chr2.12781_chr2_94840049_94840432_-_0.R 

fitVsDatCorrelation=0.79672879141965
cont.fitVsDatCorrelation=0.264547497517813

fstatistic=17943.4015883428,64,968
cont.fstatistic=7037.31373278767,64,968

residuals=-0.330034621661811,-0.0765190657360902,-0.00265806888095414,0.0712509106860601,0.659849081045344
cont.residuals=-0.397404177461374,-0.121689774116823,-0.027805823892454,0.0811735725133925,0.763370266890918

predictedValues:
Include	Exclude	Both
chr2.12781_chr2_94840049_94840432_-_0.R.tl.Lung	46.8879347687767	43.7390488156702	54.26727664582
chr2.12781_chr2_94840049_94840432_-_0.R.tl.cerebhem	49.6556899360221	46.8497543552403	54.378655857342
chr2.12781_chr2_94840049_94840432_-_0.R.tl.cortex	45.8049901975789	43.7194994808845	51.2089236112872
chr2.12781_chr2_94840049_94840432_-_0.R.tl.heart	46.4194097942394	46.2814912822365	53.0453887897063
chr2.12781_chr2_94840049_94840432_-_0.R.tl.kidney	46.0411164163767	43.8867168590385	54.3205458939291
chr2.12781_chr2_94840049_94840432_-_0.R.tl.liver	48.4801628729509	44.8534806908791	53.9503067507392
chr2.12781_chr2_94840049_94840432_-_0.R.tl.stomach	46.5652007323741	44.1203738222011	53.8626008086295
chr2.12781_chr2_94840049_94840432_-_0.R.tl.testicle	46.580014454297	45.7861610537964	52.3599795864103


diffExp=3.14888595310658,2.8059355807818,2.08549071669446,0.137918512002955,2.15439955733821,3.62668218207172,2.44482691017301,0.793853400500616
diffExpScore=0.945048884770204
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	48.9301471817147	53.5757909083001	50.5597144456167
cerebhem	49.8069121441513	51.3670089746026	50.5824753723862
cortex	48.8839596861701	47.7071373939985	50.0217514995793
heart	52.4260185234031	51.6638676426286	50.8165180118746
kidney	51.9348713268737	49.1129278007607	50.4878610373617
liver	51.6450293469747	48.4753653962993	53.5860285408591
stomach	47.5232620472413	48.91789433667	49.310759052267
testicle	49.3648678566747	49.2180440271328	49.4823473005878
cont.diffExp=-4.64564372658541,-1.56009683045136,1.17682229217157,0.762150880774485,2.82194352611304,3.16966395067532,-1.3946322894287,0.146823829541901
cont.diffExpScore=10.6143815592537

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=0.58705352948663
cont.tran.correlation=0.0601175861570427

tran.covariance=0.000447967145783224
cont.tran.covariance=9.24012968827118e-05

tran.mean=45.9794403457852
cont.tran.mean=50.0345690370998

weightedLogRatios:
wLogRatio
Lung	0.265076459376369
cerebhem	0.225457851062096
cortex	0.177126502522468
heart	0.0114149378163535
kidney	0.182375002620912
liver	0.298750590923868
stomach	0.205689935043847
testicle	0.0658808047553359

cont.weightedLogRatios:
wLogRatio
Lung	-0.356986249262213
cerebhem	-0.121012252757933
cortex	0.0944823696894845
heart	0.0578756654511887
kidney	0.219118087417063
liver	0.24782500666204
stomach	-0.112100005816105
testicle	0.0116101856261618

varWeightedLogRatios=0.00935937785507436
cont.varWeightedLogRatios=0.0394689465051437

