fitVsDatCorrelation=0.95609971453308 cont.fitVsDatCorrelation=0.207687486804753 fstatistic=10549.7722675533,52,692 cont.fstatistic=934.669350028788,52,692 residuals=-0.722726601340143,-0.100363629010059,0.0033124210842964,0.105148844902656,0.703832663244665 cont.residuals=-1.25351495859746,-0.449097756862671,-0.146199384445014,0.53316140898876,1.47808843627878 predictedValues: Include Exclude Both Lung 103.051749406063 333.847528934535 87.6609706034134 cerebhem 88.6978612904381 194.461066697821 82.9671398320324 cortex 93.8690348426282 204.372331632841 102.011426990084 heart 101.121846318510 251.266601988033 99.810571158026 kidney 114.167162454127 345.399219188324 110.945170630123 liver 107.010760794156 313.058415060889 102.839729878506 stomach 95.7264335729672 245.906094060888 84.8786140746791 testicle 118.542373792136 278.428532757829 119.140055396104 diffExp=-230.795779528472,-105.763205407383,-110.503296790212,-150.144755669524,-231.232056734197,-206.047654266733,-150.179660487921,-159.886158965693 diffExpScore=0.999256810901414 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 126.860691056042 99.720862943618 136.196128110885 cerebhem 129.599791211094 129.593334877173 150.463409505646 cortex 116.086915653527 119.741826147436 115.411625197961 heart 137.022001379168 122.565080882368 117.061123881244 kidney 126.009372500358 127.215970323214 98.9534298141762 liver 141.345081789572 136.310772807839 127.258577397199 stomach 118.711154413990 147.178903652041 152.540105955088 testicle 132.610446029291 114.486514018653 117.486363804887 cont.diffExp=27.1398281124245,0.00645633392065292,-3.65491049390991,14.4569204968003,-1.20659782285556,5.03430898173275,-28.4677492380505,18.1239320106381 cont.diffExpScore=3.02448611666007 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=1,0,0,0,0,0,-1,0 cont.diffExp1.2Score=2 tran.correlation=0.750058940704984 cont.tran.correlation=-0.0343524397592947 tran.covariance=0.0168055669175536 cont.tran.covariance=-0.000230433115541341 tran.mean=186.807938299512 cont.tran.mean=126.566169980337 weightedLogRatios: wLogRatio Lung -6.13934200602655 cerebhem -3.82900252929495 cortex -3.83646897492980 heart -4.61594771017405 kidney -5.85753561256396 liver -5.59236330501063 stomach -4.74861985916155 testicle -4.44212501344602 cont.weightedLogRatios: wLogRatio Lung 1.13683084382656 cerebhem 0.000242339453752039 cortex -0.147859238318586 heart 0.54237650588433 kidney -0.0461354341121522 liver 0.178907265078230 stomach -1.04987997132697 testicle 0.707450412553059 varWeightedLogRatios=0.788741987031533 cont.varWeightedLogRatios=0.432407302479838 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.7029859387479 0.0958361973931996 59.5076400553491 2.35029896463756e-274 *** df.mm.trans1 -1.37723423856506 0.0860749202507283 -16.00041260048 2.90577430947265e-49 *** df.mm.trans2 -0.00571817878758899 0.079157579216705 -0.0722379188976292 0.942433442198214 df.mm.exp2 -0.635415779396002 0.108426280095782 -5.86034842138534 7.14647846444317e-09 *** df.mm.exp3 -0.735679691279532 0.108426280095782 -6.78506807233119 2.49409509585687e-11 *** df.mm.exp4 -0.432872282196721 0.108426280095782 -3.99231885308919 7.24231023326944e-05 *** df.mm.exp5 -0.0991104089260892 0.108426280095782 -0.914081058932728 0.360992630448985 df.mm.exp6 -0.186291460221704 0.108426280095782 -1.71813936673965 0.0862185188524987 . df.mm.exp7 -0.347216866055799 0.108426280095782 -3.20233125907366 0.00142563289265059 ** df.mm.exp8 -0.348306748939068 0.108426280095782 -3.21238309228518 0.00137738729712697 ** df.mm.trans1:exp2 0.485420273224531 0.103810259756883 4.67603370188412 3.51722315751783e-06 *** df.mm.trans2:exp2 0.0949633626453814 0.090355233413152 1.05100013644100 0.293625406816758 df.mm.trans1:exp3 0.642348972248041 0.103810259756884 6.18772146175511 1.04481515655062e-09 *** df.mm.trans2:exp3 0.244938788062305 0.090355233413152 2.71084229224792 0.00687765685703586 ** df.mm.trans1:exp4 0.413967187640035 0.103810259756883 3.98772904151784 7.38059178153261e-05 *** df.mm.trans2:exp4 0.148702428398531 0.090355233413152 1.64575335352835 0.100268631746256 df.mm.trans1:exp5 