fitVsDatCorrelation=0.842882034276394
cont.fitVsDatCorrelation=0.226977986896567

fstatistic=15209.4674377184,51,669
cont.fstatistic=4633.99458665512,51,669

residuals=-0.45448180488452,-0.0737510919751482,-0.00341040986851525,0.0648037724771334,0.672154823059747
cont.residuals=-0.443414764164081,-0.154223001869863,-0.0398139628850127,0.102744617288326,1.12139228533344

predictedValues:
Include	Exclude	Both
Lung	51.0339503729628	46.9852644741367	72.0682405137079
cerebhem	60.3491041181456	54.7018233349236	56.5122075572957
cortex	55.9272957501643	46.7689522412523	70.6818670496872
heart	49.8469627204827	45.8486586019914	67.5062732791479
kidney	51.1938414353377	46.1740366128864	67.2688638410172
liver	51.2794010906726	50.5307379727766	62.4339448079546
stomach	51.4285194436365	49.1216614590322	67.5979352737463
testicle	53.1541523019141	48.8350643642341	55.059289820899


diffExp=4.04868589882608,5.64728078322204,9.15834350891201,3.99830411849132,5.01980482245131,0.748663117895987,2.30685798460433,4.31908793767999
diffExpScore=0.972411531360516
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	51.5489875256044	54.4768045249836	50.4235936963981
cerebhem	51.4708133402487	49.8074835499587	55.1974525709445
cortex	52.8006760633712	47.9893530106599	53.8073996968647
heart	54.385635755245	51.666500151057	52.3539079590445
kidney	53.9614669627382	53.7224809466068	53.8545957206147
liver	52.1199865020482	52.7283351434983	56.6742280189871
stomach	53.0109218749242	54.138947194005	46.6830719715475
testicle	52.9567723065871	49.1821391932475	51.4007515012043
cont.diffExp=-2.92781699937920,1.66332979028998,4.81132305271128,2.71913560418799,0.238986016131420,-0.608348641450085,-1.12802531908084,3.77463311333961
cont.diffExpScore=1.87270175814750

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=0.718026994539911
cont.tran.correlation=0.0418134361889873

tran.covariance=0.00261659038854916
cont.tran.covariance=4.12277195526884e-05

tran.mean=50.8237141434094
cont.tran.mean=52.247956502799

weightedLogRatios:
wLogRatio
Lung	0.321632112743774
cerebhem	0.398009063269973
cortex	0.703642723585382
heart	0.323338838913358
kidney	0.400836834316772
liver	0.0577987774054288
stomach	0.179773267965639
testicle	0.333127918495653

cont.weightedLogRatios:
wLogRatio
Lung	-0.219320968066428
cerebhem	0.12892149267716
cortex	0.3744164180316
heart	0.203646474955153
kidney	0.0176927388654509
liver	-0.0459462323059671
stomach	-0.0838239740906362
testicle	0.290790646978578

varWeightedLogRatios=0.0350391590860129
cont.varWeightedLogRatios=0.0406977866141533

