chr16.9548_chr16_36659489_36663303_+_2.R 

fitVsDatCorrelation=0.705777596679838
cont.fitVsDatCorrelation=0.231280899050783

fstatistic=15076.4538320944,59,853
cont.fstatistic=7987.36280365133,59,853

residuals=-0.365765089071955,-0.0745431249948689,-0.00771899018193127,0.0760820427786808,0.774134092997853
cont.residuals=-0.383867720690392,-0.108498797232301,-0.0259892969689273,0.0863576240851712,0.719445399977963

predictedValues:
Include	Exclude	Both
chr16.9548_chr16_36659489_36663303_+_2.R.tl.Lung	45.1428592263448	41.3504393765454	49.5142048559932
chr16.9548_chr16_36659489_36663303_+_2.R.tl.cerebhem	45.4804317794243	38.3450430596826	56.056732757135
chr16.9548_chr16_36659489_36663303_+_2.R.tl.cortex	49.2981301264937	42.0623626273028	57.7622122712104
chr16.9548_chr16_36659489_36663303_+_2.R.tl.heart	47.8098064205754	45.2743546298115	49.6832695947603
chr16.9548_chr16_36659489_36663303_+_2.R.tl.kidney	45.8884026831062	40.5360592494258	48.4710235404569
chr16.9548_chr16_36659489_36663303_+_2.R.tl.liver	49.3666779337246	43.7532254029242	49.9981298083474
chr16.9548_chr16_36659489_36663303_+_2.R.tl.stomach	45.1824340122773	43.435964712741	49.8363653397584
chr16.9548_chr16_36659489_36663303_+_2.R.tl.testicle	45.9494367383123	41.4110025112713	48.3503966444841


diffExp=3.79241984979944,7.13538871974163,7.23576749919087,2.53545179076396,5.35234343368041,5.61345253080041,1.74646929953627,4.53843422704101
diffExpScore=0.974325879331585
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	48.4229140218436	48.6937370002743	47.0291823275856
cerebhem	48.5038801898567	44.9367536105798	47.4897021565067
cortex	49.6823154039976	47.338536957229	47.3941194689396
heart	48.3721008305022	46.2973564812793	47.327532094258
kidney	49.3513871590581	44.814093697888	45.8103571613144
liver	49.580049218986	47.6873195558115	48.525801295854
stomach	48.8311334761964	47.6965703967679	50.5730150880151
testicle	49.3331981301251	44.7681702339641	47.5013400121219
cont.diffExp=-0.270822978430708,3.56712657927696,2.34377844676852,2.07474434922294,4.53729346117007,1.89272966317451,1.13456307942844,4.565027896161
cont.diffExpScore=0.97801072937364

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=0.497694401718025
cont.tran.correlation=-0.0816674485878329

tran.covariance=0.000970911713120584
cont.tran.covariance=-2.95593252568573e-05

tran.mean=44.3929144056227
cont.tran.mean=47.7693447727725

weightedLogRatios:
wLogRatio
Lung	0.330459250652016
cerebhem	0.636883646068607
cortex	0.606124370878866
heart	0.209240918142629
kidney	0.466838199952808
liver	0.463397349528426
stomach	0.149443439021281
testicle	0.392637647819415

cont.weightedLogRatios:
wLogRatio
Lung	-0.0216552738002643
cerebhem	0.293592578722248
cortex	0.187570307911316
heart	0.169084709988939
kidney	0.371378072260087
liver	0.151181853466189
stomach	0.0911337422193913
testicle	0.373838992237081

varWeightedLogRatios=0.0301095654535412
cont.varWeightedLogRatios=0.0189705563000517

