fitVsDatCorrelation=0.860309717218863
cont.fitVsDatCorrelation=0.249283275112527

fstatistic=11124.3045656151,61,899
cont.fstatistic=3071.73389960700,61,899

residuals=-0.489431287900413,-0.0868752950403963,-0.00195101359464783,0.0776399156428731,1.17433516483807
cont.residuals=-0.547816673799369,-0.216262760397534,-0.0587776699257865,0.167405907725709,1.10137735907045

predictedValues:
Include	Exclude	Both
Lung	52.6232696270131	49.2673502119166	88.4241506324972
cerebhem	73.0187460800128	73.187184808136	79.1233056494828
cortex	61.435233695704	48.0264092143392	84.382575526424
heart	54.9061989399972	49.6342200159889	78.5724666083022
kidney	51.2263288724259	48.0906618174422	79.6279619600417
liver	53.1803443034987	52.1021305870729	76.3683413584383
stomach	56.3268266511922	51.5373377442331	84.3530269287585
testicle	55.2006104164398	63.3109200627775	88.0759846280726


diffExp=3.35591941509649,-0.168438728123235,13.4088244813648,5.27197892400834,3.13566705498374,1.07821371642576,4.7894889069591,-8.1103096463377
diffExpScore=1.65473975998526
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=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	61.780292042706	60.047683561455	64.5035158234688
cerebhem	61.479418897662	69.7472937098952	64.2099659740758
cortex	62.899895089564	59.7051433049146	66.930692892335
heart	67.1517329316207	62.4182562453735	61.541775265232
kidney	65.6030618125131	75.0083025068009	62.9608705720464
liver	67.6302335914998	62.0964488138467	65.8199936714127
stomach	63.982901409967	63.0911653112495	63.3994087120821
testicle	62.2921545806608	62.7571622356332	63.4811679642519
cont.diffExp=1.73260848125092,-8.26787481223315,3.19475178464947,4.73347668624715,-9.40524069428778,5.53378477765305,0.891736098717523,-0.465007654972389
cont.diffExpScore=11.2146502944398

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=0.748249502891842
cont.tran.correlation=0.0737410162757296

tran.covariance=0.0124651060063074
cont.tran.covariance=0.000224151802720101

tran.mean=55.8171108155119
cont.tran.mean=64.2306966278351

weightedLogRatios:
wLogRatio
Lung	0.258988325825021
cerebhem	-0.0098890263614629
cortex	0.98366598860149
heart	0.399255884975464
kidney	0.246640947427904
liver	0.0811833633923075
stomach	0.354279012420944
testicle	-0.55923560810378

cont.weightedLogRatios:
wLogRatio
Lung	0.116892609714390
cerebhem	-0.527642244913124
cortex	0.214524924111049
heart	0.304843890200717
kidney	-0.569481668767426
liver	0.356094687267699
stomach	0.0582682360396655
testicle	-0.0307570361332036

varWeightedLogRatios=0.187843283970946
cont.varWeightedLogRatios=0.126532695488185

