chr16.9700_chr16_33400660_33418104_+_2.R 

fitVsDatCorrelation=0.897031788346143
cont.fitVsDatCorrelation=0.219502618873139

fstatistic=8469.64114660374,56,784
cont.fstatistic=1727.02850122340,56,784

residuals=-0.82207744814532,-0.0942709954126731,-0.0110675412764512,0.0786022340908064,1.00461591966724
cont.residuals=-0.645584377726937,-0.223380970764229,-0.0868453767480971,0.104788081247606,1.90509865704751

predictedValues:
Include	Exclude	Both
chr16.9700_chr16_33400660_33418104_+_2.R.tl.Lung	82.2340187250057	47.9933528650607	68.9020283741763
chr16.9700_chr16_33400660_33418104_+_2.R.tl.cerebhem	86.1159264059767	45.507078218873	78.0216831633304
chr16.9700_chr16_33400660_33418104_+_2.R.tl.cortex	76.0142190182586	47.4959899749963	71.194482772172
chr16.9700_chr16_33400660_33418104_+_2.R.tl.heart	80.1677635573648	52.0905667861119	76.4314203879396
chr16.9700_chr16_33400660_33418104_+_2.R.tl.kidney	84.5973684574397	50.4297702440022	76.7150127602464
chr16.9700_chr16_33400660_33418104_+_2.R.tl.liver	85.716798901977	52.4283931321148	74.2402963534245
chr16.9700_chr16_33400660_33418104_+_2.R.tl.stomach	87.0577540110462	48.5625696256598	90.9823606636322
chr16.9700_chr16_33400660_33418104_+_2.R.tl.testicle	79.4841696027956	45.2134229885942	76.0515312919706


diffExp=34.2406658599449,40.6088481871037,28.5182290432623,28.0771967712530,34.1675982134376,33.2884057698621,38.4951843853864,34.2707466142014
diffExpScore=0.996332521137485
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
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	60.7727283631723	75.4153229321431	68.2840902041724
cerebhem	61.0466585211816	57.5959429841083	65.9918282610737
cortex	63.7989590629265	66.8746255178066	61.2981916971938
heart	65.020071620174	56.5030044125889	64.5830575515972
kidney	67.582860880399	63.0471748008877	65.6977686842908
liver	63.566050002351	58.0210538511716	63.9156576612296
stomach	64.8521417661052	63.0596392417767	59.1448272755542
testicle	69.5516292521324	59.2661710464657	66.468668227442
cont.diffExp=-14.6425945689709,3.45071553707337,-3.07566645488012,8.51706720758514,4.53568607951126,5.54499615117948,1.79250252432846,10.2854582056666
cont.diffExpScore=2.97818223102585

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

tran.correlation=0.188613337157336
cont.tran.correlation=-0.361840993365788

tran.covariance=0.000511434745279216
cont.tran.covariance=-0.00153155630210973

tran.mean=65.6943226572048
cont.tran.mean=63.4983771409619

weightedLogRatios:
wLogRatio
Lung	2.22958717524832
cerebhem	2.63854858829418
cortex	1.92614480934193
heart	1.79721984214696
kidney	2.16201187533004
liver	2.06730241361666
stomach	2.43685759364773
testicle	2.30939121342499

cont.weightedLogRatios:
wLogRatio
Lung	-0.909905610140871
cerebhem	0.237548347002613
cortex	-0.196771832636299
heart	0.576280133102893
kidney	0.290293502093159
liver	0.37480973219035
stomach	0.116547271299158
testicle	0.666056299297038

varWeightedLogRatios=0.0739161269996474
cont.varWeightedLogRatios=0.252918602282375

