fitVsDatCorrelation=0.890922347571943 cont.fitVsDatCorrelation=0.287043584716610 fstatistic=3474.75561654439,56,784 cont.fstatistic=770.194496806412,56,784 residuals=-1.01375076144549,-0.143999906554984,0.000748117683735374,0.145264521264386,1.08918144381179 cont.residuals=-0.979815205681753,-0.371318899498586,-0.155438047607506,0.154648747633194,2.50863263332776 predictedValues: Include Exclude Both Lung 57.1811470168899 51.4997836482486 56.5192821220639 cerebhem 58.3397214262955 62.2871293906898 57.1074694127109 cortex 67.3871405988642 49.9538560796923 69.1606186370996 heart 75.1608425515133 53.5834849038234 72.919133839634 kidney 234.091090169624 60.7480114361231 277.624121267731 liver 90.2310231101801 54.9068276237744 97.6967231485128 stomach 58.2465060926559 51.465985760547 58.2235513902959 testicle 57.7514324887316 54.5582562237292 59.0742098357924 diffExp=5.6813633686413,-3.94740796439429,17.4332845191719,21.5773576476898,173.343078733501,35.3241954864057,6.78052033210885,3.19317626500241 diffExpScore=1.02647925525009 diffExp1.5=0,0,0,0,1,1,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=0,0,0,1,1,1,0,0 diffExp1.4Score=0.75 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 80.4779568336262 52.1167074410797 59.5821999856483 cerebhem 75.9104048768088 68.204654803547 72.3446600446309 cortex 73.7425372698043 76.0877723797954 59.9524421441698 heart 79.3221377768749 61.8517534075806 55.2191044047993 kidney 70.7476174713165 83.1460469505727 66.6291399248656 liver 69.4390456120223 73.7258730705021 121.677679099901 stomach 69.1979186806801 66.4486407906121 61.3734421237599 testicle 77.0225532170241 89.2054803594542 65.5704061316531 cont.diffExp=28.3612493925465,7.70575007326175,-2.34523510999112,17.4703843692943,-12.3984294792563,-4.28682745847976,2.74927789006796,-12.1829271424301 cont.diffExpScore=3.35593399239951 cont.diffExp1.5=1,0,0,0,0,0,0,0 cont.diffExp1.5Score=0.5 cont.diffExp1.4=1,0,0,0,0,0,0,0 cont.diffExp1.4Score=0.5 cont.diffExp1.3=1,0,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=1,0,0,1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.516316242211497 cont.tran.correlation=-0.422412961459046 tran.covariance=0.0184953964268359 cont.tran.covariance=-0.00462901180502976 tran.mean=71.0870149075864 cont.tran.mean=72.9154438088313 weightedLogRatios: wLogRatio Lung 0.417948311632773 cerebhem -0.268369431599714 cortex 1.21561188114303 heart 1.40446376533773 kidney 6.4497540112391 liver 2.11311597421887 stomach 0.49539882234663 testicle 0.229092447574896 cont.weightedLogRatios: wLogRatio Lung 1.81217469675595 cerebhem 0.457710652083446 cortex -0.135131664916989 heart 1.05708470725547 kidney -0.70079953656933 liver -0.255816145214110 stomach 0.170951155519332 testicle -0.648687240559589 varWeightedLogRatios=4.55542417569222 cont.varWeightedLogRatios=0.749950176809717 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.96349469038415 0.134988584434337 29.3617027468876 2.06376069925692e-128 *** df.mm.trans1 0.173690994675834 0.117654652309983 1.47627816890924 0.140270754849902 df.mm.trans2 0.00650834344627944 0.104994080682156 0.061987717821749 0.950588412011874 df.mm.exp2 0.199883085418659 0.137364861684947 1.45512529890723 0.146034841816969 df.mm.exp3 -0.0680976941453513 0.137364861684947 -0.495743185775826 0.620214546293717 df.mm.exp4 