fitVsDatCorrelation=0.96948932127968 cont.fitVsDatCorrelation=0.28259248772297 fstatistic=5198.87648784631,43,485 cont.fstatistic=328.973627995833,43,485 residuals=-0.739105759403064,-0.121299176968130,-0.00715883689479772,0.124878441148662,1.22589486321979 cont.residuals=-1.50267675793331,-0.550514904945101,-0.23292809636404,0.146046487198598,3.32527974064202 predictedValues: Include Exclude Both Lung 103.605701328804 62.7930946673165 75.4755346503041 cerebhem 99.1783306918796 71.0856165339594 73.1242409873433 cortex 82.2796552884591 57.5745808719022 56.7294194235519 heart 96.3834170283326 59.6894555301523 66.3956245207707 kidney 97.4542635462378 64.9849516584066 65.4546360129128 liver 974.569251029175 80.0924570409069 1228.47826847200 stomach 112.946197864694 66.533106804358 82.72216073062 testicle 296.173300271546 63.926723738462 310.757697696302 diffExp=40.8126066614872,28.0927141579202,24.7050744165569,36.6939614981804,32.4693118878313,894.476793988268,46.4130910603362,232.246576533084 diffExpScore=0.999252006565432 diffExp1.5=1,0,0,1,0,1,1,1 diffExp1.5Score=0.833333333333333 diffExp1.4=1,0,1,1,1,1,1,1 diffExp1.4Score=0.875 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 96.1622310283442 143.410100464936 128.394578118767 cerebhem 83.4935268129017 127.422635231619 150.094335886416 cortex 98.7105520178164 176.734823312214 112.946234288455 heart 93.4828465298492 113.954120445390 113.674371521720 kidney 97.467706609997 212.868004887206 102.085408480105 liver 86.586856252611 123.931306333845 102.352210539523 stomach 77.24156728724 153.52061729887 97.5494540146208 testicle 120.899946923269 118.888132772721 106.957911421927 cont.diffExp=-47.2478694365922,-43.9291084187171,-78.0242712943974,-20.4712739155404,-115.400298277209,-37.3444500812341,-76.27905001163,2.01181415054805 cont.diffExpScore=1.00723902430749 cont.diffExp1.5=0,-1,-1,0,-1,0,-1,0 cont.diffExp1.5Score=0.8 cont.diffExp1.4=-1,-1,-1,0,-1,-1,-1,0 cont.diffExp1.4Score=0.857142857142857 cont.diffExp1.3=-1,-1,-1,0,-1,-1,-1,0 cont.diffExp1.3Score=0.857142857142857 cont.diffExp1.2=-1,-1,-1,-1,-1,-1,-1,0 cont.diffExp1.2Score=0.875 tran.correlation=0.803846938813255 cont.tran.correlation=-0.0351005260868741 tran.covariance=0.0655867028235101 cont.tran.covariance=-0.00126118374987710 tran.mean=149.329381493412 cont.tran.mean=120.298435888052 weightedLogRatios: wLogRatio Lung 2.19838996091057 cerebhem 1.47547695363616 cortex 1.51086282861119 heart 2.07424265532576 kidney 1.77358710708014 liver 14.0747907570202 stomach 2.36150528200492 testicle 7.55003561247046 cont.weightedLogRatios: wLogRatio Lung -1.90478432088366 cerebhem -1.95988286236062 cortex -2.84439056945746 heart -0.918167068369416 kidney -3.88239684406248 liver -1.66396538731456 stomach -3.22181286797651 testicle 0.0803202692660722 varWeightedLogRatios=20.1627620160843 cont.varWeightedLogRatios=1.62264574060580 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.70195585139886 0.117186253268958 40.12378346637 3.3506262108611e-156 *** df.mm.trans1 -0.114351164550135 0.093813724060649 -1.21891722874372 0.223468128684661 df.mm.trans2 -0.604405870947601 0.093813724060649 -6.44261675996215 2.83418063490703e-10 *** df.mm.exp2 0.112015730259174 0.125622967748274 0.891681929403494 0.373005599856069 df.mm.exp3 -0.0317168069535576 0.125622967748274 -0.252476179492212 0.800779987554256 df.mm.exp4 0.0052294752950845 0.125622967748274 0.0416283374674242 0.966812114098595 df.mm.exp5 0.115552672546123 0.125622967748274 0.919837149347323 0.358115129181436 df.mm.exp6 -0.304983153367901 0.125622967748274 -2.42776586825295 0.0155550275688594 * df.mm.exp7 