chr19.11715_chr19_5472361_5473524_+_2.R 

fitVsDatCorrelation=0.893347903723241
cont.fitVsDatCorrelation=0.250591767750481

fstatistic=1867.45651505757,53,715
cont.fstatistic=391.777488270087,53,715

residuals=-2.36208137096215,-0.134911684956602,-0.00597631001454895,0.113187834640344,2.09161571333613
cont.residuals=-1.16878109359181,-0.411118433974548,-0.135448059061954,0.152780869831155,4.51968072485287

predictedValues:
Include	Exclude	Both
chr19.11715_chr19_5472361_5473524_+_2.R.tl.Lung	67.2939440698217	97.6021714986339	61.6518904298889
chr19.11715_chr19_5472361_5473524_+_2.R.tl.cerebhem	64.7113834770986	125.358923903891	59.7038214494773
chr19.11715_chr19_5472361_5473524_+_2.R.tl.cortex	62.1863120859848	89.9736141423384	71.6155733908267
chr19.11715_chr19_5472361_5473524_+_2.R.tl.heart	65.4860094886094	83.9396776573571	60.1828479467074
chr19.11715_chr19_5472361_5473524_+_2.R.tl.kidney	71.4261295373945	98.8177284333921	67.3705739881881
chr19.11715_chr19_5472361_5473524_+_2.R.tl.liver	70.4966942816616	94.49314773962	64.1748331265328
chr19.11715_chr19_5472361_5473524_+_2.R.tl.stomach	68.052675227203	129.455956236069	76.7408988841594
chr19.11715_chr19_5472361_5473524_+_2.R.tl.testicle	80.6445746047377	2842.94071387425	891.967885074997


diffExp=-30.3082274288121,-60.6475404267923,-27.7873020563535,-18.4536681687477,-27.3915988959977,-23.9964534579585,-61.4032810088656,-2762.29613926951
diffExpScore=0.999668136182958
diffExp1.5=0,-1,0,0,0,0,-1,-1
diffExp1.5Score=0.75
diffExp1.4=-1,-1,-1,0,0,0,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,-1,-1,0,-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	67.6775075235854	93.5586799569172	75.8875772095695
cerebhem	76.4090413888163	81.2356456439429	94.6614490560309
cortex	68.1412289971178	68.2875128737466	68.853876160755
heart	80.8792248582796	97.0369510764366	65.6868819151263
kidney	98.9174719769943	89.6454634699082	95.6002386285898
liver	81.234481726277	74.381316196883	83.2880067607601
stomach	73.2508416943033	77.256493553935	69.4699630533419
testicle	76.1221941949144	108.426713789852	73.5735809910644
cont.diffExp=-25.8811724333318,-4.82660425512655,-0.146283876628829,-16.157726218157,9.27200850708607,6.85316552939398,-4.00565185963171,-32.3045195949373
cont.diffExpScore=1.45823785445222

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,-1
cont.diffExp1.4Score=0.5
cont.diffExp1.3=-1,0,0,0,0,0,0,-1
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,0,0,0,0,0,0,-1
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.846685098317835
cont.tran.correlation=0.188508599675817

tran.covariance=0.0776442027943144
cont.tran.covariance=0.00418089133515803

tran.mean=257.054978516129
cont.tran.mean=82.0287980576193

weightedLogRatios:
wLogRatio
Lung	-1.63418504534062
cerebhem	-2.97596716706467
cortex	-1.59381694893774
heart	-1.06900763773995
kidney	-1.43835176208786
liver	-1.28963017542151
stomach	-2.92065121206807
testicle	-21.9856014923428

cont.weightedLogRatios:
wLogRatio
Lung	-1.41731913048778
cerebhem	-0.267475621690717
cortex	-0.00905537242796274
heart	-0.816697133036553
kidney	0.447341160086727
liver	0.383675762300244
stomach	-0.230029297001936
testicle	-1.59506272649335

varWeightedLogRatios=51.2178104397552
cont.varWeightedLogRatios=0.593796497293568