0.0583002195302036 0.137364861684947 0.424418725539272 0.671376904322283 df.mm.exp5 -0.0170434517482783 0.137364861684947 -0.124074319583768 0.901288233998796 df.mm.exp6 -0.0270770048606873 0.137364861684947 -0.197117403450598 0.843786754567387 df.mm.exp7 -0.0119047044262742 0.137364861684947 -0.0866648448536878 0.930960049690517 df.mm.exp8 0.0234027623587347 0.137364861684947 0.17036935116937 0.864763628947388 df.mm.trans1:exp2 -0.179824142443693 0.128269011505798 -1.40192974384592 0.161332011881251 df.mm.trans2:exp2 -0.00970587846371352 0.100049138982840 -0.097011114362296 0.92274236173296 df.mm.trans1:exp3 0.232327655109026 0.128269011505798 1.81125318096432 0.0704842561186487 . df.mm.trans2:exp3 0.0376197883968932 0.100049138982840 0.376013114948902 0.7070088692307 df.mm.trans1:exp4 0.215105918607811 0.128269011505798 1.67699053795302 0.0939429566005015 . df.mm.trans2:exp4 -0.0186369230533518 0.100049138982840 -0.186277695568658 0.852275185045366 df.mm.trans1:exp5 1.42652951913788 0.128269011505798 11.1213885753957 8.81614588797888e-27 *** df.mm.trans2:exp5 0.182200193215437 0.100049138982840 1.82110705866932 0.068971596423601 . df.mm.trans1:exp6 0.483226063266775 0.128269011505798 3.76728609345315 0.000177393984255294 *** df.mm.trans2:exp6 0.0911371037193102 0.100049138982840 0.91092341869071 0.362615708000591 df.mm.trans1:exp7 0.0303645673814753 0.128269011505798 0.236725667602909 0.812931457974054 df.mm.trans2:exp7 0.0112482165641532 0.100049138982840 0.112426920196510 0.910513690336547 df.mm.trans1:exp8 -0.0134788547144912 0.128269011505798 -0.105082705138660 0.916337060614092 df.mm.trans2:exp8 0.0342886833009579 0.100049138982840 0.342718424661696 0.731902165504882 df.mm.trans1:probe2 -0.126859201735127 0.0815135342190963 -1.55629617768932 0.120041240631000 df.mm.trans1:probe3 -0.0798595212161956 0.0815135342190963 -0.97970873157757 0.327532131411170 df.mm.trans1:probe4 -0.321512272611622 0.0815135342190963 -3.94428085705921 8.72094708336395e-05 *** df.mm.trans1:probe5 -0.064636283714323 0.0815135342190964 -0.792951555021405 0.428045942284921 df.mm.trans1:probe6 0.00160608949728891 0.0815135342190963 0.0197033475811756 0.984285033561149 df.mm.trans1:probe7 -0.195997092286033 0.0815135342190963 -2.40447300149227 0.0164267632068715 * df.mm.trans1:probe8 0.79264143367053 0.0815135342190963 9.72404694832673 3.53408012815686e-21 *** df.mm.trans1:probe9 0.135476095278601 0.0815135342190963 1.66200737799519 0.096911041539397 . df.mm.trans1:probe10 -0.242510058362544 0.0815135342190964 -2.97508948281782 0.00301898259599562 ** df.mm.trans1:probe11 -0.515116237855137 0.0815135342190964 -6.31939521197278 4.40130569771778e-10 *** df.mm.trans1:probe12 -0.626408772376284 0.0815135342190964 -7.68472105126235 4.58444683631029e-14 *** df.mm.trans1:probe13 -0.625307694064657 0.0815135342190963 -7.67121313110927 5.05668390542647e-14 *** df.mm.trans1:probe14 -0.544440607626605 0.0815135342190963 -6.67914368874289 4.55312813586847e-11 *** df.mm.trans1:probe15 -0.52437423132792 0.0815135342190963 -6.43297136299451 2.17552912163155e-10 *** df.mm.trans1:probe16 -0.585654956918952 0.0815135342190963 -7.18475726184069 1.56856042698000e-12 *** df.mm.trans1:probe17 0.289889337735138 0.0815135342190963 3.55633381023522 0.000398632823503051 *** df.mm.trans1:probe18 -0.00128773531220936 