chr8.23637_chr8_122050244_122062976_+_2.R 

fitVsDatCorrelation=0.942528969842811
cont.fitVsDatCorrelation=0.231730842008514

fstatistic=9385.71639082202,74,1198
cont.fstatistic=1092.99368805107,74,1198

residuals=-0.792948718578174,-0.0969524813543453,-0.00953276073386471,0.0795848852611438,1.12787800800630
cont.residuals=-0.950142918946026,-0.312802836606813,-0.137602486990688,0.116841567411254,2.26036250709979

predictedValues:
Include	Exclude	Both
chr8.23637_chr8_122050244_122062976_+_2.R.tl.Lung	69.312159899917	70.5799280891919	68.5174730992791
chr8.23637_chr8_122050244_122062976_+_2.R.tl.cerebhem	63.6771527611958	84.1766062909689	73.8652876686111
chr8.23637_chr8_122050244_122062976_+_2.R.tl.cortex	61.462489437765	56.9221815673712	65.3484342021807
chr8.23637_chr8_122050244_122062976_+_2.R.tl.heart	63.0629549444948	61.0943225140754	61.3395784907099
chr8.23637_chr8_122050244_122062976_+_2.R.tl.kidney	64.7214306618453	63.275093166313	59.8254823425234
chr8.23637_chr8_122050244_122062976_+_2.R.tl.liver	61.0395535113417	62.860312277569	56.718115889956
chr8.23637_chr8_122050244_122062976_+_2.R.tl.stomach	60.1632104203073	60.6118106024651	65.1390497011099
chr8.23637_chr8_122050244_122062976_+_2.R.tl.testicle	61.5678832632353	63.9223477531646	60.984605397051


diffExp=-1.26776818927496,-20.4994535297731,4.54030787039374,1.96863243041947,1.44633749553234,-1.82075876622733,-0.448600182157861,-2.35446448992924
diffExpScore=1.76717092336667
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,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	68.3063071159127	87.737996826874	66.6669895471503
cerebhem	74.9669279772417	92.1501124779873	67.6569449562991
cortex	71.3311335065151	75.5467008340398	76.0936758689681
heart	70.4959135540162	62.7949765148778	59.5601119920203
kidney	71.5014256126128	68.4719841434457	74.0524387478564
liver	75.1816945068976	65.3140384161925	66.7465903294878
stomach	75.8592458965569	72.1958211179764	73.7194369442728
testicle	75.6751226835546	71.3022280321293	72.1072853278255
cont.diffExp=-19.4316897109612,-17.1831845007456,-4.21556732752467,7.70093703913841,3.02944146916708,9.86765609070513,3.66342477858055,4.37289465142524
cont.diffExpScore=5.26404477952085

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=-1,-1,0,0,0,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.433434689800562
cont.tran.correlation=-0.12479699440388

tran.covariance=0.00254052829296900
cont.tran.covariance=-0.000659607825895075

tran.mean=64.2780898225763
cont.tran.mean=73.6769768260519

weightedLogRatios:
wLogRatio
Lung	-0.0769911292738563
cerebhem	-1.19824226724218
cortex	0.313111506613778
heart	0.130926512058899
kidney	0.093991192412575
liver	-0.121281639891851
stomach	-0.0304635352319424
testicle	-0.155327594074220

cont.weightedLogRatios:
wLogRatio
Lung	-1.08882972411644
cerebhem	-0.912211651973737
cortex	-0.24667070367728
heart	0.48559025846332
kidney	0.183910537461309
liver	0.597916041683257
stomach	0.213043419169205
testicle	0.255747066774116

varWeightedLogRatios=0.209882386609429
cont.varWeightedLogRatios=0.397677243019375

