fitVsDatCorrelation=0.874216451258836
cont.fitVsDatCorrelation=0.238403849795332

fstatistic=16879.9905533910,67,1037
cont.fstatistic=4207.57785757574,67,1037

residuals=-0.426989936017409,-0.0774501462209814,-0.00414574874485204,0.0730850103295226,0.479425478860232
cont.residuals=-0.446241776821457,-0.168278999264267,-0.0560351156313288,0.119675620336679,0.971541744083948

predictedValues:
Include	Exclude	Both
Lung	50.2123372807311	51.5461726029923	71.9754245258964
cerebhem	51.40435138458	53.2284363293118	58.1810756139823
cortex	49.5253871695453	50.6304380240628	60.5882029117179
heart	51.0308792353037	53.0599802156827	63.010422367856
kidney	50.9299826984354	51.6753307768863	65.023137764356
liver	52.4582156914239	50.8980878729171	64.6735900205754
stomach	50.1976507053103	53.1150976034057	66.8678796247834
testicle	50.1393486291863	51.1209937435053	61.3575657161857


diffExp=-1.33383532226117,-1.82408494473182,-1.10505085451751,-2.02910098037901,-0.745348078450895,1.56012781850681,-2.91744689809538,-0.981645114319022
diffExpScore=1.20433472397916
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	56.605979059252	54.9563673834274	53.9035989706836
cerebhem	56.0458654896756	55.8179289190534	53.7914637028837
cortex	54.071499937556	56.6289859018178	55.0521910441178
heart	55.1966567407919	54.6427625178587	60.1347711342324
kidney	55.2981922727333	56.7220967639234	56.2564008075172
liver	55.6486788347284	59.3226858486004	53.4264179939167
stomach	52.4784453111424	54.8274793341193	59.1610971459052
testicle	53.6846676173281	61.2936557770516	56.1181482170554
cont.diffExp=1.64961167582459,0.227936570622205,-2.55748596426182,0.553894222933181,-1.4239044911901,-3.67400701387196,-2.34903402297691,-7.6089881597235
cont.diffExpScore=1.23871526298424

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=0.144488798706865
cont.tran.correlation=-0.158538101062960

tran.covariance=5.60995525835235e-05
cont.tran.covariance=-0.000152902756529351

tran.mean=51.323293122705
cont.tran.mean=55.8276217318162

weightedLogRatios:
wLogRatio
Lung	-0.103017071564220
cerebhem	-0.137985874187065
cortex	-0.086361690833
heart	-0.154093737756121
kidney	-0.057209918202553
liver	0.119103499853670
stomach	-0.222820462635127
testicle	-0.0760927232564433

cont.weightedLogRatios:
wLogRatio
Lung	0.118931016445188
cerebhem	0.0163993773646352
cortex	-0.185474831384541
heart	0.0404015480359122
kidney	-0.102341602812129
liver	-0.258996035622511
stomach	-0.174380788917825
testicle	-0.536743772162675

varWeightedLogRatios=0.00988959913920967
cont.varWeightedLogRatios=0.0428722898854324