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.76166089767814	0.189222644515684	25.1643290889715	1.44639741289368e-100	***
df.mm.trans1	-0.416575655818997	0.164174322570458	-2.53739835375424	0.0113793149719667	*  
df.mm.trans2	-0.165783710504598	0.147612807499706	-1.12309841749286	0.261772661821792	   
df.mm.exp2	0.243256015641408	0.193605780903369	1.25645016644839	0.209363147791094	   
df.mm.exp3	-0.310127289606811	0.193605780903369	-1.60184932577813	0.109630609380422	   
df.mm.exp4	-0.153918549497390	0.193605780903369	-0.79501009101692	0.426871450324242	   
df.mm.exp5	-0.0167336439095082	0.193605780903369	-0.0864315302540482	0.931147581211754	   
df.mm.exp6	-0.0259840837737402	0.193605780903369	-0.134211301194096	0.893273258042214	   
df.mm.exp7	0.0747218622943921	0.193605780903369	0.385948508075214	0.699649613224066	   
df.mm.exp8	0.88074956327254	0.193605780903369	4.549190417574	6.32560843717097e-06	***
df.mm.trans1:exp2	-0.282389135649774	0.178985511046892	-1.57772064340890	0.115072100270908	   
df.mm.trans2:exp2	0.00702525618974133	0.142243260496459	0.0493890266942821	0.960623065855926	   
df.mm.trans1:exp3	0.231191953710076	0.178985511046892	1.29167971394906	0.196885463060137	   
df.mm.trans2:exp3	0.228743998623338	0.142243260496459	1.60811835882399	0.108250705860027	   
df.mm.trans1:exp4	0.126684825231878	0.178985511046892	0.707793745375782	0.479304000536606	   
df.mm.trans2:exp4	0.00311722510712318	0.142243260496459	0.0219147472874525	0.982522075151443	   
df.mm.trans1:exp5	0.0763271577173575	0.178985511046892	0.426443220297093	0.669913271154719	   
df.mm.trans2:exp5	0.0291109280183181	0.142243260496459	0.204655938824201	0.837899190492928	   
df.mm.trans1:exp6	0.0724796543726079	0.178985511046892	0.404947048220116	0.685637556151275	   
df.mm.trans2:exp6	-0.00638833718039597	0.142243260496459	-0.0449113522714491	0.964190507986474	   
df.mm.trans1:exp7	-0.0635100697976799	0.178985511046892	-0.354833580808901	0.722818904331422	   
df.mm.trans2:exp7	0.207719112577802	0.142243260496459	1.46030899357073	0.144644420180336	   
df.mm.trans1:exp8	-0.69976828032729	0.178985511046892	-3.90963646294229	0.000101230771258577	***
df.mm.trans2:exp8	2.49094495265989	0.142243260496459	17.5118662491704	2.09460389202499e-57	***
df.mm.trans1:probe2	0.116153722689335	0.113743307196094	1.02119171274918	0.307509009593388	   
df.mm.trans1:probe3	-0.440225548182866	0.113743307196094	-3.87034243187527	0.000118623153080905	***
df.mm.trans1:probe4	-0.266327632435420	0.113743307196094	-2.34147959120153	0.0194811784292099	*  
df.mm.trans1:probe5	-0.270199951799906	0.113743307196094	-2.37552396233811	0.0177867177025741	*  
df.mm.trans1:probe6	0.0361891135329888	0.113743307196094	0.318164773164179	0.750452857511628	   
df.mm.trans1:probe7	-0.315016033427905	0.113743307196094	-2.76953467587166	0.00575920309290328	** 
df.mm.trans1:probe8	-0.427104511349991	0.113743307196094	-3.75498587018980	0.000187443399260919	***
df.mm.trans1:probe9	-0.551200647426501	0.113743307196094	-4.84600510583211	1.54573118050939e-06	***
df.mm.trans1:probe10	-0.511538161961261	0.113743307196094	-4.49730339807481	8.02861753541036e-06	***
df.mm.trans1:probe11	-0.531053142107489	0.113743307196094	-4.66887375792539	3.61715701118923e-06	***
df.mm.trans1:probe12	-0.366704655720578	0.113743307196094	-3.22396688438448	0.00132172754326252	** 
