fitVsDatCorrelation=0.958945015656734 cont.fitVsDatCorrelation=0.253811016602069 fstatistic=6700.23976765979,51,669 cont.fstatistic=563.976928416653,51,669 residuals=-0.826454126899696,-0.106653624534548,-0.00412424869800457,0.104085959702442,0.943034798901567 cont.residuals=-1.35391249495704,-0.587698198732652,-0.177732773512782,0.622370850329167,1.79756807566742 predictedValues: Include Exclude Both Lung 131.491293945201 151.808741988148 67.7504586076184 cerebhem 107.155575871682 91.4816358463016 58.6749844477692 cortex 121.595129237059 126.561595360426 67.2276852161675 heart 135.530885039340 137.244869372277 71.4417568371168 kidney 131.908696800678 147.182861362036 86.1257690397306 liver 134.490460526364 128.549071017958 81.317816485844 stomach 135.636121317657 145.947764383695 72.1534740834562 testicle 120.875525699249 117.191047153966 65.0835867539207 diffExp=-20.3174480429471,15.6739400253806,-4.96646612336656,-1.71398433293726,-15.2741645613582,5.94138950840556,-10.3116430660384,3.68447854528324 diffExpScore=2.75363438500253 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 90.0069760508729 83.5636416489893 100.117338557255 cerebhem 81.865044382546 97.7801869246203 85.4935583166 cortex 98.3286884781444 103.877919128590 98.4499638003542 heart 107.573480186100 74.2334315561928 85.204641650542 kidney 94.1116025262173 89.7458774638226 88.9268940752963 liver 91.7104533084422 107.126170063174 97.6491670202521 stomach 106.542308565810 99.2497875152839 81.576351538988 testicle 92.1776556289987 104.189572866528 85.093825781236 cont.diffExp=6.44333440188359,-15.9151425420743,-5.54923065044585,33.3400486299075,4.36572506239466,-15.4157167547315,7.2925210505265,-12.0119172375292 cont.diffExpScore=28.2660062006807 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,1,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=0,0,0,1,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.884593886956496 cont.tran.correlation=-0.321807674901598 tran.covariance=0.0123683476984836 cont.tran.covariance=-0.00377791793588813 tran.mean=129.040704682627 cont.tran.mean=95.1301747683958 weightedLogRatios: wLogRatio Lung -0.711332223532713 cerebhem 0.726702628133832 cortex -0.192983730019703 heart -0.0617736130021882 kidney -0.540914500452408 liver 0.220441301402790 stomach -0.362453406652612 testicle 0.147946627506688 cont.weightedLogRatios: wLogRatio Lung 0.331486939068904 cerebhem -0.798340257332042 cortex -0.253407978829702 heart 1.66660784908546 kidney 0.214731348719706 liver -0.714134806389117 stomach 0.328497228390245 testicle -0.561632073081147 varWeightedLogRatios=0.213519566691866 cont.varWeightedLogRatios=0.651821846579948 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 6.11661933375126 0.0998730852685745 61.2439208952312 1.74527059729553e-276 *** df.mm.trans1 -0.864631222677614 0.083746127918687 -10.3244322354476 2.7439656730883e-23 *** df.mm.trans2 -1.34178141429134 0.0769573806686644 -17.4353830994886 2.10219810843202e-56 *** df.mm.exp2 -0.567324334100632 0.0998730852685745 -5.68045267225906 2.00462842535680e-08 *** df.mm.exp3 -0.252389989442995 0.0998730852685745 -2.52710716570214 0.0117299495185050 * df.mm.exp4 -0.123647148757455 0.0998730852685745 -1.23804274620082 0.216134496738692 df.mm.exp5 -0.267753764723331 0.0998730852685745 -2.68094015523100 0.00752249037233845 ** df.mm.exp6 -0.32629202636611 0.0998730852685745 -3.26706665252865 0.00114220604162872 ** df.mm.exp7 -0.071301800968479 0.0998730852685745 -0.71392408451924 0.475523118712476 df.mm.exp8 -0.302836473189 0.0998730852685745 -3.03221305694748 0.00252154499987807 ** df.mm.trans1:exp2 0.362665448979915 0.086492628996915 4.19302145380331 3.12301871490968e-05 *** df.mm.trans2:exp2 0.0608411329475367 0.0706209358514313 0.861516945563156 0.389262069338256 df.mm.trans1:exp3 0.174146258870319 0.086492628996915 2.01342311928720 0.0444701295776953 * df.mm.trans2:exp3 0.0704976467907468 0.0706209358514314 0.998254213722911 0.318517027060808 df.mm.trans1:exp4 0.153906053287693 0.086492628996915 1.77941236233185 0.0756260877627822 . df.mm.trans2:exp4 