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.14172429743527	0.0842130889197138	61.0561180381025	3.63802107431455e-300	***
df.mm.trans1	-0.0350784244460197	0.073399256228353	-0.477912532749474	0.632845681566305	   
df.mm.trans2	-1.28879202328391	0.0655009154261558	-19.6759391055666	2.34837487439277e-70	***
df.mm.exp2	-0.131370500128423	0.0856955375892962	-1.53299114310979	0.125681447636192	   
df.mm.exp3	-0.121795597142517	0.0856955375892962	-1.42125950275536	0.155638961828427	   
df.mm.exp4	-0.0472344891510222	0.0856955375892962	-0.551189600763086	0.581660750802046	   
df.mm.exp5	-0.029558554078584	0.0856955375892962	-0.344925242434981	0.730243085075496	   
df.mm.exp6	0.055244072286139	0.0856955375892962	0.644655180890531	0.519339242204424	   
df.mm.exp7	-0.209186821300428	0.0856955375892962	-2.44104684076988	0.0148650064932929	*  
df.mm.exp8	-0.192405236881122	0.0856955375892962	-2.24521885612343	0.0250320377924298	*  
df.mm.trans1:exp2	0.177495800584571	0.0800210604240832	2.21811357714964	0.0268329294272668	*  
df.mm.trans2:exp2	0.0781758598957894	0.0624159966771197	1.25249718113445	0.210762232553402	   
df.mm.trans1:exp3	0.043146942723948	0.0800210604240832	0.539194838149913	0.589905504025893	   
df.mm.trans2:exp3	0.111378363699910	0.0624159966771197	1.78445221785170	0.074736820696569	.  
df.mm.trans1:exp4	0.0217869030125564	0.0800210604240832	0.272264612554414	0.785490250970931	   
df.mm.trans2:exp4	0.129155842409044	0.0624159966771197	2.06927469374834	0.0388474541410549	*  
df.mm.trans1:exp5	0.0578926449521291	0.0800210604240832	0.723467605219409	0.469608441300261	   
df.mm.trans2:exp5	0.0790777148774827	0.0624159966771197	1.26694628120023	0.205550728445152	   
df.mm.trans1:exp6	-0.0137643155828978	0.0800210604240832	-0.17200866259397	0.86347510839948	   
df.mm.trans2:exp6	0.0331417066170078	0.0624159966771197	0.530980972529383	0.595582348077616	   
df.mm.trans1:exp7	0.26618948936784	0.0800210604240832	3.32649290020815	0.0009204111674741	***
df.mm.trans2:exp7	0.220977363819812	0.0624159966771197	3.54039630197586	0.000423092926838602	***
df.mm.trans1:exp8	0.158394044669539	0.0800210604240832	1.9794044696497	0.0481198946583231	*  
df.mm.trans2:exp8	0.132736729066542	0.0624159966771197	2.12664599034113	0.0337612405646954	*  
df.mm.trans1:probe2	-0.874894276911564	0.0508524964101095	-17.2045492094590	1.60174740714148e-56	***
df.mm.trans1:probe3	-0.880425833130565	0.0508524964101095	-17.3133257024435	4.09057042973993e-57	***
df.mm.trans1:probe4	-1.16789663752413	0.0508524964101095	-22.966357995592	1.15973469782618e-89	***
df.mm.trans1:probe5	-1.17564067171307	0.0508524964101095	-23.1186422438712	1.42579924947191e-90	***
df.mm.trans1:probe6	-0.753110219396262	0.0508524964101095	-14.809700065119	6.15553100189347e-44	***
df.mm.trans1:probe7	-0.944936080291115	0.0508524964101095	-18.5819015190621	3.83089242998147e-64	***
df.mm.trans1:probe8	-1.07931917954390	0.0508524964101095	-21.2245072658680	2.41422336552193e-79	***
df.mm.trans1:probe9	-1.18272358657248	0.0508524964101095	-23.2579257669907	2.09168270389068e-91	***
df.mm.trans1:probe10	-1.05270524741497	0.0508524964101095	-20.7011517964670	2.77237509015686e-76	***
df.mm.trans1:probe11	-1.1813192447205	0.0508524964101095	-23.2303097805372	3.06083655053791e-91	***
df.mm.trans1:probe12	-0.946011942286475	0.0508524964101095	-18.6030580417761	2.91341949370578e-64	***
df.mm.trans1:probe13	-1.00835518979795	0.0508524964101095	-19.8290204214534	3.10246417559791e-71	***
df.mm.trans1:probe14	-1.13437233117264	0.0508524964101095	-22.3071119660336	9.79864898640388e-86	***
