fitVsDatCorrelation=0.861755992693338
cont.fitVsDatCorrelation=0.281207174080282

fstatistic=6426.30331639001,44,508
cont.fstatistic=1787.68481821142,44,508

residuals=-0.562645176045418,-0.0861771320869135,0.00096675393971425,0.0857538831115502,0.706852996162413
cont.residuals=-0.572523566435553,-0.212145959078135,-0.0668315478194657,0.108887024392917,1.40710900370854

predictedValues:
Include	Exclude	Both
Lung	49.7875597472562	49.3401651737409	72.3153617958126
cerebhem	44.4968597049391	45.2640459287315	68.5510909552217
cortex	49.9512490938421	51.0433690305983	68.1707461838281
heart	50.2202860749376	54.3627182894454	61.4538390319111
kidney	58.670100009293	52.6202521142569	88.346477405071
liver	89.2281117624831	61.9449832577546	148.996907283714
stomach	49.0799972032587	54.0822689826046	58.7463793670005
testicle	46.1876213197266	47.5879588607660	61.3271645988261


diffExp=0.447394573515353,-0.7671862237924,-1.09211993675613,-4.14243221450781,6.04984789503612,27.2831285047285,-5.00227177934583,-1.40033754103938
diffExpScore=2.06402711041437
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,1,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,1,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,0,1,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	57.6521120055758	68.2985195434999	67.1851703390674
cerebhem	58.2790587467335	68.1206861312684	54.9297292874619
cortex	56.4055226728614	57.8928942987932	56.3919614630784
heart	64.7211254143176	53.0744612251634	58.4883986585418
kidney	58.6984175123703	61.1436621435602	54.8256539973906
liver	54.482135200043	57.6595207346299	68.8269625399426
stomach	58.3769988619896	57.6505587708692	51.4006895602754
testicle	55.4787631831824	67.256674601292	61.3182476413056
cont.diffExp=-10.6464075379240,-9.84162738453483,-1.4873716259318,11.6466641891542,-2.44524463118984,-3.17738553458689,0.726440091120381,-11.7779114181096
cont.diffExpScore=1.84799274980962

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=0,0,0,1,0,0,0,-1
cont.diffExp1.2Score=2

tran.correlation=0.86124766603423
cont.tran.correlation=-0.428545428576141

tran.covariance=0.0182852599490978
cont.tran.covariance=-0.00215527750502059

tran.mean=53.3667216596022
cont.tran.mean=59.6994444403844

weightedLogRatios:
wLogRatio
Lung	0.0352334432146745
cerebhem	-0.0650265551852137
cortex	-0.0848225432117337
heart	-0.313554622527242
kidney	0.437222856053320
liver	1.57246580070344
stomach	-0.382588075754246
testicle	-0.114921248900909

cont.weightedLogRatios:
wLogRatio
Lung	-0.701426634036805
cerebhem	-0.646506701000076
cortex	-0.105296679259947
heart	0.807631564963609
kidney	-0.167042062214606
liver	-0.228215989646735
stomach	0.0508476695346249
testicle	-0.791675359196702

varWeightedLogRatios=0.398212829884317
cont.varWeightedLogRatios=0.26940678594358

