chr9.25324_chr9_65083620_65216114_+_2.R 

fitVsDatCorrelation=0.91847575471453
cont.fitVsDatCorrelation=0.293646938036640

fstatistic=10673.6876618368,50,646
cont.fstatistic=1816.21335947217,50,646

residuals=-0.575162033012472,-0.0827252242696713,-0.00414770829506124,0.0824628305074477,0.705531528306712
cont.residuals=-0.760772492259043,-0.20964125249832,-0.0660840867135294,0.114405253433940,1.62698221032438

predictedValues:
Include	Exclude	Both
chr9.25324_chr9_65083620_65216114_+_2.R.tl.Lung	59.471741791066	47.7933632555127	72.4783977677692
chr9.25324_chr9_65083620_65216114_+_2.R.tl.cerebhem	78.8053596532277	60.7849016828577	82.3228272562304
chr9.25324_chr9_65083620_65216114_+_2.R.tl.cortex	56.6964395495106	45.4369645950613	66.4040814609834
chr9.25324_chr9_65083620_65216114_+_2.R.tl.heart	54.9580514758279	43.5297409842764	64.0340015172772
chr9.25324_chr9_65083620_65216114_+_2.R.tl.kidney	60.3019473230408	46.7318325671773	74.4329248689919
chr9.25324_chr9_65083620_65216114_+_2.R.tl.liver	60.5325986083982	46.1980422660209	71.8651084886056
chr9.25324_chr9_65083620_65216114_+_2.R.tl.stomach	55.2620340481616	46.9568685713448	65.640932341066
chr9.25324_chr9_65083620_65216114_+_2.R.tl.testicle	59.5571810606682	49.8316530614615	64.7594297339143


diffExp=11.6783785355533,18.0204579703700,11.2594749544493,11.4283104915515,13.5701147558634,14.3345563423773,8.30516547681686,9.7255279992067
diffExpScore=0.989931735812228
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=0,0,0,0,0,1,0,0
diffExp1.3Score=0.5
diffExp1.2=1,1,1,1,1,1,0,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	56.6919749377828	60.8664875860273	60.6765479910827
cerebhem	61.6152322995524	57.7986415963068	55.7777923489099
cortex	56.103603868445	56.2468133995018	50.6853298569615
heart	61.0927974375472	58.879133258012	64.9059053148165
kidney	53.9938408166547	57.2650570902457	60.7351711104252
liver	58.4324821366539	65.5547755558977	59.873631239001
stomach	55.2182139492772	59.4940752282554	70.3740958368231
testicle	60.5162883725029	54.4079421981614	70.1604017055886
cont.diffExp=-4.17451264824454,3.81659070324562,-0.143209531056804,2.21366417953514,-3.27121627359093,-7.12229341924373,-4.27586127897818,6.10834617434148
cont.diffExpScore=3.96581838084074

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=0.955515922943133
cont.tran.correlation=-0.0755723515945473

tran.covariance=0.0110004084991355
cont.tran.covariance=-0.000210054137138622

tran.mean=54.5530450308509
cont.tran.mean=58.3860849831765

weightedLogRatios:
wLogRatio
Lung	0.869253613953766
cerebhem	1.10013475928350
cortex	0.869384770376952
heart	0.906861019063817
kidney	1.01259024081878
liver	1.07234678580800
stomach	0.640133712371229
testicle	0.712752600415127

cont.weightedLogRatios:
wLogRatio
Lung	-0.289398148274353
cerebhem	0.261462361969675
cortex	-0.0102699447311699
heart	0.151095973519650
kidney	-0.236357789419067
liver	-0.474476855872946
stomach	-0.301959121717613
testicle	0.430899082157846

varWeightedLogRatios=0.0267030677456338
cont.varWeightedLogRatios=0.100771470804428

