chr5.18343_chr5_120938626_120953873_+_2.R 

fitVsDatCorrelation=0.962839494253486
cont.fitVsDatCorrelation=0.276369436471092

fstatistic=11699.0478099669,59,853
cont.fstatistic=910.58125871327,59,853

residuals=-1.12820548693118,-0.0873661962481598,0.00177363921815896,0.0904578056671651,0.621097954989542
cont.residuals=-0.996914511993098,-0.375579850946123,-0.175191861120134,0.292366100622971,2.384866612662

predictedValues:
Include	Exclude	Both
chr5.18343_chr5_120938626_120953873_+_2.R.tl.Lung	93.3290295225972	51.1321344420617	68.5749646382431
chr5.18343_chr5_120938626_120953873_+_2.R.tl.cerebhem	103.068364210741	56.6665029465835	75.3937120086207
chr5.18343_chr5_120938626_120953873_+_2.R.tl.cortex	96.7960771644457	47.7745691317389	84.4237977646884
chr5.18343_chr5_120938626_120953873_+_2.R.tl.heart	92.6848449609629	47.5536276071212	71.7645699075136
chr5.18343_chr5_120938626_120953873_+_2.R.tl.kidney	94.1906595417059	50.7613616053383	69.1153230545928
chr5.18343_chr5_120938626_120953873_+_2.R.tl.liver	93.4083566266841	50.6581947649555	66.3473934169738
chr5.18343_chr5_120938626_120953873_+_2.R.tl.stomach	89.636474070887	49.4679488345952	63.7041398602761
chr5.18343_chr5_120938626_120953873_+_2.R.tl.testicle	95.5099718760076	51.7893351340259	67.3904178658807


diffExp=42.1968950805355,46.4018612641572,49.0215080327069,45.1312173538417,43.4292979363676,42.7501618617287,40.1685252362918,43.7206367419817
diffExpScore=0.997173704970163
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
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	67.7907127124269	83.2713426316199	75.1065228772366
cerebhem	77.8887132516251	59.7882570403167	80.2813090402933
cortex	70.8021429119669	66.411847244813	69.4093707306266
heart	70.5890517407982	67.3800005758856	82.4298020647224
kidney	69.7303825758905	82.0677892297962	92.3654147577125
liver	87.1809045321032	65.4965715366453	81.6999874573686
stomach	74.9810111069422	86.060324916994	82.2606513611333
testicle	71.6279534809802	69.2575596264869	69.2733147876389
cont.diffExp=-15.480629919193,18.1004562113084,4.39029566715389,3.20905116491262,-12.3374066539057,21.6843329954580,-11.0793138100519,2.37039385449323
cont.diffExpScore=7.47664148968969

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=0,1,0,0,0,1,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,1,0,0,0,1,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.721000380178558
cont.tran.correlation=-0.445353643499173

tran.covariance=0.00155944250286530
cont.tran.covariance=-0.005044421713503

tran.mean=72.7767157775282
cont.tran.mean=73.1452853197057

weightedLogRatios:
wLogRatio
Lung	2.5484397054867
cerebhem	2.59400747227783
cortex	2.97947907482798
heart	2.79987494886038
kidney	2.61877510468989
liver	2.58888847917272
stomach	2.49577116336517
testicle	2.60316038103964

cont.weightedLogRatios:
wLogRatio
Lung	-0.888383190702537
cerebhem	1.11687628651798
cortex	0.270642834880282
heart	0.196976451121125
kidney	-0.704761195679911
liver	1.23689362717141
stomach	-0.604470006828523
testicle	0.143182486045117

varWeightedLogRatios=0.0250082742434411
cont.varWeightedLogRatios=0.641090866363777

