chr2.14049_chr2_145079275_145112913_+_2.R 

fitVsDatCorrelation=0.943711388579599
cont.fitVsDatCorrelation=0.256710719131495

fstatistic=9106.27126608998,64,968
cont.fstatistic=1053.24196008952,64,968

residuals=-0.601552782656681,-0.0915955179958166,-0.00310303440898402,0.0824137633275237,1.06160051728207
cont.residuals=-0.718631009133686,-0.310136986705552,-0.144580802367443,0.066649121858603,2.10631130210499

predictedValues:
Include	Exclude	Both
chr2.14049_chr2_145079275_145112913_+_2.R.tl.Lung	64.0554719149586	62.012179714298	56.5695596394928
chr2.14049_chr2_145079275_145112913_+_2.R.tl.cerebhem	63.3309416142329	73.6441974571594	56.4293281920402
chr2.14049_chr2_145079275_145112913_+_2.R.tl.cortex	59.862527782533	56.1835006664503	53.0416334204516
chr2.14049_chr2_145079275_145112913_+_2.R.tl.heart	62.1273973436365	64.3915963702512	56.5828531008904
chr2.14049_chr2_145079275_145112913_+_2.R.tl.kidney	64.7076208029688	64.1739818570449	57.2632927409024
chr2.14049_chr2_145079275_145112913_+_2.R.tl.liver	65.1473731721212	61.4377108745594	58.3496089253602
chr2.14049_chr2_145079275_145112913_+_2.R.tl.stomach	63.9217364756999	59.1023831396269	53.1821512739585
chr2.14049_chr2_145079275_145112913_+_2.R.tl.testicle	61.8368033513852	63.2585366138034	57.4975037869676


diffExp=2.04329220066057,-10.3132558429265,3.6790271160827,-2.26419902661463,0.533638945923997,3.70966229756173,4.819353336073,-1.42173326241822
diffExpScore=16.1184855445733
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	65.731176913678	61.7750954123697	70.7855758312545
cerebhem	56.6497898653803	76.6369548403867	53.3417069977144
cortex	64.8675106096829	56.9436611642476	68.6141113525169
heart	61.6697703190411	48.3231088965816	50.4222407208154
kidney	64.459249810964	60.7604201305025	63.4678741931597
liver	60.4529076130277	65.6359339851404	58.685822267273
stomach	63.4065222781608	53.9257472075062	57.3236797314824
testicle	63.2398088525958	68.3311580594126	82.6416162540853
cont.diffExp=3.95608150130827,-19.9871649750064,7.92384944543527,13.3466614224595,3.69882968046151,-5.18302637211276,9.4807750706546,-5.09134920681672
cont.diffExpScore=7.50905593619395

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,-1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,-1,0,1,0,0,0,0
cont.diffExp1.2Score=2

tran.correlation=0.27850480451634
cont.tran.correlation=-0.547276826725988

tran.covariance=0.000682708710094697
cont.tran.covariance=-0.00346209704203359

tran.mean=63.0746224469206
cont.tran.mean=62.0505509974174

weightedLogRatios:
wLogRatio
Lung	0.134327951964604
cerebhem	-0.63725180889474
cortex	0.257537420306044
heart	-0.148449310267746
kidney	0.0344969789045140
liver	0.243150760598215
stomach	0.322839168623605
testicle	-0.0940138805681295

cont.weightedLogRatios:
wLogRatio
Lung	0.257884776033526
cerebhem	-1.26557153292469
cortex	0.535105155687499
heart	0.975499768762317
kidney	0.244444170725400
liver	-0.340797071525550
stomach	0.65894305371732
testicle	-0.32410268783414

varWeightedLogRatios=0.0977552521487263
cont.varWeightedLogRatios=0.507506713858488

