chr2.13316_chr2_24478239_24486742_+_2.R 

fitVsDatCorrelation=0.890323769219502
cont.fitVsDatCorrelation=0.272104544665422

fstatistic=13995.4857252894,65,991
cont.fstatistic=3121.77698522115,65,991

residuals=-0.481453749264788,-0.0837323146981455,-0.00548779133593101,0.0733593122135405,0.616814377119275
cont.residuals=-0.607561611779305,-0.181052613635217,-0.0475119083143267,0.124827728221438,1.43926669991734

predictedValues:
Include	Exclude	Both
chr2.13316_chr2_24478239_24486742_+_2.R.tl.Lung	62.9362445159377	57.9516541990837	53.5663106365469
chr2.13316_chr2_24478239_24486742_+_2.R.tl.cerebhem	65.3657598945408	60.7306075591108	55.6737302285857
chr2.13316_chr2_24478239_24486742_+_2.R.tl.cortex	59.2116762670716	56.0120203410762	51.2930139182851
chr2.13316_chr2_24478239_24486742_+_2.R.tl.heart	60.5061245952046	58.638185972112	51.9135478227382
chr2.13316_chr2_24478239_24486742_+_2.R.tl.kidney	63.9027539085255	57.735825153201	53.0067532091821
chr2.13316_chr2_24478239_24486742_+_2.R.tl.liver	65.1308160682122	59.7783661373994	55.883191017063
chr2.13316_chr2_24478239_24486742_+_2.R.tl.stomach	68.7273987582831	55.934002528162	59.6748764096487
chr2.13316_chr2_24478239_24486742_+_2.R.tl.testicle	60.7388275601969	58.1387725130469	52.8001583572994


diffExp=4.98459031685402,4.63515233542996,3.19965592599543,1.86793862309257,6.16692875532454,5.35244993081279,12.7933962301211,2.60005504715006
diffExpScore=0.97652591370987
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=0,0,0,0,0,0,1,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	61.9720304762047	64.049418162588	59.068587078997
cerebhem	59.2255257401765	68.0091156107287	66.6279834828741
cortex	60.3483861346169	58.2721615120333	62.315067107893
heart	58.1433445601002	67.251752501039	60.9944065533202
kidney	58.0784360280671	70.9472582979622	55.2787192911049
liver	59.2426846518526	62.776322807218	63.5271865294592
stomach	58.3636858419674	65.2527438196397	56.5708025097382
testicle	60.6331234982149	65.3979144350009	69.1556403008932
cont.diffExp=-2.07738768638330,-8.78358987055223,2.07622462258351,-9.10840794093875,-12.8688222698951,-3.53363815536545,-6.8890579776723,-4.764790936786
cont.diffExpScore=1.06714557652579

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,-1,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=0.0968009771196943
cont.tran.correlation=-0.535921054296224

tran.covariance=0.000151848578554800
cont.tran.covariance=-0.000723336400895737

tran.mean=60.7149397481978
cont.tran.mean=62.3727440048381

weightedLogRatios:
wLogRatio
Lung	0.338375169787669
cerebhem	0.304737207355306
cortex	0.225173068204469
heart	0.128164119601548
kidney	0.416758664437063
liver	0.354465849170787
stomach	0.850093753230537
testicle	0.178707503522957

cont.weightedLogRatios:
wLogRatio
Lung	-0.136607667971224
cerebhem	-0.573968613206823
cortex	0.142931751670248
heart	-0.601871967709765
kidney	-0.83296542805291
liver	-0.238150954393866
stomach	-0.459962590510029
testicle	-0.313388663509832

varWeightedLogRatios=0.0501266813367483
cont.varWeightedLogRatios=0.0936562501111699