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.30961927848082	0.0532694802342079	62.1297460371219	0	***
df.mm.trans1	0.526080786761674	0.0448496364615143	11.7298785066654	8.36405816020434e-30	***
df.mm.trans2	0.459593596946952	0.0399052565062287	11.5171192265163	7.442904991063e-29	***
df.mm.exp2	0.124006777592163	0.0504494843777901	2.45803855324943	0.0141441105063695	*  
df.mm.exp3	0.0341931988718925	0.0504494843777901	0.67777102766477	0.498078847870747	   
df.mm.exp4	0.0692316204173541	0.0504494843777901	1.37229589699895	0.17028923779278	   
df.mm.exp5	-0.0158362547519033	0.0504494843777901	-0.313903203317477	0.753662190145869	   
df.mm.exp6	0.0644122652892896	0.0504494843777901	1.27676756430134	0.201990513631897	   
df.mm.exp7	0.00925853802639011	0.0504494843777901	0.183520964397926	0.854427704003422	   
df.mm.exp8	0.0749306572529354	0.0504494843777901	1.48526111172551	0.137800244386199	   
df.mm.trans1:exp2	-0.0666541806997957	0.0443896280299065	-1.50157105743010	0.133534036658706	   
df.mm.trans2:exp2	-0.0553022808248674	0.031821168233024	-1.73790856513793	0.0825449359490838	.  
df.mm.trans1:exp3	-0.0575605453676021	0.0443896280299065	-1.29671159507852	0.195039451343648	   
df.mm.trans2:exp3	-0.0346402526017346	0.031821168233024	-1.08859147935948	0.276605022209395	   
df.mm.trans1:exp4	-0.0792743220828142	0.0443896280299065	-1.78587489017490	0.0744324678986965	.  
df.mm.trans2:exp4	-0.0127307642576789	0.031821168233024	-0.400072183536835	0.689191619321104	   
df.mm.trans1:exp5	-0.00238930081268457	0.0443896280299065	-0.0538256551975347	0.957085175441565	   
df.mm.trans2:exp5	0.0192066831189731	0.031821168233024	0.603581960860895	0.546263015213774	   
df.mm.trans1:exp6	-0.0310179519028641	0.0443896280299065	-0.698765754062333	0.484866278018481	   
df.mm.trans2:exp6	-0.039252341103787	0.0318211682330240	-1.23352922860484	0.217677811209225	   
df.mm.trans1:exp7	-0.0161654292222204	0.0443896280299065	-0.364171315229974	0.715809616185917	   
df.mm.trans2:exp7	-0.000578139511111164	0.031821168233024	-0.0181683936578787	0.985508260689121	   
df.mm.trans1:exp8	-0.0815194704451209	0.0443896280299065	-1.8364531099517	0.0665971808010772	.  
df.mm.trans2:exp8	-0.0291900408753725	0.031821168233024	-0.917315186595792	0.35920598764946	   
df.mm.trans1:probe2	0.207657610247220	0.0339031384196633	6.12502617535789	1.31680422380270e-09	***
df.mm.trans1:probe3	-0.00957617039128595	0.0339031384196633	-0.282456752904383	0.777653685333178	   
df.mm.trans1:probe4	-0.030148017291684	0.0339031384196633	-0.889239719299809	0.374095186357008	   
df.mm.trans1:probe5	0.0450932718476032	0.0339031384196633	1.33006187478649	0.183811175557035	   
df.mm.trans1:probe6	0.0411570539634574	0.0339031384196633	1.21396000140173	0.225059089818035	   
df.mm.trans1:probe7	-0.0630797742022097	0.0339031384196633	-1.86058805003210	0.0631054350232397	.  
df.mm.trans1:probe8	0.124201581202212	0.0339031384196633	3.66342430204564	0.000262363491341343	***
df.mm.trans1:probe9	0.0597729212759526	0.0339031384196633	1.76304979604145	0.078207581203112	.  
df.mm.trans1:probe10	0.0333799357432389	0.0339031384196633	0.984567721431918	0.325082471516345	   