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.56387583695113	0.0716583761415309	49.7342533957537	2.47112383961180e-260	***
df.mm.trans1	0.40427189718054	0.0614496775807682	6.57890998124723	8.03601567178378e-11	***
df.mm.trans2	0.335914121149217	0.0540996800082255	6.20917020392992	8.12835473043367e-10	***
df.mm.exp2	0.834454196990983	0.0689096780132938	12.109390452093	2.24794667534905e-31	***
df.mm.exp3	0.176098756778141	0.0689096780132938	2.55550108279665	0.0107670572819294	*  
df.mm.exp4	0.168010555619992	0.0689096780132938	2.43812713197673	0.0149563343768715	*  
df.mm.exp5	0.0537014659661196	0.0689096780132938	0.779302233218383	0.436006792449434	   
df.mm.exp6	0.213051597160128	0.0689096780132938	3.09175145353352	0.00205115779906377	** 
df.mm.exp7	0.160191897503062	0.0689096780132938	2.32466472230734	0.0203118204074576	*  
df.mm.exp8	0.302557060131373	0.0689096780132938	4.39063232994652	1.26454448636858e-05	***
df.mm.trans1:exp2	-0.506896403457377	0.0630242199265237	-8.0428826258911	2.75798385994494e-15	***
df.mm.trans2:exp2	-0.43869545642341	0.0450646616649267	-9.73479973477402	2.32538772890963e-21	***
df.mm.trans1:exp3	-0.0212736577803053	0.0630242199265237	-0.337547339818042	0.735783128635556	   
df.mm.trans2:exp3	-0.201609299350841	0.0450646616649267	-4.47377816458236	8.66981583841694e-06	***
df.mm.trans1:exp4	-0.125542710446927	0.0630242199265237	-1.99197563402277	0.0466758443499864	*  
df.mm.trans2:exp4	-0.160591634242972	0.0450646616649267	-3.56358237940481	0.000385074741038511	***
df.mm.trans1:exp5	-0.0806062405700475	0.0630242199265237	-1.27897244367359	0.201236793321887	   
df.mm.trans2:exp5	-0.0778750428576123	0.0450646616649267	-1.72807339455122	0.0843184584448778	.  
df.mm.trans1:exp6	-0.202521147327033	0.0630242199265237	-3.21338602148732	0.00135850231767137	** 
df.mm.trans2:exp6	-0.157107349197175	0.0450646616649267	-3.48626492228721	0.000513521626576582	***
df.mm.trans1:exp7	-0.0921793914174318	0.0630242199265237	-1.46260265537437	0.143925629529503	   
df.mm.trans2:exp7	-0.115146941925845	0.0450646616649267	-2.55514937140784	0.0107778630397079	*  
df.mm.trans1:exp8	-0.254741458905675	0.0630242199265237	-4.04196131586656	5.7544727621059e-05	***
df.mm.trans2:exp8	-0.0517608271656689	0.0450646616649267	-1.14859016473996	0.251030529100422	   
df.mm.trans1:probe2	0.0619239120882878	0.0451474251169558	1.37159343922432	0.170532250740612	   
df.mm.trans1:probe3	0.166907931230500	0.0451474251169558	3.6969534984137	0.000231416760877715	***
df.mm.trans1:probe4	0.126082531677540	0.0451474251169558	2.79268488404199	0.00533822880835104	** 
df.mm.trans1:probe5	0.0679479808601782	0.0451474251169558	1.50502449883148	0.132669019470004	   
df.mm.trans1:probe6	0.0785164510994893	0.0451474251169558	1.73911249414756	0.0823572724923325	.  
df.mm.trans1:probe7	0.0681848030336985	0.0451474251169558	1.5102700288458	0.131325920092340	   
df.mm.trans1:probe8	-0.0324878680699364	0.0451474251169558	-0.719595148245456	0.471961321809045	   
df.mm.trans1:probe9	0.108179447725428	0.0451474251169558	2.39613770763640	0.0167723572567518	*  
df.mm.trans1:probe10	-0.192686109182893	0.0451474251169558	-4.26793130912192	2.18235176406002e-05	***
df.mm.trans1:probe11	-0.0179408431206226	0.0451474251169558	-0.397383529052794	0.691178989045926	   
df.mm.trans1:probe12	-0.168659609332270	0.0451474251169558	-3.73575256828826	0.000198953485505842	***
df.mm.trans1:probe13	0.0513639153541639	0.0451474251169558	1.13769312914533	0.255551761529382	   
df.mm.trans1:probe14	-0.108816922979723	0.0451474251169558	-2.41025756613648	0.0161411324438014	*  
df.mm.trans1:probe15	0.104152618263943	0.0451474251169558	2.30694481455215	0.0212843961183001	*  
df.mm.trans1:probe16	-0.109391054215141	0.0451474251169558	-2.42297437631848	0.0155906293847865	*  
df.mm.trans1:probe17	-0.0475661997418991	0.0451474251169558	-1.05357502933284	0.292360607048151	   
df.mm.trans1:probe18	-0.129604088278740	0.0451474251169558	-2.87068615636433	0.00419194916052932	** 
