fitVsDatCorrelation=0.744919378158477 cont.fitVsDatCorrelation=0.262096207845925 fstatistic=12864.3967311573,55,761 cont.fstatistic=6141.00565784115,55,761 residuals=-0.368428807978007,-0.0779632292324172,-0.00789934744804378,0.067405831135838,0.80331634704555 cont.residuals=-0.400215381379947,-0.127084864369123,-0.0298688446339918,0.0880020116091657,0.89841741940093 predictedValues: Include Exclude Both Lung 45.9602983743470 43.5977917355715 60.1252511984495 cerebhem 50.3997744952821 54.227642212192 58.5552181808172 cortex 46.3881399930227 45.9727854951935 63.2704225183195 heart 46.6506949475461 44.837086822082 59.7816235133666 kidney 46.3164330780547 43.1996007595596 63.5960166170163 liver 48.7612967690906 49.1610979564905 60.9805897340664 stomach 47.8727530160958 43.3208574613509 59.9021316237482 testicle 48.1624656599794 47.8022190225882 59.2708250744358 diffExp=2.3625066387755,-3.82786771690984,0.415354497829206,1.81360812546409,3.11683231849504,-0.399801187399895,4.55189555474493,0.360246637391199 diffExpScore=1.79373112983993 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 48.6946699922716 49.158009090199 53.4175276395371 cerebhem 48.655350831579 50.2166730301231 50.1616761478415 cortex 52.6184327622134 51.2999230519334 49.2936394065502 heart 53.3248644492264 48.6482465422795 46.2204726316509 kidney 50.9360127363751 47.7153145262348 49.6142312674678 liver 49.2407548766946 51.3597832759573 52.2419125295102 stomach 48.638385803076 46.2582266367256 51.8405855301267 testicle 50.3435815944331 51.2736261143446 50.7964161365938 cont.diffExp=-0.463339097927339,-1.56132219854408,1.31850971028000,4.67661790694695,3.22069821014023,-2.11902839926265,2.38015916635039,-0.930044519911561 cont.diffExpScore=2.21605470239798 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=0.883425198629807 cont.tran.correlation=0.135808480985284 tran.covariance=0.00218909203581195 cont.tran.covariance=0.000199892940212422 tran.mean=47.0394336124029 cont.tran.mean=49.8988659571042 weightedLogRatios: wLogRatio Lung 0.200604957565615 cerebhem -0.289638445037159 cortex 0.0344707729591301 heart 0.151584864076869 kidney 0.264775429239369 liver -0.0317729763084632 stomach 0.381524132307465 testicle 0.0290619120125663 cont.weightedLogRatios: wLogRatio Lung -0.0368419904382160 cerebhem -0.123200240015007 cortex 0.100249682934490 heart 0.360769481767168 kidney 0.254602940551711 liver -0.165071349598846 stomach 0.193636786659671 testicle -0.0719039447023559 varWeightedLogRatios=0.0424652535703128 cont.varWeightedLogRatios=0.0369539910330372 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.02603917111595 0.0646071472414287 46.8375295972755 2.28591804366805e-226 *** df.mm.trans1 0.71264423642361 0.0565729942905205 12.5968979609591 3.48917912281163e-33 *** df.mm.trans2 0.738754193774184 0.0507329442589134 14.5616266622332 1.48905031833404e-42 *** df.mm.exp2 0.336852734094306 0.0669046256044088 5.03481980582384 5.97585684826642e-07 *** df.mm.exp3 0.0113209240931952 0.0669046256044088 0.169209886325844 0.86567652044763 df.mm.exp4 0.0486705979657412 0.0669046256044088 0.72746237686941 0.467166495387284 df.mm.exp5 -0.0575772982887024 0.0669046256044088 -0.860587706284217 0.389736205351345 df.mm.exp6 0.165129366079986 