chr15.8780_chr15_37465701_37470892_+_0.R 

fitVsDatCorrelation=0.77393587868803
cont.fitVsDatCorrelation=0.241491006799849

fstatistic=9335.65668924761,38,370
cont.fstatistic=3970.07775231099,38,370

residuals=-0.502532552543775,-0.0743211535408707,-0.0077882799690899,0.0735519821426001,0.807263326896261
cont.residuals=-0.392849608954459,-0.147966454769581,-0.0302099721915645,0.121178814318951,0.966320115739737

predictedValues:
Include	Exclude	Both
chr15.8780_chr15_37465701_37470892_+_0.R.tl.Lung	46.5126988318571	65.7057355495667	61.794303231124
chr15.8780_chr15_37465701_37470892_+_0.R.tl.cerebhem	53.4067186197335	59.2729965516455	62.6875159098095
chr15.8780_chr15_37465701_37470892_+_0.R.tl.cortex	45.9128036238698	55.3026090249379	54.9746295184688
chr15.8780_chr15_37465701_37470892_+_0.R.tl.heart	42.0912637897390	59.1889073046299	59.5895566781588
chr15.8780_chr15_37465701_37470892_+_0.R.tl.kidney	46.9281715965146	59.0312509728192	62.3973791943046
chr15.8780_chr15_37465701_37470892_+_0.R.tl.liver	47.498320269838	55.997308572125	61.3094992659797
chr15.8780_chr15_37465701_37470892_+_0.R.tl.stomach	47.4561977810164	68.54071376123	61.076181510929
chr15.8780_chr15_37465701_37470892_+_0.R.tl.testicle	47.1255372810577	68.0135345498138	65.3229781553598


diffExp=-19.1930367177095,-5.86627793191194,-9.38980540106815,-17.0976435148909,-12.1030793763045,-8.49898830228704,-21.0845159802135,-20.8879972687560
diffExpScore=0.991313513541708
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=-1,0,0,-1,0,0,-1,-1
diffExp1.4Score=0.8
diffExp1.3=-1,0,0,-1,0,0,-1,-1
diffExp1.3Score=0.8
diffExp1.2=-1,0,-1,-1,-1,0,-1,-1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	58.8095812417497	52.988846131262	51.4864026199093
cerebhem	60.0160161729914	53.5386573626969	56.8266236873849
cortex	60.6435064822565	57.9219947705204	56.8960911988165
heart	56.8749877326924	55.1200527335169	57.7715384413666
kidney	59.3200586547676	56.5230493491214	55.7691996417087
liver	55.8327211242367	55.5075738197116	56.7058065339273
stomach	57.9662041898459	53.2833080896207	57.482836300333
testicle	54.7574052563701	54.987620442537	56.7280616079494
cont.diffExp=5.8207351104877,6.47735881029443,2.72151171173611,1.75493499917543,2.79700930564622,0.325147304525125,4.68289610022522,-0.230215186166873
cont.diffExpScore=0.978714679928345

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=0.0292639158130876
cont.tran.correlation=0.163579529573547

tran.covariance=0.000246205067385384
cont.tran.covariance=0.000160530475365265

tran.mean=54.2490480050246
cont.tran.mean=56.5057239721186

weightedLogRatios:
wLogRatio
Lung	-1.38614639859148
cerebhem	-0.420000337330577
cortex	-0.729377611413545
heart	-1.33299320067286
kidney	-0.909384246817022
liver	-0.64905422719167
stomach	-1.48651789808104
testicle	-1.4808705207273

cont.weightedLogRatios:
wLogRatio
Lung	0.419206428139992
cerebhem	0.461113671325109
cortex	0.187429087152307
heart	0.126157651100448
kidney	0.196035876869471
liver	0.0234760241715644
stomach	0.338442667782127
testicle	-0.0168028673557254

varWeightedLogRatios=0.178500204217693
cont.varWeightedLogRatios=0.0309869586537869