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93822471586676	0.082449280841726	47.7654222773245	1.19802413351901e-279	***
df.mm.trans1	-0.0600188977184335	0.0713469427166293	-0.841225922697379	0.400389257904185	   
df.mm.trans2	0.30944160637999	0.0613378552508202	5.04487163945714	5.23927354528856e-07	***
df.mm.exp2	0.0162225159641092	0.077012989006101	0.210646491890141	0.833198952965151	   
df.mm.exp3	-0.287898682565732	0.077012989006101	-3.73831331936648	0.000194001182292104	***
df.mm.exp4	-0.128150197297189	0.077012989006101	-1.66400757782609	0.0963724348137632	.  
df.mm.exp5	-0.0421249028191648	0.077012989006101	-0.546984390072532	0.584491325948313	   
df.mm.exp6	-0.0539339557359818	0.077012989006101	-0.70032284724995	0.4838616851567	   
df.mm.exp7	-0.243250762991460	0.077012989006101	-3.15856800431664	0.00162509614271043	** 
df.mm.exp8	-0.101089435708713	0.077012989006101	-1.31262838922801	0.189559603511271	   
df.mm.trans1:exp2	-0.101017045426716	0.0723221170075284	-1.39676560375300	0.162742757461504	   
df.mm.trans2:exp2	0.159948732265459	0.0468501567116204	3.41404903402995	0.000661450758892329	***
df.mm.trans1:exp3	0.16770538512083	0.0723221170075284	2.3188672021779	0.0205698272636372	*  
df.mm.trans2:exp3	0.0728379825655579	0.0468501567116204	1.55470093758495	0.120281431271094	   
df.mm.trans1:exp4	0.0336633512347546	0.0723221170075284	0.465464129475778	0.64168372008295	   
df.mm.trans2:exp4	-0.0161766614438904	0.0468501567116204	-0.345285108510173	0.729940655556644	   
df.mm.trans1:exp5	-0.0264030776038997	0.0723221170075284	-0.365076116358033	0.715119028180834	   
df.mm.trans2:exp5	-0.0671291177388144	0.0468501567116204	-1.43284724002138	0.152162389037082	   
df.mm.trans1:exp6	-0.0731643303387532	0.0723221170075284	-1.01164530804784	0.311911948428675	   
df.mm.trans2:exp6	-0.061896844471803	0.0468501567116204	-1.32116622048461	0.186698224439953	   
df.mm.trans1:exp7	0.101691447573956	0.0723221170075284	1.40609058171473	0.159956540485753	   
df.mm.trans2:exp7	0.0909947321161036	0.0468501567116204	1.94225032535556	0.0523409198231369	.  
df.mm.trans1:exp8	-0.0173905636466592	0.0723221170075284	-0.240459825655393	0.810014945167069	   
df.mm.trans2:exp8	0.00201266665606812	0.0468501567116204	0.0429596568578588	0.96574085208255	   
df.mm.trans1:probe2	-0.00285772642386271	0.0529343694475795	-0.0539862182866407	0.956955145462808	   
df.mm.trans1:probe3	0.176110585123842	0.0529343694475795	3.32696104556876	0.000904626331372904	***
df.mm.trans1:probe4	0.198169098862999	0.0529343694475795	3.7436754405707	0.000189952625440122	***
df.mm.trans1:probe5	0.204650021013603	0.0529343694475795	3.86610860107187	0.000116523501646086	***
df.mm.trans1:probe6	0.296662667939083	0.0529343694475795	5.60434876310118	2.59208688913665e-08	***
df.mm.trans1:probe7	0.170939470254886	0.0529343694475795	3.22927187078645	0.00127474520633079	** 
df.mm.trans1:probe8	0.606170573073349	0.0529343694475795	11.4513609852978	6.92324318901447e-29	***
df.mm.trans1:probe9	0.629118330601016	0.0529343694475795	11.8848743673810	7.13668257072322e-31	***
df.mm.trans1:probe10	0.607715210945201	0.0529343694475795	11.4805412303441	5.10967018695916e-29	***
df.mm.trans1:probe11	1.62083611701484	0.0529343694475795	30.6197303175576	1.43891060624982e-152	***
df.mm.trans1:probe12	2.12773703815712	0.0529343694475795	40.1957567524102	2.29696542736667e-224	***
df.mm.trans1:probe13	1.92307319250424	0.0529343694475795	36.3293869856833	1.92286472340798e-195	***
df.mm.trans1:probe14	1.74491674821519	0.0529343694475795	32.9637769642872	3.8969667694262e-170	***
