fitVsDatCorrelation=0.957071500326838
cont.fitVsDatCorrelation=0.248416733340338

fstatistic=13673.7516285137,62,922
cont.fstatistic=1210.80443151873,62,922

residuals=-0.56178164505091,-0.0917171031099883,-0.0024301987122015,0.0857678097038839,0.54640938765971
cont.residuals=-0.80959960364548,-0.29495821396856,-0.0824985455554415,0.199481548778060,2.51704971382974

predictedValues:
Include	Exclude	Both
Lung	63.827033036436	75.6368286097809	77.4834212381011
cerebhem	66.9852647666865	64.7043172957532	61.7588254648707
cortex	55.1780408135656	61.2436910434004	68.170082839978
heart	56.7749633580073	87.4300439746529	106.274835228754
kidney	62.7295349204005	80.203782370276	80.454675367799
liver	64.1832551857401	86.6284336146272	93.8764816642893
stomach	60.6181312792193	76.644539539458	90.6440710648086
testicle	62.1465010525051	80.7342098739013	91.636959924878


diffExp=-11.8097955733449,2.28094747093327,-6.06565022983484,-30.6550806166456,-17.4742474498755,-22.4451784288870,-16.0264082602387,-18.5877088213962
diffExpScore=1.02924785377501
diffExp1.5=0,0,0,-1,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,-1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,-1,0,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,0,-1,-1,-1,-1,-1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	83.1802377807039	54.4765958554163	72.8648721366811
cerebhem	77.9102358887074	75.3004850476989	71.653891433358
cortex	78.701692111748	76.8767395063118	78.626825069882
heart	79.4617902782262	62.0875573983851	68.8547797618943
kidney	76.0320592715421	73.6733495115493	65.910152140551
liver	71.994641328743	83.9675611020369	62.646301173292
stomach	77.24324373079	71.4298510872603	69.3251770244509
testicle	78.7575600165877	80.8174497899984	75.2654657641665
cont.diffExp=28.7036419252876,2.60975084100848,1.82495260543614,17.3742328798411,2.35870975999276,-11.9729197732939,5.81339264352967,-2.05988977341076
cont.diffExpScore=1.59286987447124

cont.diffExp1.5=1,0,0,0,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=1,0,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=1,0,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=1,0,0,1,0,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.0476271657595141
cont.tran.correlation=-0.786018257454789

tran.covariance=0.000781086291403134
cont.tran.covariance=-0.00456017322653799

tran.mean=69.1042856709007
cont.tran.mean=75.1194406066066

weightedLogRatios:
wLogRatio
Lung	-0.719989921844056
cerebhem	0.145062729844239
cortex	-0.423723807932246
heart	-1.83705457816342
kidney	-1.04726323580366
liver	-1.29301291578302
stomach	-0.99038830238372
testicle	-1.11479112308617

cont.weightedLogRatios:
wLogRatio
Lung	1.78157660844013
cerebhem	0.147816765261003
cortex	0.102148962236501
heart	1.04907686028625
kidney	0.135995273657593
liver	-0.669739172870441
stomach	0.337060673740545
testicle	-0.113067109875673

varWeightedLogRatios=0.351299646264664
cont.varWeightedLogRatios=0.562940962834043