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0696365420241	0.0723836242184779	56.2231663026513	2.49273305666065e-183	***
df.mm.trans1	-0.264057207328864	0.0596107962734329	-4.42968763775007	1.24447892787684e-05	***
df.mm.trans2	0.129426787266737	0.0596107962734329	2.17119708773995	0.0305513054227446	*  
df.mm.exp2	0.0208276578101523	0.0814863044460818	0.255597035007689	0.798404037249883	   
df.mm.exp3	-0.068408095955297	0.0814863044460818	-0.839504213871443	0.401728515025524	   
df.mm.exp4	-0.168006388993032	0.0814863044460818	-2.06177455383559	0.0399271402444788	*  
df.mm.exp5	-0.107938534141070	0.0814863044460818	-1.32462178613697	0.186114180741991	   
df.mm.exp6	-0.131037228189489	0.0814863044460818	-1.60808897986280	0.108668618680341	   
df.mm.exp7	0.074012691858109	0.0814863044460818	0.90828381973172	0.364319499388897	   
df.mm.exp8	-0.0079225003576901	0.0814863044460818	-0.0972249313739868	0.922600412523328	   
df.mm.trans1:exp2	0.117383528622284	0.0675648743500806	1.73734547353811	0.0831583362549908	.  
df.mm.trans2:exp2	-0.123860046403340	0.0675648743500806	-1.83320175749269	0.0675758244047758	.  
df.mm.trans1:exp3	0.0554267517090074	0.0675648743500806	0.820348624076754	0.412545571066945	   
df.mm.trans2:exp3	-0.103958037915250	0.0675648743500806	-1.53864029076116	0.124746861993834	   
df.mm.trans1:exp4	0.0681212287470486	0.0675648743500806	1.00823437329411	0.314000849418589	   
df.mm.trans2:exp4	0.0635543159544346	0.0675648743500806	0.940641369732064	0.347502372023499	   
df.mm.trans1:exp5	0.116831334492359	0.0675648743500806	1.72917267465058	0.0846122780996578	.  
df.mm.trans2:exp5	0.000819294562200129	0.0675648743500806	0.0121260428600079	0.990331590270943	   
df.mm.trans1:exp6	0.152006207396175	0.0675648743500806	2.24978154489818	0.0250499753524454	*  
df.mm.trans2:exp6	-0.0288453642027761	0.0675648743500807	-0.426928407404664	0.66967978672967	   
df.mm.trans1:exp7	-0.0539309267082004	0.0675648743500806	-0.798209531609025	0.425260920051632	   
df.mm.trans2:exp7	-0.0317709824353298	0.0675648743500806	-0.470229283202862	0.638468261482694	   
df.mm.trans1:exp8	0.0210121787820463	0.0675648743500806	0.310992642022448	0.755981346069334	   
df.mm.trans2:exp8	0.0424430024653392	0.0675648743500806	0.628181475561178	0.530272608365725	   
df.mm.trans1:probe2	0.126192232814188	0.0394493875079578	3.19883883593204	0.00149884731998387	** 
df.mm.trans1:probe3	0.0629821445768309	0.0394493875079578	1.59653035333251	0.111224127501386	   
df.mm.trans1:probe4	0.100911462055068	0.0394493875079578	2.55799819540194	0.0109253279485244	*  
df.mm.trans1:probe5	0.0362821550617784	0.0394493875079578	0.919714027358714	0.358321428344234	   
df.mm.trans1:probe6	0.0492383769967387	0.0394493875079578	1.24814046825965	0.212768879253329	   
df.mm.trans2:probe2	-0.0294398293522248	0.0394493875079578	-0.746268350713587	0.455979209843224	   
df.mm.trans2:probe3	0.120029700080358	0.0394493875079578	3.04262518793595	0.00251269324685280	** 
df.mm.trans2:probe4	-0.0736784142398636	0.0394493875079578	-1.86766940868223	0.0625985379642127	.  
df.mm.trans2:probe5	-0.0838983264975742	0.0394493875079578	-2.12673331064139	0.0341034962883072	*  
df.mm.trans2:probe6	-0.0856613238256548	0.0394493875079578	-2.17142341711579	0.0305340730196135	*  
df.mm.trans3:probe2	0.174737390285887	0.0394493875079578	4.42940692680308	1.24601776115678e-05	***
df.mm.trans3:probe3	0.230814312038492	0.0394493875079578	5.85089722855475	1.07712590722436e-08	***
df.mm.trans3:probe4	0.45049066710858	0.0394493875079578	11.4194590985147	4.45940916969766e-26	***
