chr15.8535_chr15_83900811_83903041_+_2.R 

fitVsDatCorrelation=0.803865510960865
cont.fitVsDatCorrelation=0.242773939012383

fstatistic=12030.9959456907,64,968
cont.fstatistic=4513.72204079269,64,968

residuals=-0.525041485881977,-0.087984962718129,-0.00455897917713877,0.0778240473583302,0.688366123533617
cont.residuals=-0.515750265842944,-0.166620377325145,-0.0352176869957623,0.151576699703448,0.82190563851096

predictedValues:
Include	Exclude	Both
chr15.8535_chr15_83900811_83903041_+_2.R.tl.Lung	58.1316335291259	62.8815540047106	58.1866584375452
chr15.8535_chr15_83900811_83903041_+_2.R.tl.cerebhem	59.7850734628489	59.4162496792013	55.9333677084303
chr15.8535_chr15_83900811_83903041_+_2.R.tl.cortex	55.2532093881803	57.1019384921735	54.3069229246778
chr15.8535_chr15_83900811_83903041_+_2.R.tl.heart	56.5642507515027	61.6062824945672	65.8668308860386
chr15.8535_chr15_83900811_83903041_+_2.R.tl.kidney	57.9386716821176	65.4067163905682	68.3096527826665
chr15.8535_chr15_83900811_83903041_+_2.R.tl.liver	57.3158322404343	66.4028245379783	64.1347395610175
chr15.8535_chr15_83900811_83903041_+_2.R.tl.stomach	60.4067013616313	59.3165401145926	58.3445062429513
chr15.8535_chr15_83900811_83903041_+_2.R.tl.testicle	57.8355003602079	64.3035195056598	71.978709239357


diffExp=-4.74992047558469,0.368823783647571,-1.84872910399324,-5.04203174306448,-7.46804470845058,-9.0869922975441,1.09016124703873,-6.46801914545191
diffExpScore=1.05607320399544
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	59.9240261090659	58.8334723667631	63.4057355870246
cerebhem	62.9761286542044	63.1941253962297	62.8999858423502
cortex	61.0504804625844	67.1247308763589	61.9875301028809
heart	61.3479702730495	62.5475699004759	56.6679277213608
kidney	63.6338423815772	66.9503629239699	61.6644338042644
liver	62.4633069774208	53.9891050125456	59.562817045299
stomach	62.2749719065169	52.6445488769969	57.3058918401431
testicle	58.6653124564305	61.3112241364304	59.1002807020283
cont.diffExp=1.09055374230280,-0.217996742025377,-6.07425041377451,-1.19959962742644,-3.31652054239273,8.47420196487521,9.63042302951995,-2.64591167999988
cont.diffExpScore=4.84348663306563

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=-0.0130559799867567
cont.tran.correlation=0.0371421308007469

tran.covariance=1.64706767385110e-05
cont.tran.covariance=3.20198282043351e-05

tran.mean=59.9791561247188
cont.tran.mean=61.1831986694137

weightedLogRatios:
wLogRatio
Lung	-0.32218143051061
cerebhem	0.0252955536902113
cortex	-0.132580658114604
heart	-0.348212903573767
kidney	-0.499508929138793
liver	-0.606627952668996
stomach	0.0745228186817027
testicle	-0.435772131049098

cont.weightedLogRatios:
wLogRatio
Lung	0.0750071599766994
cerebhem	-0.0143216769417937
cortex	-0.394499304120789
heart	-0.0799059647936427
kidney	-0.212295547379637
liver	0.59218098987201
stomach	0.67997777464446
testicle	-0.180599807784758

varWeightedLogRatios=0.0608840397705009
cont.varWeightedLogRatios=0.147355468465061

