fitVsDatCorrelation=0.751784204488347
cont.fitVsDatCorrelation=0.219844916260224

fstatistic=9566.22143469733,53,715
cont.fstatistic=4363.51310044909,53,715

residuals=-0.631796009548603,-0.0850269635462234,-0.00595164311366421,0.0689375428579011,1.71324618822956
cont.residuals=-0.541315116453945,-0.153387886245972,-0.0190483788967485,0.131289064613713,1.524683211539

predictedValues:
Include	Exclude	Both
Lung	56.2130818378253	75.7793251765736	75.1598278902454
cerebhem	57.2599632052451	78.826688849247	58.4288284917105
cortex	56.6841193444109	94.4413765457853	71.1983124281692
heart	59.5550486699802	74.540903847301	64.4965007888465
kidney	56.830992841892	74.7976727849209	76.208173944222
liver	56.7027965907735	67.0355546897426	63.2648809978793
stomach	59.4117990588003	82.8732850046824	63.5346584672452
testicle	58.2555859498159	77.7075699081045	63.9494228062425


diffExp=-19.5662433387482,-21.5667256440018,-37.7572572013745,-14.9858551773209,-17.9666799430289,-10.3327580989692,-23.4614859458821,-19.4519839582886
diffExpScore=0.993979131282761
diffExp1.5=0,0,-1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,-1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=-1,-1,-1,0,-1,0,-1,-1
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,-1,-1,-1,0,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	61.2386448531231	64.350484905418	63.0169445637414
cerebhem	62.0547992278811	67.3730789749113	65.0483801361772
cortex	64.6177398952041	58.3092956896614	65.6730102887439
heart	61.4967958412207	64.7024730786556	64.8796737785451
kidney	61.3257696573903	64.2300391209183	64.081479049637
liver	62.565842958011	63.7615626827845	62.7451878789955
stomach	63.4775445191518	62.4388120468433	67.6573982710168
testicle	59.8174401758057	67.3053283633991	59.370980588401
cont.diffExp=-3.11184005229494,-5.3182797470302,6.30844420554278,-3.20567723743491,-2.904269463528,-1.19571972477351,1.03873247230851,-7.48788818759338
cont.diffExpScore=1.81144521635323

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=0.0261296185361211
cont.tran.correlation=-0.84469057191736

tran.covariance=0.000119712024006342
cont.tran.covariance=-0.000917371458827609

tran.mean=67.9322352690688
cont.tran.mean=63.0666032493987

weightedLogRatios:
wLogRatio
Lung	-1.2480139238009
cerebhem	-1.34490381486427
cortex	-2.19137854103577
heart	-0.942480684805572
kidney	-1.14756186536427
liver	-0.689941414132849
stomach	-1.41478508934869
testicle	-1.21263645833801

cont.weightedLogRatios:
wLogRatio
Lung	-0.205181616402372
cerebhem	-0.342817956032255
cortex	0.422941811894291
heart	-0.210594603435563
kidney	-0.191530615895340
liver	-0.0784821846965066
stomach	0.0683466577783834
testicle	-0.489491556417680

varWeightedLogRatios=0.191126859877991
cont.varWeightedLogRatios=0.0768825466097127

