fitVsDatCorrelation=0.907743983652383
cont.fitVsDatCorrelation=0.211030157890475

fstatistic=6870.72047269273,61,899
cont.fstatistic=1253.59232060602,61,899

residuals=-0.971045221690765,-0.110238403778446,0.00295302068582443,0.114585067677791,0.950044014406185
cont.residuals=-0.768377910522926,-0.312064101367926,-0.126692889722914,0.179955318284937,2.28663620534016

predictedValues:
Include	Exclude	Both
Lung	50.3844540988757	52.171907334769	68.8709319457769
cerebhem	51.5965070223561	48.291937212475	64.960494333495
cortex	53.2541826000666	48.599053982953	66.5902472195337
heart	60.5129161350767	50.4615169039999	76.0015854547209
kidney	158.410314869851	55.1137354689344	201.104925955316
liver	72.2673195215606	51.468884979437	98.1219094194426
stomach	65.170821004871	51.5973421938688	88.6146781442365
testicle	51.5505144851338	49.0813444522194	67.5806678168476


diffExp=-1.78745323589337,3.30456980988117,4.65512861711363,10.0513992310769,103.296579400917,20.7984345421236,13.5734788110022,2.46917003291438
diffExpScore=1.01636302161855
diffExp1.5=0,0,0,0,1,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,1,1,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,0,0,0,1,1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,0,0,1,1,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	80.3860426208813	65.0482993670872	72.0298570870254
cerebhem	74.8485244168162	63.5942639601756	72.6210511417508
cortex	68.896579729499	76.7038451977405	73.0496915050855
heart	77.3736584153093	83.8803240224576	72.869829322959
kidney	72.8675676039578	61.8639838873563	73.8055108996031
liver	74.0100933368704	74.0589122459137	79.8814738612511
stomach	80.5128569052909	59.2297195243327	69.5016519291444
testicle	75.6475815131418	69.6355865601846	68.4998574252271
cont.diffExp=15.3377432537941,11.2542604566406,-7.80726546824145,-6.50666560714824,11.0035837166015,-0.0488189090433195,21.2831373809582,6.01199495295725
cont.diffExpScore=1.5380669971884

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

tran.correlation=0.815848652178922
cont.tran.correlation=-0.333860868546676

tran.covariance=0.0137455719618436
cont.tran.covariance=-0.00217580380532647

tran.mean=60.620797016653
cont.tran.mean=72.4098649566884

weightedLogRatios:
wLogRatio
Lung	-0.137253676891449
cerebhem	0.258824185143371
cortex	0.359425717970404
heart	0.728769222277513
kidney	4.79042771758944
liver	1.39514066224326
stomach	0.948235483687629
testicle	0.192308903309275

cont.weightedLogRatios:
wLogRatio
Lung	0.90632954390942
cerebhem	0.689900328057052
cortex	-0.46011194402436
heart	-0.354389899026093
kidney	0.688674743251355
liver	-0.0028384404033702
stomach	1.30009274978798
testicle	0.354813254574572

