chr15.8598_chr15_103388280_103390228_+_1.R 

fitVsDatCorrelation=0.927082502864621
cont.fitVsDatCorrelation=0.245577915373689

fstatistic=8167.50240018027,47,577
cont.fstatistic=1210.89767925126,47,577

residuals=-0.726394294726483,-0.0990655836359335,0.00362627216344084,0.0883147159654372,1.02681224174169
cont.residuals=-1.08026780380775,-0.364148712848014,-0.0400320489883897,0.35217874580703,1.58829896449313

predictedValues:
Include	Exclude	Both
chr15.8598_chr15_103388280_103390228_+_1.R.tl.Lung	114.546241174913	179.093423850574	90.5250868057897
chr15.8598_chr15_103388280_103390228_+_1.R.tl.cerebhem	106.466305029791	123.258824248356	63.7409136277903
chr15.8598_chr15_103388280_103390228_+_1.R.tl.cortex	102.327557082583	214.324366303234	73.8487921361102
chr15.8598_chr15_103388280_103390228_+_1.R.tl.heart	114.510745678306	223.873503379275	83.689072028251
chr15.8598_chr15_103388280_103390228_+_1.R.tl.kidney	98.6972795167104	152.538613434818	73.7669296151805
chr15.8598_chr15_103388280_103390228_+_1.R.tl.liver	104.849825343359	193.208478045582	67.5126102216722
chr15.8598_chr15_103388280_103390228_+_1.R.tl.stomach	127.789838383690	325.413085711723	81.084926400222
chr15.8598_chr15_103388280_103390228_+_1.R.tl.testicle	119.188150945574	191.759184664161	87.6942351860557


diffExp=-64.5471826756616,-16.7925192185655,-111.996809220652,-109.362757700969,-53.8413339181078,-88.3586527022222,-197.623247328033,-72.5710337185876
diffExpScore=0.998603534386148
diffExp1.5=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	117.398834961899	93.8022182269507	107.333339317326
cerebhem	122.057134780747	106.575186224476	98.2634147936142
cortex	122.631134280618	122.095708774956	89.2714570105011
heart	105.277516947914	121.037302054808	98.1078418970198
kidney	102.134312675133	97.412319464748	97.8083677195343
liver	122.069501123247	99.2199205581363	132.377545692938
stomach	113.038266598704	104.708029908201	110.445053864261
testicle	110.625304361848	106.901637148540	100.534857348884
cont.diffExp=23.5966167349486,15.4819485562712,0.53542550566145,-15.7597851068946,4.72199321038475,22.8495805651111,8.33023669050294,3.72366721330752
cont.diffExpScore=1.47332072086947

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=1,0,0,0,0,1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.711103492134147
cont.tran.correlation=0.0255818821127837

tran.covariance=0.0161682820777693
cont.tran.covariance=0.000163771508930575

tran.mean=155.740338924541
cont.tran.mean=110.436520505683

weightedLogRatios:
wLogRatio
Lung	-2.21875356764619
cerebhem	-0.694365114916712
cortex	-3.69495717601091
heart	-3.40293003615901
kidney	-2.09396919726175
liver	-3.03062279573939
stomach	-4.97053728560324
testicle	-2.38646901095181

cont.weightedLogRatios:
wLogRatio
Lung	1.04416547832280
cerebhem	0.642475014847015
cortex	0.0210339885853761
heart	-0.659320520633494
kidney	0.217869894032836
liver	0.974283193651551
stomach	0.358980269965955
testicle	0.160551048899912

varWeightedLogRatios=1.62412186762262
cont.varWeightedLogRatios=0.305330528605111