0.0669046256044088 2.46813078450445 0.0138008991151335 * df.mm.exp7 0.0381140972900755 0.0669046256044088 0.569678059562505 0.569064194710084 df.mm.exp8 0.153180322669267 0.0669046256044088 2.28953261879061 0.022321375986503 * df.mm.trans1:exp2 -0.244643978330536 0.062762102068001 -3.89795705162124 0.000105562996753183 *** df.mm.trans2:exp2 -0.118668452547134 0.0500668373204162 -2.37020069367841 0.0180268012723718 * df.mm.trans1:exp3 -0.00205504613538754 0.062762102068001 -0.0327434242588139 0.97388778068683 df.mm.trans2:exp3 0.0417221768446068 0.0500668373204162 0.833329586560351 0.404920342384675 df.mm.trans1:exp4 -0.0337607187334284 0.062762102068001 -0.537915678745903 0.590792537883781 df.mm.trans2:exp4 -0.0206414709633422 0.0500668373204162 -0.41227830771977 0.680251556563136 df.mm.trans1:exp5 0.065296177497832 0.062762102068001 1.04037588522904 0.298495718630376 df.mm.trans2:exp5 0.0484020510135875 0.0500668373204162 0.966748722389343 0.333976788904125 df.mm.trans1:exp6 -0.10597041188791 0.062762102068001 -1.68844586774825 0.091735362543865 . df.mm.trans2:exp6 -0.0450332480903372 0.0500668373204162 -0.89946260839555 0.368690824410537 df.mm.trans1:exp7 0.00265446971605181 0.062762102068001 0.0422941493128411 0.9662753033822 df.mm.trans2:exp7 -0.0444863824987988 0.0500668373204162 -0.888539897459394 0.374531164757276 df.mm.trans1:exp8 -0.106378270724114 0.062762102068001 -1.6949443568486 0.0904950740796457 . df.mm.trans2:exp8 -0.0611147619882891 0.0500668373204162 -1.22066352218673 0.222591527404237 df.mm.trans1:probe2 0.142395360710517 0.0384337813127698 3.70495319083281 0.000226737304544187 *** df.mm.trans1:probe3 0.186847392511723 0.0384337813127698 4.86154071053222 1.41531616669476e-06 *** df.mm.trans1:probe4 0.237513244013798 0.0384337813127698 6.17980422173248 1.04543367550734e-09 *** df.mm.trans1:probe5 0.064826815723304 0.0384337813127699 1.68671448681436 0.0920681092988006 . df.mm.trans1:probe6 0.095269941760066 0.0384337813127698 2.47880740603610 0.0133979063868062 * df.mm.trans1:probe7 0.124762309014868 0.0384337813127698 3.24616274416422 0.00122091902594371 ** df.mm.trans1:probe8 0.0569448071787509 0.0384337813127698 1.48163426115532 0.138851571124530 df.mm.trans1:probe9 0.123543685436115 0.0384337813127698 3.21445564855381 0.00136205887642798 ** df.mm.trans1:probe10 0.271822136208735 0.0384337813127699 7.07247965004215 3.45255758465836e-12 *** df.mm.trans1:probe11 0.0260536794667257 0.0384337813127699 0.677884886077269 0.498050733709577 df.mm.trans1:probe12 0.00844808855731935 0.0384337813127698 0.219808935492705 0.826078908059493 df.mm.trans1:probe13 0.0704460652922691 0.0384337813127698 1.83292049041407 0.0672049121617277 . df.mm.trans1:probe14 0.0481026523797975 0.0384337813127699 1.25157220384701 0.211110364138454 df.mm.trans1:probe15 -0.000103312808491893 0.0384337813127698 -0.00268807296506022 0.997855935136507 df.mm.trans1:probe16 0.0875711963449233 0.0384337813127698 2.27849546294388 0.0229733780357345 * df.mm.trans1:probe17 0.168869255416715 0.0384337813127698 4.39377156367924 1.2720924340619e-05 *** df.mm.trans1:probe18 0.195636238090418 0.0384337813127699 5.09021572710612 4.51117474463665e-07 *** df.mm.trans1:probe19 0.111737948433636 