df.mm.trans1:probe15	-0.870844412069107	0.0508524964101095	-17.1249097595135	4.34068084179e-56	***
df.mm.trans1:probe16	-1.14462675759878	0.0508524964101095	-22.5087623696529	6.20233736271223e-87	***
df.mm.trans1:probe17	-0.865279880394998	0.0508524964101095	-17.0154848135043	1.70207068388998e-55	***
df.mm.trans1:probe18	-0.749605820821077	0.0508524964101095	-14.7407870554818	1.36999581057777e-43	***
df.mm.trans1:probe19	-0.892020990884926	0.0508524964101095	-17.5413412095063	2.31123831503730e-58	***
df.mm.trans1:probe20	-0.649677811589726	0.0508524964101095	-12.7757309365951	4.33664257729798e-34	***
df.mm.trans1:probe21	-0.893336274943295	0.0508524964101095	-17.5672058995652	1.66663437738362e-58	***
df.mm.trans1:probe22	-0.768124910936853	0.0508524964101095	-15.1049597396786	1.95219933153246e-45	***
df.mm.trans2:probe2	0.092587169150676	0.0508524964101095	1.82070056903381	0.0690334635246456	.  
df.mm.trans2:probe3	-0.0383411016880968	0.0508524964101095	-0.753966951374183	0.451095447258301	   
df.mm.trans2:probe4	0.043380072638722	0.0508524964101095	0.853056893979703	0.393888291491113	   
df.mm.trans2:probe5	0.144777772996263	0.0508524964101095	2.84701407436668	0.00452846870957812	** 
df.mm.trans2:probe6	-0.00671072563904681	0.0508524964101095	-0.131964527069171	0.895046208090896	   
df.mm.trans3:probe2	0.231438052329858	0.0508524964101095	4.55116402670544	6.181390533061e-06	***
df.mm.trans3:probe3	0.124876158560072	0.0508524964101095	2.45565443932162	0.0142788351546614	*  
df.mm.trans3:probe4	1.69115808623072	0.0508524964101095	33.2561468092354	5.91647309614754e-152	***
df.mm.trans3:probe5	0.419038134607797	0.0508524964101095	8.24026673594124	7.19275257529608e-16	***
df.mm.trans3:probe6	0.296108381196648	0.0508524964101095	5.82288780492951	8.43463107847196e-09	***
df.mm.trans3:probe7	0.141203557992381	0.0508524964101095	2.77672814434947	0.00562191516744409	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07349904022342	0.185895039926082	21.9128979549061	2.12525919991983e-83	***
df.mm.trans1	-0.0203891436597617	0.162024191751507	-0.125840119548519	0.899890745866595	   
df.mm.trans2	0.243846690899797	0.144589106569273	1.6864803766041	0.0921011531417245	.  
df.mm.exp2	-0.230915142069165	0.189167451117211	-1.22069172421257	0.222569763320582	   
df.mm.exp3	0.0363313183462815	0.189167451117211	0.192059036222728	0.847745695363626	   
df.mm.exp4	-0.165437099252744	0.189167451117211	-0.874553726212852	0.382084598069896	   
df.mm.exp5	-0.0343021149896277	0.189167451117211	-0.181332014503772	0.856153846753819	   
df.mm.exp6	-0.151153718682966	0.189167451117211	-0.799047181691472	0.424505055127996	   
df.mm.exp7	0.0297269158370985	0.189167451117211	0.157146039984856	0.87517021394058	   
df.mm.exp8	-0.0790974850421885	0.189167451117211	-0.418134750851925	0.675963100373127	   
df.mm.trans1:exp2	0.235412465963543	0.176641403531038	1.3327139688526	0.183012870624813	   
df.mm.trans2:exp2	-0.038643203292124	0.13777934455511	-0.280471673144498	0.779189647090394	   
df.mm.trans1:exp3	0.0122644145566235	0.176641403531038	0.0694311430472105	0.94466414493781	   
df.mm.trans2:exp3	-0.156522189158369	0.13777934455511	-1.13603522838478	0.256288963369709	   
df.mm.trans1:exp4	0.232991973951175	0.176641403531038	1.31901111117607	0.187550517000845	   
df.mm.trans2:exp4	-0.123279564843421	0.13777934455511	-0.894760860138295	0.371189544760207	   
df.mm.trans1:exp5	0.140515386933707	0.176641403531038	0.795483868022012	0.426572860607165	   
df.mm.trans2:exp5	-0.144825108750000	0.13777934455511	-1.05113802956198	0.293519081756076	   
df.mm.trans1:exp6	0.196092099823327	0.176641403531038	1.11011402708240	0.267290259154268	   