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.75293707932618	0.0644099204600145	58.2664448662996	2.74652459819021e-264	***
df.mm.trans1	0.254803160311608	0.0577853574943954	4.40947622996573	1.20710591645216e-05	***
df.mm.trans2	0.159649020241966	0.0532279634702111	2.99934489004663	0.00280608441157915	** 
df.mm.exp2	0.562874786626326	0.0729349557520503	7.71748993088959	4.32800062935807e-14	***
df.mm.exp3	0.106371383039675	0.0729349557520503	1.45844172993393	0.145188127366486	   
df.mm.exp4	0.0173712966977972	0.0729349557520503	0.238175186625912	0.81181815232435	   
df.mm.exp5	0.0546277395300579	0.0729349557520503	0.748992564220788	0.454124946922459	   
df.mm.exp6	0.221050161916786	0.0729349557520503	3.03078489096872	0.00253333723982382	** 
df.mm.exp7	0.116203859543078	0.0729349557520503	1.59325330830562	0.111575704875851	   
df.mm.exp8	0.348522530990033	0.0729349557520503	4.77853900638359	2.17188829563304e-06	***
df.mm.trans1:exp2	-0.39521978879886	0.0696917633401676	-5.67096841659438	2.11344560394427e-08	***
df.mm.trans2:exp2	-0.410811775116467	0.0608319582516698	-6.75322292629286	3.14265415456010e-11	***
df.mm.trans1:exp3	-0.0148099308251505	0.0696917633401676	-0.212506186030374	0.831776884043022	   
df.mm.trans2:exp3	-0.110985844645675	0.0608319582516698	-1.82446608387177	0.068527516273794	.  
df.mm.trans1:exp4	-0.0409048353577694	0.0696917633401676	-0.58693930813763	0.557442480588131	   
df.mm.trans2:exp4	-0.0418593853752461	0.0608319582516698	-0.688115039829366	0.491618859980123	   
df.mm.trans1:exp5	-0.0514996040215516	0.0696917633401676	-0.738962562479307	0.460188909483501	   
df.mm.trans2:exp5	-0.0720441082122181	0.0608319582516698	-1.18431348065703	0.236709582388037	   
df.mm.trans1:exp6	-0.216252133407767	0.0696917633401676	-3.10297979335426	0.00199653417593248	** 
df.mm.trans2:exp6	-0.148302368798271	0.0608319582516698	-2.43790226487079	0.0150320723542093	*  
df.mm.trans1:exp7	-0.108502092803066	0.0696917633401676	-1.55688545680017	0.119970596830469	   
df.mm.trans2:exp7	-0.0717377825094665	0.0608319582516698	-1.17927787582767	0.238706757389979	   
df.mm.trans1:exp8	-0.307817409876883	0.0696917633401676	-4.41684060101073	1.16784710346475e-05	***
df.mm.trans2:exp8	-0.309907974822457	0.0608319582516698	-5.09449282464863	4.5538652328712e-07	***
df.mm.trans1:probe2	0.302673247778309	0.0348458816700838	8.6860550880583	2.86500580454736e-17	***
df.mm.trans1:probe3	-0.149819689162442	0.0348458816700838	-4.29949486085373	1.96669431554319e-05	***
df.mm.trans1:probe4	-0.128543703639971	0.0348458816700838	-3.68892097083398	0.000243504177352003	***
df.mm.trans1:probe5	0.306860089016625	0.0348458816700838	8.80620820336635	1.10245277551713e-17	***
df.mm.trans1:probe6	0.101190197122513	0.0348458816700838	2.90393562374366	0.00380651722303716	** 
df.mm.trans1:probe7	-0.0787900420752353	0.0348458816700838	-2.26110054614801	0.0240734003839125	*  
df.mm.trans1:probe8	-0.089961127445927	0.0348458816700838	-2.58168607405796	0.0100434731581711	*  
df.mm.trans1:probe9	0.0281365814439458	0.0348458816700838	0.80745787150227	0.419689726021939	   
df.mm.trans1:probe10	-0.258304041587239	0.0348458816700838	-7.41275666469938	3.75071647150865e-13	***
df.mm.trans1:probe11	-0.0791974338463884	0.0348458816700838	-2.27279179204645	0.0233550739118188	*  
