chr15.8368_chr15_89924480_89924844_+_0.R 

fitVsDatCorrelation=0.86190157649547
cont.fitVsDatCorrelation=0.283085333503669

fstatistic=6605.36085327385,37,347
cont.fstatistic=1839.61417680971,37,347

residuals=-0.470991185858891,-0.0837462678970545,-0.00379218809534631,0.0745860360738609,1.71997105557717
cont.residuals=-0.55808134365169,-0.229171770599245,-0.0624795614260305,0.211194319905503,2.11101787001619

predictedValues:
Include	Exclude	Both
chr15.8368_chr15_89924480_89924844_+_0.R.tl.Lung	65.94683589197	120.196830281605	87.8372444491236
chr15.8368_chr15_89924480_89924844_+_0.R.tl.cerebhem	59.933610700708	78.7289163394325	89.4439737860664
chr15.8368_chr15_89924480_89924844_+_0.R.tl.cortex	64.3717589208534	84.8660585495516	73.0359390119831
chr15.8368_chr15_89924480_89924844_+_0.R.tl.heart	65.4762390028587	110.321277803994	95.5619001414072
chr15.8368_chr15_89924480_89924844_+_0.R.tl.kidney	65.7216072859772	96.3139905201928	85.0669769225137
chr15.8368_chr15_89924480_89924844_+_0.R.tl.liver	60.7656794438034	104.616619758597	95.905624151977
chr15.8368_chr15_89924480_89924844_+_0.R.tl.stomach	67.680909357595	126.874021423187	109.156130292608
chr15.8368_chr15_89924480_89924844_+_0.R.tl.testicle	62.2825340023066	104.614560472783	94.7698657947884


diffExp=-54.2499943896345,-18.7953056387244,-20.4942996286982,-44.8450388011352,-30.5923832342156,-43.8509403147935,-59.1931120655916,-42.3320264704759
diffExpScore=0.996828951425316
diffExp1.5=-1,0,0,-1,0,-1,-1,-1
diffExp1.5Score=0.833333333333333
diffExp1.4=-1,0,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.857142857142857
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	89.0301725023382	75.0791681881482	78.6222930961455
cerebhem	80.8180942788837	84.7046447409442	78.1667003207844
cortex	90.4028152507387	81.191216411835	81.2436226566179
heart	86.6976672027563	84.5219486306491	90.5688647165546
kidney	78.6277111522656	78.4365011016926	85.7550101386239
liver	86.0566166649416	89.4572761371075	81.3164430040095
stomach	87.5490357279054	73.6906572906473	89.6107887072104
testicle	77.8267386628088	92.4434383672705	79.3972449514387
cont.diffExp=13.9510043141901,-3.88655046206044,9.21159883890378,2.17571857210721,0.191210050573048,-3.40065947216584,13.8583784372581,-14.6166997044617
cont.diffExpScore=3.31593907959476

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=0.649607701430576
cont.tran.correlation=-0.460598903314024

tran.covariance=0.00447512699639084
cont.tran.covariance=-0.00211472866202286

tran.mean=83.6694656097134
cont.tran.mean=83.5333563944333

weightedLogRatios:
wLogRatio
Lung	-2.69465864183712
cerebhem	-1.15372744184530
cortex	-1.18931119346787
heart	-2.31771641096521
kidney	-1.67264422555312
liver	-2.37882855654783
stomach	-2.84598012809855
testicle	-2.27717071594531

cont.weightedLogRatios:
wLogRatio
Lung	0.750542358620958
cerebhem	-0.207402804352167
cortex	0.478292279147066
heart	0.113092855702914
kidney	0.0106242759599265
liver	-0.173408020572097
stomach	0.755815235109346
testicle	-0.764270056659379

varWeightedLogRatios=0.423564192445626
cont.varWeightedLogRatios=0.273920262896148