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.4430525010097	0.0671637719879715	66.1525159993309	0	***
df.mm.trans1	-0.144654449687959	0.0593359827005978	-2.43788748587628	0.0149482483538978	*  
df.mm.trans2	-0.361375158362298	0.0524460946699504	-6.89041120480897	9.87950962293931e-12	***
df.mm.exp2	0.0461269811789137	0.0688439035641215	0.670022744075673	0.502999382942141	   
df.mm.exp3	-0.0516804879059606	0.0688439035641215	-0.750690841605534	0.45301694004375	   
df.mm.exp4	0.00373992258083673	0.0688439035641215	0.0543246734600596	0.956687441326467	   
df.mm.exp5	0.0220100012548274	0.0688439035641215	0.319708792142026	0.74925648052967	   
df.mm.exp6	0.022966990701799	0.0688439035641215	0.333609651875818	0.738744708402807	   
df.mm.exp7	-0.0554016938220748	0.0688439035641215	-0.804743644010155	0.421160647172176	   
df.mm.exp8	-0.0179092500294207	0.0688439035641214	-0.260142860910552	0.794807616094505	   
df.mm.trans1:exp2	-0.00825063100011274	0.0658273624944768	-0.125337408145511	0.900281859098917	   
df.mm.trans2:exp2	0.000711718286921563	0.0507233893924334	0.0140313629559568	0.988807783667466	   
df.mm.trans1:exp3	-0.00932297758269676	0.0658273624944768	-0.141627694463362	0.887402875555885	   
df.mm.trans2:exp3	0.0176376898948995	0.0507233893924334	0.347723015085632	0.728122051086623	   
df.mm.trans1:exp4	-0.0431175518607412	0.0658273624944768	-0.65500956178153	0.512613601506634	   
df.mm.trans2:exp4	0.0080370848675831	0.0507233893924334	0.1584492866871	0.874135060924587	   
df.mm.trans1:exp5	-0.00676976581154637	0.0658273624944768	-0.102841213061125	0.918109801472722	   
df.mm.trans2:exp5	-0.0257412485340039	0.0507233893924334	-0.507482816947635	0.611929006325191	   
df.mm.trans1:exp6	0.0113085894423372	0.0658273624944768	0.171791623024332	0.863636424678421	   
df.mm.trans2:exp6	0.00806771986232024	0.0507233893924334	0.159053248589175	0.873659329270006	   
df.mm.trans1:exp7	0.143427408814524	0.0658273624944768	2.17884179738415	0.0295784172390730	*  
df.mm.trans2:exp7	0.0199650486059828	0.0507233893924334	0.393606358824299	0.69395635367254	   
df.mm.trans1:exp8	-0.0176298153088594	0.0658273624944768	-0.267818953103864	0.788894412267735	   
df.mm.trans2:exp8	0.0211329176703735	0.0507233893924335	0.416630629843636	0.677038824250281	   
df.mm.trans1:probe2	-0.209122934082194	0.0403108623061727	-5.1877564040641	2.58070950301281e-07	***
df.mm.trans1:probe3	0.676595345163678	0.0403108623061727	16.7844423675370	7.75369453456392e-56	***
df.mm.trans1:probe4	-0.320007914744686	0.0403108623061727	-7.93850333228133	5.51795238781236e-15	***
df.mm.trans1:probe5	-0.495051919499967	0.0403108623061727	-12.2808566023695	2.26727381956623e-32	***
df.mm.trans1:probe6	-0.30732962288377	0.0403108623061727	-7.62399029198414	5.7466812752214e-14	***
df.mm.trans1:probe7	-0.371820751851525	0.0403108623061727	-9.22383522901192	1.68089356195094e-19	***
df.mm.trans1:probe8	-0.308514645034842	0.0403108623061727	-7.65338738456111	4.63224204295496e-14	***
df.mm.trans1:probe9	-0.475869269043521	0.0403108623061727	-11.8049885767552	3.47766622816053e-30	***
df.mm.trans1:probe10	-0.0372771160450323	0.0403108623061727	-0.924741221408308	0.355325648987005	   
