chr15.8652_chr15_5008926_5031476_+_2.R 

fitVsDatCorrelation=0.845624464971
cont.fitVsDatCorrelation=0.252657480569104

fstatistic=10823.1484930216,64,968
cont.fstatistic=3283.47680243611,64,968

residuals=-0.59457144067955,-0.0897446109712246,0.00399796175781949,0.0867249303365712,1.0252288619852
cont.residuals=-0.821284384861061,-0.193057658657238,-0.0495452173720374,0.134055863517827,1.48434987231675

predictedValues:
Include	Exclude	Both
chr15.8652_chr15_5008926_5031476_+_2.R.tl.Lung	56.9620426561399	63.6272278236974	78.1829784096259
chr15.8652_chr15_5008926_5031476_+_2.R.tl.cerebhem	58.7027272210803	57.5334690249998	81.6737845233973
chr15.8652_chr15_5008926_5031476_+_2.R.tl.cortex	77.311819908746	56.2359462231207	111.980938866968
chr15.8652_chr15_5008926_5031476_+_2.R.tl.heart	61.8470923638141	61.1579801562123	82.6737858822627
chr15.8652_chr15_5008926_5031476_+_2.R.tl.kidney	61.9584129699095	64.9764991116445	83.539333313806
chr15.8652_chr15_5008926_5031476_+_2.R.tl.liver	56.7040499353978	57.5989227865627	75.0202220591064
chr15.8652_chr15_5008926_5031476_+_2.R.tl.stomach	54.7894616815189	56.0426983005408	75.9770363709718
chr15.8652_chr15_5008926_5031476_+_2.R.tl.testicle	54.2542549062779	60.2411090313865	72.3123944724188


diffExp=-6.6651851675575,1.16925819608048,21.0758736856252,0.689112207601809,-3.01808614173504,-0.89487285116492,-1.25323661902186,-5.98685412510861
diffExpScore=6.6632468596863
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=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	68.5102524747246	69.0914921283088	67.3350517384815
cerebhem	61.0986543436283	64.8189526704886	69.781799812347
cortex	62.5368100591876	65.9283275605054	66.4338145966392
heart	67.0559026296751	59.3746677553636	66.9348548025338
kidney	65.0277068803973	66.1002843743736	74.9259888006745
liver	64.5920475746904	72.1124013215252	70.0771367379857
stomach	67.0812277986543	66.3489803412448	67.9056393888152
testicle	65.5748146198968	70.988538083413	65.6803851251722
cont.diffExp=-0.581239653584205,-3.72029832686026,-3.39151750131784,7.6812348743115,-1.07257749397621,-7.52035374683477,0.732247457409457,-5.41372346351625
cont.diffExpScore=2.10784769953128

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=-0.187840485810121
cont.tran.correlation=0.0271833821718845

tran.covariance=-0.00105073134356261
cont.tran.covariance=4.67591849002922e-05

tran.mean=59.9964821313156
cont.tran.mean=66.0150662885049

weightedLogRatios:
wLogRatio
Lung	-0.453438045055656
cerebhem	0.0817333306313944
cortex	1.33322468734805
heart	0.0461529926105613
kidney	-0.197394971714846
liver	-0.0633481069252064
stomach	-0.0907988474553943
testicle	-0.423511181117899

cont.weightedLogRatios:
wLogRatio
Lung	-0.035746061465377
cerebhem	-0.244828753304001
cortex	-0.219815673731389
heart	0.504239596805048
kidney	-0.0684321391961112
liver	-0.465116441673196
stomach	0.046103128944632
testicle	-0.334985282657306

varWeightedLogRatios=0.316532067521568
cont.varWeightedLogRatios=0.0879132594262887