0.0227923946675452 0.0706209358514313 0.322742744665613 0.74699091830733 df.mm.trans1:exp5 0.270923113391019 0.086492628996915 3.13232603209093 0.00180994040603647 ** df.mm.trans2:exp5 0.236808081116981 0.0706209358514314 3.35322773993204 0.00084377995102599 *** df.mm.trans1:exp6 0.348844653751924 0.086492628996915 4.03322985782252 6.13556653079631e-05 *** df.mm.trans2:exp6 0.159981281273493 0.0706209358514313 2.26535204248855 0.0238099911717582 * df.mm.trans1:exp7 0.102336878713990 0.086492628996915 1.18318612696626 0.237155671183783 df.mm.trans2:exp7 0.0319291283069708 0.0706209358514313 0.45211986958289 0.651329184623465 df.mm.trans1:exp8 0.218657132381577 0.086492628996915 2.52804354448951 0.0116990129093188 * df.mm.trans2:exp8 0.0440205057847234 0.0706209358514313 0.62333506705903 0.533276805333616 df.mm.trans1:probe2 0.0156301104584301 0.0611595244863707 0.255562982048908 0.798366858062795 df.mm.trans1:probe3 0.131241727301508 0.0611595244863707 2.14589188525747 0.0322407454647858 * df.mm.trans1:probe4 0.113928892248311 0.0611595244863708 1.86281520670913 0.0629263412583214 . df.mm.trans1:probe5 -0.00113366980520550 0.0611595244863707 -0.018536275661498 0.985216565486115 df.mm.trans1:probe6 -0.895249582202099 0.0611595244863708 -14.6379421638833 2.70089347643334e-42 *** df.mm.trans1:probe7 -1.42197969801890 0.0611595244863708 -23.2503393373469 4.23765220649222e-88 *** df.mm.trans1:probe8 -1.38139595467675 0.0611595244863707 -22.5867674132193 2.15701940639932e-84 *** df.mm.trans1:probe9 -1.41146414713338 0.0611595244863707 -23.0784028977844 3.87920061212612e-87 *** df.mm.trans1:probe10 -1.42807134241524 0.0611595244863707 -23.3499418840884 1.17415292817469e-88 *** df.mm.trans1:probe11 -1.32230998954515 0.0611595244863707 -21.6206715250021 5.05895213107702e-79 *** df.mm.trans1:probe12 -1.35233556351005 0.0611595244863707 -22.1116101681172 9.53999573472975e-82 *** df.mm.trans2:probe2 0.546150777153036 0.0611595244863708 8.92993825147781 4.08019561907961e-18 *** df.mm.trans2:probe3 1.08092432640875 0.0611595244863708 17.6738510556869 1.18572565974725e-57 *** df.mm.trans2:probe4 1.19430530481426 0.0611595244863708 19.5277074968169 1.51121612306136e-67 *** df.mm.trans2:probe5 0.606670104880715 0.0611595244863708 9.91947059719063 9.9269086689883e-22 *** df.mm.trans2:probe6 1.03205307531029 0.0611595244863708 16.8747727190110 1.71003833244051e-53 *** df.mm.trans3:probe2 0.0672092480522044 0.0611595244863707 1.09891711252892 0.272199534381286 df.mm.trans3:probe3 0.125863042715734 0.0611595244863708 2.05794671840168 0.0399821213918358 * df.mm.trans3:probe4 0.775590798954506 0.0611595244863708 12.6814393255681 3.53061433409769e-33 *** df.mm.trans3:probe5 0.611157038104597 0.0611595244863707 9.99283502017404 5.22161250567433e-22 *** df.mm.trans3:probe6 1.41559859351933 0.0611595244863707 23.1460039202037 1.62461185922778e-87 *** df.mm.trans3:probe7 0.282299337928256 0.0611595244863708 4.61578699800332 4.6955130009826e-06 *** df.mm.trans3:probe8 0.115312404372419 0.0611595244863708 1.88543657493798 0.0598039938298709 . df.mm.trans3:probe9 -0.171353378067772 0.0611595244863708 -2.80174477330931 0.00522967710200008 ** df.mm.trans3:probe10 0.252753141844426 0.0611595244863707 4.13268651067998 4.04073868880777e-05 *** df.mm.trans3:probe11 0.371237209838043 0.0611595244863708 6.06998195221045 2.14294101582868e-09 *** df.mm.trans3:probe12 -0.178173052412941 0.0611595244863708 -2.91325110699065 0.00369617622518636 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22519991304913 0.340639380990965 12.4037329470170 6.02860284154586e-32 *** df.mm.trans1 0.216057678821045 0.285634804390968 0.756412298150166 0.449668314970551 df.mm.trans2 0.177357730760152 0.262480271267924 0.675699281715218 0.499465086149778 df.mm.exp2 0.220200215984088 0.340639380990965 0.646432057689562 0.51822128081471 df.mm.exp3 0.322830936146443 0.340639380990965 0.947720534270833 0.343613942796577 df.mm.exp4 0.221180035311685 0.340639380990965 0.649308470054881 0.516361864125127 df.mm.exp5 0.23449600210471 0.340639380990965 0.688399566199686 