fitVsDatCorrelation=0.84503456401502 cont.fitVsDatCorrelation=0.277301553854687 fstatistic=12528.4356948321,52,692 cont.fstatistic=3871.29581557406,52,692 residuals=-0.394250510348451,-0.0908358430258705,-0.00636654612347681,0.0780928268147692,0.631659982349875 cont.residuals=-0.600173791405038,-0.178681635805413,-0.0132523159985244,0.157035842147641,0.785590092641171 predictedValues: Include Exclude Both Lung 58.3581221813496 93.693295907683 54.6400422836453 cerebhem 57.3297920488247 76.5824613010311 53.498275000488 cortex 56.2071582529571 79.548866111912 49.2358544367214 heart 56.8012001525856 82.2491806872995 52.7533405560293 kidney 65.376938693 97.2119093786192 68.0290294686444 liver 58.9845292927675 87.7826336674106 51.9035886350198 stomach 60.0463040472544 85.3695398986885 50.1234716829661 testicle 55.9183149707586 85.2626017941826 51.9559031662812 diffExp=-35.3351737263335,-19.2526692522065,-23.3417078589549,-25.4479805347138,-31.8349706856192,-28.7981043746431,-25.3232358514342,-29.344286823424 diffExpScore=0.995447885485626 diffExp1.5=-1,0,0,0,0,0,0,-1 diffExp1.5Score=0.666666666666667 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 62.8576567610337 66.4202653235949 58.3702724704332 cerebhem 63.073176272141 66.0611624231405 59.3318609013023 cortex 65.0633357129684 69.1259255871168 53.613945505507 heart 59.2255818776279 63.5366794542984 66.239509910242 kidney 62.4668129387815 62.9236063239284 58.8540039728838 liver 63.8900968646546 66.1294216940142 64.9529780225129 stomach 62.9004066521309 71.4973859287531 73.5947522077283 testicle 63.7287316468383 60.394729749232 68.9087311532574 cont.diffExp=-3.56260856256117,-2.98798615099945,-4.06258987414844,-4.31109757667058,-0.456793385146867,-2.23932482935967,-8.59697927662218,3.33400189760629 cont.diffExpScore=1.23732002452364 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=0.741835006083664 cont.tran.correlation=0.302713468247504 tran.covariance=0.00293999221138515 cont.tran.covariance=0.000434245912008289 tran.mean=72.2951780241452 cont.tran.mean=64.3309359506409 weightedLogRatios: wLogRatio Lung -2.03730906793013 cerebhem -1.21424542863058 cortex -1.45971549716126 heart -1.56395043348573 kidney -1.73706696639256 liver -1.70011600358016 stomach -1.50286966925447 testicle -1.78643069428544 cont.weightedLogRatios: wLogRatio Lung -0.229803694955870 cerebhem -0.192891697868266 cortex -0.254730308295973 heart -0.289239885019724 kidney -0.0301513716288504 liver -0.143805139886592 stomach -0.538772963472241 testicle 0.22180012612915 varWeightedLogRatios=0.0612772635277453 cont.varWeightedLogRatios=0.0477217053777881 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.27382046258929 0.074921616463099 57.043890192816 8.50392797670266e-264 *** df.mm.trans1 -0.460172803775352 0.0672905680476686 -6.83859294291238 1.75976087313980e-11 *** df.mm.trans2 0.328571210328076 0.0618828162170192 5.30957106373112 1.48215000802363e-07 *** df.mm.exp2 -0.198319100717221 0.0847641328936239 -2.33965822509038 0.0195848251977693 * df.mm.exp3 -0.0970646818180713 0.084764132893624 -1.1451150209946 0.252557396835123 