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00129016333075	0.0863871032658922	46.3181425474485	8.5021164260329e-248	***
df.mm.trans1	0.0231333255937731	0.0767505058475502	0.301409421844371	0.763167045437217	   
df.mm.trans2	0.0686421464120644	0.0683220487356866	1.00468512994416	0.315299563698634	   
df.mm.exp2	0.163021102069652	0.0906228354683474	1.79889650579949	0.0723464509971846	.  
df.mm.exp3	-0.102012524692238	0.0906228354683474	-1.12568233122510	0.260579017256208	   
df.mm.exp4	0.00685497119160781	0.0906228354683474	0.0756428681157533	0.93971886005655	   
df.mm.exp5	0.0322077953560537	0.0906228354683474	0.355404851212179	0.722363742771959	   
df.mm.exp6	-0.0233860875773128	0.0906228354683474	-0.258059543783322	0.796415807166643	   
df.mm.exp7	0.0115986328454749	0.0906228354683474	0.127987971084021	0.898185076197895	   
df.mm.exp8	-0.0316219834387254	0.0906228354683474	-0.348940565314471	0.727209814927116	   
df.mm.trans1:exp2	-0.174396539897328	0.0869915956181577	-2.00475159304845	0.0452676972713058	*  
df.mm.trans2:exp2	0.00889343960392734	0.0690847916763355	0.128732234521216	0.897596268985665	   
df.mm.trans1:exp3	0.0343137978454719	0.0869915956181577	0.394449574141501	0.693336035599924	   
df.mm.trans2:exp3	0.00330484328390516	0.0690847916763355	0.0478374936612454	0.961855622754143	   
df.mm.trans1:exp4	-0.0374173551081527	0.0869915956181577	-0.430126092552585	0.667199658097238	   
df.mm.trans2:exp4	0.0307973495021096	0.0690847916763355	0.445790582193491	0.65584821198635	   
df.mm.trans1:exp5	-0.0220782706529235	0.0869915956181577	-0.253797743288147	0.799705712989268	   
df.mm.trans2:exp5	0.00205925341605193	0.0690847916763355	0.0298076228658197	0.976226623548601	   
df.mm.trans1:exp6	0.0402886140229717	0.0869915956181577	0.463132257049465	0.643373672203977	   
df.mm.trans2:exp6	0.0140791050648739	0.0690847916763355	0.203794564957726	0.838556874736246	   
df.mm.trans1:exp7	-0.0136886219868355	0.0869915956181577	-0.157355683495225	0.874997352978052	   
df.mm.trans2:exp7	-0.0596581981515654	0.0690847916763355	-0.86355038068387	0.388048844151263	   
df.mm.trans1:exp8	-0.00362876248735220	0.0869915956181577	-0.0417139433018374	0.966735340637208	   
df.mm.trans2:exp8	0.0515212554086619	0.0690847916763355	0.745768412388658	0.455988377448402	   
df.mm.trans1:probe2	0.123819031593323	0.0507921490047464	2.43775926042729	0.0149576837168896	*  
df.mm.trans1:probe3	-0.2111988545418	0.0507921490047464	-4.15810038913816	3.49365880398867e-05	***
df.mm.trans1:probe4	-0.100006739159392	0.0507921490047464	-1.96894089183048	0.0492448006211648	*  
df.mm.trans1:probe5	0.116854373415749	0.0507921490047464	2.30063849837953	0.0216239135147792	*  
df.mm.trans1:probe6	-0.0104221675518277	0.0507921490047464	-0.205192490494028	0.837464875985383	   
df.mm.trans1:probe7	-0.145465187549468	0.0507921490047464	-2.86393055619431	0.00427458293078946	** 
df.mm.trans1:probe8	-0.195934553547694	0.0507921490047464	-3.85757557785917	0.000122094918959761	***
df.mm.trans1:probe9	-0.119450729320590	0.0507921490047464	-2.35175576661321	0.0188846210847041	*  
