chr15.8746_chr15_34237825_34240362_+_1.R 

fitVsDatCorrelation=0.920119297775137
cont.fitVsDatCorrelation=0.249705074672891

fstatistic=7393.43144522062,50,646
cont.fstatistic=1198.61191843927,50,646

residuals=-0.819035118402961,-0.108574819897847,0.000764958282914009,0.106336669869242,0.663603754474869
cont.residuals=-0.900531855338227,-0.353320756622930,-0.0754308172423764,0.274607290466104,1.40735162128197

predictedValues:
Include	Exclude	Both
chr15.8746_chr15_34237825_34240362_+_1.R.tl.Lung	54.0007058749053	129.408715573425	86.9806621071897
chr15.8746_chr15_34237825_34240362_+_1.R.tl.cerebhem	61.4054144191092	89.9888092792665	66.9570091364973
chr15.8746_chr15_34237825_34240362_+_1.R.tl.cortex	59.0715500088113	191.269003445025	110.703512535976
chr15.8746_chr15_34237825_34240362_+_1.R.tl.heart	57.3555214895361	200.244445538202	98.9142288617907
chr15.8746_chr15_34237825_34240362_+_1.R.tl.kidney	52.8826632979676	117.610343560446	78.9889575367499
chr15.8746_chr15_34237825_34240362_+_1.R.tl.liver	59.3922098376193	126.049885864246	71.3337743155952
chr15.8746_chr15_34237825_34240362_+_1.R.tl.stomach	68.0206582016963	251.210461247787	86.7655953017722
chr15.8746_chr15_34237825_34240362_+_1.R.tl.testicle	63.4115804496583	171.852396460885	99.4146844287003


diffExp=-75.4080096985196,-28.5833948601573,-132.197453436213,-142.888924048666,-64.7276802624789,-66.6576760266268,-183.189803046090,-108.440816011227
diffExpScore=0.998754815373924
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	79.8008100252655	102.332096420561	98.3188097358273
cerebhem	79.5076879101755	70.968064066932	87.3184332666947
cortex	71.904675989644	77.2027192933749	87.1402207526057
heart	66.2130254694604	94.1482092320967	79.4403376497395
kidney	93.5483539491216	96.0669707301729	84.0278087588673
liver	69.6962176360281	88.2004498340653	84.6262352322314
stomach	73.5361998967265	76.6496664679758	98.3416819438062
testicle	74.3525061765966	94.710592488611	95.020865619329
cont.diffExp=-22.5312863952952,8.53962384324359,-5.29804330373086,-27.9351837626363,-2.51861678105128,-18.5042321980372,-3.11346657124929,-20.3580863120144
cont.diffExpScore=1.17341857805095

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,-1,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,-1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=-1,0,0,-1,0,-1,0,-1
cont.diffExp1.2Score=0.8

tran.correlation=0.567154805848919
cont.tran.correlation=0.205448603733990

tran.covariance=0.0132780203623952
cont.tran.covariance=0.00230583894224653

tran.mean=109.573397784287
cont.tran.mean=81.8023903491755

weightedLogRatios:
wLogRatio
Lung	-3.86821748782394
cerebhem	-1.64668904757832
cortex	-5.48248272287296
heart	-5.84426581343263
kidney	-3.49112984647603
liver	-3.35653182753174
stomach	-6.36654218131864
testicle	-4.63414774749585

cont.weightedLogRatios:
wLogRatio
Lung	-1.12006827134622
cerebhem	0.490745715016249
cortex	-0.306475611631536
heart	-1.53781310496509
kidney	-0.120927129522379
liver	-1.02707427153516
stomach	-0.179077675385259
testicle	-1.07205433724519

varWeightedLogRatios=2.41795126837585
cont.varWeightedLogRatios=0.462442840532272