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.21415352264583	0.0913502875854156	35.184930530629	2.71994565615319e-138	***
df.mm.trans1	0.547658115716425	0.0787176313051627	6.95724841609286	1.07381779566914e-11	***
df.mm.trans2	0.690347891973994	0.0735183389731744	9.39014539251072	2.01146695874530e-19	***
df.mm.exp2	-0.145114700865832	0.0988413365206879	-1.46815801944826	0.142680194815973	   
df.mm.exp3	0.096240597189688	0.0988413365206879	0.973687736097584	0.330675012488776	   
df.mm.exp4	0.26834430762403	0.098841336520688	2.71489962671502	0.00685517064895943	** 
df.mm.exp5	0.0282979395631611	0.098841336520688	0.286296609892949	0.774767671238368	   
df.mm.exp6	0.0880501915247122	0.0988413365206879	0.890823562531279	0.37344553700764	   
df.mm.exp7	0.285261398678507	0.0988413365206879	2.8860536362618	0.00406703313280257	** 
df.mm.exp8	0.0536016750752192	0.0988413365206879	0.542300184943373	0.587849533714379	   
df.mm.trans1:exp2	0.0327681705672645	0.0892212015399572	0.367268877819242	0.713571414175436	   
df.mm.trans2:exp2	0.0588892713505961	0.0784298145048902	0.75085312546448	0.453088711430017	   
df.mm.trans1:exp3	-0.0929582340764252	0.0892212015399572	-1.04188502813196	0.297960565861181	   
df.mm.trans2:exp3	-0.0623034114383643	0.0784298145048901	-0.794384276332562	0.427342695586870	   
df.mm.trans1:exp4	-0.259690406028736	0.0892212015399573	-2.91063560618419	0.00376527831814321	** 
df.mm.trans2:exp4	-0.171404173026299	0.0784298145048901	-2.18544662011934	0.0293120717492796	*  
df.mm.trans1:exp5	0.135867139427801	0.0892212015399573	1.52281225855218	0.128427910408425	   
df.mm.trans2:exp5	0.0360646685028695	0.0784298145048902	0.459833658036012	0.645832355679194	   
df.mm.trans1:exp6	0.495380804146138	0.0892212015399573	5.55227676377217	4.55078733851500e-08	***
df.mm.trans2:exp6	0.139457974007386	0.0784298145048901	1.77812449114859	0.0759814673642569	.  
df.mm.trans1:exp7	-0.299574984371476	0.0892212015399573	-3.35766588210889	0.000844999417761456	***
df.mm.trans2:exp7	-0.193493470544232	0.0784298145048901	-2.46709075835144	0.0139503411580982	*  
df.mm.trans1:exp8	-0.128654998243819	0.0892212015399572	-1.44197787099069	0.149924813104819	   
df.mm.trans2:exp8	-0.0897603697084635	0.0784298145048901	-1.14446744870048	0.252968776858019	   
df.mm.trans1:probe2	-0.0348700980759608	0.0520939583967594	-0.669369330899799	0.503564010362517	   
df.mm.trans1:probe3	0.0700327159745738	0.0520939583967594	1.34435389687972	0.179433974391802	   
df.mm.trans1:probe4	0.00098346268856116	0.0520939583967594	0.0188786323563835	0.9849453377833	   
df.mm.trans1:probe5	0.0212628360558113	0.0520939583967594	0.408163186484481	0.683325851035703	   
df.mm.trans1:probe6	0.111484702170138	0.0520939583967594	2.14006970484072	0.0328247872072353	*  
df.mm.trans1:probe7	0.447013312900959	0.0520939583967594	8.58090509260986	1.14980915226274e-16	***
df.mm.trans1:probe8	0.409521350221078	0.0520939583967594	7.86120622860084	2.29396507183941e-14	***
df.mm.trans1:probe9	0.42911872545381	0.0520939583967594	8.2373990892676	1.50278619328507e-15	***
df.mm.trans1:probe10	0.238458928067019	0.0520939583967594	4.5774776078804	5.92409393282233e-06	***
df.mm.trans1:probe11	0.373167927987819	0.0520939583967594	7.16336288261467	2.7818355246099e-12	***
df.mm.trans1:probe12	0.41503580978797	0.0520939583967594	7.96706225752636	1.07569528109635e-14	***
df.mm.trans2:probe2	-0.0376713633006942	0.0520939583967594	-0.723142653391408	0.469925060568557	   
df.mm.trans2:probe3	0.00268479742105389	0.0520939583967594	0.0515375967517358	0.958917402322128	   
df.mm.trans2:probe4	0.0206978835058283	0.0520939583967594	0.397318309892839	0.691299629352236	   
df.mm.trans2:probe5	-0.0443543630964197	0.0520939583967594	-0.851430078678354	0.394931661446575	   
df.mm.trans2:probe6	-0.00474946899235385	0.0520939583967594	-0.0911712056162217	0.92739249243595	   
df.mm.trans3:probe2	-0.378233060217292	0.0520939583967594	-7.2605935862386	1.45536825767280e-12	***
df.mm.trans3:probe3	-0.088159564496657	0.0520939583967594	-1.69231840332066	0.0911984661479794	.  
df.mm.trans3:probe4	0.0183546958494178	0.0520939583967594	0.352338282870045	0.724730705161423	   