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9409444020068	0.0683821498266644	57.6311860916385	0	***
df.mm.trans1	0.0287482384169287	0.0585768907456484	0.490777814441515	0.623694838115116	   
df.mm.trans2	0.246209814448834	0.051283124789676	4.80099088069609	1.82819070460283e-06	***
df.mm.exp2	0.0108559025285543	0.0649038753822213	0.167261237709205	0.867199436467898	   
df.mm.exp3	-0.0781939727453711	0.0649038753822213	-1.20476585234524	0.228588169038786	   
df.mm.exp4	-0.171800714739140	0.0649038753822213	-2.6470024128359	0.00825277971982102	** 
df.mm.exp5	-0.124347821411154	0.0649038753822214	-1.91587668192177	0.0556751120362579	.  
df.mm.exp6	-0.0569764444922688	0.0649038753822213	-0.877858897588663	0.380238125956392	   
df.mm.exp7	-0.0226837165082165	0.0649038753822213	-0.349497104365975	0.726792161624867	   
df.mm.exp8	-0.195459976876227	0.0649038753822213	-3.01153013938159	0.00266686093387957	** 
df.mm.trans1:exp2	0.0171901369981871	0.0593775232005363	0.289505793970737	0.772256341717267	   
df.mm.trans2:exp2	-0.0675410115796852	0.0411602095827395	-1.64092972957087	0.101136856195481	   
df.mm.trans1:exp3	0.0274104173020085	0.0593775232005363	0.461629516095427	0.644450742206409	   
df.mm.trans2:exp3	-0.0182208237250214	0.0411602095827395	-0.442680538066606	0.658095720983235	   
df.mm.trans1:exp4	0.144467905605015	0.0593775232005362	2.43304027884595	0.0151528225765007	*  
df.mm.trans2:exp4	0.151311706995362	0.0411602095827395	3.67616463884125	0.000249780216111459	***
df.mm.trans1:exp5	0.121022905112807	0.0593775232005363	2.03819389205701	0.0418019328155132	*  
df.mm.trans2:exp5	0.163719910073868	0.0411602095827395	3.977625763658	7.47950660197491e-05	***
df.mm.trans1:exp6	0.0428433517905146	0.0593775232005363	0.721541578044933	0.470750609326917	   
df.mm.trans2:exp6	0.111463176661555	0.0411602095827395	2.70803229117417	0.00688767685629709	** 
df.mm.trans1:exp7	0.0610737823624017	0.0593775232005363	1.02856736135889	0.303939959068829	   
df.mm.trans2:exp7	-0.0356809553363328	0.0411602095827395	-0.866879826367442	0.386222683711735	   
df.mm.trans1:exp8	0.190352774470359	0.0593775232005363	3.20580523083589	0.00139096212483017	** 
df.mm.trans2:exp8	0.217821480693244	0.0411602095827395	5.29204012567971	1.49642948471378e-07	***
df.mm.trans1:probe2	0.184139194572212	0.0434598969171799	4.23699105690747	2.48173663039479e-05	***
df.mm.trans1:probe3	0.112366139011712	0.0434598969171799	2.58551324283728	0.00986859527744297	** 
df.mm.trans1:probe4	0.221957615597879	0.0434598969171799	5.10718228395378	3.93730171467243e-07	***
df.mm.trans1:probe5	0.154416955170373	0.0434598969171799	3.55309069104882	0.000399106994381312	***
df.mm.trans1:probe6	0.0570725956996113	0.0434598969171799	1.31322436885602	0.189418554857113	   
df.mm.trans1:probe7	0.107873507626634	0.0434598969171799	2.48213905873281	0.0132282868209045	*  
df.mm.trans1:probe8	0.0655088249224689	0.0434598969171799	1.50733962962008	0.132049894315279	   
df.mm.trans1:probe9	-0.174639922105342	0.0434598969171799	-4.01841547020112	6.3135373271028e-05	***
df.mm.trans1:probe10	-0.075916289892543	0.0434598969171799	-1.74681247029219	0.0809870237571007	.  