0.0815135342190963 -0.0157978098305505 0.98739971497735 df.mm.trans1:probe19 0.383404067817078 0.0815135342190963 4.70356329767943 3.0209079699081e-06 *** df.mm.trans1:probe20 -0.0797243098896363 0.0815135342190963 -0.978049972356114 0.328351303622289 df.mm.trans1:probe21 -0.121075381311688 0.0815135342190963 -1.48534083905937 0.137855528126470 df.mm.trans1:probe22 0.413865612912094 0.0815135342190963 5.07726252918546 4.78717043238883e-07 *** df.mm.trans2:probe2 -0.148218034702307 0.0815135342190963 -1.81832423440160 0.0693960526744664 . df.mm.trans2:probe3 0.0500406734460567 0.0815135342190963 0.613894047478726 0.539463425001788 df.mm.trans2:probe4 -0.0925521750855505 0.0815135342190963 -1.13542095766310 0.256545968072077 df.mm.trans2:probe5 -0.0459743171481056 0.0815135342190963 -0.564008389386398 0.572909722099916 df.mm.trans2:probe6 -0.132826699777915 0.0815135342190963 -1.62950485524154 0.103607879674777 df.mm.trans3:probe2 -0.0913061318928163 0.0815135342190963 -1.12013462264314 0.262999419924874 df.mm.trans3:probe3 0.119028378455798 0.0815135342190963 1.46022840987201 0.144627905883139 df.mm.trans3:probe4 -0.159379946837836 0.0815135342190963 -1.95525747183831 0.0509076101479345 . df.mm.trans3:probe5 -0.00268567698117669 0.0815135342190963 -0.0329476203786967 0.973724743805733 df.mm.trans3:probe6 -0.308184571638013 0.0815135342190963 -3.78077793571873 0.000168213454857310 *** df.mm.trans3:probe7 -0.321257255657155 0.0815135342190963 -3.94115233420824 8.833239938061e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.28998596096371 0.284721604449780 15.0673004574207 3.03794735606687e-45 *** df.mm.trans1 0.183256277784053 0.248160401985503 0.738458981843358 0.460456580268814 df.mm.trans2 -0.331347474768324 0.221456378958434 -1.49622005167219 0.134998753701939 df.mm.exp2 0.0165129084654267 0.289733713245840 0.0569933967312095 0.95456497709322 df.mm.exp3 0.284803732309874 0.289733713245840 0.982984441538623 0.325918341308356 df.mm.exp4 0.232836689809270 0.28973371324584 0.803623048214989 0.421858288866209 df.mm.exp5 0.226463480828467 0.289733713245840 0.781626267414423 0.434670219073041 df.mm.exp6 -0.51468447575866 0.28973371324584 -1.77640520322172 0.0760538934030023 . df.mm.exp7 0.0623108878827136 0.289733713245840 0.215062607608396 0.82977443722301 df.mm.exp8 0.39780426160409 0.28973371324584 1.37299956276249 0.170145047516622 df.mm.trans1:exp2 -0.074942465581418 0.270548497935268 -0.277001965094439 0.781851613315189 df.mm.trans2:exp2 0.252514328702886 0.211026373040231 1.19660080901237 0.231823910045683 df.mm.trans1:exp3 -0.372207250544961 0.270548497935268 -1.37575057110099 0.169291763767708 df.mm.trans2:exp3 0.093598263723482 0.211026373040231 0.443538228776922 0.65749884840443 df.mm.trans1:exp4 -0.247302753996926 0.270548497935268 -0.914079197941422 0.360956258345060 df.mm.trans2:exp4 -0.0615818196214791 0.211026373040231 -0.291820490179865 0.77050107610225 df.mm.trans1:exp5 -0.355327938962259 0.270548497935268 -1.31336134435784 0.189445450764374 df.mm.trans2:exp5 0.240649604893172 0.211026373040231 1.14037691794710 0.25447755235724 df.mm.trans1:exp6 0.367150483176774 0.270548497935268 1.35705977293808 0.175152778215880 df.mm.trans2:exp6 0.861552695044854 0.211026373040231 4.08267783136568 4.9087077161449e-05 *** df.mm.trans1:exp7 -0.213323421353224 0.270548497935268 -0.788484959189328 0.430651441618042 df.mm.trans2:exp7 0.180632864845567 0.211026373040231 0.855972939510886 0.392274276790505 df.mm.trans1:exp8 -0.441689302487561 0.270548497935268 -1.63256978271319 0.102961047975291 df.mm.trans2:exp8 0.139652637498461 0.211026373040231 0.661778125105895 0.508307882531716 df.mm.trans1:probe2 0.0746475192904884 0.171930569866243 0.434172464783673 0.66428266413774 df.mm.trans1:probe3 -0.123683288095280 0.171930569866243 -0.719379271478606 0.472121638085474 df.mm.trans1:probe4 -0.165014135499299 0.171930569866243 -0.959771933680408 0.337465902976479 df.mm.trans1:probe5 -0.280764030733990 0.171930569866243 -1.63300820181319 0.102868786277246 df.mm.trans1:probe6 -0.160326524670167 0.171930569866243 -0.932507376639858 0.351361455302282 df.mm.trans1:probe7 -0.0416662738913434 0.171930569866243 -0.242343603721889 0.808577310101074 df.mm.trans1:probe8 -0.136620883895068 0.171930569866243 -0.794628226971825 0.427070267319954 df.mm.trans1:probe9 -0.0442600833223432 0.171930569866243 -0.257429980932281 0.796914411398509 df.mm.trans1:probe10 -0.0794994436447744 0.171930569866243 -0.462392718797027 0.643928016126121 df.mm.trans1:probe11 0.0277252887620836 0.171930569866243 0.161258633549886 0.87193124743751 df.mm.trans1:probe12 -0.191114479297724 0.171930569866243 -1.11157939769760 0.266659791570386 df.mm.trans1:probe13 0.086048331078102 0.171930569866243 0.500483021402447 0.616875468679898 df.mm.trans1:probe14 -0.0738277141489893 0.171930569866243 -0.429404231059229 0.667747054069001 df.mm.trans1:probe15 -0.138041313208972 0.171930569866243 -0.802889871861434 0.422281718315904 df.mm.trans1:probe16 -0.119924834149079 0.171930569866243 -0.697518970840248 0.485684866525227 df.mm.trans1:probe17 -0.294348786445756 0.171930569866243 -1.71202123435495 0.0872882150253013 . df.mm.trans1:probe18 -0.158191342352714 0.171930569866243 -0.920088512914147 0.357809514551122 df.mm.trans1:probe19 0.0237392720388083 0.171930569866243 0.138074759231455 0.890216788282464 df.mm.trans1:probe20 -0.234800077091652 0.171930569866243 -1.36566799769418 0.172434856016539 df.mm.trans1:probe21 -0.0557080671341475 0.171930569866243 -0.324014904257496 0.746013170215586 df.mm.trans1:probe22 -0.38687781285531 0.171930569866243 -2.25019793255085 0.0247128786619179 * df.mm.trans2:probe2 -0.109391541740283 0.171930569866243 -0.636254168327286 0.524796471957034 df.mm.trans2:probe3 -0.166521770278213 0.171930569866243 -0.96854079183104 0.333073044093962 df.mm.trans2:probe4 -0.0273117728772401 0.171930569866243 -0.158853500564139 0.873825206975956 df.mm.trans2:probe5 0.0930449575347276 0.171930569866243 0.541177509078889 0.588538973486724 df.mm.trans2:probe6 0.143192315665889 0.171930569866243 0.832849654237106 0.405183161643283 df.mm.trans3:probe2 0.0262783676156915 0.171930569866243 0.152842904180072 0.878561489934538 df.mm.trans3:probe3 -0.0428942523967831 0.171930569866243 -0.249485896720714 0.80305029883152 df.mm.trans3:probe4 -0.170016495046158 0.171930569866243 -0.988867164102493 0.323033222526206 df.mm.trans3:probe5 -0.188297726814041 0.171930569866243 -1.09519631651620 0.273766970097596 df.mm.trans3:probe6 0.00608537152150699 0.171930569866243 0.0353943543969011 0.971774295923148 df.mm.trans3:probe7 -0.0129205121997718 0.171930569866243 -0.0751495921279361 0.940114861342912