fitVsDatCorrelation=0.813661989187283
cont.fitVsDatCorrelation=0.233123731840058

fstatistic=9169.32867801735,53,715
cont.fstatistic=3268.23218307205,53,715

residuals=-0.764770806050716,-0.0842844919890069,-0.00286304239966114,0.0748594910446893,0.736250988693463
cont.residuals=-0.634839128379394,-0.183842948110211,-0.0462242442236506,0.129569816469529,1.23436590019782

predictedValues:
Include	Exclude	Both
Lung	59.5238652019803	46.051851264305	68.6231170082095
cerebhem	63.9784371879486	53.8748874780826	65.9842172543705
cortex	66.3110022324635	42.9027171074338	75.9318469189554
heart	58.8367851772043	43.8230602538219	65.6311457270308
kidney	57.2359883012699	44.0169421098402	66.3558757717188
liver	57.4050958407642	49.6632692283465	63.1165200221472
stomach	61.5562566761628	45.6497781537913	71.0439127793761
testicle	66.9330099288308	48.0196844639318	71.3535555546136


diffExp=13.4720139376753,10.1035497098660,23.4082851250297,15.0137249233825,13.2190461914296,7.74182661241773,15.9064785223715,18.9133254648990
diffExpScore=0.991580950250578
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=0,0,1,1,1,0,1,1
diffExp1.3Score=0.833333333333333
diffExp1.2=1,0,1,1,1,0,1,1
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	57.3062459834269	54.2777489231385	66.9756903846893
cerebhem	56.1979208531447	57.3463740251661	59.4654250185455
cortex	56.7855347148458	51.2982137537669	57.9486344996615
heart	59.1181753447738	51.7202262112128	61.5391080958179
kidney	57.4058421835064	60.5319527707076	59.948313431906
liver	58.5493135066422	56.974773517631	52.2887372639031
stomach	54.4459798313633	56.1749903915162	69.7361608596828
testicle	56.352375488576	51.3451558938829	59.5547512542659
cont.diffExp=3.02849706028837,-1.14845317202140,5.48732096107882,7.39794913356099,-3.12611058720123,1.57453998901125,-1.72901056015292,5.00721959469311
cont.diffExpScore=1.62926929909970

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=0.150482694276136
cont.tran.correlation=-0.104292226281885

tran.covariance=0.000693592207656883
cont.tran.covariance=-0.000171371873913606

tran.mean=54.1114144128861
cont.tran.mean=55.9894264620813

weightedLogRatios:
wLogRatio
Lung	1.01567850776791
cerebhem	0.700006107024427
cortex	1.73151364505145
heart	1.15705865128774
kidney	1.02834209721164
liver	0.576239228236279
stomach	1.18698446520197
testicle	1.34082839500920

cont.weightedLogRatios:
wLogRatio
Lung	0.218335466184188
cerebhem	-0.0817082788696305
cortex	0.405330894829788
heart	0.53645494988596
kidney	-0.216166020231592
liver	0.11057617793665
stomach	-0.125451953224913
testicle	0.370828460259207

varWeightedLogRatios=0.130527654385779
cont.varWeightedLogRatios=0.0761613046393087