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	6.12324411062099	0.09799613059383	62.4845498849373	5.24343261296472e-259	***
df.mm.trans1	-0.891598632720032	0.0862015370195337	-10.3431871814303	4.0644128669497e-23	***
df.mm.trans2	-0.919453277169506	0.079553087005336	-11.5577322236136	6.1600861739573e-28	***
df.mm.exp2	-0.0959708560608466	0.107642287274916	-0.891572062341436	0.372993986187316	   
df.mm.exp3	0.270390785926958	0.107642287274917	2.51193831692172	0.0122788616864019	*  
df.mm.exp4	0.301382264217706	0.107642287274917	2.79985005751486	0.00528380525322809	** 
df.mm.exp5	-0.104694543824164	0.107642287274917	-0.972615377047643	0.331152048417499	   
df.mm.exp6	0.280725325482835	0.107642287274917	2.60794649193831	0.00934455116679148	** 
df.mm.exp7	0.816726185046012	0.107642287274917	7.58741016864597	1.31584730510341e-13	***
df.mm.exp8	0.139828348657811	0.107642287274916	1.29900945249047	0.194459527410370	   
df.mm.trans1:exp2	0.0228208119297213	0.100376950453845	0.227351118225242	0.820231258665513	   
df.mm.trans2:exp2	-0.27765032788402	0.0869289890474415	-3.19399007082082	0.00147972616629937	** 
df.mm.trans1:exp3	-0.383190368571656	0.100376950453845	-3.81751355106025	0.000149382985817814	***
df.mm.trans2:exp3	-0.0908077787163352	0.0869289890474415	-1.04462020910857	0.296635970491478	   
df.mm.trans1:exp4	-0.3016921914317	0.100376950453845	-3.00559232042443	0.00276583187546065	** 
df.mm.trans2:exp4	-0.0782086795725226	0.0869289890474415	-0.899684678604052	0.368663394975232	   
df.mm.trans1:exp5	-0.0442266677937729	0.100376950453845	-0.440605812328490	0.659663415941022	   
df.mm.trans2:exp5	-0.0557952799678511	0.0869289890474415	-0.641848945665304	0.521226166245859	   
df.mm.trans1:exp6	-0.369174828436156	0.100376950453845	-3.67788448211434	0.000257164402824538	***
df.mm.trans2:exp6	-0.204863113084022	0.0869289890474415	-2.35667198398245	0.0187727692040323	*  
df.mm.trans1:exp7	-0.707317752652132	0.100376950453845	-7.04661527824926	5.24709859683938e-12	***
df.mm.trans2:exp7	-0.219538367476026	0.0869289890474415	-2.52549086192884	0.0118203311499831	*  
df.mm.trans1:exp8	-0.100103598310167	0.100376950453845	-0.997276743889493	0.319048234362006	   
df.mm.trans2:exp8	-0.0714956010366845	0.0869289890474415	-0.822459824048635	0.411154992005294	   
df.mm.trans1:probe2	-0.134561177270691	0.0549787200171492	-2.44751382405262	0.0146815586670241	*  
df.mm.trans1:probe3	-0.393910451513886	0.0549787200171492	-7.16478032575177	2.38906933827180e-12	***
df.mm.trans1:probe4	-0.274016462159326	0.0549787200171492	-4.98404586490653	8.24444576340944e-07	***
df.mm.trans1:probe5	-0.582597857325726	0.0549787200171492	-10.5967883054389	4.27878859877498e-24	***
df.mm.trans1:probe6	-0.377644857065326	0.0549787200171492	-6.8689277769204	1.67915962107758e-11	***
df.mm.trans1:probe7	-1.03445059129307	0.0549787200171492	-18.8154724404352	6.0840497782395e-62	***
df.mm.trans1:probe8	-0.928846493569304	0.0549787200171492	-16.8946547551412	2.52787760922976e-52	***
df.mm.trans1:probe9	-0.500650694576071	0.0549787200171492	-9.10626319455793	1.38585885740043e-18	***
df.mm.trans1:probe10	-0.94301632037632	0.0549787200171492	-17.1523876889489	1.34903997633539e-53	***
df.mm.trans1:probe11	-0.850780413206826	0.0549787200171492	-15.4747220914100	1.95273545841679e-45	***
df.mm.trans1:probe12	-1.04507203420110	0.0549787200171492	-19.0086643318562	6.33756188241483e-63	***
df.mm.trans1:probe13	-0.734426252969293	0.0549787200171492	-13.3583730712575	1.1739013477778e-35	***
df.mm.trans1:probe14	-1.00487588620991	0.0549787200171492	-18.2775423999770	3.20979668591739e-59	***
df.mm.trans1:probe15	-0.803220782035059	0.0549787200171492	-14.6096668271745	2.32873998016795e-41	***
df.mm.trans1:probe16	-0.205267393775174	0.0549787200171492	-3.73357898676336	0.000207492515371489	***
df.mm.trans2:probe2	0.365380248078439	0.0549787200171492	6.64584857494805	6.98974320222064e-11	***
df.mm.trans2:probe3	-0.161767373799255	0.0549787200171492	-2.94236340440076	0.0033880179733279	** 
df.mm.trans2:probe4	0.112476888834226	0.0549787200171492	2.04582589043801	0.0412263469458167	*  
df.mm.trans2:probe5	-0.231440295587818	0.0549787200171492	-4.20963411872131	2.96635722900547e-05	***
df.mm.trans2:probe6	-0.243481895637297	0.0549787200171492	-4.42865704333147	1.13453185056734e-05	***
df.mm.trans3:probe2	0.240554173061084	0.0549787200171492	4.37540512012737	1.43866480430856e-05	***
df.mm.trans3:probe3	0.150143191531808	0.0549787200171492	2.73093283155692	0.00650864336377176	** 
