chr5.18032_chr5_120451580_120452567_+_2.R 

fitVsDatCorrelation=0.924951232805945
cont.fitVsDatCorrelation=0.21542975344172

fstatistic=13244.0919955219,63,945
cont.fstatistic=1993.70139010299,63,945

residuals=-0.398640880686126,-0.0901545753160626,-0.00716289818399431,0.077484114405688,0.656820746726137
cont.residuals=-0.631959943750446,-0.253622885777525,-0.108921882317112,0.182668443908896,1.4231550367545

predictedValues:
Include	Exclude	Both
chr5.18032_chr5_120451580_120452567_+_2.R.tl.Lung	56.3001279807335	52.7988262307945	61.5341306222185
chr5.18032_chr5_120451580_120452567_+_2.R.tl.cerebhem	62.3064565709242	62.9200791588595	67.3087014027164
chr5.18032_chr5_120451580_120452567_+_2.R.tl.cortex	57.6872208925506	51.8128867266101	62.737927479123
chr5.18032_chr5_120451580_120452567_+_2.R.tl.heart	58.5310955055083	50.224601850215	63.1765174662598
chr5.18032_chr5_120451580_120452567_+_2.R.tl.kidney	54.7085878152651	49.3602951870478	63.0205363163818
chr5.18032_chr5_120451580_120452567_+_2.R.tl.liver	57.2227902129242	49.994874371109	59.693885550934
chr5.18032_chr5_120451580_120452567_+_2.R.tl.stomach	63.0714428783243	52.7838288248748	63.0407156669718
chr5.18032_chr5_120451580_120452567_+_2.R.tl.testicle	60.7347526875667	52.7489214451314	71.0967603432357


diffExp=3.50130174993905,-0.613622587935339,5.87433416594052,8.30649365529325,5.34829262821725,7.22791584181515,10.2876140534495,7.98583124243535
diffExpScore=1.00464541537111
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	60.4636953139739	65.4874701632654	66.5430756026515
cerebhem	63.4260809313721	59.8682080787762	60.4231979081829
cortex	68.0413372410847	66.0372194849541	59.0251210483804
heart	62.8526953046936	66.4315056984072	67.0609957391148
kidney	59.919480676661	60.6609444485412	60.7828953510326
liver	61.3398128528656	65.8160716547723	61.4841903693941
stomach	64.885796707469	63.3407080754678	64.1685643686442
testicle	57.6002585866982	60.9087695093818	68.2634298211224
cont.diffExp=-5.02377484929151,3.55787285259593,2.00411775613055,-3.57881039371358,-0.74146377188012,-4.47625880190678,1.54508863200115,-3.30851092268360
cont.diffExpScore=2.19891769198105

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=0.624741819867248
cont.tran.correlation=0.399665010365673

tran.covariance=0.00243215945577641
cont.tran.covariance=0.000895497535257113

tran.mean=55.8254242711524
cont.tran.mean=62.942503420524

weightedLogRatios:
wLogRatio
Lung	0.256741045510634
cerebhem	-0.0405434134753397
cortex	0.429730728761159
heart	0.61114616700691
kidney	0.406413655993247
liver	0.537350303541052
stomach	0.722088491791168
testicle	0.568970167139647

cont.weightedLogRatios:
wLogRatio
Lung	-0.330592805620259
cerebhem	0.237904007774289
cortex	0.125721483304716
heart	-0.230840598837241
kidney	-0.0504129948568651
liver	-0.292421023148391
stomach	0.100272150206820
testicle	-0.227949532227502

varWeightedLogRatios=0.0572308441522625
cont.varWeightedLogRatios=0.0470312676535103