0.491439832065842 df.mm.exp6 0.292109727339061 0.340639380990965 0.857533637153974 0.391457125461045 df.mm.exp7 0.545489725748561 0.340639380990965 1.60137011804583 0.109767024992166 df.mm.exp8 0.407022517545542 0.340639380990965 1.19487804481519 0.232558105983717 df.mm.trans1:exp2 -0.315015303752423 0.295002357467582 -1.06783995374356 0.285977882796344 df.mm.trans2:exp2 -0.0630867641354029 0.240868416237900 -0.261913808048182 0.793468514334543 df.mm.trans1:exp3 -0.234402284408616 0.295002357467582 -0.794577665144167 0.427140991900465 df.mm.trans2:exp3 -0.105223098030339 0.240868416237900 -0.436848880703448 0.662361909014266 df.mm.trans1:exp4 -0.0428930636365899 0.295002357467582 -0.145399053772997 0.884439607055835 df.mm.trans2:exp4 -0.339573943539159 0.240868416237900 -1.40979024499323 0.159066337075910 df.mm.trans1:exp5 -0.189901842157146 0.295002357467582 -0.643729913846586 0.519971199434856 df.mm.trans2:exp5 -0.163122426327633 0.240868416237900 -0.677226300049739 0.498496495003767 df.mm.trans1:exp6 -0.273360538936792 0.295002357467582 -0.926638489547772 0.354448424211122 df.mm.trans2:exp6 -0.0437109450563541 0.240868416237900 -0.181472298191150 0.856051841938294 df.mm.trans1:exp7 -0.376834734961365 0.295002357467582 -1.27739567302534 0.201905712480915 df.mm.trans2:exp7 -0.373458464073105 0.240868416237900 -1.55046672330942 0.121502444909435 df.mm.trans1:exp8 -0.383191942113534 0.295002357467582 -1.29894535556599 0.194410190037613 df.mm.trans2:exp8 -0.186418978678493 0.240868416237900 -0.773945300052838 0.439236545257748 df.mm.trans1:probe2 0.301269877672982 0.208598167431345 1.44425946489744 0.149134157204777 df.mm.trans1:probe3 0.424343035391319 0.208598167431345 2.03426061032382 0.0423192682117024 * df.mm.trans1:probe4 0.117820650382073 0.208598167431345 0.564821119154129 0.572384777683641 df.mm.trans1:probe5 0.165162851309598 0.208598167431345 0.79177517877264 0.428772419183383 df.mm.trans1:probe6 0.376894651253405 0.208598167431345 1.80679751837921 0.0712430331287398 . df.mm.trans1:probe7 0.028872406283766 0.208598167431345 0.138411600827072 0.88995678012715 df.mm.trans1:probe8 0.165899800365598 0.208598167431345 0.795308043251145 0.426716408992808 df.mm.trans1:probe9 0.00439241842190644 0.208598167431345 0.0210568408917211 0.983206591821997 df.mm.trans1:probe10 0.0307147886500784 0.208598167431345 0.147243808650368 0.882983945584378 df.mm.trans1:probe11 -0.133670505213300 0.208598167431345 -0.64080383283949 0.521869579113085 df.mm.trans1:probe12 -0.0745898833354424 0.208598167431345 -0.357576886958951 0.720772755678324 df.mm.trans2:probe2 0.112629844786095 0.208598167431345 0.539936885222947 0.589420236334869 df.mm.trans2:probe3 0.28533326695832 0.208598167431345 1.36786085166463 0.171814919979541 df.mm.trans2:probe4 0.00200993495359952 0.208598167431345 0.00963543917163627 0.992315023335545 df.mm.trans2:probe5 0.168779366369207 0.208598167431345 0.809112411904368 0.418738147334481 df.mm.trans2:probe6 -0.153836695893307 0.208598167431345 -0.737478654715117 0.461089894057111 df.mm.trans3:probe2 -0.201087602310685 0.208598167431345 -0.963995057036483 0.335396721491961 df.mm.trans3:probe3 -0.151632236811295 0.208598167431345 -0.726910685163143 0.467534946676834 df.mm.trans3:probe4 -0.249746454725657 0.208598167431345 -1.19726102007993 0.231628886809524 df.mm.trans3:probe5 -0.0411443187168828 0.208598167431345 -0.197241995092907 0.843698078461091 df.mm.trans3:probe6 0.0227775986632235 0.208598167431345 0.109193666194216 0.913081627627726 df.mm.trans3:probe7 0.0807196559138214 0.208598167431345 0.386962440311889 0.698907073708763 df.mm.trans3:probe8 0.0187247487291161 0.208598167431345 0.0897646847030853 0.928501079025322 df.mm.trans3:probe9 0.235420466376989 0.208598167431345 1.12858357902148 0.259478086673080 df.mm.trans3:probe10 0.0877846435216868 0.208598167431345 0.420831326577109 0.674013407226925 df.mm.trans3:probe11 -0.0894734250713338 0.208598167431345 -0.428927186528528 0.668114296508725 df.mm.trans3:probe12 0.140387657049115 0.208598167431345 0.6730052271208 0.501176370444395