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12731715269679	0.0821846875652061	50.2200260775115	1.16040423681806e-236	***
df.mm.trans1	-0.324544530034840	0.0729815931016646	-4.44693677188909	1.00963064899985e-05	***
df.mm.trans2	0.342706633791632	0.0663862494847149	5.16231352805281	3.16451888919677e-07	***
df.mm.exp2	0.309685762375028	0.0894985294943986	3.46023296834626	0.000571748762510376	***
df.mm.exp3	0.282646142482746	0.0894985294943987	3.15810934637128	0.00165474968979543	** 
df.mm.exp4	0.194280002597639	0.0894985294943987	2.17076195212568	0.0302775153717126	*  
df.mm.exp5	-0.0159582476272667	0.0894985294943986	-0.178307372393928	0.858532096596265	   
df.mm.exp6	0.058358186511314	0.0894985294943987	0.652057490117381	0.514573638728832	   
df.mm.exp7	0.312861920278742	0.0894985294943987	3.49572134923538	0.000501963297647703	***
df.mm.exp8	0.222342040531080	0.0894985294943986	2.48430942706154	0.0132081423883320	*  
df.mm.trans1:exp2	-0.291233608023439	0.0849830526008883	-3.42696101293489	0.00064530441679591	***
df.mm.trans2:exp2	-0.270259632391211	0.0715621344700786	-3.77657310521127	0.000172217528725412	***
df.mm.trans1:exp3	-0.27430155581579	0.0849830526008883	-3.22772067395615	0.00130475450831559	** 
df.mm.trans2:exp3	-0.0624923550125343	0.0715621344700786	-0.873260076369906	0.382814517995559	   
df.mm.trans1:exp4	-0.136528432764944	0.0849830526008883	-1.60653716931224	0.108597442431039	   
df.mm.trans2:exp4	-0.210757483469396	0.0715621344700786	-2.94509778153022	0.0033333396976561	** 
df.mm.trans1:exp5	0.0268905704398762	0.0849830526008883	0.316422740968887	0.751774013002328	   
df.mm.trans2:exp5	0.00291951910517111	0.0715621344700786	0.040796981906567	0.967469134477097	   
df.mm.trans1:exp6	-0.0496841571326045	0.0849830526008883	-0.584636061097259	0.558976881768861	   
df.mm.trans2:exp6	-0.180960541380674	0.0715621344700786	-2.52871917139835	0.0116619644758369	*  
df.mm.trans1:exp7	-0.257518579009665	0.0849830526008883	-3.03023451298069	0.00253177333261182	** 
df.mm.trans2:exp7	-0.223374666263788	0.0715621344700786	-3.12140866001118	0.00187235351837881	** 
df.mm.trans1:exp8	-0.186651559276107	0.0849830526008883	-2.19633860591819	0.0283880665702525	*  
df.mm.trans2:exp8	-0.197214863605703	0.0715621344700786	-2.75585496528422	0.00600297998927359	** 
df.mm.trans1:probe2	0.646360931963621	0.0465471349151546	13.8861593337978	5.46273266252292e-39	***
df.mm.trans1:probe3	0.395558548175156	0.0465471349151546	8.49802139049319	1.11769868264389e-16	***
df.mm.trans1:probe4	0.235777791503894	0.0465471349151546	5.06535562142903	5.19342748987905e-07	***
df.mm.trans1:probe5	0.0510544260835389	0.0465471349151546	1.09683283786639	0.273083739058647	   
df.mm.trans1:probe6	0.141709049783516	0.0465471349151546	3.04442045771068	0.00241684042261863	** 
df.mm.trans1:probe7	0.156368169559361	0.0465471349151546	3.35935111461503	0.00082270894584236	***
df.mm.trans1:probe8	0.0326708680786507	0.0465471349151546	0.701887842038026	0.482977517034696	   
df.mm.trans1:probe9	0.301552521298504	0.0465471349151546	6.47843356735424	1.72464872237568e-10	***
df.mm.trans1:probe10	0.146557242330864	0.0465471349151546	3.14857708423959	0.00170890691540569	** 
df.mm.trans1:probe11	0.236965821752531	0.0465471349151546	5.0908787873726	4.56206127950446e-07	***
df.mm.trans1:probe12	0.348290641681139	0.0465471349151546	7.4825366226299	2.14539145853241e-13	***