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.75154177655923	0.0955791576605521	49.7131581074846	6.7854136976321e-160	***
df.mm.trans1	-0.576726677899308	0.0796305548338104	-7.24252994472965	2.86459078370309e-12	***
df.mm.trans2	0.0202408315544235	0.0796305548338103	0.254184233635773	0.799503785382764	   
df.mm.exp2	-0.536858408786808	0.109714176383551	-4.89324558122734	1.52140438784203e-06	***
df.mm.exp3	-0.187696337372016	0.109714176383551	-1.71077561313359	0.0880162748123	.  
df.mm.exp4	-0.177184021766246	0.109714176383551	-1.61496014103799	0.107228187982845	   
df.mm.exp5	-0.192891516590425	0.109714176383551	-1.75812755423778	0.0796073970642845	.  
df.mm.exp6	-0.308530996566129	0.109714176383551	-2.81213428142169	0.00520127020085227	** 
df.mm.exp7	-0.137274402022323	0.109714176383551	-1.25120022359211	0.211704325394473	   
df.mm.exp8	-0.271981655432917	0.109714176383551	-2.47900193391685	0.0136495935310391	*  
df.mm.trans1:exp2	0.441246969442118	0.092725402974593	4.75864170213443	2.86482965442136e-06	***
df.mm.trans2:exp2	0.113738270239602	0.092725402974593	1.22661392230095	0.220799385488932	   
df.mm.trans1:exp3	0.163522447592824	0.092725402974593	1.76351293547497	0.0786940396884456	.  
df.mm.trans2:exp3	-0.16036008214161	0.092725402974593	-1.72940830664871	0.084625352227058	.  
df.mm.trans1:exp4	0.170022434642673	0.092725402974593	1.83361224851467	0.0675679315588066	.  
df.mm.trans2:exp4	0.0914501865049605	0.092725402974593	0.986247388215914	0.324698906448887	   
df.mm.trans1:exp5	0.189470365398312	0.092725402974593	2.04334906422815	0.0417728134086233	*  
df.mm.trans2:exp5	-0.0286255459582348	0.092725402974593	-0.308713093067693	0.757725040896253	   
df.mm.trans1:exp6	0.226707242690537	0.092725402974593	2.44493132860965	0.0149847233292721	*  
df.mm.trans2:exp6	0.169702772896938	0.092725402974593	1.83016484644922	0.0680829522217521	.  
df.mm.trans1:exp7	0.163229652729588	0.092725402974593	1.76035528014166	0.0792285318657686	.  
df.mm.trans2:exp7	0.191338387483877	0.092725402974593	2.06349480666376	0.0398089153497361	*  
df.mm.trans1:exp8	0.214813788191941	0.092725402974593	2.31666599767489	0.0211037859059698	*  
df.mm.trans2:exp8	0.133133747452696	0.092725402974593	1.43578505114909	0.151964340223580	   
df.mm.trans1:probe2	0.0397169520541736	0.0507877948629412	0.78201765131477	0.43473748064634	   
df.mm.trans1:probe3	0.0169727520173492	0.0507877948629412	0.334189583602769	0.738438387441232	   
df.mm.trans1:probe4	-0.0516131048884472	0.0507877948629412	-1.01625016458646	0.310218316950142	   
df.mm.trans1:probe5	0.119501347706604	0.0507877948629412	2.35295405183662	0.0191826023781744	*  
df.mm.trans1:probe6	0.0157600728622801	0.0507877948629412	0.310312209947510	0.75650984029279	   
df.mm.trans2:probe2	0.108555162040839	0.0507877948629412	2.13742617362680	0.0332618798061153	*  
df.mm.trans2:probe3	-0.141340704154114	0.0507877948629412	-2.78296595738294	0.00568124883178504	** 
df.mm.trans2:probe4	0.304849068149275	0.0507877948629412	6.00240803862342	4.89763552554087e-09	***
df.mm.trans2:probe5	0.0945198974239947	0.0507877948629412	1.86107504133762	0.0635790975861233	.  
df.mm.trans2:probe6	-0.193102990755965	0.0507877948629412	-3.80215347559554	0.000169424160396184	***
df.mm.trans3:probe2	0.470497906465162	0.0507877948629412	9.26399556694427	2.11586070151203e-18	***
df.mm.trans3:probe3	0.103078162940483	0.0507877948629412	2.02958532101375	0.0431614250050346	*  