df.mm.trans1:probe19	-0.0764396196676085	0.0451474251169558	-1.69311138940015	0.0907807131711092	.  
df.mm.trans1:probe20	-0.0261141912555638	0.0451474251169558	-0.578420390263988	0.563125199332388	   
df.mm.trans1:probe21	-0.0931900969541125	0.0451474251169558	-2.06412872301578	0.0392919433392322	*  
df.mm.trans2:probe2	-0.108297220460448	0.0451474251169558	-2.39874633337120	0.0166541300743951	*  
df.mm.trans2:probe3	-0.103253404985787	0.0451474251169558	-2.28702754848822	0.0224258470407592	*  
df.mm.trans2:probe4	0.105651589764388	0.0451474251169558	2.34014651977812	0.0194941027114299	*  
df.mm.trans2:probe5	0.0114651882268237	0.0451474251169558	0.253949991547087	0.799592261551821	   
df.mm.trans2:probe6	0.0463949323202169	0.0451474251169558	1.02763185718852	0.304399440759471	   
df.mm.trans3:probe2	-0.0956356942957595	0.0451474251169558	-2.11829786633484	0.0344236656718822	*  
df.mm.trans3:probe3	0.176683971264331	0.0451474251169558	3.91348943614449	9.7838025574869e-05	***
df.mm.trans3:probe4	0.568980963813846	0.0451474251169558	12.6027334303093	1.18643536711137e-33	***
df.mm.trans3:probe5	0.116831208877498	0.0451474251169558	2.58777125328506	0.00981573575577764	** 
df.mm.trans3:probe6	0.0787923327062023	0.0451474251169558	1.74522317722635	0.0812876896634197	.  
df.mm.trans3:probe7	0.303391709972430	0.0451474251169558	6.72002244173358	3.22276192894774e-11	***
df.mm.trans3:probe8	0.150310094301257	0.0451474251169558	3.32931709642075	0.000905993240746071	***
df.mm.trans3:probe9	0.428615038144832	0.0451474251169558	9.49367626247768	1.94563582978259e-20	***
df.mm.trans3:probe10	0.281796139401515	0.0451474251169558	6.24168795167195	6.66266974689818e-10	***
df.mm.trans3:probe11	-0.266209930588388	0.0451474251169558	-5.89645876589335	5.25170870038439e-09	***
df.mm.trans3:probe12	0.466179186807279	0.0451474251169558	10.3257092868448	1.06943068777070e-23	***
df.mm.trans3:probe13	0.105347544862451	0.0451474251169558	2.33341202935815	0.0198461967358191	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03246355220024	0.136131849662531	29.6217495185488	4.15662262970508e-135	***
df.mm.trans1	0.111498827368984	0.116738038463419	0.955119932085574	0.339773802074992	   
df.mm.trans2	0.0732602112708832	0.102774998572741	0.712821330948806	0.476141296643095	   
df.mm.exp2	0.149418998903617	0.130910054521349	1.14138672884938	0.254012960764700	   
df.mm.exp3	-0.0246986405712177	0.130910054521349	-0.188668782253007	0.850395001606523	   
df.mm.exp4	0.169092657283713	0.130910054521349	1.29167051302493	0.196803249039071	   
df.mm.exp5	0.306704088904628	0.130910054521349	2.34286121128007	0.0193537258625210	*  
df.mm.exp6	0.103816624489556	0.130910054521349	0.79303782180173	0.42796502706777	   
df.mm.exp7	0.101738439902131	0.130910054521349	0.777162917501801	0.437267103759446	   
df.mm.exp8	0.0683612484383673	0.130910054521349	0.522200137249342	0.601659634648924	   
df.mm.trans1:exp2	-0.154300946789391	0.119729250006872	-1.28874896301894	0.197816910694926	   
df.mm.trans2:exp2	0.000320648568152663	0.08561086752435	0.00374541898038193	0.997012425932099	   
df.mm.trans1:exp3	0.0426587217645467	0.119729250006872	0.356293234628115	0.721704573731998	   
df.mm.trans2:exp3	0.0189778371511521	0.08561086752435	0.221675561759193	0.824616798621406	   
df.mm.trans1:exp4	-0.0857223423921979	0.119729250006872	-0.715968256606282	0.474196874119717	   
df.mm.trans2:exp4	-0.130373829282019	0.08561086752435	-1.52286541477853	0.128144041197648	   
df.mm.trans1:exp5	-0.246666134691755	0.119729250006872	-2.06019944731632	0.0396667971861696	*  
df.mm.trans2:exp5	-0.0842442540232802	0.08561086752435	-0.984036915632457	0.325362114863636	   
df.mm.trans1:exp6	-0.0133459135651474	0.119729250006872	-0.111467444792157	0.91127056123292	   
df.mm.trans2:exp6	-0.070266794766294	0.08561086752435	-0.820769568142834	0.411995117196389	   
df.mm.trans1:exp7	-0.0667069723259877	0.119729250006872	-0.557148502326365	0.577564651844725	   
