chr14.7833_chr14_50622174_50631516_+_2.R 

fitVsDatCorrelation=0.917206425670124
cont.fitVsDatCorrelation=0.335929434421057

fstatistic=8414.93202428892,39,393
cont.fstatistic=1497.35646153714,39,393

residuals=-0.545867712606679,-0.0768249564637046,-0.00787408715749792,0.0696665866345422,1.18757432964047
cont.residuals=-0.583106965787809,-0.231302868305409,-0.102587909688333,0.171649138199972,1.04932444297050

predictedValues:
Include	Exclude	Both
chr14.7833_chr14_50622174_50631516_+_2.R.tl.Lung	46.8659462726058	43.2461088851148	79.3375963712887
chr14.7833_chr14_50622174_50631516_+_2.R.tl.cerebhem	57.2474667430205	46.7591597338589	73.3119342143876
chr14.7833_chr14_50622174_50631516_+_2.R.tl.cortex	47.2663534784069	44.398850305445	83.5239697284563
chr14.7833_chr14_50622174_50631516_+_2.R.tl.heart	47.5310299414912	45.5238409646578	79.6218454978041
chr14.7833_chr14_50622174_50631516_+_2.R.tl.kidney	44.7955716169491	41.4114209455178	94.0427269741807
chr14.7833_chr14_50622174_50631516_+_2.R.tl.liver	50.8553294764528	50.0030666222022	83.0354104071647
chr14.7833_chr14_50622174_50631516_+_2.R.tl.stomach	48.8014574529237	46.9886744823972	83.2510534407156
chr14.7833_chr14_50622174_50631516_+_2.R.tl.testicle	49.3813742111053	44.9666638474549	96.9813833869282


diffExp=3.61983738749104,10.4883070091615,2.86750317296191,2.00718897683335,3.38415067143136,0.852262854250561,1.81278297052651,4.41471036365042
diffExpScore=0.96715576484962
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	53.917886340024	44.2958125306965	57.9429296937602
cerebhem	51.5823274055847	49.9109438864458	56.6942754568614
cortex	56.9030917511447	53.5197674105149	60.2983006446272
heart	56.30715418978	57.8534090642115	59.7769146001186
kidney	52.1528559531018	49.8094365959172	51.5635992812056
liver	49.8335587412206	67.314932515901	62.8053790047479
stomach	53.240777178033	56.9284800010106	60.4290393716158
testicle	57.0278125623728	52.7577551029424	54.7138403859197
cont.diffExp=9.62207380932745,1.67138351913889,3.38332434062983,-1.54625487443152,2.34341935718468,-17.4813737746805,-3.68770282297756,4.27005745943035
cont.diffExpScore=18.1460888826771

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

tran.correlation=0.616400820797212
cont.tran.correlation=-0.275951532895015

tran.covariance=0.00272756797371723
cont.tran.covariance=-0.00153491828000577

tran.mean=47.2526446862253
cont.tran.mean=53.9597500768063

weightedLogRatios:
wLogRatio
Lung	0.306030090532239
cerebhem	0.79860457170724
cortex	0.239357192142065
heart	0.165674698816672
kidney	0.295580223852174
liver	0.0662593436946485
stomach	0.146449171219049
testicle	0.360817114499463

cont.weightedLogRatios:
wLogRatio
Lung	0.764503606517434
cerebhem	0.129341210236251
cortex	0.245850343011332
heart	-0.109565062691647
kidney	0.180734526391168
liver	-1.22052540494811
stomach	-0.268441149923682
testicle	0.311673193938579

varWeightedLogRatios=0.0503197426468237
cont.varWeightedLogRatios=0.337864141771753