0.0384337813127698 2.90728480563296 0.00375180858862352 ** df.mm.trans1:probe20 0.0771609179023491 0.0384337813127698 2.00763274564171 0.0450353912452739 * df.mm.trans1:probe21 0.122651071051469 0.0384337813127698 3.19123091359001 0.00147484818735204 ** df.mm.trans1:probe22 0.274147556307737 0.0384337813127699 7.13298423792221 2.28966287639014e-12 *** df.mm.trans2:probe2 0.0269281370144348 0.0384337813127698 0.70063720234282 0.483743611535906 df.mm.trans2:probe3 0.0256313827236868 0.0384337813127698 0.666897241130177 0.505039919916584 df.mm.trans2:probe4 0.143590578799478 0.0384337813127699 3.73605130421475 0.000200910980399093 *** df.mm.trans2:probe5 -0.0742752455314235 0.0384337813127698 -1.93255107861961 0.0536623044893902 . df.mm.trans2:probe6 0.00068277797957874 0.0384337813127698 0.0177650482533157 0.985830944302308 df.mm.trans3:probe2 -0.574691788289227 0.0384337813127698 -14.9527777038759 1.64063917107114e-44 *** df.mm.trans3:probe3 -0.400292141849249 0.0384337813127698 -10.4151121273162 7.74075203220445e-24 *** df.mm.trans3:probe4 -0.323746347271434 0.0384337813127698 -8.42348413851925 1.81391256719818e-16 *** df.mm.trans3:probe5 -0.532330245878001 0.0384337813127698 -13.8505821622379 4.53852697496967e-39 *** df.mm.trans3:probe6 -0.45498564647063 0.0384337813127698 -11.8381702484075 8.51463872080297e-30 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.77338449104755 0.0934545069654445 40.3766989262785 2.19865753312922e-191 *** df.mm.trans1 0.110459168762940 0.0818330713353223 1.34981086448922 0.177478023265428 df.mm.trans2 0.108046595300727 0.0733854146957578 1.47231702305789 0.141348724007579 df.mm.exp2 0.083387142079782 0.0967778189648698 0.861634855710598 0.389159896178218 df.mm.exp3 0.200490250107971 0.0967778189648699 2.07165497479074 0.0386340202394592 * df.mm.exp4 0.225125218975404 0.0967778189648698 2.32620678357222 0.0202693027041425 * df.mm.exp5 0.0890744464841545 0.0967778189648699 0.920401466336912 0.357654667448268 df.mm.exp6 0.0772215380835344 0.0967778189648699 0.797926001117731 0.425162347403405 df.mm.exp7 -0.0319914136695564 0.0967778189648699 -0.330565557394605 0.741063654349468 df.mm.exp8 0.125751402198301 0.0967778189648699 1.29938247775504 0.194206202339408 df.mm.trans1:exp2 -0.0841949315902095 0.0907856414548329 -0.927403609656684 0.354011050553312 df.mm.trans2:exp2 -0.0620798237572954 0.0724218882411553 -0.85719697821987 0.39160589200817 df.mm.trans1:exp3 -0.122993337364831 0.0907856414548329 -1.35476640792390 0.175894040667988 df.mm.trans2:exp3 -0.157840783200396 0.0724218882411553 -2.17946241162350 0.0296032616660523 * df.mm.trans1:exp4 -0.134292074950421 0.0907856414548329 -1.47922152444375 0.139494928936005 df.mm.trans2:exp4 -0.235549238595057 0.0724218882411553 -3.25245922628680 0.00119455430792439 ** df.mm.trans1:exp5 -0.0440738320778458 0.0907856414548329 -0.4854713958239 0.627481731948164 df.mm.trans2:exp5 -0.118861826173859 0.0724218882411553 -1.64124174418188 0.101160430682079 df.mm.trans1:exp6 -0.0660694846814259 0.0907856414548329 -0.72775257874117 0.466988902298779 df.mm.trans2:exp6 -0.0334058837213270 0.0724218882411553 -0.461267781503981 0.644738304201582 df.mm.trans1:exp7 0.0308348858073769 0.0907856414548329 0.339644962719327 0.734217628080478 