df.mm.trans3:probe5	-0.407576824983405	0.0520939583967594	-7.823878958846	2.99070601053347e-14	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.989519747831	0.172797290912430	23.0878605027019	3.50761546217657e-81	***
df.mm.trans1	0.0696906599120026	0.148901484561361	0.468032001946118	0.639962437749539	   
df.mm.trans2	0.245012488084328	0.139066555155262	1.7618361784455	0.0786984145119375	.  
df.mm.exp2	0.20960661191107	0.186967284202240	1.12108710786183	0.262780461529498	   
df.mm.exp3	-0.0120273221482861	0.186967284202240	-0.0643284850587889	0.948733977669582	   
df.mm.exp4	0.00209266900981966	0.18696728420224	0.0111927015399980	0.991074096820682	   
df.mm.exp5	0.110618328996776	0.18696728420224	0.591645375118791	0.554351272660205	   
df.mm.exp6	-0.250029759717487	0.186967284202240	-1.33729149880058	0.181725964739339	   
df.mm.exp7	0.110807880487060	0.186967284202240	0.592659196820767	0.553672970984877	   
df.mm.exp8	0.0375766621336685	0.186967284202240	0.200979878880962	0.840794749742807	   
df.mm.trans1:exp2	-0.198790662257285	0.168769933029942	-1.17787960620934	0.239396102594096	   
df.mm.trans2:exp2	-0.212213774327594	0.148357053178814	-1.43042592030872	0.153209597089146	   
df.mm.trans1:exp3	-0.0098324854162495	0.168769933029942	-0.0582596985122051	0.953564678185124	   
df.mm.trans2:exp3	-0.153266115037448	0.148357053178814	-1.03308950773454	0.302053575175637	   
df.mm.trans1:exp4	0.113568111555688	0.168769933029943	0.67291673058577	0.501306254704712	   
df.mm.trans2:exp4	-0.254284903221265	0.148357053178814	-1.71400616130314	0.0871375889239716	.  
df.mm.trans1:exp5	-0.0926324424358359	0.168769933029943	-0.548868158994893	0.583337083298536	   
df.mm.trans2:exp5	-0.221280207208803	0.148357053178814	-1.49153816732997	0.136440955207110	   
df.mm.trans1:exp6	0.193475732149392	0.168769933029942	1.14638744399493	0.252174566344281	   
df.mm.trans2:exp6	0.080697049420918	0.148357053178814	0.543938071644321	0.586722728236189	   
df.mm.trans1:exp7	-0.0983128043434	0.168769933029942	-0.582525587220312	0.560471170662851	   
df.mm.trans2:exp7	-0.280296031900609	0.148357053178814	-1.88933404846462	0.0594163022786948	.  
df.mm.trans1:exp8	-0.0760032409630669	0.168769933029942	-0.450336381596968	0.652660034214778	   
df.mm.trans2:exp8	-0.0529484885510881	0.148357053178814	-0.356899031199208	0.721315596513999	   
df.mm.trans1:probe2	0.0785194435786718	0.0985404110025159	0.79682480293965	0.42592503028467	   
df.mm.trans1:probe3	0.0507731377792711	0.0985404110025159	0.515251938394846	0.606601113973082	   
df.mm.trans1:probe4	-0.0249632340122592	0.0985404110025159	-0.253329915699478	0.800115767391232	   
df.mm.trans1:probe5	0.0137546927007324	0.0985404110025159	0.139584283856713	0.889043780185492	   
df.mm.trans1:probe6	0.0181228027476866	0.0985404110025159	0.183912392523143	0.854155569853192	   
df.mm.trans1:probe7	-0.0443697785005659	0.0985404110025159	-0.45026987455363	0.652707950652604	   
df.mm.trans1:probe8	-0.0113607925815915	0.0985404110025159	-0.115290696131778	0.908260269145667	   
df.mm.trans1:probe9	-0.117328528262432	0.0985404110025159	-1.19066408460013	0.234341552110532	   
df.mm.trans1:probe10	-0.032865214690604	0.0985404110025159	-0.333520170620812	0.738879283599923	   
df.mm.trans1:probe11	1.60609385546750e-05	0.0985404110025159	0.000162988345504921	0.999870018071187	   
df.mm.trans1:probe12	-0.011618544221235	0.0985404110025159	-0.117906390921572	0.90618843330025	   
df.mm.trans2:probe2	0.0805013231686737	0.0985404110025159	0.816937156539954	0.414347287949658	   
df.mm.trans2:probe3	0.0911315786437221	0.0985404110025159	0.924814273825135	0.355501617558478	   
df.mm.trans2:probe4	-0.0682065617339171	0.0985404110025159	-0.692168431611024	0.489147810101107	   
df.mm.trans2:probe5	-0.1028925233414	0.0985404110025159	-1.04416576199152	0.296905318922214	   
df.mm.trans2:probe6	-0.117619415742592	0.0985404110025159	-1.19361604590414	0.233185301207618	   
df.mm.trans3:probe2	-0.106001500415585	0.0985404110025159	-1.07571603707720	0.282564894262069	   
df.mm.trans3:probe3	-0.135917495092327	0.0985404110025159	-1.37930716656801	0.168406967095547	   
df.mm.trans3:probe4	-0.0968962733638621	0.0985404110025159	-0.983315092539935	0.325920464534640	   
df.mm.trans3:probe5	-0.145036491776671	0.0985404110025159	-1.47184784700125	0.141681182604161