df.mm.trans1:probe13	-0.486681279536871	0.113743307196094	-4.27876849666267	2.13514095269094e-05	***
df.mm.trans1:probe14	0.178495019616273	0.113743307196094	1.56927931863760	0.117025285682303	   
df.mm.trans1:probe15	0.0367578450267477	0.113743307196094	0.323164904668870	0.746664846429877	   
df.mm.trans1:probe16	0.180509933862728	0.113743307196094	1.58699389276178	0.112956137275306	   
df.mm.trans1:probe17	-0.103213538806106	0.113743307196094	-0.907425160657284	0.364487731081941	   
df.mm.trans1:probe18	-0.162568002946245	0.113743307196094	-1.42925335084532	0.153368176257203	   
df.mm.trans1:probe19	0.347337643588110	0.113743307196094	3.0536974187791	0.00234428877756511	** 
df.mm.trans2:probe2	-0.00472501534927161	0.113743307196094	-0.0415410406620731	0.966876172456966	   
df.mm.trans2:probe3	0.156352848565291	0.113743307196094	1.37461141599951	0.169682595460000	   
df.mm.trans2:probe4	0.050474903630901	0.113743307196094	0.443761526503551	0.657349264770206	   
df.mm.trans2:probe5	-0.404930605208144	0.113743307196094	-3.5600389613258	0.000395339770483737	***
df.mm.trans2:probe6	0.0081210828336148	0.113743307196094	0.0713983357246155	0.943100699949296	   
df.mm.trans3:probe2	0.0847228131617885	0.113743307196094	0.744859765820999	0.45660125418923	   
df.mm.trans3:probe3	0.148420676064142	0.113743307196094	1.30487392817112	0.192355582622351	   
df.mm.trans3:probe4	0.00579365188305454	0.113743307196094	0.0509362003433421	0.959390585349517	   
df.mm.trans3:probe5	-0.694106746055207	0.113743307196094	-6.10239637975853	1.71205611063963e-09	***
df.mm.trans3:probe6	-0.191835994283224	0.113743307196094	-1.68656951351430	0.0921223815679833	.  
df.mm.trans3:probe7	0.242422131080751	0.113743307196094	2.13130897154955	0.0334041379271602	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10489651789341	0.407652594446056	10.0695949782226	2.15333126429133e-22	***
df.mm.trans1	-0.120237255839632	0.35368963745628	-0.339951310715159	0.73399303567416	   
df.mm.trans2	0.474918588800023	0.318010268299224	1.49340645929445	0.135771958072163	   
df.mm.exp2	-0.240941385743084	0.417095422627768	-0.577664900336508	0.56367224376291	   
df.mm.exp3	-0.210766873487729	0.417095422627768	-0.505320514331862	0.613489463062873	   
df.mm.exp4	0.359059874661193	0.417095422627768	0.860857864128688	0.389604910076498	   
df.mm.exp5	0.105883444078010	0.417095422627768	0.253859041201955	0.799677440797508	   
df.mm.exp6	-0.139849691534864	0.417095422627768	-0.335294237116744	0.737501490539395	   
df.mm.exp7	-0.0239634850691064	0.417095422627768	-0.0574532439558616	0.954200202377287	   
df.mm.exp8	0.296038634000565	0.417095422627768	0.709762365972454	0.478082908143231	   
df.mm.trans1:exp2	0.362288530449525	0.385598183205135	0.939549370897297	0.347766044962912	   
df.mm.trans2:exp2	0.0997066897849534	0.306442362288417	0.325368493573716	0.744997385510894	   
df.mm.trans1:exp3	0.217595434661622	0.385598183205135	0.564306171914361	0.572722752325857	   
df.mm.trans2:exp3	-0.104095036830259	0.306442362288417	-0.339688795155178	0.734190657341525	   
df.mm.trans1:exp4	-0.180856771093699	0.385598183205135	-0.469029105869737	0.639191809318948	   
df.mm.trans2:exp4	-0.322556862326834	0.306442362288417	-1.05258574538481	0.292886480956065	   
df.mm.trans1:exp5	0.273648554761586	0.385598183205135	0.709672832187614	0.478138406979721	   
df.mm.trans2:exp5	-0.148609680547874	0.306442362288417	-0.484951491164937	0.627859294845971	   
