chr15.8730_chr15_101468255_101473365_-_2.R 

fitVsDatCorrelation=0.856816049479313
cont.fitVsDatCorrelation=0.245730572626915

fstatistic=12217.8266442920,57,807
cont.fstatistic=3446.90520812099,57,807

residuals=-0.455519831518449,-0.074480171995886,-0.00589569996093012,0.0716498557198048,1.03444137783336
cont.residuals=-0.575841766003309,-0.172773179821361,-0.0601046962753667,0.136435102734634,1.50196138175146

predictedValues:
Include	Exclude	Both
chr15.8730_chr15_101468255_101473365_-_2.R.tl.Lung	55.6340683734788	44.7938266027189	65.3275873948764
chr15.8730_chr15_101468255_101473365_-_2.R.tl.cerebhem	63.1145422383309	52.8465133076584	69.659883808302
chr15.8730_chr15_101468255_101473365_-_2.R.tl.cortex	58.0865975293497	46.4589833331781	70.6182001366539
chr15.8730_chr15_101468255_101473365_-_2.R.tl.heart	56.3152184324767	46.7608942351085	65.6770179969895
chr15.8730_chr15_101468255_101473365_-_2.R.tl.kidney	57.6421425011311	44.0660740083033	70.9735546942655
chr15.8730_chr15_101468255_101473365_-_2.R.tl.liver	56.3549096158137	50.1682043044794	64.9945382119219
chr15.8730_chr15_101468255_101473365_-_2.R.tl.stomach	56.6594063508437	45.4442855894411	62.1233921970125
chr15.8730_chr15_101468255_101473365_-_2.R.tl.testicle	57.2729472350132	47.1825343740466	64.9782550366325


diffExp=10.8402417707599,10.2680289306725,11.6276141961716,9.55432419736817,13.5760684928278,6.18670531133429,11.2151207614026,10.0904128609666
diffExpScore=0.988145832320972
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=0,0,0,0,1,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,0,1,1,1,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	56.0214756512619	52.7094021575156	54.2426340548958
cerebhem	55.960002241063	52.282318344451	51.0317766299578
cortex	60.1823470000694	50.0322447415219	56.7483676166384
heart	55.3419897544861	56.3314897985344	54.4711408418941
kidney	56.3066539869217	55.061461341657	57.2722797849723
liver	56.9427084297662	45.6017078535197	57.0559041814687
stomach	53.5124520462525	55.7220028768099	54.9301801027115
testicle	56.1841624017444	54.9707380849904	54.9747900361606
cont.diffExp=3.31207349374628,3.67768389661199,10.1501022585475,-0.989500044048278,1.24519264526472,11.3410005762465,-2.20955083055743,1.21342431675399
cont.diffExpScore=1.18782260536099

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=0,0,1,0,0,1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.702732001459027
cont.tran.correlation=-0.573232543313479

tran.covariance=0.00163832082387790
cont.tran.covariance=-0.00130205731713294

tran.mean=52.4250717519608
cont.tran.mean=54.5726972944103

weightedLogRatios:
wLogRatio
Lung	0.847490256262002
cerebhem	0.720211655035956
cortex	0.882348575603025
heart	0.732144425513228
kidney	1.05276240281424
liver	0.462072997303942
stomach	0.866131888336834
testicle	0.765707640428465

cont.weightedLogRatios:
wLogRatio
Lung	0.243476460613703
cerebhem	0.271280070376184
cortex	0.739773386500593
heart	-0.0712839261442324
kidney	0.0898897139495082
liver	0.87307596050387
stomach	-0.161848729267868
testicle	0.0877224543686136

varWeightedLogRatios=0.0290010882710905
cont.varWeightedLogRatios=0.136108503601327

