fitVsDatCorrelation=0.909339992635908
cont.fitVsDatCorrelation=0.283073979048856

fstatistic=7790.3443898413,55,761
cont.fstatistic=1454.75272755993,55,761

residuals=-0.660463081106404,-0.0987587527703852,-0.00535553489865233,0.0808791779444824,1.24051914175856
cont.residuals=-0.838445202991018,-0.316151346540525,-0.0770145026740185,0.204961805746172,1.70640533817456

predictedValues:
Include	Exclude	Both
Lung	71.8855112038547	87.0728682084129	61.8526212517033
cerebhem	76.6500223789632	94.5076786124312	87.4948523365649
cortex	70.333609793272	95.9271488328716	56.4768096435979
heart	74.7827837519268	86.2785891729085	54.8175080229766
kidney	75.5218259140682	82.8065932120259	62.4959726835275
liver	79.7171844008355	84.8788562875979	63.355968286149
stomach	81.4890536428662	96.2070640634246	57.7706289568531
testicle	79.4690459988096	98.9055645019191	65.8970719115127


diffExp=-15.1873570045582,-17.8576562334680,-25.5935390395996,-11.4958054209816,-7.28476729795767,-5.16167188676239,-14.7180104205583,-19.4365185031095
diffExpScore=0.991506372508458
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,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=-1,-1,-1,0,0,0,0,-1
diffExp1.2Score=0.8

cont.predictedValues:
Include	Exclude	Both
Lung	77.8912196754983	61.115453309554	107.278956945071
cerebhem	88.5657101525012	57.071950958944	83.109105971443
cortex	83.1633620209528	66.4229166746473	81.76952701072
heart	79.9933023751507	81.5515937697982	81.5817462379452
kidney	79.841651880856	82.683535883923	76.8808189179747
liver	83.2248860228656	92.1168876617009	76.8073519559113
stomach	79.904566153567	75.5312507928701	98.680373377493
testicle	85.9633778541688	81.865214712982	80.3475006582266
cont.diffExp=16.7757663659443,31.4937591935573,16.7404453463054,-1.55829139464757,-2.84188400306701,-8.89200163883528,4.37331536069688,4.09816314118683
cont.diffExpScore=1.41811829233593

cont.diffExp1.5=0,1,0,0,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,1,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=1,1,1,0,0,0,0,0
cont.diffExp1.2Score=0.75

tran.correlation=0.199550309046892
cont.tran.correlation=-0.172606902071440

tran.covariance=0.00064012131191588
cont.tran.covariance=-0.00136369786114559

tran.mean=83.5270874985117
cont.tran.mean=78.5566799937487

weightedLogRatios:
wLogRatio
Lung	-0.837774959158467
cerebhem	-0.930704977163895
cortex	-1.36810562314249
heart	-0.62718279150495
kidney	-0.402458554690223
liver	-0.276673619301569
stomach	-0.744411456958825
testicle	-0.981257562992383

cont.weightedLogRatios:
wLogRatio
Lung	1.02695972727735
cerebhem	1.87375066672883
cortex	0.968382013396076
heart	-0.084726764991489
kidney	-0.153804496802578
liver	-0.453991796049634
stomach	0.244997719558634
testicle	0.216368540384892

varWeightedLogRatios=0.118897087140504
cont.varWeightedLogRatios=0.598973122243593

