chr5.18825_chr5_73875401_73889553_+_2.R 

fitVsDatCorrelation=0.888712700340194
cont.fitVsDatCorrelation=0.248462009113403

fstatistic=10207.0180177210,61,899
cont.fstatistic=2275.13179553745,61,899

residuals=-0.661184057997551,-0.0932822523042207,-0.00183628977352186,0.0990316018112951,0.619974422588329
cont.residuals=-0.65616254912975,-0.227405677238023,-0.0669086745866425,0.172949663024253,1.24208943719536

predictedValues:
Include	Exclude	Both
chr5.18825_chr5_73875401_73889553_+_2.R.tl.Lung	65.248250971007	59.8344269071053	74.8393795521473
chr5.18825_chr5_73875401_73889553_+_2.R.tl.cerebhem	61.290518335104	62.9349446521462	76.1410356630431
chr5.18825_chr5_73875401_73889553_+_2.R.tl.cortex	72.4800822740804	72.4578725839167	88.061599491272
chr5.18825_chr5_73875401_73889553_+_2.R.tl.heart	65.6107907767871	61.6813113830156	72.061411962327
chr5.18825_chr5_73875401_73889553_+_2.R.tl.kidney	60.6019877757766	55.1785471611798	65.2034200136419
chr5.18825_chr5_73875401_73889553_+_2.R.tl.liver	62.4744134067964	56.9945798690147	63.6316115329081
chr5.18825_chr5_73875401_73889553_+_2.R.tl.stomach	67.8369664672029	63.7107669572913	69.7024851174083
chr5.18825_chr5_73875401_73889553_+_2.R.tl.testicle	63.94321591427	65.6616174452303	73.2870324459349


diffExp=5.41382406390172,-1.64442631704224,0.0222096901636917,3.92947939377149,5.42344061459684,5.47983353778169,4.12619950991161,-1.71840153096035
diffExpScore=1.25987719614079
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	67.9945257312115	82.44021142177	66.6777445509303
cerebhem	71.207957217038	65.7557763313594	74.7469007275068
cortex	66.8565025586501	68.7761992653325	67.2797548649326
heart	73.1322932839801	68.7605244185173	65.9443513085824
kidney	71.560185341191	62.3688935306572	64.3087906880086
liver	66.8175431459381	81.136243383402	70.9850709023601
stomach	72.5700537244071	66.8018268008095	74.776145532622
testicle	70.9371176670017	60.9576038092159	70.4068696535706
cont.diffExp=-14.4456856905585,5.4521808856786,-1.91969670668236,4.37176886546283,9.1912918105338,-14.3187002374639,5.7682269235976,9.97951385778583
cont.diffExpScore=12.8860715383126

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

tran.correlation=0.808526211575938
cont.tran.correlation=-0.659027597624849

tran.covariance=0.00395348137946994
cont.tran.covariance=-0.00265984465020618

tran.mean=63.6212683049953
cont.tran.mean=69.8795911019051

weightedLogRatios:
wLogRatio
Lung	0.358156105809588
cerebhem	-0.109317494655874
cortex	0.00131266686355421
heart	0.256477288633538
kidney	0.380400045645454
liver	0.375360776430424
stomach	0.262670072093015
testicle	-0.110617958439800

cont.weightedLogRatios:
wLogRatio
Lung	-0.831412553514447
cerebhem	0.336612856669757
cortex	-0.119371385378276
heart	0.262676755547014
kidney	0.577631062355027
liver	-0.834720690299867
stomach	0.351425142743605
testicle	0.634659198998797

varWeightedLogRatios=0.0461085206715782
cont.varWeightedLogRatios=0.346671220288892

