chr11.4162_chr11_76248959_76251163_+_1.R 

fitVsDatCorrelation=0.9186521127722
cont.fitVsDatCorrelation=0.275621901020292

fstatistic=9123.47814035455,41,439
cont.fstatistic=1532.14750067211,41,439

residuals=-0.7185960575583,-0.0943204435914975,-0.00066803226249675,0.0962733338787895,0.602563611283145
cont.residuals=-0.733341591715671,-0.258583544772817,-0.0382203181556285,0.167015634913781,1.50567196748039

predictedValues:
Include	Exclude	Both
chr11.4162_chr11_76248959_76251163_+_1.R.tl.Lung	55.8915540596911	85.4968621887889	110.788701271897
chr11.4162_chr11_76248959_76251163_+_1.R.tl.cerebhem	63.4202119252004	77.9366871993285	91.957572549942
chr11.4162_chr11_76248959_76251163_+_1.R.tl.cortex	56.0582591194136	80.0581564384317	95.1374282645683
chr11.4162_chr11_76248959_76251163_+_1.R.tl.heart	54.4090423487474	96.0120110818236	91.9918730178265
chr11.4162_chr11_76248959_76251163_+_1.R.tl.kidney	52.5958005657703	79.738103443525	99.4901297716066
chr11.4162_chr11_76248959_76251163_+_1.R.tl.liver	56.8842209593385	85.7381859406152	85.9044295209806
chr11.4162_chr11_76248959_76251163_+_1.R.tl.stomach	52.1573794209928	98.2071278598138	82.6205639184634
chr11.4162_chr11_76248959_76251163_+_1.R.tl.testicle	54.8721399829627	89.45136623134	91.3864673286858


diffExp=-29.6053081290977,-14.5164752741281,-23.9998973190181,-41.6029687330762,-27.1423028777546,-28.8539649812767,-46.0497484388209,-34.5792262483774
diffExpScore=0.995957143979696
diffExp1.5=-1,0,0,-1,-1,-1,-1,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	80.4968377147253	108.78990321282	68.78253456776
cerebhem	70.0730467401772	80.6630646464203	71.917535930045
cortex	75.1843381690217	76.2623727330361	78.4452663436605
heart	71.7866022730702	91.5081487717397	80.5772152783911
kidney	72.5092701666177	99.7153376489065	79.6141416525434
liver	83.5736097871865	80.9067552996444	77.2442438049451
stomach	73.6119887502303	80.2390816630041	82.4830938601603
testicle	77.8992887655974	91.092165076908	77.3917061184335
cont.diffExp=-28.2930654980948,-10.5900179062431,-1.07803456401446,-19.7215464986694,-27.2060674822887,2.66685448754212,-6.62709291277385,-13.1928763113106
cont.diffExpScore=1.04125697626057

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=-1,0,0,0,-1,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,0,0,-1,-1,0,0,0
cont.diffExp1.2Score=0.75

tran.correlation=-0.567248460438471
cont.tran.correlation=0.172619155662935

tran.covariance=-0.00298264328336588
cont.tran.covariance=0.00122019358575049

tran.mean=71.1829442978615
cont.tran.mean=82.1444882136941

weightedLogRatios:
wLogRatio
Lung	-1.80055850753610
cerebhem	-0.876570584088444
cortex	-1.49834913760069
heart	-2.43108079101236
kidney	-1.73547194436270
liver	-1.74211509099592
stomach	-2.70253743386339
testicle	-2.07661221797534

cont.weightedLogRatios:
wLogRatio
Lung	-1.36709489735222
cerebhem	-0.607995124732814
cortex	-0.0616031923979694
heart	-1.06681435411186
kidney	-1.41556779676579
liver	0.143002588719879
stomach	-0.374284720034498
testicle	-0.69366570647271

varWeightedLogRatios=0.316250101845598
cont.varWeightedLogRatios=0.331623408796413