df.mm.trans3:probe5	0.0879829182001497	0.0394493875079578	2.23027336438117	0.0263284816280468	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15518981358819	0.110919448397706	37.4613277798642	5.73254915236084e-128	***
df.mm.trans1	-0.058913186083463	0.0913465816693564	-0.644941332306322	0.519364886676256	   
df.mm.trans2	-0.188275942296334	0.0913465816693564	-2.06111645182114	0.0399902271677023	*  
df.mm.exp2	-0.0680580024380132	0.124868242488743	-0.545038522858596	0.586055398643417	   
df.mm.exp3	0.0198146260682742	0.124868242488743	0.158684271303495	0.874004206307778	   
df.mm.exp4	-0.109195477861018	0.124868242488743	-0.874485583240769	0.382421130990332	   
df.mm.exp5	-0.00669420810414232	0.124868242488743	-0.0536101731771057	0.957274684604095	   
df.mm.exp6	-0.102065522195125	0.124868242488743	-0.817385751259585	0.414234033950506	   
df.mm.exp7	-0.119071619667026	0.124868242488743	-0.953578085939355	0.340919800765827	   
df.mm.exp8	-0.131316792574443	0.124868242488743	-1.05164283533727	0.2936497847044	   
df.mm.trans1:exp2	0.0883646773369667	0.103535277141568	0.853474098650858	0.393948628259227	   
df.mm.trans2:exp2	0.0783805218899958	0.103535277141568	0.757041696839452	0.44950668720648	   
df.mm.trans1:exp3	0.0108931502509994	0.103535277141568	0.105211967860044	0.916264627262733	   
df.mm.trans2:exp3	0.069201120464114	0.103535277141568	0.668382046918099	0.504306496406669	   
df.mm.trans1:exp4	0.0757463515246461	0.103535277141568	0.731599447221016	0.464876182289118	   
df.mm.trans2:exp4	0.148627620219036	0.103535277141568	1.43552636668766	0.15198197951886	   
df.mm.trans1:exp5	0.0153369262405453	0.103535277141568	0.148132372501157	0.882318995603746	   
df.mm.trans2:exp5	0.071261275097307	0.103535277141568	0.688280140496155	0.491707678179119	   
df.mm.trans1:exp6	0.05012083187444	0.103535277141568	0.48409424553824	0.628605388518285	   
df.mm.trans2:exp6	0.148503557806221	0.103535277141568	1.43432810445048	0.152323269273215	   
df.mm.trans1:exp7	0.104626986085258	0.103535277141568	1.01054431855335	0.312894961508219	   
df.mm.trans2:exp7	0.124613291690098	0.103535277141568	1.20358292487795	0.229520300440653	   
df.mm.trans1:exp8	0.0599246200303839	0.103535277141568	0.578784561985055	0.563086705126038	   
df.mm.trans2:exp8	0.16834342855821	0.103535277141568	1.62595236334787	0.104811168679464	   
df.mm.trans1:probe2	-0.0549545286656843	0.0604515779536362	-0.909066901575871	0.363906572669499	   
df.mm.trans1:probe3	-0.0430692312250892	0.0604515779536362	-0.71245834572129	0.476630025208251	   
df.mm.trans1:probe4	-0.0398653724206202	0.0604515779536362	-0.659459583523118	0.510010929696988	   
df.mm.trans1:probe5	-0.0100559555224923	0.0604515779536362	-0.166347279308487	0.867974524300867	   
df.mm.trans1:probe6	-0.0937451476141935	0.0604515779536362	-1.55074773542044	0.121817049295656	   
df.mm.trans2:probe2	0.0208619506938496	0.0604515779536362	0.345101838530168	0.730213907772338	   
df.mm.trans2:probe3	0.0441511910900496	0.0604515779536362	0.730356304742147	0.465634602427892	   
df.mm.trans2:probe4	-0.0364006400373672	0.0604515779536362	-0.602145407441389	0.54744616532331	   
df.mm.trans2:probe5	-0.019519546543881	0.0604515779536362	-0.322895567074389	0.746956728478038	   
df.mm.trans2:probe6	0.0257503121456490	0.0604515779536362	0.425965922103776	0.670380360582565	   
df.mm.trans3:probe2	0.119994521103728	0.0604515779536362	1.98496921280961	0.0478859647624818	*  
df.mm.trans3:probe3	0.0332101067439821	0.0604515779536362	0.549367078051344	0.583084968233797	   
df.mm.trans3:probe4	0.0264882801387028	0.0604515779536362	0.43817351069013	0.66151621191911	   
df.mm.trans3:probe5	-0.0131076039753659	0.0604515779536362	-0.216828152697997	0.828461775953172