df.mm.exp4 -0.122174410885055 0.0847641328936239 -1.44134561062967 0.149939419673332 df.mm.exp5 -0.068729908447359 0.084764132893624 -0.810837156012824 0.417737967097402 df.mm.exp6 -0.00310724771971119 0.084764132893624 -0.0366575768976564 0.970768606458845 df.mm.exp7 0.0217577694184131 0.0847641328936239 0.256686037781078 0.797497446044735 df.mm.exp8 -0.0866256470465807 0.084764132893624 -1.02196110653657 0.307156438997499 df.mm.trans1:exp2 0.180540973695374 0.0811554785978162 2.22463075586165 0.0264277992365410 * df.mm.trans2:exp2 -0.00333945169858421 0.0706367774113533 -0.0472763880370266 0.96230658694879 df.mm.trans1:exp3 0.0595102549864821 0.0811554785978162 0.733286969834757 0.463631754980901 df.mm.trans2:exp3 -0.066590455485751 0.0706367774113533 -0.942716498771759 0.34615492675515 df.mm.trans1:exp4 0.0951333191665775 0.0811554785978162 1.17223532915173 0.241505987916923 df.mm.trans2:exp4 -0.0080987989751421 0.0706367774113533 -0.114654140122768 0.908752523435441 df.mm.trans1:exp5 0.182300938683304 0.0811554785978163 2.24631709199493 0.0249983107091806 * df.mm.trans2:exp5 0.105596498672207 0.0706367774113533 1.49492236964982 0.135390565339086 df.mm.trans1:exp6 0.0137838951948558 0.0811554785978162 0.169845529014313 0.865181260441163 df.mm.trans2:exp6 -0.0620557036313371 0.0706367774113533 -0.878518328631495 0.379967427277505 df.mm.trans1:exp7 0.00675968262164739 0.0811554785978162 0.0832929919019575 0.933642660519733 df.mm.trans2:exp7 -0.114795046114593 0.0706367774113533 -1.62514557319177 0.104586868729169 df.mm.trans1:exp8 0.0439190649940256 0.0811554785978162 0.541171905493603 0.588563267426299 df.mm.trans2:exp8 -0.00766506428912004 0.0706367774113533 -0.108513788001434 0.913619600090559 df.mm.trans1:probe2 0.606980847175086 0.0405777392989081 14.9584687974822 4.86138987194096e-44 *** df.mm.trans1:probe3 0.0869866488424808 0.0405777392989081 2.14370367461110 0.0324045164016833 * df.mm.trans1:probe4 0.337940904595236 0.0405777392989081 8.32823391431099 4.39126566100095e-16 *** df.mm.trans1:probe5 0.140903638020257 0.0405777392989081 3.47243686944502 0.000547839556740062 *** df.mm.trans1:probe6 0.0799499175046864 0.0405777392989081 1.97029008727546 0.0492034100377079 * df.mm.trans1:probe7 0.0972217709153629 0.0405777392989081 2.39593857605515 0.0168424638924908 * df.mm.trans1:probe8 0.279183550510123 0.0405777392989081 6.88021450513965 1.33964842719289e-11 *** df.mm.trans1:probe9 0.123620760117042 0.0405777392989081 3.04651669247549 0.00240312826874055 ** df.mm.trans1:probe10 0.45388131358642 0.0405777392989081 11.1854756186142 8.17663187718422e-27 *** df.mm.trans1:probe11 0.177714665277732 0.0405777392989081 4.37960981435242 1.37296407995232e-05 *** df.mm.trans1:probe12 0.222041375082173 0.0405777392989081 5.47199964607559 6.22530443281349e-08 *** df.mm.trans1:probe13 0.365326728963131 0.0405777392989081 9.00313164989361 2.09845008938337e-18 *** df.mm.trans1:probe14 0.132222736701482 0.0405777392989081 3.25850426825133 0.00117476744123229 ** df.mm.trans1:probe15 0.28134074378226 0.0405777392989081 6.93337649270743 9.43649902159625e-12 *** df.mm.trans1:probe16 0.452810063006522 0.0405777392989081 11.1590756614355 1.05183562415579e-26 *** df.mm.trans1:probe17 0.553901112519655 0.0405777392989081 13.6503689483401 9.70107845555594e-38 *** df.mm.trans1:probe18 0.355492678170008 0.0405777392989081 8.76078077074081 1.48393199721205e-17 *** df.mm.trans1:probe19 0.262644527504478 0.0405777392989081 6.47262592846185 1.82420670161089e-10 *** df.mm.trans1:probe20 0.301825263385253 0.0405777392989081 7.43819810073487 3.03090473631916e-13 *** df.mm.trans1:probe21 0.467951358136758 0.0405777392989081 11.5322185568221 2.88358318149504e-28 *** df.mm.trans1:probe22 0.543831592587552 0.0405777392989081 13.4022151550021 1.39942292181738e-36 *** df.mm.trans2:probe2 -0.183120651298134 0.0405777392989081 -4.51283522596493 7.51565056552572e-06 *** df.mm.trans2:probe3 -0.228590167341408 0.0405777392989081 -5.63338843639223 2.57157382271338e-08 *** df.mm.trans2:probe4 -0.093859784653 0.0405777392989081 -2.3130856049323 0.0210102723599726 * df.mm.trans2:probe5 0.117808839096258 0.0405777392989081 2.90328739677787 0.00381023129099329 ** df.mm.trans2:probe6 -0.173523548193198 0.0405777392989081 -4.27632369844388 2.16711857464479e-05 *** df.mm.trans3:probe2 -0.183187093553084 0.0405777392989081 -4.51447263248629 7.45947396328338e-06 *** df.mm.trans3:probe3 -0.241168546509838 0.0405777392989081 -5.94337069232262 4.42572043864045e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.28417289780825 0.134620979190629 31.8239617893560 1.3037860846853e-137 *** df.mm.trans1 -0.182877860179232 0.12090932615332 -1.5125207128136 0.130857985945954 df.mm.trans2 -0.0963934185236073 0.111192546390293 -0.866905396565527 0.386294336029775 df.mm.exp2 -0.0183380852437703 0.152306251641067 -0.120402708662195 0.90419909471787 df.mm.exp3 0.159413592338424 0.152306251641067 1.04666479951268 0.295619649810407 df.mm.exp4 -0.230374405103005 0.152306251641067 -1.51257353273928 0.130844561559788 df.mm.exp5 -0.0685712899609729 0.152306251641067 -0.450219798741891 0.652692970632652 df.mm.exp6 -0.0949537026255224 0.152306251641067 -0.623439298140535 0.533201308188037 df.mm.exp7 -0.157428441279138 0.152306251641067 -1.03363085613940 0.301669857077126 df.mm.exp8 -0.247313776574038 0.152306251641067 -1.62379268026943 0.104875457969411 df.mm.trans1:exp2 0.0217609121453413 0.145822134001915 0.149229143396403 0.881416303142115 df.mm.trans2:exp2 0.0129168909519372 0.126921876367556 0.101770406502114 0.918968396850857 df.mm.trans1:exp3 -0.124925154841172 0.145822134001915 -0.85669542347756 0.391909799253255 df.mm.trans2:exp3 -0.119485953111556 0.126921876367556 -0.941413383816786 0.346821573613094 df.mm.trans1:exp4 0.170855226397271 0.145822134001915 1.17166867407815 0.241733351771659 df.mm.trans2:exp4 0.185989562864978 0.126921876367556 1.46538617445560 0.143269783823579 df.mm.trans1:exp5 0.0623339591298455 0.145822134001916 0.427465690009904 0.669173151113846 df.mm.trans2:exp5 0.0144904719530638 0.126921876367556 0.114168434692067 0.909137389476884 df.mm.trans1:exp6 0.111245319787387 0.145822134001915 0.762883635936408 0.445792771104712 