0.201542837138377 0.103810259756884 1.94145393345875 0.0526088290388974 . df.mm.trans2:exp5 0.133126925482087 0.090355233413152 1.47337260337052 0.141105305671225 df.mm.trans1:exp6 0.223989574320319 0.103810259756884 2.1576824376019 0.0312959809161005 * df.mm.trans2:exp6 0.121996874383466 0.090355233413152 1.35019156915495 0.177395970246396 df.mm.trans1:exp7 0.273480055782285 0.103810259756883 2.63442222784874 0.00861655886758147 ** df.mm.trans2:exp7 0.0414822090975948 0.090355233413152 0.459101344002026 0.646305564219829 df.mm.trans1:exp8 0.488345946863493 0.103810259756884 4.70421659677151 3.07772266233386e-06 *** df.mm.trans2:exp8 0.166783771949268 0.090355233413152 1.84586731337016 0.0653384189823491 . df.mm.trans1:probe2 -0.487401242335535 0.0519051298784418 -9.39023259313665 8.47918287954637e-20 *** df.mm.trans1:probe3 0.378381798355865 0.0519051298784418 7.28987287464667 8.49729954263307e-13 *** df.mm.trans1:probe4 0.246788283790033 0.0519051298784418 4.75460295288721 2.42013112626490e-06 *** df.mm.trans1:probe5 -0.12306316159555 0.0519051298784418 -2.37092483698154 0.0180165936157883 * df.mm.trans1:probe6 0.221275442247586 0.0519051298784418 4.2630746279953 2.29622694772414e-05 *** df.mm.trans1:probe7 0.0164329652721487 0.0519051298784417 0.316596169022861 0.7516455234011 df.mm.trans1:probe8 -0.0204965317655527 0.0519051298784418 -0.394884509749888 0.693049804612117 df.mm.trans1:probe9 0.0400806945796101 0.0519051298784418 0.772191393672964 0.440264710612215 df.mm.trans1:probe10 -0.0509131219657695 0.0519051298784418 -0.980888056440751 0.326990749194344 df.mm.trans1:probe11 0.928059557072305 0.0519051298784418 17.8799197544782 4.57725987591607e-59 *** df.mm.trans1:probe12 1.18695691514353 0.0519051298784418 22.8678151451177 1.21638252171996e-86 *** df.mm.trans1:probe13 0.960476763860043 0.0519051298784418 18.5044670172180 2.0549744089063e-62 *** df.mm.trans1:probe14 1.01076238222640 0.0519051298784418 19.4732656404778 1.11289579974405e-67 *** df.mm.trans1:probe15 1.10842870411002 0.0519051298784417 21.3548970343758 4.11093623265335e-78 *** df.mm.trans1:probe16 0.972095032584545 0.0519051298784418 18.7283036351344 1.26925326645149e-63 *** df.mm.trans1:probe17 0.435585626358425 0.0519051298784418 8.39195716066093 2.68864018654430e-16 *** df.mm.trans1:probe18 0.156906659284217 0.0519051298784418 3.02295090392185 0.00259571415919993 ** df.mm.trans1:probe19 0.239518635231831 0.0519051298784418 4.61454649651715 4.69494720294883e-06 *** df.mm.trans1:probe20 0.0622744381648758 0.0519051298784418 1.19977424795426 0.230637632505384 df.mm.trans1:probe21 0.344132063482023 0.0519051298784418 6.63002027522052 6.76118803059812e-11 *** df.mm.trans1:probe22 0.110707678236347 0.0519051298784418 2.13288510202396 0.0332854300760895 * df.mm.trans2:probe2 0.133544196005452 0.0519051298784418 2.57285159132062 0.0102936972667326 * df.mm.trans2:probe3 0.311364989565595 0.0519051298784418 5.99873250090676 3.20497097524628e-09 *** df.mm.trans2:probe4 0.282745168647735 0.0519051298784418 5.44734536470489 7.11166159265676e-08 *** df.mm.trans2:probe5 0.118584341135387 0.0519051298784418 2.28463624718989 0.0226360236004953 * df.mm.trans2:probe6 0.174510961977847 0.0519051298784418 3.36211396419851 0.00081603953734287 *** df.mm.trans3:probe2 -0.00957733896687765 0.0519051298784418 -0.184516231619247 0.853662554643053 df.mm.trans3:probe3 0.469907076660712 0.0519051298784418 9.05319142368399 1.39381231013256e-18 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.49634972016758 0.319908623436926 14.0551063358694 1.17693107485617e-39 *** df.mm.trans1 0.340537089775674 0.2873247269701 1.18519938526249 0.236345469675033 df.mm.trans2 0.0703152066244096 0.264234108725315 0.266109500259506 0.790234144857617 df.mm.exp2 0.183764088127374 0.361935291187686 0.507726360489342 0.611807043005253 df.mm.exp3 0.259803561930418 0.361935291187686 0.717817710115739 0.473111943732141 df.mm.exp4 0.434718933123224 0.361935291187686 1.20109573094323 0.23012500768505 df.mm.exp5 0.556224640925126 0.361935291187686 1.53680686705041 0.124797696608215 df.mm.exp6 0.488552092479835 