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.99531712025815	0.082202401094721	48.6034114216979	8.43308765847419e-229	***
df.mm.trans1	0.319810774787866	0.0729973230586888	4.3811301755647	1.35747565471987e-05	***
df.mm.trans2	-0.171814789329787	0.066400557926159	-2.58755038656233	0.00986226304365374	** 
df.mm.exp2	0.268279156133760	0.0895178193997411	2.99693578253690	0.00282149970900745	** 
df.mm.exp3	-0.0640610279623262	0.0895178193997412	-0.715623195380376	0.474457673481738	   
df.mm.exp4	-0.0166387699462643	0.0895178193997412	-0.185871037273193	0.852598612996611	   
df.mm.exp5	-0.0507906701181365	0.0895178193997411	-0.567380555723003	0.570633751559183	   
df.mm.exp6	0.122900314503755	0.0895178193997412	1.37291452503936	0.170209344851584	   
df.mm.exp7	-0.00986375944950545	0.0895178193997412	-0.110187664485647	0.912291464697606	   
df.mm.exp8	0.120140281531481	0.0895178193997411	1.34208230648465	0.179995317664002	   
df.mm.trans1:exp2	-0.196110376448084	0.0850013692710017	-2.30714373344792	0.0213316509702401	*  
df.mm.trans2:exp2	-0.111382659358433	0.0715775584866276	-1.55611146445071	0.120124086259818	   
df.mm.trans1:exp3	0.172039529611844	0.0850013692710017	2.02396186187715	0.0433454722375933	*  
df.mm.trans2:exp3	-0.00677177534424672	0.0715775584866276	-0.0946075206730032	0.924653082322448	   
df.mm.trans1:exp4	0.0050286995318538	0.0850013692710017	0.0591602179468578	0.952841026053464	   
df.mm.trans2:exp4	-0.0329690244777167	0.0715775584866276	-0.460605602856321	0.645221647188718	   
df.mm.trans1:exp5	0.0115962088022342	0.0850013692710017	0.136423788248201	0.89152467121054	   
df.mm.trans2:exp5	0.00559731480284237	0.0715775584866276	0.078199297673559	0.937691399322681	   
df.mm.trans1:exp6	-0.159144565370836	0.0850013692710017	-1.87225884401286	0.0615787614893086	.  
df.mm.trans2:exp6	-0.047402667437045	0.0715775584866276	-0.662255998098914	0.508020567972299	   
df.mm.trans1:exp7	0.0434379308759145	0.0850013692710017	0.511026248735188	0.609490430949802	   
df.mm.trans2:exp7	0.00109454368634712	0.0715775584866276	0.0152917158602386	0.987803717341874	   
df.mm.trans1:exp8	-0.00282534210002121	0.0850013692710017	-0.0332387833778706	0.973493447003462	   
df.mm.trans2:exp8	-0.0782972247626936	0.0715775584866276	-1.09387951221222	0.274376206091679	   
df.mm.trans1:probe2	-0.47290534774463	0.0465571673685541	-10.1575197649168	9.81797442075042e-23	***
df.mm.trans1:probe3	-0.441560252650859	0.0465571673685541	-9.48425940855457	3.52880571091374e-20	***
df.mm.trans1:probe4	-0.271319844392814	0.0465571673685541	-5.8276707911588	8.50214745916245e-09	***
df.mm.trans1:probe5	-0.0192212827958475	0.0465571673685541	-0.412853356040515	0.679837828829682	   
df.mm.trans1:probe6	-0.223045607753585	0.0465571673685541	-4.7907899118501	2.02072314325319e-06	***
df.mm.trans1:probe7	-0.237660222389808	0.0465571673685541	-5.10469678080822	4.25189559862859e-07	***
df.mm.trans1:probe8	0.0226319235870721	0.0465571673685541	0.486110407188524	0.627037834399297	   
df.mm.trans1:probe9	0.23419582096208	0.0465571673685541	5.03028500656296	6.20019348913178e-07	***
df.mm.trans1:probe10	-0.433727296245668	0.0465571673685541	-9.31601557311708	1.4644409601354e-19	***
df.mm.trans1:probe11	-0.308085668253746	0.0465571673685541	-6.61736281795002	7.1724965210505e-11	***
df.mm.trans1:probe12	-0.555421590560347	0.0465571673685541	-11.9298836667519	4.75333170962806e-30	***