df.mm.trans3:probe4	0.548142638354794	0.0507877948629412	10.7928024800849	1.31145852935352e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46133540332994	0.180780665783166	24.6781666833831	2.31982805817179e-78	***
df.mm.trans1	-0.000416569664203844	0.150615103458697	-0.00276578945031287	0.99779481144122	   
df.mm.trans2	-0.144752759959384	0.150615103458697	-0.961077319839172	0.337182502535016	   
df.mm.exp2	0.0296644120892613	0.207515972498009	0.142950018411454	0.88641262564734	   
df.mm.exp3	0.0607669749557745	0.207515972498009	0.292830350475106	0.769826856667187	   
df.mm.exp4	-0.0495354482214097	0.207515972498009	-0.238706677009573	0.81147395004453	   
df.mm.exp5	-0.167344096246368	0.207515972498009	-0.806415497717765	0.420555641083563	   
df.mm.exp6	0.107555140184814	0.207515972498009	0.518298128525241	0.604580599276615	   
df.mm.exp7	-0.166263828698112	0.207515972498009	-0.801209789765495	0.423558474677908	   
df.mm.exp8	0.0637551924122254	0.207515972498009	0.307230290009782	0.758852389953378	   
df.mm.trans1:exp2	-0.126438861696839	0.175383007080818	-0.72092994527443	0.471438028944965	   
df.mm.trans2:exp2	0.0909628929707927	0.175383007080818	0.518652830082199	0.604333442869196	   
df.mm.trans1:exp3	-0.0454668950474688	0.175383007080818	-0.259243445555231	0.795601138178217	   
df.mm.trans2:exp3	0.0174969614429208	0.175383007080818	0.0997642915020727	0.920589069103821	   
df.mm.trans1:exp4	0.022987095944209	0.175383007080818	0.131067977033923	0.89579748802301	   
df.mm.trans2:exp4	0.168003563314520	0.175383007080818	0.957923838294675	0.338768096892061	   
df.mm.trans1:exp5	0.0430929635771645	0.175383007080818	0.245707747258016	0.80605373722989	   
df.mm.trans2:exp5	0.211090357883028	0.175383007080818	1.20359641105797	0.229566055349195	   
df.mm.trans1:exp6	-0.141525056383454	0.175383007080818	-0.80694851079978	0.420248890757963	   
df.mm.trans2:exp6	0.0676628768762596	0.175383007080818	0.385800642847227	0.699880734781953	   
df.mm.trans1:exp7	0.149487544251393	0.175383007080818	0.852349077254145	0.394608269337827	   
df.mm.trans2:exp7	0.147596720459627	0.175383007080818	0.841567965541912	0.400609417538195	   
df.mm.trans1:exp8	-0.198245464821076	0.175383007080818	-1.13035731409099	0.259106385687601	   
df.mm.trans2:exp8	0.144298655317339	0.175383007080818	0.822763035707582	0.411207798703979	   
df.mm.trans1:probe2	-0.0235509486240979	0.0960612291812525	-0.24516601364387	0.806472815942292	   
df.mm.trans1:probe3	0.0820562052039902	0.0960612291812525	0.854207320720027	0.393579450536743	   
df.mm.trans1:probe4	0.0250519203411963	0.0960612291812525	0.260791169910258	0.794408252667568	   
df.mm.trans1:probe5	0.130103251062308	0.0960612291812525	1.35437837066215	0.176496953727738	   
df.mm.trans1:probe6	0.066904526950336	0.0960612291812525	0.696477939336979	0.486595838062236	   
df.mm.trans2:probe2	0.113649642845564	0.0960612291812525	1.18309586306797	0.237581090346141	   
df.mm.trans2:probe3	-0.0253991695203220	0.0960612291812525	-0.264406043278894	0.791624027654215	   
df.mm.trans2:probe4	-0.110598046890347	0.0960612291812525	-1.15132866644529	0.250389733003167	   
df.mm.trans2:probe5	0.0482116341280572	0.0960612291812525	0.501884418292102	0.616067120535234	   
df.mm.trans2:probe6	-0.00625916776997637	0.0960612291812525	-0.0651581061717032	0.94808563808886	   
df.mm.trans3:probe2	-0.0192866648028642	0.0960612291812525	-0.200774703459949	0.8409923710269	   
df.mm.trans3:probe3	0.181988726780484	0.0960612291812525	1.89450757950536	0.0589888838031257	.  
df.mm.trans3:probe4	0.0312547887889761	0.0960612291812525	0.325363198611619	0.745102297615944