346 tran.covariance=0.00437803586506155 cont.tran.covariance=-0.000137348827688343 tran.mean=47.404652009511 cont.tran.mean=54.7135199892552 weightedLogRatios: wLogRatio Lung -0.146601784854843 cerebhem 0.0466274496179194 cortex -0.425064416990103 heart -0.0958291777187574 kidney -0.271508321781583 liver -0.115082483420552 stomach -0.329525475850929 testicle -0.259940237476931 cont.weightedLogRatios: wLogRatio Lung -0.233043225441203 cerebhem -0.432198451935698 cortex -0.0689525959283773 heart -0.096667103063685 kidney -0.338751486615465 liver -0.615080780928464 stomach -0.0910057277710143 testicle 0.062886345528235 varWeightedLogRatios=0.0225535633696665 cont.varWeightedLogRatios=0.0499615606139236 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 2.95634956209903 0.0670009101213897 44.124020953489 2.89816630725469e-200 *** df.mm.trans1 0.779196963046572 0.0561819710946679 13.8691638592318 1.25660330695242e-38 *** df.mm.trans2 0.824912280591572 0.0516276685704951 15.9781044434574 6.41796991816011e-49 *** df.mm.exp2 0.439768381616707 0.0670009101213897 6.56361802876933 1.05340196631615e-10 *** df.mm.exp3 0.138430575360241 0.0670009101213897 2.06609992475383 0.0392034414803546 * df.mm.exp4 0.172676952394846 0.0670009101213897 2.57723293731379 0.0101724824376657 * df.mm.exp5 -0.0808616275251665 0.0670009101213897 -1.20687356901070 0.227907398933551 df.mm.exp6 0.210462279656661 0.0670009101213897 3.14118538502467 0.00175684978176266 ** df.mm.exp7 0.239559231651212 0.0670009101213897 3.57546235143952 0.000374788045613240 *** df.mm.exp8 0.136225772319242 0.0670009101213897 2.03319286368548 0.0424272976124639 * df.mm.trans1:exp2 -0.208709446070067 0.0580244902418014 -3.59691994191292 0.000345738230970511 *** df.mm.trans2:exp2 -0.259154585333671 0.047376797892505 -5.47007389401192 6.36134257420138e-08 *** df.mm.trans1:exp3 -0.121886718363600 0.0580244902418014 -2.10060817175076 0.0360490617545279 * df.mm.trans2:exp3 -0.0498140308182579 0.0473767978925051 -1.05144359758721 0.293434540377042 df.mm.trans1:exp4 -0.130112106415811 0.0580244902418014 -2.24236535079591 0.0252645638588294 * df.mm.trans2:exp4 -0.143771210299451 0.047376797892505 -3.03463333730699 0.00250167580293833 ** df.mm.trans1:exp5 0.0619395159821171 0.0580244902418014 1.06747195406631 0.286143819463804 df.mm.trans2:exp5 0.0950046367657221 0.0473767978925051 2.00529881697116 0.0453334203126431 * df.mm.trans1:exp6 -0.102273520534041 0.0580244902418014 -1.76259231417362 0.0784258456673227 . df.mm.trans2:exp6 -0.111416940491137 0.0473767978925051 -2.3517195219469 0.0189758763959152 * df.mm.trans1:exp7 -0.188284388705199 0.0580244902418014 -3.24491241406129 0.00123338063265386 ** df.mm.trans2:exp7 -0.141612416327543 0.0473767978925051 -2.98906685607694 0.00290091702238363 ** df.mm.trans1:exp8 -0.0335430073283559 0.0580244902418014 -0.578083619323055 0.563402194784502 df.mm.trans2:exp8 -0.00563673516909108 0.047376797892505 -0.118976702095411 0.905329529829411 df.mm.trans1:probe2 0.0924974107236602 0.0410295105248704 2.25441175242858 0.0244929432169294 * df.mm.trans1:probe3 0.0477023857065802 0.0410295105248704 1.16263599288285 0.245391780464485 df.mm.trans1:probe4 0.0583816257384101 0.0410295105248704 1.42291791911633 0.155226080951528 df.mm.trans1:probe5 0.0865014359413738 0.0410295105248704 2.10827365071636 0.0353785857860039 * df.mm.trans1:probe6 0.0344009702529128 0.0410295105248704 0.838444568624828 0.402080532274028 