df.mm.trans2:exp7 -0.0288090502687896 0.0724218882411553 -0.397794796137588 0.690893014005268 df.mm.trans1:exp8 -0.0924498452953428 0.0907856414548329 -1.01833113490020 0.308844247324779 df.mm.trans2:exp8 -0.0836146783590662 0.0724218882411553 -1.15454982450389 0.248637244734963 df.mm.trans1:probe2 -0.0222658928158116 0.0555946243839011 -0.400504420392474 0.688897436840173 df.mm.trans1:probe3 -0.0531321375979518 0.0555946243839011 -0.955706386845157 0.339524085172255 df.mm.trans1:probe4 -0.0385282317716155 0.0555946243839011 -0.693020812688005 0.48850793005608 df.mm.trans1:probe5 0.0250147203820994 0.0555946243839011 0.449948545552959 0.652875680965123 df.mm.trans1:probe6 0.0202334767754622 0.0555946243839011 0.363946640519462 0.715998946442953 df.mm.trans1:probe7 0.0457246321150058 0.0555946243839011 0.822464988687765 0.411069931956495 df.mm.trans1:probe8 0.026778836138741 0.0555946243839011 0.481680314158135 0.630171547407684 df.mm.trans1:probe9 -0.0456314112468397 0.0555946243839011 -0.820788192249276 0.412023958614994 df.mm.trans1:probe10 -0.0188090567535889 0.0555946243839011 -0.338325098191247 0.73521152934076 df.mm.trans1:probe11 0.0464380835843882 0.0555946243839011 0.835298090400185 0.403812054901324 df.mm.trans1:probe12 0.0243377615943095 0.0555946243839011 0.437771850498502 0.661675898483617 df.mm.trans1:probe13 -0.0608590370404678 0.0555946243839011 -1.09469283613131 0.273997578726962 df.mm.trans1:probe14 0.0405023487082439 0.0555946243839011 0.728529946143002 0.466513366374136 df.mm.trans1:probe15 0.0322009005698768 0.0555946243839011 0.579208888030574 0.56261963969063 df.mm.trans1:probe16 -0.0140800997570881 0.0555946243839011 -0.253263690745707 0.800132853562151 df.mm.trans1:probe17 0.0281013110202343 0.0555946243839011 0.50546813350486 0.613376439336674 df.mm.trans1:probe18 -0.00748081774641187 0.0555946243839011 -0.134560091543278 0.892995276464302 df.mm.trans1:probe19 0.00521722214613788 0.0555946243839011 0.0938440038754657 0.92525777749579 df.mm.trans1:probe20 -0.0330401728039533 0.0555946243839011 -0.594305171949699 0.55248465372549 df.mm.trans1:probe21 -0.0120338611324332 0.0555946243839011 -0.216457279202662 0.828689332372807 df.mm.trans1:probe22 0.0596371448783676 0.0555946243839011 1.07271423342213 0.283739399190442 df.mm.trans2:probe2 0.00956951652139988 0.0555946243839011 0.172130248696689 0.863380896911012 df.mm.trans2:probe3 -0.0305663564711387 0.0555946243839011 -0.549807770263307 0.582612545414908 df.mm.trans2:probe4 0.0416384124592413 0.0555946243839011 0.748964723130656 0.454109899746859 df.mm.trans2:probe5 0.134753097347011 0.0555946243839011 2.42385120576573 0.0155889971620287 * df.mm.trans2:probe6 0.00790971763276445 0.0555946243839011 0.14227486416933 0.886900558161933 df.mm.trans3:probe2 -0.0119257202357893 0.0555946243839011 -0.214512110981052 0.830205190680791 df.mm.trans3:probe3 -0.0102717926410974 0.0555946243839011 -0.184762335476303 0.853464677837883 df.mm.trans3:probe4 -0.0246754566088232 0.0555946243839011 -0.4438460891188 0.657280034831104 df.mm.trans3:probe5 -0.0203139591667731 0.0555946243839011 -0.365394305508709 0.714918625541819 df.mm.trans3:probe6 -0.0153210516570043 0.0555946243839011 -0.275585127641242 0.782941546503298