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87666257439947	0.0669387005181905	57.9136216327653	0	***
df.mm.trans1	0.197138836406761	0.0573228784446512	3.43909520519124	0.000609809146456687	***
df.mm.trans2	0.448415267349714	0.050389623157977	8.89896052494544	2.93503344974297e-18	***
df.mm.exp2	0.119007568298713	0.0640056399448066	1.85932940286724	0.0632988411820928	.  
df.mm.exp3	-0.228636034473232	0.0640056399448066	-3.57212324836357	0.000372422791306207	***
df.mm.exp4	-0.288150242491442	0.0640056399448066	-4.50195080839626	7.59517513894013e-06	***
df.mm.exp5	0.00365293919078604	0.0640056399448066	0.0570721454224354	0.954500090694282	   
df.mm.exp6	-0.0506655823347144	0.0640056399448066	-0.791579966678005	0.428809224391599	   
df.mm.exp7	-0.195224259560039	0.0640056399448066	-3.05011026728871	0.00235297952349677	** 
df.mm.exp8	-0.129233902798858	0.0640056399448066	-2.01910179962733	0.0437656279085502	*  
df.mm.trans1:exp2	-0.0707117178644737	0.0584288880088738	-1.21021844286570	0.226505251423256	   
df.mm.trans2:exp2	-0.275122956597460	0.041315462928264	-6.65907960598568	4.72806881667194e-11	***
df.mm.trans1:exp3	0.0830242813465022	0.0584288880088738	1.42094577144602	0.155670631151352	   
df.mm.trans2:exp3	0.0175535597307748	0.041315462928264	0.424866587148085	0.671033063486954	   
df.mm.trans1:exp4	0.171068869179885	0.0584288880088738	2.92781319325989	0.00349749839299317	** 
df.mm.trans2:exp4	0.433045903164926	0.0413154629282640	10.4814486507587	2.31001466783038e-24	***
df.mm.trans1:exp5	-0.0209973670131302	0.0584288880088738	-0.359366192455027	0.719403448549993	   
df.mm.trans2:exp5	0.0549744208047157	0.0413154629282640	1.33060159340748	0.183649121810136	   
df.mm.trans1:exp6	0.0562311204450597	0.0584288880088738	0.962385600022367	0.336108270989888	   
df.mm.trans2:exp6	0.186350361257097	0.0413154629282640	4.51042655822727	7.304228886502e-06	***
df.mm.trans1:exp7	0.143641487998420	0.0584288880088738	2.45839845482948	0.0141387443001870	*  
df.mm.trans2:exp7	0.208459307966138	0.041315462928264	5.04555179081705	5.44642246088453e-07	***
df.mm.trans1:exp8	0.102551604826225	0.0584288880088738	1.75515243094563	0.0795653328671631	.  
df.mm.trans2:exp8	0.194452986934243	0.0413154629282640	4.70654261509478	2.90578474850013e-06	***
df.mm.trans1:probe2	0.217736290921589	0.0423357514498751	5.14308317355334	3.30136055329927e-07	***
df.mm.trans1:probe3	0.102108897087103	0.0423357514498751	2.41188342217095	0.0160648229474247	*  
df.mm.trans1:probe4	0.0345255696507809	0.0423357514498751	0.815518054324811	0.414986452867064	   
df.mm.trans1:probe5	2.34676183609125	0.0423357514498751	55.4321526303786	8.54810641100614e-296	***
df.mm.trans1:probe6	0.0969011233619896	0.0423357514498751	2.28887217170857	0.0223121625410991	*  
df.mm.trans1:probe7	0.77176842109657	0.0423357514498751	18.2297088079406	1.20484015878384e-63	***
df.mm.trans1:probe8	0.394797465884941	0.0423357514498751	9.32539171655842	7.96822963111772e-20	***
df.mm.trans1:probe9	0.198870793461156	0.0423357514498751	4.69746695524269	3.03471837446341e-06	***
df.mm.trans1:probe10	-0.134203476012038	0.0423357514498751	-3.16997977869682	0.00157478728167429	** 
df.mm.trans1:probe11	-0.144631618257833	0.0423357514498751	-3.41629977748417	0.000662514079995528	***
df.mm.trans1:probe12	-0.271199871846011	0.0423357514498751	-6.40593027307211	2.37943628183218e-10	***
df.mm.trans1:probe13	-0.256502177016657	0.0423357514498751	-6.0587604620731	1.99639726888173e-09	***
df.mm.trans1:probe14	-0.251054958574391	0.0423357514498751	-5.93009336025692	4.27720961297645e-09	***
df.mm.trans1:probe15	-0.15060040219166	0.0423357514498751	-3.55728662026866	0.000393790409009896	***
df.mm.trans1:probe16	0.0858058138456926	0.0423357514498751	2.02679321630290	0.0429709314268984	*  
df.mm.trans1:probe17	-0.18504240955949	0.0423357514498751	-4.37083087514291	1.37846696393019e-05	***
df.mm.trans1:probe18	-0.197899025134466	0.0423357514498751	-4.67451310906282	3.38581326243465e-06	***