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.66605887551206	0.0740342881150186	49.518391664907	1.48467576500603e-267	***
df.mm.trans1	0.34487260042887	0.0634185736678065	5.43803779371247	6.82233790938467e-08	***
df.mm.trans2	0.479608298977814	0.0555219402393937	8.63817613199195	2.32784652211623e-17	***
df.mm.exp2	-0.114254701367253	0.07026851630153	-1.62597287349819	0.104280910293794	   
df.mm.exp3	-0.177300310122396	0.07026851630153	-2.52318277735624	0.0117889801851311	*  
df.mm.exp4	-0.0131517921518909	0.07026851630153	-0.187164790778491	0.851570650782368	   
df.mm.exp5	0.038796814827704	0.07026851630153	0.552122299853644	0.580992011342342	   
df.mm.exp6	-0.0627828635337582	0.07026851630153	-0.893470743915383	0.371827205226518	   
df.mm.exp7	-0.137194061786215	0.07026851630153	-1.95242576629198	0.0511756202836055	.  
df.mm.exp8	-0.0253337916963908	0.07026851630153	-0.360528342276087	0.71853072087761	   
df.mm.trans1:exp2	0.144355759747971	0.0642853825351739	2.24554562880583	0.0249581003126733	*  
df.mm.trans2:exp2	0.0135800606650745	0.0445623137448489	0.304743168023772	0.760627274924499	   
df.mm.trans1:exp3	0.482762035884033	0.0642853825351739	7.50967042344173	1.34439624692586e-13	***
df.mm.trans2:exp3	0.0538149858997406	0.0445623137448489	1.20763446458076	0.22748287134992	   
df.mm.trans1:exp4	0.0954317510918431	0.0642853825351739	1.4845015667384	0.13800145450383	   
df.mm.trans2:exp4	-0.0264293420698467	0.0445623137448489	-0.593087293922249	0.55326126929585	   
df.mm.trans1:exp5	0.0452814589092455	0.064285382535174	0.704381884707143	0.481364404528887	   
df.mm.trans2:exp5	-0.0178126516047851	0.0445623137448489	-0.399724567866365	0.689447577100265	   
df.mm.trans1:exp6	0.0582433714495738	0.0642853825351739	0.906012675863067	0.365154600180900	   
df.mm.trans2:exp6	-0.0367547597298921	0.0445623137448489	-0.82479468952935	0.409691357934917	   
df.mm.trans1:exp7	0.0983068044662862	0.0642853825351739	1.52922485002710	0.126535408481568	   
df.mm.trans2:exp7	0.0102664424731211	0.0445623137448489	0.230383963721090	0.817842099180569	   
df.mm.trans1:exp8	-0.0233699152056327	0.0642853825351739	-0.363533890349735	0.716285478517594	   
df.mm.trans2:exp8	-0.029352703934302	0.0445623137448489	-0.658688956376171	0.510252165078275	   
df.mm.trans1:probe2	0.075525230376143	0.0470520821292004	1.60514108958575	0.108788925812999	   
df.mm.trans1:probe3	0.245434214584473	0.0470520821292004	5.21622430885278	2.23338717884361e-07	***
df.mm.trans1:probe4	0.0342199188819358	0.0470520821292004	0.72727746219543	0.467231854538955	   
df.mm.trans1:probe5	-0.273571127642512	0.0470520821292004	-5.81421937697278	8.26752460093945e-09	***
df.mm.trans1:probe6	0.0531197736114672	0.0470520821292004	1.12895691768975	0.259195708781449	   
df.mm.trans1:probe7	0.095007792629713	0.0470520821292004	2.01920485407704	0.0437411592365678	*  
df.mm.trans1:probe8	-0.138513038661995	0.0470520821292004	-2.94382378832144	0.00331929282095368	** 
df.mm.trans1:probe9	0.126182848398174	0.0470520821292005	2.68176970472186	0.0074480483992306	** 
df.mm.trans1:probe10	0.0799018191742615	0.0470520821292004	1.69815692650665	0.0897995277048061	.  