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.78875894776592	0.079694345912365	34.9931844704837	4.48517952870224e-129	***
df.mm.trans1	1.22590710532399	0.0643013725744497	19.0650223508776	1.77620777778328e-59	***
df.mm.trans2	1.6620978586376	0.0643013725744496	25.8485595577790	3.21584485472760e-90	***
df.mm.exp2	0.220083943215161	0.086614521450561	2.54095894694498	0.0113981130462554	*  
df.mm.exp3	0.0895541985794391	0.086614521450561	1.03393977221887	0.301733550358675	   
df.mm.exp4	0.275035263118808	0.086614521450561	3.17539436243144	0.00160173737703051	** 
df.mm.exp5	-0.0229427634183949	0.086614521450561	-0.264883567260606	0.79122348150304	   
df.mm.exp6	0.274812741206539	0.086614521450561	3.17282525613676	0.0016156536440662	** 
df.mm.exp7	0.362817706389261	0.086614521450561	4.18887849650425	3.38998667830251e-05	***
df.mm.exp8	0.219335267895083	0.086614521450561	2.53231518481896	0.0116787494539867	*  
df.mm.trans1:exp2	-0.0937146110751606	0.0690944855599713	-1.35632547685473	0.175692774994415	   
df.mm.trans2:exp2	-0.31266682443013	0.0690944855599713	-4.52520663401926	7.77849011584579e-06	***
df.mm.trans1:exp3	-0.0865759857855481	0.0690944855599713	-1.25300861688019	0.210869554723657	   
df.mm.trans2:exp3	-0.15528054825225	0.0690944855599713	-2.24736528528710	0.0251125657520090	*  
df.mm.trans1:exp4	-0.301918182003032	0.0690944855599713	-4.36964223057973	1.55489722343555e-05	***
df.mm.trans2:exp4	-0.159041639764534	0.0690944855599713	-2.30179931836228	0.0218138271464836	*  
df.mm.trans1:exp5	-0.0378342357570098	0.0690944855599713	-0.547572435779569	0.584263797056729	   
df.mm.trans2:exp5	-0.0467893548889533	0.0690944855599713	-0.677179293104976	0.498648890181794	   
df.mm.trans1:exp6	-0.257208028898203	0.0690944855599713	-3.72255508979738	0.00022287130343729	***
df.mm.trans2:exp6	-0.271994113662327	0.0690944855599713	-3.93655313384231	9.60945296256875e-05	***
df.mm.trans1:exp7	-0.431965309774197	0.0690944855599713	-6.25180586082037	9.62379094103022e-10	***
df.mm.trans2:exp7	-0.224218584255468	0.0690944855599713	-3.24510100101773	0.00126388607972700	** 
df.mm.trans1:exp8	-0.237742795198735	0.0690944855599713	-3.44083602724538	0.000635416170851472	***
df.mm.trans2:exp8	-0.174119860092513	0.0690944855599712	-2.52002542144096	0.0120883486684553	*  
df.mm.trans1:probe2	0.0407208562368197	0.0452329585975145	0.900247463340973	0.368482141613204	   
df.mm.trans1:probe3	-0.0253782540547668	0.0452329585975145	-0.561056690555751	0.575045150737399	   
df.mm.trans1:probe4	0.0717182329128317	0.0452329585975145	1.58553044365249	0.113565893440107	   
df.mm.trans1:probe5	-0.0764882343268637	0.0452329585975145	-1.69098455414912	0.091549515244413	.  
df.mm.trans1:probe6	0.111888556082740	0.0452329585975145	2.4736068466874	0.0137530206283753	*  
df.mm.trans2:probe2	-0.0870191052653694	0.0452329585975145	-1.92379866282173	0.0550260808904791	.  
df.mm.trans2:probe3	0.0104534955550011	0.0452329585975145	0.231103511225453	0.817342056842651	   
df.mm.trans2:probe4	-0.09254257772134	0.0452329585975145	-2.04591034039558	0.0413605377431319	*  
df.mm.trans2:probe5	0.0942231911300387	0.0452329585975145	2.0830649608495	0.0378234430564783	*  
df.mm.trans2:probe6	0.0416051667734819	0.0452329585975145	0.919797600322524	0.358183664517793	   
df.mm.trans3:probe2	-1.24567722076239	0.0452329585975145	-27.5391497568509	1.06200982544494e-97	***
df.mm.trans3:probe3	-1.2362414429432	0.0452329585975145	-27.330545718739	8.76920725820626e-97	***
df.mm.trans3:probe4	-0.875522169324679	0.0452329585975145	-19.3558457476798	8.4677457427857e-61	***
df.mm.trans3:probe5	-1.27274846511550	0.0452329585975145	-28.1376346933325	2.54807337878738e-100	***
df.mm.trans3:probe6	-1.12022898428218	0.0452329585975145	-24.7657685682257	2.26146584908342e-85	***
df.mm.trans3:probe7	-1.22694065343835	0.0452329585975145	-27.1249259716956	7.05463005936746e-96	***