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.2635201873473	0.0765745661835501	42.618853099665	1.97823142398495e-149	***
df.mm.trans1	0.613573993057342	0.0625228714748925	9.81359266750467	1.77218106620654e-20	***
df.mm.trans2	0.480041254826053	0.0625228714748925	7.67785041700819	1.29633624857089e-13	***
df.mm.exp2	0.357183688695783	0.0849518537757541	4.20454260643487	3.24231640981434e-05	***
df.mm.exp3	-0.0166078402499811	0.084951853775754	-0.195497090549907	0.845104850934978	   
df.mm.exp4	0.0618439680573762	0.0849518537757541	0.727988446498468	0.467054166775565	   
df.mm.exp5	-0.258569685015564	0.0849518537757541	-3.04372033714657	0.00249384112341549	** 
df.mm.exp6	0.1813156510885	0.084951853775754	2.13433424969296	0.0334337305676952	*  
df.mm.exp7	0.07531953826642	0.0849518537757541	0.886614416505138	0.375828749315308	   
df.mm.exp8	-0.109510773027382	0.084951853775754	-1.28909221117712	0.198124262475608	   
df.mm.trans1:exp2	-0.157091615721700	0.0693628981513753	-2.26477872044603	0.0240702039096831	*  
df.mm.trans2:exp2	-0.279080783373173	0.0693628981513753	-4.02348792814447	6.87909165018255e-05	***
df.mm.trans1:exp3	0.0251152202211567	0.0693628981513754	0.362084354756141	0.71748361008798	   
df.mm.trans2:exp3	0.0429141539750916	0.0693628981513754	0.618690324637779	0.536478812801104	   
df.mm.trans1:exp4	-0.0477525278243809	0.0693628981513753	-0.688444818441226	0.491578885886912	   
df.mm.trans2:exp4	-0.0105150634396898	0.0693628981513754	-0.151594926393388	0.879584222604964	   
df.mm.trans1:exp5	0.213387652263738	0.0693628981513753	3.07639470020482	0.00224183974311777	** 
df.mm.trans2:exp5	0.215219134695103	0.0693628981513754	3.10279905296655	0.00205554443587519	** 
df.mm.trans1:exp6	-0.0996220457404094	0.0693628981513753	-1.43624399204020	0.151728444485919	   
df.mm.trans2:exp6	-0.0361385764533702	0.0693628981513753	-0.521007302412632	0.602655118832077	   
df.mm.trans1:exp7	-0.0348506794657323	0.0693628981513754	-0.502439782571877	0.61563961173169	   
df.mm.trans2:exp7	0.00767980459013789	0.0693628981513753	0.110719199958712	0.911895539982198	   
df.mm.trans1:exp8	0.161792766411643	0.0693628981513754	2.33255487766027	0.0201756522566738	*  
df.mm.trans2:exp8	0.148524923521087	0.0693628981513753	2.14127332449331	0.0328664781048355	*  
df.mm.trans1:probe2	-0.0393273049145541	0.0424759268878770	-0.92587278950653	0.355080289538628	   
df.mm.trans1:probe3	-0.106458227302163	0.0424759268878770	-2.50631911066189	0.0126026600109949	*  
df.mm.trans1:probe4	-0.0814161208370583	0.042475926887877	-1.91675913399067	0.0559934246732986	.  
df.mm.trans1:probe5	-0.0533689266060197	0.042475926887877	-1.25645113635535	0.209698779267136	   
df.mm.trans1:probe6	-0.0770637517834125	0.0424759268878770	-1.81429241054201	0.0703950941674996	.  
df.mm.trans2:probe2	0.0344128474141279	0.0424759268878770	0.810172959967816	0.418331072793028	   
df.mm.trans2:probe3	0.0904749423339084	0.0424759268878770	2.13002867654268	0.0337899112381988	*  
df.mm.trans2:probe4	0.0298094522797527	0.0424759268878770	0.70179639301198	0.48322154151195	   
df.mm.trans2:probe5	0.0905879185503476	0.0424759268878770	2.13268844702250	0.033569497899384	*  
df.mm.trans2:probe6	0.0348646696172857	0.0424759268878770	0.820810095782426	0.412252045858534	   
df.mm.trans3:probe2	0.237446703331511	0.042475926887877	5.59014766077489	4.25083539072086e-08	***
df.mm.trans3:probe3	-0.461253545472001	0.042475926887877	-10.8591755205146	3.35556183240094e-24	***
df.mm.trans3:probe4	-0.177374829614307	0.0424759268878770	-4.17589073647575	3.65881509150272e-05	***
df.mm.trans3:probe5	0.185712516342847	0.042475926887877	4.37218278562983	1.57709420074952e-05	***