df.mm.trans1:probe11	0.0956907686051255	0.0470520821292004	2.03372017294300	0.0422521167346253	*  
df.mm.trans1:probe12	0.0905220611624769	0.0470520821292004	1.92386940314165	0.0546639176854896	.  
df.mm.trans1:probe13	0.0928916613400996	0.0470520821292004	1.97423062140009	0.0486394613504208	*  
df.mm.trans1:probe14	0.157670965521955	0.0470520821292004	3.35098806231370	0.00083637166521839	***
df.mm.trans1:probe15	0.000435480250525501	0.0470520821292004	0.00925528118670104	0.99261736648687	   
df.mm.trans1:probe16	0.183849483261200	0.0470520821292004	3.90736126737955	9.97986643005464e-05	***
df.mm.trans1:probe17	0.0704103580928436	0.0470520821292004	1.49643448082709	0.134866429701469	   
df.mm.trans1:probe18	-0.00225932677985814	0.0470520821292004	-0.0480175728175907	0.961712142534447	   
df.mm.trans1:probe19	-0.0263882758568873	0.0470520821292004	-0.56083120369525	0.575042400684618	   
df.mm.trans1:probe20	-0.0344283409613383	0.0470520821292004	-0.73170706594453	0.464524481071869	   
df.mm.trans1:probe21	0.0745471731464821	0.0470520821292004	1.58435439566272	0.113439713641641	   
df.mm.trans1:probe22	0.163535675919310	0.0470520821292004	3.47563101395294	0.000532272816587692	***
df.mm.trans2:probe2	0.0371106299806600	0.0470520821292004	0.788713874101422	0.430472373677873	   
df.mm.trans2:probe3	0.00383924586175586	0.0470520821292004	0.0815956635290584	0.934985117331196	   
df.mm.trans2:probe4	0.0513512436432075	0.0470520821292004	1.09137027139845	0.275381555306586	   
df.mm.trans2:probe5	-0.0662279817465526	0.0470520821292004	-1.40754624980669	0.159586499128706	   
df.mm.trans2:probe6	0.128787470510973	0.0470520821292004	2.73712585465048	0.00631143258468512	** 
df.mm.trans3:probe2	-0.329431459158759	0.0470520821292004	-7.00142149404084	4.73320210896932e-12	***
df.mm.trans3:probe3	-0.137142270939883	0.0470520821292004	-2.9146908007876	0.00364242375762102	** 
df.mm.trans3:probe4	-0.20797204692692	0.0470520821292004	-4.42003918882589	1.09836239080610e-05	***
df.mm.trans3:probe5	-0.0162231056436904	0.0470520821292004	-0.344790387790775	0.730326857402415	   
df.mm.trans3:probe6	0.139822563228583	0.0470520821292004	2.97165517233953	0.0030352418747836	** 
df.mm.trans3:probe7	-0.270419135375469	0.0470520821292004	-5.74722994474346	1.21490972589554e-08	***
df.mm.trans3:probe8	-0.130470603666427	0.0470520821292004	-2.77289755867057	0.00566267371964611	** 
df.mm.trans3:probe9	0.250830475586396	0.0470520821292004	5.33091128459821	1.21628236542421e-07	***
df.mm.trans3:probe10	-0.0691954772601931	0.0470520821292004	-1.47061456430746	0.141720351080157	   
df.mm.trans3:probe11	-0.265024276004896	0.0470520821292004	-5.63257275793162	2.32648116830463e-08	***
df.mm.trans3:probe12	-0.481968759806668	0.0470520821292004	-10.2433035478266	1.89559690544602e-23	***
df.mm.trans3:probe13	-0.521521636574908	0.0470520821292004	-11.0839226018279	5.8126316052222e-27	***
df.mm.trans3:probe14	0.478468210489038	0.0470520821292004	10.1689062170556	3.78678929067609e-23	***