df.mm.trans1:probe15	0.883603226701576	0.0529343694475795	16.6924294352198	2.10775761657783e-56	***
df.mm.trans1:probe16	1.98274028132077	0.0529343694475795	37.4565769274774	6.65888998174977e-204	***
df.mm.trans1:probe17	0.253791498292836	0.0529343694475795	4.7944558694359	1.83587536631623e-06	***
df.mm.trans1:probe18	0.287509899026693	0.0529343694475795	5.43144089609704	6.76442821340301e-08	***
df.mm.trans1:probe19	0.179923311085140	0.0529343694475795	3.39898846369214	0.000698596537184063	***
df.mm.trans1:probe20	0.081532052755344	0.0529343694475795	1.54024792599229	0.123764009832202	   
df.mm.trans1:probe21	0.0632470813213885	0.0529343694475795	1.19482071821072	0.232393570286080	   
df.mm.trans1:probe22	0.269662894452571	0.0529343694475795	5.09428745948539	4.06342333638511e-07	***
df.mm.trans1:probe23	-0.00698939881238213	0.0529343694475795	-0.132038954753276	0.894975651787099	   
df.mm.trans1:probe24	0.195579726166315	0.0529343694475795	3.69475877784842	0.000230026428892238	***
df.mm.trans1:probe25	0.328152827808317	0.0529343694475795	6.19923938327601	7.79293269636226e-10	***
df.mm.trans1:probe26	0.191666130915973	0.0529343694475795	3.62082580592139	0.000305931341054612	***
df.mm.trans1:probe27	0.226130211301069	0.0529343694475795	4.27189770391059	2.09165591305656e-05	***
df.mm.trans1:probe28	0.144283318223490	0.0529343694475795	2.72570202930958	0.00650999819194848	** 
df.mm.trans1:probe29	0.261568153376666	0.0529343694475795	4.94136713266596	8.8583725713646e-07	***
df.mm.trans1:probe30	0.129566927303835	0.0529343694475795	2.44769001040325	0.0145202670535662	*  
df.mm.trans1:probe31	0.578561832716539	0.0529343694475795	10.9297954949569	1.42280301506685e-26	***
df.mm.trans1:probe32	0.585712087512554	0.0529343694475795	11.0648732312299	3.65090446062084e-27	***
df.mm.trans2:probe2	0.269037351566595	0.0529343694475795	5.082470129979	4.31892334333872e-07	***
df.mm.trans2:probe3	0.0975256588047066	0.0529343694475795	1.84238822191479	0.0656653721347942	.  
df.mm.trans2:probe4	-0.0850499569117134	0.0529343694475795	-1.60670577168087	0.108382446178522	   
df.mm.trans2:probe5	-0.00396228101659705	0.0529343694475795	-0.0748527102135575	0.940344399419841	   
df.mm.trans2:probe6	-0.0868817524076087	0.0529343694475795	-1.64131080268458	0.100995391510085	   
df.mm.trans3:probe2	0.0856260612217394	0.0529343694475795	1.61758914133348	0.106014416920525	   
df.mm.trans3:probe3	0.204737814640611	0.0529343694475795	3.86776713838749	0.000115743795005024	***
df.mm.trans3:probe4	-0.226951762013827	0.0529343694475795	-4.28741787957965	1.95283127136718e-05	***
df.mm.trans3:probe5	0.0406156932048537	0.0529343694475795	0.76728397124056	0.443063883292031	   
df.mm.trans3:probe6	-0.0962312047239388	0.0529343694475795	-1.81793427839423	0.0693238442794056	.  
df.mm.trans3:probe7	-0.140627282388909	0.0529343694475795	-2.65663469417863	0.00799736189371461	** 
df.mm.trans3:probe8	0.143407259689793	0.0529343694475795	2.70915212906822	0.00684170873913983	** 
df.mm.trans3:probe9	-0.147992884680426	0.0529343694475795	-2.79578062844372	0.00526016239826928	** 
df.mm.trans3:probe10	-0.136501233693086	0.0529343694475795	-2.57868819667839	0.0100360853953875	*  
df.mm.trans3:probe11	-0.0501155274104073	0.0529343694475795	-0.946748359022891	0.343957940823091	   
df.mm.trans3:probe12	0.249793712670462	0.0529343694475795	4.71893243042842	2.64990143365031e-06	***
df.mm.trans3:probe13	0.358158376334739	0.0529343694475795	6.76608373864585	2.06495800305113e-11	***
df.mm.trans3:probe14	0.337877438229366	0.0529343694475795	6.38295008999709	2.47809519986290e-10	***