df.mm.trans1:probe12	-0.275201654587358	0.0348458816700838	-7.89768091371401	1.16800248669883e-14	***
df.mm.trans1:probe13	-0.207914820472947	0.0348458816700838	-5.96669708178019	3.92473600657868e-09	***
df.mm.trans1:probe14	-0.266907072639935	0.0348458816700838	-7.65964469394049	6.55627222224696e-14	***
df.mm.trans1:probe15	-0.24391325601209	0.0348458816700838	-6.99977283747412	6.24693102353338e-12	***
df.mm.trans1:probe16	-0.192294539792493	0.0348458816700838	-5.51842945496723	4.89451341171906e-08	***
df.mm.trans1:probe17	-0.104134042587829	0.0348458816700838	-2.98841750005801	0.00290700580948819	** 
df.mm.trans1:probe18	-0.116609537923808	0.0348458816700838	-3.34643671891708	0.000864368620741142	***
df.mm.trans1:probe19	-0.0937293971943224	0.0348458816700838	-2.68982711018019	0.00732720032760124	** 
df.mm.trans1:probe20	-0.0981928960878206	0.0348458816700838	-2.81791969040985	0.00497632180062401	** 
df.mm.trans1:probe21	-0.161326095545114	0.0348458816700838	-4.62970336272527	4.39989691472359e-06	***
df.mm.trans2:probe2	-0.120495456901931	0.0348458816700838	-3.45795402862141	0.00057878514179322	***
df.mm.trans2:probe3	-0.131486824238735	0.0348458816700838	-3.77338204507593	0.000175336428038050	***
df.mm.trans2:probe4	-0.158065555488665	0.0348458816700838	-4.53613304967309	6.79071008727267e-06	***
df.mm.trans2:probe5	-0.131033487116217	0.0348458816700838	-3.76037226886164	0.000184510638885932	***
df.mm.trans2:probe6	-0.0236872958433214	0.0348458816700838	-0.679773181450526	0.496883232216846	   
df.mm.trans3:probe2	0.403979376873748	0.0348458816700838	11.5933177038989	1.89929907324701e-28	***
df.mm.trans3:probe3	-0.073306791320587	0.0348458816700838	-2.10374333514204	0.0357735375953532	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07377734234595	0.116574990099473	34.9455516905454	4.9820700199219e-153	***
df.mm.trans1	-0.067193272983799	0.104585247578213	-0.642473719188254	0.520785754593805	   
df.mm.trans2	-0.106824757250190	0.0963368572766214	-1.10886695154954	0.267886042531218	   
df.mm.exp2	-0.181585023786308	0.132004381995457	-1.3755984539404	0.169406371753605	   
df.mm.exp3	-0.167756210890221	0.132004381995457	-1.27083819759858	0.204227938420663	   
df.mm.exp4	-0.0369652906248019	0.132004381995457	-0.280030784327100	0.7795404498452	   
df.mm.exp5	-0.0340343535994349	0.132004381995457	-0.257827453035660	0.796619365240343	   
df.mm.exp6	-0.138466483252181	0.132004381995457	-1.04895368743855	0.294578223036830	   
df.mm.exp7	0.0988218077937221	0.132004381995457	0.748625206980804	0.454346245072973	   
df.mm.exp8	-0.0944946772731281	0.132004381995457	-0.71584500336042	0.474336836412338	   
df.mm.trans1:exp2	0.180067369904174	0.126134554481221	1.42758160636292	0.153878923458773	   
df.mm.trans2:exp2	0.0919752625872288	0.110099265458996	0.835384888389496	0.403799273385618	   
df.mm.trans1:exp3	0.191747636109322	0.126134554481221	1.52018324318788	0.128937385928400	   
df.mm.trans2:exp3	0.0409603788887202	0.110099265458996	0.372031354777522	0.70998731539427	   
df.mm.trans1:exp4	0.0905327913612504	0.126134554481221	0.717747739575428	0.473163391174287	   
df.mm.trans2:exp4	-0.0160001100395134	0.110099265458996	-0.145324403144835	0.884498520731749	   
df.mm.trans1:exp5	0.0797720010422953	0.126134554481221	0.632435745861944	0.527318266270447	   
