fitVsDatCorrelation=0.821162159363203
cont.fitVsDatCorrelation=0.265244001550709

fstatistic=11028.3015680199,64,968
cont.fstatistic=3853.83692363708,64,968

residuals=-0.522082266364726,-0.0851265530170151,-0.00284365493754893,0.0813429703380819,0.997149084867782
cont.residuals=-0.59515928953778,-0.178627679106645,-0.0513255708290738,0.143973956286081,1.01294657596627

predictedValues:
Include	Exclude	Both
Lung	65.8691897453173	50.6573456933868	87.228207603601
cerebhem	77.6580498396248	65.2002359620867	85.116332961271
cortex	75.6184664220459	54.4963756679818	88.6703316887157
heart	65.438319966034	49.5566926923264	75.2633540863653
kidney	62.6594837676064	49.7705449331446	90.3471300592474
liver	64.3233674145385	48.7436226344251	93.3534925090076
stomach	64.2212157320748	50.5792140293341	77.6023501860881
testicle	63.6100447511458	52.9492230690489	79.833371864354


diffExp=15.2118440519305,12.4578138775381,21.1220907540641,15.8816272737076,12.8889388344618,15.5797447801134,13.6420017027407,10.6608216820969
diffExpScore=0.991557254521785
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=1,0,1,1,0,1,0,0
diffExp1.3Score=0.8
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	61.828042508776	68.922639625306	70.5525185890061
cerebhem	63.6292328463641	62.2402611150412	59.120573304294
cortex	66.472375436635	57.796261484247	65.6905852265972
heart	63.1187400080931	72.597183161794	73.9266540954376
kidney	62.4761707914771	64.1442558699107	64.8229383667996
liver	64.4105280785664	67.48619204122	70.4037130529654
stomach	64.248117084801	60.1780942810128	68.940344606849
testicle	64.4194253005578	67.0559727929875	54.0614011869877
cont.diffExp=-7.09459711653,1.38897173132286,8.6761139523881,-9.47844315370088,-1.66808507843357,-3.07566396265358,4.07002280378823,-2.63654749242973
cont.diffExpScore=3.52076552442876

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=0.843813079111983
cont.tran.correlation=-0.614202143738764

tran.covariance=0.0065989978781708
cont.tran.covariance=-0.00104726832674020

tran.mean=60.0844620200076
cont.tran.mean=64.4389682766743

weightedLogRatios:
wLogRatio
Lung	1.06515019236127
cerebhem	0.745724928653228
cortex	1.36330395415553
heart	1.12366972234386
kidney	0.926364318343068
liver	1.11639711045134
stomach	0.965424758874232
testicle	0.744951310002825

cont.weightedLogRatios:
wLogRatio
Lung	-0.453919350833705
cerebhem	0.0914186020856447
cortex	0.577191722133262
heart	-0.589710007299638
kidney	-0.109295956173208
liver	-0.195381083776226
stomach	0.270285766164744
testicle	-0.167889567657386

varWeightedLogRatios=0.0430107364311947
cont.varWeightedLogRatios=0.143572799667013