df.mm.trans1:probe10	-0.100451549057063	0.0507921490047464	-1.97769834561786	0.0482460319193972	*  
df.mm.trans1:probe11	1.28193654057697	0.0507921490047464	25.2388718669331	2.10159860188034e-108	***
df.mm.trans1:probe12	1.40292361458274	0.0507921490047464	27.6208753138528	2.65895527712068e-124	***
df.mm.trans1:probe13	1.14957789273364	0.0507921490047464	22.6329839406128	2.28925827931450e-91	***
df.mm.trans1:probe14	1.24093084588012	0.0507921490047464	24.431548382884	4.46218835042444e-103	***
df.mm.trans1:probe15	1.39401196670592	0.0507921490047464	27.4454220587448	4.00703322322002e-123	***
df.mm.trans1:probe16	1.28207056341755	0.0507921490047464	25.2415105196226	2.01873225550558e-108	***
df.mm.trans1:probe17	-0.160296995278595	0.0507921490047464	-3.15594040456165	0.00164923869673425	** 
df.mm.trans1:probe18	-0.157835423713870	0.0507921490047464	-3.10747678148290	0.00194195454614295	** 
df.mm.trans1:probe19	-0.155596203427591	0.0507921490047464	-3.06339082863083	0.00224898138892369	** 
df.mm.trans1:probe20	-0.232779516855244	0.0507921490047464	-4.58298224069021	5.18314834777147e-06	***
df.mm.trans1:probe21	-0.105551858433076	0.0507921490047464	-2.07811365538427	0.0379619238886891	*  
df.mm.trans1:probe22	-0.123851630075184	0.0507921490047464	-2.43840106201472	0.0149313160225031	*  
df.mm.trans1:probe23	-0.0177741270041241	0.0507921490047464	-0.349938471838691	0.726460996338955	   
df.mm.trans1:probe24	-0.224734183434930	0.0507921490047464	-4.42458505573232	1.07593788631685e-05	***
df.mm.trans1:probe25	-0.116614166112489	0.0507921490047464	-2.29590927727023	0.0218940418901056	*  
df.mm.trans1:probe26	-0.0854867485810046	0.0507921490047464	-1.6830701251293	0.0926839733031358	.  
df.mm.trans1:probe27	-0.221623419574975	0.0507921490047464	-4.36334008144181	1.41841178362413e-05	***
df.mm.trans1:probe28	-0.0315579104159902	0.0507921490047464	-0.621314731397586	0.534538843093106	   
df.mm.trans1:probe29	-0.100392328804788	0.0507921490047464	-1.97653241242868	0.0483780126188707	*  
df.mm.trans1:probe30	-0.193765879660141	0.0507921490047464	-3.81487854829758	0.000144885783484183	***
df.mm.trans1:probe31	-0.125527860568316	0.0507921490047464	-2.47140282559389	0.0136295736593402	*  
df.mm.trans1:probe32	-0.0487459914615065	0.0507921490047464	-0.95971508228469	0.337438278093611	   
df.mm.trans2:probe2	0.0968657804559117	0.0507921490047464	1.90710143898144	0.0568032332405442	.  
df.mm.trans2:probe3	0.0225631091236975	0.0507921490047464	0.444224345018145	0.656979682772334	   
df.mm.trans2:probe4	0.0527326889840353	0.0507921490047464	1.03820551044429	0.299433647472845	   
df.mm.trans2:probe5	0.271832083922857	0.0507921490047464	5.35185238760925	1.08716609558815e-07	***
df.mm.trans2:probe6	0.187389870666377	0.0507921490047464	3.68934715971292	0.000237358434453317	***
df.mm.trans3:probe2	-0.00987274326326892	0.0507921490047464	-0.194375379989264	0.845922750219229	   
df.mm.trans3:probe3	0.233807630926231	0.0507921490047464	4.60322383493524	4.71360808826408e-06	***
df.mm.trans3:probe4	-0.252458218933584	0.0507921490047464	-4.97041814296916	7.8983310135084e-07	***