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.29062394342325	0.056691804579042	58.0440853463278	0	***
df.mm.trans1	0.634172613887876	0.048422025747994	13.0967799073162	2.22348631307002e-36	***
df.mm.trans2	0.640401978697908	0.0422511972255251	15.1570137830562	5.02932713019487e-47	***
df.mm.exp2	0.268341470841115	0.053144038109477	5.04932407071382	5.23515528895086e-07	***
df.mm.exp3	0.140524159181891	0.053144038109477	2.64421305156395	0.0083117577427164	** 
df.mm.exp4	0.178139767676263	0.053144038109477	3.35201791232525	0.000831213929277574	***
df.mm.exp5	0.118275149924227	0.053144038109477	2.22555820241924	0.0262581823822202	*  
df.mm.exp6	0.138075405033514	0.053144038109477	2.59813536843169	0.00950605926722749	** 
df.mm.exp7	0.103296726325869	0.053144038109477	1.94371240877626	0.0522003756032276	.  
df.mm.exp8	0.1498688810535	0.053144038109477	2.82005068460867	0.00489310113100441	** 
df.mm.trans1:exp2	-0.24487940374305	0.048422025747994	-5.05719039962294	5.02881805608082e-07	***
df.mm.trans2:exp2	-0.236226661230694	0.0326461230994891	-7.23597900157372	8.97726480467551e-13	***
df.mm.trans1:exp3	-0.154299508036548	0.048422025747994	-3.18655623454456	0.00148278822527937	** 
df.mm.trans2:exp3	-0.158449183166664	0.0326461230994891	-4.85353751450945	1.39900227472681e-06	***
df.mm.trans1:exp4	-0.161969602043435	0.048422025747994	-3.34495716652549	0.000852431380573518	***
df.mm.trans2:exp4	-0.149194753235498	0.0326461230994891	-4.57006036462052	5.46223094523704e-06	***
df.mm.trans1:exp5	-0.104084107807786	0.048422025747994	-2.14951989719468	0.0318240289449214	*  
df.mm.trans2:exp5	-0.115772604677903	0.0326461230994892	-3.54628953413811	0.000408151362515254	***
df.mm.trans1:exp6	-0.0943192029851028	0.048422025747994	-1.94785743735660	0.0517015681231019	.  
df.mm.trans2:exp6	-0.150728009931942	0.0326461230994892	-4.61702633028119	4.38063619806235e-06	***
df.mm.trans1:exp7	-0.103589258489654	0.048422025747994	-2.13930038839702	0.0326443207173785	*  
df.mm.trans2:exp7	-0.0733134759109726	0.0326461230994892	-2.24570236678792	0.0249330307010224	*  
df.mm.trans1:exp8	-0.151323538516472	0.048422025747994	-3.12509722959580	0.00182685641563625	** 
df.mm.trans2:exp8	-0.158151593148089	0.0326461230994891	-4.84442188330056	1.46328748377762e-06	***
df.mm.trans1:probe2	-0.163194676747032	0.0363165193109955	-4.49367615187786	7.78722761885945e-06	***
df.mm.trans1:probe3	-0.0862398403949663	0.0363165193109955	-2.37467251903889	0.0177456801208841	*  
df.mm.trans1:probe4	-0.112000299988711	0.0363165193109955	-3.08400425243398	0.00209638449144577	** 
df.mm.trans1:probe5	-0.123354529244746	0.0363165193109955	-3.39665065884764	0.00070804729278773	***
df.mm.trans1:probe6	0.604256255196953	0.0363165193109955	16.63860597494	2.79903511349589e-55	***
df.mm.trans1:probe7	-0.123014601872433	0.0363165193109955	-3.38729052801015	0.000732375819541087	***
df.mm.trans1:probe8	-0.122070327263345	0.0363165193109955	-3.36128928595824	0.000804098299721824	***
df.mm.trans1:probe9	-0.0585319419012147	0.0363165193109955	-1.6117167341941	0.107327883683830	   
df.mm.trans1:probe10	-0.0275904268286058	0.0363165193109955	-0.759721122840433	0.447593970724892	   
df.mm.trans1:probe11	-0.00596461437099747	0.0363165193109955	-0.164239703698465	0.86957445220575	   