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.70008832247055	0.078263394742723	47.2773808832844	8.93416710157605e-254	***
df.mm.trans1	0.855603558797081	0.0695330079232105	12.3049985086504	1.96261288518004e-32	***
df.mm.trans2	0.177711967131592	0.0618971497791126	2.87108481999218	0.00417983090454763	** 
df.mm.exp2	0.441532066455316	0.082100805291898	5.37792613465251	9.44878526371127e-08	***
df.mm.exp3	0.194682071918420	0.082100805291898	2.37125654524647	0.0179226298845089	*  
df.mm.exp4	0.119004699235309	0.082100805291898	1.44949490826800	0.147523308658204	   
df.mm.exp5	-0.102748153270526	0.082100805291898	-1.251487764404	0.211058921303758	   
df.mm.exp6	-0.130123223772851	0.082100805291898	-1.58492018817862	0.113311079730356	   
df.mm.exp7	0.0900493184100561	0.082100805291898	1.09681407009223	0.272995454108848	   
df.mm.exp8	0.0979358922714196	0.0821008052918981	1.19287371083904	0.233211139515101	   
df.mm.trans1:exp2	-0.276887655745609	0.0788110415765182	-3.51331044745522	0.000463022438158441	***
df.mm.trans2:exp2	-0.189153227155197	0.0625881655626549	-3.02218838744909	0.00257558028011998	** 
df.mm.trans1:exp3	-0.0566523554808213	0.0788110415765182	-0.718837796678745	0.472414346796095	   
df.mm.trans2:exp3	-0.121632122641815	0.0625881655626549	-1.94337254572596	0.0522607259140756	.  
df.mm.trans1:exp4	-0.125567481923016	0.0788110415765182	-1.59327271168091	0.111425479604326	   
df.mm.trans2:exp4	-0.140971626784946	0.0625881655626549	-2.25236872686138	0.0245223362041907	*  
df.mm.trans1:exp5	0.0527923983712558	0.0788110415765182	0.669860432183215	0.503106519411925	   
df.mm.trans2:exp5	0.0850872464331229	0.0625881655626549	1.35947819636837	0.174311702083844	   
df.mm.trans1:exp6	0.106375399592924	0.0788110415765182	1.34975248981633	0.177410940633274	   
df.mm.trans2:exp6	0.0916133462275877	0.0625881655626549	1.46374870399224	0.143587266082591	   
df.mm.trans1:exp7	-0.115386500950586	0.0788110415765182	-1.46409054673584	0.143493869988287	   
df.mm.trans2:exp7	-0.091592865124393	0.0625881655626549	-1.46342146795631	0.143676715164739	   
df.mm.trans1:exp8	-0.132835299819169	0.0788110415765182	-1.68549098149145	0.092216174575608	.  
df.mm.trans2:exp8	-0.053686741924925	0.062588165562655	-0.857777847334109	0.391227469665011	   
df.mm.trans1:probe2	-0.542284617077202	0.046015734491692	-11.7847649954411	4.73624572653652e-30	***
df.mm.trans1:probe3	-0.577947822014396	0.046015734491692	-12.5597869598005	1.25607821885377e-33	***
df.mm.trans1:probe4	-0.61366896426923	0.046015734491692	-13.3360680003929	2.27365869716659e-37	***
df.mm.trans1:probe5	-0.199490560304057	0.046015734491692	-4.33526841433064	1.6081286181326e-05	***
df.mm.trans1:probe6	-0.41886103540994	0.046015734491692	-9.10256111386343	4.92099066792953e-19	***
df.mm.trans1:probe7	-0.350010774445975	0.046015734491692	-7.60632810303546	6.66611287509667e-14	***
df.mm.trans1:probe8	-0.353816066388053	0.046015734491692	-7.68902355458292	3.63565049530709e-14	***
df.mm.trans1:probe9	-0.217424816156643	0.046015734491692	-4.725010228749	2.64152129715501e-06	***
df.mm.trans1:probe10	-0.38948225312573	0.046015734491692	-8.46411031852702	9.46178777322754e-17	***
df.mm.trans1:probe11	-0.565255859906887	0.046015734491692	-12.2839690847256	2.45803105034573e-32	***
df.mm.trans1:probe12	-0.518956239451357	0.046015734491692	-11.2777997609720	8.39601532262933e-28	***
df.mm.trans1:probe13	-0.679438344397206	0.046015734491692	-14.7653482423469	1.19856278003453e-44	***
df.mm.trans1:probe14	-0.645129805027651	0.046015734491692	-14.0197654596631	8.61975028674097e-41	***
df.mm.trans1:probe15	-0.497459457759996	0.046015734491692	-10.8106382144093	8.51100917691536e-26	***
df.mm.trans1:probe16	-0.401237930140809	0.046015734491692	-8.71958112965146	1.19838839617514e-17	***
df.mm.trans1:probe17	-0.445833519564486	0.046015734491692	-9.6887189673128	2.99258015714862e-21	***
df.mm.trans1:probe18	-0.603667277799253	0.046015734491692	-13.1187143803658	2.63118832775806e-36	***
df.mm.trans1:probe19	-0.55162803693289	0.046015734491692	-11.9878133648499	5.68070121552753e-31	***
df.mm.trans1:probe20	-0.574597620632887	0.046015734491692	-12.4869813984308	2.76646129863614e-33	***