df.mm.trans1:probe7 -0.00849925280172932 0.0410295105248704 -0.207149748875932 0.835955945907585 df.mm.trans1:probe8 0.135883744085263 0.0410295105248704 3.31185389118635 0.000976651210739622 *** df.mm.trans1:probe9 0.0677396339536091 0.0410295105248704 1.65099785708016 0.0992083259492077 . df.mm.trans1:probe10 0.0573631776921638 0.0410295105248704 1.39809558920749 0.162547695637997 df.mm.trans1:probe11 0.0297770333366591 0.0410295105248704 0.72574673584296 0.468247847375282 df.mm.trans1:probe12 0.0668511026532531 0.0410295105248704 1.62934194919851 0.103711390505033 df.mm.trans2:probe2 0.0294973248527180 0.0410295105248704 0.71892948454352 0.472435398416882 df.mm.trans2:probe3 0.0991802654104952 0.0410295105248704 2.41729097280788 0.0159028787003477 * df.mm.trans2:probe4 -0.00400717959949259 0.0410295105248704 -0.0976657909936217 0.92222693320265 df.mm.trans2:probe5 0.106285764650389 0.0410295105248704 2.59047118258851 0.00979323955149181 ** df.mm.trans2:probe6 0.145207823196266 0.0410295105248704 3.53910688523196 0.000429285018060795 *** df.mm.trans3:probe2 -0.594771676876456 0.0410295105248704 -14.496192356863 1.30464421033887e-41 *** df.mm.trans3:probe3 -0.703472856869623 0.0410295105248704 -17.1455337358511 6.79093782998349e-55 *** df.mm.trans3:probe4 -0.68795310953967 0.0410295105248704 -16.7672755716318 6.12095533399523e-53 *** df.mm.trans3:probe5 -0.262866041754754 0.0410295105248704 -6.40675548872111 2.80346794633422e-10 *** df.mm.trans3:probe6 -0.39405603830834 0.0410295105248704 -9.60421007385596 1.50899780181964e-20 *** df.mm.trans3:probe7 -0.392454472198535 0.0410295105248704 -9.56517558162544 2.10413307647350e-20 *** df.mm.trans3:probe8 -0.55118026695166 0.0410295105248704 -13.4337519483094 1.34531814817965e-36 *** df.mm.trans3:probe9 -0.365127915840295 0.0410295105248704 -8.89915358895079 5.23016711073053e-18 *** df.mm.trans3:probe10 -0.336651979574612 0.0410295105248704 -8.20511810323809 1.18244842317878e-15 *** df.mm.trans3:probe11 -0.287997758505246 0.0410295105248704 -7.01928331147586 5.48599200496527e-12 *** df.mm.trans3:probe12 -0.367693687116461 0.0410295105248704 -8.96168836558702 3.1562172295027e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.06354240152014 0.110140493570544 36.8941727950173 2.04309577911132e-163 *** df.mm.trans1 -0.140918607521110 0.0923556115121678 -1.52582615407776 0.127525762458642 df.mm.trans2 -0.0352212350810962 0.0848689501075217 -0.415007314647747 0.6782696029879 df.mm.exp2 0.133299890664445 0.110140493570544 1.21027141193141 0.226602196857071 df.mm.exp3 0.167043799399423 0.110140493570544 1.51664291655306 0.129829222105306 df.mm.exp4 0.063092574407045 0.110140493570544 0.572837222366676 0.566947385356087 df.mm.exp5 -0.146478335675004 0.110140493570544 -1.32992263722866 0.183996994190590 df.mm.exp6 0.0182031945386212 0.110140493570544 0.165272498320178 0.86877943225588 df.mm.exp7 -0.0546664774171691 0.110140493570544 -0.496334051582544 0.619821777652897 df.mm.exp8 0.0219144257512873 0.110140493570544 0.198967927606492 0.842348286085131 df.mm.trans1:exp2 -0.0831340855670026 0.0953844654174475 -0.871568396417263 0.383756542551223 df.mm.trans2:exp2 -0.0349984727546390 0.0778810898869648 -0.449383448606525 0.653300617498751 df.mm.trans1:exp3 -0.0987984721197673 0.0953844654174475 -1.03579206202371 0.300673411696628 df.mm.trans2:exp3 -0.140308296977316 0.0778810898869648 -1.80157079441181 0.0720630763315656 . df.mm.trans1:exp4 -0.0125849329103709 