fitVsDatCorrelation=0.86740256110151 cont.fitVsDatCorrelation=0.217793066997058 fstatistic=10859.5049989110,53,715 cont.fstatistic=2812.86722428041,53,715 residuals=-0.611862889026077,-0.0878888562475529,-0.00805172540517867,0.0731521628045815,0.612593320784375 cont.residuals=-0.496494502149412,-0.199082762815947,-0.0764086477453968,0.118316487508573,1.11700953934181 predictedValues: Include Exclude Both Lung 51.0340612791613 68.6028823497387 48.9288751029074 cerebhem 54.979515565586 62.2590733899999 55.9642398883696 cortex 50.4293465505991 68.9375486551659 50.3901742803111 heart 50.6599779636279 75.5103722644437 52.4256796774992 kidney 49.1241135604919 74.1906144432629 48.0195219844718 liver 51.5651420965497 80.346471660198 54.6444797597279 stomach 53.429717458518 89.1773018542135 53.2912292803628 testicle 52.4291522250233 84.3135791049237 55.3195621388181 diffExp=-17.5688210705774,-7.27955782441389,-18.5082021045668,-24.8503943008158,-25.0665008827710,-28.7813295636483,-35.7475843956955,-31.8844268799004 diffExpScore=0.994755798981727 diffExp1.5=0,0,0,0,-1,-1,-1,-1 diffExp1.5Score=0.8 diffExp1.4=0,0,0,-1,-1,-1,-1,-1 diffExp1.4Score=0.833333333333333 diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.875 diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.875 cont.predictedValues: Include Exclude Both Lung 59.1619022516529 52.9837276913784 50.3486581631374 cerebhem 54.624621511072 54.2737795977714 60.1645746620269 cortex 57.7484540638995 51.8040758553587 63.4072472085461 heart 57.6854991291037 55.2267934723005 56.4353271258027 kidney 55.8490846005863 60.678288941094 48.4700997704334 liver 57.2629470701074 53.6820853150663 50.8250367064347 stomach 56.8064591578976 52.695485133545 52.778155296288 testicle 57.0382036046798 53.2829553881994 54.4259926758685 cont.diffExp=6.17817456027453,0.350841913300656,5.94437820854071,2.45870565680315,-4.82920434050763,3.58086175504116,4.11097402435255,3.75524821648042 cont.diffExpScore=1.38396524889199 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=0.0199253136677577 cont.tran.correlation=-0.430476455511686 tran.covariance=-8.93563388490186e-05 cont.tran.covariance=-0.00050466265402256 tran.mean=63.561804401344 cont.tran.mean=55.6752726739821 weightedLogRatios: wLogRatio Lung -1.20715484510762 cerebhem -0.505970623346328 cortex -1.27454794919945 heart -1.64630850477804 kidney -1.69058287324309 liver -1.84700826494506 stomach -2.16916099465538 testicle -1.99391277080883 cont.weightedLogRatios: wLogRatio Lung 0.443943606900987 cerebhem 0.0257563422197529 cortex 0.434704647047126 heart 0.175677699572576 kidney -0.33704889859876 liver 0.259289996361092 stomach 0.300638018657861 testicle 0.273077650808024 varWeightedLogRatios=0.282935965621287 cont.varWeightedLogRatios=0.0646972274938304 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.0139327108401 0.0750831325415395 53.4598461061731 5.37063471378032e-252 *** df.mm.trans1 -0.0595743663944952 0.0666752748022234 -0.893500125364441 0.371890042699341 df.mm.trans2 0.121720395802384 0.0606498329149431 2.00693703432106 0.0451323417371452 * df.mm.exp2 -0.156908073267352 0.081764987510225 -1.91901299132138 0.0553804771528126 . df.mm.exp3 -0.0364820335808767 0.081764987510225 -0.446181607699927 0.655601154588217 df.mm.exp4 0.0195495824126203 0.081764987510225 0.239094788709845 0.811100599027236 df.mm.exp5 0.0589199278947068 0.081764987510225 0.720600952667408 0.471390587503344 df.mm.exp6 0.0578857702930872 0.081764987510225 0.707953025564254 0.479205139129605 df.mm.exp7 0.222761764265234 0.081764987510225 2.72441507114982 0.00659891825727464 ** df.mm.exp8 0.110419004336520 0.081764987510225 1.35044360304846 0.17730100346284 df.mm.trans1:exp2 0.231375466844712 0.0776396916658537 2.98011831165568 0.00297907935463537 ** df.mm.trans2:exp2 0.059877804650485 0.0653784711794312 0.915864252716317 0.360046881658589 df.mm.trans1:exp3 0.0245620340140522 0.0776396916658537 0.316359242122734 0.751822184263843 df.mm.trans2:exp3 0.0413484857717806 0.0653784711794312 0.632448037187956 0.527296342995063 df.mm.trans1:exp4 -0.0269066508432647 0.0776396916658537 -0.346557930176562 0.72902541948237 df.mm.trans2:exp4 0.0763858947832647 0.0653784711794312 1.16836465284763 0.243049120873487 df.mm.trans1:exp5 -0.097063180456674 0.0776396916658537 -1.25017472859649 0.211644735040303 df.mm.trans2:exp5 0.0193831736859148 0.0653784711794312 0.296476398671325 0.766952384595602 df.mm.trans1:exp6 -0.0475331448690346 0.0776396916658537 -0.61222737815096 0.540581972075391 df.mm.trans2:exp6 0.100127858185121 0.0653784711794312 1.5315111592365 0.126085417754753 df.mm.trans1:exp7 -0.176887944396146 0.0776396916658537 -2.27831848118920 0.0230017796998795 * df.mm.trans2:exp7 0.0395302288124102 0.0653784711794312 0.60463678791019 0.545612179063688 df.mm.trans1:exp8 -0.0834495055555892 0.0776396916658537 -1.07483046061981 0.282813211631751 df.mm.trans2:exp8 0.0957893778126845 0.0653784711794312 1.46515169419901 0.143319110574672 df.mm.trans1:probe2 0.0299756109304646 0.0425250104831339 0.704893675272659 0.481105953821138 df.mm.trans1:probe3 0.0194152086102468 0.0425250104831339 0.456559760707105 0.648126165505542 df.mm.trans1:probe4 -0.11518826636144 0.0425250104831339 -2.70871811794439 0.00691596722605987 ** df.mm.trans1:probe5 -0.183392244166044 0.0425250104831339 -4.31257375559685 1.84039713095899e-05 *** df.mm.trans1:probe6 0.111416594673557 0.0425250104831339 2.62002509600194 0.00897891184743974 ** df.mm.trans1:probe7 -0.153050168529069 0.0425250104831339 -3.59906245266586 0.000341405827272458 *** df.mm.trans1:probe8 -0.00636066569866849 0.0425250104831339 -0.149574700309392 0.881142349768057 df.mm.trans1:probe9 -0.116815380479825 0.0425250104831339 -2.74698063922068 0.00616606633199989 ** df.mm.trans1:probe10 -0.0698041546769002 0.0425250104831339 -1.64148471414453 0.101136588518759 df.mm.trans1:probe11 -0.0539963791462537 0.0425250104831339 -1.26975581035235 0.204584721032190 df.mm.trans1:probe12 -0.146850180753327 0.0425250104831339 -3.45326618582658 0.000586470390948872 *** df.mm.trans1:probe13 -0.108923910186528 0.0425250104831339 -2.56140819129788 0.0106288923520014 * df.mm.trans1:probe14 -0.0723040944378454 0.0425250104831339 -1.70027223077399 0.0895146076535135 . df.mm.trans1:probe15 0.320810643629465 0.0425250104831339 7.5440461973949 1.38774247773232e-13 *** df.mm.trans1:probe16 -0.0594151508510859 