fitVsDatCorrelation=0.718718127682267 cont.fitVsDatCorrelation=0.309334570518377 fstatistic=10206.7719983049,42,462 cont.fstatistic=5451.40951787712,42,462 residuals=-0.39300104281507,-0.0809842198679776,-0.00356889938043384,0.0664037756612457,0.631189366278981 cont.residuals=-0.401150548285144,-0.105417794810454,-0.0240703415260914,0.0628170628481824,1.11175055280386 predictedValues: Include Exclude Both Lung 44.0486274211258 48.2337861022814 62.7842429245963 cerebhem 52.5336913474681 53.97051815538 75.2386946162242 cortex 44.6469649516089 52.8414984626798 59.5766394228065 heart 46.1139561303229 48.5784999147738 54.6277238676165 kidney 44.3770224709013 47.6502840956234 61.2248485216241 liver 45.4850312614517 48.7714600858387 57.7973443511203 stomach 45.8291742540834 49.1661691370932 56.8008715787537 testicle 46.7601913082551 52.3501257152555 56.6542884313716 diffExp=-4.18515868115561,-1.43682680791192,-8.19453351107084,-2.46454378445090,-3.27326162472212,-3.28642882438704,-3.33699488300985,-5.58993440700041 diffExpScore=0.969482126199298 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 49.3462892689914 47.3474741188884 49.8572240343031 cerebhem 49.6359226003559 49.1116371634579 52.1215161053327 cortex 48.4771787375653 48.96443183649 48.8202780508472 heart 48.4797465510964 43.9027774039744 48.1227052874817 kidney 52.8483269117305 46.6790606511851 50.1644992477519 liver 53.0551445064202 47.8083626476511 49.4720259235975 stomach 51.2118033686995 46.7475662449743 51.1904369207016 testicle 50.2657719234429 50.048321609153 53.7820900266115 cont.diffExp=1.99881515010294,0.524285436898019,-0.487253098924711,4.576969147122,6.16926626054538,5.24678185876913,4.46423712372521,0.217450314289927 cont.diffExpScore=0.998924790871863 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=0.676281598786287 cont.tran.correlation=0.0360008284572061 tran.covariance=0.0018187842278117 cont.tran.covariance=7.84012547029222e-05 tran.mean=48.2098125508839 cont.tran.mean=48.9956134715048 weightedLogRatios: wLogRatio Lung -0.347693461692068 cerebhem -0.107256905377041 cortex -0.654333305832149 heart -0.200823987640635 kidney -0.272448111048138 liver -0.268741734785322 stomach -0.271304114719926 testicle -0.440564857118957 cont.weightedLogRatios: wLogRatio Lung 0.160359575144716 cerebhem 0.0414070117348681 cortex -0.0388648534189042 heart 0.3799703529607 kidney 0.484773891459982 liver 0.408118128393296 stomach 0.354831908197727 testicle 0.0169737541682308 varWeightedLogRatios=0.0276861065755392 cont.varWeightedLogRatios=0.0418091493327323 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.45418594589405 0.0727620402374045 47.472362438215 8.28041746210738e-180 *** df.mm.trans1 0.296277897736978 0.0641161406647103 4.62095651212598 4.96179701700433e-06 *** df.mm.trans2 0.387304330131529 0.0606207647855837 6.3889713615694 4.0817788730246e-10 *** df.mm.exp2 0.107577195262716 0.0835226791282804 1.28799981496631 0.198390759921977 df.mm.exp3 0.157169836140272 0.0835226791282804 1.88176238813986 0.0604966949526358 . df.mm.exp4 0.192105376452075 0.0835226791282804 2.30003848603833 0.0218909182783916 * df.mm.exp5 0.0204074959083684 0.0835226791282804 0.244334785729575 0.807080017039676 df.mm.exp6 0.125935975731188 0.0835226791282804 1.50780574863704 0.132287737015665 df.mm.exp7 0.158925187119235 0.0835226791282804 1.90277884734931 0.0576908111498328 . df.mm.exp8 0.244369090388534 0.0835226791282804 2.92578127209275 0.00360516380934512 ** df.mm.trans1:exp2 0.0685833161287017 0.0773269751238153 0.886926147297067 0.375580119648573 df.mm.trans2:exp2 0.00480101014535542 0.0705895476315908 0.0680130459315599 0.94580467387551 df.mm.trans1:exp3 -0.143677697667106 0.0773269751238153 -1.85805402884375 0.0637972398968852 . df.mm.trans2:exp3 -0.0659327303771244 0.0705895476315908 -0.934029648712717 0.350776503353535 df.mm.trans1:exp4 -0.146283928579996 0.0773269751238153 -1.89175806173418 0.0591483431627695 . df.mm.trans2:exp4 -0.184984063993400 0.0705895476315908 -2.62055885325739 0.00906779434836494 ** df.mm.trans1:exp5 -0.0129798646732412 0.0773269751238153 -0.167856878566088 0.866769327049155 df.mm.trans2:exp5 -0.0325786356733983 0.0705895476315908 -0.461522091676054 0.6446412948182 df.mm.trans1:exp6 -0.0938468794968517 0.0773269751238153 -1.21363701795635 0.225506438905693 df.mm.trans2:exp6 -0.114850400241133 0.0705895476315908 -1.62701708814654 0.104415265200732 df.mm.trans1:exp7 -0.11929849867634 0.0773269751238153 -1.54277984474784 0.123568852486699 df.mm.trans2:exp7 -0.139779151284405 0.0705895476315908 -1.98016782900943 0.0482775309622417 * df.mm.trans1:exp8 -0.184631054843891 0.0773269751238153 -2.38766684650811 0.0173566864482021 * df.mm.trans2:exp8 -0.162474483617499 0.0705895476315908 -2.30167905970242 0.0217973353879512 * df.mm.trans1:probe2 0.0200569967531831 0.0386634875619077 0.518758084641691 0.60417783134288 df.mm.trans1:probe3 0.112986581348301 0.0386634875619077 2.92230702590883 0.00364487745268065 ** df.mm.trans1:probe4 -0.0093848690881778 0.0386634875619077 -0.24273208859265 0.808320671207444 df.mm.trans1:probe5 0.084605827168898 0.0386634875619077 2.18826165211888 0.0291506393544343 * df.mm.trans1:probe6 0.184073876355226 0.0386634875619077 4.76092271967158 2.58262243018808e-06 *** df.mm.trans1:probe7 0.0318250811773627 0.0386634875619077 0.82313012054085 0.41085867131294 df.mm.trans1:probe8 -0.0149573843298931 0.0386634875619077 -0.386860712085102 0.699037455584728 df.mm.trans1:probe9 0.0243834705610899 0.0386634875619077 0.630658848921667 0.528575445798228 df.mm.trans1:probe10 0.0317829213854229 0.0386634875619077 0.822039691440983 0.411478341176094 df.mm.trans1:probe11 -0.00850455226471409 0.0386634875619077 -0.219963402191710 0.82599681587531 df.mm.trans1:probe12 0.0655872814314686 0.0386634875619077 1.69636226753861 0.0904909101980197 . df.mm.trans2:probe2 -0.0613002920241667 0.0386634875619077 -1.58548273551405 0.113540891648645 df.mm.trans2:probe3 0.0777240571708997 0.0386634875619077 2.01027020768493 0.0449833075546582 * df.mm.trans2:probe4 0.0582361559926731 0.0386634875619077 1.50623132223718 0.132691202764172 df.mm.trans2:probe5 0.0551596152336594 0.0386634875619077 1.42665907066294 0.154353614767636 df.mm.trans2:probe6 0.181305567218301 0.0386634875619077 4.68932263102224 3.61416364070833e-06 *** df.mm.trans3:probe2 -0.167879833023007 0.0386634875619077 -4.34207681741588 1.73583653521545e-05 *** df.mm.trans3:probe3 0.173687711177108 0.0386634875619077 4.49229291328157 8.91399386179433e-06 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.79765321871996 0.099515526213883 38.1614142355825 6.42447135243617e-145 *** df.mm.trans1 0.126583306964421 0.087690662002245 1.44352094138804 0.149551493361949 df.mm.trans2 0.0298135932828614 0.082910090033789 0.359589445273925 0.719318481488329 df.mm.exp2 -0.00197964975490268 0.114232686949469 -0.0173299762770911 0.986180852989699 df.mm.exp3 0.0368288960755653 