chr16.9641_chr16_35211350_35212054_-_0.R 

fitVsDatCorrelation=0.799106699646454
cont.fitVsDatCorrelation=0.294565229018136

fstatistic=8592.40814485792,48,600
cont.fstatistic=3393.05432724668,48,600

residuals=-0.559057047551015,-0.0968604239800693,-0.0109334187171662,0.0821899611728724,0.95154841919422
cont.residuals=-0.549859211138869,-0.178325155276684,-0.0377124518504826,0.135586291380383,1.22712506000297

predictedValues:
Include	Exclude	Both
chr16.9641_chr16_35211350_35212054_-_0.R.tl.Lung	74.4815312210986	76.723257406108	77.7069857445106
chr16.9641_chr16_35211350_35212054_-_0.R.tl.cerebhem	84.1816613017618	67.6374952781452	73.669719412853
chr16.9641_chr16_35211350_35212054_-_0.R.tl.cortex	61.3530050412749	62.5380348813067	64.5219792228825
chr16.9641_chr16_35211350_35212054_-_0.R.tl.heart	59.6430829063922	68.4209167990438	68.6441935941381
chr16.9641_chr16_35211350_35212054_-_0.R.tl.kidney	67.7971057044279	77.1153557243014	77.9763311539742
chr16.9641_chr16_35211350_35212054_-_0.R.tl.liver	91.6739409625106	80.26098717431	128.523821755743
chr16.9641_chr16_35211350_35212054_-_0.R.tl.stomach	79.2737328467569	78.995816370161	85.811579724422
chr16.9641_chr16_35211350_35212054_-_0.R.tl.testicle	67.5814312000988	66.1317028148856	66.8675423210812


diffExp=-2.24172618500934,16.5441660236166,-1.18502984003180,-8.77783389265158,-9.31825001987352,11.4129537882007,0.277916476595962,1.44972838521321
diffExpScore=5.5891754283514
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	78.2330614916697	80.9518713033877	73.660514509921
cerebhem	80.4468936344407	79.1331663702532	83.2222188733513
cortex	83.5331169208326	79.6439945292074	79.9560606857388
heart	85.0231808814114	75.2312080787698	70.8252409767423
kidney	78.7249255320912	72.4465245988415	75.2311372395232
liver	70.9038997661185	80.3300481628709	76.0002628684752
stomach	87.280299431083	73.0574686483131	75.770249023477
testicle	87.6724526139479	82.30798446074	78.3737701919542
cont.diffExp=-2.71880981171805,1.31372726418749,3.88912239162524,9.79197280264155,6.27840093324967,-9.4261483967524,14.2228307827698,5.36446815320787
cont.diffExpScore=1.78376154406855

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=0.598563779534376
cont.tran.correlation=-0.192867287839698

tran.covariance=0.00871282560914192
cont.tran.covariance=-0.00069609927407312

tran.mean=72.7380661020365
cont.tran.mean=79.6825060264987

weightedLogRatios:
wLogRatio
Lung	-0.128263447481965
cerebhem	0.946060217480852
cortex	-0.078937598135999
heart	-0.570761653329671
kidney	-0.551308222027949
liver	0.591881634468945
stomach	0.0153512296161375
testicle	0.0911310459119582

cont.weightedLogRatios:
wLogRatio
Lung	-0.149521339732132
cerebhem	0.0721070131948703
cortex	0.209843703020844
heart	0.536140637984694
kidney	0.359406094730279
liver	-0.539681261919771
stomach	0.77914039708463
testicle	0.280468592349635

varWeightedLogRatios=0.270553059443125
cont.varWeightedLogRatios=0.166526774982829

