chr11.4677_chr11_84509799_84515405_+_1.R 

fitVsDatCorrelation=0.883248082090744
cont.fitVsDatCorrelation=0.221417029561675

fstatistic=5839.84058977519,52,692
cont.fstatistic=1339.98648338132,52,692

residuals=-0.686916462765611,-0.115856237540531,-0.00864639769133696,0.104982915894484,0.878902906959912
cont.residuals=-0.763079936493016,-0.298982476662155,-0.109937762786106,0.212465201062374,1.47516050147037

predictedValues:
Include	Exclude	Both
chr11.4677_chr11_84509799_84515405_+_1.R.tl.Lung	56.1861417590069	51.3378181733986	75.6240195376171
chr11.4677_chr11_84509799_84515405_+_1.R.tl.cerebhem	48.0179851022526	51.7290293714724	56.5490929533212
chr11.4677_chr11_84509799_84515405_+_1.R.tl.cortex	56.3629429995011	51.4909610096695	65.3009924803505
chr11.4677_chr11_84509799_84515405_+_1.R.tl.heart	66.4842403722412	56.4699770757534	74.0036838546069
chr11.4677_chr11_84509799_84515405_+_1.R.tl.kidney	114.881877784474	71.6189907694486	132.713169233072
chr11.4677_chr11_84509799_84515405_+_1.R.tl.liver	63.0972750036651	53.8166520503311	75.3626801089295
chr11.4677_chr11_84509799_84515405_+_1.R.tl.stomach	62.8813276966275	55.8844086337452	75.3688877493195
chr11.4677_chr11_84509799_84515405_+_1.R.tl.testicle	50.4567903787711	52.3181989096397	63.4106132825073


diffExp=4.84832358560833,-3.7110442692198,4.87198198983166,10.0142632964878,43.2628870150258,9.28062295333404,6.99691906288227,-1.86140853086867
diffExpScore=1.13580401559516
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,1,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,1,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,1,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	70.3539359428198	61.2508999983816	62.1922036562171
cerebhem	72.266133712719	61.3262219130202	71.8048665676185
cortex	65.9383299138034	68.735886005464	68.8996309377661
heart	72.1086773596536	63.1551662519725	69.465386618906
kidney	76.563783064155	64.2146541859542	66.3702539104804
liver	69.2227922670279	67.2552420083338	77.533602392737
stomach	80.3989597602093	65.260932171786	70.2099384801983
testicle	73.658332862575	64.4799008385653	72.7410364313857
cont.diffExp=9.10303594443825,10.9399117996987,-2.79755609166061,8.95351110768113,12.3491288782009,1.96755025869410,15.1380275884232,9.17843202400965
cont.diffExpScore=1.06980054207584

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

tran.correlation=0.983448790494152
cont.tran.correlation=-0.288892573844839

tran.covariance=0.0290377077062037
cont.tran.covariance=-0.00074011982727337

tran.mean=60.1896635681249
cont.tran.mean=68.5118655160275

weightedLogRatios:
wLogRatio
Lung	0.359485259460902
cerebhem	-0.290984518804096
cortex	0.360411889081996
heart	0.6718526998948
kidney	2.13005542816345
liver	0.646740460940686
stomach	0.481559918479141
testicle	-0.142706310013902

cont.weightedLogRatios:
wLogRatio
Lung	0.579771658480397
cerebhem	0.689139903224367
cortex	-0.174910905630855
heart	0.558410530000735
kidney	0.747575316858873
liver	0.121768582975353
stomach	0.893403496275997
testicle	0.563329458392747

varWeightedLogRatios=0.540084224226936
cont.varWeightedLogRatios=0.123446509859690