df.mm.trans1:probe13	-0.481547211304019	0.0465571673685541	-10.3431380928314	1.84023255959193e-23	***
df.mm.trans1:probe14	-0.355938212821040	0.0465571673685541	-7.64518618590721	6.73680514254069e-14	***
df.mm.trans1:probe15	-0.340441065394471	0.0465571673685541	-7.31232342164386	7.05361056595245e-13	***
df.mm.trans1:probe16	-0.44328116559702	0.0465571673685541	-9.52122284605363	2.57466962132765e-20	***
df.mm.trans1:probe17	-0.223532821931151	0.0465571673685541	-4.80125477054111	1.92105358877289e-06	***
df.mm.trans1:probe18	-0.366071787408422	0.0465571673685541	-7.86284493020243	1.38508155543250e-14	***
df.mm.trans1:probe19	-0.295632160156119	0.0465571673685541	-6.34987429144576	3.82988180285432e-10	***
df.mm.trans1:probe20	-0.28116154242126	0.0465571673685541	-6.03906032760842	2.49120049557572e-09	***
df.mm.trans1:probe21	-0.130927155004218	0.0465571673685541	-2.81218043116278	0.00505557724820502	** 
df.mm.trans1:probe22	-0.322862253810964	0.0465571673685541	-6.93474865545694	9.1145545290784e-12	***
df.mm.trans2:probe2	0.00123042268481911	0.0465571673685541	0.0264282119029898	0.978923166399947	   
df.mm.trans2:probe3	0.0485084464629923	0.0465571673685541	1.04191146508102	0.297804892230921	   
df.mm.trans2:probe4	0.0163695881840178	0.0465571673685541	0.351601893097007	0.725240435238056	   
df.mm.trans2:probe5	-0.0460952704205659	0.0465571673685541	-0.990078929322917	0.322470471672365	   
df.mm.trans2:probe6	0.0426431345304017	0.0465571673685541	0.915930606190701	0.360012100332016	   
df.mm.trans3:probe2	0.478189384041857	0.0465571673685541	10.2710154218884	3.53625441395295e-23	***
df.mm.trans3:probe3	-0.166439918302929	0.0465571673685541	-3.57495800776201	0.000373841203905872	***
df.mm.trans3:probe4	0.0295159537677742	0.0465571673685541	0.633972284742349	0.526301670605227	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.69067566155621	0.137505977367330	26.8401107516732	2.63775647393135e-110	***
df.mm.trans1	0.321776895886785	0.122107969094693	2.63518342228140	0.00859136995132076	** 
df.mm.trans2	0.281260433803059	0.111073076866107	2.53221070072727	0.0115475161083555	*  
df.mm.exp2	0.154400063612312	0.149743013396528	1.03110028381393	0.302842445504689	   
df.mm.exp3	0.0791862868606124	0.149743013396528	0.528814567467816	0.597098205429934	   
df.mm.exp4	0.0675202017403817	0.149743013396528	0.45090719232145	0.652193149937226	   
df.mm.exp5	0.221640438979530	0.149743013396528	1.48013876542349	0.139276693688615	   
df.mm.exp6	0.317502704085648	0.149743013396528	2.12031731487120	0.0343230477515953	*  
df.mm.exp7	-0.0572325908101938	0.149743013396528	-0.382205416546805	0.702422578613754	   
df.mm.exp8	0.0451046520368954	0.149743013396528	0.301213732873505	0.763339094033971	   
df.mm.trans1:exp2	-0.173929925619117	0.142188016451031	-1.22323898989770	0.221642439130649	   
df.mm.trans2:exp2	-0.0994048099393033	0.119733024901909	-0.830220484454812	0.406691251545190	   
df.mm.trans1:exp3	-0.0883142868072825	0.142188016451031	-0.621109211673246	0.534725720300188	   
df.mm.trans2:exp3	-0.135644717440713	0.119733024901909	-1.13289309738762	0.257638896618062	   
df.mm.trans1:exp4	-0.0363914118895511	0.142188016451031	-0.255938670486230	0.798071808223135	   
df.mm.trans2:exp4	-0.115785636615535	0.119733024901909	-0.967031750098957	0.333855137743208	   
df.mm.trans1:exp5	-0.219903983359257	0.142188016451031	-1.54657184795170	0.122408998554105	   
df.mm.trans2:exp5	-0.112583430927755	0.119733024901909	-0.940287201630366	0.347387813820055	   