df.mm.trans1:probe19	-0.115012550232242	0.0423357514498751	-2.71667671633076	0.00671759642643422	** 
df.mm.trans1:probe20	0.00710128609644429	0.0423357514498751	0.167737334362710	0.866826707770925	   
df.mm.trans1:probe21	0.332908170078324	0.0423357514498751	7.86352335029372	1.04074266623221e-14	***
df.mm.trans2:probe2	-0.0975701694641843	0.0423357514498751	-2.30467550764290	0.0214061645295006	*  
df.mm.trans2:probe3	-0.104827018153235	0.0423357514498751	-2.47608733902712	0.0134616812979265	*  
df.mm.trans2:probe4	0.114341818112930	0.0423357514498751	2.70083355549528	0.0070434266664042	** 
df.mm.trans2:probe5	-0.0434525497291194	0.0423357514498751	-1.02637955489149	0.304981906672801	   
df.mm.trans2:probe6	0.148817394030447	0.0423357514498751	3.5151707229443	0.000460863092679989	***
df.mm.trans3:probe2	-0.457729149192296	0.0423357514498751	-10.8118820031868	9.80135438522813e-26	***
df.mm.trans3:probe3	-0.328446127158782	0.0423357514498751	-7.75812678198608	2.28033217073239e-14	***
df.mm.trans3:probe4	0.022364763905367	0.0423357514498751	0.528271334260987	0.5974381483049	   
df.mm.trans3:probe5	-0.275267931902508	0.0423357514498751	-6.50202069115087	1.29680236455572e-10	***
df.mm.trans3:probe6	0.368208650118988	0.0423357514498751	8.69734532892233	1.53903520187576e-17	***
df.mm.trans3:probe7	-0.811860222289595	0.0423357514498750	-19.1767051365753	3.08520842270325e-69	***
df.mm.trans3:probe8	-0.0610259030558197	0.0423357514498751	-1.44147442683458	0.149790242289078	   
df.mm.trans3:probe9	-0.594463293810616	0.0423357514498751	-14.0416379407946	9.87681090758331e-41	***
df.mm.trans3:probe10	-0.110735866407567	0.0423357514498751	-2.61565845922627	0.00905109633782424	** 
df.mm.trans3:probe11	-0.364020872405593	0.0423357514498751	-8.5984270962236	3.43048126233275e-17	***
df.mm.trans3:probe12	0.100456751597365	0.0423357514498751	2.37285859249019	0.0178552687210784	*  
df.mm.trans3:probe13	0.325797502807866	0.0423357514498751	7.69556442605266	3.61714413999956e-14	***
df.mm.trans3:probe14	-0.223415880853625	0.0423357514498751	-5.27723905215539	1.63551531145091e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40484784115738	0.223701768457123	19.6907153284381	2.53341733033390e-72	***
df.mm.trans1	0.0911458511480922	0.191566749606029	0.475791604417469	0.634335561429551	   
df.mm.trans2	-0.394927271998584	0.168396573657181	-2.34522154116134	0.0192269797253911	*  
df.mm.exp2	0.27502214452453	0.213899802894921	1.28575221109313	0.198852377536775	   
df.mm.exp3	0.21298077314226	0.213899802894921	0.995703456757682	0.319655490531466	   
df.mm.exp4	0.141647930661895	0.213899802894921	0.662216274839111	0.507998109644288	   
df.mm.exp5	0.312329417234325	0.213899802894921	1.46016692398617	0.144584851213691	   
df.mm.exp6	0.439343287747956	0.213899802894921	2.05396770731848	0.0402603324248214	*  
df.mm.exp7	0.246692862199829	0.213899802894921	1.15331037645237	0.249081905390408	   
df.mm.exp8	0.307371461306941	0.213899802894921	1.43698805303686	0.151060639338596	   
df.mm.trans1:exp2	-0.340474595417643	0.195262911819095	-1.74367263217442	0.081549486981424	.  
df.mm.trans2:exp2	0.0486932562065723	0.138071729061513	0.35266637520617	0.724419173370213	   
df.mm.trans1:exp3	-0.268325910104444	0.195262911819095	-1.37417755171570	0.169720512281331	   
df.mm.trans2:exp3	0.131451404862295	0.138071729061513	0.952051558677383	0.341320344213143	   
df.mm.trans1:exp4	-0.187381442909152	0.195262911819095	-0.959636631265414	0.337489705787892	   
df.mm.trans2:exp4	-0.0108735013436578	0.138071729061513	-0.0787525543249585	0.9372465310102	   
df.mm.trans1:exp5	-0.40218412631303	0.195262911819095	-2.05970566845608	0.0397069431194631	*  
df.mm.trans2:exp5	-0.0104594667384082	0.138071729061513	-0.075753862209898	0.939631356855729	   
df.mm.trans1:exp6	-0.583761390430334	0.195262911819095	-2.98961735739744	0.00286744561174037	** 
df.mm.trans2:exp6	-0.00668391643752944	0.138071729061513	-0.0484090152485281	0.961400758528386	   
df.mm.trans1:exp7	-0.320743202981406	0.195262911819095	-1.64262224706843	0.100802091315033	   
