fitVsDatCorrelation=0.859772224256799 cont.fitVsDatCorrelation=0.275634252152871 fstatistic=5916.02801913968,55,761 cont.fstatistic=1659.77449713485,55,761 residuals=-0.698997258993447,-0.138817580134457,0.00922478624461744,0.128131819575679,1.02312705485521 cont.residuals=-1.03755019614083,-0.296564530656687,0.00177266697814306,0.263695113462113,1.59718182485061 predictedValues: Include Exclude Both Lung 159.645195513484 128.036893190451 153.075160455801 cerebhem 79.3500623367813 107.446007645078 66.673937738696 cortex 90.9135732141913 114.574988570855 99.2032471592781 heart 94.7154328938218 123.095027072288 84.1643989226037 kidney 114.495363765461 143.886640177883 102.169898207558 liver 119.211412764089 123.289467607618 111.539430839656 stomach 131.457353197126 118.070094111694 121.937501057840 testicle 203.505600131125 115.440763287786 205.778707496661 diffExp=31.6083023230333,-28.0959453082967,-23.661415356664,-28.3795941784665,-29.3912764124219,-4.07805484352954,13.3872590854317,88.0648368433383 diffExpScore=12.0595155885043 diffExp1.5=0,0,0,0,0,0,0,1 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,0,1 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,0,0,1 diffExp1.3Score=2 diffExp1.2=1,-1,-1,-1,-1,0,0,1 diffExp1.2Score=2 cont.predictedValues: Include Exclude Both Lung 110.240437242480 104.228415033863 113.142774690529 cerebhem 111.999435985915 113.494437319294 105.434262340567 cortex 108.031024694182 99.5174881255939 113.066420190154 heart 111.348973554662 93.159860150464 110.273247759102 kidney 114.74593163279 121.29600579486 113.200248554270 liver 111.548147624655 93.7823359885958 126.216441514486 stomach 106.316499901322 109.925876111384 112.841619159310 testicle 119.444298860736 92.0123988380172 96.3870151461995 cont.diffExp=6.01202220861707,-1.49500133337870,8.51353656858791,18.1891134041979,-6.55007416207016,17.7658116360596,-3.60937621006184,27.4319000227192 cont.diffExpScore=1.33169178270828 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=0,0,0,0,0,0,0,1 cont.diffExp1.2Score=0.5 tran.correlation=0.0999354132780788 cont.tran.correlation=-0.157168004091914 tran.covariance=0.00588876614711107 cont.tran.covariance=-0.000669022749129924 tran.mean=122.945867217483 cont.tran.mean=107.568222928676 weightedLogRatios: wLogRatio Lung 1.09493308222201 cerebhem -1.37174452811565 cortex -1.06998773744367 heart -1.22703526382948 kidney -1.10927567244659 liver -0.161378355691522 stomach 0.518222312849648 testicle 2.85294979587657 cont.weightedLogRatios: wLogRatio Lung 0.26214818181997 cerebhem -0.0626550941505653 cortex 0.380987633799133 heart 0.824610305127978 kidney -0.264825849145940 liver 0.802815283445888 stomach -0.156349447988890 testicle 1.21393241468509 varWeightedLogRatios=2.19535137836519 cont.varWeightedLogRatios=0.283572229113416 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.60991219210061 0.114786982565431 48.8723726917628 6.40394783372425e-237 *** df.mm.trans1 -0.170494401174837 0.100512769663603 -1.69624617593814 0.0902482441169972 . df.mm.trans2 -0.767935726027942 0.0901368012176616 -8.5196691656889 8.52818848819499e-17 *** df.mm.exp2 -0.043299668213791 0.118868893314567 -0.364264081261406 0.715762007233175 df.mm.exp3 -0.240375135945314 0.118868893314567 -2.02218704357918 0.0435064162388814 * df.mm.exp4 