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15692774515817	0.0762490639045784	54.517754478407	2.89258719511608e-287	***
df.mm.trans1	-0.0457133716509316	0.0655879499946459	-0.696978204909032	0.485996617936125	   
df.mm.trans2	-0.0485902531236919	0.0576926973594862	-0.84222536555924	0.399885813989601	   
df.mm.exp2	-0.0292968559457052	0.0736399953243475	-0.39783891642941	0.690843378742883	   
df.mm.exp3	0.133844332367640	0.0736399953243475	1.81754944141594	0.0694657925874043	.  
df.mm.exp4	0.0737662408401172	0.0736399953243475	1.00171436072495	0.316751110188179	   
df.mm.exp5	-0.0170462141231756	0.0736399953243475	-0.23148038030279	0.816994300884818	   
df.mm.exp6	0.0701666513344823	0.0736399953243475	0.952833457218909	0.340930570537567	   
df.mm.exp7	0.172788651346023	0.0736399953243475	2.34639682668331	0.0191722260715104	*  
df.mm.exp8	0.093690075204638	0.0736399953243475	1.27227160718819	0.203605607707979	   
df.mm.trans1:exp2	-0.0332772303029749	0.0677389434776396	-0.491257002169801	0.623364592437042	   
df.mm.trans2:exp2	0.0798172281518854	0.0486189873992183	1.64168841067160	0.101004340606065	   
df.mm.trans1:exp3	-0.0287317763070231	0.0677389434776396	-0.424154479417107	0.671554630405771	   
df.mm.trans2:exp3	0.0575797967074433	0.0486189873992183	1.18430678604320	0.236604659191920	   
df.mm.trans1:exp4	-0.0682253056705744	0.0677389434776396	-1.00717994949383	0.314119311775850	   
df.mm.trans2:exp4	-0.0433664468296987	0.0486189873992183	-0.891965241349226	0.372650237001048	   
df.mm.trans1:exp5	-0.0568253325605467	0.0677389434776396	-0.838887199049754	0.40175561244003	   
df.mm.trans2:exp5	-0.0639607426098449	0.0486189873992183	-1.31555069390181	0.188660290199369	   
df.mm.trans1:exp6	-0.113608804706005	0.0677389434776396	-1.6771564313445	0.0938595207498486	.  
df.mm.trans2:exp6	-0.118791674130736	0.0486189873992183	-2.44331855691107	0.0147443190673230	*  
df.mm.trans1:exp7	-0.133880617654192	0.0677389434776396	-1.97642022123338	0.0484124995765916	*  
df.mm.trans2:exp7	-0.110016273126237	0.0486189873992183	-2.26282526665716	0.0238843100231615	*  
df.mm.trans1:exp8	-0.113893877826444	0.0677389434776396	-1.68136483947436	0.0930393977706543	.  
df.mm.trans2:exp8	-0.00075672588025293	0.0486189873992183	-0.015564410546837	0.987585351897845	   
df.mm.trans1:probe2	0.977932764259856	0.0478986662834512	20.416701343472	2.02519116056256e-76	***
df.mm.trans1:probe3	0.134747556666783	0.0478986662834512	2.813179721318	0.00501236581079518	** 
df.mm.trans1:probe4	0.616605254121043	0.0478986662834512	12.8731194825372	6.30190978407593e-35	***
df.mm.trans1:probe5	0.948816874333073	0.0478986662834512	19.8088370293702	9.488107967517e-73	***
df.mm.trans1:probe6	0.0217061794787092	0.0478986662834512	0.453168765707546	0.650536575089513	   
df.mm.trans1:probe7	0.00592841594486158	0.0478986662834512	0.123769958640995	0.901525084679656	   
df.mm.trans1:probe8	-0.132984190298909	0.0478986662834512	-2.77636520215290	0.00561128304459471	** 
df.mm.trans1:probe9	0.0185369545249179	0.0478986662834512	0.387003563214501	0.698845161754908	   
df.mm.trans1:probe10	-0.121692695938123	0.0478986662834512	-2.54062806713613	0.0112325219943144	*  
df.mm.trans1:probe11	-0.000229516431966896	0.0478986662834512	-0.00479170819931981	0.996177847691419	   
df.mm.trans1:probe12	0.150145828396072	0.0478986662834512	3.13465572313747	0.00177640603602573	** 
df.mm.trans1:probe13	0.243197209388543	0.0478986662834512	5.07732737169275	4.65325403282311e-07	***
df.mm.trans1:probe14	0.129609457671585	0.0478986662834512	2.70590953210663	0.00694074994290631	** 
df.mm.trans1:probe15	0.362625455157844	0.0478986662834512	7.57067958869514	9.19719670449106e-14	***
df.mm.trans1:probe16	0.123041360004105	0.0478986662834512	2.56878467713444	0.0103659391465091	*  
df.mm.trans1:probe17	-0.110850646256062	0.0478986662834512	-2.31427417206313	0.0208772956579222	*  
df.mm.trans1:probe18	-0.169828096654254	0.0478986662834512	-3.54557046848147	0.000411981313935081	***