df.mm.trans1:probe11	-0.0582754268618322	0.0339031384196633	-1.71888000870248	0.0859559414912643	.  
df.mm.trans1:probe12	-0.0407135332604984	0.0339031384196633	-1.20087800593957	0.23009229061644	   
df.mm.trans1:probe13	-0.0372602637679139	0.0339031384196633	-1.09902107901325	0.272032134656273	   
df.mm.trans1:probe14	0.00451458760183503	0.0339031384196633	0.133161347659090	0.894093455018133	   
df.mm.trans1:probe15	0.0870562682935491	0.0339031384196633	2.56779378994181	0.0103839134562782	*  
df.mm.trans1:probe16	-0.00197036288775165	0.0339031384196633	-0.0581174186106874	0.953667094397524	   
df.mm.trans2:probe2	0.0955600578635069	0.0339031384196633	2.81861981863258	0.00492136542057678	** 
df.mm.trans2:probe3	-0.0127444374195899	0.0339031384196633	-0.375907305743657	0.707068173581847	   
df.mm.trans2:probe4	0.0809451029721775	0.0339031384196633	2.38754011413971	0.0171525646129579	*  
df.mm.trans2:probe5	0.00203150611914196	0.0339031384196633	0.0599208867921124	0.952231012644213	   
df.mm.trans2:probe6	0.077974388422472	0.0339031384196633	2.2999165285904	0.0216649628376632	*  
df.mm.trans3:probe2	-0.290696365484238	0.0339031384196633	-8.57432022622539	3.90456465870597e-17	***
df.mm.trans3:probe3	-0.508542663120142	0.0339031384196633	-14.9998698299033	6.91198994074672e-46	***
df.mm.trans3:probe4	-0.560792826604798	0.0339031384196633	-16.5410299088873	2.57820771483355e-54	***
df.mm.trans3:probe5	-0.441441210940503	0.0339031384196633	-13.0206591931464	7.86993840250307e-36	***
df.mm.trans3:probe6	-0.395666525187752	0.0339031384196633	-11.6704984739192	1.54405414219508e-29	***
df.mm.trans3:probe7	-0.37816808884043	0.0339031384196633	-11.1543681932732	2.88617859965893e-27	***
df.mm.trans3:probe8	0.200321971695466	0.0339031384196633	5.90865568891647	4.77331140937124e-09	***
df.mm.trans3:probe9	-0.225963292582385	0.0339031384196633	-6.66496681768345	4.438407372786e-11	***
df.mm.trans3:probe10	0.0141979743900651	0.0339031384196633	0.418780533362968	0.675469434042264	   
df.mm.trans3:probe11	-0.440532704940005	0.0339031384196633	-12.9938620869536	1.06066327054391e-35	***
df.mm.trans3:probe12	-0.232875094692785	0.0339031384196633	-6.86883591159574	1.15659398993002e-11	***
df.mm.trans3:probe13	-0.470680722231688	0.0339031384196633	-13.8831018062534	4.25024857877953e-40	***
df.mm.trans3:probe14	-0.395439433092401	0.0339031384196633	-11.6638002121671	1.65437223487997e-29	***
df.mm.trans3:probe15	-0.316331123737793	0.0339031384196633	-9.330437784908	6.980636479182e-20	***
df.mm.trans3:probe16	-0.380759783664052	0.0339031384196633	-11.2308122909122	1.34504159640340e-27	***
df.mm.trans3:probe17	-0.407152769196766	0.0339031384196633	-12.0092943655217	4.53202458887204e-31	***
df.mm.trans3:probe18	-0.260617991361791	0.0339031384196633	-7.68713468752604	3.68657559910385e-14	***
df.mm.trans3:probe19	-0.198001051584275	0.0339031384196633	-5.84019830652131	7.11356853873978e-09	***
df.mm.trans3:probe20	-0.351532918315509	0.0339031384196633	-10.3687426799292	5.84987014514858e-24	***