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.36022654557953	0.0995269752580508	33.7619679174137	1.99292578715755e-148	***
df.mm.trans1	0.646131410509765	0.081446726378576	7.93317840064503	8.6009566641478e-15	***
df.mm.trans2	0.551887564198724	0.0769272929146335	7.17414513482433	1.87688065117716e-12	***
df.mm.exp2	0.141161724093936	0.100103205725788	1.41016187314339	0.158941200551862	   
df.mm.exp3	0.152887082443899	0.100103205725788	1.52729456899414	0.127144703899896	   
df.mm.exp4	0.285235312596314	0.100103205725788	2.84941236924677	0.00451019538559688	** 
df.mm.exp5	0.485750682694251	0.100103205725788	4.8524987703677	1.50756537251048e-06	***
df.mm.exp6	0.166624462437870	0.100103205725788	1.66452673747835	0.0964598958447377	.  
df.mm.exp7	0.200816264023774	0.100103205725788	2.00609223818335	0.0452351185615742	*  
df.mm.exp8	0.0875065025908263	0.100103205725788	0.874162839804874	0.382332848142215	   
df.mm.trans1:exp2	-0.298256232308232	0.0821498766301533	-3.63063518221714	0.000303588388933027	***
df.mm.trans2:exp2	-0.133570280556402	0.0715022898041343	-1.86805598705007	0.0621766796764969	.  
df.mm.trans1:exp3	-0.149745317474372	0.0821498766301533	-1.82283070428139	0.0687603935985404	.  
df.mm.trans2:exp3	-0.149908481497511	0.0715022898041343	-2.09655497618544	0.0363954745268530	*  
df.mm.trans1:exp4	-0.116940518477746	0.0821498766301533	-1.42350205836855	0.155041366356716	   
df.mm.trans2:exp4	-0.189953871809583	0.0715022898041342	-2.65661242919524	0.00807495231435534	** 
df.mm.trans1:exp5	0.229483629515198	0.0821498766301533	2.79347503524997	0.00535868179649739	** 
df.mm.trans2:exp5	-0.152818086629352	0.0715022898041342	-2.13724745106717	0.0329277805128666	*  
df.mm.trans1:exp6	-0.0506170177589221	0.0821498766301533	-0.61615451946209	0.537995162649508	   
df.mm.trans2:exp6	-0.119469202440199	0.0715022898041343	-1.67084442704506	0.0952045667642651	.  
df.mm.trans1:exp7	-0.0882371399313073	0.0821498766301533	-1.07409948195734	0.283152480798410	   
df.mm.trans2:exp7	-0.115958515027952	0.0715022898041342	-1.62174547620217	0.105313356859992	   
df.mm.trans1:exp8	-0.195059307212750	0.0821498766301533	-2.37443213811416	0.0178477476399390	*  
df.mm.trans2:exp8	-0.0685898974878664	0.0715022898041343	-0.959268544766248	0.337758511605901	   
df.mm.trans1:probe2	0.616391707799001	0.0606717047890429	10.1594591736331	1.07183003220891e-22	***
df.mm.trans1:probe3	0.166144240590581	0.0606717047890429	2.73841391416755	0.00633241813556576	** 
df.mm.trans1:probe4	0.0639296170633389	0.0606717047890429	1.05369739132309	0.292389251440316	   
df.mm.trans1:probe5	0.056595241555112	0.0606717047890429	0.93281113085408	0.351242837996762	   
df.mm.trans1:probe6	0.0361781055459354	0.0606717047890429	0.596292879386324	0.551174588297303	   
df.mm.trans1:probe7	-0.255120471812013	0.0606717047890429	-4.20493329961757	2.95466850029639e-05	***
df.mm.trans1:probe8	-0.008182602637353	0.0606717047890429	-0.134866865300788	0.892756355171205	   
df.mm.trans1:probe9	-0.140443458476113	0.0606717047890429	-2.31480982715813	0.0209151035184812	*  
df.mm.trans2:probe2	-0.0545277842338378	0.0606717047890429	-0.898734993905847	0.369106439040535	   
df.mm.trans2:probe3	0.688590009801989	0.0606717047890429	11.3494422514784	1.69575277534870e-27	***
df.mm.trans2:probe4	-0.013790001759894	0.0606717047890429	-0.227288845893521	0.82026627761243	   