df.mm.trans1:probe21	-0.395746004764993	0.046015734491692	-8.60023227134283	3.16659280126192e-17	***
df.mm.trans1:probe22	-0.578471964039824	0.046015734491692	-12.5711774554911	1.10978987466781e-33	***
df.mm.trans1:probe23	-0.0129808572146187	0.046015734491692	-0.282096056012369	0.777930157139375	   
df.mm.trans1:probe24	-0.549336038728501	0.046015734491692	-11.9380043543080	9.58078385143802e-31	***
df.mm.trans1:probe25	-0.62993686676346	0.046015734491692	-13.6895971285038	3.99825834856031e-39	***
df.mm.trans1:probe26	-0.193540807368773	0.046015734491692	-4.20597018621351	2.84085265092156e-05	***
df.mm.trans1:probe27	-0.303307692260953	0.046015734491692	-6.59139087121851	7.14891701099874e-11	***
df.mm.trans1:probe28	-0.440938459198713	0.046015734491692	-9.58234099856265	7.70048411862675e-21	***
df.mm.trans1:probe29	-0.443247282677968	0.046015734491692	-9.63251565088014	4.93591162650429e-21	***
df.mm.trans1:probe30	-0.31680817808303	0.046015734491692	-6.88477933868966	1.03958841228550e-11	***
df.mm.trans1:probe31	-0.233910080441175	0.046015734491692	-5.08326299742985	4.45243909491046e-07	***
df.mm.trans1:probe32	-0.372364709362913	0.046015734491692	-8.09211704379385	1.74817054242071e-15	***
df.mm.trans2:probe2	0.138469916236954	0.046015734491692	3.00918626566646	0.00268732772163783	** 
df.mm.trans2:probe3	0.0992529455676364	0.046015734491692	2.15693494114619	0.0312563365813384	*  
df.mm.trans2:probe4	0.178949593843238	0.046015734491692	3.88887835476247	0.000107584513897822	***
df.mm.trans2:probe5	0.0298226345232852	0.046015734491692	0.648096457716428	0.517076257534488	   
df.mm.trans2:probe6	0.0736284596451594	0.046015734491692	1.60007137685595	0.109909048910603	   
df.mm.trans3:probe2	-0.0260814697741553	0.046015734491692	-0.566794598896694	0.570985136968607	   
df.mm.trans3:probe3	-0.130179782035153	0.046015734491692	-2.82902758096053	0.00476541649949517	** 
df.mm.trans3:probe4	-0.599627258883539	0.046015734491692	-13.0309179133455	7.01948845823541e-36	***
df.mm.trans3:probe5	-0.568491929809848	0.046015734491692	-12.3542943753834	1.15669680922921e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08278779577393	0.132224939787091	30.8775923993464	2.82134798400488e-146	***
df.mm.trans1	0.0240545831353505	0.117475070128066	0.204763300921016	0.83780010699564	   
df.mm.trans2	0.157145886784873	0.104574391763101	1.50271863058850	0.133237762766323	   
df.mm.exp2	0.103511744373763	0.138708192659919	0.746255447416505	0.455694309133648	   
df.mm.exp3	-0.0322294255916402	0.138708192659919	-0.232354160007401	0.81631206408407	   
df.mm.exp4	0.0258860377521022	0.138708192659919	0.186622269785958	0.85199590826609	   
df.mm.exp5	0.0232758761438419	0.138708192659919	0.167804624207808	0.866772035209775	   
df.mm.exp6	0.0219697522434366	0.138708192659919	0.158388281341834	0.874183883072683	   
df.mm.exp7	-0.0741650963180209	0.138708192659919	-0.534684324666076	0.592990950014746	   
df.mm.exp8	0.279838090971718	0.138708192659919	2.01745899507044	0.0439232117112363	*  
df.mm.trans1:exp2	-0.0747957675014148	0.133150181656055	-0.561739883274231	0.574423289368773	   
df.mm.trans2:exp2	-0.20549437999049	0.105741853520557	-1.94335897422623	0.0522623669280065	.  
df.mm.trans1:exp3	0.104658856157575	0.133150181656055	0.78602112934343	0.4320474702815	   
df.mm.trans2:exp3	-0.143831191796365	0.105741853520557	-1.36021061677724	0.174079955506988	   
df.mm.trans1:exp4	-0.00522534708529527	0.133150181656055	-0.0392440101868811	0.968703937586703	   
df.mm.trans2:exp4	0.0260553731304634	0.105741853520557	0.246405489056403	0.805420598514085	   
df.mm.trans1:exp5	-0.0128476832613262	0.133150181656055	-0.09649016697937	0.923151255685437	   
df.mm.trans2:exp5	-0.0951260420306678	0.105741853520557	-0.89960634189351	0.368553512241154	   
df.mm.trans1:exp6	0.0189503231637988	0.133150181656055	0.142322923845122	0.88685455668884	   
df.mm.trans2:exp6	-0.0430314484141231	0.105741853520557	-0.406948119230362	0.684136057444907	   
df.mm.trans1:exp7	0.112560489768179	0.133150181656055	0.845364898254054	0.398116037833310	   
df.mm.trans2:exp7	-0.0615112107348063	0.105741853520557	-0.581711107634859	0.560896725898794	   