df.mm.trans1:exp6	-0.296042961745017	0.142188016451031	-2.08205282789758	0.037692695591567	*  
df.mm.trans2:exp6	-0.269008466616095	0.119733024901909	-2.24673574259465	0.0249612550184975	*  
df.mm.trans1:exp7	0.00603198243877621	0.142188016451031	0.0424225795487737	0.966173672308452	   
df.mm.trans2:exp7	0.0915898753000949	0.119733024901909	0.76495081766396	0.444553232643024	   
df.mm.trans1:exp8	-0.0618898792020199	0.142188016451031	-0.435267899129421	0.663499338798823	   
df.mm.trans2:exp8	-0.100648417723397	0.119733024901909	-0.840606990476127	0.400849156680108	   
df.mm.trans1:probe2	0.0780516866779942	0.0778795840171454	1.00220985593363	0.316581297468515	   
df.mm.trans1:probe3	0.0386086193489503	0.0778795840171454	0.495747631888359	0.620224815153096	   
df.mm.trans1:probe4	0.0124244930914491	0.0778795840171454	0.159534661724873	0.873292706589539	   
df.mm.trans1:probe5	0.0814584184338311	0.0778795840171454	1.04595343518910	0.295935997634311	   
df.mm.trans1:probe6	0.0189012836452847	0.0778795840171454	0.242698826448836	0.808308319604401	   
df.mm.trans1:probe7	0.00498593089229593	0.0778795840171454	0.064021026244802	0.94897137860014	   
df.mm.trans1:probe8	0.128628009375054	0.0778795840171454	1.65162681591540	0.0990497299327023	.  
df.mm.trans1:probe9	0.0770322026856	0.0778795840171454	0.989119339269212	0.32293935904738	   
df.mm.trans1:probe10	0.00358788865738606	0.0778795840171454	0.0460696946788542	0.963267564407472	   
df.mm.trans1:probe11	0.0559851653524064	0.0778795840171454	0.718868315219572	0.472456921304407	   
df.mm.trans1:probe12	0.0163779960122493	0.0778795840171454	0.210298966268794	0.833494255385123	   
df.mm.trans1:probe13	0.0342201767242159	0.0778795840171454	0.439398555553177	0.660505538335093	   
df.mm.trans1:probe14	-0.0071159120493103	0.0778795840171454	-0.0913706992546817	0.927223634832582	   
df.mm.trans1:probe15	0.0426862271472422	0.0778795840171454	0.548105484716568	0.583790550326204	   
df.mm.trans1:probe16	0.0640251916914532	0.0778795840171454	0.822104952144555	0.411291228154041	   
df.mm.trans1:probe17	0.0549980235135341	0.0778795840171454	0.706193082662411	0.48029811267759	   
df.mm.trans1:probe18	0.0622543832936881	0.0778795840171454	0.799367177924097	0.424343005398439	   
df.mm.trans1:probe19	0.0248202660330957	0.0778795840171454	0.318700547086017	0.750046674314443	   
df.mm.trans1:probe20	0.0853835555220062	0.0778795840171454	1.09635351292078	0.273293222413355	   
df.mm.trans1:probe21	-0.0070352537503943	0.0778795840171454	-0.0903350196226713	0.92804629173138	   
df.mm.trans1:probe22	0.0646053450719227	0.0778795840171454	0.82955431628525	0.407067680354639	   
df.mm.trans2:probe2	0.0475581072852818	0.0778795840171454	0.61066206099421	0.54161738732131	   
df.mm.trans2:probe3	0.0151664833653811	0.0778795840171454	0.194742737224201	0.845649673624732	   
df.mm.trans2:probe4	0.0558725701123485	0.0778795840171454	0.71742255454328	0.473347717653529	   
df.mm.trans2:probe5	0.0581856668301756	0.0778795840171454	0.747123492818938	0.455234636381885	   
df.mm.trans2:probe6	0.0449998441734129	0.0778795840171454	0.577813103926005	0.563572224746185	   
df.mm.trans3:probe2	-0.142687582760807	0.0778795840171454	-1.83215645745352	0.0673439000326282	.  
df.mm.trans3:probe3	-0.160542350900009	0.0778795840171454	-2.06141767352873	0.0396241876341362	*  
df.mm.trans3:probe4	-0.0543031374316233	0.0778795840171454	-0.697270512123798	0.48586017050087