df.mm.trans1:probe12	-0.0139445039126972	0.0363165193109955	-0.383971376587161	0.701078376908834	   
df.mm.trans1:probe13	-0.000643042711534202	0.0363165193109955	-0.0177066173668110	0.985876307643685	   
df.mm.trans1:probe14	0.0101953138751258	0.0363165193109955	0.280734885075812	0.778969748059398	   
df.mm.trans1:probe15	-0.0565830608107925	0.0363165193109955	-1.55805297105279	0.119525807548227	   
df.mm.trans1:probe16	0.0339832717198902	0.0363165193109955	0.935752444469564	0.3496185173133	   
df.mm.trans1:probe17	-0.0554676609032348	0.0363165193109955	-1.52733967779894	0.126981505592548	   
df.mm.trans1:probe18	-0.0749192981558784	0.0363165193109955	-2.06295370749353	0.039365547366916	*  
df.mm.trans1:probe19	-0.0531536217686161	0.0363165193109955	-1.46362104015081	0.143600561346262	   
df.mm.trans1:probe20	0.0589049726231088	0.0363165193109955	1.62198838822294	0.105109802390491	   
df.mm.trans1:probe21	0.0317245557601792	0.0363165193109955	0.873557167979312	0.382561701875307	   
df.mm.trans1:probe22	-0.00382385205046727	0.0363165193109955	-0.105292360694642	0.916164186837299	   
df.mm.trans2:probe2	0.035306808709021	0.0363165193109955	0.972196933485616	0.331179295140516	   
df.mm.trans2:probe3	-0.00870999993152712	0.0363165193109955	-0.239835757852763	0.81050498298085	   
df.mm.trans2:probe4	0.0444400939670235	0.0363165193109955	1.22368814000213	0.22134786079454	   
df.mm.trans2:probe5	0.132861680574726	0.0363165193109955	3.65843652132433	0.000266524201493451	***
df.mm.trans2:probe6	0.0709503594177305	0.0363165193109955	1.95366628641223	0.0510092645618343	.  
df.mm.trans3:probe2	0.0493035568911641	0.0363165193109955	1.35760689147973	0.174883805721886	   
df.mm.trans3:probe3	-0.520667027551684	0.0363165193109955	-14.3369198764057	1.15504493254362e-42	***
df.mm.trans3:probe4	-0.544898319669025	0.0363165193109955	-15.0041449457973	3.35893151146633e-46	***
df.mm.trans3:probe5	0.10329327192177	0.0363165193109955	2.84425032688901	0.00453911061438155	** 
df.mm.trans3:probe6	0.0462842512174406	0.0363165193109955	1.27446826115374	0.202783024304020	   
df.mm.trans3:probe7	-0.313248889879143	0.0363165193109955	-8.62552072230945	2.36058185818803e-17	***
df.mm.trans3:probe8	-0.322657803892089	0.0363165193109955	-8.88460155360753	2.78025625667638e-18	***
df.mm.trans3:probe9	-0.182781777546587	0.0363165193109955	-5.03302026224871	5.68916016086838e-07	***
df.mm.trans3:probe10	-0.587530004388768	0.0363165193109955	-16.1780373101693	1.15366763641999e-52	***
df.mm.trans3:probe11	-0.509915819308701	0.0363165193109955	-14.0408780627364	3.96050741823451e-41	***
df.mm.trans3:probe12	-0.613678833006037	0.0363165193109955	-16.8980630481356	9.02300422688101e-57	***
df.mm.trans3:probe13	-0.285142069546035	0.0363165193109955	-7.85158035394935	1.02072307899390e-14	***
df.mm.trans3:probe14	-0.408262816090383	0.0363165193109955	-11.2417936475199	9.49831868019615e-28	***
df.mm.trans3:probe15	-0.308249871263122	0.0363165193109955	-8.48786935288133	7.19713397135326e-17	***
df.mm.trans3:probe16	-0.231300134288428	0.0363165193109955	-6.36900613485823	2.85429963003132e-10	***
df.mm.trans3:probe17	-0.623017338592549	0.0363165193109955	-17.1552051356397	2.91247511576709e-58	***