0.0367183420822867 0.118868893314567 0.30889782060238 0.757483800771382 df.mm.exp5 0.188579773616046 0.118868893314567 1.58645183241506 0.113052252875279 df.mm.exp6 -0.0132879175265805 0.118868893314567 -0.111786331613404 0.911022302969827 df.mm.exp7 -0.0478909020229811 0.118868893314567 -0.402888431847729 0.687143457660498 df.mm.exp8 -0.15669353562215 0.118868893314567 -1.31820471489951 0.187831753489481 df.mm.trans1:exp2 -0.655784924058286 0.111508906111083 -5.88100939134856 6.10405110482115e-09 *** df.mm.trans2:exp2 -0.132030315295194 0.0889533345456189 -1.48426493475052 0.138152712244562 df.mm.trans1:exp3 -0.322669378567178 0.111508906111083 -2.89366463917904 0.00391656199254922 ** df.mm.trans2:exp3 0.129286216147691 0.088953334545619 1.45341618510532 0.146520476391464 df.mm.trans1:exp4 -0.558795213860847 0.111508906111083 -5.01121599474919 6.73097082953208e-07 *** df.mm.trans2:exp4 -0.0760801576113012 0.088953334545619 -0.855281682242999 0.392664414819252 df.mm.trans1:exp5 -0.520999267409774 0.111508906111083 -4.67226597031418 3.52223551306885e-06 *** df.mm.trans2:exp5 -0.0718724554884101 0.088953334545619 -0.807979328212265 0.419354971381222 df.mm.trans1:exp6 -0.278767412597119 0.111508906111083 -2.49995648167704 0.0126302755971145 * df.mm.trans2:exp6 -0.0244955472547683 0.088953334545619 -0.275375255800062 0.783102704391936 df.mm.trans1:exp7 -0.146380434084063 0.111508906111083 -1.31272415082470 0.189671655487171 df.mm.trans2:exp7 -0.0331490824147227 0.088953334545619 -0.372656995761327 0.709507510327373 df.mm.trans1:exp8 0.399433234361129 0.111508906111083 3.58207472650857 0.000362552003657135 *** df.mm.trans2:exp8 0.053132611612679 0.088953334545619 0.597308823599305 0.550478892066037 df.mm.trans1:probe2 -0.727770105961023 0.0682849804370178 -10.6578357539734 8.14616490590001e-25 *** df.mm.trans1:probe3 -0.920953438235331 0.0682849804370178 -13.4869107721978 2.50801407962303e-37 *** df.mm.trans1:probe4 -0.118253185663796 0.0682849804370178 -1.73175982341923 0.0837215721735887 . df.mm.trans1:probe5 -0.978638739282734 0.0682849804370178 -14.3316836736210 2.04381337741352e-41 *** df.mm.trans1:probe6 -0.487376015565634 0.0682849804370178 -7.13738237085918 2.22206115510488e-12 *** df.mm.trans1:probe7 -0.562924449379202 0.0682849804370178 -8.24375207807831 7.29520552158154e-16 *** df.mm.trans1:probe8 -0.400487395853823 0.0682849804370178 -5.8649412109477 6.69731719841723e-09 *** df.mm.trans1:probe9 -0.235876321426825 0.0682849804370178 -3.45429287549382 0.000582278623228453 *** df.mm.trans1:probe10 -0.198001353464787 0.0682849804370178 -2.89963257216442 0.00384357823054201 ** df.mm.trans1:probe11 -0.544134801528763 0.0682849804370178 -7.96858691393553 5.8598509572235e-15 *** df.mm.trans1:probe12 -0.588025923070286 0.0682849804370178 -8.61135083157339 4.12737750350906e-17 *** df.mm.trans1:probe13 -0.449919293388914 0.0682849804370178 -6.5888470716323 8.27302427722757e-11 *** df.mm.trans1:probe14 -0.444145010642582 0.0682849804370178 -6.50428553687932 1.41364900427249e-10 *** df.mm.trans1:probe15 -0.745382825222907 0.0682849804370178 -10.915765376991 7.14610417601025e-26 *** df.mm.trans1:probe16 -0.820298941769198 0.0682849804370178 -12.0128751083966 1.45391615354400e-30 *** df.mm.trans1:probe17 -0.317774064000062 0.0682849804370178 -4.65364509100444 3.84622459830404e-06 *** df.mm.trans1:probe18 -0.220644473959436 0.0682849804370178 -3.23122995052984 0.00128561344240659 ** df.mm.trans1:probe19 -0.309332391471431 0.0682849804370178 -4.53002094299114 6.8452227337813e-06 *** df.mm.trans1:probe20 -0.373111808975245 0.0682849804370178 -5.46403918676351 6.31003012427995e-08 *** df.mm.trans1:probe21 -0.274186083243400 0.0682849804370178 -4.01532052127179 6.5246971652125e-05 *** df.mm.trans1:probe22 -0.543754428383177 0.0682849804370178 -7.96301653604053 6.10865489234768e-15 *** df.mm.trans2:probe2 -0.0106269073555589 0.0682849804370178 -0.155625838764948 0.876369227062797 df.mm.trans2:probe3 -0.00230874666943625 0.0682849804370178 -0.0338104610217426 0.973037159929777 df.mm.trans2:probe4 -0.0385985706730355 0.0682849804370178 -0.565257109630962 0.57206549902257 df.mm.trans2:probe5 0.049544020625248 0.0682849804370178 0.725547848270157 0.468339057812388 df.mm.trans2:probe6 0.126094016484242 0.0682849804370178 1.84658493972249 0.0651954148799816 . df.mm.trans3:probe2 0.173795082278740 0.0682849804370178 2.54514361967254 0.0111195672136288 * df.mm.trans3:probe3 -0.126389569598325 0.0682849804370178 -1.85091316991589 0.0645693555889985 . df.mm.trans3:probe4 0.341740631282103 0.0682849804370178 5.00462369755389 6.95781687124632e-07 *** df.mm.trans3:probe5 0.725214680623072 0.0682849804370178 10.6204128050087 1.15553057174871e-24 *** df.mm.trans3:probe6 1.04232105950963 0.0682849804370178 15.2642799754627 4.3243080725876e-46 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.62708670323748 0.216066669366379 21.4150878375019 5.40604365782967e-80 *** df.mm.trans1 0.107036632631672 0.189197928934366 0.565738923436123 0.571738038539577 df.mm.trans2 -0.0560866883410844 0.169666960409366 -0.330569300032019 0.74106082805809 df.mm.exp2 0.171561959103565 0.223750161348704 0.766756806204906 0.443463915568457 df.mm.exp3 -0.0658216437183896 0.223750161348704 -0.294174731860012 0.768704607019302 df.mm.exp4 -0.0765732855433123 0.223750161348704 -0.342226727711612 0.732274765184977 df.mm.exp5 0.191197871016279 0.223750161348704 0.854515008453137 0.393088617491131 df.mm.exp6 -0.203163434216280 0.223750161348704 -0.907992347320187 0.364169706395164 df.mm.exp7 0.0196434946450621 0.223750161348704 0.0877920915303774 0.93006501970035 df.mm.exp8 0.115803938712765 0.223750161348704 0.517559129409029 0.604916312392919 df.mm.trans1:exp2 -0.155731897196615 0.209896256610599 -0.741946996632389 0.458348371320575 df.mm.trans2:exp2 -0.0863929229920587 0.167439288800448 -0.515965659021764 0.606028268404233 df.mm.trans1:exp3 0.0455763217411701 0.209896256610599 0.217137372896190 0.828159489766984 df.mm.trans2:exp3 0.0195702432889644 0.167439288800448 0.116879636966733 0.906986273042015 df.mm.trans1:exp4 0.0865786874798447 0.209896256610599 0.412483237566575 0.680101439311155 df.mm.trans2:exp4 -0.0356945598236608 0.167439288800448 -0.213179117514057 0.831244350863592 df.mm.trans1:exp5 -0.151141250420038 0.209896256610599 -0.720075969246257 0.47169932187549 df.mm.trans2:exp5 -0.0395487731339992 0.167439288800448 -0.236197689427198 0.81334282878598 df.mm.trans1:exp6 0.214955975636275 0.209896256610599 1.02410580878087 0.306110726034211 df.mm.trans2:exp6 0.0975551676020193 0.167439288800448 0.582630088200413 