df.mm.trans1:probe13	0.307282997705103	0.0465471349151546	6.6015448268774	7.93226858169827e-11	***
df.mm.trans1:probe14	0.242701077833961	0.0465471349151546	5.21409273151512	2.42085187761666e-07	***
df.mm.trans1:probe15	0.413260142710348	0.0465471349151546	8.87831535632929	5.41677565142725e-18	***
df.mm.trans1:probe16	0.308410979366369	0.0465471349151546	6.62577793302501	6.79785246462821e-11	***
df.mm.trans1:probe17	0.216333641133499	0.0465471349151546	4.64762528408739	3.99816073723499e-06	***
df.mm.trans1:probe18	0.440478287247816	0.0465471349151546	9.46305907013854	4.22638065217125e-20	***
df.mm.trans1:probe19	0.319729039569116	0.0465471349151546	6.86893060447036	1.40741697899391e-11	***
df.mm.trans1:probe20	0.606193612919784	0.0465471349151546	13.0232207422593	6.05904137712838e-35	***
df.mm.trans1:probe21	0.209398249128982	0.0465471349151546	4.49862810054088	7.980133484882e-06	***
df.mm.trans1:probe22	0.129144841872691	0.0465471349151546	2.77449604810466	0.0056730244543309	** 
df.mm.trans2:probe2	-0.282997027933379	0.0465471349151546	-6.07979478112288	1.9579986413346e-09	***
df.mm.trans2:probe3	-0.375620731911587	0.0465471349151546	-8.06968533286233	2.9797915762177e-15	***
df.mm.trans2:probe4	-0.231005477037807	0.0465471349151546	-4.96282912920162	8.6918900769159e-07	***
df.mm.trans2:probe5	-0.130516760160466	0.0465471349151546	-2.80396979101657	0.00518466845640895	** 
df.mm.trans2:probe6	-0.401842862666334	0.0465471349151546	-8.63303108556105	3.86190658830979e-17	***
df.mm.trans3:probe2	0.0966712805760639	0.0465471349151546	2.07684706593166	0.03817223438764	*  
df.mm.trans3:probe3	0.302716050756435	0.0465471349151546	6.50343036812516	1.47451172627513e-10	***
df.mm.trans3:probe4	0.297163181377408	0.0465471349151546	6.38413474683399	3.10044791530342e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.19201458356466	0.121584630617596	34.4781619376651	3.39558364812242e-154	***
df.mm.trans1	-0.0866002822505673	0.107969505050552	-0.802080941373404	0.422772635392786	   
df.mm.trans2	-0.0296543977560773	0.098212305245823	-0.301941774830080	0.762784252593218	   
df.mm.exp2	0.0274128067762904	0.132404782104463	0.207037890479384	0.836039214712554	   
df.mm.exp3	-0.0861567878564234	0.132404782104463	-0.650707523452202	0.515444350360717	   
df.mm.exp4	-0.0194691438293084	0.132404782104463	-0.147042603143653	0.88313984432572	   
df.mm.exp5	-0.0172035028940622	0.132404782104463	-0.129931129530422	0.89665745915035	   
df.mm.exp6	0.0165688846872621	0.132404782104463	0.125138113774394	0.900449385039797	   
df.mm.exp7	-0.0653026859186204	0.132404782104463	-0.493204889435934	0.622019265130586	   
df.mm.exp8	0.0810118423052003	0.132404782104463	0.611849821566753	0.540831624832513	   
df.mm.trans1:exp2	-0.0141733956747818	0.125724552411746	-0.112733713526091	0.910273314969542	   
df.mm.trans2:exp2	0.0184882401631657	0.105869547521829	0.174632277136668	0.861418021001986	   
df.mm.trans1:exp3	0.139867330242180	0.125724552411746	1.11249018237995	0.266301343902889	   
df.mm.trans2:exp3	-0.0124261551569448	0.105869547521829	-0.117372327055452	0.906597974289595	   
df.mm.trans1:exp4	0.0236757750569031	0.125724552411746	0.188314649785870	0.85068343546547	   
df.mm.trans2:exp4	0.0249240989355627	0.105869547521829	0.235422739767767	0.813948049865942	   
df.mm.trans1:exp5	0.0186252013420181	0.125724552411746	0.148142912301018	0.882271751595552	   
df.mm.trans2:exp5	0.0153300337875847	0.105869547521829	0.144801164701529	0.8849086689707	   