0.0425250104831339 -1.3971813334332 0.162792476353635 df.mm.trans1:probe17 -0.0989993425228784 0.0425250104831339 -2.32802629318911 0.0201887761303127 * df.mm.trans1:probe18 -0.133284906085695 0.0425250104831339 -3.13427097539594 0.00179325821060238 ** df.mm.trans1:probe19 -0.119813106741659 0.0425250104831339 -2.81747389078666 0.0049739022849498 ** df.mm.trans1:probe20 -0.0747411888628578 0.0425250104831339 -1.75758190330139 0.0792465799560956 . df.mm.trans1:probe21 0.572576598859088 0.0425250104831339 13.4644669655327 5.40807483868309e-37 *** df.mm.trans1:probe22 -0.109747244308759 0.0425250104831339 -2.58076936517833 0.0100562244825627 * df.mm.trans2:probe2 0.135691226543414 0.0425250104831339 3.19085698044051 0.00148052437060191 ** df.mm.trans2:probe3 -0.00367923201033574 0.0425250104831339 -0.0865192499316369 0.931077876979554 df.mm.trans2:probe4 -0.0417348250656029 0.0425250104831339 -0.981418336913888 0.326718447753691 df.mm.trans2:probe5 0.316031938606895 0.0425250104831339 7.43167220928114 3.06901138331887e-13 *** df.mm.trans2:probe6 0.520505331592818 0.0425250104831339 12.2399812646550 2.07228211293544e-31 *** df.mm.trans3:probe2 -0.335054114813933 0.0425250104831339 -7.87898958771146 1.22997248154251e-14 *** df.mm.trans3:probe3 -0.172529177577367 0.0425250104831339 -4.05712251724887 5.51510164274983e-05 *** df.mm.trans3:probe4 -0.235260808158701 0.0425250104831339 -5.53229277279095 4.43689968648551e-08 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.15770787688187 0.147266314975173 28.2325790360328 2.13654581805584e-118 *** df.mm.trans1 -0.0142102259252372 0.130775337785062 -0.108661358983394 0.913501577798592 df.mm.trans2 -0.226814548896772 0.118957175798468 -1.90669076812172 0.056961215538693 . df.mm.exp2 -0.233848428604291 0.160371950357293 -1.45816290244833 0.145234745169434 df.mm.exp3 -0.277303418746531 0.160371950357294 -1.72912668411605 0.0842180565860956 . df.mm.exp4 -0.097931943153497 0.160371950357294 -0.610655061158225 0.541622019751828 df.mm.exp5 0.116001318045089 0.160371950357293 0.723326727564569 0.469715733008815 df.mm.exp6 -0.028946640880643 0.160371950357294 -0.180496906199324 0.85681362937639 df.mm.exp7 -0.0932082268144213 0.160371950357294 -0.581200307203112 0.561288623045919 df.mm.exp8 -0.108794735372134 0.160371950357293 -0.67839004969229 0.497743910606876 df.mm.trans1:exp2 0.154055361456100 0.152280690754521 1.01165394438905 0.312045764050770 df.mm.trans2:exp2 0.257904816864806 0.128231817232435 2.01123888307162 0.0446750575949449 * df.mm.trans1:exp3 0.253122206652992 0.152280690754521 1.66220815914888 0.096909348240462 . df.mm.trans2:exp3 0.254787407663475 0.128231817232435 1.98692815217337 0.0473115761390984 * df.mm.trans1:exp4 0.0726599784816037 0.152280690754521 0.477145054449042 0.633404639978712 df.mm.trans2:exp4 0.139395325944949 0.128231817232435 1.08705724486677 0.277377830459397 df.mm.trans1:exp5 -0.173625974665795 0.152280690754521 -1.14017065332126 0.254596995787544 df.mm.trans2:exp5 0.0195997962479464 0.128231817232435 0.152846591984418 0.878562377277552 df.mm.trans1:exp6 -0.00367738423764358 0.152280690754521 -0.024148723120593 0.980740717017761 df.mm.trans2:exp6 