df.mm.trans3:probe15	-0.412069754794451	0.0470520821292004	-8.75773687682827	8.763308607412e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.16616889190016	0.13419860818704	31.0447995562930	2.08756609075989e-147	***
df.mm.trans1	0.00491382951488415	0.114955982371369	0.04274531358455	0.96591337275986	   
df.mm.trans2	0.075151802418585	0.100642111833303	0.746723225989751	0.455411968382425	   
df.mm.exp2	-0.214019421019245	0.127372563809670	-1.68026311646714	0.0932287779033056	.  
df.mm.exp3	-0.124616707704348	0.127372563809669	-0.978363816956377	0.328138859494953	   
df.mm.exp4	-0.167059599359812	0.127372563809669	-1.31158229341639	0.189972099189137	   
df.mm.exp5	-0.203248397620147	0.127372563809670	-1.59569998075768	0.110882194403050	   
df.mm.exp6	-0.0560133320085699	0.127372563809670	-0.439759790752658	0.660209254573522	   
df.mm.exp7	-0.070020527478059	0.127372563809670	-0.54973006261136	0.582631339399107	   
df.mm.exp8	0.00817572343091099	0.127372563809670	0.0641874763793545	0.948834201484471	   
df.mm.trans1:exp2	0.0995258578859644	0.116527207630997	0.854099741247813	0.393261039653843	   
df.mm.trans2:exp2	0.150185861950930	0.0807760921920689	1.85928605699096	0.0632898555823957	.  
df.mm.trans1:exp3	0.0333886467918433	0.116527207630997	0.286530909567268	0.774532836472466	   
df.mm.trans2:exp3	0.077753314465578	0.0807760921920688	0.962578311918045	0.335999574503446	   
df.mm.trans1:exp4	0.145602833234286	0.116527207630997	1.24951791254933	0.211777740031166	   
df.mm.trans2:exp4	0.0154956668542156	0.0807760921920689	0.191834816883318	0.847911830147766	   
df.mm.trans1:exp5	0.151078431158917	0.116527207630997	1.29650777900156	0.195109587015418	   
df.mm.trans2:exp5	0.158989847507095	0.0807760921920689	1.96827852391089	0.0493210439768424	*  
df.mm.trans1:exp6	-0.00287877257201850	0.116527207630997	-0.0247047245921711	0.980295578231704	   
df.mm.trans2:exp6	0.0988077640487819	0.0807760921920689	1.22323030698040	0.221540416991472	   
df.mm.trans1:exp7	0.0489413625312546	0.116527207630997	0.419999444990013	0.674579064024224	   
df.mm.trans2:exp7	0.0295173211138304	0.0807760921920689	0.365421504219891	0.714876622934866	   
df.mm.trans1:exp8	-0.0519674299079689	0.116527207630997	-0.445968207463897	0.655719942963406	   
df.mm.trans2:exp8	0.0189111059878063	0.0807760921920689	0.23411761419257	0.81494317718384	   
df.mm.trans1:probe2	0.0708187353630777	0.0852891828206839	0.830336661941883	0.406553201740828	   
df.mm.trans1:probe3	0.123645647701666	0.0852891828206839	1.44972250421984	0.147459817913606	   
df.mm.trans1:probe4	0.248541087279804	0.0852891828206839	2.91409858859064	0.00364927974845287	** 
df.mm.trans1:probe5	0.106872842145394	0.0852891828206839	1.25306444042369	0.210484850962769	   
df.mm.trans1:probe6	0.102731830027108	0.0852891828206839	1.20451183408682	0.228686228445123	   
df.mm.trans1:probe7	0.0844113036490696	0.0852891828206839	0.989707027989	0.322564659162239	   
df.mm.trans1:probe8	0.118287045373079	0.0852891828206839	1.38689387635207	0.16579339697433	   
df.mm.trans1:probe9	0.126116761784872	0.0852891828206839	1.47869586287426	0.139546936104611	   
df.mm.trans1:probe10	0.0751690468270091	0.0852891828206839	0.881343264655826	0.37835083453841	   