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.62611094185189	0.0575348707012988	63.024577923837	0	***
df.mm.trans1	0.141694874221636	0.0496857069873384	2.85182364935946	0.00445192034029174	** 
df.mm.trans2	0.0886520417222813	0.0438970975479235	2.01954221746660	0.0437431728760524	*  
df.mm.exp2	-0.192112398717087	0.0564657031111163	-3.40228471677821	0.000699350391869391	***
df.mm.exp3	-0.0489508642995814	0.0564657031111163	-0.866913216386472	0.386233352384080	   
df.mm.exp4	0.144647711662223	0.0564657031111163	2.56169149930848	0.0105872077207869	*  
df.mm.exp5	0.0177826207881645	0.0564657031111163	0.314927820046284	0.75289347854875	   
df.mm.exp6	0.136199830827877	0.0564657031111163	2.41208066708841	0.0160719530242104	*  
df.mm.exp7	0.0435956532675442	0.0564657031111163	0.772073149992559	0.440284951077988	   
df.mm.exp8	0.042958209077748	0.0564657031111163	0.760784099211738	0.446996281065922	   
df.mm.trans1:exp2	0.199562450700339	0.0521924235889664	3.82359041748977	0.000141093636237534	***
df.mm.trans2:exp2	0.116654615681215	0.0385466594243446	3.02632231750647	0.00254984969170286	** 
df.mm.trans1:exp3	0.137004905475429	0.0521924235889664	2.6249960445292	0.00882006896757186	** 
df.mm.trans2:exp3	0.0660211585743244	0.0385466594243446	1.71275953766899	0.0871201896607003	.  
df.mm.trans1:exp4	-0.0872490486489132	0.0521924235889664	-1.67168034456553	0.0949541342175638	.  
df.mm.trans2:exp4	-0.053990009649927	0.0385466594243446	-1.40064043048641	0.161685281299913	   
df.mm.trans1:exp5	-0.00140231118768582	0.0521924235889664	-0.0268680986866889	0.978571222244383	   
df.mm.trans2:exp5	-0.037673738172445	0.0385466594243446	-0.97735416596572	0.328670941491788	   
df.mm.trans1:exp6	-0.0467562806749669	0.0521924235889664	-0.895844213773036	0.37058870501152	   
df.mm.trans2:exp6	-0.0797175444526505	0.0385466594243446	-2.06807919656726	0.0389335183284448	*  
df.mm.trans1:exp7	-0.0427193806965772	0.0521924235889664	-0.818497738924854	0.413301626441621	   
df.mm.trans2:exp7	0.0056090774838103	0.0385466594243446	0.145513971056797	0.88433946324072	   
df.mm.trans1:exp8	-0.0252487291793551	0.0521924235889664	-0.483762344094187	0.628678704965533	   
df.mm.trans2:exp8	-0.0414946496192596	0.0385466594243446	-1.07647848708397	0.28201767565292	   
df.mm.trans1:probe2	0.00468956413832156	0.0357337096631771	0.131236420246456	0.895619231380944	   
df.mm.trans1:probe3	0.447230910658231	0.0357337096631771	12.5156586000668	4.07999854607657e-33	***
df.mm.trans1:probe4	0.0229467598418753	0.0357337096631771	0.64216002363509	0.520941825190378	   
df.mm.trans1:probe5	0.00636984102810232	0.0357337096631771	0.178258599181107	0.858562216546355	   
df.mm.trans1:probe6	0.187381201626806	0.0357337096631771	5.24382168526707	1.98472029414483e-07	***
df.mm.trans1:probe7	0.0136055649327854	0.0357337096631771	0.380748740084093	0.703484499503286	   
df.mm.trans1:probe8	-0.081708497251786	0.0357337096631771	-2.28659431170073	0.0224637744910562	*  
df.mm.trans1:probe9	0.129040896225945	0.0357337096631771	3.61118107921829	0.000322602649149768	***
df.mm.trans1:probe10	0.00784841434040162	0.0357337096631771	0.219636147894526	0.826207084577174	   
df.mm.trans1:probe11	0.00456775829595969	0.0357337096631770	0.127827710557202	0.898315448939997	   
df.mm.trans1:probe12	0.043502094739991	0.0357337096631771	1.21739654656732	0.223790053343364	   
df.mm.trans1:probe13	0.0698085922768334	0.0357337096631771	1.95357809012396	0.0510776783113668	.  
df.mm.trans1:probe14	0.0512174941538161	0.0357337096631770	1.43331030101794	0.152135595696623	   
df.mm.trans1:probe15	0.00314737770159794	0.0357337096631771	0.0880786722471543	0.929834820846155	   
df.mm.trans1:probe16	0.0721022960941248	0.0357337096631771	2.01776688661085	0.0439282260724446	*  
df.mm.trans1:probe17	0.0521840247367708	0.0357337096631771	1.46035844665032	0.144559794017603	   