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85195481938841	0.0723630502075013	53.2309626023629	2.16190581913533e-238	***
df.mm.trans1	0.234092230507287	0.0628462243418232	3.72484159484346	0.000212527903179494	***
df.mm.trans2	0.0235585460269937	0.0571103062355822	0.412509537767383	0.680102852696076	   
df.mm.exp2	0.394574503311666	0.0756237211847237	5.21760232279302	2.44505356647958e-07	***
df.mm.exp3	-0.0108206383113301	0.0756237211847237	-0.143085240210527	0.886267479889955	   
df.mm.exp4	-0.0484991189863154	0.0756237211847237	-0.641321508999116	0.521541279816952	   
df.mm.exp5	-0.0352079154148838	0.0756237211847237	-0.465567084815657	0.641682322666398	   
df.mm.exp6	-0.00777090877414536	0.0756237211847237	-0.102757556126650	0.918187281507506	   
df.mm.exp7	0.00801664588422562	0.0756237211847237	0.106007027406700	0.915609685211936	   
df.mm.exp8	0.155808438072989	0.0756237211847237	2.06031170685718	0.0397686700958654	*  
df.mm.trans1:exp2	-0.113094764713278	0.0697216259488483	-1.62209017896759	0.105271950897903	   
df.mm.trans2:exp2	-0.154119858593277	0.0573445258359443	-2.68761239798539	0.00738184435268605	** 
df.mm.trans1:exp3	-0.0369692193242558	0.0697216259488482	-0.530240349692627	0.596127504512263	   
df.mm.trans2:exp3	-0.0397401756225109	0.0573445258359443	-0.69300731051823	0.488554026934155	   
df.mm.trans1:exp4	-0.0304319590810348	0.0697216259488483	-0.436478046329003	0.662635763903279	   
df.mm.trans2:exp4	-0.0449432615787452	0.0573445258359443	-0.783741096880326	0.433479298727657	   
df.mm.trans1:exp5	0.0490710406846534	0.0697216259488483	0.703813773945185	0.481802358972853	   
df.mm.trans2:exp5	0.0127467016023738	0.0573445258359443	0.222282797120698	0.824163998818498	   
df.mm.trans1:exp6	0.0254516768167337	0.0697216259488482	0.365047092209331	0.71519573375942	   
df.mm.trans2:exp6	-0.0261784550611235	0.0573445258359443	-0.45651184100845	0.648175404474991	   
df.mm.trans1:exp7	-0.0814317902336164	0.0697216259488482	-1.16795598389170	0.243255391888839	   
df.mm.trans2:exp7	-0.0256739413115428	0.0573445258359443	-0.447713900102563	0.654509783263237	   
df.mm.trans1:exp8	-0.154372832638123	0.0697216259488482	-2.21413127616070	0.0271681550430490	*  
df.mm.trans2:exp8	-0.114044838139777	0.0573445258359443	-1.98876591056041	0.0471484841046653	*  
df.mm.trans1:probe2	-0.0949270426755677	0.0426956019029673	-2.22334475788173	0.0265377297833272	*  
df.mm.trans1:probe3	-0.040414325463646	0.0426956019029673	-0.946568818856193	0.344212464935339	   
df.mm.trans1:probe4	-0.222163033097747	0.0426956019029673	-5.20341728880291	2.63119761669156e-07	***
df.mm.trans1:probe5	0.867323208484547	0.0426956019029673	20.3141112861151	2.56172948896140e-71	***
df.mm.trans1:probe6	-0.170502619295064	0.0426956019029673	-3.99344690543439	7.26024255747128e-05	***
df.mm.trans1:probe7	-0.262242605074844	0.0426956019029673	-6.14214563998495	1.42288239960784e-09	***
df.mm.trans1:probe8	-0.142164915667989	0.0426956019029673	-3.32973208788769	0.000918743476602704	***
df.mm.trans1:probe9	-0.203211772362519	0.0426956019029673	-4.7595481338886	2.39611022808536e-06	***
df.mm.trans1:probe10	-0.239380373751190	0.0426956019029673	-5.6066752330889	3.05814531479111e-08	***