df.mm.trans3:probe5	-0.259449720523259	0.0507921490047464	-5.10806740031839	3.91938971314367e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87669405689512	0.252417434765999	15.3582658047728	8.37656469617644e-48	***
df.mm.trans1	0.181894940716972	0.224259930830212	0.811089792294126	0.417513483847554	   
df.mm.trans2	0.190374387045802	0.199632533420397	0.953624060086945	0.340512037934006	   
df.mm.exp2	0.349830762218667	0.264793965711928	1.32114325671323	0.186765802824233	   
df.mm.exp3	-0.0635073436119855	0.264793965711928	-0.239836823476090	0.810507535191626	   
df.mm.exp4	0.0298529990318684	0.264793965711928	0.112740480892778	0.9102596817992	   
df.mm.exp5	0.0730195758668785	0.264793965711927	0.275759969342796	0.782791288168424	   
df.mm.exp6	0.164371431490139	0.264793965711928	0.620752180089183	0.534908816331215	   
df.mm.exp7	0.0390425875796009	0.264793965711928	0.147445155989981	0.882811369859061	   
df.mm.exp8	-0.0926319309681377	0.264793965711928	-0.349826442302363	0.726545049100072	   
df.mm.trans1:exp2	-0.49851583148997	0.254183721666774	-1.96124216067426	0.05013712186826	.  
df.mm.trans2:exp2	-0.134251659026705	0.201861218133576	-0.665069101772027	0.50616471341004	   
df.mm.trans1:exp3	0.0502808876484514	0.254183721666774	0.197813169618973	0.843232773168339	   
df.mm.trans2:exp3	-0.0179305743584673	0.201861218133576	-0.0888262466869799	0.929238371691287	   
df.mm.trans1:exp4	-0.0936324817796354	0.254183721666774	-0.368365374327095	0.712681353244135	   
df.mm.trans2:exp4	-0.275443404115646	0.201861218133576	-1.36451868596859	0.172721493858455	   
df.mm.trans1:exp5	-0.092559684991935	0.254183721666774	-0.364144817712903	0.715829395406303	   
df.mm.trans2:exp5	-0.0895812799607407	0.201861218133576	-0.443776574762681	0.657303302992871	   
df.mm.trans1:exp6	-0.248080103504444	0.254183721666774	-0.975987375893679	0.329314555910661	   
df.mm.trans2:exp6	-0.103748407737634	0.201861218133576	-0.513959088808139	0.607397895274498	   
df.mm.trans1:exp7	-0.0750492037673018	0.254183721666774	-0.295255743661227	0.767861837566562	   
df.mm.trans2:exp7	-0.174934835367071	0.201861218133576	-0.866609430897782	0.386370795992211	   
df.mm.trans1:exp8	0.0539925733441574	0.254183721666774	0.212415543332628	0.831827580599756	   
df.mm.trans2:exp8	0.193497491267096	0.201861218133576	0.958566945430074	0.338016301229425	   
df.mm.trans1:probe2	0.0445184623501636	0.148411319205473	0.299966758522836	0.764266913232496	   
df.mm.trans1:probe3	0.125702693046962	0.148411319205473	0.846988583619616	0.397210834849199	   
df.mm.trans1:probe4	0.160978015408736	0.148411319205473	1.08467478269541	0.278335787342627	   
df.mm.trans1:probe5	0.147431050519884	0.148411319205473	0.993394919667605	0.320765785835851	   
df.mm.trans1:probe6	0.127190054391291	0.148411319205473	0.857010469768808	0.391651212553492	   
df.mm.trans1:probe7	0.246518675357028	0.148411319205473	1.66105036109629	0.0970271545886947	.  
df.mm.trans1:probe8	0.209145361573987	0.148411319205473	1.40922783176955	0.159088969563773	   
df.mm.trans1:probe9	0.143722103105644	0.148411319205473	0.968403918751395	0.333084574194568	   