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15014862245942	0.073823548542037	56.2171380869917	4.37855886598366e-289	***
df.mm.trans1	0.55851671067095	0.0637522107361302	8.76074263499105	1.03268704223987e-17	***
df.mm.trans2	-0.236738563089315	0.0563247900305182	-4.20309712581341	2.91057420329510e-05	***
df.mm.exp2	0.107235047396517	0.0724516892759723	1.48009036736263	0.139218361801188	   
df.mm.exp3	-0.239366219842857	0.0724516892759723	-3.30380453837450	0.000993559595587284	***
df.mm.exp4	-0.124944681696284	0.0724516892759723	-1.72452406486153	0.0849755456946222	.  
df.mm.exp5	-0.00593680378306417	0.0724516892759723	-0.0819415508788287	0.934712415618574	   
df.mm.exp6	0.0245605139275373	0.0724516892759723	0.338991598028654	0.734699492758499	   
df.mm.exp7	0.000220891336306309	0.0724516892759723	0.00304880864081612	0.997568119272365	   
df.mm.exp8	0.0532952289227315	0.0724516892759723	0.735596774282614	0.462178336515452	   
df.mm.trans1:exp2	-0.00797375031703781	0.0669686030294605	-0.119066994924921	0.905250313455673	   
df.mm.trans2:exp2	-0.0044649418123696	0.0494595911741961	-0.09027453940418	0.928090244897418	   
df.mm.trans1:exp3	0.275841486963487	0.0669686030294605	4.11896731431205	4.1762765962104e-05	***
df.mm.trans2:exp3	0.171446537615381	0.0494595911741961	3.46639617403120	0.000553834537093503	***
df.mm.trans1:exp4	0.118018454943694	0.0669686030294605	1.76229530862061	0.078377532147708	.  
df.mm.trans2:exp4	0.0523896045324097	0.0494595911741961	1.05924054947996	0.289790089518291	   
df.mm.trans1:exp5	0.0151266236608719	0.0669686030294605	0.225876350656702	0.821351700734613	   
df.mm.trans2:exp5	-0.00134088297016179	0.0494595911741961	-0.0271106763790105	0.978377800509468	   
df.mm.trans1:exp6	-0.0237109024973401	0.0669686030294605	-0.354059983704741	0.723381430247884	   
df.mm.trans2:exp6	-0.0338726579325832	0.0494595911741961	-0.684855194481573	0.493621346347821	   
df.mm.trans1:exp7	-0.040589778684699	0.0669686030294605	-0.606101618497895	0.54460852662996	   
df.mm.trans2:exp7	-0.0333090833861114	0.0494595911741961	-0.67346054820383	0.500836767786405	   
df.mm.trans1:exp8	-0.0301957705174119	0.0669686030294605	-0.450894436369359	0.652180226843079	   
df.mm.trans2:exp8	-0.0405241399140205	0.0494595911741961	-0.8193383518132	0.412822256689559	   
df.mm.trans1:probe2	-0.394039123083161	0.0458502681548054	-8.59404184404674	3.96904608014728e-17	***
df.mm.trans1:probe3	-0.567223951013033	0.0458502681548054	-12.3712242881089	1.88574420136929e-32	***
df.mm.trans1:probe4	1.48746195524456	0.0458502681548054	32.4417285897306	4.94420417923527e-151	***
df.mm.trans1:probe5	-0.446214698260799	0.0458502681548054	-9.73199756987752	2.68746941705616e-21	***
df.mm.trans1:probe6	-0.507117077783296	0.0458502681548054	-11.0602859741431	1.14894937071655e-26	***
df.mm.trans1:probe7	-0.870970771387292	0.0458502681548054	-18.9959798805672	2.25094579971233e-67	***
df.mm.trans1:probe8	-0.423260482572229	0.0458502681548054	-9.23136329635333	2.06288402002072e-19	***
df.mm.trans1:probe9	-0.585806301785038	0.0458502681548054	-12.7765076489229	2.49350725046834e-34	***
df.mm.trans1:probe10	-0.580372003503758	0.0458502681548054	-12.6579849335719	8.9214649365555e-34	***
df.mm.trans1:probe11	0.587680984506425	0.0458502681548054	12.8173947101514	1.60334257097316e-34	***
df.mm.trans1:probe12	0.254774108003103	0.0458502681548054	5.55665469922449	3.67654185828535e-08	***
df.mm.trans1:probe13	0.298451304713094	0.0458502681548054	6.50925974315843	1.28737733753146e-10	***
df.mm.trans1:probe14	0.57339053534252	0.0458502681548054	12.5057182524335	4.53505711651757e-33	***
df.mm.trans1:probe15	0.237282730722183	0.0458502681548054	5.17516560472535	2.84072082034953e-07	***
df.mm.trans1:probe16	0.530987998058341	0.0458502681548054	11.5809136876921	6.57721776880553e-29	***