df.mm.trans1:exp6	0.00487216277005382	0.125724552411746	0.0387526754050200	0.969098393479565	   
df.mm.trans2:exp6	-0.0257628111305067	0.105869547521829	-0.243344868600622	0.807808046656908	   
df.mm.trans1:exp7	0.101210457571046	0.125724552411746	0.805017441935951	0.421077222738074	   
df.mm.trans2:exp7	0.035145286418387	0.105869547521829	0.331967853278495	0.740010821149034	   
df.mm.trans1:exp8	-0.104493024216781	0.125724552411746	-0.831126635269844	0.406179551024975	   
df.mm.trans2:exp8	-0.0361169049354408	0.105869547521829	-0.341145360312360	0.733094378338034	   
df.mm.trans1:probe2	0.0264455949922863	0.0688621733881537	0.384036600808707	0.701065497955727	   
df.mm.trans1:probe3	-0.00625006228926603	0.0688621733881537	-0.0907619086321376	0.92770719757506	   
df.mm.trans1:probe4	-0.0335873960758103	0.0688621733881537	-0.487748126776206	0.625877779292042	   
df.mm.trans1:probe5	0.0360000073913651	0.0688621733881537	0.522783490849828	0.60128688084208	   
df.mm.trans1:probe6	0.0161281980805818	0.0688621733881537	0.234209832293157	0.814889129467828	   
df.mm.trans1:probe7	-0.0410990424465415	0.0688621733881537	-0.596830457483233	0.550809509686802	   
df.mm.trans1:probe8	-0.0756777758067513	0.0688621733881537	-1.09897454703005	0.272149074708208	   
df.mm.trans1:probe9	0.0278456909768126	0.0688621733881537	0.404368459587464	0.686062718741337	   
df.mm.trans1:probe10	0.0513225783044136	0.0688621733881537	0.745294198240373	0.456338807184306	   
df.mm.trans1:probe11	-0.00690010433144469	0.0688621733881537	-0.100201663583155	0.920212313965486	   
df.mm.trans1:probe12	0.0568146055913475	0.0688621733881537	0.825048104001917	0.409619450600416	   
df.mm.trans1:probe13	0.0664965296658364	0.0688621733881537	0.96564668807383	0.334547497355971	   
df.mm.trans1:probe14	0.0437318810571355	0.0688621733881537	0.63506390962471	0.525589903947105	   
df.mm.trans1:probe15	-0.0155278479013404	0.0688621733881537	-0.225491690682125	0.821661224094494	   
df.mm.trans1:probe16	0.0365280429437075	0.0688621733881537	0.530451496757309	0.595963631909771	   
df.mm.trans1:probe17	-0.0464050096175531	0.0688621733881537	-0.673882442774252	0.500603843831683	   
df.mm.trans1:probe18	0.0241077406254836	0.0688621733881537	0.350086839252024	0.726376628277029	   
df.mm.trans1:probe19	-0.033679360529412	0.0688621733881537	-0.48908361256001	0.624932493412537	   
df.mm.trans1:probe20	-0.0136559655434635	0.0688621733881537	-0.198308663110141	0.842859944338517	   
df.mm.trans1:probe21	0.135448601140124	0.0688621733881537	1.96695216656385	0.0495750200743061	*  
df.mm.trans1:probe22	-0.00461924618749919	0.0688621733881537	-0.0670795875329405	0.94653709664823	   
df.mm.trans2:probe2	0.00327661395016751	0.0688621733881537	0.0475822035371771	0.962062502665999	   
df.mm.trans2:probe3	0.00813792080084168	0.0688621733881537	0.118176938084293	0.905960653659911	   
df.mm.trans2:probe4	-0.0163141601168846	0.0688621733881537	-0.236910328474924	0.812794216025785	   
df.mm.trans2:probe5	0.0185311315824282	0.0688621733881537	0.269104657472459	0.78792678073377	   
df.mm.trans2:probe6	0.00621133021236644	0.0688621733881537	0.0901994506818016	0.928153982033602	   
df.mm.trans3:probe2	0.111312796871382	0.0688621733881537	1.61645779380136	0.106436468628654	   
df.mm.trans3:probe3	0.0896113681076384	0.0688621733881537	1.30131483946242	0.193569869477049	   
df.mm.trans3:probe4	0.0696508270175015	0.0688621733881537	1.01145263924364	0.312141991202491