fitVsDatCorrelation=0.916199740472067 cont.fitVsDatCorrelation=0.244017477035358 fstatistic=3676.21468196203,65,991 cont.fstatistic=615.052193813695,65,991 residuals=-1.35806680856373,-0.152486795075901,0.00459803920097933,0.154209378143131,1.3236097163836 cont.residuals=-1.21366603059733,-0.444222560201114,-0.191413647070467,0.235009436012044,3.29673644938232 predictedValues: Include Exclude Both Lung 62.7584551619337 63.4449468547077 49.392968831918 cerebhem 53.1534428353032 60.1781551315619 48.5855233403922 cortex 54.2864292240188 101.152430745895 70.6374788376894 heart 65.230630386165 109.784192945510 68.6894322772129 kidney 66.4713879071959 874.054089685011 439.964032640956 liver 67.6231660674041 174.729892173844 114.404701116383 stomach 66.7184727854231 67.3277717555953 49.8910335328754 testicle 65.903466202541 64.4220405088701 48.5668751796013 diffExp=-0.686491692773991,-7.02471229625876,-46.8660015218764,-44.5535625593445,-807.582701777815,-107.106726106440,-0.609298970172134,1.48142569367094 diffExpScore=1.00193585001728 diffExp1.5=0,0,-1,-1,-1,-1,0,0 diffExp1.5Score=0.8 diffExp1.4=0,0,-1,-1,-1,-1,0,0 diffExp1.4Score=0.8 diffExp1.3=0,0,-1,-1,-1,-1,0,0 diffExp1.3Score=0.8 diffExp1.2=0,0,-1,-1,-1,-1,0,0 diffExp1.2Score=0.8 cont.predictedValues: Include Exclude Both Lung 78.6673993111331 69.170248927104 74.8165678248654 cerebhem 70.2904637153515 86.2593587421269 78.0226684382856 cortex 86.7144998873745 81.1999006502694 83.8394229080768 heart 72.9729489059718 71.7783265123062 79.8551867815117 kidney 84.2100625006992 58.2744803993836 81.8754373805584 liver 83.5041962925118 74.507171145053 71.7345996983407 stomach 81.1973175484355 65.4792501282454 83.2570427111671 testicle 73.2354322244528 86.8293205490293 78.9223145288967 cont.diffExp=9.49715038402914,-15.9688950267753,5.5145992371051,1.19462239366564,25.9355821013156,8.99702514745883,15.7180674201901,-13.5938883245765 cont.diffExpScore=2.51786616700642 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,1,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,0,1,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,0,1,0,1,0 cont.diffExp1.2Score=1.5 tran.correlation=0.300066275877676 cont.tran.correlation=-0.485940801585033 tran.covariance=0.0301116803994838 cont.tran.covariance=-0.00530150455845458 tran.mean=126.077435648186 cont.tran.mean=76.5181485899655 weightedLogRatios: wLogRatio Lung -0.045091538656583 cerebhem -0.500881127604179 cortex -2.67951630348714 heart -2.31048292526383 kidney -14.1312821225214 liver -4.45084049961839 stomach -0.0382276501778203 testicle 0.0949609976823322 cont.weightedLogRatios: wLogRatio Lung 0.553345120337325 cerebhem -0.891565591075919 cortex 0.291067128386517 heart 0.0706769999065129 kidney 1.56435811374384 liver 0.497946745187849 stomach 0.922839892595102 testicle -0.745558436949987 varWeightedLogRatios=22.8451444251525 cont.varWeightedLogRatios=0.664749301863036 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.36415773982277 0.13357218499399 32.672653666773 1.72099153222655e-159 *** df.mm.trans1 -0.424185072208188 0.113412950608491 -3.74018196275048 0.000194431497635425 *** df.mm.trans2 -0.204934982226667 0.0997900920767662 -2.05366061862148 0.0402705153150165 * df.mm.exp2 -0.202491100482039 0.125820371914024 -1.60936657078399 0.107854691541047 df.mm.exp3 -0.0363157784591738 0.125820371914024 -0.288631943354843 0.772923408818779 df.mm.exp4 0.257192562074136 0.125820371914024 2.04412495497853 0.0412050991860791 * df.mm.exp5 0.493563098704421 0.125820371914024 3.92275981382160 9.35751798250946e-05 *** df.mm.exp6 0.247791973106509 0.125820371914024 1.96941059175879 0.0491841814324919 * df.mm.exp7 0.110555596608335 0.125820371914024 0.878678030644196 0.379788866559551 df.mm.exp8 0.081047357651628 