varWeightedLogRatios=2.495602325008
cont.varWeightedLogRatios=0.386625406256059

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.7877946584584	0.0942244009728457	40.1997212967158	4.92616396229426e-203	***
df.mm.trans1	-0.0304672867967009	0.0805548100176762	-0.378218095108354	0.705357872994623	   
df.mm.trans2	0.176869633437526	0.0710195951930377	2.49043426615962	0.0129382690697423	*  
df.mm.exp2	0.00494663448335236	0.0903199719010604	0.0547678921863603	0.956335548766256	   
df.mm.exp3	0.0181296326378706	0.0903199719010604	0.200726730271023	0.840957658738611	   
df.mm.exp4	0.0513209830323983	0.0903199719010604	0.568213009284558	0.570032230990983	   
df.mm.exp5	0.128768120491809	0.0903199719010604	1.42568822577654	0.154305473280475	   
df.mm.exp6	-0.0068538665066455	0.0903199719010604	-0.0758842851961188	0.939528042603772	   
df.mm.exp7	-0.00580816199565204	0.0903199719010604	-0.0643065080004067	0.948740467363464	   
df.mm.exp8	-0.0192734118524695	0.0903199719010603	-0.2133903659047	0.83107087632617	   
df.mm.trans1:exp2	0.0188246652700998	0.0821090653646003	0.229264152338235	0.818715760559629	   
df.mm.trans2:exp2	-0.0822261955359033	0.058637601363584	-1.40227761067609	0.161177563698926	   
df.mm.trans1:exp3	0.0372640382696009	0.0821090653646003	0.453835859708452	0.65005650758534	   
df.mm.trans2:exp3	-0.0890697436499531	0.058637601363584	-1.51898682037954	0.129117399192697	   
df.mm.trans1:exp4	0.131853171977091	0.0821090653646003	1.60582965342017	0.108662520734790	   
df.mm.trans2:exp4	-0.0846541551149971	0.058637601363584	-1.44368379924167	0.149176314926730	   
df.mm.trans1:exp5	1.01673779881863	0.0821090653646003	12.3827715527375	1.25179570520213e-32	***
df.mm.trans2:exp5	-0.0739133291986223	0.058637601363584	-1.26051078966073	0.207812218979306	   
df.mm.trans1:exp6	0.36754320425558	0.0821090653646003	4.47628045239009	8.57106638175643e-06	***
df.mm.trans2:exp6	-0.0067128599253246	0.058637601363584	-0.114480465933477	0.908882492797259	   
df.mm.trans1:exp7	0.26313732307884	0.0821090653646003	3.20472924530799	0.00139954465193538	** 
df.mm.trans2:exp7	-0.00526585107190621	0.058637601363584	-0.0898033164633588	0.92846351540793	   
df.mm.trans1:exp8	0.0421529255010341	0.0821090653646003	0.513377241768088	0.607813599837992	   
df.mm.trans2:exp8	-0.0417917519607389	0.058637601363584	-0.712712508508117	0.476208614064143	   
df.mm.trans1:probe2	0.262429968941853	0.0594936700237959	4.4110569886996	1.15322913981469e-05	***
df.mm.trans1:probe3	0.213695679523294	0.0594936700237959	3.59190615468539	0.000346068884800655	***
df.mm.trans1:probe4	0.560618739478179	0.0594936700237959	9.42316618312413	3.59219991099099e-20	***
df.mm.trans1:probe5	0.0731891152608323	0.0594936700237959	1.23020004029939	0.218944013419212	   
df.mm.trans1:probe6	0.138446619671169	0.0594936700237959	2.32708151330712	0.0201822284703227	*  
df.mm.trans1:probe7	0.424875731832109	0.0594936700237959	7.14152836196136	1.90467500404119e-12	***
df.mm.trans1:probe8	0.619110708018609	0.0594936700237959	10.4063290728405	5.03504201510547e-24	***
df.mm.trans1:probe9	0.0678064050174956	0.0594936700237959	1.13972469660007	0.254704583386900	   
df.mm.trans1:probe10	0.0983981834113348	0.0594936700237959	1.65392693663003	0.0984914511444516	.  
df.mm.trans1:probe11	0.102515178551662	0.0594936700237959	1.72312749424700	0.0852093345012263	.  
df.mm.trans1:probe12	0.161432052498189	0.0594936700237959	2.7134324111056	0.00678638268794381	** 
df.mm.trans1:probe13	0.154464066217023	0.0594936700237959	2.59631093787359	0.00957688078890221	** 
df.mm.trans1:probe14	0.149120110175894	0.0594936700237959	2.5064869946038	0.0123693182728821	*  
df.mm.trans1:probe15	0.436742259622368	0.0594936700237959	7.34098702345447	4.74509334146706e-13	***
df.mm.trans1:probe16	0.407930055291228	0.0594936700237959	6.85669677342256	1.30908052552157e-11	***
df.mm.trans1:probe17	0.379271983726	0.0594936700237959	6.37499726566375	2.92098568906018e-10	***