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86137223845271	0.0955804086706834	40.3992020138441	6.00031095388722e-179	***
df.mm.trans1	0.158313660403441	0.0754338905512163	2.09870734820383	0.0362303321103075	*  
df.mm.trans2	1.00042788110550	0.0754338905512163	13.2623131830415	1.12065969264360e-35	***
df.mm.exp2	0.0268455440283135	0.099887251232597	0.268758462136485	0.788201367319931	   
df.mm.exp3	0.239287651477193	0.099887251232597	2.39557749887410	0.0168778261871815	*  
df.mm.exp4	0.368267831996115	0.099887251232597	3.68683518118412	0.00024614057343545	***
df.mm.exp5	-0.0201425508776269	0.099887251232597	-0.201652869901515	0.840251591235485	   
df.mm.exp6	0.267183877624781	0.099887251232597	2.67485464188636	0.00766544613243006	** 
df.mm.exp7	0.89660532689068	0.099887251232597	8.97617379421974	3.02790481907914e-18	***
df.mm.exp8	0.310695584094821	0.099887251232597	3.11046284947152	0.00195020403340322	** 
df.mm.trans1:exp2	0.101655351722219	0.0757432584545468	1.34210428487470	0.180033680372627	   
df.mm.trans2:exp2	-0.390135956305092	0.0757432584545468	-5.1507680586413	3.44964038658735e-07	***
df.mm.trans1:exp3	-0.149535348535322	0.0757432584545468	-1.97423970907005	0.0487800443151215	*  
df.mm.trans2:exp3	0.151417447177029	0.0757432584545467	1.99908810719958	0.0460172793301423	*  
df.mm.trans1:exp4	-0.307995834228889	0.0757432584545468	-4.06631349790313	5.36594625903409e-05	***
df.mm.trans2:exp4	0.0682952823961778	0.0757432584545468	0.901668132447213	0.367569229904967	   
df.mm.trans1:exp5	-0.00077900814468576	0.0757432584545468	-0.0102848512274296	0.991797195962004	   
df.mm.trans2:exp5	-0.0754561956142613	0.0757432584545468	-0.99621005425245	0.319521096623917	   
df.mm.trans1:exp6	-0.172017925607940	0.0757432584545468	-2.27106582312097	0.0234712395749352	*  
df.mm.trans2:exp6	-0.293481863005796	0.0757432584545467	-3.87469286368124	0.000117650006581405	***
df.mm.trans1:exp7	-0.665790989004139	0.0757432584545468	-8.79010228222064	1.34502378967266e-17	***
df.mm.trans2:exp7	-0.233289981603612	0.0757432584545467	-3.08000984329989	0.00215781247594895	** 
df.mm.trans1:exp8	-0.150046200633059	0.0757432584545468	-1.98098423139667	0.0480166946975776	*  
df.mm.trans2:exp8	-0.0270353693572194	0.0757432584545467	-0.356934331963593	0.721257528976972	   
df.mm.trans1:probe2	-0.098281100709754	0.0563942933116353	-1.74274904318158	0.0818532934100499	.  
df.mm.trans1:probe3	-0.112892824919903	0.0563942933116353	-2.00184838377273	0.0457186937593811	*  
df.mm.trans1:probe4	-0.155859464488625	0.0563942933116353	-2.76374532485662	0.00587706722013618	** 
df.mm.trans1:probe5	-0.228484767030591	0.0563942933116353	-4.05155829807072	5.70688158748076e-05	***
df.mm.trans1:probe6	-0.110323797106779	0.0563942933116353	-1.95629363590264	0.0508610767031403	.  
df.mm.trans2:probe2	-0.173319521331590	0.0563942933116353	-3.07335212756059	0.00220582461391683	** 
df.mm.trans2:probe3	0.07084314592455	0.0563942933116354	1.25621125409038	0.209493487864023	   
df.mm.trans2:probe4	-0.07943853637622	0.0563942933116354	-1.40862721582913	0.159426538379216	   
df.mm.trans2:probe5	0.113118973627180	0.0563942933116353	2.00585851838028	0.0452878242882122	*  