df.mm.trans1:probe11	-0.20274122550109	0.0426956019029673	-4.74852716590932	2.52575373201121e-06	***
df.mm.trans1:probe12	0.192528551453954	0.0426956019029673	4.50932983428847	7.72342548353137e-06	***
df.mm.trans1:probe13	0.0917799153086433	0.0426956019029673	2.14963394864951	0.0319548578528360	*  
df.mm.trans1:probe14	-0.00156559646601687	0.0426956019029673	-0.0366687995071469	0.970760414246946	   
df.mm.trans1:probe15	0.388734381518024	0.0426956019029673	9.10478747674025	1.06548442452600e-18	***
df.mm.trans1:probe16	0.00277799995051207	0.0426956019029673	0.0650652485664807	0.948142178655722	   
df.mm.trans1:probe17	0.0236165577355424	0.0426956019029673	0.553137950583643	0.580360316876518	   
df.mm.trans2:probe2	0.118674495106859	0.0426956019029673	2.77954847378814	0.00560184221400583	** 
df.mm.trans2:probe3	0.0729824295510124	0.0426956019029673	1.70936645223732	0.0878633489100246	.  
df.mm.trans2:probe4	-0.100675845809017	0.0426956019029673	-2.35799101832127	0.0186712199854921	*  
df.mm.trans2:probe5	-0.114507387317987	0.0426956019029673	-2.6819480746102	0.00750657548027674	** 
df.mm.trans2:probe6	-0.0799926465466624	0.0426956019029673	-1.87355706399124	0.0614427529574469	.  
df.mm.trans3:probe2	0.00185576445010515	0.0426956019029673	0.0434650026558398	0.96534429310813	   
df.mm.trans3:probe3	-0.0559562364530476	0.0426956019029673	-1.31058549262796	0.190463588685543	   
df.mm.trans3:probe4	-0.103380014420136	0.0426956019029673	-2.42132701759502	0.0157384033955106	*  
df.mm.trans3:probe5	1.13304140719490	0.0426956019029673	26.5376609462006	1.65566884100673e-105	***
df.mm.trans3:probe6	0.0665368375158956	0.0426956019029673	1.55840026959009	0.119628135887845	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.157552622511	0.174909787521059	23.7696968330628	3.24460035231717e-90	***
df.mm.trans1	-0.170045981944541	0.151906528464573	-1.11941194143080	0.263380476539465	   
df.mm.trans2	-0.0405803030796447	0.138042156878191	-0.293970363817577	0.768874975805286	   
df.mm.exp2	0.115740449161005	0.18279120305242	0.633183912728085	0.52683763521818	   
df.mm.exp3	0.0905547841379911	0.18279120305242	0.495400121153654	0.620486179650259	   
df.mm.exp4	-0.0258160174584678	0.18279120305242	-0.141232275007591	0.887730479183216	   
df.mm.exp5	-0.110720498988482	0.18279120305242	-0.60572115692422	0.544912682703232	   
df.mm.exp6	0.117763663796140	0.18279120305242	0.644252359137699	0.519640483723299	   
df.mm.exp7	-0.197413783701379	0.18279120305242	-1.07999608517684	0.280546967173321	   
df.mm.exp8	-0.192119514435720	0.18279120305242	-1.05103260565896	0.293636620155809	   
df.mm.trans1:exp2	-0.0324639968272327	0.168525162294384	-0.192635903061905	0.847304668028724	   
df.mm.trans2:exp2	-0.167457912862097	0.138608292501484	-1.20813776607417	0.227436353812164	   
df.mm.trans1:exp3	-0.100987398821188	0.168525162294384	-0.599242258225991	0.549221438042256	   
df.mm.trans2:exp3	-0.169488132642948	0.138608292501484	-1.22278494009392	0.221856841260735	   
df.mm.trans1:exp4	0.100577329913795	0.168525162294384	0.596808978223112	0.550844017375623	   
df.mm.trans2:exp4	-0.00737996602064307	0.138608292501484	-0.0532433225130745	0.957554498641084	   
df.mm.trans1:exp5	0.0619578148689252	0.168525162294384	0.367647264215036	0.713256686051027	   