0.0953844654174475 -0.131939020209352 0.89507219453101 df.mm.trans2:exp4 -0.0471153670784668 0.0778810898869648 -0.604965430592319 0.545407104435446 df.mm.trans1:exp5 0.137075210810945 0.0953844654174475 1.43708108244921 0.151162468519474 df.mm.trans2:exp5 0.163601258231263 0.0778810898869648 2.10065445243140 0.0360449813709479 * df.mm.trans1:exp6 -0.0410948863726645 0.0953844654174475 -0.430834163538201 0.666727730672179 df.mm.trans2:exp6 0.054114182516902 0.0778810898869648 0.694830832432396 0.487402565775286 df.mm.trans1:exp7 0.0788359174755942 0.0953844654174475 0.826506885901923 0.408811284494404 df.mm.trans2:exp7 0.0430462039919641 0.0778810898869648 0.552717020966715 0.580641820683886 df.mm.trans1:exp8 0.0527075404703631 0.0953844654174475 0.552579922104611 0.580735664672601 df.mm.trans2:exp8 -0.0216358313755796 0.0778810898869648 -0.277805965568555 0.7812471411937 df.mm.trans1:probe2 -0.036099811614312 0.0674470023165308 -0.535232261989858 0.592667123237956 df.mm.trans1:probe3 0.0354592854252496 0.0674470023165308 0.525735528746527 0.599246189641726 df.mm.trans1:probe4 0.0236871054097377 0.0674470023165308 0.351195821847995 0.725552015540624 df.mm.trans1:probe5 -0.00801963528131569 0.0674470023165308 -0.118902768186484 0.905388082265684 df.mm.trans1:probe6 0.0296682141805368 0.0674470023165308 0.43987446678954 0.660170095723878 df.mm.trans1:probe7 0.142334615081644 0.0674470023165308 2.1103178820856 0.0352015948217805 * df.mm.trans1:probe8 0.0552101119022636 0.0674470023165308 0.818570284905486 0.413323101186586 df.mm.trans1:probe9 -0.0189609640162117 0.0674470023165308 -0.281123895280435 0.77870229776346 df.mm.trans1:probe10 0.141668839794736 0.0674470023165308 2.10044679420859 0.0360632928585195 * df.mm.trans1:probe11 0.0691801572784967 0.0674470023165308 1.02569654547184 0.305405363172499 df.mm.trans1:probe12 0.0632358952671128 0.0674470023165308 0.937564207380853 0.348806753677989 df.mm.trans2:probe2 -0.112002679575055 0.0674470023165308 -1.66060278037893 0.0972618188928531 . df.mm.trans2:probe3 -0.0149507760302098 0.0674470023165308 -0.221667020278312 0.824640749979932 df.mm.trans2:probe4 -0.134120233975561 0.0674470023165308 -1.98852772353219 0.0471603604050218 * df.mm.trans2:probe5 -0.0854789830195141 0.0674470023165308 -1.26735036522985 0.205471004646856 df.mm.trans2:probe6 -0.127070293501272 0.0674470023165308 -1.88400209256042 0.0599980915630044 . df.mm.trans3:probe2 0.152349509153244 0.0674470023165308 2.25880326657460 0.0242167836645889 * df.mm.trans3:probe3 0.035251149590633 0.0674470023165308 0.522649611990141 0.601391142138232 df.mm.trans3:probe4 0.116720962486528 0.0674470023165308 1.73055819350952 0.0839917172679976 . df.mm.trans3:probe5 0.0533527564156293 0.0674470023165308 0.7910322858419 0.429205491860792 df.mm.trans3:probe6 0.0989043597588755 0.0674470023165308 1.46640112031539 0.143008918492420 df.mm.trans3:probe7 0.0485051442637994 0.0674470023165308 0.719159378442992 0.472293848280078 df.mm.trans3:probe8 0.0195259715894836 0.0674470023165308 0.289500955103202 0.772287744773702 df.mm.trans3:probe9 0.0273608185186448 0.0674470023165308 0.40566396695052 0.685119226173354 df.mm.trans3:probe10 0.0969860105136232 0.0674470023165308 1.43795879998439 0.1509133376214 df.mm.trans3:probe11 0.0374329305731853 0.0674470023165308 0.554997691335656 0.579081749920533 df.mm.trans3:probe12 -0.000368398742781346 0.0674470023165308 -0.00546204768378644 0.995643566451942