df.mm.trans2:probe5	0.0618705831275172	0.0606717047890429	1.01976008985808	0.308198633019929	   
df.mm.trans2:probe6	-0.103244329300403	0.0606717047890429	-1.70168828549298	0.0892629959271689	.  
df.mm.trans3:probe2	0.0748384050655112	0.0606717047890429	1.23349764648490	0.217808959840332	   
df.mm.trans3:probe3	-0.212236293262458	0.0606717047890429	-3.49810993444818	0.000498547303710105	***
df.mm.trans3:probe4	-0.69072449407963	0.0606717047890429	-11.3846231366219	1.20753775898297e-27	***
df.mm.trans3:probe5	-0.0896130407676156	0.0606717047890429	-1.47701537445177	0.140126447514851	   
df.mm.trans3:probe6	-0.0662087240390564	0.0606717047890429	-1.0912619691381	0.275537432176872	   
df.mm.trans3:probe7	0.00153101429925636	0.0606717047890429	0.0252344038226672	0.979875270297531	   
df.mm.trans3:probe8	-0.475941922417044	0.0606717047890429	-7.84454506547832	1.65001208396075e-14	***
df.mm.trans3:probe9	0.245734714850839	0.0606717047890429	4.05023586703662	5.69549401919028e-05	***
df.mm.trans3:probe10	-0.622065745704814	0.0606717047890429	-10.2529795044948	4.63923670444547e-23	***
df.mm.trans3:probe11	-0.653524984732458	0.0606717047890429	-10.7714953289146	4.04779784859200e-25	***
df.mm.trans3:probe12	-0.401984087173961	0.0606717047890429	-6.62556110087347	6.95592667874759e-11	***
df.mm.trans3:probe13	-0.253537834126159	0.0606717047890429	-4.17884803151183	3.305315013243e-05	***
df.mm.trans3:probe14	-0.644266643086888	0.0606717047890429	-10.6188979743856	1.66218670346761e-24	***
df.mm.trans3:probe15	-0.395155686551756	0.0606717047890429	-6.51301439321217	1.41675576395082e-10	***
df.mm.trans3:probe16	0.463610220719842	0.0606717047890429	7.64129213661998	7.18797775339718e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.35860496654358	0.206985280609292	21.0575600048147	1.89393427776537e-76	***
df.mm.trans1	-0.075852638461298	0.169383963196592	-0.447814757842578	0.654427046901498	   
df.mm.trans2	-0.248210428707138	0.159984941460990	-1.55146119653806	0.121248398185791	   
df.mm.exp2	-0.115676756492671	0.208183661498000	-0.555647622202003	0.578631359627707	   
df.mm.exp3	-0.0519471149841281	0.208183661498000	-0.249525417174138	0.80302843132893	   
df.mm.exp4	-0.0553471752424916	0.208183661498000	-0.265857439744488	0.790428190550328	   
df.mm.exp5	0.0668190047162394	0.208183661498000	0.320961809564874	0.748336239326977	   
df.mm.exp6	-0.143173906150593	0.208183661498000	-0.687728830977296	0.491854003705646	   
df.mm.exp7	0.0756173135874261	0.208183661498000	0.363224054391763	0.716548445996406	   
df.mm.exp8	-0.059402474110353	0.208183661498000	-0.285336868815345	0.775471450276244	   
df.mm.trans1:exp2	0.142493632528996	0.17084629792299	0.83404577249444	0.404542999980437	   
df.mm.trans2:exp2	0.116905728472832	0.148702615355715	0.786171300304167	0.432036257252866	   
df.mm.trans1:exp3	-0.0128717050640783	0.17084629792299	-0.075340848590587	0.939965295995272	   
df.mm.trans2:exp3	0.167239992821027	0.148702615355715	1.12466073593237	0.261122776081723	   
df.mm.trans1:exp4	0.0799828340107426	0.17084629792299	0.468156670546033	0.639819970805762	   
df.mm.trans2:exp4	0.0859632867405054	0.148702615355715	0.578088600088646	0.563392370123262	   
df.mm.trans1:exp5	0.0177664242614876	0.17084629792299	0.103990689160241	0.917206868395384	   