df.mm.trans1:probe11	0.130928747072589	0.0852891828206839	1.53511550635747	0.125082224629067	   
df.mm.trans1:probe12	0.0928718526387207	0.0852891828206839	1.08890541059561	0.276466616616554	   
df.mm.trans1:probe13	-0.00105617593909243	0.0852891828206839	-0.0123834688545790	0.99012222558452	   
df.mm.trans1:probe14	0.140521175619442	0.0852891828206839	1.64758496883339	0.099762402253897	.  
df.mm.trans1:probe15	0.0510167478329887	0.0852891828206839	0.598161995997181	0.549871769241973	   
df.mm.trans1:probe16	0.277814690181545	0.0852891828206839	3.25732620472676	0.00116376666760531	** 
df.mm.trans1:probe17	0.119459589905097	0.0852891828206839	1.4006417455805	0.161641695653882	   
df.mm.trans1:probe18	0.0733515398691841	0.0852891828206839	0.860033329471593	0.389983616121884	   
df.mm.trans1:probe19	0.0297581235279942	0.0852891828206839	0.348908531467105	0.727233857117893	   
df.mm.trans1:probe20	0.0432001330185885	0.0852891828206839	0.506513623297512	0.612611473095154	   
df.mm.trans1:probe21	-0.00740326254234315	0.0852891828206839	-0.0868018932472144	0.930846934235863	   
df.mm.trans1:probe22	0.0612678399076643	0.0852891828206839	0.718354167333	0.472712283562698	   
df.mm.trans2:probe2	-0.0189506002893238	0.0852891828206839	-0.222192306956047	0.824211051371458	   
df.mm.trans2:probe3	-0.0151789669640370	0.0852891828206839	-0.177970599108096	0.858783301358869	   
df.mm.trans2:probe4	-0.0482563420171383	0.0852891828206839	-0.565796744923619	0.57166308649677	   
df.mm.trans2:probe5	0.0092252703560048	0.0852891828206839	0.108164600139275	0.913887540446874	   
df.mm.trans2:probe6	-0.050510359721282	0.0852891828206839	-0.592224688416554	0.553838439802891	   
df.mm.trans3:probe2	-0.0216108044711041	0.0852891828206839	-0.253382712278294	0.80002628837665	   
df.mm.trans3:probe3	-0.0301647809635256	0.0852891828206839	-0.353676515191211	0.723658339178491	   
df.mm.trans3:probe4	-0.115081786263002	0.0852891828206839	-1.34931280212821	0.177552018447456	   
df.mm.trans3:probe5	-0.06765310188517	0.0852891828206839	-0.793220190975533	0.427843925043112	   
df.mm.trans3:probe6	-0.0244370575783934	0.0852891828206839	-0.286520010747096	0.774541180248111	   
df.mm.trans3:probe7	-0.0103581348657073	0.0852891828206839	-0.121447228395713	0.90336201612852	   
df.mm.trans3:probe8	0.101907356139758	0.0852891828206839	1.19484502922267	0.232440248768421	   
df.mm.trans3:probe9	-0.129028342941300	0.0852891828206839	-1.5128336170435	0.130648328737516	   
df.mm.trans3:probe10	-0.0975299050058614	0.0852891828206839	-1.14352021886425	0.253105378269636	   
df.mm.trans3:probe11	0.0427194350081785	0.0852891828206839	0.500877527435032	0.61657118777479	   
df.mm.trans3:probe12	-0.0467079902809313	0.0852891828206839	-0.547642605266044	0.584063574993781	   
df.mm.trans3:probe13	-0.0872705218595172	0.0852891828206839	-1.02323083623628	0.306454356580739	   
df.mm.trans3:probe14	0.0187573941465571	0.0852891828206839	0.219927000426227	0.825974377644196	   
df.mm.trans3:probe15	-0.0818456775446578	0.0852891828206839	-0.959625533248854	0.33748333817004