0.125820371914024 0.644151312054693 0.519626375613503 df.mm.trans1:exp2 0.0363806664497187 0.112990029493697 0.321981210313323 0.747534834715218 df.mm.trans2:exp2 0.149627964184998 0.0782817887327157 1.91140195704886 0.0562411622584468 . df.mm.trans1:exp3 -0.108703260597555 0.112990029493697 -0.962060644506855 0.336253821936019 df.mm.trans2:exp3 0.502771821696215 0.0782817887327157 6.42258984925438 2.0758252947446e-10 *** df.mm.trans1:exp4 -0.218556724811277 0.112990029493697 -1.93430098027781 0.0533605335755165 . df.mm.trans2:exp4 0.291151443203573 0.0782817887327157 3.71927427715885 0.000211008519662738 *** df.mm.trans1:exp5 -0.43608481321564 0.112990029493697 -3.85949818023515 0.000120974271509728 *** df.mm.trans2:exp5 2.12940661135836 0.0782817887327157 27.201813420858 3.74695171060193e-122 *** df.mm.trans1:exp6 -0.173134668009998 0.112990029493697 -1.53230040549424 0.125767474692385 df.mm.trans2:exp6 0.765276783953425 0.0782817887327157 9.775923574848 1.30437751482736e-21 *** df.mm.trans1:exp7 -0.0493670412778114 0.112990029493697 -0.436915022493780 0.662268160020044 df.mm.trans2:exp7 -0.0511553401650542 0.0782817887327157 -0.653476894092422 0.51360046783308 df.mm.trans1:exp8 -0.0321496321635222 0.112990029493697 -0.284535124980347 0.776059764783291 df.mm.trans2:exp8 -0.0657640903561175 0.0782817887327157 -0.84009437470395 0.401058103337558 df.mm.trans1:probe2 0.085783010177484 0.0853056015513771 1.00559645108205 0.314855200446952 df.mm.trans1:probe3 0.0104403736958654 0.0853056015513771 0.122387903091891 0.902616632256248 df.mm.trans1:probe4 -0.00699492002864276 0.0853056015513771 -0.0819983670642065 0.93466456993707 df.mm.trans1:probe5 -0.0496364955378687 0.0853056015513771 -0.581866778208861 0.560788755445602 df.mm.trans1:probe6 0.0578814792079894 0.0853056015513771 0.678519090837535 0.497601033877389 df.mm.trans1:probe7 0.0444155234573696 0.0853056015513771 0.520663621727343 0.602717427135238 df.mm.trans1:probe8 0.517146569879726 0.0853056015513771 6.06228149705108 1.90546332706214e-09 *** df.mm.trans1:probe9 0.483878862243171 0.0853056015513771 5.67229881090218 1.84827043311507e-08 *** df.mm.trans1:probe10 0.391076903694843 0.0853056015513771 4.58442231908193 5.13383534399497e-06 *** df.mm.trans1:probe11 0.527049499115716 0.0853056015513771 6.17836917542031 9.4433994575663e-10 *** df.mm.trans1:probe12 0.401096815944463 0.0853056015513771 4.70188133780281 2.9424983026021e-06 *** df.mm.trans1:probe13 0.397135802567768 0.0853056015513771 4.65544812234381 3.67212718419601e-06 *** df.mm.trans1:probe14 1.23000484506502 0.0853056015513771 14.4188051276354 6.30066605272537e-43 *** df.mm.trans1:probe15 0.390220585485718 0.0853056015513771 4.57438407782284 5.3808419756873e-06 *** df.mm.trans1:probe16 0.197997550832410 0.0853056015513771 2.32103809400092 0.020486928427969 * df.mm.trans1:probe17 1.19070529883035 0.0853056015513771 13.9581138539094 1.480311546071e-40 *** df.mm.trans1:probe18 0.649550502067321 0.0853056015513771 7.6143944858781 6.16469513022933e-14 *** df.mm.trans1:probe19 1.05643230089525 0.0853056015513771 12.3840906304259 7.46537548141373e-33 *** df.mm.trans2:probe2 -0.0478517791426902 0.0853056015513771 -0.560945333863808 0.57496161049598 df.mm.trans2:probe3 -0.213661890598856 0.0853056015513771 -2.50466425080155 0.0124164033392702 * df.mm.trans2:probe4 0.177492466611726 0.0853056015513771 2.08066602173630 0.037720875509742 * df.mm.trans2:probe5 -0.150468489741023 0.0853056015513771 -1.76387584173355 0.0780609665428856 . df.mm.trans2:probe6 0.0082345326998476 0.0853056015513771 0.0965298005065726 0.923119324239042 df.mm.trans3:probe2 0.0194303374160359 0.0853056015513771 0.227773288772057 0.81986947768649 df.mm.trans3:probe3 0.120709516640017 0.0853056015513771 1.41502450536401 0.157375454324465 