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.5976937885348	0.0659526389210816	54.5496563502147	3.18350523905829e-294	***
df.mm.trans1	0.244015836657804	0.0565642341516468	4.31395987796114	1.77222038408200e-05	***
df.mm.trans2	0.365977760735745	0.0495896108837739	7.38012971292532	3.46242400289084e-13	***
df.mm.exp2	0.187047094437625	0.0629188603371648	2.97283029977483	0.00302554233024576	** 
df.mm.exp3	-0.0138853758969491	0.0629188603371648	-0.220687021706071	0.8253838071855	   
df.mm.exp4	-0.0374633075456278	0.0629188603371648	-0.595422538565897	0.551703623783256	   
df.mm.exp5	-0.119887369108291	0.0629188603371648	-1.90542817313994	0.0570276996006222	.  
df.mm.exp6	-0.00795063662844644	0.0629188603371648	-0.126363328671899	0.899471208555689	   
df.mm.exp7	0.0890983933174221	0.0629188603371648	1.41608403012973	0.157080415744536	   
df.mm.exp8	-0.0695761571994231	0.0629188603371648	-1.10580765173723	0.269091217919572	   
df.mm.trans1:exp2	-0.0856788455771645	0.0576556061424141	-1.48604535291036	0.137600652812578	   
df.mm.trans2:exp2	-0.0116707181486721	0.0403963456561758	-0.288905294751281	0.77271721445845	   
df.mm.trans1:exp3	0.0382242415471851	0.0576556061424141	0.662975278635838	0.507508061186762	   
df.mm.trans2:exp3	-0.00496468725960528	0.0403963456561758	-0.122899415255555	0.902212880561854	   
df.mm.trans1:exp4	0.0763246593295382	0.0576556061424141	1.32380291243509	0.185888642785510	   
df.mm.trans2:exp4	-0.0125206690952798	0.0403963456561758	-0.309945587698614	0.75667067044983	   
df.mm.trans1:exp5	0.091211256328689	0.0576556061424141	1.58200151609524	0.113983830180285	   
df.mm.trans2:exp5	0.0525447689884552	0.0403963456561758	1.30073075014453	0.193667722771965	   
df.mm.trans1:exp6	0.0242060776223157	0.0576556061424141	0.419839097043308	0.674698433417865	   
df.mm.trans2:exp6	-0.0466178357637381	0.0403963456561758	-1.1540112108287	0.248787336665062	   
df.mm.trans1:exp7	0.0244728961721668	0.0576556061424141	0.424466895928849	0.671321958447641	   
df.mm.trans2:exp7	-0.089382481760029	0.0403963456561758	-2.21263780938968	0.0271610249834763	*  
df.mm.trans1:exp8	0.145395415015895	0.0576556061424141	2.52179145696182	0.0118392878284508	*  
df.mm.trans2:exp8	0.0686305228210345	0.0403963456561758	1.69892899236895	0.0896617056086961	.  
df.mm.trans1:probe2	0.0111176113509649	0.0417754555069818	0.266127830709274	0.790198813756754	   
df.mm.trans1:probe3	0.381640156436843	0.0417754555069818	9.13551155350205	3.87158083614098e-19	***
df.mm.trans1:probe4	0.0366533130531877	0.0417754555069818	0.877388710867843	0.380498540647513	   
df.mm.trans1:probe5	0.123379644940623	0.0417754555069818	2.95340035059589	0.00322078706598172	** 
df.mm.trans1:probe6	-0.0172095425464403	0.0417754555069818	-0.411953438630109	0.680466974619667	   
df.mm.trans1:probe7	-0.0248121363208885	0.0417754555069818	-0.593940533257423	0.552694033150369	   
df.mm.trans1:probe8	0.154936989249498	0.0417754555069818	3.70880430552347	0.000220374334753085	***
df.mm.trans1:probe9	0.0907481287253522	0.0417754555069818	2.17228340478982	0.0300822840569619	*  
df.mm.trans1:probe10	0.256385166931066	0.0417754555069818	6.13722014086038	1.23409600991280e-09	***
df.mm.trans1:probe11	0.0352131721743456	0.0417754555069818	0.842915337415303	0.399489085554972	   
df.mm.trans1:probe12	0.0344573411767372	0.0417754555069818	0.824822632298491	0.409680443735553	   
df.mm.trans1:probe13	0.180486382117037	0.0417754555069818	4.32039291796287	1.72230734632663e-05	***
df.mm.trans1:probe14	-0.0470117066727992	0.0417754555069818	-1.12534276651854	0.260729541459164	   
df.mm.trans1:probe15	-0.0137955651211820	0.0417754555069818	-0.330231351250651	0.741298353495878	   
df.mm.trans1:probe16	-0.0329860434280716	0.0417754555069818	-0.789603441249342	0.429957469062848	   
df.mm.trans1:probe17	0.906675658824715	0.0417754555069818	21.7035493167318	4.57046089008473e-85	***
df.mm.trans1:probe18	1.00855741951019	0.0417754555069818	24.1423440455756	1.08554382192168e-100	***
df.mm.trans1:probe19	1.14981062171582	0.0417754555069818	27.5235926876644	6.1156785965614e-123	***