df.mm.trans3:probe21	-0.288142776462447	0.0339031384196633	-8.49900008948222	7.15699205274927e-17	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94798974891925	0.0850049368939375	46.4442406897525	1.3832797635479e-248	***
df.mm.trans1	-0.0481051922474986	0.071568945301607	-0.672151755831707	0.50164748454892	   
df.mm.trans2	0.0331644633878793	0.0636789357833839	0.520807437811062	0.60262002887013	   
df.mm.exp2	-0.0247912048712260	0.0805049198342249	-0.307946457462176	0.758189322646758	   
df.mm.exp3	-0.106263518527113	0.0805049198342249	-1.31996303761224	0.187159412602119	   
df.mm.exp4	0.0276042822787306	0.0805049198342249	0.342889382854776	0.73175612554227	   
df.mm.exp5	-0.0259560925161749	0.0805049198342249	-0.322416227102933	0.747207013245932	   
df.mm.exp6	-0.104174595114217	0.0805049198342249	-1.29401526426873	0.19596879157408	   
df.mm.exp7	-0.0951156234214219	0.0805049198342249	-1.18148833161108	0.237698964988572	   
df.mm.exp8	-0.0544526020013253	0.0805049198342249	-0.676388500397909	0.498955594297547	   
df.mm.trans1:exp2	0.0425512640434754	0.0708348854322864	0.600710564911574	0.548173400809452	   
df.mm.trans2:exp2	-0.0173099813719823	0.0507787270618523	-0.340890415604501	0.73326005257998	   
df.mm.trans1:exp3	0.105319125116869	0.0708348854322864	1.48682565764220	0.137386496363106	   
df.mm.trans2:exp3	-0.00975276775884252	0.0507787270618523	-0.192064045775762	0.847732320232695	   
df.mm.trans1:exp4	0.0414050092187504	0.0708348854322864	0.584528498437834	0.559001001049668	   
df.mm.trans2:exp4	-0.0639429340419953	0.0507787270618523	-1.25924649438550	0.208244871805553	   
df.mm.trans1:exp5	0.0855528383105842	0.0708348854322864	1.20777831132892	0.227427546754972	   
df.mm.trans2:exp5	-0.0610189158964092	0.0507787270618523	-1.20166296847268	0.229788039487253	   
df.mm.trans1:exp6	0.158174835514787	0.0708348854322864	2.23300757175633	0.0257763835659396	*  
df.mm.trans2:exp6	0.0041330301562189	0.0507787270618523	0.0813929453407636	0.935146284651566	   
df.mm.trans1:exp7	0.0659412285929554	0.0708348854322864	0.930914593713728	0.352129847163837	   
df.mm.trans2:exp7	0.00416158643061828	0.0507787270618523	0.0819553122225603	0.934699191906993	   
df.mm.trans1:exp8	0.0632978828963711	0.0708348854322864	0.893597589804529	0.371759343383444	   
df.mm.trans2:exp8	-0.0303843971053527	0.0507787270618523	-0.598368625277712	0.54973397463812	   
df.mm.trans1:probe2	-0.0938555865399271	0.0541010373894962	-1.73482045943447	0.0830909099337803	.  
df.mm.trans1:probe3	0.0104904986384951	0.0541010373894962	0.193905683600290	0.846290415332316	   
df.mm.trans1:probe4	-0.058516889884529	0.0541010373894962	-1.08162232570960	0.279689757935023	   
df.mm.trans1:probe5	-0.0273278959665397	0.0541010373894962	-0.505127023162137	0.61358460347646	   
df.mm.trans1:probe6	-0.05561163193654	0.0541010373894962	-1.02792172978437	0.304243427823528	   
df.mm.trans1:probe7	-0.057179304417069	0.0541010373894962	-1.05689848431946	0.290821607399261	   
df.mm.trans1:probe8	0.0678970308225557	0.0541010373894962	1.25500423094915	0.209780123558898	   
df.mm.trans1:probe9	-0.0556247512909483	0.0541010373894962	-1.02816422706430	0.304129422248292	   
df.mm.trans1:probe10	0.0438024973698291	0.0541010373894962	0.809642466825107	0.41834466568484	   