0.0420411382211309 0.128231817232435 0.327852627596523 0.743119070617474 df.mm.trans1:exp7 0.0525804719639473 0.152280690754521 0.345286534382142 0.729980523069039 df.mm.trans2:exp7 0.087753165882115 0.128231817232435 0.684332233419513 0.493987141959782 df.mm.trans1:exp8 0.0722382256947985 0.152280690754521 0.474375479496922 0.635377014870296 df.mm.trans2:exp8 0.114426387344238 0.128231817232435 0.892340058917102 0.372510901241488 df.mm.trans1:probe2 -0.100750126028141 0.0834075693987197 -1.20792545274299 0.227475319724328 df.mm.trans1:probe3 -0.0814615781480126 0.0834075693987197 -0.976668889109998 0.32906342391299 df.mm.trans1:probe4 -0.113200833073999 0.0834075693987197 -1.35720095777946 0.175145623096738 df.mm.trans1:probe5 -0.109845992266239 0.0834075693987197 -1.31697869939278 0.188267688933168 df.mm.trans1:probe6 -0.0835280800910937 0.0834075693987197 -1.00144484119658 0.316950585013056 df.mm.trans1:probe7 -0.0910619552159014 0.0834075693987197 -1.09177087730001 0.2753015683801 df.mm.trans1:probe8 -0.0272206373718532 0.0834075693987197 -0.326356919019283 0.744249829959838 df.mm.trans1:probe9 -0.106279794106732 0.0834075693987197 -1.27422241018287 0.202998598165320 df.mm.trans1:probe10 -0.0321926043221181 0.0834075693987197 -0.385967419434383 0.699635613416586 df.mm.trans1:probe11 -0.0634426740747244 0.0834075693987197 -0.760634490755203 0.447126171401754 df.mm.trans1:probe12 -0.0552029876970595 0.0834075693987197 -0.661846258019682 0.508282998628244 df.mm.trans1:probe13 0.00435457629991042 0.0834075693987197 0.0522084066386577 0.958377216925913 df.mm.trans1:probe14 -0.181962512077645 0.0834075693987197 -2.18160669816183 0.0294635396032306 * df.mm.trans1:probe15 -0.0410932097436079 0.0834075693987197 -0.492679621763906 0.622390235311169 df.mm.trans1:probe16 -0.162653289097350 0.0834075693987197 -1.95010225414681 0.0515543818484773 . df.mm.trans1:probe17 -0.0981873257918547 0.0834075693987197 -1.17719922184139 0.239507715956996 df.mm.trans1:probe18 -0.0573321602069 0.0834075693987197 -0.687373587555712 0.492070234793221 df.mm.trans1:probe19 -0.0728786792676343 0.0834075693987197 -0.873765772015807 0.382539193137329 df.mm.trans1:probe20 -0.0666250628483246 0.0834075693987197 -0.798789166602273 0.424677923368556 df.mm.trans1:probe21 -0.068938943184468 0.0834075693987197 -0.8265310172859 0.408778658450091 df.mm.trans1:probe22 -0.0342124728568337 0.0834075693987197 -0.410184268687715 0.681793655781961 df.mm.trans2:probe2 0.151309202406399 0.0834075693987197 1.81409437413388 0.070082218761225 . df.mm.trans2:probe3 0.0012604434290298 0.0834075693987197 0.0151118590089156 0.987947155580434 df.mm.trans2:probe4 0.0642901660670561 0.0834075693987197 0.770795343042846 0.441082871696925 df.mm.trans2:probe5 0.0600776610441924 0.0834075693987197 0.720290274339473 0.47158169334759 df.mm.trans2:probe6 0.113977664396488 0.0834075693987197 1.36651463671878 0.172207052453499 df.mm.trans3:probe2 0.0237321524091469 0.0834075693987197 0.284532358156827 0.776084826174594 df.mm.trans3:probe3 0.0318367988290066 0.0834075693987197 0.381701553690106 0.702796155676062 df.mm.trans3:probe4 -0.0464134849836219 0.0834075693987197 -0.556466101556657 0.578066374085355