df.mm.trans1:probe19	-0.251093855589443	0.0478986662834512	-5.24218887648224	1.97861903971114e-07	***
df.mm.trans1:probe20	-0.249836915863721	0.0478986662834512	-5.21594723296166	2.27078439960545e-07	***
df.mm.trans1:probe21	-0.161172759333380	0.0478986662834512	-3.36486945961301	0.000798209778087817	***
df.mm.trans1:probe22	-0.257719145576397	0.0478986662834512	-5.38050775884416	9.47835383682605e-08	***
df.mm.trans2:probe2	-0.0127599270010667	0.0478986662834512	-0.266394202409664	0.789996716512477	   
df.mm.trans2:probe3	-0.151606382921201	0.0478986662834512	-3.16514831590582	0.00160219704790321	** 
df.mm.trans2:probe4	-0.0541203890906532	0.0478986662834512	-1.12989344568351	0.258822498992070	   
df.mm.trans2:probe5	0.0218967829924690	0.0478986662834512	0.457148073035892	0.647675063723689	   
df.mm.trans2:probe6	-0.105023410940931	0.0478986662834512	-2.19261660271356	0.0285906524544741	*  
df.mm.trans3:probe2	0.282151480587890	0.0478986662834512	5.89059158595764	5.43442496818509e-09	***
df.mm.trans3:probe3	0.158600867670637	0.0478986662834512	3.31117502796593	0.000966060500041259	***
df.mm.trans3:probe4	0.240510800711910	0.0478986662834512	5.02124212162052	6.19048014226913e-07	***
df.mm.trans3:probe5	0.129672528350156	0.0478986662834512	2.70722628439776	0.00691350455198648	** 
df.mm.trans3:probe6	0.305407905984399	0.0478986662834512	6.37612546823492	2.90048462011737e-10	***
df.mm.trans3:probe7	0.0737676466475829	0.0478986662834512	1.54007725833212	0.123893412134541	   
df.mm.trans3:probe8	-0.207066667078219	0.0478986662834512	-4.32301529760463	1.71103378878801e-05	***
df.mm.trans3:probe9	0.485590473986346	0.0478986662834512	10.1378704599572	6.0780527175019e-23	***
df.mm.trans3:probe10	0.124817175226655	0.0478986662834512	2.60585909611806	0.00931597719179373	** 
df.mm.trans3:probe11	0.943057145192584	0.0478986662834512	19.6885888139730	4.99229554977535e-72	***
df.mm.trans3:probe12	0.496131730687093	0.0478986662834512	10.3579445772273	7.91733033248732e-24	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.38154603830249	0.161098649241523	27.1979067418099	2.46284665950192e-119	***
df.mm.trans1	-0.07530399587988	0.138573899921984	-0.543421206462945	0.586974546266235	   
df.mm.trans2	0.0449819235203321	0.121892848774438	0.369028404640629	0.712193446716415	   
df.mm.exp2	-0.294184944800207	0.155586221907599	-1.89081617377996	0.0589702191716212	.  
df.mm.exp3	-0.207082344488432	0.155586221907599	-1.33098125238504	0.183532626779961	   
df.mm.exp4	-0.0975406655123825	0.155586221907599	-0.626923543206228	0.530868525428442	   
df.mm.exp5	-0.191720123150113	0.155586221907599	-1.23224358043718	0.218180285236892	   
df.mm.exp6	-0.0960034454126552	0.155586221907599	-0.617043361780907	0.537362363427188	   
df.mm.exp7	-0.259845429254145	0.155586221907599	-1.67010565632517	0.0952465625104534	.  
df.mm.exp8	-0.313947703966077	0.155586221907599	-2.01783744162466	0.0439048515134588	*  
df.mm.trans1:exp2	0.34036231765715	0.143118508431162	2.37818519343261	0.0176061974017846	*  
df.mm.trans2:exp2	0.0680591443754493	0.102721958754885	0.662556917726344	0.507784189501523	   
df.mm.trans1:exp3	0.190203716207980	0.143118508431162	1.32899453951105	0.184186961908283	   
df.mm.trans2:exp3	0.0258667680932847	0.102721958754885	0.251813423408407	0.801242834127595	   
df.mm.trans1:exp4	0.170383505325083	0.143118508431162	1.19050643549038	0.234161662538274	   
df.mm.trans2:exp4	-0.0839028477779189	0.102721958754885	-0.816795637416998	0.414261614906598	   
df.mm.trans1:exp5	0.242831773815615	0.143118508431162	1.69671817067891	0.090096116795576	.  
df.mm.trans2:exp5	-0.0872865474509745	0.102721958754885	-0.849736010771148	0.395698103821403	   
df.mm.trans1:exp6	0.0785419154386123	0.143118508431162	0.548789365537511	0.583286273928641	   
df.mm.trans2:exp6	0.080059883585429	0.102721958754885	0.779384316224613	0.435958477624603	   
df.mm.trans1:exp7	0.324970584872722	0.143118508431162	2.27063982454113	0.0234046341911089	*  