df.mm.trans3:probe4 0.423580697068626 0.0853056015513771 4.96544997474187 8.06729119511784e-07 *** df.mm.trans3:probe5 0.0499853701407437 0.0853056015513771 0.585956481540535 0.558038182375714 df.mm.trans3:probe6 0.345499978737657 0.0853056015513771 4.05014409903167 5.51827270549519e-05 *** df.mm.trans3:probe7 -0.172490401080089 0.0853056015513771 -2.02202901032476 0.0434416388243429 * df.mm.trans3:probe8 0.0808235454114827 0.0853056015513771 0.947458829685469 0.343635995674123 df.mm.trans3:probe9 0.270035678948401 0.0853056015513771 3.16550934566432 0.00159535264998722 ** df.mm.trans3:probe10 0.198740594677927 0.0853056015513771 2.32974846977935 0.0200195485357065 * df.mm.trans3:probe11 0.654281449076723 0.0853056015513771 7.669853294249 4.10406851442845e-14 *** df.mm.trans3:probe12 -0.222909641348319 0.0853056015513771 -2.61307156030155 0.00910916984930605 ** df.mm.trans3:probe13 -0.0229913055199014 0.0853056015513771 -0.269516949670115 0.787588007393466 df.mm.trans3:probe14 -0.0682870747752255 0.0853056015513771 -0.80049930524314 0.423613425174616 df.mm.trans3:probe15 0.559121609644521 0.0853056015513771 6.55433640319361 8.97405241896828e-11 *** df.mm.trans3:probe16 -0.0178215371534601 0.0853056015513771 -0.20891403177934 0.834558283235893 df.mm.trans3:probe17 0.341047789765059 0.0853056015513771 3.99795304836642 6.86349666186256e-05 *** df.mm.trans3:probe18 0.578020995792574 0.0853056015513771 6.7758855840721 2.11824933757140e-11 *** df.mm.trans3:probe19 -0.00614005401680847 0.0853056015513771 -0.0719771492744294 0.942634616207628 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.33741535001867 0.323252638602846 13.4180354065035 7.66557774354704e-38 *** df.mm.trans1 -0.0186263575004737 0.274466091406518 -0.0678639660186137 0.945907613000082 df.mm.trans2 -0.145418074123436 0.241497962855718 -0.602150313832321 0.547211827490374 df.mm.exp2 0.0662349745178224 0.304492789520735 0.217525592714608 0.827843542226114 df.mm.exp3 0.143871482692861 0.304492789520735 0.472495532387851 0.636677163867674 df.mm.exp4 -0.103303763778389 0.304492789520735 -0.339265057609366 0.734481942900768 df.mm.exp5 -0.193480661756168 0.304492789520735 -0.63541951867137 0.525301533402213 df.mm.exp6 0.176058754545883 0.304492789520735 0.578203361803727 0.563258193583793 df.mm.exp7 -0.130077525923568 0.304492789520735 -0.427194109024082 0.669330749794358 df.mm.exp8 0.102399385818534 0.304492789520735 0.336294944716781 0.736719655249018 df.mm.trans1:exp2 -0.178827665769145 0.273442597134234 -0.653986129605688 0.513272467370558 df.mm.trans2:exp2 0.154552741283600 0.189446588476032 0.815811688808287 0.4148038838202 df.mm.trans1:exp3 -0.046479200412099 0.273442597134234 -0.169977907243480 0.865062204037391 df.mm.trans2:exp3 0.0164716995765083 0.189446588476032 0.0869464037806741 0.930731678622593 df.mm.trans1:exp4 0.0281637437282294 0.273442597134234 0.102996914245968 0.91798625794024 df.mm.trans2:exp4 0.140315493725175 0.189446588476032 0.740659912928055 0.459075119454207 df.mm.trans1:exp5 0.261566253406987 0.273442597134234 0.956567323995184 0.339018977614661 df.mm.trans2:exp5 0.0220740889040794 0.189446588476032 0.116518798684370 0.90726499617592 df.mm.trans1:exp6 -0.11639069854292 0.273442597134234 -0.425649477304311 0.670455702100965 df.mm.trans2:exp6 -0.101734218190863 0.189446588476032 -0.53700739089178 0.591383050999722 df.mm.trans1:exp7 0.161730907836490 0.273442597134234 0.591462008960865 0.554345791064162 df.mm.trans2:exp7 0.0752399850617603 0.189446588476032 0.397156716660956 0.691337417096411 df.mm.trans1:exp8 -0.173948864773227 0.273442597134234 -0.636143989986445 0.524829463714809 df.mm.trans2:exp8 0.124974131773096 0.189446588476032 0.659680033187333 0.509612456445871 df.mm.trans1:probe2 0.0942909242349902 