df.mm.trans1:probe18	0.389680754414501	0.0594936700237959	6.54995320104877	9.67310132348253e-11	***
df.mm.trans1:probe19	0.416662490756083	0.0594936700237959	7.0034760099592	4.88906416799283e-12	***
df.mm.trans1:probe20	0.463690281732028	0.0594936700237959	7.79394314633076	1.79146798294864e-14	***
df.mm.trans2:probe2	0.00765724023211666	0.0594936700237959	0.128706805766966	0.897618437149688	   
df.mm.trans2:probe3	-0.0722172912766013	0.0594936700237959	-1.21386512628514	0.225118023995278	   
df.mm.trans2:probe4	-0.0297781026426708	0.0594936700237959	-0.500525562312097	0.616827569279334	   
df.mm.trans2:probe5	-0.0737959586647964	0.0594936700237959	-1.24040017425854	0.215151043404882	   
df.mm.trans2:probe6	-0.0342681984117082	0.0594936700237959	-0.575997385906801	0.564761109263101	   
df.mm.trans3:probe2	0.0406926878371787	0.0594936700237959	0.683983486325566	0.494161857727415	   
df.mm.trans3:probe3	0.443447668261458	0.0594936700237959	7.4536949575323	2.13263795525822e-13	***
df.mm.trans3:probe4	1.07629232226686	0.0594936700237959	18.0908712109435	1.27191128753455e-62	***
df.mm.trans3:probe5	-0.0187959586047919	0.0594936700237959	-0.315932074744658	0.752127397710939	   
df.mm.trans3:probe6	0.412705395228525	0.0594936700237959	6.93696312672346	7.65635673491132e-12	***
df.mm.trans3:probe7	0.156376694040802	0.0594936700237959	2.62845936346263	0.00872356944854346	** 
df.mm.trans3:probe8	0.359641438097529	0.0594936700237959	6.04503702584967	2.18707775115992e-09	***
df.mm.trans3:probe9	0.0470723497752842	0.0594936700237959	0.791216103435147	0.429026586232554	   
df.mm.trans3:probe10	0.606299074118792	0.0594936700237959	10.1909845850204	3.72798588270221e-23	***
df.mm.trans3:probe11	0.258984931956122	0.0594936700237959	4.35315104703635	1.49606173286973e-05	***
df.mm.trans3:probe12	0.29309682245747	0.0594936700237959	4.92652112973093	9.96186466375993e-07	***
df.mm.trans3:probe13	-0.0649826188336436	0.0594936700237959	-1.09226105580060	0.275010990767316	   
df.mm.trans3:probe14	0.561689941897336	0.0594936700237959	9.44117150064326	3.07262493591019e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31636604527352	0.219540005429744	19.6609544434708	7.30767018979138e-72	***
df.mm.trans1	0.129636633506841	0.187690271798801	0.69069447374347	0.489935850661689	   
df.mm.trans2	-0.164866281664840	0.165473509550790	-0.996330361955825	0.319357747756612	   
df.mm.exp2	-0.102155074848320	0.210442803741331	-0.485429166653211	0.627490222945134	   
df.mm.exp3	-0.00347144753594932	0.210442803741331	-0.0164959194338444	0.98684241758859	   
df.mm.exp4	0.204472863414409	0.210442803741331	0.971631530179288	0.331495235208299	   
df.mm.exp5	-0.172741492405547	0.210442803741331	-0.820847704623224	0.411950626691542	   
df.mm.exp6	-0.056371499257752	0.210442803741331	-0.267870881092431	0.788860154712073	   
df.mm.exp7	-0.0564001095516504	0.210442803741331	-0.268006833918519	0.788755537885074	   
df.mm.exp8	0.0576396360479741	0.210442803741331	0.273896921269034	0.784226754853095	   
df.mm.trans1:exp2	0.0307809097773869	0.191311639764847	0.160894077408054	0.872212974477247	   
df.mm.trans2:exp2	0.0795482906778232	0.136623838292782	0.582243125883732	0.560548914850587	   
df.mm.trans1:exp3	-0.150762578629946	0.191311639764847	-0.788047077612518	0.430876903291601	   
df.mm.trans2:exp3	0.168293226464887	0.136623838292782	1.23179987158784	0.218345948449488	   
df.mm.trans1:exp4	-0.242667033167930	0.191311639764847	-1.26843841527994	0.204969789030783	   
df.mm.trans2:exp4	0.0497881443966704	0.136623838292782	0.364417696200097	0.715631820605518	   
df.mm.trans1:exp5	0.0745445817211054	0.191311639764847	0.389650006725848	0.696887656967541	   
df.mm.trans2:exp5	0.122549598115726	0.136623838292782	0.896985472279765	0.369966805664663	   
df.mm.trans1:exp6	-0.0262675822670904	0.191311639764847	-0.137302582840111	0.890822372600323	   
df.mm.trans2:exp6	0.186102326020715	0.136623838292782	1.36215120542801	0.173491358328751	   