df.mm.trans1:probe11	-0.0145148785403374	0.0434598969171799	-0.333983271244245	0.738464521358477	   
df.mm.trans1:probe12	0.087881317417352	0.0434598969171799	2.02212438710622	0.0434381459386524	*  
df.mm.trans1:probe13	0.169124634283287	0.0434598969171799	3.89151024922085	0.000106441816969520	***
df.mm.trans1:probe14	-0.0297232074320099	0.0434598969171799	-0.683922639960524	0.494187702166413	   
df.mm.trans1:probe15	-0.029722869148188	0.0434598969171799	-0.683914856144962	0.494192615435616	   
df.mm.trans1:probe16	0.166021482052389	0.0434598969171799	3.82010758950419	0.000141893142275759	***
df.mm.trans1:probe17	0.396282427068479	0.0434598969171799	9.11834714711038	4.30377740537506e-19	***
df.mm.trans1:probe18	0.31847444761264	0.0434598969171799	7.3280074322207	4.92099184915282e-13	***
df.mm.trans1:probe19	0.699250451592899	0.0434598969171799	16.0895561470253	8.50373952552226e-52	***
df.mm.trans1:probe20	0.275086502214488	0.0434598969171799	6.32966301642895	3.75262969582954e-10	***
df.mm.trans1:probe21	0.41544567521487	0.0434598969171799	9.55928809510458	9.4403717045645e-21	***
df.mm.trans1:probe22	0.335257058619221	0.0434598969171799	7.7141705894542	3.02019951107455e-14	***
df.mm.trans2:probe2	-0.305003286339148	0.0434598969171799	-7.01803980162177	4.22733216471717e-12	***
df.mm.trans2:probe3	-0.177669811810128	0.0434598969171799	-4.08813237980539	4.70938991726933e-05	***
df.mm.trans2:probe4	-0.176616564980624	0.0434598969171799	-4.06389746660458	5.21717804857475e-05	***
df.mm.trans2:probe5	-0.0779549788376954	0.0434598969171799	-1.79372212930582	0.0731695549544223	.  
df.mm.trans2:probe6	-0.226683809350977	0.0434598969171799	-5.21593067243029	2.23683144975898e-07	***
df.mm.trans3:probe2	-0.362493739053169	0.0434598969171799	-8.3408789428093	2.5168737170055e-16	***
df.mm.trans3:probe3	-0.339836732254831	0.0434598969171799	-7.81954759125283	1.38062006730155e-14	***
df.mm.trans3:probe4	0.140249345953492	0.0434598969171799	3.22709798922810	0.00129250737930985	** 
df.mm.trans3:probe5	-0.0794581827451746	0.0434598969171799	-1.82831042826897	0.0678105955949817	.  
df.mm.trans3:probe6	-0.209416458687063	0.0434598969171799	-4.81861379206998	1.67737116534438e-06	***
df.mm.trans3:probe7	0.0995176002332982	0.0434598969171799	2.28987198066635	0.0222431560866168	*  
df.mm.trans3:probe8	-0.414055336037373	0.0434598969171799	-9.52729678182221	1.25163736681096e-20	***
df.mm.trans3:probe9	-0.362783088638651	0.0434598969171799	-8.3475367953586	2.38802779126635e-16	***
df.mm.trans3:probe10	-0.111098508621833	0.0434598969171799	-2.55634542423213	0.0107294787034002	*  
df.mm.trans3:probe11	-0.00612725743234012	0.0434598969171799	-0.14098646952653	0.887909964347736	   
df.mm.trans3:probe12	-0.31302122011657	0.0434598969171799	-7.20253020187978	1.18671317253346e-12	***
df.mm.trans3:probe13	-0.136894683591715	0.0434598969171799	-3.1499081521659	0.00168331539985184	** 
df.mm.trans3:probe14	0.172518058887124	0.0434598969171799	3.96959199456653	7.73200145098623e-05	***