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27926600448394	0.0935574097361228	45.7394664575852	1.40307640519550e-220	***
df.mm.trans1	-0.383712814590787	0.0819231777416043	-4.68381262993807	3.33468042969205e-06	***
df.mm.trans2	0.177108555431010	0.0734662193861336	2.41074819026876	0.0161558946585089	*  
df.mm.exp2	-0.200714563795877	0.096884381034855	-2.0716916560954	0.0386305871905061	*  
df.mm.exp3	0.16594312122009	0.096884381034855	1.71279539021249	0.0871575192112214	.  
df.mm.exp4	0.151093945765621	0.096884381034855	1.55952842090475	0.119287110207261	   
df.mm.exp5	-0.0112383180240859	0.096884381034855	-0.115997211356935	0.90768535416412	   
df.mm.exp6	0.0538754959945792	0.096884381034855	0.556080303338027	0.578319417562984	   
df.mm.exp7	0.293425402853479	0.096884381034855	3.02861410393814	0.00253982243919803	** 
df.mm.exp8	0.164373493841126	0.0968843810348551	1.69659435386176	0.0901823203150749	.  
df.mm.trans1:exp2	0.264889729815334	0.0908856055373245	2.91453996757001	0.00366664822970371	** 
df.mm.trans2:exp2	0.282650316446376	0.0725016319924173	3.89853729742162	0.000105315374535455	***
df.mm.trans1:exp3	-0.187768076971141	0.0908856055373245	-2.06598257073866	0.0391680430001397	*  
df.mm.trans2:exp3	-0.06909941786749	0.0725016319924173	-0.953073964938015	0.340855215349691	   
df.mm.trans1:exp4	-0.111580982282536	0.0908856055373245	-1.22770797006697	0.219936224502171	   
df.mm.trans2:exp4	-0.160257809812532	0.0725016319924173	-2.21040279244049	0.0273740091467446	*  
df.mm.trans1:exp5	0.0605852861952938	0.0908856055373245	0.666610359661551	0.505223094891004	   
df.mm.trans2:exp5	-0.0389993292964691	0.0725016319924174	-0.537909674923564	0.590796680961218	   
df.mm.trans1:exp6	0.0495349488603124	0.0908856055373245	0.545025238787341	0.585895847656426	   
df.mm.trans2:exp6	-0.0793958099486075	0.0725016319924173	-1.09508996924250	0.273823685295256	   
df.mm.trans1:exp7	-0.168031434018998	0.0908856055373245	-1.84882339756202	0.064871008424171	.  
df.mm.trans2:exp7	-0.193667950571192	0.0725016319924173	-2.67122194699628	0.00771906446076293	** 
df.mm.trans1:exp8	-0.0640806377673968	0.0908856055373245	-0.705069162366756	0.480982934321481	   
df.mm.trans2:exp8	-0.0369533265790565	0.0725016319924173	-0.509689583027887	0.610416725623636	   
df.mm.trans1:probe2	0.068625134852311	0.0556558396325786	1.23302667438586	0.217946558485363	   
df.mm.trans1:probe3	0.201310962541403	0.0556558396325786	3.6170681076845	0.000317625791308065	***
df.mm.trans1:probe4	-0.0596219153452067	0.0556558396325786	-1.07126072913123	0.284391835265225	   
df.mm.trans1:probe5	0.208878681485278	0.0556558396325786	3.75304160110108	0.000187997237477482	***
df.mm.trans1:probe6	1.66638454353259	0.0556558396325786	29.9408751091261	9.1887681778623e-131	***
df.mm.trans1:probe7	1.46867592035176	0.0556558396325786	26.388532273477	1.78294583459277e-109	***
df.mm.trans1:probe8	0.255135060957958	0.0556558396325786	4.58415617556532	5.32694137562277e-06	***
df.mm.trans1:probe9	0.461392666787061	0.0556558396325786	8.29010342549897	5.1070332506362e-16	***
df.mm.trans1:probe10	0.0667015641157914	0.0556558396325786	1.19846478925002	0.231109208467349	   
df.mm.trans1:probe11	0.319131711069713	0.0556558396325786	5.73402024255702	1.41439147017557e-08	***
df.mm.trans1:probe12	0.0692653189123777	0.0556558396325786	1.24452922406066	0.213687995604314	   
df.mm.trans1:probe13	0.180200359658859	0.0556558396325786	3.23776194642794	0.00125693384307008	** 
df.mm.trans1:probe14	0.354025551195923	0.0556558396325786	6.36097763564583	3.45783505522442e-10	***