df.mm.trans1:exp7	0.0579764324605334	0.191311639764847	0.303047072994596	0.76192409840188	   
df.mm.trans2:exp7	-0.0373065166795183	0.136623838292782	-0.273060083406317	0.784869742548596	   
df.mm.trans1:exp8	-0.118394727619808	0.191311639764847	-0.61885794176107	0.536166723914924	   
df.mm.trans2:exp8	0.0105060406307246	0.136623838292782	0.0768975660617172	0.938722143766836	   
df.mm.trans1:probe2	-0.0788975699941911	0.138618452388183	-0.569170760709753	0.569382432165476	   
df.mm.trans1:probe3	-0.200164573601027	0.138618452388183	-1.4439965975128	0.149088326934589	   
df.mm.trans1:probe4	-0.169125144079792	0.138618452388183	-1.22007670094439	0.222755754204478	   
df.mm.trans1:probe5	-0.178418462294960	0.138618452388183	-1.28711913328337	0.198384056780835	   
df.mm.trans1:probe6	-0.045304914912003	0.138618452388183	-0.326831775506570	0.743871202868706	   
df.mm.trans1:probe7	-0.185065819447496	0.138618452388183	-1.33507347874035	0.182190267269322	   
df.mm.trans1:probe8	-0.110764804898056	0.138618452388183	-0.799062484032593	0.424465237648388	   
df.mm.trans1:probe9	-0.0476205228892478	0.138618452388183	-0.343536679776893	0.731275076014916	   
df.mm.trans1:probe10	-0.108904787699942	0.138618452388183	-0.785644232955136	0.432282951470747	   
df.mm.trans1:probe11	-0.149743644536224	0.138618452388183	-1.08025765658446	0.280317174324313	   
df.mm.trans1:probe12	-0.185538871102149	0.138618452388183	-1.33848609550603	0.181076426522558	   
df.mm.trans1:probe13	-0.0445037182342114	0.138618452388183	-0.321051905193577	0.748245676076157	   
df.mm.trans1:probe14	-0.108837747461797	0.138618452388183	-0.78516060154106	0.432566274575445	   
df.mm.trans1:probe15	-0.117240805498363	0.138618452388183	-0.845780655305868	0.397900173672918	   
df.mm.trans1:probe16	-0.108057821795145	0.138618452388183	-0.779534181297467	0.435870273618283	   
df.mm.trans1:probe17	0.00464759813985615	0.138618452388183	0.0335279903922252	0.97326098763893	   
df.mm.trans1:probe18	-0.0727721264326115	0.138618452388183	-0.524981524312668	0.599725471328905	   
df.mm.trans1:probe19	0.0181353149624518	0.138618452388183	0.130829010496136	0.895939842796054	   
df.mm.trans1:probe20	-0.123333553107317	0.138618452388183	-0.889734021571215	0.373846743735822	   
df.mm.trans2:probe2	0.0253389987809165	0.138618452388183	0.182796722545696	0.8549987343901	   
df.mm.trans2:probe3	0.0177408154137258	0.138618452388183	0.127983072297221	0.898190991937752	   
df.mm.trans2:probe4	0.0059634291837944	0.138618452388183	0.043020457096827	0.965694779460718	   
df.mm.trans2:probe5	0.202110012804279	0.138618452388183	1.45803108693131	0.145181216557700	   
df.mm.trans2:probe6	0.221452694520914	0.138618452388183	1.59757009767187	0.110490089456465	   
df.mm.trans3:probe2	0.046970782550479	0.138618452388183	0.338849422578628	0.73480229600095	   
df.mm.trans3:probe3	0.0761261116397806	0.138618452388183	0.549177330494204	0.58302013675234	   
df.mm.trans3:probe4	-0.130957895681674	0.138618452388183	-0.944736385563901	0.345047308676694	   
df.mm.trans3:probe5	-0.188834581326172	0.138618452388183	-1.36226150323309	0.173456571572406	   
df.mm.trans3:probe6	-0.0299498151726459	0.138618452388183	-0.216059367686311	0.828990484238946	   
df.mm.trans3:probe7	0.0310268241389697	0.138618452388183	0.223828960751076	0.822941248233664	   
df.mm.trans3:probe8	-0.0545179983433025	0.138618452388183	-0.393295390361391	0.69419457145212	   
df.mm.trans3:probe9	-0.116381222844923	0.138618452388183	-0.839579585833291	0.401367356308582	   
df.mm.trans3:probe10	0.0762167284552815	0.138618452388183	0.549831044440217	0.582571828784306	   
df.mm.trans3:probe11	0.0969572207833863	0.138618452388183	0.6994539263205	0.484449328841825	   
df.mm.trans3:probe12	0.0138771986455556	0.138618452388183	0.100110760194424	0.920278712885368	   
df.mm.trans3:probe13	0.148460686440975	0.138618452388183	1.07100233686948	0.284455819721642	   
df.mm.trans3:probe14	-0.0257735913728848	0.138618452388183	-0.185931893834085	0.852540114203505