df.mm.trans1:probe18	0.0244506156551061	0.0357337096631771	0.684245097572447	0.494006259852208	   
df.mm.trans1:probe19	0.101197635702367	0.0357337096631771	2.83199356171659	0.00473480775180983	** 
df.mm.trans1:probe20	0.02415338247756	0.0357337096631771	0.675927092519298	0.499270145940009	   
df.mm.trans1:probe21	0.135879325870056	0.0357337096631771	3.80255302768291	0.000153414173697572	***
df.mm.trans1:probe22	0.0252261743159959	0.0357337096631771	0.705948935998408	0.48041264886606	   
df.mm.trans2:probe2	0.0284643659177329	0.0357337096631771	0.796569015252982	0.425923021321413	   
df.mm.trans2:probe3	0.0376044290436104	0.0357337096631771	1.05235167011952	0.292936248664519	   
df.mm.trans2:probe4	0.0119373477768703	0.0357337096631771	0.334064050147345	0.738413338897044	   
df.mm.trans2:probe5	0.0498963625918191	0.0357337096631771	1.39633872503409	0.162975763005700	   
df.mm.trans2:probe6	-0.0107814872149898	0.0357337096631771	-0.301717546725913	0.762940932643572	   
df.mm.trans3:probe2	0.0851687823002057	0.0357337096631771	2.38342962717836	0.0173700986348688	*  
df.mm.trans3:probe3	0.108801535623813	0.0357337096631771	3.0447870274138	0.00239999202357595	** 
df.mm.trans3:probe4	-0.0150648274277272	0.0357337096631771	-0.421585879824038	0.673433532037333	   
df.mm.trans3:probe5	-0.0152126157699508	0.0357337096631771	-0.42572170405322	0.670418035231851	   
df.mm.trans3:probe6	-0.0369112931082558	0.0357337096631771	-1.03295441352657	0.301918002623951	   
df.mm.trans3:probe7	-0.123900269346651	0.0357337096631771	-3.46732176744380	0.000551957406399024	***
df.mm.trans3:probe8	0.0425834758146997	0.0357337096631771	1.19168919812938	0.233714585586881	   
df.mm.trans3:probe9	0.227809237980395	0.0357337096631771	6.37519138448556	2.98971066506979e-10	***
df.mm.trans3:probe10	0.184743347928307	0.0357337096631771	5.17000193010135	2.91788643084719e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94991127196259	0.0790122585853	49.9911196399778	1.00309435322676e-255	***
df.mm.trans1	-0.0136252807680818	0.068233053809373	-0.199687394999902	0.841772665062045	   
df.mm.trans2	-0.0731922057428241	0.0602835946326787	-1.21413140986035	0.225033578606464	   
df.mm.exp2	-0.0883683067339787	0.077543977783122	-1.13958954983107	0.254777243628202	   
df.mm.exp3	-0.0102796580645887	0.0775439777831219	-0.132565524215682	0.89456826357775	   
df.mm.exp4	-0.0578393580091243	0.0775439777831219	-0.745891037094998	0.455938735648419	   
df.mm.exp5	-0.0377768893556589	0.0775439777831219	-0.487167287978375	0.62626494964331	   
df.mm.exp6	-0.0285968379000354	0.0775439777831219	-0.368782189379247	0.712381592855539	   
df.mm.exp7	-0.0849457894106037	0.0775439777831219	-1.09545308145248	0.27362738772263	   
df.mm.exp8	-0.0754185162834671	0.0775439777831219	-0.972590244137342	0.331032706225968	   
df.mm.trans1:exp2	0.0900389734986007	0.0716755111906742	1.25620273930207	0.209386409381354	   
df.mm.trans2:exp2	0.00807391444908032	0.0529358732349956	0.152522551450851	0.878810874199204	   
df.mm.trans1:exp3	0.0359555690711841	0.0716755111906741	0.50164370611231	0.616047614435082	   
df.mm.trans2:exp3	-0.0179460614565551	0.0529358732349956	-0.339015120745965	0.73468177872663	   
df.mm.trans1:exp4	0.0567894445174723	0.0716755111906741	0.792313072820628	0.428398441373097	   
df.mm.trans2:exp4	0.00737380390665923	0.0529358732349956	0.139296916363787	0.889248407182922	   
df.mm.trans1:exp5	0.0567696315397343	0.0716755111906741	0.792036646780424	0.428559510883216	   
df.mm.trans2:exp5	-0.0452508473541218	0.0529358732349956	-0.854823857410305	0.392888688660391	   
df.mm.trans1:exp6	0.0522122253318991	0.0716755111906741	0.72845277926239	0.466536395221741	   
df.mm.trans2:exp6	0.00771194492102044	0.0529358732349956	0.145684664287773	0.88420474686308	   