df.mm.trans3:probe15	-0.0301759117807241	0.0529343694475795	-0.570062741761893	0.568742031556197	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32141950981633	0.240045316150777	18.0025154379683	2.57607833473471e-64	***
df.mm.trans1	-0.025899224221497	0.207721634997418	-0.124682362633141	0.900795938335987	   
df.mm.trans2	0.155323035767219	0.178580876696286	0.86976298157265	0.384604173685596	   
df.mm.exp2	0.127368701004630	0.224217932587837	0.568057601524506	0.570102305160695	   
df.mm.exp3	-0.238528502435864	0.224217932587837	-1.06382437694818	0.287622643688692	   
df.mm.exp4	-0.190203515530927	0.224217932587838	-0.848297517222064	0.396441757148463	   
df.mm.exp5	-0.307278665062788	0.224217932587837	-1.3704464291339	0.170804316292794	   
df.mm.exp6	-0.200435691210107	0.224217932587838	-0.893932474074463	0.371537568351191	   
df.mm.exp7	-0.190651984787528	0.224217932587837	-0.85029766614604	0.39532951722136	   
df.mm.exp8	-0.183425282461934	0.224217932587838	-0.818066959876536	0.413481631925374	   
df.mm.trans1:exp2	-0.0343237516398813	0.210560786759219	-0.163011129318829	0.87053716442484	   
df.mm.trans2:exp2	-0.0783048609846968	0.13640095540849	-0.574078537428066	0.566022421837994	   
df.mm.trans1:exp3	0.281859283003837	0.210560786759219	1.33861241374516	0.180950851291031	   
df.mm.trans2:exp3	0.0889244568664838	0.13640095540849	0.6519342668838	0.514568630014611	   
df.mm.trans1:exp4	0.221756153279832	0.210560786759219	1.05316928518801	0.292475779479813	   
df.mm.trans2:exp4	-0.144276470687614	0.13640095540849	-1.05773797738834	0.290388148413598	   
df.mm.trans1:exp5	0.352993946585386	0.210560786759219	1.67644674974093	0.0939116233575768	.  
df.mm.trans2:exp5	0.0593482713729963	0.13640095540849	0.435101581182196	0.663567065484126	   
df.mm.trans1:exp6	0.296341361806506	0.210560786759219	1.40739102644682	0.159570866331089	   
df.mm.trans2:exp6	-0.0947123769023568	0.13640095540849	-0.694367401010606	0.487586460910137	   
df.mm.trans1:exp7	0.295529473656751	0.210560786759219	1.40353518907913	0.160716450973049	   
df.mm.trans2:exp7	-0.00432091493303483	0.13640095540849	-0.0316780400847975	0.974734084165955	   
df.mm.trans1:exp8	0.285872651941888	0.210560786759219	1.35767279530918	0.174823205117275	   
df.mm.trans2:exp8	-0.0240022066004495	0.13640095540849	-0.175968024040362	0.860348802825547	   
df.mm.trans1:probe2	-0.048032320958708	0.154114715368817	-0.31166602646451	0.755348578688682	   
df.mm.trans1:probe3	-0.105326208788168	0.154114715368817	-0.683427332270699	0.494469006588454	   
df.mm.trans1:probe4	-0.193283941989283	0.154114715368817	-1.25415630510512	0.210029850733238	   
df.mm.trans1:probe5	0.0344482003344571	0.154114715368817	0.223523109081557	0.823166508230277	   
df.mm.trans1:probe6	-0.0695450850032267	0.154114715368817	-0.45125531872019	0.651887214824581	   
df.mm.trans1:probe7	-0.263217407846352	0.154114715368817	-1.70793170020422	0.0879080850923202	.  
df.mm.trans1:probe8	-0.0156086336687601	0.154114715368817	-0.101279320611316	0.91934567893509	   
df.mm.trans1:probe9	-0.117252514350405	0.154114715368817	-0.760813229741261	0.4469183487145	   
df.mm.trans1:probe10	-0.168271488282028	0.154114715368817	-1.09185867085653	0.275114755273885	   
df.mm.trans1:probe11	-0.118164216955714	0.154114715368817	-0.766728969864634	0.443393736528457	   
df.mm.trans1:probe12	-0.126184712998249	0.154114715368817	-0.818771346371907	0.413079718653942	   
df.mm.trans1:probe13	0.0160596893511611	0.154114715368817	0.104206073461111	0.917023256566146	   
df.mm.trans1:probe14	-0.169889711333296	0.154114715368817	-1.10235879115584	0.270527123839886	   