df.mm.trans2:probe6	0.0958350596113619	0.0563942933116354	1.69937513148319	0.0897299496233313	.  
df.mm.trans3:probe2	-0.933086245348164	0.0563942933116353	-16.5457565039768	1.57093932209999e-51	***
df.mm.trans3:probe3	-0.593950624186289	0.0563942933116353	-10.5321050997858	4.79552896978386e-24	***
df.mm.trans3:probe4	-0.702751636329795	0.0563942933116353	-12.4613962701223	4.28441612095628e-32	***
df.mm.trans3:probe5	-0.610099380842028	0.0563942933116353	-10.8184595464405	3.4884713100071e-25	***
df.mm.trans3:probe6	-0.8120903400122	0.0563942933116354	-14.4002219431066	5.62491995251783e-41	***
df.mm.trans3:probe7	-0.706682427293536	0.0563942933116353	-12.5310981979755	2.11511691366126e-32	***
df.mm.trans3:probe8	-0.743903191912613	0.0563942933116353	-13.1911076144141	2.36128318624350e-35	***
df.mm.trans3:probe9	-0.525993490306712	0.0563942933116354	-9.32706945009608	1.70703895213679e-19	***
df.mm.trans3:probe10	0.0337843827564464	0.0563942933116353	0.599074494466197	0.549333231735026	   
df.mm.trans3:probe11	-0.606449026117289	0.0563942933116353	-10.7537303954861	6.33491691111972e-25	***
df.mm.trans3:probe12	-0.661279161075082	0.0563942933116353	-11.7259942849332	6.34054956730596e-29	***
df.mm.trans3:probe13	-0.731690100077321	0.0563942933116353	-12.9745415202562	2.24641890229522e-34	***
df.mm.trans3:probe14	-0.489996514825558	0.0563942933116353	-8.68876061834543	2.99972470063616e-17	***
df.mm.trans3:probe15	-0.704921363616685	0.0563942933116353	-12.4998705050045	2.9027117927346e-32	***
df.mm.trans3:probe16	-0.591690233195046	0.0563942933116353	-10.4920231897465	6.89413695113775e-24	***
df.mm.trans3:probe17	-0.0444759785618263	0.0563942933116354	-0.788660978798895	0.430599503073859	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29426286428714	0.236321704871192	18.1712588212232	6.53998073332723e-60	***
df.mm.trans1	0.0409607481937978	0.186509619157949	0.219617349382444	0.826238554683419	   
df.mm.trans2	0.326893468104737	0.186509619157949	1.75268959092078	0.0801296650652131	.  
df.mm.exp2	-0.251019562539501	0.246970334553766	-1.01639560473144	0.309821546401335	   
df.mm.exp3	-0.265284244024867	0.246970334553766	-1.07415428862819	0.283154587062777	   
df.mm.exp4	-0.0568005002906572	0.246970334553766	-0.229989162031489	0.818172997328698	   
df.mm.exp5	0.252834548779786	0.246970334553766	1.02374460979946	0.306339120970303	   
df.mm.exp6	-0.134027882239902	0.246970334553766	-0.542688183510248	0.58753168432573	   
df.mm.exp7	-0.370966578357801	0.246970334553766	-1.50206938427676	0.133567828147853	   
df.mm.exp8	-0.113994946069714	0.246970334553766	-0.461573436646486	0.644542630185176	   
df.mm.trans1:exp2	0.247339627700180	0.187274528529695	1.32073288151924	0.187058124788411	   
df.mm.trans2:exp2	-0.114973835235130	0.187274528529695	-0.613932050118069	0.539476358679899	   
df.mm.trans1:exp3	0.161091886166895	0.187274528529695	0.860191118523368	0.390002816989612	   
df.mm.trans2:exp3	-0.0165044473078474	0.187274528529695	-0.0881296962134946	0.929800906362027	   
df.mm.trans1:exp4	-0.129855951860351	0.187274528529695	-0.69339889882441	0.488308480774817	   
df.mm.trans2:exp4	-0.0265526369083319	0.187274528529695	-0.141784561503363	0.887294383836859	   
df.mm.trans1:exp5	-0.0938897467242018	0.187274528529695	-0.50134819433981	0.616296890196381	   