df.mm.trans3:probe18	0.052741201822814	0.0363165193109955	1.45226477711606	0.146730483650081	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02466743855331	0.113394503464564	35.4926148586295	2.95270073699761e-181	***
df.mm.trans1	0.000800395346818776	0.0968533566220605	0.00826399181953049	0.993407953041563	   
df.mm.trans2	-0.0171785920129006	0.0845105137461607	-0.203271655222673	0.838962587094465	   
df.mm.exp2	0.00769379947979323	0.106298288761015	0.0723793352599574	0.942313986821174	   
df.mm.exp3	-0.036910367025034	0.106298288761015	-0.347233877941515	0.728486078317285	   
df.mm.exp4	-0.140325991083471	0.106298288761015	-1.32011524097965	0.18708785797485	   
df.mm.exp5	-0.0344726796118662	0.106298288761015	-0.324301360009374	0.74577529125267	   
df.mm.exp6	0.0682878678706075	0.106298288761015	0.642417377236767	0.520744216121683	   
df.mm.exp7	-0.171127067170593	0.106298288761015	-1.60987603060412	0.107729263960635	   
df.mm.exp8	0.0158876067290560	0.106298288761015	0.149462488194663	0.881217768854402	   
df.mm.trans1:exp2	-0.017638034081748	0.0968533566220605	-0.182110715590115	0.85553143141242	   
df.mm.trans2:exp2	0.00786177500649604	0.0652985197136965	0.120397446082488	0.904191636543389	   
df.mm.trans1:exp3	-0.0088970060237186	0.0968533566220605	-0.0918605852602129	0.926826549093659	   
df.mm.trans2:exp3	0.0668917894668934	0.0652985197136965	1.02439978364261	0.305885384519174	   
df.mm.trans1:exp4	0.115113759523116	0.0968533566220605	1.18853660356151	0.234894177188891	   
df.mm.trans2:exp4	0.134603213507856	0.0652985197136965	2.06135168297885	0.0395183360775688	*  
df.mm.trans1:exp5	0.0110982814243253	0.0968533566220605	0.114588505875257	0.908793472825449	   
df.mm.trans2:exp5	0.0660969779903005	0.0652985197136965	1.01222781588472	0.311665254685367	   
df.mm.trans1:exp6	-0.085344147765899	0.0968533566220605	-0.88116871466755	0.378430690295483	   
df.mm.trans2:exp6	0.00816437557397688	0.0652985197136965	0.125031556760763	0.900522800840706	   
df.mm.trans1:exp7	0.0954149702418388	0.0968533566220605	0.985148822607826	0.324780766484677	   
df.mm.trans2:exp7	0.168779033058510	0.0652985197136965	2.5847298499036	0.0098811876257056	** 
df.mm.trans1:exp8	-0.0688747819321929	0.0968533566220605	-0.711124367129112	0.477167087935302	   
df.mm.trans2:exp8	0.093249186106356	0.0652985197136965	1.42804441073412	0.153580162145685	   
df.mm.trans1:probe2	0.0293783692733178	0.0726400174665454	0.404437805743206	0.685974204349251	   
df.mm.trans1:probe3	-0.00679719951025801	0.0726400174665454	-0.0935737593040708	0.925465830554677	   
df.mm.trans1:probe4	-0.00740137795552571	0.0726400174665454	-0.101891191847998	0.918862739198347	   
df.mm.trans1:probe5	-0.01822379398412	0.0726400174665454	-0.250878160822484	0.801958009030992	   
df.mm.trans1:probe6	0.0276723870721087	0.0726400174665454	0.380952373598386	0.703316617204127	   
df.mm.trans1:probe7	-0.00610178496613115	0.0726400174665454	-0.084000323498564	0.9330723871041	   
df.mm.trans1:probe8	0.0359597329752958	0.0726400174665454	0.495040257828368	0.620676574104854	   
df.mm.trans1:probe9	0.0531494528785968	0.0726400174665454	0.731682820740991	0.464527494800854	   
df.mm.trans1:probe10	0.0756922181355499	0.0726400174665454	1.04201817091261	0.297646118586099	   
df.mm.trans1:probe11	0.102576172652214	0.0726400174665454	1.41211657471662	0.158215643525004	   