df.mm.trans1:probe11	-0.396725044669827	0.0403108623061727	-9.8416412344788	7.20496966924015e-22	***
df.mm.trans1:probe12	-0.480283943458397	0.0403108623061727	-11.9145043291434	1.10628420270816e-30	***
df.mm.trans1:probe13	-0.318846308886528	0.0403108623061727	-7.9096871325847	6.86238861202296e-15	***
df.mm.trans1:probe14	-0.491434375432533	0.0403108623061727	-12.1911154293834	5.92276609449107e-32	***
df.mm.trans1:probe15	-0.399614400801473	0.0403108623061727	-9.91331809690116	3.75757843593792e-22	***
df.mm.trans1:probe16	-0.436056114491988	0.0403108623061727	-10.8173353172159	7.42162438107308e-26	***
df.mm.trans1:probe17	-0.0761639631975299	0.0403108623061727	-1.88941537938441	0.0591279027518793	.  
df.mm.trans1:probe18	0.0380336743569888	0.0403108623061727	0.94350932183271	0.34565040873259	   
df.mm.trans1:probe19	-0.0269158427472021	0.0403108623061727	-0.667706945655701	0.504476135541031	   
df.mm.trans1:probe20	0.110232075678947	0.0403108623061727	2.73455017760976	0.00635797740777053	** 
df.mm.trans1:probe21	0.0597947036517565	0.0403108623061727	1.48333973105309	0.138302120680289	   
df.mm.trans1:probe22	-0.0660721483239649	0.0403108623061727	-1.63906561517161	0.101516992803401	   
df.mm.trans1:probe23	-0.476948889512962	0.0403108623061727	-11.8317709477509	2.62999802278684e-30	***
df.mm.trans1:probe24	-0.410801821449511	0.0403108623061727	-10.190846782918	2.91610928053243e-23	***
df.mm.trans1:probe25	-0.0180924217245487	0.0403108623061727	-0.448822493231018	0.653657835480965	   
df.mm.trans1:probe26	-0.184123064893087	0.0403108623061727	-4.5675794155585	5.55471901154324e-06	***
df.mm.trans1:probe27	0.721859834510788	0.0403108623061727	17.9073280305455	2.40079257158850e-62	***
df.mm.trans1:probe28	-0.319377000014033	0.0403108623061727	-7.92285209848083	6.21224043404075e-15	***
df.mm.trans1:probe29	-0.324420208676154	0.0403108623061727	-8.04796003151925	2.39556838042509e-15	***
df.mm.trans1:probe30	-0.335754724170163	0.0403108623061727	-8.32913773017327	2.68722007967722e-16	***
df.mm.trans1:probe31	-0.263563537261468	0.0403108623061727	-6.53827584385634	9.94793966238198e-11	***
df.mm.trans1:probe32	0.00519084182848847	0.0403108623061727	0.128770299902357	0.897565535202731	   
df.mm.trans2:probe2	-0.0170904514737129	0.0403108623061727	-0.423966407463723	0.671682321550801	   
df.mm.trans2:probe3	-0.0875572884660228	0.0403108623061727	-2.17205198442543	0.0300882627341326	*  
df.mm.trans2:probe4	-0.100041023576312	0.0403108623061727	-2.48173861468086	0.0132391385790923	*  
df.mm.trans2:probe5	0.0828184018230037	0.0403108623061727	2.05449343142237	0.0401897535957887	*  
df.mm.trans2:probe6	-0.142948374153215	0.0403108623061727	-3.54615024276784	0.000409187025615525	***
df.mm.trans3:probe2	0.319517338420897	0.0403108623061727	7.9263335027187	6.05071730885642e-15	***
df.mm.trans3:probe3	-0.0971130491275396	0.0403108623061727	-2.4091037395811	0.0161732988536836	*  
df.mm.trans3:probe4	0.0931760219991165	0.0403108623061727	2.31143708341979	0.0210131139092728	*  
df.mm.trans3:probe5	-0.0209291965326004	0.0403108623061727	-0.519194959751471	0.603740721204127	   