df.mm.trans1:probe15	-0.171142370921478	0.154114715368817	-1.11048688966470	0.267012109160620	   
df.mm.trans1:probe16	0.116665122803027	0.154114715368817	0.75700183803884	0.44919761290444	   
df.mm.trans1:probe17	-0.0677821630424809	0.154114715368817	-0.439816294506786	0.660149494887458	   
df.mm.trans1:probe18	-0.0240423020538821	0.154114715368817	-0.156002637362342	0.87605722489073	   
df.mm.trans1:probe19	-0.183448048120592	0.154114715368817	-1.19033440565086	0.234150801421589	   
df.mm.trans1:probe20	-0.0508051630890782	0.154114715368817	-0.329658092463753	0.741715951777283	   
df.mm.trans1:probe21	-0.202024341400397	0.154114715368817	-1.31086989919766	0.190152943494773	   
df.mm.trans1:probe22	-0.132548986730566	0.154114715368817	-0.860067037812444	0.389924168019678	   
df.mm.trans1:probe23	-0.172780156503221	0.154114715368817	-1.12111394482828	0.262464039661131	   
df.mm.trans1:probe24	-0.0984378151798093	0.154114715368817	-0.638730798316273	0.523120163829923	   
df.mm.trans1:probe25	-0.185791049992089	0.154114715368817	-1.20553737874717	0.228233971555452	   
df.mm.trans1:probe26	-0.121083434638084	0.154114715368817	-0.785670818963108	0.432215721049146	   
df.mm.trans1:probe27	-0.120000478660245	0.154114715368817	-0.778643871696929	0.43634329284607	   
df.mm.trans1:probe28	-0.194923827361862	0.154114715368817	-1.26479698512490	0.206190092340339	   
df.mm.trans1:probe29	-0.0976150466272247	0.154114715368817	-0.633392122183915	0.526598503939468	   
df.mm.trans1:probe30	-0.173489473655813	0.154114715368817	-1.12571647191918	0.260511012520933	   
df.mm.trans1:probe31	0.187902912679066	0.154114715368817	1.21924056524641	0.222992900145217	   
df.mm.trans1:probe32	-0.325739436996834	0.154114715368817	-2.11361670569418	0.0347542860463041	*  
df.mm.trans2:probe2	0.0386987657050629	0.154114715368817	0.251103638042946	0.801777045019248	   
df.mm.trans2:probe3	0.00323548182309377	0.154114715368817	0.0209939837046114	0.983253950904533	   
df.mm.trans2:probe4	0.0429596116457969	0.154114715368817	0.278750874262649	0.78048412523662	   
df.mm.trans2:probe5	-0.119766532959136	0.154114715368817	-0.77712587453131	0.437237930231636	   
df.mm.trans2:probe6	-0.0152644249400780	0.154114715368817	-0.0990458627104376	0.921118425958928	   
df.mm.trans3:probe2	-0.272188444566672	0.154114715368817	-1.76614182438899	0.0776267091584215	.  
df.mm.trans3:probe3	-0.438560667449542	0.154114715368817	-2.84567678303794	0.00450721100567581	** 
df.mm.trans3:probe4	-0.135351183486036	0.154114715368817	-0.87824957637642	0.379984395864686	   
df.mm.trans3:probe5	-0.244842848849967	0.154114715368817	-1.58870519446521	0.112390825248356	   
df.mm.trans3:probe6	-0.305733486546585	0.154114715368817	-1.98380463419684	0.047505568671311	*  
df.mm.trans3:probe7	-0.388493084430794	0.154114715368817	-2.52080460649769	0.0118376840162428	*  
df.mm.trans3:probe8	-0.404431700181618	0.154114715368817	-2.62422507295140	0.0087950657691087	** 
df.mm.trans3:probe9	-0.274857838719277	0.154114715368817	-1.78346265028298	0.0747641132754583	.  
df.mm.trans3:probe10	-0.301133501288944	0.154114715368817	-1.95395683383181	0.0509387444041152	.  
df.mm.trans3:probe11	-0.290995866388997	0.154114715368817	-1.88817703548040	0.0592436431591315	.  
df.mm.trans3:probe12	-0.239082723105814	0.154114715368817	-1.55132962179282	0.121086830000833	   
df.mm.trans3:probe13	-0.126220257865725	0.154114715368817	-0.819001985395512	0.412948169561582	   
df.mm.trans3:probe14	-0.0654356301216778	0.154114715368817	-0.424590409586014	0.671211524626951	   
df.mm.trans3:probe15	-0.279673771297357	0.154114715368817	-1.81471166220605	0.0698182355372874	.