fitVsDatCorrelation=0.738270188205248
cont.fitVsDatCorrelation=0.265262061025873

fstatistic=9941.26915717375,53,715
cont.fstatistic=4858.29658045603,53,715

residuals=-0.434053108972885,-0.0901499904239839,-0.00526153656565536,0.0698392747512825,1.46747656444484
cont.residuals=-0.484912869801896,-0.150292922906577,-0.0248442661014154,0.128416689249962,1.35247672158441

predictedValues:
Include	Exclude	Both
Lung	59.9403639336974	48.8972670431661	62.3867149631013
cerebhem	66.2403433060339	60.4077871317621	75.522206288418
cortex	60.7837067114519	49.4245980406358	77.504162877438
heart	57.4666139069282	44.4353135690152	62.8191662825901
kidney	59.8829189691072	45.9623553136978	65.640668869294
liver	59.3657783740819	47.9788726671554	68.7874208398874
stomach	59.9540504577111	48.0248266901253	72.4498463955464
testicle	60.7983980302372	48.3245878602142	60.6634518079966


diffExp=11.0430968905313,5.8325561742718,11.3591086708161,13.0313003379131,13.9205636554094,11.3869057069265,11.9292237675858,12.4738101700231
diffExpScore=0.989127665335845
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=0,0,0,0,1,0,0,0
diffExp1.3Score=0.5
diffExp1.2=1,0,1,1,1,1,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	61.3993761673876	57.1837037087015	55.8272749439621
cerebhem	55.1143219301284	57.1238122290022	59.312729988907
cortex	60.5743161420732	54.2868982079624	63.2978495379142
heart	59.5600312617268	55.0973205533688	54.430378603782
kidney	58.4323146795744	58.5431324201394	55.1472278218045
liver	58.5691301778636	62.758116155694	61.5277271367589
stomach	60.7275142364931	63.4888447718372	57.2547703577324
testicle	59.2020316471851	59.4773331071313	60.1201277119622
cont.diffExp=4.21567245868612,-2.00949029887378,6.28741793411078,4.46271070835801,-0.110817740565075,-4.18898597783038,-2.76133053534407,-0.275301459946192
cont.diffExpScore=3.67253562768853

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=0.97254579069521
cont.tran.correlation=0.0135357574576825

tran.covariance=0.00360438937122499
cont.tran.covariance=1.57511369094758e-05

tran.mean=54.8679863753138
cont.tran.mean=58.8461373372668

weightedLogRatios:
wLogRatio
Lung	0.812790959707212
cerebhem	0.382254706106005
cortex	0.828297844590562
heart	1.00877862978678
kidney	1.04771999167271
liver	0.846981666027586
stomach	0.883591712433523
testicle	0.916827461191413

cont.weightedLogRatios:
wLogRatio
Lung	0.290343803081011
cerebhem	-0.144223993559728
cortex	0.443730506110872
heart	0.315276343026404
kidney	-0.00770926189343308
liver	-0.283556562698665
stomach	-0.183589217896608
testicle	-0.0189440471800106

varWeightedLogRatios=0.0414123899253108
cont.varWeightedLogRatios=0.0706821225035512