df.mm.trans2:exp6 0.0905652487122334 0.126921876367556 0.713551133218073 0.475745358868724 df.mm.trans1:exp7 0.158108316455069 0.145822134001915 1.08425457861557 0.278629546539962 df.mm.trans2:exp7 0.231087119299041 0.126921876367556 1.82070361637131 0.069083671879424 . df.mm.trans1:exp8 0.261076530155275 0.145822134001915 1.79037655663334 0.0738304398300698 . df.mm.trans2:exp8 0.152213411325689 0.126921876367556 1.19926852392957 0.230834026156218 df.mm.trans1:probe2 0.092130869419279 0.0729110670009577 1.26360610547736 0.206796962133222 df.mm.trans1:probe3 0.0530252029134954 0.0729110670009577 0.727258632942497 0.467313497567636 df.mm.trans1:probe4 0.00828324572927306 0.0729110670009577 0.113607523109822 0.909581874385085 df.mm.trans1:probe5 0.183039323015975 0.0729110670009577 2.51044636356194 0.0122848573734687 * df.mm.trans1:probe6 0.0409886664762151 0.0729110670009577 0.562173455446437 0.574179891794929 df.mm.trans1:probe7 -0.0361319311582472 0.0729110670009577 -0.495561684178516 0.620361024509924 df.mm.trans1:probe8 -0.038179927699732 0.0729110670009577 -0.523650650994183 0.600689199018394 df.mm.trans1:probe9 0.0484665392692922 0.0729110670009577 0.664735015723409 0.50644144727969 df.mm.trans1:probe10 0.071182314588654 0.0729110670009577 0.97628957463644 0.329262146197701 df.mm.trans1:probe11 0.0513959755854441 0.0729110670009577 0.704913227847413 0.481101420107897 df.mm.trans1:probe12 0.103351402019355 0.0729110670009577 1.41749951372948 0.156787027584007 df.mm.trans1:probe13 0.0176859760865817 0.0729110670009577 0.242569157386620 0.808411052018062 df.mm.trans1:probe14 -0.00120735284006988 0.0729110670009577 -0.016559253481423 0.986793004155776 df.mm.trans1:probe15 0.036296685797413 0.0729110670009577 0.497821349904757 0.61876812389767 df.mm.trans1:probe16 0.113649774109135 0.0729110670009577 1.55874517798022 0.119513898859776 df.mm.trans1:probe17 0.0376042739134977 0.0729110670009577 0.515755364175424 0.6061900185234 df.mm.trans1:probe18 0.0309597263302507 0.0729110670009577 0.424623141639718 0.671243406361464 df.mm.trans1:probe19 -0.053225314018416 0.0729110670009577 -0.730003224582036 0.465635256377359 df.mm.trans1:probe20 0.00693163614384943 0.0729110670009577 0.0950697394643584 0.92428695573143 df.mm.trans1:probe21 0.119849839337526 0.0729110670009577 1.64378117434424 0.100675625803707 df.mm.trans1:probe22 0.103345973437954 0.0729110670009577 1.41742505889533 0.156808773943466 df.mm.trans2:probe2 0.0190169527671592 0.0729110670009577 0.260823953747781 0.794305869046557 df.mm.trans2:probe3 0.0250777888550483 0.0729110670009577 0.343950375252619 0.730988091359726 df.mm.trans2:probe4 -0.0542196825278751 0.0729110670009577 -0.743641325769692 0.457345902257477 df.mm.trans2:probe5 0.0836287583963147 0.0729110670009577 1.14699677067154 0.25177937312395 df.mm.trans2:probe6 0.000500764119759025 0.0729110670009577 0.00686814965624419 0.994522031966868 df.mm.trans3:probe2 0.0864207608986051 0.0729110670009577 1.18529003144982 0.236309663987944 df.mm.trans3:probe3 0.0992945633622152 0.0729110670009577 1.36185859632134 0.173685682907827