df.mm.trans1:exp8	-0.238779892064904	0.133150181656055	-1.79331255200016	0.0732350340852085	.  
df.mm.trans2:exp8	-0.307295116295918	0.105741853520557	-2.90608785513843	0.00374318121306009	** 
df.mm.trans1:probe2	0.100172177676142	0.0777429568756152	1.28850485885702	0.197878154858801	   
df.mm.trans1:probe3	-0.0302904638390047	0.0777429568756151	-0.389623254071336	0.696900858206458	   
df.mm.trans1:probe4	0.00589210420894045	0.0777429568756151	0.0757895563242794	0.939602185293697	   
df.mm.trans1:probe5	-0.0424441319279236	0.0777429568756151	-0.54595469009279	0.585222877725945	   
df.mm.trans1:probe6	0.130192434288016	0.0777429568756151	1.6746524639694	0.0943254465359373	.  
df.mm.trans1:probe7	0.0330541608830917	0.0777429568756151	0.425172417045787	0.670805486775519	   
df.mm.trans1:probe8	-0.0485540992865081	0.0777429568756151	-0.624546598660922	0.532415849077812	   
df.mm.trans1:probe9	0.00926347032803145	0.0777429568756151	0.119155106781603	0.905177195732094	   
df.mm.trans1:probe10	0.0431196140158608	0.0777429568756151	0.554643349684397	0.579266755420839	   
df.mm.trans1:probe11	0.0187447167207674	0.0777429568756151	0.241111445642053	0.809519802128295	   
df.mm.trans1:probe12	0.028526640711321	0.0777429568756151	0.366935370839602	0.713747422934803	   
df.mm.trans1:probe13	-0.00946142916789046	0.0777429568756151	-0.121701431848396	0.903160738018223	   
df.mm.trans1:probe14	-0.00173372306867123	0.0777429568756151	-0.0223007091362	0.982212679162485	   
df.mm.trans1:probe15	0.205864971827810	0.0777429568756152	2.64802086389875	0.00822814023653342	** 
df.mm.trans1:probe16	0.0991217393510884	0.0777429568756151	1.27499317410422	0.202617591477204	   
df.mm.trans1:probe17	0.008032540950487	0.0777429568756151	0.103321783391114	0.917728988471879	   
df.mm.trans1:probe18	0.0801944408189101	0.0777429568756151	1.03153319659834	0.302548503884968	   
df.mm.trans1:probe19	0.0270230583723065	0.0777429568756151	0.347594939250151	0.728219971797615	   
df.mm.trans1:probe20	-0.0207994596227761	0.0777429568756151	-0.267541401288018	0.789109334333708	   
df.mm.trans1:probe21	-0.00794755206483273	0.0777429568756152	-0.102228579722642	0.91859641546226	   
df.mm.trans1:probe22	0.0495001620922272	0.0777429568756151	0.636715711384956	0.524460573535799	   
df.mm.trans1:probe23	0.0143526552129135	0.0777429568756151	0.184616790893059	0.853568284983889	   
df.mm.trans1:probe24	0.0268635602139416	0.0777429568756151	0.345543330142715	0.729761017557313	   
df.mm.trans1:probe25	-0.044144174963109	0.0777429568756151	-0.567822176274276	0.570287394042841	   
df.mm.trans1:probe26	-0.04445398141624	0.0777429568756152	-0.571807186178475	0.567585359870875	   
df.mm.trans1:probe27	0.0769753282484001	0.0777429568756151	0.990126068546078	0.322359929039993	   
df.mm.trans1:probe28	0.0083590738015104	0.0777429568756151	0.107521943304581	0.914397198591366	   
df.mm.trans1:probe29	-0.108078593642591	0.0777429568756151	-1.39020430899626	0.164786413397968	   
df.mm.trans1:probe30	0.0502571332753255	0.0777429568756151	0.646452557184497	0.518139548650684	   
df.mm.trans1:probe31	-0.00493805082106854	0.0777429568756151	-0.0635176615287372	0.949367409179285	   
df.mm.trans1:probe32	-0.00462246138334547	0.0777429568756151	-0.0594582656631028	0.952599378102001	   
df.mm.trans2:probe2	-5.93431148953916e-05	0.0777429568756151	-0.000763324644190439	0.99939111237152	   
df.mm.trans2:probe3	-0.0585663651941049	0.0777429568756151	-0.753333389258762	0.451432779804603	   
df.mm.trans2:probe4	-0.0249480139649580	0.0777429568756151	-0.320903847339812	0.748352524954933	   
df.mm.trans2:probe5	0.0546426170174325	0.0777429568756151	0.702862602780314	0.482310372688348	   
df.mm.trans2:probe6	-0.0475075844077103	0.0777429568756151	-0.611085380811024	0.541286522537196	   
df.mm.trans3:probe2	-0.0599383195067416	0.0777429568756152	-0.770980702504536	0.440906510361844	   
df.mm.trans3:probe3	0.00854739308731696	0.0777429568756151	0.109944275736674	0.912476356811705	   
df.mm.trans3:probe4	-0.0437623461463014	0.0777429568756151	-0.562910749797168	0.57362600824206	   
df.mm.trans3:probe5	0.0569988291433928	0.0777429568756151	0.733170327372396	0.463632060536261