df.mm.trans1:probe10	0.169848762686602	0.148411319205473	1.14444614868930	0.252721561035905	   
df.mm.trans1:probe11	0.0721762157429226	0.148411319205473	0.486325545310974	0.626846448970693	   
df.mm.trans1:probe12	0.00942229047520262	0.148411319205473	0.0634876808968838	0.949391275864943	   
df.mm.trans1:probe13	-0.0170846598301745	0.148411319205473	-0.115116959552938	0.908376291599828	   
df.mm.trans1:probe14	0.293947767346556	0.148411319205473	1.98062903099453	0.0479156228802753	*  
df.mm.trans1:probe15	0.128069128954875	0.148411319205473	0.862933701017545	0.388387662885539	   
df.mm.trans1:probe16	0.260864412485589	0.148411319205473	1.75771237586283	0.0791125103024386	.  
df.mm.trans1:probe17	0.425590063542121	0.148411319205473	2.86763884197336	0.00422522907011593	** 
df.mm.trans1:probe18	0.143873206788050	0.148411319205473	0.969422059976839	0.332576803022826	   
df.mm.trans1:probe19	0.0622481851661534	0.148411319205473	0.419430172168822	0.674994840141395	   
df.mm.trans1:probe20	0.227251576531040	0.148411319205473	1.53122806095682	0.12603976023186	   
df.mm.trans1:probe21	0.191637824670364	0.148411319205473	1.29126151358472	0.196921273033914	   
df.mm.trans1:probe22	0.224327296083314	0.148411319205473	1.5115241700179	0.130981326828903	   
df.mm.trans1:probe23	0.288757855701918	0.148411319205473	1.94565924787811	0.0519848421962937	.  
df.mm.trans1:probe24	0.118895303754245	0.148411319205473	0.801120186726706	0.423258661333595	   
df.mm.trans1:probe25	0.137509199317280	0.148411319205473	0.926541183337241	0.354395747865871	   
df.mm.trans1:probe26	0.301293279288367	0.148411319205473	2.03012331472663	0.0426170363116936	*  
df.mm.trans1:probe27	0.088455472978261	0.148411319205473	0.596015677589902	0.551304089127461	   
df.mm.trans1:probe28	0.288005049266189	0.148411319205473	1.94058681512999	0.0525984717576575	.  
df.mm.trans1:probe29	-0.0307681574602363	0.148411319205473	-0.207316784359543	0.835806070663156	   
df.mm.trans1:probe30	-0.000710989353838223	0.148411319205473	-0.00479066797360564	0.996178601682533	   
df.mm.trans1:probe31	0.14652113516957	0.148411319205473	0.987263882256272	0.323759994823012	   
df.mm.trans1:probe32	-0.0369156932953491	0.148411319205473	-0.248739068508919	0.803615388746618	   
df.mm.trans2:probe2	-0.00820920166956955	0.148411319205473	-0.0553138514873253	0.955899841349802	   
df.mm.trans2:probe3	0.250277849799348	0.148411319205473	1.68637979326121	0.0920449007985011	.  
df.mm.trans2:probe4	0.144098827809447	0.148411319205473	0.970942301307523	0.33181955508102	   
df.mm.trans2:probe5	0.203326876616781	0.148411319205473	1.37002270248187	0.170997485987645	   
df.mm.trans2:probe6	0.0312560243592594	0.148411319205473	0.210604046420382	0.833240577616075	   
df.mm.trans3:probe2	0.093270661606907	0.148411319205473	0.628460565583784	0.529850524216391	   
df.mm.trans3:probe3	-0.00820193841936461	0.148411319205473	-0.0552649114856878	0.955938820053722	   
df.mm.trans3:probe4	0.0194355737945461	0.148411319205473	0.130957489621381	0.895836146745233	   
df.mm.trans3:probe5	-0.0510449765381177	0.148411319205473	-0.343942610384366	0.730964142235732