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.92308968799048	0.0668032889159837	58.725996154537	1.33602927403865e-293	***
df.mm.trans1	0.322566359647465	0.0580098622689949	5.56054344952083	3.6570930955691e-08	***
df.mm.trans2	-0.141584570871166	0.0515624342303799	-2.74588608905796	0.00616907563421052	** 
df.mm.exp2	0.227266679820925	0.0670154007922221	3.39126047347764	0.000729553393113281	***
df.mm.exp3	0.00176523242615859	0.0670154007922221	0.0263406978887077	0.978992105980136	   
df.mm.exp4	0.0498113401292056	0.0670154007922221	0.743281985041665	0.457527302195417	   
df.mm.exp5	-0.0638148745665105	0.0670154007922221	-0.952241929647864	0.341259415361621	   
df.mm.exp6	0.131295904279019	0.067015400792222	1.95919001791984	0.0504344819592648	.  
df.mm.exp7	0.0829708297534602	0.0670154007922221	1.23808600370394	0.216044227271771	   
df.mm.exp8	0.0863478344674081	0.0670154007922221	1.28847747602264	0.197949078732601	   
df.mm.trans1:exp2	-0.101111227599272	0.0623339974001371	-1.62208797472452	0.105175096616259	   
df.mm.trans2:exp2	-0.0619452739253674	0.0476525200590902	-1.29993699910422	0.193993730316405	   
df.mm.trans1:exp3	0.041373971326081	0.0623339974001371	0.663746479477186	0.507042158176901	   
df.mm.trans2:exp3	0.0347342811709636	0.0476525200590902	0.728907539997723	0.466269699717723	   
df.mm.trans1:exp4	-0.0376422860130652	0.0623339974001371	-0.603880507958286	0.546092798335458	   
df.mm.trans2:exp4	-0.00683441065449335	0.0476525200590902	-0.143421809508050	0.885992863358372	   
df.mm.trans1:exp5	0.0992730612565521	0.0623339974001371	1.59259898926896	0.111641768972261	   
df.mm.trans2:exp5	0.0474347332154328	0.0476525200590902	0.99542968885198	0.319825699203604	   
df.mm.trans1:exp6	-0.118422294560316	0.0623339974001371	-1.89980266787857	0.0578152633759735	.  
df.mm.trans2:exp6	-0.0179847895196138	0.0476525200590902	-0.377415286690238	0.705964240769896	   
df.mm.trans1:exp7	-0.0647085669380967	0.0623339974001371	-1.03809429263323	0.299537093088260	   
df.mm.trans2:exp7	-0.0685540775191845	0.0476525200590902	-1.43862438826270	0.150644678551461	   
df.mm.trans1:exp8	-0.0573152014141348	0.0623339974001371	-0.919485414134676	0.358116482778793	   
df.mm.trans2:exp8	-0.0343943756830292	0.0476525200590902	-0.721774538689233	0.470642178074611	   
df.mm.trans1:probe2	-0.143767953802239	0.0408071802079012	-3.52310434266180	0.000450500049598859	***
df.mm.trans1:probe3	-0.0750425183448145	0.0408071802079012	-1.83895378123393	0.0662892402089235	.  
df.mm.trans1:probe4	-0.165386000873620	0.0408071802079012	-4.0528652073244	5.54881835651009e-05	***
df.mm.trans1:probe5	-0.355574430374257	0.0408071802079013	-8.71352611385311	1.65467406945215e-17	***
df.mm.trans1:probe6	0.163807710005352	0.0408071802079012	4.01418841416627	6.52071127218175e-05	***
df.mm.trans1:probe7	-0.283997149718148	0.0408071802079012	-6.95948968468934	7.07086335532714e-12	***
df.mm.trans1:probe8	-0.331824128132273	0.0408071802079012	-8.13151328863501	1.59311772118658e-15	***
df.mm.trans1:probe9	0.0903004012831993	0.0408071802079012	2.21285569900061	0.0271867560584966	*  
df.mm.trans1:probe10	-0.368510954428203	0.0408071802079012	-9.03054199164809	1.23605322521091e-18	***
df.mm.trans1:probe11	-0.436368478066559	0.0408071802079012	-10.6934239475353	4.74256952245175e-25	***
df.mm.trans1:probe12	-0.431523674047688	0.0408071802079012	-10.5746996447487	1.44910313078408e-24	***
df.mm.trans1:probe13	-0.419437320124306	0.0408071802079012	-10.2785176037009	2.25778873117816e-23	***
df.mm.trans1:probe14	-0.431041666208676	0.0408071802079013	-10.5628878058380	1.61860735305231e-24	***
df.mm.trans1:probe15	-0.480821274299611	0.0408071802079012	-11.7827615593619	1.10716201716359e-29	***