df.mm.trans1:probe17	-0.847887769621067	0.0458502681548054	-18.4925367668150	1.83297420365597e-64	***
df.mm.trans1:probe18	-0.820988265385908	0.0458502681548054	-17.9058552637901	4.10301300902689e-61	***
df.mm.trans1:probe19	-0.844287040697482	0.0458502681548054	-18.4140044251627	5.17930331747272e-64	***
df.mm.trans1:probe20	-0.845852579413663	0.0458502681548054	-18.4481490175322	3.29787196429728e-64	***
df.mm.trans1:probe21	-0.907440070522918	0.0458502681548054	-19.7913797899525	4.91086695522789e-72	***
df.mm.trans1:probe22	-0.849661703970918	0.0458502681548054	-18.5312264936420	1.09802481538000e-64	***
df.mm.trans2:probe2	0.0211667334652938	0.0458502681548054	0.461649065907924	0.644450706618972	   
df.mm.trans2:probe3	0.0359927372284302	0.0458502681548054	0.785006035447973	0.432667996187872	   
df.mm.trans2:probe4	0.0645116841059156	0.0458502681548054	1.40700778211598	0.159789326728009	   
df.mm.trans2:probe5	0.0790629723382998	0.0458502681548054	1.72437317206865	0.0850027797798233	.  
df.mm.trans2:probe6	0.135315380794598	0.0458502681548054	2.951245134221	0.00325162949398129	** 
df.mm.trans3:probe2	-0.478391425420079	0.0458502681548054	-10.4337759553526	4.54363445395513e-24	***
df.mm.trans3:probe3	-0.548894137283581	0.0458502681548054	-11.9714487913208	1.22430905789439e-30	***
df.mm.trans3:probe4	-0.607442301512239	0.0458502681548054	-13.2483914698452	1.44177296493025e-36	***
df.mm.trans3:probe5	-0.320796447569426	0.0458502681548054	-6.99661006313666	5.30296649200505e-12	***
df.mm.trans3:probe6	0.42877420685994	0.0458502681548054	9.35161830269473	7.39325584571443e-20	***
df.mm.trans3:probe7	0.149876566139308	0.0458502681548054	3.26882638141344	0.00112317306137999	** 
df.mm.trans3:probe8	-0.287985152919427	0.0458502681548054	-6.28099168247164	5.35539896308424e-10	***
df.mm.trans3:probe9	-0.249327215842409	0.0458502681548054	-5.43785730981113	7.04501740885977e-08	***
df.mm.trans3:probe10	-0.52580658128164	0.0458502681548054	-11.4679063491264	2.04675024044337e-28	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.16834484278844	0.262698966341093	15.8673819727794	7.07024930551771e-50	***
df.mm.trans1	-0.0103648872419113	0.226860401499183	-0.0456883932736435	0.96356930861244	   
df.mm.trans2	0.227807082837985	0.200430139333803	1.13659095181583	0.256028525297628	   
df.mm.exp2	-0.259068761879471	0.257817244745775	-1.00485428015077	0.315251982353707	   
df.mm.exp3	-0.103879411031526	0.257817244745775	-0.402918785102828	0.68710893347604	   
df.mm.exp4	-0.264346000548435	0.257817244745775	-1.02532319282636	0.30550138893181	   
df.mm.exp5	-0.193193135994410	0.257817244745775	-0.749341403384058	0.453858055702992	   
df.mm.exp6	-0.0726929858006826	0.257817244745775	-0.281955483126672	0.778046036887918	   
df.mm.exp7	0.0427682600348009	0.257817244745775	0.165885955677531	0.868286008895975	   
df.mm.exp8	-0.0483643091118351	0.257817244745775	-0.187591443541821	0.85124154682487	   
df.mm.trans1:exp2	0.397924611682323	0.238305840623858	1.66980637419797	0.095324571416927	.  
df.mm.trans2:exp2	-0.0722264309617251	0.176000527388843	-0.410376218942532	0.68163304252823	   
df.mm.trans1:exp3	0.147343473470363	0.238305840623858	0.618295687107938	0.536545497808335	   
df.mm.trans2:exp3	-0.122349589991114	0.176000527388843	-0.695166041865335	0.487140573206523	   
df.mm.trans1:exp4	0.304795853608421	0.238305840623858	1.27901126053185	0.201240922683353	   
df.mm.trans2:exp4	0.0525897831223817	0.176000527388843	0.298804690546146	0.765161850402824	   
df.mm.trans1:exp5	0.221404058823507	0.238305840623858	0.929075251550262	0.353112921499312	   
df.mm.trans2:exp5	0.178634275705199	0.176000527388843	1.01496443422886	0.310410579015399	   
df.mm.trans1:exp6	0.324253103090401	0.238305840623858	1.36065948799888	0.173980728080372	   
df.mm.trans2:exp6	-0.167413679831324	0.176000527388843	-0.951211239620052	0.341766592455089	   