df.mm.trans3:probe6	0.314929260020977	0.0424759268878770	7.4142998892594	7.5833016191672e-13	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.64806468670747	0.181030047704971	20.1517081443452	1.54288985089378e-62	***
df.mm.trans1	0.300077232741736	0.147810414996292	2.03014945022151	0.0430143901371422	*  
df.mm.trans2	0.081897267518725	0.147810414996292	0.554069667694118	0.579846292333797	   
df.mm.exp2	0.0968523151849903	0.20083480596922	0.482248655643056	0.629897788357717	   
df.mm.exp3	0.203202966649758	0.20083480596922	1.01179158497507	0.312260233066104	   
df.mm.exp4	0.279220580149945	0.20083480596922	1.39029974810611	0.165224734215065	   
df.mm.exp5	0.200673558950383	0.20083480596922	0.999197116166895	0.318314028352517	   
df.mm.exp6	0.259136138663170	0.20083480596922	1.2902949636274	0.197706910902508	   
df.mm.exp7	0.1962567026288	0.20083480596922	0.977204631844933	0.329068989918345	   
df.mm.exp8	0.288239307414778	0.20083480596922	1.43520594462572	0.152023774682402	   
df.mm.trans1:exp2	-0.141135459411476	0.163980932405151	-0.860682137498581	0.389937903399045	   
df.mm.trans2:exp2	0.0224978325709530	0.163980932405151	0.137197857342140	0.890944664122665	   
df.mm.trans1:exp3	-0.149315556328514	0.163980932405151	-0.910566577092005	0.363081926549738	   
df.mm.trans2:exp3	-0.0140420439984768	0.163980932405151	-0.0856321755982144	0.931802427940659	   
df.mm.trans1:exp4	-0.235861246345058	0.163980932405151	-1.43834556180172	0.151131879415188	   
df.mm.trans2:exp4	-0.0121983461906658	0.163980932405151	-0.0743888085751769	0.940738862297917	   
df.mm.trans1:exp5	-0.233956880712071	0.163980932405151	-1.42673222600069	0.154451047851173	   
df.mm.trans2:exp5	-0.083359250309711	0.163980932405151	-0.508347215051525	0.611495087238343	   
df.mm.trans1:exp6	-0.337909777185916	0.163980932405151	-2.06066505556289	0.0399926020983915	*  
df.mm.trans2:exp6	0.159355805938618	0.163980932405151	0.971794730041505	0.331750404819268	   
df.mm.trans1:exp7	-0.208894377484798	0.163980932405151	-1.27389431454553	0.203453718209298	   
df.mm.trans2:exp7	0.0546488932231812	0.163980932405151	0.333263705856721	0.739112957555354	   
df.mm.trans1:exp8	-0.232162484803681	0.163980932405151	-1.41578951527164	0.157629147209555	   
df.mm.trans2:exp8	-0.113418677011747	0.163980932405151	-0.691657715005062	0.489560701127325	   
df.mm.trans1:probe2	0.265048110830404	0.10041740298461	2.63946390717778	0.00863418682867747	** 
df.mm.trans1:probe3	0.148262118945807	0.10041740298461	1.47645840799656	0.140621678502129	   
df.mm.trans1:probe4	0.0433869759422973	0.10041740298461	0.432066301783833	0.665930184549334	   
df.mm.trans1:probe5	0.0805742477775973	0.10041740298461	0.8023932643423	0.422810453350935	   
df.mm.trans1:probe6	-0.0654272959624657	0.10041740298461	-0.651553356468431	0.515070169351269	   
df.mm.trans2:probe2	0.302144284472762	0.10041740298461	3.00888367446696	0.00279097058809805	** 
df.mm.trans2:probe3	0.129348270699739	0.10041740298461	1.28810611363414	0.198466917719524	   
df.mm.trans2:probe4	0.0407962262538418	0.10041740298461	0.406266494066713	0.684767929936534	   
df.mm.trans2:probe5	0.0873639837669727	0.10041740298461	0.870008396655729	0.384826884145712	   
df.mm.trans2:probe6	0.171485551894565	0.10041740298461	1.70772741375164	0.0884766630179142	.  
df.mm.trans3:probe2	0.0417825331297277	0.10041740298461	0.41608856520748	0.677572322009784	   
df.mm.trans3:probe3	0.0328261863272534	0.10041740298461	0.326897383835791	0.743919535043358	   
df.mm.trans3:probe4	0.0981801056122454	0.10041740298461	0.977720023562973	0.328814273940939	   
df.mm.trans3:probe5	0.0150119084330149	0.10041740298461	0.149495087373606	0.881239672172448	   
df.mm.trans3:probe6	-0.0112845233170961	0.10041740298461	-0.112376171676393	0.910582523829562