0.114232686949469 0.322402431905121 0.747293684720197 df.mm.exp4 -0.05784305350778 0.114232686949469 -0.506361664532733 0.612844329540427 df.mm.exp5 0.0482015054533631 0.114232686949469 0.421958957112555 0.6732512536985 df.mm.exp6 0.089912390143407 0.114232686949469 0.78709861900718 0.431627719311796 df.mm.exp7 -0.00203314313853942 0.114232686949469 -0.0177982606628064 0.98580747596331 df.mm.exp8 -0.00183981777970110 0.114232686949469 -0.0161058785259506 0.987156876557835 df.mm.trans1:exp2 0.0078318963864285 0.105758917628844 0.0740542411176536 0.940999277455142 df.mm.trans2:exp2 0.0385621926049808 0.0965442414044148 0.3994250930353 0.68976458619798 df.mm.trans1:exp3 -0.0545983212997339 0.105758917628844 -0.516252648229098 0.60592495953215 df.mm.trans2:exp3 -0.00324821573914620 0.0965442414044148 -0.0336448419076569 0.973174894442626 df.mm.trans1:exp4 0.0401265964140273 0.105758917628844 0.379415725063012 0.704553504569224 df.mm.trans2:exp4 -0.0176928351141597 0.0965442414044148 -0.1832614235379 0.854673298658066 df.mm.trans1:exp5 0.0203619781277343 0.105758917628844 0.192532020790848 0.84741008963699 df.mm.trans2:exp5 -0.0624192946983418 0.0965442414044148 -0.64653565857826 0.51825356067978 df.mm.trans1:exp6 -0.0174431260465417 0.105758917628844 -0.164932910033719 0.8690689283856 df.mm.trans2:exp6 -0.0802252884675789 0.0965442414044148 -0.83096917330908 0.406420278321954 df.mm.trans1:exp7 0.0391406122251908 0.105758917628844 0.370092783688963 0.711482957790115 df.mm.trans2:exp7 -0.0107181347200661 0.0965442414044148 -0.111017856312825 0.91165039903422 df.mm.trans1:exp8 0.0203016136608002 0.105758917628844 0.191961246540435 0.84785690374408 df.mm.trans2:exp8 0.0573153153888284 0.0965442414044148 0.593668918571123 0.553024118051768 df.mm.trans1:probe2 -0.0828047441190579 0.0528794588144222 -1.56591512045645 0.118053148674120 df.mm.trans1:probe3 -0.116599435145833 0.0528794588144222 -2.20500432039278 0.0279457466064426 * df.mm.trans1:probe4 -0.0034501636545619 0.0528794588144222 -0.0652458200578428 0.94800650203771 df.mm.trans1:probe5 -0.00735361959152409 0.0528794588144222 -0.139063820931512 0.889460314220386 df.mm.trans1:probe6 -0.046434424864509 0.0528794588144222 -0.87811838293331 0.380335912264865 df.mm.trans1:probe7 0.0150756818405182 0.0528794588144222 0.285095236950620 0.775698937927105 df.mm.trans1:probe8 -0.0866311413306657 0.0528794588144222 -1.63827586879611 0.102044978574605 df.mm.trans1:probe9 -0.0254631443112821 0.0528794588144222 -0.481531862885431 0.630366539814535 df.mm.trans1:probe10 0.0362166128321606 0.0528794588144222 0.684890005384906 0.49375678763033 df.mm.trans1:probe11 0.0193612823334434 0.0528794588144222 0.366139948621464 0.714428251341928 df.mm.trans1:probe12 -0.082526225233626 0.0528794588144222 -1.56064806796241 0.119291521156359 df.mm.trans2:probe2 0.0565515181912801 0.0528794588144222 1.06944207560340 0.285428939471660 df.mm.trans2:probe3 0.0416897815659434 0.0528794588144222 0.78839274267635 0.430871355901006 df.mm.trans2:probe4 0.0258555336125451 0.0528794588144222 0.488952311393424 0.625107489000592 df.mm.trans2:probe5 0.0550665464231734 0.0528794588144222 1.04135987125789 0.298253192513288 df.mm.trans2:probe6 0.0912565719460862 0.0528794588144222 1.72574708577004 0.0850615520318868 . df.mm.trans3:probe2 -0.0416825649390242 0.0528794588144222 -0.788256269514919 0.430951082591312 df.mm.trans3:probe3 -0.0929775879397645 0.0528794588144222 -1.75829310708464 0.079359745576728 .