df.mm.trans1:exp7	0.093340748022651	0.0716755111906741	1.30226832668611	0.193176188144198	   
df.mm.trans2:exp7	0.0642548671532698	0.0529358732349956	1.21382463774663	0.225150666177301	   
df.mm.trans1:exp8	0.094042628876899	0.0716755111906741	1.31206080451555	0.189852592251297	   
df.mm.trans2:exp8	-0.0086345007872064	0.0529358732349956	-0.163112465319608	0.870468520213127	   
df.mm.trans1:probe2	-0.110226040275923	0.0490728678748077	-2.24617074667667	0.0249482530881425	*  
df.mm.trans1:probe3	-0.105010216891593	0.0490728678748077	-2.13988343129834	0.0326473166513209	*  
df.mm.trans1:probe4	-0.0228874222551899	0.0490728678748077	-0.466396671854988	0.641050677196243	   
df.mm.trans1:probe5	-0.0877212069259276	0.0490728678748077	-1.78757041772487	0.0742002625378868	.  
df.mm.trans1:probe6	-0.0905817869554669	0.0490728678748077	-1.84586291525807	0.065258459160839	.  
df.mm.trans1:probe7	-0.0516469479824437	0.0490728678748077	-1.05245424241768	0.292889235959340	   
df.mm.trans1:probe8	-0.0745428739884866	0.0490728678748077	-1.51902420251159	0.129126946447457	   
df.mm.trans1:probe9	-0.140932305975712	0.0490728678748077	-2.87189871061238	0.0041812502952116	** 
df.mm.trans1:probe10	-0.112762237481567	0.0490728678748077	-2.29785301664537	0.0218113614474951	*  
df.mm.trans1:probe11	-0.0990696035531227	0.0490728678748077	-2.01882644816814	0.0438177022309688	*  
df.mm.trans1:probe12	-0.0982292388884293	0.0490728678748077	-2.00170161521896	0.0456331088908100	*  
df.mm.trans1:probe13	-0.0333401363441109	0.0490728678748077	-0.67940060950109	0.497068376253445	   
df.mm.trans1:probe14	-0.0636302503405368	0.0490728678748077	-1.29664829255276	0.195102879143281	   
df.mm.trans1:probe15	-0.0733577692370868	0.0490728678748077	-1.49487430455936	0.135317088067321	   
df.mm.trans1:probe16	-0.0534053777025093	0.0490728678748077	-1.08828727594960	0.276775692227254	   
df.mm.trans1:probe17	-0.0990383042869896	0.0490728678748077	-2.01818863612478	0.0438842047763714	*  
df.mm.trans1:probe18	-0.082002848803502	0.0490728678748077	-1.67104252012953	0.095080086288361	.  
df.mm.trans1:probe19	-0.111900671429008	0.0490728678748077	-2.28029614479601	0.0228360983378687	*  
df.mm.trans1:probe20	-0.104175079228928	0.0490728678748077	-2.12286511346136	0.0340526032932622	*  
df.mm.trans1:probe21	-0.09093768792155	0.0490728678748077	-1.85311541509141	0.0642109856936244	.  
df.mm.trans1:probe22	-0.0966134911724822	0.0490728678748077	-1.96877613550033	0.0493021907517902	*  
df.mm.trans2:probe2	0.101429149583974	0.0490728678748077	2.06690894534094	0.0390439571331431	*  
df.mm.trans2:probe3	0.00266615267142957	0.0490728678748077	0.0543304841736848	0.956684584392393	   
df.mm.trans2:probe4	0.0344864892689772	0.0490728678748077	0.702760828182233	0.482396552464186	   
df.mm.trans2:probe5	-0.0123754506885917	0.0490728678748077	-0.252185193662684	0.800958672718399	   
df.mm.trans2:probe6	0.0150952896945024	0.0490728678748077	0.30760969041004	0.758454435301878	   
df.mm.trans3:probe2	-0.0555052471804345	0.0490728678748077	-1.13107812084749	0.258340113288704	   
df.mm.trans3:probe3	0.0138894381037329	0.0490728678748077	0.283037016282949	0.777217133633751	   
df.mm.trans3:probe4	-0.063705057529969	0.0490728678748077	-1.29817270293821	0.194578882519850	   
df.mm.trans3:probe5	-0.0491640512883581	0.0490728678748077	-1.00185812277740	0.316696234108402	   
df.mm.trans3:probe6	-0.00273515439535259	0.0490728678748077	-0.0557365915994635	0.95556470100664	   
df.mm.trans3:probe7	0.0114261862684107	0.0490728678748077	0.232841216811713	0.815940563457436	   
df.mm.trans3:probe8	0.0273981321978915	0.0490728678748077	0.558315284686199	0.57677562339617	   
df.mm.trans3:probe9	0.0228854255035554	0.0490728678748077	0.466355982331002	0.641079785374386	   
df.mm.trans3:probe10	-0.0277444300166772	0.0490728678748077	-0.565372092934478	0.571969373958909