df.mm.trans2:exp7	0.024251826637713	0.138071729061513	0.175646577344653	0.860610175584498	   
df.mm.trans1:exp8	-0.362006980937289	0.195262911819095	-1.85394644361688	0.0640660276313431	.  
df.mm.trans2:exp8	0.087050268060511	0.138071729061513	0.630471340166448	0.528542479030938	   
df.mm.trans1:probe2	-0.142280312770142	0.141481420986424	-1.00564661973387	0.31484943653801	   
df.mm.trans1:probe3	-0.165594980111759	0.141481420986424	-1.17043622376149	0.242127898737358	   
df.mm.trans1:probe4	-0.225398483913525	0.141481420986424	-1.59313132665775	0.111473474923246	   
df.mm.trans1:probe5	-0.182184746057320	0.141481420986424	-1.28769378189099	0.198175691431287	   
df.mm.trans1:probe6	-0.216647493039212	0.141481420986424	-1.53127874691052	0.126043536340330	   
df.mm.trans1:probe7	-0.308864688526746	0.141481420986424	-2.18307595706424	0.0292817906593957	*  
df.mm.trans1:probe8	0.0156483519802182	0.141481420986424	0.110603582230912	0.911954777094414	   
df.mm.trans1:probe9	-0.0798884862292712	0.141481420986424	-0.564657081278093	0.572444376854725	   
df.mm.trans1:probe10	-0.0276342867818854	0.141481420986424	-0.195320958675819	0.845184724563025	   
df.mm.trans1:probe11	-0.134252809132338	0.141481420986424	-0.9489076954155	0.342916208648753	   
df.mm.trans1:probe12	-0.0374140221516829	0.141481420986424	-0.264444772259342	0.791496316394987	   
df.mm.trans1:probe13	-0.0793020885364003	0.141481420986424	-0.560512383770939	0.575266167242889	   
df.mm.trans1:probe14	-0.210092530119108	0.141481420986424	-1.48494783735079	0.137899445267616	   
df.mm.trans1:probe15	-0.193492450915127	0.141481420986424	-1.36761738443166	0.171765135459453	   
df.mm.trans1:probe16	-0.0141814825890926	0.141481420986424	-0.100235652781954	0.920179032446126	   
df.mm.trans1:probe17	-0.207071992276174	0.141481420986424	-1.46359847697631	0.143644517691915	   
df.mm.trans1:probe18	-0.169859338882257	0.141481420986424	-1.20057699235687	0.230223688744848	   
df.mm.trans1:probe19	-0.0533017359777874	0.141481420986424	-0.376740179778814	0.706453385321305	   
df.mm.trans1:probe20	-0.115694459721368	0.141481420986424	-0.817736059722428	0.413719182982606	   
df.mm.trans1:probe21	-0.0769284420756193	0.141481420986424	-0.543735294282923	0.586755087573081	   
df.mm.trans2:probe2	-0.121357192873349	0.141481420986424	-0.857760630528262	0.391247564100838	   
df.mm.trans2:probe3	0.114667582378475	0.141481420986424	0.810478023043591	0.417874633060639	   
df.mm.trans2:probe4	-0.100131411134920	0.141481420986424	-0.707735407495857	0.479288444422823	   
df.mm.trans2:probe5	-0.112990053925687	0.141481420986424	-0.79862114147503	0.424715787521118	   
df.mm.trans2:probe6	-0.0231767958104353	0.141481420986424	-0.163815118966463	0.869912591088523	   
df.mm.trans3:probe2	0.177804853844527	0.141481420986424	1.25673641531766	0.209167388307632	   
df.mm.trans3:probe3	0.290760328848286	0.141481420986424	2.0551131506955	0.04014934140368	*  
df.mm.trans3:probe4	-0.0945855915220885	0.141481420986424	-0.668537189283422	0.503958086343216	   
df.mm.trans3:probe5	0.185367296758946	0.141481420986424	1.31018825981917	0.190458466729532	   
df.mm.trans3:probe6	0.272268834178186	0.141481420986424	1.92441404871324	0.0546101906685964	.  
df.mm.trans3:probe7	0.188178569783593	0.141481420986424	1.33005852267804	0.183827905636278	   
df.mm.trans3:probe8	0.269333698028535	0.141481420986424	1.90366831313052	0.0572645300902801	.  
df.mm.trans3:probe9	0.152443098930347	0.141481420986424	1.07747786152766	0.281548662504713	   
df.mm.trans3:probe10	0.321484972390348	0.141481420986424	2.27227695445041	0.0232993300140308	*  
df.mm.trans3:probe11	0.310918538221789	0.141481420986424	2.19759270195359	0.0282261840317477	*  
df.mm.trans3:probe12	0.193390047910284	0.141481420986424	1.36689359325025	0.171991847393893	   
df.mm.trans3:probe13	0.22744860966447	0.141481420986424	1.60762175046500	0.108260410939740	   
df.mm.trans3:probe14	0.130750087279146	0.141481420986424	0.924150226704978	0.355649900880469