df.mm.trans1:probe15	0.0147966906244038	0.0556558396325786	0.265860522850551	0.790418638277394	   
df.mm.trans1:probe16	0.509528444342265	0.0556558396325786	9.15498621000066	4.9216619560768e-19	***
df.mm.trans1:probe17	1.16675777977172	0.0556558396325786	20.9637980034849	2.24145584526566e-77	***
df.mm.trans1:probe18	0.774586378621363	0.0556558396325786	13.9174322718860	2.15599104542558e-39	***
df.mm.trans1:probe19	0.810464467594027	0.0556558396325786	14.5620742215811	1.48144469132192e-42	***
df.mm.trans1:probe20	0.535925442472183	0.0556558396325786	9.62927602943709	8.69713366580642e-21	***
df.mm.trans1:probe21	0.819119305807114	0.0556558396325786	14.7175806027664	2.48707040494716e-43	***
df.mm.trans1:probe22	0.735319088959482	0.0556558396325786	13.2118946334080	4.99297938011915e-36	***
df.mm.trans2:probe2	0.0067439369063591	0.0556558396325786	0.121172134871746	0.90358668120224	   
df.mm.trans2:probe3	-0.0325120726592783	0.0556558396325786	-0.584162827726834	0.55928388571624	   
df.mm.trans2:probe4	0.0797374365642382	0.0556558396325786	1.43268769442054	0.152357755815774	   
df.mm.trans2:probe5	-0.0724638722731805	0.0556558396325786	-1.30199944429125	0.193310523861565	   
df.mm.trans2:probe6	0.142943856956564	0.0556558396325786	2.56835325637402	0.0104079601037529	*  
df.mm.trans3:probe2	-0.166814469919780	0.0556558396325786	-2.99725008231003	0.00281287674284106	** 
df.mm.trans3:probe3	-0.00492093458812022	0.0556558396325786	-0.0884172194796917	0.929568337608073	   
df.mm.trans3:probe4	0.12599215719911	0.0556558396325786	2.26377246360613	0.0238688780064298	*  
df.mm.trans3:probe5	0.138588734723971	0.0556558396325786	2.49010230802173	0.0129829530561205	*  
df.mm.trans3:probe6	0.219710913007216	0.0556558396325786	3.94767044137102	8.6229178945857e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97959908599941	0.21567097421757	18.4521774450036	4.06550869418582e-63	***
df.mm.trans1	0.412679735345861	0.188851440034141	2.18520830591101	0.0291778176349747	*  
df.mm.trans2	0.0851306340946522	0.169356239679769	0.502671966829349	0.615340355074892	   
df.mm.exp2	0.315257789013974	0.223340394985153	1.4115574078524	0.158489042237117	   
df.mm.exp3	0.420298904236829	0.223340394985153	1.88187588843823	0.060234279451872	.  
df.mm.exp4	0.588927774232601	0.223340394985153	2.63690665663841	0.00853724572286369	** 
df.mm.exp5	0.660163934878169	0.223340394985153	2.95586445489207	0.00321428318071503	** 
df.mm.exp6	0.810658868387316	0.223340394985153	3.62970105986069	0.000302731253658049	***
df.mm.exp7	0.32084794243308	0.223340394985153	1.43658715412592	0.151246306111803	   
df.mm.exp8	0.67998908240717	0.223340394985153	3.04463096544793	0.00240995519893187	** 
df.mm.trans1:exp2	-0.186826258666511	0.209511861688710	-0.891721629318031	0.372824015128419	   
df.mm.trans2:exp2	-0.383709772204208	0.167132647732242	-2.29583972617330	0.0219560799423583	*  
df.mm.trans1:exp3	-0.354805247479587	0.209511861688710	-1.69348525004638	0.0907723728791435	.  
df.mm.trans2:exp3	-0.337021529178451	0.167132647732242	-2.01649129449791	0.0440994583237087	*  
df.mm.trans1:exp4	-0.562298097131829	0.209511861688710	-2.68384850671263	0.00743619881115492	** 
df.mm.trans2:exp4	-0.300456654411168	0.167132647732242	-1.79771372312919	0.072618679536998	.  
df.mm.trans1:exp5	-0.635431846956266	0.209511861688710	-3.03291585418863	0.00250432738149046	** 
df.mm.trans2:exp5	-0.357908187609779	0.167132647732242	-2.14146184163356	0.0325537557295902	*  
df.mm.trans1:exp6	-0.744425688190888	0.209511861688710	-3.55314339813821	0.00040412204579843	***