df.mm.trans1:exp6	0.322435613115245	0.385598183205135	0.836195882550911	0.403324075261949	   
df.mm.trans2:exp6	-0.0895343571121282	0.306442362288417	-0.292173563875155	0.77023867963988	   
df.mm.trans1:exp7	0.103099336357845	0.385598183205135	0.267375057374161	0.789257506246267	   
df.mm.trans2:exp7	-0.167494376297726	0.306442362288417	-0.546577095434617	0.584839830129166	   
df.mm.trans1:exp8	-0.178452653765881	0.385598183205135	-0.462794332386535	0.643652607919234	   
df.mm.trans2:exp8	-0.148552970742465	0.306442362288417	-0.484766432529488	0.627990510549689	   
df.mm.trans1:probe2	0.377626554537004	0.245043368873957	1.54106008365908	0.123744597040947	   
df.mm.trans1:probe3	0.453428237275190	0.245043368873957	1.85039994903278	0.0646682596955741	.  
df.mm.trans1:probe4	0.551733246504326	0.245043368873957	2.25157387053441	0.0246519963611557	*  
df.mm.trans1:probe5	0.308104505758157	0.245043368873957	1.25734684098567	0.209038603220704	   
df.mm.trans1:probe6	-0.0149626425636414	0.245043368873957	-0.0610612016656436	0.951327531615025	   
df.mm.trans1:probe7	0.391971147761365	0.245043368873957	1.59959908143029	0.110129305010179	   
df.mm.trans1:probe8	0.44507929497126	0.245043368873957	1.8163286646626	0.0697386017532499	.  
df.mm.trans1:probe9	0.307858716587198	0.245043368873957	1.25634379743428	0.209401671502684	   
df.mm.trans1:probe10	0.214681815384656	0.245043368873957	0.876097224630803	0.381271412793241	   
df.mm.trans1:probe11	0.116553471186319	0.245043368873957	0.475644257267253	0.634473121749653	   
df.mm.trans1:probe12	0.656419850506648	0.245043368873957	2.67879050766842	0.0075586862043608	** 
df.mm.trans1:probe13	0.331659749878131	0.245043368873957	1.35347367856637	0.176332070293354	   
df.mm.trans1:probe14	0.598916325862183	0.245043368873957	2.4441237835342	0.0147610297916312	*  
df.mm.trans1:probe15	0.174975853433614	0.245043368873957	0.714060756827157	0.475422643006138	   
df.mm.trans1:probe16	0.34485071602966	0.245043368873957	1.40730482777128	0.159771574448848	   
df.mm.trans1:probe17	0.161368359927632	0.245043368873957	0.658529796864795	0.510409747831641	   
df.mm.trans1:probe18	0.25045507419648	0.245043368873957	1.02208468381491	0.307086508491668	   
df.mm.trans1:probe19	0.311739977856043	0.245043368873957	1.27218287639684	0.203721734845100	   
df.mm.trans2:probe2	-0.293099808342137	0.245043368873957	-1.19611401724116	0.232048567525207	   
df.mm.trans2:probe3	0.216209034867313	0.245043368873957	0.882329670298174	0.377895087877533	   
df.mm.trans2:probe4	-0.283008283540703	0.245043368873957	-1.15493141006510	0.248504347386327	   
df.mm.trans2:probe5	-0.0258920866907712	0.245043368873957	-0.105663282421200	0.915879149557635	   
df.mm.trans2:probe6	-0.150150419995225	0.245043368873957	-0.612750390615374	0.540236234549798	   
df.mm.trans3:probe2	-0.302884706844552	0.245043368873957	-1.23604531000529	0.216847632306521	   
df.mm.trans3:probe3	0.0186809719066030	0.245043368873957	0.0762353700589708	0.939253169293377	   
df.mm.trans3:probe4	-0.328251967571633	0.245043368873957	-1.33956682476266	0.180811833366471	   
df.mm.trans3:probe5	-0.150207366959294	0.245043368873957	-0.612982786065744	0.540082645053416	   
df.mm.trans3:probe6	-0.0428173757798408	0.245043368873957	-0.174733868443772	0.861338219775967	   
df.mm.trans3:probe7	-0.106793525585563	0.245043368873957	-0.435814795055704	0.66310265109981