df.mm.trans1:probe11	-0.0457606459662390	0.0541010373894962	-0.845836756082676	0.397852849332401	   
df.mm.trans1:probe12	0.00814175886645111	0.0541010373894962	0.150491732863367	0.88040802491377	   
df.mm.trans1:probe13	-0.0140910048279640	0.0541010373894962	-0.260457202077603	0.794566517304283	   
df.mm.trans1:probe14	-0.0241339635427738	0.0541010373894962	-0.44609058730285	0.655631574391532	   
df.mm.trans1:probe15	0.0177883583347735	0.0541010373894962	0.328798839968771	0.7423788581426	   
df.mm.trans1:probe16	-0.000743770748473352	0.0541010373894962	-0.0137478093648858	0.989034013533036	   
df.mm.trans2:probe2	-0.0405594703995031	0.0541010373894962	-0.749698570611472	0.453618426899486	   
df.mm.trans2:probe3	0.00838925571826628	0.0541010373894962	0.155066448317220	0.876801259402612	   
df.mm.trans2:probe4	-0.000502941425533941	0.0541010373894962	-0.00929633607416902	0.992584619296362	   
df.mm.trans2:probe5	-0.0316664765753355	0.0541010373894962	-0.585321060432818	0.558468275631196	   
df.mm.trans2:probe6	0.0628031053682667	0.0541010373894962	1.16084844946910	0.245989727931477	   
df.mm.trans3:probe2	-0.062073188890602	0.0541010373894962	-1.14735672153033	0.251517713787845	   
df.mm.trans3:probe3	0.0135144749295705	0.0541010373894962	0.249800661533975	0.802794508347187	   
df.mm.trans3:probe4	-0.078260525017982	0.0541010373894962	-1.44656237281647	0.148343249276407	   
df.mm.trans3:probe5	0.0190828367338223	0.0541010373894962	0.352725893155004	0.724370733040572	   
df.mm.trans3:probe6	-0.0298441132686945	0.0541010373894962	-0.551636617498369	0.581324659845162	   
df.mm.trans3:probe7	0.05429956013646	0.0541010373894962	1.00366948133609	0.315788772260501	   
df.mm.trans3:probe8	-0.0416160219285772	0.0541010373894962	-0.769227799255786	0.441945738253722	   
df.mm.trans3:probe9	-0.0448378992313075	0.0541010373894962	-0.828780766411198	0.407432777765918	   
df.mm.trans3:probe10	0.0235462314153009	0.0541010373894962	0.435226985497184	0.663494696178855	   
df.mm.trans3:probe11	0.000254046469168693	0.0541010373894962	0.00469577814820269	0.996254292408905	   
df.mm.trans3:probe12	-0.0535420487656302	0.0541010373894962	-0.989667691215576	0.322583882240948	   
df.mm.trans3:probe13	0.0435382792962672	0.0541010373894962	0.804758677413461	0.421156559598382	   
df.mm.trans3:probe14	0.0588716456702271	0.0541010373894962	1.08817960820946	0.276786679157816	   
df.mm.trans3:probe15	-0.0536008092965105	0.0541010373894962	-0.99075381698535	0.322053389677697	   
df.mm.trans3:probe16	-0.0622536343053165	0.0541010373894962	-1.15069206265171	0.250143110398992	   
df.mm.trans3:probe17	0.0109547160133089	0.0541010373894962	0.202486246879912	0.839579158214826	   
df.mm.trans3:probe18	-0.0165111155531057	0.0541010373894962	-0.305190368795245	0.760286776719473	   
df.mm.trans3:probe19	-0.000859027096705373	0.0541010373894962	-0.0158782000892307	0.987334833554028	   
df.mm.trans3:probe20	0.0106133954351445	0.0541010373894962	0.196177299868285	0.844512570434055	   
df.mm.trans3:probe21	0.000950917807676636	0.0541010373894962	0.017576701918497	0.985980165019645