df.mm.trans2:exp7	0.0495025361681025	0.102721958754885	0.481908024030435	0.629988579605108	   
df.mm.trans1:exp8	0.356314323928281	0.143118508431162	2.48964531446094	0.0129668200677799	*  
df.mm.trans2:exp8	0.0120529864080805	0.102721958754885	0.117336025852479	0.906619986035716	   
df.mm.trans1:probe2	-0.0990047504862346	0.101200067824979	-0.978307155460204	0.328185615029370	   
df.mm.trans1:probe3	-0.103931185206565	0.101200067824979	-1.02698730781792	0.304702680089753	   
df.mm.trans1:probe4	-0.17060224946598	0.101200067824979	-1.68579184908284	0.0921829021951217	.  
df.mm.trans1:probe5	-0.197924902919809	0.101200067824979	-1.95577836234371	0.0508006689277943	.  
df.mm.trans1:probe6	-0.242478847275718	0.101200067824979	-2.39603443443412	0.0167770529167005	*  
df.mm.trans1:probe7	-0.157835501093007	0.101200067824979	-1.55963829358273	0.119197321287271	   
df.mm.trans1:probe8	-0.159349372460702	0.101200067824979	-1.57459748679507	0.115701210782745	   
df.mm.trans1:probe9	-0.166443189868991	0.101200067824979	-1.6446944497789	0.100382431096051	   
df.mm.trans1:probe10	-0.146907416508832	0.101200067824979	-1.45165334041971	0.146946890378188	   
df.mm.trans1:probe11	0.0111266446568230	0.101200067824979	0.109947007901873	0.912475939421166	   
df.mm.trans1:probe12	-0.212994451385924	0.101200067824979	-2.10468684422514	0.0355956202427005	*  
df.mm.trans1:probe13	-0.113236938227151	0.101200067824979	-1.11894132742075	0.263464051807192	   
df.mm.trans1:probe14	0.0136260603340202	0.101200067824979	0.134644774720763	0.892922876841514	   
df.mm.trans1:probe15	-0.181121998786759	0.101200067824979	-1.78974187151734	0.0738319016461346	.  
df.mm.trans1:probe16	-0.112468329124264	0.101200067824979	-1.11134638090137	0.26671640836398	   
df.mm.trans1:probe17	-0.191895547153638	0.101200067824979	-1.89619978798348	0.0582540346656713	.  
df.mm.trans1:probe18	-0.14991271366627	0.101200067824979	-1.48134993274449	0.138863744801739	   
df.mm.trans1:probe19	-0.173339564148604	0.101200067824979	-1.71284039501226	0.0870867078979619	.  
df.mm.trans1:probe20	-0.121710290282224	0.101200067824979	-1.20267004655290	0.229420681332710	   
df.mm.trans1:probe21	-0.217084841619357	0.101200067824979	-2.14510569296056	0.0322117571403069	*  
df.mm.trans1:probe22	-0.0582153262670309	0.101200067824979	-0.575249874018976	0.565266259429889	   
df.mm.trans2:probe2	-0.0265866275865613	0.101200067824979	-0.262713535257128	0.792831575299784	   
df.mm.trans2:probe3	-0.0485836789663315	0.101200067824979	-0.480075557363804	0.63129045142741	   
df.mm.trans2:probe4	-0.0891205252740959	0.101200067824979	-0.880637011313333	0.378749678562291	   
df.mm.trans2:probe5	-0.0558264997091006	0.101200067824979	-0.551644884326068	0.581328767261847	   
df.mm.trans2:probe6	-0.040066210974677	0.101200067824979	-0.395910910296719	0.692264691984163	   
df.mm.trans3:probe2	-0.209401296426928	0.101200067824979	-2.06918138423660	0.0388143498930446	*  
df.mm.trans3:probe3	-0.116184232159929	0.101200067824979	-1.14806476573578	0.251247228422084	   
df.mm.trans3:probe4	-0.159177200202339	0.101200067824979	-1.57289618103447	0.116094706266712	   
df.mm.trans3:probe5	-0.136240206308998	0.101200067824979	-1.34624619565098	0.178562477238943	   
df.mm.trans3:probe6	-0.0903118444554406	0.101200067824979	-0.89240893209312	0.372412588419249	   
df.mm.trans3:probe7	-0.136061650243863	0.101200067824979	-1.34448180883807	0.179131767921782	   
df.mm.trans3:probe8	-0.243723463948784	0.101200067824979	-2.40833300991748	0.0162259194177496	*  
df.mm.trans3:probe9	-0.171874603605328	0.101200067824979	-1.6983645100177	0.0897850147008312	.  
df.mm.trans3:probe10	-0.264647285712133	0.101200067824979	-2.61509000339634	0.0090698008442592	** 
df.mm.trans3:probe11	-0.145088895268111	0.101200067824979	-1.43368377498557	0.152010222991846	   
df.mm.trans3:probe12	-0.14352212490983	0.101200067824979	-1.41820186482528	0.156478375731756