df.mm.trans3:probe4	0.383041890746683	0.0549787200171492	6.96709364327149	8.85707617665075e-12	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.80931545688735	0.253416882154735	18.9778810945626	9.09055841490362e-63	***
df.mm.trans1	0.00486632124174076	0.222916196956572	0.0218302721299736	0.982590893237814	   
df.mm.trans2	-0.292994520659583	0.205723380632601	-1.42421595327970	0.154924544819984	   
df.mm.exp2	0.254862008957966	0.278361733916575	0.91557846465473	0.360270507321345	   
df.mm.exp3	0.491478194970456	0.278361733916575	1.76560976271886	0.0779901773565504	.  
df.mm.exp4	0.235805185184589	0.278361733916575	0.847117819920102	0.397280673720398	   
df.mm.exp5	-0.0085948842769258	0.278361733916575	-0.0308766731547293	0.975378570128022	   
df.mm.exp6	-0.114554839843559	0.278361733916575	-0.411532282946229	0.680835091463325	   
df.mm.exp7	0.0435578907793947	0.278361733916575	0.156479449120147	0.875709850590402	   
df.mm.exp8	0.136727305380479	0.278361733916575	0.491185708095412	0.623481876958282	   
df.mm.trans1:exp2	-0.215949739657530	0.259573655307317	-0.83194012659667	0.405786910198234	   
df.mm.trans2:exp2	-0.127199803159634	0.224797379649322	-0.565842018968647	0.571721158804142	   
df.mm.trans1:exp3	-0.447874237302812	0.259573655307317	-1.72542254633876	0.0849867680330899	.  
df.mm.trans2:exp3	-0.227861463847168	0.224797379649322	-1.01363042666523	0.31118398398308	   
df.mm.trans1:exp4	-0.344782286788109	0.259573655307317	-1.32826378848004	0.184616108532345	   
df.mm.trans2:exp4	0.0191050901403769	0.224797379649322	0.084988046436219	0.932300373686336	   
df.mm.trans1:exp5	-0.130693361396189	0.259573655307317	-0.503492395025438	0.614810283766111	   
df.mm.trans2:exp5	0.0463590659305414	0.224797379649322	0.206226006739315	0.836687119079873	   
df.mm.trans1:exp6	0.15356842002689	0.259573655307317	0.591617896835777	0.554338200387498	   
df.mm.trans2:exp6	0.170705141844815	0.224797379649322	0.759373361518318	0.447939360412798	   
df.mm.trans1:exp7	-0.0814084706082736	0.259573655307317	-0.313623778622263	0.753920141122835	   
df.mm.trans2:exp7	0.0664294143918951	0.224797379649322	0.29550795696784	0.767712183724275	   
df.mm.trans1:exp8	-0.196155434443839	0.259573655307317	-0.755683138227586	0.450147572410382	   
df.mm.trans2:exp8	-0.00600667690774996	0.224797379649322	-0.0267204044687719	0.978691977165738	   
df.mm.trans1:probe2	0.0880983140396114	0.142174346345888	0.619649861623291	0.535732938157661	   
df.mm.trans1:probe3	-0.0814477614588481	0.142174346345888	-0.572872417227074	0.566954249258928	   
df.mm.trans1:probe4	0.0202173296103328	0.142174346345888	0.142200967544083	0.886970905596486	   
df.mm.trans1:probe5	-0.0302946319271525	0.142174346345888	-0.213080859562739	0.831339155715043	   
df.mm.trans1:probe6	-0.146805621142184	0.142174346345888	-1.03257461641518	0.302235609147813	   
df.mm.trans1:probe7	0.0148540065364117	0.142174346345888	0.104477403400711	0.916826805421596	   
df.mm.trans1:probe8	0.0427698467334099	0.142174346345888	0.300826751327961	0.763654974088606	   
df.mm.trans1:probe9	-0.117193250176457	0.142174346345888	-0.824292519631806	0.410113972113223	   
df.mm.trans1:probe10	-0.211050036815652	0.142174346345888	-1.48444527609925	0.138236899094289	   
df.mm.trans1:probe11	-0.144249866614796	0.142174346345888	-1.01459841611551	0.310722544988297	   
df.mm.trans1:probe12	-0.105356327583903	0.142174346345888	-0.7410361312834	0.458973000711954	   
df.mm.trans1:probe13	-0.112623472542616	0.142174346345888	-0.79215045074743	0.428598504642205	   
df.mm.trans1:probe14	-0.0420753727448065	0.142174346345888	-0.295942086784374	0.767380785792991	   
df.mm.trans1:probe15	0.00459162239508501	0.142174346345888	0.0322957165838787	0.974247393241263	   
df.mm.trans1:probe16	-0.151530667266169	0.142174346345888	-1.06580878450123	0.286955858909579	   
df.mm.trans2:probe2	-0.0660190134212167	0.142174346345888	-0.464352501826192	0.642570366847153	   
df.mm.trans2:probe3	0.143003063750039	0.142174346345888	1.00582888140829	0.314919505382825	   
df.mm.trans2:probe4	0.155978706061107	0.142174346345888	1.09709458893263	0.273057685461465	   
df.mm.trans2:probe5	0.0111311692363574	0.142174346345888	0.0782923890451869	0.93762261417931	   
df.mm.trans2:probe6	0.00458175417112856	0.142174346345888	0.0322263072691178	0.974302721022727	   
df.mm.trans3:probe2	0.239423815020537	0.142174346345888	1.68401558490767	0.092719494500675	.  
df.mm.trans3:probe3	0.203441503261541	0.142174346345888	1.43092975976551	0.152991698531495	   
df.mm.trans3:probe4	0.176142902350992	0.142174346345888	1.23892183701315	0.215878185872113