df.mm.trans1:probe12	0.00266490528959851	0.0726400174665454	0.0366864626763869	0.970742063277049	   
df.mm.trans1:probe13	-0.0267738675921021	0.0726400174665454	-0.368582890339101	0.712513882122826	   
df.mm.trans1:probe14	0.025889091860601	0.0726400174665454	0.356402610620576	0.721611573950099	   
df.mm.trans1:probe15	-0.0247275658727544	0.0726400174665454	-0.340412443927932	0.733614893958302	   
df.mm.trans1:probe16	0.0189316901693065	0.0726400174665454	0.260623425345754	0.794434665899615	   
df.mm.trans1:probe17	-0.0154245957511397	0.0726400174665454	-0.212342952123374	0.83188121833444	   
df.mm.trans1:probe18	0.0880253878612946	0.0726400174665454	1.21180295560687	0.225863858200564	   
df.mm.trans1:probe19	-0.0261270715659011	0.0726400174665454	-0.359678762163487	0.719160608054672	   
df.mm.trans1:probe20	0.0784624705484929	0.0726400174665454	1.08015489650218	0.280324357803609	   
df.mm.trans1:probe21	-0.037897113710438	0.0726400174665454	-0.521711241711797	0.601982802981783	   
df.mm.trans1:probe22	0.0569438049546613	0.0726400174665454	0.783917831254474	0.433267340413618	   
df.mm.trans2:probe2	0.0366567313689541	0.0726400174665454	0.504635497724604	0.613922084113958	   
df.mm.trans2:probe3	-0.00452536217895955	0.0726400174665454	-0.062298473166581	0.950337149450292	   
df.mm.trans2:probe4	0.0232492825831186	0.0726400174665454	0.32006163260941	0.74898608430493	   
df.mm.trans2:probe5	0.019627025229392	0.0726400174665454	0.270195766932894	0.787063410893078	   
df.mm.trans2:probe6	-0.097790791906431	0.0726400174665454	-1.34623855165603	0.178519793090401	   
df.mm.trans3:probe2	0.0128910323358290	0.0726400174665454	0.177464609528295	0.859178118193537	   
df.mm.trans3:probe3	0.00195352860605863	0.0726400174665454	0.0268932838150587	0.978550024597581	   
df.mm.trans3:probe4	-0.00186015705784321	0.0726400174665454	-0.0256078828546526	0.979575025324003	   
df.mm.trans3:probe5	-0.00382212667725907	0.0726400174665454	-0.052617370019485	0.958046910787302	   
df.mm.trans3:probe6	-0.144609233289652	0.0726400174665454	-1.99076539809826	0.0467687936649547	*  
df.mm.trans3:probe7	-0.0942559856396832	0.0726400174665454	-1.29757658281254	0.194721440503824	   
df.mm.trans3:probe8	-0.0344888286131027	0.0726400174665454	-0.47479102863634	0.635035899765619	   
df.mm.trans3:probe9	-0.064678494538288	0.0726400174665454	-0.890397563134892	0.373458972677726	   
df.mm.trans3:probe10	0.124279208183929	0.0726400174665454	1.71089177175882	0.0874001604820953	.  
df.mm.trans3:probe11	0.0357082431757956	0.0726400174665454	0.491578119350551	0.623121643781276	   
df.mm.trans3:probe12	0.0212944355103145	0.0726400174665454	0.293150198100127	0.769466000245019	   
df.mm.trans3:probe13	-0.0144275097143857	0.0726400174665454	-0.198616550732939	0.842601637302569	   
df.mm.trans3:probe14	-0.0102861913896488	0.0726400174665454	-0.141605023627453	0.887419502581733	   
df.mm.trans3:probe15	-0.0650502434849679	0.0726400174665454	-0.895515251148268	0.370719532896132	   
df.mm.trans3:probe16	0.0194125466940145	0.0726400174665454	0.267243144633810	0.789335096897336	   
df.mm.trans3:probe17	-0.0880163950611485	0.0726400174665454	-1.21167915607516	0.225911242207072	   
df.mm.trans3:probe18	-0.073699738061648	0.0726400174665454	-1.01458866107226	0.310538605050941