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.71525797144039	0.0773877340920058	48.0083570740466	7.16488869287627e-226	***
df.mm.trans1	0.352534038079932	0.0680841125739395	5.17791926416136	2.91977768537778e-07	***
df.mm.trans2	0.177729174081514	0.0615594185170681	2.88711586891023	0.00400524041757037	** 
df.mm.exp2	0.120261691183956	0.082026861542347	1.46612571690153	0.143053679418677	   
df.mm.exp3	-0.192280930073308	0.082026861542347	-2.34412150432016	0.0193448021200966	*  
df.mm.exp4	-0.144740855316165	0.082026861542347	-1.76455434957049	0.0780656075109225	.  
df.mm.exp5	-0.113700740348814	0.082026861542347	-1.38614032294915	0.166136173131806	   
df.mm.exp6	-0.126261479908349	0.082026861542347	-1.53926991151753	0.124180831202799	   
df.mm.exp7	-0.167317293850086	0.082026861542347	-2.03978661019119	0.0417386345395883	*  
df.mm.exp8	0.0304432439088075	0.082026861542347	0.371137494917942	0.710645065735072	   
df.mm.trans1:exp2	-0.0203221318635505	0.0771688150279771	-0.263346428945200	0.792359449209643	   
df.mm.trans2:exp2	0.091134825237934	0.0634158971288676	1.43709746867948	0.151127746348445	   
df.mm.trans1:exp3	0.20625256790903	0.0771688150279771	2.67274504389182	0.00769485403217114	** 
df.mm.trans2:exp3	0.203007660152245	0.0634158971288675	3.20121088470470	0.00142906732348197	** 
df.mm.trans1:exp4	0.102594873280988	0.0771688150279771	1.32948618225889	0.184111634260565	   
df.mm.trans2:exp4	0.0490538530529306	0.0634158971288675	0.773526123161961	0.43946673659883	   
df.mm.trans1:exp5	0.112741912187882	0.0771688150279771	1.46097762609169	0.144460876473007	   
df.mm.trans2:exp5	0.0518019328194285	0.0634158971288675	0.816860364115984	0.414280313970911	   
df.mm.trans1:exp6	0.116629285103308	0.0771688150279771	1.51135254650501	0.131140472944442	   
df.mm.trans2:exp6	0.107300734921796	0.0634158971288675	1.69201635204734	0.0910785589353022	.  
df.mm.trans1:exp7	0.167545603469722	0.0771688150279771	2.17115687741194	0.0302475371550164	*  
df.mm.trans2:exp7	0.149313887524174	0.0634158971288675	2.35451825621505	0.01881620430214	*  
df.mm.trans1:exp8	-0.0162299369740353	0.0771688150279771	-0.210317301984633	0.833479951018662	   
df.mm.trans2:exp8	-0.0422242535824485	0.0634158971288675	-0.665830737940118	0.50573404114133	   
df.mm.trans1:probe2	0.129829253589403	0.045056880766107	2.88145231942164	0.00407707425590298	** 
df.mm.trans1:probe3	0.0491954231719915	0.045056880766107	1.09185150715088	0.275266145232786	   
df.mm.trans1:probe4	-0.125512538122026	0.045056880766107	-2.78564640933687	0.00548358234458121	** 
df.mm.trans1:probe5	-0.0812569804873494	0.045056880766107	-1.80343110987108	0.071741378776884	.  
df.mm.trans1:probe6	-0.0990574856666419	0.045056880766107	-2.19849851970125	0.0282332703262276	*  
df.mm.trans1:probe7	0.206891614833656	0.045056880766107	4.59178734337254	5.19199991080823e-06	***
df.mm.trans1:probe8	-0.0609347562799938	0.045056880766107	-1.35239624323552	0.176676149695472	   
df.mm.trans1:probe9	-0.0364823718298216	0.045056880766107	-0.809695904587888	0.418384349615295	   
df.mm.trans1:probe10	0.256959767968784	0.045056880766107	5.70300836630654	1.72296972119500e-08	***
df.mm.trans1:probe11	0.359375841427957	0.045056880766107	7.97604794911345	5.9979598205874e-15	***
df.mm.trans1:probe12	0.0831501586814136	0.045056880766107	1.84544862555069	0.0653855937218656	.  
df.mm.trans1:probe13	0.0162711567762124	0.045056880766107	0.361124793806232	0.718112834913016	   