df.mm.trans3:probe6	-0.0312664471151018	0.0403108623061727	-0.775633298975944	0.438150584377963	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.3867092223275	0.141940597346715	30.9052470140886	2.12454507053734e-147	***
df.mm.trans1	-0.245658056605660	0.125397734215784	-1.95903106337578	0.0503892367590447	.  
df.mm.trans2	-0.147970343636100	0.110836985261762	-1.33502678087681	0.182174177362816	   
df.mm.exp2	-0.105769129918612	0.145491304409184	-0.726979047635339	0.467410481419778	   
df.mm.exp3	-0.174583408033341	0.145491304409184	-1.19995767954858	0.230442561772563	   
df.mm.exp4	-0.0470665115328187	0.145491304409184	-0.323500512446073	0.746384472628273	   
df.mm.exp5	0.103704346369706	0.145491304409184	0.712787247257364	0.476145212494314	   
df.mm.exp6	-0.137886509474981	0.145491304409184	-0.9477302443257	0.343497839215297	   
df.mm.exp7	0.00183013495617595	0.145491304409184	0.0125789988866195	0.98996620749014	   
df.mm.exp8	-0.158666760746554	0.145491304409184	-1.09055837660452	0.275732342299009	   
df.mm.trans1:exp2	0.0604385950138486	0.139116295551389	0.434446552607655	0.664058774960558	   
df.mm.trans2:exp2	0.165755935020473	0.107196305042269	1.54628403427817	0.122355249854407	   
df.mm.trans1:exp3	0.148034451548839	0.139116295551389	1.06410576102608	0.287540000099801	   
df.mm.trans2:exp3	0.0800529390777642	0.107196305042269	0.746788231611137	0.455368532393546	   
df.mm.trans1:exp4	-0.0167052309251959	0.139116295551389	-0.120081050598599	0.90444328270975	   
df.mm.trans2:exp4	0.0958546449813444	0.107196305042269	0.894197285471243	0.371433456291758	   
df.mm.trans1:exp5	-0.168593066014839	0.139116295551389	-1.21188582075607	0.225844956092356	   
df.mm.trans2:exp5	-0.00142253043404040	0.107196305042269	-0.0132703308521640	0.989414789639336	   
df.mm.trans1:exp6	0.0928456541684714	0.139116295551389	0.667395963934179	0.504674618517479	   
df.mm.trans2:exp6	0.117809542305549	0.107196305042269	1.09900749152776	0.272031717838104	   
df.mm.trans1:exp7	-0.0618194182130832	0.139116295551389	-0.444372228056111	0.656870511411482	   
df.mm.trans2:exp7	0.0167830170493349	0.107196305042269	0.156563391272835	0.875620844772223	   
df.mm.trans1:exp8	0.136824934966698	0.139116295551389	0.983529171937698	0.325587119372063	   
df.mm.trans2:exp8	0.179502185139271	0.107196305042269	1.67451839938411	0.0943442808710846	.  
df.mm.trans1:probe2	0.00779668625331816	0.0851909847517801	0.0915200860282958	0.927097842592155	   
df.mm.trans1:probe3	-0.0660351239761369	0.0851909847517801	-0.775142160506098	0.438440577530911	   
df.mm.trans1:probe4	-0.0435228835307906	0.0851909847517801	-0.510886024590544	0.609544657151845	   
df.mm.trans1:probe5	-0.138435206077029	0.0851909847517801	-1.62499830798277	0.104480886460162	   
df.mm.trans1:probe6	-0.0436081121910187	0.0851909847517801	-0.511886466837765	0.608844516567008	   
df.mm.trans1:probe7	0.167811735156326	0.0851909847517801	1.96982973779768	0.0491360321017523	*  
df.mm.trans1:probe8	0.0159668022485452	0.0851909847517801	0.187423614072164	0.851366874763136	   
df.mm.trans1:probe9	-0.0775813677795013	0.0851909847517801	-0.91067579516247	0.362687669903747	   
df.mm.trans1:probe10	-0.055683333693192	0.0851909847517802	-0.653629416955747	0.513502215836342	   