df.mm.trans2:exp6	-0.111050815483099	0.13777934455511	-0.806004817642893	0.420484474041841	   
df.mm.trans1:exp7	0.0352418791560523	0.176641403531038	0.199510864675958	0.841914878384802	   
df.mm.trans2:exp7	-0.208656458887951	0.13777934455511	-1.51442481862360	0.130321158865911	   
df.mm.trans1:exp8	0.214025687074397	0.176641403531038	1.21163941633191	0.226015494492088	   
df.mm.trans2:exp8	-0.161874319405503	0.13777934455511	-1.17488089327319	0.240399077016908	   
df.mm.trans1:probe2	0.128561443816561	0.112253652867557	1.14527626079344	0.252444220135249	   
df.mm.trans1:probe3	-0.0658334853232029	0.112253652867557	-0.586470761899188	0.557728019543279	   
df.mm.trans1:probe4	-0.0679012964934946	0.112253652867557	-0.604891642801223	0.545426025667614	   
df.mm.trans1:probe5	0.191875326664550	0.112253652867557	1.70930140590557	0.0877908652287395	.  
df.mm.trans1:probe6	-0.0260902223168857	0.112253652867557	-0.232422033941901	0.816270894779874	   
df.mm.trans1:probe7	0.143500216060328	0.112253652867557	1.27835676073399	0.201501979785823	   
df.mm.trans1:probe8	0.0777312496824186	0.112253652867557	0.692460759153472	0.488853065180051	   
df.mm.trans1:probe9	0.0629587299056047	0.112253652867557	0.560861302036086	0.575052344678336	   
df.mm.trans1:probe10	0.135210662939803	0.112253652867557	1.20451013829663	0.228755957382418	   
df.mm.trans1:probe11	0.0355274254383044	0.112253652867557	0.316492377136463	0.751713058289976	   
df.mm.trans1:probe12	0.0692988130943524	0.112253652867557	0.617341274195461	0.537188900290555	   
df.mm.trans1:probe13	0.103242057350988	0.112253652867557	0.919721137919663	0.358001390530898	   
df.mm.trans1:probe14	0.0463032403893676	0.112253652867557	0.412487604692908	0.68009483279271	   
df.mm.trans1:probe15	0.128625015951797	0.112253652867557	1.14584258655312	0.252209916385555	   
df.mm.trans1:probe16	0.0849281317188283	0.112253652867557	0.756573434799769	0.449532747204376	   
df.mm.trans1:probe17	0.0395401412707692	0.112253652867557	0.352239239086686	0.724753514113389	   
df.mm.trans1:probe18	0.0937009575556904	0.112253652867557	0.834725242003877	0.404126703531338	   
df.mm.trans1:probe19	0.166063973184564	0.112253652867557	1.47936364601423	0.13944483170331	   
df.mm.trans1:probe20	-0.00950580612338402	0.112253652867557	-0.0846814859076302	0.932536233858202	   
df.mm.trans1:probe21	0.0349209443889780	0.112253652867557	0.311089603740377	0.75581519912461	   
df.mm.trans1:probe22	0.194248589410231	0.112253652867557	1.73044337042124	0.0839445882475264	.  
df.mm.trans2:probe2	0.0801921509298394	0.112253652867557	0.71438344215359	0.475202747683028	   
df.mm.trans2:probe3	0.0040892134867008	0.112253652867557	0.0364283333525501	0.970950095449701	   
df.mm.trans2:probe4	-0.00162987621493661	0.112253652867557	-0.0145195828670238	0.98841915002125	   
df.mm.trans2:probe5	0.022055072820593	0.112253652867557	0.196475324029008	0.84428906239486	   
df.mm.trans2:probe6	-0.0310648739989886	0.112253652867557	-0.276738201434217	0.782054078452259	   
df.mm.trans3:probe2	-0.100149044013520	0.112253652867557	-0.892167350061045	0.372576980261642	   
df.mm.trans3:probe3	-0.0884746414766756	0.112253652867557	-0.788167148387261	0.430837180891753	   
df.mm.trans3:probe4	-0.109913912315795	0.112253652867557	-0.979156664464874	0.327804620063421	   
df.mm.trans3:probe5	-0.0532046817175175	0.112253652867557	-0.473968377494953	0.635654422528118	   
df.mm.trans3:probe6	-0.109127624388591	0.112253652867557	-0.972152100184627	0.331274718325303	   
df.mm.trans3:probe7	-0.0520886116052619	0.112253652867557	-0.464025982893573	0.642757942570758