df.mm.trans3:probe15	-0.36079524112767	0.0434598969171799	-8.30179698344028	3.42389026026799e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06637953948576	0.111525077420213	36.4615710972712	7.05145286153406e-184	***
df.mm.trans1	0.0355756152523238	0.0955335901547865	0.372388551447539	0.70968508924172	   
df.mm.trans2	0.00705336931586518	0.0836381201383183	0.0843319924479475	0.932809913789035	   
df.mm.exp2	0.129186994741542	0.105852327620907	1.22044547951939	0.222593263043944	   
df.mm.exp3	0.173086221386323	0.105852327620907	1.63516689029459	0.102339177117638	   
df.mm.exp4	0.197046914619795	0.105852327620907	1.86152651574641	0.062972782293862	.  
df.mm.exp5	0.217155452299163	0.105852327620907	2.05149435236672	0.0404874727311118	*  
df.mm.exp6	0.0180959036458029	0.105852327620907	0.170954234569223	0.864295477861454	   
df.mm.exp7	0.0284847100672408	0.105852327620907	0.269098570692314	0.787911175201313	   
df.mm.exp8	0.0903417216685339	0.105852327620907	0.853469391736739	0.393610193122341	   
df.mm.trans1:exp2	-0.0795087787633914	0.096839348993063	-0.821037931276128	0.411826812854453	   
df.mm.trans2:exp2	-0.057686601377285	0.0671285645739856	-0.859345075280223	0.390362919078383	   
df.mm.trans1:exp3	-0.154462679233605	0.096839348993063	-1.59504045452298	0.111029605649914	   
df.mm.trans2:exp3	-0.0412446287329674	0.0671285645739855	-0.614412493321078	0.539087161724665	   
df.mm.trans1:exp4	-0.173562356752075	0.0968393489930629	-1.79227099889435	0.073401763500083	.  
df.mm.trans2:exp4	-0.135830479850444	0.0671285645739856	-2.02343787197682	0.0433024019671918	*  
df.mm.trans1:exp5	-0.157087538915112	0.096839348993063	-1.62214575530000	0.105097769969391	   
df.mm.trans2:exp5	-0.0879149102325078	0.0671285645739856	-1.30964978605512	0.190625075949697	   
df.mm.trans1:exp6	0.0234058639734031	0.096839348993063	0.241697865761983	0.809065474083449	   
df.mm.trans2:exp6	-0.104024587341445	0.0671285645739856	-1.54963223184662	0.121556671650436	   
df.mm.trans1:exp7	0.00999737160311039	0.096839348993063	0.103236666779189	0.917796522650823	   
df.mm.trans2:exp7	-0.139632962824051	0.0671285645739856	-2.08008265497999	0.0377805626479825	*  
df.mm.trans1:exp8	-0.111570627051967	0.096839348993063	-1.15212078780041	0.249555896558689	   
df.mm.trans2:exp8	-0.0490897446281167	0.0671285645739856	-0.731279522207161	0.464785413991329	   
df.mm.trans1:probe2	-0.0209201206827628	0.0708791458099639	-0.295151986436918	0.767941070216445	   
df.mm.trans1:probe3	0.0378875823863023	0.0708791458099639	0.534537796037832	0.59309225546361	   
df.mm.trans1:probe4	0.0266377003173272	0.0708791458099639	0.375818585465833	0.707134112878199	   
df.mm.trans1:probe5	-0.0193754664446429	0.070879145809964	-0.27335919787452	0.784635425026981	   
df.mm.trans1:probe6	-0.0672693050748952	0.0708791458099639	-0.949070481961688	0.342821643969558	   
df.mm.trans1:probe7	0.0509544866662417	0.0708791458099639	0.718892504755309	0.472380650757805	   
df.mm.trans1:probe8	0.0117261840855082	0.0708791458099639	0.165439128131532	0.868632901911003	   
df.mm.trans1:probe9	-0.0436260444246066	0.0708791458099639	-0.615499014922862	0.538369898156394	   