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03369968175528	0.084807461628961	47.5630281142363	9.93258512689255e-206	***
df.mm.trans1	0.274839286686022	0.067123205672899	4.09454947705199	4.80980704822305e-05	***
df.mm.trans2	0.272831480696405	0.067123205672899	4.06463722882891	5.45119959590716e-05	***
df.mm.exp2	0.049736819763918	0.0890861221310933	0.558300424063005	0.576847525990766	   
df.mm.exp3	-0.212397766937844	0.0890861221310933	-2.38418467272931	0.0174268274885704	*  
df.mm.exp4	-0.212690708946684	0.0890861221310933	-2.38747297400264	0.0172732008350402	*  
df.mm.exp5	-0.0923943211841446	0.0890861221310933	-1.03713484181277	0.300090809813046	   
df.mm.exp6	-0.250403363863837	0.0890861221310933	-2.81080103021389	0.00510317761261669	** 
df.mm.exp7	-0.00766314913116057	0.0890861221310933	-0.086019561159975	0.931479563862743	   
df.mm.exp8	-0.0955430921844218	0.0890861221310933	-1.07248008891696	0.28393561920607	   
df.mm.trans1:exp2	0.0726890869162053	0.0680406496726359	1.06831853114182	0.285806476210071	   
df.mm.trans2:exp2	-0.175779214649614	0.0680406496726359	-2.58344409548323	0.0100173306968538	*  
df.mm.trans1:exp3	0.0184907272430945	0.0680406496726359	0.27176000423363	0.785900041811576	   
df.mm.trans2:exp3	0.00796780864136587	0.0680406496726359	0.117103653179407	0.90681710211343	   
df.mm.trans1:exp4	-0.0094823021479745	0.0680406496726359	-0.139362310524616	0.889210618289186	   
df.mm.trans2:exp4	0.0981643999398054	0.0680406496726359	1.44273166720341	0.149617897785646	   
df.mm.trans1:exp5	-0.00163736508591302	0.0680406496726359	-0.0240645128138969	0.980809150729045	   
df.mm.trans2:exp5	0.0974918602124487	0.0680406496726359	1.43284728587266	0.152422298050291	   
df.mm.trans1:exp6	0.458090334088477	0.068040649672636	6.73259788512437	3.90448126697885e-11	***
df.mm.trans2:exp6	0.29548214028845	0.0680406496726359	4.34272955520125	1.65197632726449e-05	***
df.mm.trans1:exp7	0.0700187935748428	0.068040649672636	1.02907297199137	0.303860041926637	   
df.mm.trans2:exp7	0.0368531548128068	0.068040649672636	0.541634375775634	0.588271470785387	   
df.mm.trans1:exp8	-0.00167484046891365	0.068040649672636	-0.0246152921374474	0.980370005803278	   
df.mm.trans2:exp8	-0.053013544997057	0.068040649672636	-0.779145191177938	0.436201383374828	   
df.mm.trans1:probe2	-0.0699408463792659	0.0498006562342432	-1.40441615970462	0.160712399659769	   
df.mm.trans1:probe3	-0.0485186557543178	0.0498006562342432	-0.974257357696344	0.330321384831506	   
df.mm.trans1:probe4	-0.131161183570055	0.0498006562342432	-2.63372400060599	0.00866320886244256	** 
df.mm.trans1:probe5	0.236691203865258	0.0498006562342432	4.75277278981934	2.51421278796393e-06	***
df.mm.trans1:probe6	0.0551861677503009	0.0498006562342432	1.10814137650569	0.268244772563929	   
df.mm.trans2:probe2	0.0155289780515396	0.0498006562342432	0.311822759493314	0.75528355008508	   
df.mm.trans2:probe3	0.0256540023398942	0.0498006562342432	0.515133821113272	0.606649236523863	   
df.mm.trans2:probe4	0.267215401630371	0.0498006562342432	5.36570041112494	1.15325970295885e-07	***
df.mm.trans2:probe5	0.128398551215248	0.0498006562342432	2.57825018632907	0.0101674580871069	*  
df.mm.trans2:probe6	0.270351304187906	0.0498006562342432	5.42866951223045	8.2533528799639e-08	***
df.mm.trans3:probe2	-0.33383353955726	0.0498006562342432	-6.70339639676703	4.70415185107854e-11	***
df.mm.trans3:probe3	-0.20917038937426	0.0498006562342432	-4.20015327489667	3.07244546782253e-05	***
df.mm.trans3:probe4	-0.369593327336201	0.0498006562342432	-7.42145496231567	3.98364174416161e-13	***
df.mm.trans3:probe5	-0.273184681726147	0.0498006562342432	-5.48556389380074	6.08296850541562e-08	***
df.mm.trans3:probe6	-0.254311873095839	0.0498006562342432	-5.10659682674969	4.41156448128409e-07	***
df.mm.trans3:probe7	-0.422531227745408	0.0498006562342432	-8.48445100317519	1.69529988459715e-16	***
df.mm.trans3:probe8	-0.425059845606031	0.0498006562342432	-8.5352257931444	1.14698752840712e-16	***
df.mm.trans3:probe9	-0.230539256643511	0.0498006562342432	-4.62924134090006	4.50131746099407e-06	***
df.mm.trans3:probe10	-0.0401318958358252	0.0498006562342432	-0.80585074315206	0.420648172572932	   
df.mm.trans3:probe11	-0.404778917231627	0.0498006562342432	-8.12798360181645	2.50437951714858e-15	***
df.mm.trans3:probe12	-0.263083355382407	0.0498006562342432	-5.28272868825189	1.7831125979436e-07	***
df.mm.trans3:probe13	-0.141153073139010	0.0498006562342432	-2.83436170951402	0.00474628993860836	** 
df.mm.trans3:probe14	0.0376455542968921	0.0498006562342432	0.755924864118694	0.449990914282203	   