df.mm.trans1:probe14	0.0169203927554619	0.045056880766107	0.375534046471096	0.707374711416362	   
df.mm.trans1:probe15	0.240395475812242	0.045056880766107	5.33537767650073	1.28097045736137e-07	***
df.mm.trans1:probe16	-0.0804892370220435	0.045056880766107	-1.78639168210218	0.0744594775924214	.  
df.mm.trans1:probe17	-0.101808305738002	0.045056880766107	-2.25955068364574	0.0241493707156425	*  
df.mm.trans1:probe18	0.0412978249225385	0.045056880766107	0.916570881524578	0.359676587876381	   
df.mm.trans1:probe19	-0.0979463585763804	0.045056880766107	-2.17383797792896	0.0300446935584822	*  
df.mm.trans1:probe20	0.00831096620970262	0.045056880766107	0.184454983753655	0.853708842187997	   
df.mm.trans1:probe21	-0.060598618868535	0.045056880766107	-1.34493595291485	0.179072362734315	   
df.mm.trans2:probe2	-0.0412156969630378	0.045056880766107	-0.914748119759799	0.360632255832149	   
df.mm.trans2:probe3	0.00607734181695226	0.045056880766107	0.134881547803988	0.89274348919016	   
df.mm.trans2:probe4	0.0544409336489019	0.045056880766107	1.20827125010069	0.227342407981282	   
df.mm.trans2:probe5	0.0197682779875130	0.045056880766107	0.438740490939248	0.660982124855403	   
df.mm.trans2:probe6	-0.074992888688193	0.045056880766107	-1.66440480151047	0.0964696939113453	.  
df.mm.trans3:probe2	-0.132665353591878	0.045056880766107	-2.94439720051976	0.00334080341966936	** 
df.mm.trans3:probe3	-0.0516066332733492	0.045056880766107	-1.14536631022556	0.252440670129005	   
df.mm.trans3:probe4	-0.277614721794798	0.045056880766107	-6.16142789013543	1.20335279277786e-09	***
df.mm.trans3:probe5	-0.0989574519674055	0.045056880766107	-2.19627835493317	0.0283923951090869	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03533361058135	0.110622474543836	36.4784247253689	2.28017867121622e-165	***
df.mm.trans1	0.0329880975979118	0.0973233432716347	0.338953600328343	0.734744205745306	   
df.mm.trans2	0.00102313612362389	0.0879965706159776	0.0116269999667250	0.990726448613133	   
df.mm.exp2	-0.169599373227504	0.117253909929589	-1.44642829675658	0.148495328002315	   
df.mm.exp3	-0.191103550624147	0.117253909929589	-1.62982667903275	0.103578566546767	   
df.mm.exp4	-0.0422426936543041	0.117253909929589	-0.360266823338096	0.718754003778243	   
df.mm.exp5	-0.0137797033560097	0.117253909929589	-0.117520203499265	0.90648083898818	   
df.mm.exp6	-0.0513983254958132	0.117253909929589	-0.438350631775759	0.661264535415666	   
df.mm.exp7	0.0683440469571297	0.117253909929589	0.582872221473643	0.560163099474549	   
df.mm.exp8	-0.0711996300287421	0.117253909929589	-0.607226062410181	0.543893681279365	   
df.mm.trans1:exp2	0.0616093067868458	0.110309539052549	0.558512956504122	0.576669017211437	   
df.mm.trans2:exp2	0.168551472139138	0.0906503278345415	1.8593586605311	0.0633868888601894	.  
df.mm.trans1:exp3	0.177574852860165	0.110309539052549	1.60978691766242	0.107885762938003	   
df.mm.trans2:exp3	0.139117505549042	0.0906503278345415	1.53466081008515	0.125309523288118	   
df.mm.trans1:exp4	0.0118277513866434	0.110309539052549	0.107223287199205	0.914641919797363	   
df.mm.trans2:exp4	0.00507482223881634	0.0906503278345414	0.0559823925632029	0.955371473534112	   
df.mm.trans1:exp5	-0.0357509012492301	0.110309539052549	-0.324096189289662	0.745959995634211	   
df.mm.trans2:exp5	0.0372745346477574	0.0906503278345414	0.411190290627436	0.681056220661098	   