df.mm.trans1:exp7	0.0580414312829705	0.238305840623858	0.243558576369864	0.807631275236943	   
df.mm.trans2:exp7	-0.00982422111223466	0.176000527388843	-0.0558192708737157	0.955498854451716	   
df.mm.trans1:exp8	0.103424513712158	0.238305840623858	0.433999072122633	0.664398861373571	   
df.mm.trans2:exp8	-0.135907851448596	0.176000527388843	-0.772201387489772	0.440209048066954	   
df.mm.trans1:probe2	0.136768049641043	0.163156855618647	0.83826112683071	0.40211888959827	   
df.mm.trans1:probe3	0.138214593444134	0.163156855618647	0.847127096928055	0.397161893485716	   
df.mm.trans1:probe4	0.0981432213487575	0.163156855618647	0.601526800554133	0.54764899446575	   
df.mm.trans1:probe5	0.0643287713820558	0.163156855618647	0.394275625980522	0.693476106082088	   
df.mm.trans1:probe6	0.392432360563045	0.163156855618647	2.40524591550289	0.0163736134694937	*  
df.mm.trans1:probe7	0.0640152926119181	0.163156855618647	0.392354292249561	0.694894468593373	   
df.mm.trans1:probe8	0.00693497029602803	0.163156855618647	0.0425049273579861	0.966106130975407	   
df.mm.trans1:probe9	0.279467411055800	0.163156855618647	1.71287568638249	0.0870988041225515	.  
df.mm.trans1:probe10	0.144712645230913	0.163156855618647	0.886954119593693	0.375353600887707	   
df.mm.trans1:probe11	0.0634267944745576	0.163156855618647	0.388747345209983	0.697560068853792	   
df.mm.trans1:probe12	0.085112956527906	0.163156855618647	0.521663378502732	0.602040146249545	   
df.mm.trans1:probe13	0.00217264859633067	0.163156855618647	0.0133163181411689	0.989378543144243	   
df.mm.trans1:probe14	-0.0484827283496816	0.163156855618647	-0.297154098525912	0.76642121069962	   
df.mm.trans1:probe15	-0.0701830852458423	0.163156855618647	-0.430157133022249	0.667189993593075	   
df.mm.trans1:probe16	0.00814866078460017	0.163156855618647	0.0499437228898695	0.960178926919323	   
df.mm.trans1:probe17	0.113231315261572	0.163156855618647	0.69400280381863	0.487869402884012	   
df.mm.trans1:probe18	0.176537039024780	0.163156855618647	1.08200809800728	0.279554712477118	   
df.mm.trans1:probe19	-0.135316501333289	0.163156855618647	-0.829364483767509	0.407130217427803	   
df.mm.trans1:probe20	-0.068969817906183	0.163156855618647	-0.42272093100022	0.672605421500203	   
df.mm.trans1:probe21	0.444620754109719	0.163156855618647	2.72511230020851	0.00655959784092491	** 
df.mm.trans1:probe22	-0.0250673715359946	0.163156855618647	-0.153639707267867	0.877930166042405	   
df.mm.trans2:probe2	0.235115876148627	0.163156855618647	1.44104196699017	0.149939800967161	   
df.mm.trans2:probe3	-0.112582883923357	0.163156855618647	-0.690028521918202	0.490363939585161	   
df.mm.trans2:probe4	0.205814821002926	0.163156855618647	1.26145371104715	0.207490284646415	   
df.mm.trans2:probe5	0.031353675664295	0.163156855618647	0.192168913438606	0.847655633697573	   
df.mm.trans2:probe6	0.0555391243053562	0.163156855618647	0.340403252408651	0.733636682143485	   
df.mm.trans3:probe2	0.066304275018342	0.163156855618647	0.406383628606557	0.6845626796308	   
df.mm.trans3:probe3	-0.174275705921347	0.163156855618647	-1.06814822619951	0.285755846509458	   
df.mm.trans3:probe4	-0.159351866319425	0.163156855618647	-0.976678949316625	0.329005020034455	   
df.mm.trans3:probe5	0.0202929115933112	0.163156855618647	0.124376701894419	0.901046322380395	   
df.mm.trans3:probe6	-0.143735406586892	0.163156855618647	-0.880964554274389	0.378585154615816	   
df.mm.trans3:probe7	-0.076625406008112	0.163156855618647	-0.469642576265453	0.63873042998136	   
df.mm.trans3:probe8	-0.065867591463835	0.163156855618647	-0.403707163968579	0.686529253579117	   
df.mm.trans3:probe9	-0.220949982359223	0.163156855618647	-1.35421819402831	0.176025407715841	   
df.mm.trans3:probe10	0.0854124970717472	0.163156855618647	0.523499283851027	0.600762819550234