df.mm.trans2:exp5	-0.0195661048871123	0.148702615355714	-0.131578754282888	0.895355736081928	   
df.mm.trans1:exp6	0.126965352555249	0.17084629792299	0.743155421561898	0.457639805154882	   
df.mm.trans2:exp6	0.236690326506050	0.148702615355715	1.59170251269528	0.111908240486699	   
df.mm.trans1:exp7	0.0578451941980755	0.17084629792299	0.338580319862415	0.735028614911695	   
df.mm.trans2:exp7	-0.0122022817237509	0.148702615355715	-0.0820582858920237	0.934624092718575	   
df.mm.trans1:exp8	0.105301021988284	0.17084629792299	0.616349451339876	0.537866603123426	   
df.mm.trans2:exp8	0.110777491276092	0.148702615355715	0.744959939077725	0.456548864330813	   
df.mm.trans1:probe2	-0.128780886863387	0.126178353237841	-1.02062583286882	0.307788419357971	   
df.mm.trans1:probe3	-0.125011869069768	0.126178353237841	-0.990755275067873	0.322151390087494	   
df.mm.trans1:probe4	-0.098521935520487	0.126178353237841	-0.780814878244429	0.435178400541855	   
df.mm.trans1:probe5	-0.120345587420296	0.126178353237841	-0.953773641295271	0.340531272414148	   
df.mm.trans1:probe6	0.000233978288447943	0.126178353237841	0.00185434571338005	0.998520981458003	   
df.mm.trans1:probe7	-0.101833911373658	0.126178353237841	-0.807063246274147	0.419907345101137	   
df.mm.trans1:probe8	-0.0522124947633867	0.126178353237841	-0.413799145602799	0.679149423063345	   
df.mm.trans1:probe9	-0.0746536470155649	0.126178353237841	-0.591651777819972	0.554277022649667	   
df.mm.trans2:probe2	0.0285847552271634	0.126178353237841	0.226542465436067	0.820846436040475	   
df.mm.trans2:probe3	0.0511311945300007	0.126178353237841	0.405229528028636	0.685434046598727	   
df.mm.trans2:probe4	-0.0244681347924743	0.126178353237841	-0.193917056013189	0.84629773667877	   
df.mm.trans2:probe5	0.0468146652333619	0.126178353237841	0.371019782966404	0.710736351032077	   
df.mm.trans2:probe6	-0.00121436290356235	0.126178353237841	-0.00962417778010887	0.992323909506024	   
df.mm.trans3:probe2	0.117813368852408	0.126178353237841	0.933705075626836	0.350781721398917	   
df.mm.trans3:probe3	0.107368018482132	0.126178353237841	0.850922648195825	0.395106663550918	   
df.mm.trans3:probe4	0.0776028192399915	0.126178353237841	0.61502482199711	0.538740513684172	   
df.mm.trans3:probe5	0.220836947630241	0.126178353237841	1.75019678069481	0.080527542565533	.  
df.mm.trans3:probe6	0.0254412389536131	0.126178353237841	0.201629188373202	0.840265879823547	   
df.mm.trans3:probe7	0.0523870505602439	0.126178353237841	0.415182550857171	0.678136965551484	   
df.mm.trans3:probe8	0.0719687328795899	0.126178353237841	0.570373055542514	0.568609843889796	   
df.mm.trans3:probe9	0.122597191128866	0.126178353237841	0.971618252916773	0.33157998715264	   
df.mm.trans3:probe10	0.120715081626599	0.126178353237841	0.956701989913092	0.339051796339381	   
df.mm.trans3:probe11	0.178132538095007	0.126178353237841	1.41175196477034	0.158472489073214	   
df.mm.trans3:probe12	0.34421888413732	0.126178353237841	2.72803436805426	0.0065329274179516	** 
df.mm.trans3:probe13	-0.0316794126566543	0.126178353237841	-0.251068522006624	0.801835664515597	   
df.mm.trans3:probe14	-0.0139377057646179	0.126178353237841	-0.110460355575777	0.912076316259704	   
df.mm.trans3:probe15	0.00280573909623068	0.126178353237841	0.0222362950873355	0.982265875553224	   
df.mm.trans3:probe16	0.134733497598949	0.126178353237841	1.06780199726479	0.285982198256494