df.mm.trans1:probe10	-0.0673381898957178	0.0708791458099639	-0.95004234498339	0.342327868510240	   
df.mm.trans1:probe11	-0.0482566117997252	0.0708791458099639	-0.680829477391099	0.496142215447839	   
df.mm.trans1:probe12	-0.0450152881993432	0.0708791458099639	-0.635099191517275	0.52551381253166	   
df.mm.trans1:probe13	-0.0130099549692167	0.0708791458099639	-0.183551238104619	0.854403959048144	   
df.mm.trans1:probe14	-0.00586789477129813	0.0708791458099639	-0.0827873234679027	0.934037764457499	   
df.mm.trans1:probe15	0.0195696983971845	0.0708791458099639	0.276099523682938	0.782530560112946	   
df.mm.trans1:probe16	-0.080039117709404	0.0708791458099639	-1.12923366661330	0.259079033548575	   
df.mm.trans1:probe17	-0.0106762935026536	0.0708791458099639	-0.150626723568002	0.880301560510706	   
df.mm.trans1:probe18	-0.0385312111336894	0.0708791458099639	-0.543618446489134	0.586829233033903	   
df.mm.trans1:probe19	0.00657215953947092	0.0708791458099639	0.0927234585627163	0.926142446250873	   
df.mm.trans1:probe20	-0.0143803486762	0.0708791458099639	-0.202885468100244	0.8392671889992	   
df.mm.trans1:probe21	-0.0588799846636555	0.0708791458099639	-0.83070956895446	0.406342559361407	   
df.mm.trans1:probe22	0.0513658444939497	0.070879145809964	0.724696155787036	0.468813581099262	   
df.mm.trans2:probe2	-0.022277144973829	0.070879145809964	-0.314297593731687	0.75336275024846	   
df.mm.trans2:probe3	-0.0386030352868900	0.070879145809964	-0.544631779146854	0.586132234121263	   
df.mm.trans2:probe4	0.0621601001047176	0.070879145809964	0.876987150372505	0.380711208449039	   
df.mm.trans2:probe5	0.0395822251205246	0.0708791458099639	0.55844670062263	0.576668536717826	   
df.mm.trans2:probe6	-0.0140232613838173	0.070879145809964	-0.197847494119293	0.843205924357415	   
df.mm.trans3:probe2	0.0738529765460765	0.070879145809964	1.04195635686815	0.29769206993276	   
df.mm.trans3:probe3	0.137283136664682	0.070879145809964	1.93686217710292	0.053052905490311	.  
df.mm.trans3:probe4	-0.00162913646525335	0.070879145809964	-0.0229847079368208	0.98166720801363	   
df.mm.trans3:probe5	0.0683550576891897	0.070879145809964	0.964388846790823	0.335091868255473	   
df.mm.trans3:probe6	0.0407723813927763	0.0708791458099639	0.575238046774609	0.565263995381444	   
df.mm.trans3:probe7	0.0824911393861304	0.070879145809964	1.16382806879896	0.244780472822296	   
df.mm.trans3:probe8	0.00445663419928182	0.070879145809964	0.06287652240097	0.949877810771411	   
df.mm.trans3:probe9	0.0993335110800008	0.070879145809964	1.40144904322530	0.161400367020342	   
df.mm.trans3:probe10	0.0771786779363526	0.0708791458099639	1.08887708866129	0.276479101212901	   
df.mm.trans3:probe11	0.00522672340871822	0.0708791458099639	0.0737413430846322	0.941231435994052	   
df.mm.trans3:probe12	-0.00207088353624922	0.0708791458099639	-0.0292171062811835	0.976697460636797	   
df.mm.trans3:probe13	-0.0226418745568441	0.0708791458099639	-0.31944338913945	0.749459238351528	   
df.mm.trans3:probe14	-0.00116795104872229	0.0708791458099639	-0.0164780632635403	0.986856398269989	   
df.mm.trans3:probe15	0.0467466068491795	0.070879145809964	0.659525539070592	0.509715223689922