0.560314933448565 df.mm.trans1:exp7 -0.0558867747338393 0.209896256610599 -0.266259034993276 0.790111843412894 df.mm.trans2:exp7 0.0335780012669404 0.167439288800448 0.200538365323316 0.84111313705551 df.mm.trans1:exp8 -0.0356175678637306 0.209896256610599 -0.169691296256934 0.865298017472923 df.mm.trans2:exp8 -0.240465389972138 0.167439288800448 -1.43613480261924 0.151374920071876 df.mm.trans1:probe2 -0.0850896002426218 0.128534681904062 -0.661997205595703 0.508173421181671 df.mm.trans1:probe3 -0.164454793971459 0.128534681904062 -1.27945852072989 0.201125587981402 df.mm.trans1:probe4 -0.0200233964763714 0.128534681904062 -0.155782051814753 0.876246131213288 df.mm.trans1:probe5 0.129411908749375 0.128534681904062 1.00682482604942 0.314338983214382 df.mm.trans1:probe6 -0.115724575020628 0.128534681904062 -0.900337351027205 0.368225570479695 df.mm.trans1:probe7 0.0397189590027169 0.128534681904062 0.309013555052503 0.757395795919538 df.mm.trans1:probe8 0.0324003807947179 0.128534681904062 0.252075006642187 0.801051150454916 df.mm.trans1:probe9 -0.153544985749087 0.128534681904062 -1.19458019792427 0.232623273171592 df.mm.trans1:probe10 -0.158616693653662 0.128534681904062 -1.23403809231856 0.217569670337752 df.mm.trans1:probe11 -0.104980940834387 0.128534681904062 -0.816751862448643 0.414325849315774 df.mm.trans1:probe12 -0.00412601243399469 0.128534681904062 -0.0321003823471888 0.974400415505835 df.mm.trans1:probe13 0.0852024555819097 0.128534681904062 0.662875220288828 0.507611193118194 df.mm.trans1:probe14 0.0876940104604588 0.128534681904062 0.682259520632052 0.495282470705398 df.mm.trans1:probe15 -0.0808996013669853 0.128534681904062 -0.62939900864553 0.529276765797523 df.mm.trans1:probe16 -0.0217729624471002 0.128534681904062 -0.169393677446150 0.865532013162896 df.mm.trans1:probe17 -0.246312184942132 0.128534681904062 -1.91630913379455 0.0557000722181451 . df.mm.trans1:probe18 -0.00448937582746012 0.128534681904062 -0.0349273500424653 0.972146830861682 df.mm.trans1:probe19 -0.159153832477376 0.128534681904062 -1.23821703309748 0.21601743552065 df.mm.trans1:probe20 -0.0404120754145284 0.128534681904062 -0.314406001678923 0.75329887370537 df.mm.trans1:probe21 -0.0220300219314732 0.128534681904062 -0.171393600584131 0.863959849304716 df.mm.trans1:probe22 0.126335592855523 0.128534681904062 0.982891084211961 0.325973423539727 df.mm.trans2:probe2 0.155253924343130 0.128534681904062 1.20787574250980 0.227470299000668 df.mm.trans2:probe3 0.0910145014055923 0.128534681904062 0.708092944700521 0.479104358486127 df.mm.trans2:probe4 0.245270302783998 0.128534681904062 1.90820328918748 0.0567409795394158 . df.mm.trans2:probe5 0.0684472110326154 0.128534681904062 0.532519394910894 0.594521760036004 df.mm.trans2:probe6 0.347031352152333 0.128534681904062 2.69990439165171 0.0070899769215207 ** df.mm.trans3:probe2 0.158155832006443 0.128534681904062 1.2304525880765 0.218907869775112 df.mm.trans3:probe3 0.141436785885522 0.128534681904062 1.10037838652053 0.271515239139549 df.mm.trans3:probe4 0.147764696453757 0.128534681904062 1.14960953934633 0.250665886498323 df.mm.trans3:probe5 0.0805307574859472 0.128534681904062 0.626529402749486 0.531155648225465 df.mm.trans3:probe6 -0.224204872918927 0.128534681904062 -1.74431421619165 0.081508046314988 .