df.mm.trans1:probe11	0.0969093443589253	0.0851909847517801	1.13755398697748	0.255581726828618	   
df.mm.trans1:probe12	0.0616201304626321	0.0851909847517801	0.7233175041018	0.469655526010671	   
df.mm.trans1:probe13	-0.0847664479385784	0.0851909847517801	-0.995016646251494	0.319971055684451	   
df.mm.trans1:probe14	-0.100089395598143	0.0851909847517801	-1.17488248187026	0.240323953099336	   
df.mm.trans1:probe15	-0.0497784554425949	0.0851909847517802	-0.584315999957434	0.559140715959269	   
df.mm.trans1:probe16	0.0554097081762506	0.0851909847517801	0.650417509994715	0.515573326035774	   
df.mm.trans1:probe17	-0.007998509784183	0.0851909847517801	-0.0938891574911143	0.925216185454747	   
df.mm.trans1:probe18	0.0277352507198934	0.0851909847517801	0.325565560730461	0.744821798337886	   
df.mm.trans1:probe19	-0.0649058288801444	0.0851909847517801	-0.761886120570852	0.446309253043667	   
df.mm.trans1:probe20	-0.0538252126584656	0.0851909847517801	-0.631818176715476	0.527651413500814	   
df.mm.trans1:probe21	-0.0766932497023928	0.0851909847517801	-0.900250770968934	0.368205522555352	   
df.mm.trans1:probe22	0.124397213954048	0.0851909847517801	1.46021570611612	0.144547737511059	   
df.mm.trans1:probe23	0.0538699596485204	0.0851909847517801	0.632343431707951	0.527308349897151	   
df.mm.trans1:probe24	-0.144335080149114	0.0851909847517802	-1.69425298427599	0.0905314245800916	.  
df.mm.trans1:probe25	-0.0411479757757902	0.0851909847517801	-0.483008570633179	0.629196335501657	   
df.mm.trans1:probe26	0.082416375704954	0.0851909847517801	0.967430719871234	0.333564792976875	   
df.mm.trans1:probe27	-0.0394285202969284	0.0851909847517801	-0.462825032623004	0.643591396473634	   
df.mm.trans1:probe28	0.0330210188200578	0.0851909847517801	0.387611657692075	0.698386675553823	   
df.mm.trans1:probe29	-0.0635527043802596	0.0851909847517801	-0.746002696945367	0.455842702021793	   
df.mm.trans1:probe30	-0.0308456673063262	0.0851909847517801	-0.362076661001171	0.717371937073344	   
df.mm.trans1:probe31	0.00137419009435633	0.0851909847517801	0.0161306985517340	0.98713336963692	   
df.mm.trans1:probe32	-0.092079485374428	0.0851909847517801	-1.08085950224333	0.280022574407018	   
df.mm.trans2:probe2	-0.206175554514308	0.0851909847517801	-2.42015695809878	0.0156927893725558	*  
df.mm.trans2:probe3	-0.166093499495992	0.0851909847517801	-1.94966051842148	0.0514983397507678	.  
df.mm.trans2:probe4	-0.141708130387554	0.0851909847517801	-1.66341697775236	0.0965451720809686	.  
df.mm.trans2:probe5	-0.196036893737241	0.0851909847517801	-2.30114599929125	0.0215901780153996	*  
df.mm.trans2:probe6	-0.238993135895109	0.0851909847517801	-2.80538059973670	0.00512406867163893	** 
df.mm.trans3:probe2	0.0921784069589398	0.0851909847517801	1.08202067657181	0.279506556105763	   
df.mm.trans3:probe3	0.110697994917764	0.0851909847517801	1.29940973496555	0.194105553753826	   
df.mm.trans3:probe4	0.087514446553268	0.0851909847517801	1.02727356431267	0.304542335823088	   
df.mm.trans3:probe5	0.143409589355880	0.0851909847517801	1.68338926676022	0.0926147114687993	.  
df.mm.trans3:probe6	0.0799101992904046	0.0851909847517801	0.938012390903074	0.34846659000799