df.mm.trans1:exp6	0.00420641954195187	0.110309539052549	0.0381328720805192	0.969592386424075	   
df.mm.trans2:exp6	0.144417278674007	0.0906503278345415	1.59312472578812	0.111574163708403	   
df.mm.trans1:exp7	-0.0793468442397366	0.110309539052549	-0.719310813201185	0.472184463986537	   
df.mm.trans2:exp7	0.0362512130283201	0.0906503278345415	0.39990162081363	0.689348416454436	   
df.mm.trans1:exp8	0.0347558147332390	0.110309539052549	0.315075332847526	0.752796386415766	   
df.mm.trans2:exp8	0.110525956218964	0.0906503278345415	1.21925600115534	0.223149112090462	   
df.mm.trans1:probe2	0.103369596774091	0.064406894762515	1.60494613434232	0.108947226203923	   
df.mm.trans1:probe3	0.0711619024050876	0.064406894762515	1.10488019438726	0.269583165312711	   
df.mm.trans1:probe4	0.0175415669524076	0.064406894762515	0.272355421218301	0.785427377980025	   
df.mm.trans1:probe5	0.0924537549207613	0.064406894762515	1.43546362950213	0.151592329542837	   
df.mm.trans1:probe6	0.0851420725455078	0.064406894762515	1.32194034286933	0.186610782677093	   
df.mm.trans1:probe7	0.103719082697769	0.064406894762515	1.61037235346011	0.107757949477190	   
df.mm.trans1:probe8	0.0322618075297165	0.064406894762515	0.500906116475172	0.616591359707411	   
df.mm.trans1:probe9	0.055606334724763	0.064406894762515	0.86335996991934	0.388229090966400	   
df.mm.trans1:probe10	0.0210786424540836	0.064406894762515	0.327273074285076	0.743557148595082	   
df.mm.trans1:probe11	-0.0274048264223028	0.064406894762515	-0.425495228784923	0.670603735450134	   
df.mm.trans1:probe12	0.149184070748782	0.064406894762515	2.31627485378487	0.0208251478565130	*  
df.mm.trans1:probe13	0.105146424974884	0.064406894762515	1.63253368078971	0.103007392429315	   
df.mm.trans1:probe14	0.0631372244139333	0.064406894762515	0.980286732449014	0.327276172719322	   
df.mm.trans1:probe15	0.0859590430425112	0.064406894762515	1.33462486212795	0.182423996386711	   
df.mm.trans1:probe16	0.083265383464546	0.064406894762515	1.29280232763227	0.196497023495627	   
df.mm.trans1:probe17	0.00309054275723895	0.064406894762515	0.0479846570562761	0.96174187023975	   
df.mm.trans1:probe18	0.0435502346006303	0.064406894762515	0.67617348672392	0.499149159225754	   
df.mm.trans1:probe19	0.00834324217560502	0.064406894762515	0.129539581226028	0.896967139482644	   
df.mm.trans1:probe20	0.0898577724316331	0.064406894762515	1.39515765762287	0.163401495834910	   
df.mm.trans1:probe21	0.0895632631392304	0.064406894762515	1.39058502151786	0.164783954038957	   
df.mm.trans2:probe2	-0.0514108552041548	0.064406894762515	-0.798219746406344	0.425008014685491	   
df.mm.trans2:probe3	-0.00218459904771647	0.064406894762515	-0.0339187140720207	0.97295143654482	   
df.mm.trans2:probe4	-0.00222877925848945	0.064406894762515	-0.0346046687502563	0.972404636720297	   
df.mm.trans2:probe5	0.0889181276746308	0.064406894762515	1.38056846246811	0.167843102818862	   
df.mm.trans2:probe6	0.07594042564206	0.064406894762515	1.17907292258185	0.238761339153779	   
df.mm.trans3:probe2	-0.0665010893727716	0.064406894762515	-1.03251506873571	0.302180005342272	   
df.mm.trans3:probe3	-0.0719144219047824	0.064406894762515	-1.11656402889706	0.264555858891596	   
df.mm.trans3:probe4	-0.0314593770187833	0.064406894762515	-0.488447349228405	0.625382777450127	   
df.mm.trans3:probe5	-0.0655365920021053	0.064406894762515	-1.01754000474259	0.309240792907703