df.mm.trans2:exp5	0.049728375476735	0.138608292501484	0.358769122534301	0.719885034071223	   
df.mm.trans1:exp6	-0.0875243928420596	0.168525162294384	-0.519355042596962	0.603691063878537	   
df.mm.trans2:exp6	-0.0435603399124978	0.138608292501484	-0.314269363876851	0.753417929584175	   
df.mm.trans1:exp7	0.171073980180109	0.168525162294384	1.01512425711997	0.310426640423911	   
df.mm.trans2:exp7	0.174607777989985	0.138608292501484	1.25972100830920	0.208224940051752	   
df.mm.trans1:exp8	0.257399407306606	0.168525162294384	1.52736483859291	0.127159767921954	   
df.mm.trans2:exp8	0.0799469166216765	0.138608292501484	0.576783071047648	0.564286931590104	   
df.mm.trans1:probe2	0.169667590190577	0.103200164110241	1.64406318200555	0.100649685128836	   
df.mm.trans1:probe3	0.0387597081613266	0.103200164110241	0.375577970204801	0.707353961772921	   
df.mm.trans1:probe4	0.171705801279576	0.103200164110241	1.66381325805021	0.0966347295565021	.  
df.mm.trans1:probe5	0.0900492184834716	0.103200164110241	0.87256855897321	0.383222486811813	   
df.mm.trans1:probe6	-0.0275497988558460	0.103200164110241	-0.266954990753858	0.78958901185189	   
df.mm.trans1:probe7	0.135694329690066	0.103200164110241	1.31486544483703	0.189021698123891	   
df.mm.trans1:probe8	0.151281921238443	0.103200164110241	1.46590775841054	0.143159983562090	   
df.mm.trans1:probe9	0.104274556190767	0.103200164110241	1.01041075941873	0.312676834270039	   
df.mm.trans1:probe10	0.101160960416112	0.103200164110241	0.980240305703866	0.327334468135488	   
df.mm.trans1:probe11	0.00269194290724989	0.103200164110241	0.0260846766132493	0.979197854768386	   
df.mm.trans1:probe12	0.0817698334564442	0.103200164110241	0.792342087451486	0.428452105525713	   
df.mm.trans1:probe13	0.0399345587349414	0.103200164110241	0.38696216308612	0.698911659258579	   
df.mm.trans1:probe14	0.0163613639119954	0.103200164110241	0.158540095871532	0.874080775987379	   
df.mm.trans1:probe15	-0.0555245417436262	0.103200164110241	-0.538027649687782	0.590743269715469	   
df.mm.trans1:probe16	0.00814126139855645	0.103200164110241	0.0788880663974503	0.937146084947306	   
df.mm.trans1:probe17	0.124479861750178	0.103200164110241	1.20619829264230	0.228182603419934	   
df.mm.trans2:probe2	0.0560731219293375	0.103200164110241	0.543343340708633	0.587080863828996	   
df.mm.trans2:probe3	-0.146327671463889	0.103200164110241	-1.41790153848571	0.156701687119420	   
df.mm.trans2:probe4	-0.0721892130930956	0.103200164110241	-0.699506766442555	0.484487179497482	   
df.mm.trans2:probe5	0.105192133136863	0.103200164110241	1.01930199475743	0.308441193697739	   
df.mm.trans2:probe6	-0.0422233559744847	0.103200164110241	-0.409140395642983	0.682572242893972	   
df.mm.trans3:probe2	0.175185535141382	0.103200164110241	1.69753155580493	0.0900778427885363	.  
df.mm.trans3:probe3	0.0477272270206433	0.103200164110241	0.462472394614218	0.643898321618072	   
df.mm.trans3:probe4	0.197178139707759	0.103200164110241	1.91063785031513	0.0564935343562042	.  
df.mm.trans3:probe5	0.348942453729338	0.103200164110241	3.38121994996625	0.000765155961356428	***
df.mm.trans3:probe6	0.182752204195398	0.103200164110241	1.77085187577975	0.0770568338868547	.