df.mm.trans1:probe20	0.702366262116293	0.0417754555069818	16.8128929677118	1.07492492447871e-55	***
df.mm.trans1:probe21	0.717792590539603	0.0417754555069818	17.1821607168266	8.64183135575273e-58	***
df.mm.trans1:probe22	1.14913312837079	0.0417754555069818	27.5073751901697	7.83756158820742e-123	***
df.mm.trans2:probe2	0.0381889757235636	0.0417754555069818	0.914148637282512	0.360871927223555	   
df.mm.trans2:probe3	0.0777719435369975	0.0417754555069818	1.86166596134421	0.0629604649736346	.  
df.mm.trans2:probe4	0.00248699754680298	0.0417754555069818	0.0595325057888916	0.952540562199127	   
df.mm.trans2:probe5	-0.0790316675079557	0.0417754555069818	-1.89182060491830	0.058820409266421	.  
df.mm.trans2:probe6	0.0169319650444092	0.0417754555069818	0.405308926950644	0.685342184126692	   
df.mm.trans3:probe2	-0.402879209771979	0.0417754555069818	-9.64392140989694	4.68448905542441e-21	***
df.mm.trans3:probe3	-0.132156702814107	0.0417754555069818	-3.16350118054415	0.00160861833502154	** 
df.mm.trans3:probe4	0.258387452336054	0.0417754555069818	6.1851498493624	9.22492573538697e-10	***
df.mm.trans3:probe5	-0.343770706888723	0.0417754555069818	-8.22901157430275	6.22016944270973e-16	***
df.mm.trans3:probe6	-0.0392441843616645	0.0417754555069818	-0.939407694910848	0.347761509951434	   
df.mm.trans3:probe7	-0.392775863198664	0.0417754555069818	-9.40207254312333	3.91864994814661e-20	***
df.mm.trans3:probe8	-0.252393134504512	0.0417754555069818	-6.04166086141968	2.19157331151890e-09	***
df.mm.trans3:probe9	0.184808869485344	0.0417754555069818	4.42386246283914	1.08226148673218e-05	***
df.mm.trans3:probe10	0.351776498850218	0.0417754555069818	8.42065022585874	1.37798938785031e-16	***
df.mm.trans3:probe11	0.14183016414055	0.0417754555069818	3.39505966887294	0.000714691104819322	***
df.mm.trans3:probe12	-0.367055150646994	0.0417754555069818	-8.78638296560642	7.16714575062562e-18	***
df.mm.trans3:probe13	-0.20959607800714	0.0417754555069818	-5.01720628688563	6.26211613953597e-07	***
df.mm.trans3:probe14	-0.0302674928435953	0.0417754555069818	-0.724528134433788	0.468920901297388	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9997323397734	0.169446022657553	23.6047578871578	3.29963140284331e-97	***
df.mm.trans1	0.0857391183192342	0.145325261558312	0.58998082920932	0.555344541207395	   
df.mm.trans2	0.170037562749237	0.127406006292574	1.33461182637468	0.182324891494728	   
df.mm.exp2	0.0545952145611459	0.161651615593971	0.337733800930735	0.73563880736801	   
df.mm.exp3	0.246318356778127	0.161651615593971	1.52376056294307	0.127903096401792	   
df.mm.exp4	0.0453102115848811	0.161651615593971	0.280295445352610	0.77931219133347	   
df.mm.exp5	0.0049408757761725	0.161651615593971	0.0305649637834907	0.975622937763177	   
df.mm.exp6	0.0984606464755292	0.161651615593971	0.609091632729721	0.542610098561381	   
df.mm.exp7	0.0735910864828541	0.161651615593971	0.455244979844165	0.649037576845834	   
df.mm.exp8	-0.146522413942693	0.161651615593971	-0.906408596068233	0.36495067658938	   
df.mm.trans1:exp2	-0.0067631740138759	0.148129222796264	-0.0456572571313488	0.963593078234135	   
df.mm.trans2:exp2	-0.144308429398925	0.103786599191723	-1.39043412659034	0.164724454281946	   
df.mm.trans1:exp3	-0.128246043157841	0.148129222796264	-0.865771390255855	0.386835283023046	   
df.mm.trans2:exp3	-0.237958671235772	0.103786599191723	-2.29276874942396	0.0220802558237625	*  
df.mm.trans1:exp4	-0.00655950021076791	0.148129222796264	-0.0442822833127923	0.964688748728778	   
df.mm.trans2:exp4	-0.0309976133490241	0.103786599191723	-0.298666818167564	0.76525993901468	   
df.mm.trans1:exp5	-0.0139823110129587	0.148129222796264	-0.0943926576337328	0.924817258085435	   
df.mm.trans2:exp5	-0.081499633217527	0.103786599191723	-0.785261621945762	0.432497016681837	   
df.mm.trans1:exp6	-0.0840746462377068	0.148129222796264	-0.56757636778627	0.570457467829611	   
df.mm.trans2:exp6	-0.093455417020543	0.103786599191723	-0.90045745547462	0.368106266903825	   
df.mm.trans1:exp7	-0.00300544307037977	0.148129222796264	-0.0202893326086875	0.983816848380312	   