0.0524948082902019 0.125622967748274 0.417875880749706 0.676222809724333 df.mm.exp8 -0.346960169683118 0.125622967748274 -2.76191667735763 0.00596439658416684 ** df.mm.trans1:exp2 -0.155688540878504 0.0985467636775211 -1.57984427969619 0.114794460469258 df.mm.trans2:exp2 0.012024177082793 0.0985467636775211 0.122014936199632 0.902937727866147 df.mm.trans1:exp3 -0.198751678206633 0.0985467636775211 -2.0168260305028 0.0442642268659486 * df.mm.trans2:exp3 -0.0550471369568271 0.0985467636775212 -0.558588987629875 0.57670004991937 df.mm.trans1:exp4 -0.0774876714463064 0.0985467636775211 -0.786303563452096 0.432073671426842 df.mm.trans2:exp4 -0.0559192046835636 0.0985467636775211 -0.567438265822209 0.57067901131014 df.mm.trans1:exp5 -0.176761856876253 0.0985467636775211 -1.79368505144094 0.0734862732425 . df.mm.trans2:exp5 -0.0812420523029102 0.0985467636775211 -0.82440101806653 0.410116981687979 df.mm.trans1:exp6 2.54638637248740 0.0985467636775211 25.8393708475303 3.20184742255674e-93 *** df.mm.trans2:exp6 0.548319723829368 0.0985467636775211 5.5640561228744 4.36524533310944e-08 *** df.mm.trans1:exp7 0.0338244115049136 0.0985467636775211 0.343232088428583 0.731572592835698 df.mm.trans2:exp7 0.00535975226756391 0.0985467636775211 0.0543879075025016 0.956648508917755 df.mm.trans1:exp8 1.39731256613208 0.0985467636775211 14.1791826944675 1.99279923524522e-38 *** df.mm.trans2:exp8 0.364852545608339 0.0985467636775211 3.70232904656577 0.000238154533252502 *** df.mm.trans1:probe2 0.133131748659921 0.0674703567941363 1.97318874518670 0.0490413896476495 * df.mm.trans1:probe3 0.00464837260182252 0.0674703567941364 0.068895035133807 0.94510156536391 df.mm.trans1:probe4 0.363267970450814 0.0674703567941364 5.38411218958285 1.13638889171154e-07 *** df.mm.trans1:probe5 0.341276286652191 0.0674703567941363 5.05816632470885 6.01492797535654e-07 *** df.mm.trans1:probe6 0.00547839913533027 0.0674703567941363 0.0811971270886533 0.93531867988274 df.mm.trans2:probe2 0.249468641818731 0.0674703567941363 3.69745550004875 0.000242643303753555 *** df.mm.trans2:probe3 0.177724380006753 0.0674703567941363 2.63411057020227 0.00870542393315606 ** df.mm.trans2:probe4 0.12165233762326 0.0674703567941363 1.80304867802081 0.0720011216102308 . df.mm.trans2:probe5 0.0659423498860242 0.0674703567941363 0.977352914958278 0.32888170318904 df.mm.trans2:probe6 0.0619343618702591 0.0674703567941363 0.917949227084 0.359101705159245 df.mm.trans3:probe2 0.72442296771185 0.0674703567941363 10.7369073194942 2.84983358256437e-24 *** df.mm.trans3:probe3 0.402078617831372 0.0674703567941363 5.95933735845185 4.87537159573672e-09 *** df.mm.trans3:probe4 0.280716760545 0.0674703567941363 4.16059398354029 3.75514010203955e-05 *** df.mm.trans3:probe5 0.119488639431664 0.0674703567941363 1.77097980667635 0.0771920619682929 . df.mm.trans3:probe6 0.051381843710975 0.0674703567941363 0.761546939313657 0.446700590830989 df.mm.trans3:probe7 0.532364044738098 0.0674703567941363 7.89033984750417 2.01855894554546e-14 *** df.mm.trans3:probe8 0.745810747439951 0.0674703567941363 11.0539025266392 1.73735835536838e-25 *** df.mm.trans3:probe9 0.526008184151594 0.0674703567941363 7.79613758018999 3.92892073118141e-14 *** df.mm.trans3:probe10 0.0238256291725046 0.0674703567941363 0.353127362957345 0.724146387780145 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.57785110218892 0.458565339259456 9.9829854336173 1.80759621991595e-21 *** df.mm.trans1 -0.0859228982663035 0.367105535001009 -0.234055033428105 0.815041020241633 df.mm.trans2 0.493389536691677 0.367105535001009 1.34399917639575 0.179577011803842 df.mm.exp2 -0.415622367876971 0.491579321100513 -0.845483831473027 0.398257668519938 df.mm.exp3 0.363293514683564 0.491579321100513 0.739033354515907 0.46024424975 df.mm.exp4 -0.136400965058231 0.491579321100513 -0.277474985629718 0.781533643183975 df.mm.exp5 0.637746717047829 0.491579321100513 1.29734244235515 0.195130143602628 df.mm.exp6 -0.0241813192595257 0.491579321100513 -0.0491910831509149 0.960787267813925 df.mm.exp7 0.123776281936864 0.491579321100513 0.251793101588901 0.80130764784607 df.mm.exp8 0.224074033831936 0.491579321100513 0.455824775806872 0.648720005657205 df.mm.trans1:exp2 0.274354801802265 0.38562654627234 0.711452062764653 0.477146182749485 df.mm.trans2:exp2 0.297423404527015 0.38562654627234 0.771273158972221 0.440920628301681 df.mm.trans1:exp3 -0.337138335677980 0.38562654627234 -0.87426122225487 0.38240870980672 df.mm.trans2:exp3 -0.154351440155276 0.38562654627234 -0.400261448925948 0.689140197986179 df.mm.trans1:exp4 0.108142253220646 0.38562654627234 0.280432595385364 0.779265209534159 df.mm.trans2:exp4 -0.0935114810706591 0.38562654627234 -0.24249233351435 0.808501206373798 df.mm.trans1:exp5 -0.624262279891682 0.38562654627234 -1.61882600128574 0.106134772220451 df.mm.trans2:exp5 -0.242782800078495 0.38562654627234 -0.62958010133212 0.529265802856972 df.mm.trans1:exp6 -0.0807073237927945 0.38562654627234 -0.209288817310302 0.83431065795727 df.mm.trans2:exp6 -0.121799611102701 0.38562654627234 -0.315848616440121 0.752253287386715 df.mm.trans1:exp7 -0.342875205175422 0.38562654627234 -0.889137971671884 0.374369715301789 df.mm.trans2:exp7 -0.0556497705627259 0.38562654627234 -0.144310009517406 0.885315619211315 df.mm.trans1:exp8 0.00485261304757263 0.38562654627234 0.0125837110916777 0.98996509068654 df.mm.trans2:exp8 -0.411599404872370 0.38562654627234 -1.06735236163354 0.286343746052411 df.mm.trans1:probe2 0.26101332229695 0.264020447707713 0.988610255618944 0.323346808590198 df.mm.trans1:probe3 -0.0414899899028082 0.264020447707713 -0.157146881095892 0.87519448841881 df.mm.trans1:probe4 0.481388199083520 0.264020447707713 1.82329892727266 0.0688735153536013 . df.mm.trans1:probe5 0.35969935846238 0.264020447707713 1.36239204798482 0.173706517934043 df.mm.trans1:probe6 0.125124594940510 0.264020447707713 0.473920092276453 0.63577016128637 df.mm.trans2:probe2 -0.0107468000163125 0.264020447707713 -0.0407044231218403 0.967548285839477 df.mm.trans2:probe3 -0.445513549016672 0.264020447707713 -1.68742062550353 0.0921654523015638 . df.mm.trans2:probe4 -0.500378465001641 0.264020447707713 -1.89522618170692 0.0586567980788565 . df.mm.trans2:probe5 -0.515075502723107 0.264020447707713 -1.95089246759148 0.0516453031490256 . df.mm.trans2:probe6 -0.216802124782662 0.264020447707713 -0.821156568231698 0.411960535186205 df.mm.trans3:probe2 -0.101448518203857 0.264020447707713 -0.384244929075218 0.700965330446543 df.mm.trans3:probe3 0.0390100543057534 0.264020447707713 0.147753913170165 0.882598386727381 df.mm.trans3:probe4 -0.203170437830426 0.264020447707713 -0.769525389394644 0.441956088528801 df.mm.trans3:probe5 0.254336257522295 0.264020447707713 0.963320302387566 0.335866856262903 df.mm.trans3:probe6 -0.32034093244602 0.264020447707713 -1.21331864720060 0.22559858986854 df.mm.trans3:probe7 -0.252575791818904 0.264020447707713 -0.956652388145789 0.339219339419886 df.mm.trans3:probe8 -0.308041363571206 0.264020447707713 -1.16673297937979 0.24389156910047 df.mm.trans3:probe9 0.0282286571755194 0.264020447707713 0.106918450523841 0.914897886867508 df.mm.trans3:probe10 -0.438093692526686 0.264020447707713 -1.65931728519634 0.0976983219527029 .