df.mm.trans2:exp6	-0.400365332223811	0.167132647732242	-2.39549446296827	0.0168386455487226	*  
df.mm.trans1:exp7	-0.295328176696477	0.209511861688710	-1.40960122408378	0.159065992935135	   
df.mm.trans2:exp7	-0.109066206554486	0.167132647732242	-0.652572720137947	0.514228848405822	   
df.mm.trans1:exp8	-0.581380949511563	0.209511861688710	-2.77493095057009	0.00565676107147204	** 
df.mm.trans2:exp8	-0.387679663072589	0.167132647732242	-2.31959266087664	0.0206267207424748	*  
df.mm.trans1:probe2	-0.214148804800318	0.128299289049476	-1.66913477375339	0.0955019683279538	.  
df.mm.trans1:probe3	-0.0903729077286726	0.128299289049476	-0.70439133683603	0.481404595660073	   
df.mm.trans1:probe4	-0.0995808533968431	0.128299289049476	-0.776160601781993	0.437895318242868	   
df.mm.trans1:probe5	0.0444410486440292	0.128299289049476	0.346385774802629	0.729148563288285	   
df.mm.trans1:probe6	0.0521771589437235	0.128299289049476	0.406683149456911	0.684355065804973	   
df.mm.trans1:probe7	-0.0524523108826841	0.128299289049476	-0.408827759462151	0.68278108919673	   
df.mm.trans1:probe8	0.0420303273847367	0.128299289049476	0.327595949253691	0.743307285142216	   
df.mm.trans1:probe9	-0.0305147078267505	0.128299289049476	-0.23784003834178	0.812069184045303	   
df.mm.trans1:probe10	0.057151714503955	0.128299289049476	0.445456205777692	0.656116791229073	   
df.mm.trans1:probe11	-0.0588790191833825	0.128299289049476	-0.458919294250158	0.646423166725058	   
df.mm.trans1:probe12	-0.0438896467673629	0.128299289049476	-0.342087996687477	0.732379121262073	   
df.mm.trans1:probe13	-0.124838515098763	0.128299289049476	-0.973025774528038	0.330849668286932	   
df.mm.trans1:probe14	-0.138289712669336	0.128299289049476	-1.07786811364175	0.281434160409581	   
df.mm.trans1:probe15	-0.0589297541517777	0.128299289049476	-0.459314736569215	0.646139339139055	   
df.mm.trans1:probe16	0.0198805359252154	0.128299289049476	0.154954373266627	0.876898375814814	   
df.mm.trans1:probe17	-0.0744421582637692	0.128299289049476	-0.580222687243903	0.561936211636385	   
df.mm.trans1:probe18	-0.0351930010839895	0.128299289049476	-0.274303944665025	0.78392549559737	   
df.mm.trans1:probe19	0.0441875953674838	0.128299289049476	0.344410290149338	0.73063290316891	   
df.mm.trans1:probe20	0.0161290851460083	0.128299289049476	0.125714532523936	0.899991101571632	   
df.mm.trans1:probe21	-0.179784984158962	0.128299289049476	-1.40129368986317	0.161533943147429	   
df.mm.trans1:probe22	-0.109717542353015	0.128299289049476	-0.855168747745005	0.392726884312979	   
df.mm.trans2:probe2	0.141966834757453	0.128299289049476	1.1065286160916	0.268847444278391	   
df.mm.trans2:probe3	0.0971523317692693	0.128299289049476	0.757232035259404	0.449145249353603	   
df.mm.trans2:probe4	0.168885133152842	0.128299289049476	1.31633724866328	0.188457199810603	   
df.mm.trans2:probe5	0.117963831205179	0.128299289049476	0.919442594570329	0.358155458319038	   
df.mm.trans2:probe6	0.0504522582554151	0.128299289049476	0.393238798353429	0.694253254208138	   
df.mm.trans3:probe2	0.146253380146198	0.128299289049476	1.13993913161747	0.254670315258934	   
df.mm.trans3:probe3	0.0959817755380534	0.128299289049476	0.748108397553474	0.454625902633843	   
df.mm.trans3:probe4	0.419052358779752	0.128299289049476	3.26620951592455	0.00113880080608758	** 
df.mm.trans3:probe5	0.224946623853676	0.128299289049476	1.75329594980787	0.0799538175431104	.  
df.mm.trans3:probe6	0.301904226194276	0.128299289049476	2.35312470108741	0.0188699616919355	*