df.mm.trans2:exp5	0.0200909010832694	0.110099265458996	0.182479883035658	0.855261421954446	   
df.mm.trans1:exp6	0.149482406888781	0.126134554481221	1.18510274606024	0.236397627626264	   
df.mm.trans2:exp6	0.105844457037033	0.110099265458996	0.961354797380123	0.33672113429119	   
df.mm.trans1:exp7	-0.0708564120016262	0.126134554481221	-0.561752584714406	0.574472741294002	   
df.mm.trans2:exp7	-0.105042975815915	0.110099265458996	-0.954075173689841	0.340390188805491	   
df.mm.trans1:exp8	0.12143807153035	0.126134554481221	0.96276608761027	0.336012780987624	   
df.mm.trans2:exp8	-0.00774979569294639	0.110099265458996	-0.0703891679988786	0.943904947852558	   
df.mm.trans1:probe2	-0.0164465577171709	0.0630672772406107	-0.260777988788464	0.7943439697699	   
df.mm.trans1:probe3	-0.118523181892446	0.0630672772406107	-1.87931344237778	0.0606361617244977	.  
df.mm.trans1:probe4	-0.093321808927762	0.0630672772406107	-1.47971837394733	0.139419148409267	   
df.mm.trans1:probe5	-0.0748117265518695	0.0630672772406107	-1.18622096632541	0.235956153452254	   
df.mm.trans1:probe6	-0.0675641672399628	0.0630672772406107	-1.07130306231861	0.284419506770860	   
df.mm.trans1:probe7	-0.129572013716895	0.0630672772406107	-2.05450463990319	0.0403147869170238	*  
df.mm.trans1:probe8	-0.0814696530802776	0.0630672772406107	-1.29178960381402	0.196876103516398	   
df.mm.trans1:probe9	-0.0248762336297845	0.0630672772406107	-0.394439632059556	0.693382195241106	   
df.mm.trans1:probe10	-0.00571844218437664	0.0630672772406107	-0.0906720954919295	0.92778028426216	   
df.mm.trans1:probe11	-0.0555243802186786	0.0630672772406107	-0.88039919666811	0.378959245062859	   
df.mm.trans1:probe12	-0.0610049454942389	0.0630672772406107	-0.967299496084099	0.333743885623818	   
df.mm.trans1:probe13	-0.057449866597083	0.0630672772406107	-0.910929869033405	0.362660423335229	   
df.mm.trans1:probe14	-0.110818573997937	0.0630672772406107	-1.75714853798346	0.0793498880661078	.  
df.mm.trans1:probe15	-0.100120798774062	0.0630672772406107	-1.58752372315181	0.112866566909775	   
df.mm.trans1:probe16	-0.069191207722242	0.0630672772406107	-1.09710155170117	0.27299173502659	   
df.mm.trans1:probe17	-0.104308135753754	0.0630672772406107	-1.65391848701195	0.0986131701068286	.  
df.mm.trans1:probe18	-0.0633848948065544	0.0630672772406107	-1.00503617057594	0.315242804019302	   
df.mm.trans1:probe19	-0.0387116679865429	0.0630672772406107	-0.613815431398004	0.539545952535582	   
df.mm.trans1:probe20	-0.0817889178788292	0.0630672772406107	-1.29685189304737	0.195129246991555	   
df.mm.trans1:probe21	-0.182628819121354	0.0630672772406107	-2.89577776482404	0.00390560121757088	** 
df.mm.trans2:probe2	0.0243252109903752	0.0630672772406107	0.385702571201402	0.699839573005268	   
df.mm.trans2:probe3	0.0481721049446583	0.0630672772406107	0.763820907645573	0.445243266732786	   
df.mm.trans2:probe4	0.0751817265439482	0.0630672772406107	1.19208771701241	0.233649534228471	   
df.mm.trans2:probe5	0.061038305001963	0.0630672772406107	0.967828447216662	0.333479801157662	   
df.mm.trans2:probe6	0.0686844409046548	0.0630672772406107	1.08906621483933	0.276516835285271	   
df.mm.trans3:probe2	0.0370311682982807	0.0630672772406107	0.587169288393432	0.557288118833723	   
df.mm.trans3:probe3	0.0250684786737237	0.0630672772406107	0.397487885485905	0.691134508039179