0.361935291187686 1.34983270317923 0.177511025533131 df.mm.exp7 0.209546085254092 0.361935291187686 0.57896008031289 0.562804507076206 df.mm.exp8 0.33018210306399 0.361935291187686 0.912268328353672 0.361945179683638 df.mm.trans1:exp2 -0.162402478818082 0.346526659036774 -0.468657964929757 0.639461732987198 df.mm.trans2:exp2 0.078262353767079 0.301612742656405 0.259479599826573 0.795342394417205 df.mm.trans1:exp3 -0.348553942095682 0.346526659036774 -1.00585029464845 0.314839170151587 df.mm.trans2:exp3 -0.0768404978948624 0.301612742656405 -0.254765422767295 0.798979974666068 df.mm.trans1:exp4 -0.357666989762482 0.346526659036774 -1.0321485531782 0.302363120147598 df.mm.trans2:exp4 -0.228451683988951 0.301612742656405 -0.757433793999883 0.449047848477807 df.mm.trans1:exp5 -0.562957915398666 0.346526659036775 -1.62457317703491 0.104708890878960 df.mm.trans2:exp5 -0.312713357329112 0.301612742656405 -1.03680419658314 0.300189275008765 df.mm.trans1:exp6 -0.380437367083209 0.346526659036774 -1.09785887221692 0.272647971571459 df.mm.trans2:exp6 -0.175989631710028 0.301612742656405 -0.583495346251049 0.559750010208407 df.mm.trans1:exp7 -0.275942380161361 0.346526659036774 -0.796309239030516 0.426125409524057 df.mm.trans2:exp7 0.179727880904134 0.301612742656405 0.595889548038356 0.551443863080435 df.mm.trans1:exp8 -0.28585581349572 0.346526659036774 -0.824917235200032 0.409702842251038 df.mm.trans2:exp8 -0.192099980787318 0.301612742656405 -0.636909366280179 0.524394517190306 df.mm.trans1:probe2 -0.0488327809712613 0.173263329518387 -0.281841409298781 0.778149381549532 df.mm.trans1:probe3 -0.0959118371299785 0.173263329518387 -0.553561087603364 0.580058069254546 df.mm.trans1:probe4 0.0911576134294006 0.173263329518387 0.526121792088306 0.598972157377877 df.mm.trans1:probe5 -0.0613926767625669 0.173263329518387 -0.354331623045785 0.723198315466137 df.mm.trans1:probe6 0.0161300097167675 0.173263329518387 0.0930953466126004 0.92585475989609 df.mm.trans1:probe7 -0.0757889002841911 0.173263329518387 -0.437420315625114 0.661943032791109 df.mm.trans1:probe8 0.0134239877158642 0.173263329518387 0.0774773736207097 0.938266186146244 df.mm.trans1:probe9 0.0968997272847645 0.173263329518387 0.559262756603562 0.576163359190151 df.mm.trans1:probe10 0.152355553322428 0.173263329518387 0.87932947927253 0.379527897654925 df.mm.trans1:probe11 0.0106896434932533 0.173263329518387 0.0616959371782064 0.950822790031956 df.mm.trans1:probe12 0.053426691895993 0.173263329518387 0.308355449733656 0.757904675860817 df.mm.trans1:probe13 0.0667926162203348 0.173263329518387 0.385497706906565 0.699987175303917 df.mm.trans1:probe14 -0.130536903268386 0.173263329518387 -0.75340179385467 0.45146475169965 df.mm.trans1:probe15 -0.0831300730355932 0.173263329518387 -0.479790347251588 0.631528083933948 df.mm.trans1:probe16 0.0278148547557573 0.173263329518387 0.160535150935129 0.87250638520287 df.mm.trans1:probe17 -0.0871616108903535 0.173263329518387 -0.503058616803873 0.615083133420342 df.mm.trans1:probe18 -0.0897230004899462 0.173263329518387 -0.517841834964995 0.604734133919147 df.mm.trans1:probe19 0.0293974318073227 0.173263329518387 0.169669092063725 0.865319969921006 df.mm.trans1:probe20 0.048152810100895 0.173263329518387 0.277916915453162 0.781159151413402 df.mm.trans1:probe21 -0.0877291064929936 0.173263329518387 -0.506333952699919 0.612783504609659 df.mm.trans1:probe22 0.309034790553000 0.173263329518387 1.78361336707549 0.0749246594068177 . df.mm.trans2:probe2 0.107392509856089 0.173263329518387 0.619822498820745 0.535578687661517 df.mm.trans2:probe3 0.162850866068051 0.173263329518387 0.93990382454684 0.347594856360017 df.mm.trans2:probe4 -0.0174833543204175 0.173263329518387 -0.100906258519995 0.919654103056192 df.mm.trans2:probe5 0.111486587981535 0.173263329518387 0.643451723405232 0.520144203031046 df.mm.trans2:probe6 -0.0428567401539301 0.173263329518387 -0.247350320884732 0.804710486260785 df.mm.trans3:probe2 0.0144179624759394 0.173263329518387 0.0832141602958712 0.933705318485237 df.mm.trans3:probe3 0.0062638838538369 0.173263329518387 0.0361523922647011 0.971171272573644