df.mm.trans2:exp5	-0.316012360507841	0.187274528529695	-1.68742841319039	0.092003727993647	.  
df.mm.trans1:exp6	-0.00135972290394840	0.187274528529695	-0.00726058644826755	0.994209182608196	   
df.mm.trans2:exp6	-0.0145834263514284	0.187274528529695	-0.0778719159830431	0.937954052434174	   
df.mm.trans1:exp7	0.289210723835517	0.187274528529695	1.54431425408533	0.123001929483476	   
df.mm.trans2:exp7	0.0819884605519606	0.187274528529695	0.43779824835581	0.661678879097064	   
df.mm.trans1:exp8	0.0432786711334021	0.187274528529695	0.231097477447606	0.817312248323726	   
df.mm.trans2:exp8	0.0365974213565472	0.187274528529695	0.195421244116197	0.845124636077608	   
df.mm.trans1:probe2	0.127851100821338	0.139434385411866	0.91692662784497	0.359523267905571	   
df.mm.trans1:probe3	0.229330838814536	0.139434385411866	1.64472226945406	0.100513584107503	   
df.mm.trans1:probe4	0.217694107482015	0.139434385411866	1.5612655862396	0.118950830344255	   
df.mm.trans1:probe5	0.235466251221896	0.139434385411866	1.68872441705371	0.091754842179021	.  
df.mm.trans1:probe6	0.208788681825	0.139434385411866	1.49739736872130	0.134778322298226	   
df.mm.trans2:probe2	0.0435408933354827	0.139434385411866	0.312267976129921	0.754937642519137	   
df.mm.trans2:probe3	0.0420378686599618	0.139434385411866	0.301488535527223	0.763139013621233	   
df.mm.trans2:probe4	-0.016512020804581	0.139434385411866	-0.118421440707091	0.905770550619804	   
df.mm.trans2:probe5	0.0152494903806722	0.139434385411866	0.109366784496004	0.912945541483892	   
df.mm.trans2:probe6	0.0782256736777781	0.139434385411866	0.561021396886518	0.574977492256155	   
df.mm.trans3:probe2	-0.107646179812285	0.139434385411866	-0.772020326939558	0.440384715632221	   
df.mm.trans3:probe3	0.0158832092372727	0.139434385411866	0.113911709728962	0.909343175564563	   
df.mm.trans3:probe4	-0.0217147620722937	0.139434385411866	-0.155734627496309	0.876290851941935	   
df.mm.trans3:probe5	-0.1257089410717	0.139434385411866	-0.901563417806711	0.367624832608168	   
df.mm.trans3:probe6	-0.250910986873878	0.139434385411866	-1.79949146785227	0.0724074990718517	.  
df.mm.trans3:probe7	-0.0667685714525013	0.139434385411866	-0.478852983468017	0.632205257571174	   
df.mm.trans3:probe8	-0.221246884844615	0.139434385411866	-1.58674550894378	0.113059622353122	   
df.mm.trans3:probe9	-0.0164646457870196	0.139434385411866	-0.118081675035794	0.906039648548522	   
df.mm.trans3:probe10	-0.0734100988171553	0.139434385411866	-0.526484902560543	0.598732052581349	   
df.mm.trans3:probe11	-0.000417986605816784	0.139434385411866	-0.00299772975354767	0.997609086750126	   
df.mm.trans3:probe12	-0.144347206056251	0.139434385411866	-1.03523392475876	0.300947047617535	   
df.mm.trans3:probe13	-0.113713311608477	0.139434385411866	-0.815532777460786	0.415067990779373	   
df.mm.trans3:probe14	-0.017012781550579	0.139434385411866	-0.122012812695564	0.90292681588556	   
df.mm.trans3:probe15	-0.16523323028603	0.139434385411866	-1.18502498360041	0.236443325185701	   
df.mm.trans3:probe16	0.0365969637569417	0.139434385411866	0.262467279135203	0.793044886068754	   
df.mm.trans3:probe17	0.0157861496349480	0.139434385411866	0.113215614558191	0.909894790240008