0.206444633591390 0.456737104736844 0.647960108536047 df.mm.trans1:probe3 -0.108147966657469 0.206444633591390 -0.523859423110619 0.600493451178148 df.mm.trans1:probe4 0.444670033930462 0.206444633591390 2.15394329314747 0.0314851810307509 * df.mm.trans1:probe5 -0.000923952618876132 0.206444633591390 -0.00447554679820297 0.996429942974842 df.mm.trans1:probe6 0.0527129124718749 0.206444633591390 0.255336801712211 0.798515960852124 df.mm.trans1:probe7 0.120236134105049 0.206444633591390 0.582413463665174 0.560420696014202 df.mm.trans1:probe8 0.252634923881941 0.206444633591390 1.22374178241888 0.221340525761511 df.mm.trans1:probe9 0.145263951363157 0.206444633591390 0.703646051903068 0.481818511805036 df.mm.trans1:probe10 0.094590496874716 0.206444633591390 0.458188208766599 0.646917682832657 df.mm.trans1:probe11 0.336376605652751 0.206444633591390 1.62937926649395 0.103550555743507 df.mm.trans1:probe12 0.0396136977058664 0.206444633591390 0.191885335146433 0.84787133455954 df.mm.trans1:probe13 -0.143006059668838 0.206444633591390 -0.692709019270925 0.4886544686672 df.mm.trans1:probe14 -0.137831290697500 0.206444633591390 -0.667642884679216 0.504517018859456 df.mm.trans1:probe15 0.327459455649168 0.206444633591390 1.58618536094912 0.113016278258539 df.mm.trans1:probe16 0.260385570724152 0.206444633591390 1.26128524725678 0.207502916997082 df.mm.trans1:probe17 0.0300145088660302 0.206444633591390 0.145387692302223 0.884434346183 df.mm.trans1:probe18 0.209276552403947 0.206444633591390 1.01371757048508 0.310964968790461 df.mm.trans1:probe19 -0.252902689445426 0.206444633591390 -1.22503881571457 0.220851685367361 df.mm.trans2:probe2 0.379134342700430 0.206444633591390 1.83649405704989 0.0665840097889967 . df.mm.trans2:probe3 0.0703487735235553 0.206444633591390 0.340763391616149 0.733353938187421 df.mm.trans2:probe4 0.159587397012485 0.206444633591390 0.773027587282077 0.439690390507223 df.mm.trans2:probe5 0.0767855312073498 0.206444633591390 0.371942490688952 0.710015183731506 df.mm.trans2:probe6 0.428483092641957 0.206444633591390 2.07553514561217 0.0381944255769727 * df.mm.trans3:probe2 0.322862031743319 0.206444633591390 1.56391583606067 0.118156590243152 df.mm.trans3:probe3 0.0272694826135040 0.206444633591390 0.132091021883754 0.8949391103244 df.mm.trans3:probe4 0.271004959493393 0.206444633591390 1.31272465057040 0.189579664717473 df.mm.trans3:probe5 0.113080973191906 0.206444633591390 0.547754481309133 0.583983843308817 df.mm.trans3:probe6 0.0323894599771893 0.206444633591390 0.156891750653576 0.875362118394458 df.mm.trans3:probe7 0.18540982425326 0.206444633591390 0.89810919774372 0.369345480971492 df.mm.trans3:probe8 0.0535531630298971 0.206444633591390 0.259406902946643 0.795375180916132 df.mm.trans3:probe9 0.482921396413494 0.206444633591390 2.33922959397107 0.0195214316783458 * df.mm.trans3:probe10 0.202361407373750 0.206444633591390 0.980221204365516 0.327216172646372 df.mm.trans3:probe11 0.214518035121897 0.206444633591390 1.03910686071156 0.299008495519087 df.mm.trans3:probe12 0.170125753644418 0.206444633591390 0.824074477911315 0.410095525968106 df.mm.trans3:probe13 -0.0549587561456412 0.206444633591390 -0.266215474771892 0.790128642792422 df.mm.trans3:probe14 0.0937257953442453 0.206444633591390 0.453999669130436 0.649928474527251 df.mm.trans3:probe15 0.185900520401208 0.206444633591390 0.900486087563578 0.368080397536197 df.mm.trans3:probe16 0.0177822166485275 0.206444633591390 0.0861355237924143 0.931376083988912 df.mm.trans3:probe17 0.0862072234316393 0.206444633591390 0.417580355235906 0.67634441561697 df.mm.trans3:probe18 0.0922776120423244 0.206444633591390 0.446984794116599 0.654983707907061 df.mm.trans3:probe19 0.195269205259818 0.206444633591390 0.945867189002879 0.344446893539925