df.mm.trans1:probe16	-0.437451018572497	0.0408071802079012	-10.7199521344971	3.69045436047001e-25	***
df.mm.trans1:probe17	-0.487348060681577	0.0408071802079013	-11.9427036663321	2.17597837653372e-30	***
df.mm.trans1:probe18	-0.393479194155928	0.0408071802079012	-9.64240097334	6.73085033292093e-21	***
df.mm.trans1:probe19	-0.484486128049964	0.0408071802079012	-11.8725706010962	4.44919052414299e-30	***
df.mm.trans1:probe20	-0.465605930423434	0.0408071802079012	-11.4099020822145	4.62937623127225e-28	***
df.mm.trans1:probe21	-0.411593833457911	0.0408071802079012	-10.0863091093517	1.30016496430496e-22	***
df.mm.trans1:probe22	-0.456657202528115	0.0408071802079013	-11.1906091085337	3.99801252267503e-27	***
df.mm.trans2:probe2	0.000348568834801659	0.0408071802079013	0.00854185055242233	0.993186783293218	   
df.mm.trans2:probe3	-0.0339507774246128	0.0408071802079012	-0.831980481171281	0.405666080790837	   
df.mm.trans2:probe4	0.109443856784955	0.0408071802079013	2.68197548145619	0.00746847660251194	** 
df.mm.trans2:probe5	0.0668505184731298	0.0408071802079013	1.63820479956089	0.101768781889887	   
df.mm.trans2:probe6	0.145220825971901	0.0408071802079013	3.55870768899104	0.000394454123802003	***
df.mm.trans3:probe2	-0.202650836253851	0.0408071802079012	-4.96605830693034	8.34118740260029e-07	***
df.mm.trans3:probe3	-0.0169833567280909	0.0408071802079012	-0.416185500727211	0.677384925862854	   
df.mm.trans3:probe4	-0.140129699899176	0.0408071802079012	-3.43394714325406	0.000625193182765285	***
df.mm.trans3:probe5	0.202624006787911	0.0408071802079012	4.96540083768586	8.3686538880999e-07	***
df.mm.trans3:probe6	-0.0262837847105562	0.0408071802079012	-0.644097057837557	0.519695514300002	   
df.mm.trans3:probe7	0.114418368251338	0.0408071802079012	2.80387832897074	0.00517042451634793	** 
df.mm.trans3:probe8	0.671748837675384	0.0408071802079013	16.4615353046452	1.02641675208139e-52	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99719968585551	0.125586092375068	31.8283626017896	1.13640859037837e-144	***
df.mm.trans1	0.0783446001310675	0.109054988755739	0.71839538039423	0.472721467477773	   
df.mm.trans2	-0.0476393099019426	0.0969342188598516	-0.491460192925472	0.623234635744692	   
df.mm.exp2	0.0517851582375106	0.125984849713448	0.411042743276639	0.681150318376715	   
df.mm.exp3	-0.0256418832458450	0.125984849713448	-0.203531482588323	0.838770950995356	   
df.mm.exp4	0.0500528391884139	0.125984849713448	0.397292526063722	0.691256812644261	   
df.mm.exp5	-0.00561573109085883	0.125984849713448	-0.0445746540447665	0.964457372722075	   
df.mm.exp6	-0.179102500731177	0.125984849713448	-1.42161935453783	0.155523114517744	   
df.mm.exp7	-0.00283521139483035	0.125984849713448	-0.0225043836721561	0.98205117831282	   
df.mm.exp8	0.0314994467703656	0.125984849713448	0.250025672467849	0.802631127934917	   
df.mm.trans1:exp2	-0.052883079385384	0.117184098008202	-0.451282044955302	0.651907526052271	   
df.mm.trans2:exp2	-0.0599207743924027	0.0895838196465475	-0.668879431897633	0.503763681699812	   
df.mm.trans1:exp3	0.0972858423633529	0.117184098008202	0.830196622382531	0.406673149202877	   
df.mm.trans2:exp3	-0.0264842730462136	0.0895838196465475	-0.295636791897322	0.76758352383607	   
df.mm.trans1:exp4	-0.0622560213822577	0.117184098008202	-0.531266805312613	0.595380094107464	   
df.mm.trans2:exp4	0.0164070123659273	0.0895838196465475	0.183147050780611	0.854728641472826	   
df.mm.trans1:exp5	0.0106933363048966	0.117184098008202	0.0912524522239194	0.927314643138907	   
df.mm.trans2:exp5	0.0492719226523921	0.0895838196465475	0.550009174053911	0.582465269414939	   
df.mm.trans1:exp6	0.195413036838647	0.117184098008202	1.66757299121737	0.0957884890184062	.  