df.mm.trans3:probe15	-0.0966535093789692	0.0498006562342432	-1.94080794687419	0.0527495729479317	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.48765473946298	0.134807258725650	33.2894146938775	1.9550102782719e-138	***
df.mm.trans1	-0.120242809972293	0.106696924773320	-1.12695665997639	0.260211302104318	   
df.mm.trans2	-0.0866773223615765	0.106696924773320	-0.81236945250038	0.416901862425918	   
df.mm.exp2	-0.116865262156016	0.141608482134902	-0.82527021258999	0.409546190269780	   
df.mm.exp3	-0.0327476841805156	0.141608482134902	-0.231255103414771	0.817195462220255	   
df.mm.exp4	0.0491943931976896	0.141608482134902	0.347397221240074	0.728414744544319	   
df.mm.exp5	-0.125836925407902	0.141608482134902	-0.88862562122532	0.374560423004405	   
df.mm.exp6	-0.137347853691661	0.141608482134902	-0.969912618375627	0.332480865831757	   
df.mm.exp7	-0.0214148172434161	0.141608482134902	-0.151225526328398	0.879848644263053	   
df.mm.exp8	0.0685063125996333	0.141608482134902	0.483772663662699	0.62872372669295	   
df.mm.trans1:exp2	0.144770183046185	0.108155264738498	1.33854032345273	0.181227333611564	   
df.mm.trans2:exp2	0.0941425491285102	0.108155264738498	0.870438895010163	0.384408707697343	   
df.mm.trans1:exp3	0.0982985078109035	0.108155264738498	0.908864751508612	0.363786441469468	   
df.mm.trans2:exp3	0.0164595228046882	0.108155264738498	0.152184203371741	0.879092811561904	   
df.mm.trans1:exp4	0.0340372029363448	0.108155264738498	0.31470685240003	0.753093647804592	   
df.mm.trans2:exp4	-0.122483043920575	0.108155264738498	-1.13247417235508	0.257887496290092	   
df.mm.trans1:exp5	0.132104407042147	0.108155264738498	1.22143297750279	0.222401830550566	   
df.mm.trans2:exp5	0.0148308267367593	0.108155264738498	0.137125333404876	0.890977728882484	   
df.mm.trans1:exp6	0.0389809500426629	0.108155264738498	0.360416574606076	0.718662476341274	   
df.mm.trans2:exp6	0.129636806802860	0.108155264738498	1.19861762731847	0.231149857696344	   
df.mm.trans1:exp7	0.130847250029751	0.108155264738498	1.20980934535289	0.226828493507387	   
df.mm.trans2:exp7	-0.0811936061997696	0.108155264738498	-0.75071339704158	0.453119463718853	   
df.mm.trans1:exp8	0.045409088702939	0.108155264738498	0.419850932016400	0.674744652041206	   
df.mm.trans2:exp8	-0.0518929897255407	0.108155264738498	-0.479800866384169	0.631543848249318	   
df.mm.trans1:probe2	-0.128478147297181	0.0791615480610514	-1.62298679654541	0.105117484327332	   
df.mm.trans1:probe3	-0.0662151097769241	0.0791615480610514	-0.83645546857999	0.403231720436073	   
df.mm.trans1:probe4	0.0299198885848029	0.0791615480610514	0.377959872155708	0.7055940768231	   
df.mm.trans1:probe5	-0.0577754151220584	0.0791615480610514	-0.729841906041309	0.465771663042296	   
df.mm.trans1:probe6	0.0604373922920872	0.0791615480610514	0.76346905501995	0.445483800099442	   
df.mm.trans2:probe2	0.0580028931458006	0.0791615480610514	0.732715498452195	0.464018109799379	   
df.mm.trans2:probe3	0.0565198990159395	0.0791615480610514	0.713981729770494	0.475516123533476	   
df.mm.trans2:probe4	-0.110693278212442	0.0791615480610514	-1.39832129264416	0.162533230413674	   
df.mm.trans2:probe5	-0.148048314469766	0.0791615480610514	-1.87020489234985	0.061942051670373	.  
df.mm.trans2:probe6	-0.005356229231305	0.0791615480610514	-0.0676620071549654	0.94607723482338	   
df.mm.trans3:probe2	0.0487958318741549	0.0791615480610514	0.6164082571569	0.537858897570665	   
df.mm.trans3:probe3	-0.0794569512656622	0.0791615480610514	-1.00373165017419	0.315912594667735	   
df.mm.trans3:probe4	-0.0590690276777719	0.0791615480610514	-0.74618333173849	0.455848925818805	   
df.mm.trans3:probe5	-0.0427690610134807	0.0791615480610514	-0.54027570280076	0.589207392061397	   
df.mm.trans3:probe6	0.0863449337559766	0.0791615480610514	1.09074337062465	0.275823650644564	   
df.mm.trans3:probe7	0.0582660074181846	0.0791615480610514	0.736039262057488	0.461994452634340	   
df.mm.trans3:probe8	0.0653212403035316	0.0791615480610514	0.8251637556804	0.409606570797119	   
df.mm.trans3:probe9	0.0508660558419923	0.0791615480610514	0.642560145523723	0.520755000731427	   
df.mm.trans3:probe10	0.0293344846137413	0.0791615480610514	0.370564817543965	0.711092475006213	   
df.mm.trans3:probe11	-0.0490760843994223	0.0791615480610514	-0.619948517954368	0.535527046822413	   
df.mm.trans3:probe12	0.0635709780516813	0.0791615480610514	0.803053750321479	0.422261663945988	   
df.mm.trans3:probe13	0.0274030526759693	0.0791615480610514	0.346166205022107	0.729339162468364	   
df.mm.trans3:probe14	0.0415833069466851	0.0791615480610514	0.525296788216104	0.59957094664289	   
df.mm.trans3:probe15	0.0398695647710168	0.0791615480610514	0.503648118910818	0.61469353170332