df.mm.trans2:exp7	-0.0522966636914683	0.08561086752435	-0.610864779247726	0.541443512370454	   
df.mm.trans1:exp8	-0.0601101748783953	0.119729250006872	-0.50205087624741	0.615754671049749	   
df.mm.trans2:exp8	-0.0242275103454179	0.08561086752435	-0.282995734607256	0.777245255141983	   
df.mm.trans1:probe2	-0.098786067724547	0.0857680960636476	-1.15178104981179	0.249717263920450	   
df.mm.trans1:probe3	-0.0863358912707584	0.0857680960636476	-1.00662012138744	0.314388216887718	   
df.mm.trans1:probe4	0.0947624961044226	0.0857680960636476	1.10486883181014	0.269512040878713	   
df.mm.trans1:probe5	-0.0145921535007858	0.0857680960636476	-0.170134982242780	0.864942261586101	   
df.mm.trans1:probe6	-0.103956290630218	0.0857680960636476	-1.21206247312605	0.225806918786697	   
df.mm.trans1:probe7	-0.059758577921647	0.0857680960636476	-0.696746000719204	0.486141879327546	   
df.mm.trans1:probe8	-0.0827501847689388	0.0857680960636476	-0.964813124772302	0.334897876369401	   
df.mm.trans1:probe9	0.059643000333257	0.0857680960636476	0.69539844150203	0.486985344285494	   
df.mm.trans1:probe10	0.0299576006872719	0.0857680960636476	0.349286063958336	0.726956356515207	   
df.mm.trans1:probe11	-0.0584993970653494	0.0857680960636476	-0.682064774084965	0.495373679535123	   
df.mm.trans1:probe12	-0.0939622450287685	0.0857680960636476	-1.09553842677165	0.273574222046097	   
df.mm.trans1:probe13	-0.0109431988247697	0.0857680960636476	-0.127590553212804	0.898501541021983	   
df.mm.trans1:probe14	-0.0191850610373626	0.0857680960636476	-0.223685285296826	0.823053016396593	   
df.mm.trans1:probe15	-0.067243335311578	0.0857680960636476	-0.784013384903373	0.433238772627961	   
df.mm.trans1:probe16	-0.0893318020543029	0.0857680960636476	-1.04155048501964	0.297900149276651	   
df.mm.trans1:probe17	0.0187598113114539	0.0857680960636476	0.218727151148749	0.826912241573797	   
df.mm.trans1:probe18	-0.0294323447315931	0.0857680960636476	-0.34316192246767	0.731556877572596	   
df.mm.trans1:probe19	-0.0426646684072138	0.0857680960636476	-0.497442176815406	0.618998909889386	   
df.mm.trans1:probe20	0.0183244155921747	0.0857680960636476	0.213650721342541	0.830867886058637	   
df.mm.trans1:probe21	-0.0568569140511611	0.0857680960636476	-0.662914494557139	0.507555244706017	   
df.mm.trans2:probe2	-0.0522734897180298	0.0857680960636476	-0.609474759463451	0.542363795808056	   
df.mm.trans2:probe3	0.0170674907044745	0.0857680960636476	0.198995797829171	0.842311027216271	   
df.mm.trans2:probe4	-0.0745260737778638	0.0857680960636476	-0.868925360340968	0.385119870823994	   
df.mm.trans2:probe5	-0.0149329458749300	0.0857680960636476	-0.174108398813569	0.861819470838349	   
df.mm.trans2:probe6	-0.0764460075270617	0.0857680960636476	-0.891310534284588	0.37300108149607	   
df.mm.trans3:probe2	-0.0397828790987662	0.0857680960636476	-0.463842395070118	0.642872953527343	   
df.mm.trans3:probe3	-0.0762669916004318	0.0857680960636476	-0.889223325463991	0.374120942496134	   
df.mm.trans3:probe4	0.113577698685058	0.0857680960636476	1.32424180899123	0.185759317735397	   
df.mm.trans3:probe5	-0.128703902080512	0.0857680960636476	-1.50060346431151	0.133809263041972	   
df.mm.trans3:probe6	0.0540430848084762	0.0857680960636476	0.630107082805842	0.52878464459531	   
df.mm.trans3:probe7	-0.0870287420561771	0.0857680960636476	-1.01469830916608	0.310522665603925	   
df.mm.trans3:probe8	-0.0604969407224132	0.0857680960636476	-0.705354828880882	0.480772162172792	   
df.mm.trans3:probe9	-0.00355076349108617	0.0857680960636476	-0.0413995839251368	0.966986536861206	   
df.mm.trans3:probe10	-0.109177991756221	0.0857680960636476	-1.27294409887799	0.203366961170062	   
df.mm.trans3:probe11	-0.058474979196515	0.0857680960636476	-0.681780077677384	0.495553623660229	   
df.mm.trans3:probe12	-0.214961691231032	0.0857680960636476	-2.50631296597177	0.0123753653303711	*  
df.mm.trans3:probe13	-0.0457130774886676	0.0857680960636476	-0.532984636323796	0.594175938655806