df.mm.trans2:exp6	0.0342538207829083	0.0895838196465475	0.382366156277512	0.702290465627152	   
df.mm.trans1:exp7	-0.0429855242496035	0.117184098008202	-0.366820455848837	0.71384910774683	   
df.mm.trans2:exp7	0.0584164563493271	0.0895838196465475	0.652087135598916	0.514530640900347	   
df.mm.trans1:exp8	-0.0285996483768817	0.117184098008202	-0.244057417883440	0.807248383816228	   
df.mm.trans2:exp8	0.0105077133676755	0.0895838196465475	0.117294768286658	0.906655656003346	   
df.mm.trans1:probe2	-0.0154537981750885	0.0767149999096729	-0.201444283299021	0.840401955759576	   
df.mm.trans1:probe3	-0.161612380797818	0.0767149999096729	-2.10665946670282	0.0354552464886284	*  
df.mm.trans1:probe4	-0.0813506693952509	0.0767149999096729	-1.06042715884816	0.289267577246425	   
df.mm.trans1:probe5	-0.0644625416268326	0.0767149999096729	-0.840286015808292	0.400996921340621	   
df.mm.trans1:probe6	-0.126809118912601	0.0767149999096729	-1.65298988544497	0.098721866523636	.  
df.mm.trans1:probe7	-0.145981819951520	0.0767149999096729	-1.90291103595652	0.0574077898646827	.  
df.mm.trans1:probe8	-0.0666112594839776	0.0767149999096729	-0.868295112590864	0.385490934779343	   
df.mm.trans1:probe9	-0.0454509886337173	0.0767149999096729	-0.592465472035886	0.55370490031305	   
df.mm.trans1:probe10	-0.0762403128760306	0.0767149999096729	-0.993812330910497	0.320612128601675	   
df.mm.trans1:probe11	-0.110156570737546	0.0767149999096729	-1.43591958374827	0.151412724978774	   
df.mm.trans1:probe12	-0.078542027744143	0.0767149999096729	-1.02381578357063	0.306229090349459	   
df.mm.trans1:probe13	-0.0171386097145269	0.0767149999096729	-0.223406240431553	0.823275886658309	   
df.mm.trans1:probe14	0.0335264559491266	0.0767149999096729	0.437026083407442	0.662209346886262	   
df.mm.trans1:probe15	-0.111919130135952	0.0767149999096729	-1.45889500446757	0.144983107905774	   
df.mm.trans1:probe16	-0.0883793997013457	0.0767149999096729	-1.15204848863204	0.24964231690442	   
df.mm.trans1:probe17	-0.00692678648268984	0.0767149999096729	-0.090292465500172	0.928077212424867	   
df.mm.trans1:probe18	-0.0604318496901222	0.0767149999096729	-0.787744896842559	0.431077246812024	   
df.mm.trans1:probe19	-0.0593216921594761	0.0767149999096729	-0.773273704351479	0.439586852747525	   
df.mm.trans1:probe20	-0.0917831376083763	0.0767149999096729	-1.19641709856541	0.231885210954548	   
df.mm.trans1:probe21	-0.0931209043817994	0.0767149999096729	-1.21385523680432	0.225158129974140	   
df.mm.trans1:probe22	-0.0261087075553123	0.0767149999096729	-0.340333801551896	0.733693734110122	   
df.mm.trans2:probe2	-0.0330585881110276	0.0767149999096729	-0.430927304307528	0.666636304722771	   
df.mm.trans2:probe3	0.0214547985033486	0.0767149999096729	0.279668885206417	0.779803215980093	   
df.mm.trans2:probe4	0.108153290401620	0.0767149999096729	1.40980630292595	0.158982152130699	   
df.mm.trans2:probe5	0.112077344756732	0.0767149999096729	1.46095737324768	0.144416366382211	   
df.mm.trans2:probe6	0.00464177283402975	0.0767149999096729	0.060506717584503	0.951767046584327	   
df.mm.trans3:probe2	-0.0104756830445836	0.0767149999096729	-0.136553256298222	0.891417984016125	   
df.mm.trans3:probe3	-0.0967806537990832	0.0767149999096729	-1.26156102343787	0.207471316567051	   
df.mm.trans3:probe4	-0.0907485612520851	0.0767149999096729	-1.18293112636298	0.237184822465012	   
df.mm.trans3:probe5	-0.055511635851191	0.0767149999096729	-0.723608628254611	0.469515733346443	   
df.mm.trans3:probe6	-0.0980848115826545	0.0767149999096729	-1.27856105974247	0.201419243987281	   
df.mm.trans3:probe7	0.0359903996901296	0.0767149999096729	0.469144231669244	0.639093261045648	   
df.mm.trans3:probe8	0.0401087804583483	0.0767149999096729	0.522828397387393	0.601237182432756