df.mm.trans3:probe8	-0.775753997688875	0.0452329585975145	-17.1501936141648	7.7834169268069e-51	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.85415930940558	0.193909859908710	25.0330710964921	1.42630174952461e-86	***
df.mm.trans1	-0.474720935396567	0.156456145101691	-3.03421086521092	0.00255487224386095	** 
df.mm.trans2	-0.112031043829456	0.156456145101691	-0.716053969990377	0.474338734336128	   
df.mm.exp2	-0.482387696834702	0.210747820667318	-2.28893326302144	0.0225573358703761	*  
df.mm.exp3	-0.554965104343509	0.210747820667318	-2.63331360953699	0.00875408119079981	** 
df.mm.exp4	-0.445776608673249	0.210747820667318	-2.11521337331854	0.0349745099521012	*  
df.mm.exp5	-0.337844389513223	0.210747820667318	-1.60307417862478	0.109637485090282	   
df.mm.exp6	-0.374633853551702	0.210747820667318	-1.77764046321073	0.0761551120211614	.  
df.mm.exp7	-0.575461322309478	0.210747820667318	-2.73056831851130	0.00657704161903096	** 
df.mm.exp8	-0.328277573684291	0.210747820667318	-1.55767956529668	0.120029813084646	   
df.mm.trans1:exp2	0.343708019044124	0.168118601916020	2.04443776671315	0.0415063320660582	*  
df.mm.trans2:exp2	0.183249951419973	0.168118601916020	1.09000401699458	0.276309315233149	   
df.mm.trans1:exp3	0.486690143973302	0.168118601916020	2.89492143300370	0.00398169100000614	** 
df.mm.trans2:exp3	0.199726243255951	0.168118601916020	1.18800799542528	0.235472588940246	   
df.mm.trans1:exp4	0.331256568841749	0.168118601916020	1.97037427784001	0.0494234728297565	*  
df.mm.trans2:exp4	0.272786105841198	0.168118601916020	1.62258133681996	0.105396805722343	   
df.mm.trans1:exp5	0.233340906961014	0.168118601916020	1.38795412465763	0.165855008578383	   
df.mm.trans2:exp5	0.250745363889464	0.168118601916020	1.49147899775373	0.136553986566574	   
df.mm.trans1:exp6	0.412143750799235	0.168118601916020	2.45150593748759	0.0146147264413247	*  
df.mm.trans2:exp6	0.0785126472170345	0.168118601916020	0.467007495436192	0.640726208486579	   
df.mm.trans1:exp7	0.486051324801171	0.168118601916020	2.89112162046154	0.00402922820537803	** 
df.mm.trans2:exp7	0.271053492257076	0.168118601916020	1.61227543631653	0.107620648902390	   
df.mm.trans1:exp8	0.295476495818259	0.168118601916020	1.75754790041532	0.0795214207556785	.  
df.mm.trans2:exp8	0.150730841942348	0.168118601916020	0.896574443425618	0.370437423222882	   
df.mm.trans1:probe2	-0.0909099333684827	0.110059459858615	-0.826007446204691	0.409248539990818	   
df.mm.trans1:probe3	0.010988528192422	0.110059459858615	0.099841741968733	0.920515545324765	   
df.mm.trans1:probe4	0.0689327800359076	0.110059459858615	0.626323081400363	0.531428574972882	   
df.mm.trans1:probe5	0.078546367477631	0.110059459858615	0.713672114859851	0.47580918808703	   
df.mm.trans1:probe6	0.0553556300207415	0.110059459858615	0.502961127483747	0.615243837483885	   
df.mm.trans2:probe2	-0.16018279471659	0.110059459858615	-1.45542050562818	0.146267584356093	   
df.mm.trans2:probe3	-0.194105821057667	0.110059459858615	-1.76364504520573	0.0784873109010088	.  
df.mm.trans2:probe4	-0.181493041112946	0.110059459858615	-1.64904535553869	0.0998537050907258	.  
df.mm.trans2:probe5	-0.128420358436040	0.110059459858615	-1.16682708238812	0.243913638011798	   
df.mm.trans2:probe6	-0.0737343000636348	0.110059459858615	-0.669949681366376	0.503242101594817	   
df.mm.trans3:probe2	-0.0473848415906515	0.110059459858615	-0.43053856207838	0.667015214031865	   
df.mm.trans3:probe3	-0.135651875639738	0.110059459858615	-1.23253263112502	0.21840985028162	   
df.mm.trans3:probe4	-0.0102653777349649	0.110059459858615	-0.0932711985698644	0.925730663657181	   
df.mm.trans3:probe5	-0.0968387263604597	0.110059459858615	-0.879876445739972	0.379407638100437	   
df.mm.trans3:probe6	0.0301386366617041	0.110059459858615	0.273839583625259	0.784336698065064	   
df.mm.trans3:probe7	0.0379507854319085	0.110059459858615	0.344820749444535	0.73039427030812	   
df.mm.trans3:probe8	-0.0696084047230496	0.110059459858615	-0.632461805759087	0.527414423048658