df.mm.trans2:exp7	-0.106921695597233	0.103786599191723	-1.03020714070917	0.303176374117343	   
df.mm.trans1:exp8	0.0980063635235784	0.148129222796264	0.661627474130311	0.508371284660014	   
df.mm.trans2:exp8	0.0740407476791497	0.103786599191723	0.713394101509924	0.475778057098145	   
df.mm.trans1:probe2	0.0133835980258874	0.107329818733045	0.124695990209167	0.900790751581044	   
df.mm.trans1:probe3	-0.0615776513121092	0.107329818733045	-0.573723612310086	0.566291360745965	   
df.mm.trans1:probe4	0.00931140002806132	0.107329818733045	0.0867550149434336	0.930884623994644	   
df.mm.trans1:probe5	0.0730944384199054	0.107329818733045	0.681026384677952	0.496021633701158	   
df.mm.trans1:probe6	0.119222686697121	0.107329818733045	1.11080674601395	0.266934089319961	   
df.mm.trans1:probe7	0.103467457046346	0.107329818733045	0.96401408543971	0.335285482019498	   
df.mm.trans1:probe8	-0.089274225977526	0.107329818733045	-0.831774683227338	0.405746263867374	   
df.mm.trans1:probe9	-0.0274353128850752	0.107329818733045	-0.255616875244273	0.798302312574881	   
df.mm.trans1:probe10	-0.0367523925323691	0.107329818733045	-0.342424807627609	0.732107366817459	   
df.mm.trans1:probe11	0.0709713331350603	0.107329818733045	0.661245252929972	0.508616224775772	   
df.mm.trans1:probe12	0.0898137438266631	0.107329818733045	0.836801411638005	0.402915759753028	   
df.mm.trans1:probe13	0.0539093483526396	0.107329818733045	0.502277456432915	0.615589379378839	   
df.mm.trans1:probe14	0.00437669208424244	0.107329818733045	0.0407779695885663	0.96748151591103	   
df.mm.trans1:probe15	-0.0729688327290224	0.107329818733045	-0.67985610700148	0.496762079200494	   
df.mm.trans1:probe16	-0.00282998086962857	0.107329818733045	-0.0263671447789120	0.978970066489988	   
df.mm.trans1:probe17	-0.00429459323162836	0.107329818733045	-0.0400130483990665	0.968091175115816	   
df.mm.trans1:probe18	0.0995986243959636	0.107329818733045	0.9279678804237	0.353661181173871	   
df.mm.trans1:probe19	0.0175535071441537	0.107329818733045	0.163547347338892	0.870122466608793	   
df.mm.trans1:probe20	0.0509315439121772	0.107329818733045	0.474533028317659	0.635229485845636	   
df.mm.trans1:probe21	0.0710513414101374	0.107329818733045	0.661990696051199	0.508138577344298	   
df.mm.trans1:probe22	0.115026652402244	0.107329818733045	1.07171197864727	0.284123038543098	   
df.mm.trans2:probe2	-0.00475117256272421	0.107329818733045	-0.0442670323942456	0.96470090205198	   
df.mm.trans2:probe3	0.0357531490873213	0.107329818733045	0.33311478123566	0.739121531899532	   
df.mm.trans2:probe4	0.098476934174573	0.107329818733045	0.917517008199825	0.359105885412228	   
df.mm.trans2:probe5	0.147063766219839	0.107329818733045	1.37020417956377	0.170948593100303	   
df.mm.trans2:probe6	-0.0347641448793621	0.107329818733045	-0.323900154586387	0.746085297905072	   
df.mm.trans3:probe2	-0.08918656687246	0.107329818733045	-0.830957956747216	0.406207275887346	   
df.mm.trans3:probe3	-0.0989518454050618	0.107329818733045	-0.921941791881517	0.35679424802475	   
df.mm.trans3:probe4	-0.0429198802861497	0.107329818733045	-0.399887755264935	0.689329560747594	   
df.mm.trans3:probe5	-0.176484871389923	0.107329818733045	-1.6443228310008	0.100442131602243	   
df.mm.trans3:probe6	0.0215101202473328	0.107329818733045	0.200411409440965	0.841201958367044	   
df.mm.trans3:probe7	0.0777225274549275	0.107329818733045	0.724146638579929	0.469154944804413	   
df.mm.trans3:probe8	-0.0400003797478439	0.107329818733045	-0.372686548994688	0.709465318798002	   
df.mm.trans3:probe9	-0.0762374226315101	0.107329818733045	-0.71030980515425	0.47768728841967	   
df.mm.trans3:probe10	-0.0224577054052780	0.107329818733045	-0.209240131683589	0.834305881010041	   
df.mm.trans3:probe11	-0.0453382735231242	0.107329818733045	-0.422420107089637	0.672814495725402	   
df.mm.trans3:probe12	-0.0774285120180864	0.107329818733045	-0.721407274623931	0.470837413685111	   
df.mm.trans3:probe13	-0.107310632816702	0.107329818733045	-0.999821243373282	0.317652963506312	   
df.mm.trans3:probe14	-0.130149970024148	0.107329818733045	-1.21261706728363	0.225579314898451