fitVsDatCorrelation=0.889446099905931
cont.fitVsDatCorrelation=0.188114706122514

fstatistic=12697.2202129027,52,692
cont.fstatistic=2739.14027379990,52,692

residuals=-0.477517951149023,-0.0783911801409976,-0.00366354603161285,0.0748083780625868,0.95760196599767
cont.residuals=-0.629087740197265,-0.224259771503293,-0.0386383404759005,0.210469530529682,1.36000146936018

predictedValues:
Include	Exclude	Both
Lung	68.7472872088748	47.1521531868285	61.8688919361766
cerebhem	73.409554650645	57.7692733486367	69.1164850716157
cortex	69.4418461438779	46.3510309540824	69.6456161757627
heart	68.9720422971955	49.903099995222	59.005095661471
kidney	73.7299303382536	45.9057063587357	67.5897043597205
liver	70.958693385767	51.0227131492667	63.9148803029504
stomach	70.6568827328761	46.1459526564579	63.0883929350486
testicle	69.6158681372182	48.8572653074767	62.6557335639198


diffExp=21.5951340220463,15.6402813020084,23.0908151897955,19.0689423019735,27.8242239795178,19.9359802365003,24.5109300764182,20.7586028297415
diffExpScore=0.994233815659137
diffExp1.5=0,0,0,0,1,0,1,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,1,0,1,0,1,1
diffExp1.4Score=0.833333333333333
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	67.3003813361855	64.712048220042	70.8262823971955
cerebhem	66.7424023536624	63.6525874692122	56.5938601024134
cortex	68.9359229055462	68.1047807306301	64.5607632620565
heart	69.3549766960786	69.3925109775182	70.031031392895
kidney	69.5710746292703	66.1890960662044	70.6926262839698
liver	70.207539681332	64.7634362890649	76.6098937179883
stomach	64.2769307638965	62.4358877807704	73.5922974643314
testicle	68.8552031100253	63.7902226939474	65.1765747516323
cont.diffExp=2.58833311614359,3.08981488445018,0.831142174916096,-0.0375342814396049,3.38197856306596,5.44410339226712,1.84104298312608,5.06498041607796
cont.diffExpScore=0.960138899848994

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=0.383973131695149
cont.tran.correlation=0.624666581102354

tran.covariance=0.000758154516850308
cont.tran.covariance=0.000659003113155268

tran.mean=59.9149562407134
cont.tran.mean=66.7678126064616

weightedLogRatios:
wLogRatio
Lung	1.52403231827106
cerebhem	1.00061858927363
cortex	1.63249403378208
heart	1.31773806584091
kidney	1.92536470417642
liver	1.35136218474968
stomach	1.72320058459326
testicle	1.43970917417161

cont.weightedLogRatios:
wLogRatio
Lung	0.164308135865529
cerebhem	0.197998504553480
cortex	0.051274904347747
heart	-0.00229376270618020
kidney	0.210167933493331
liver	0.339896785010081
stomach	0.120562676199061
testicle	0.320431134031711

varWeightedLogRatios=0.0794716974970622
cont.varWeightedLogRatios=0.0142749866904939

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.57085763905236	0.0755102123737677	47.2897311078532	5.84579233338462e-219	***
df.mm.trans1	0.371434279667353	0.0678192132511386	5.47682967497588	6.06469047831939e-08	***
df.mm.trans2	0.223380201406936	0.0623689772782123	3.58159154046239	0.000365424025469227	***
df.mm.exp2	0.157918408279963	0.085430053149324	1.84851117912728	0.0649548481005442	.  
df.mm.exp3	-0.125486018670180	0.085430053149324	-1.46887440712277	0.142321285217343	   
df.mm.exp4	0.107361104117470	0.085430053149324	1.25671353533881	0.209281420708739	   
df.mm.exp5	-0.0452568629522383	0.085430053149324	-0.529753421470233	0.596452819685631	   
df.mm.exp6	0.0780171590942777	0.085430053149324	0.913228497679976	0.361440435800335	   
df.mm.exp7	-0.0136914755362606	0.085430053149324	-0.160265328552813	0.872718839905749	   
df.mm.exp8	0.0354409796610407	0.085430053149324	0.414853770476919	0.678377533746953	   
df.mm.trans1:exp2	-0.0923015857672254	0.0817930487022274	-1.12847714117192	0.259509539068941	   
df.mm.trans2:exp2	0.0451589483050844	0.07119171095777	0.634328739926929	0.52607594055814	   
df.mm.trans1:exp3	0.135538397854066	0.0817930487022275	1.65708944714241	0.0979546601540189	.  
df.mm.trans2:exp3	0.108349878463633	0.07119171095777	1.52194513948267	0.128479631362598	   
df.mm.trans1:exp4	-0.104097142716243	0.0817930487022274	-1.27268935891135	0.203555725789934	   
df.mm.trans2:exp4	-0.0506576539144442	0.07119171095777	-0.711566743275684	0.476972902431005	   
df.mm.trans1:exp5	0.115228413142915	0.0817930487022275	1.40878002435649	0.159349381501956	   
df.mm.trans2:exp5	0.0184666190549707	0.07119171095777	0.259392825464257	0.795409311750125	   
df.mm.trans1:exp6	-0.046356511641749	0.0817930487022275	-0.566753683561945	0.571065317359554	   
df.mm.trans2:exp6	0.000874055578964414	0.07119171095777	0.0122774908371410	0.99020776436452	   
df.mm.trans1:exp7	0.0410897230733923	0.0817930487022274	0.502362043295169	0.615572691130833	   
df.mm.trans2:exp7	-0.00787894189001864	0.07119171095777	-0.110672180567374	0.911908396445708	   
df.mm.trans1:exp8	-0.0228857248174332	0.0817930487022275	-0.279800364218603	0.779714285222427	   
df.mm.trans2:exp8	8.24398589243699e-05	0.07119171095777	0.00115799800026259	0.999076385209102	   
df.mm.trans1:probe2	-0.0715477241698345	0.0408965243511137	-1.74948177883204	0.0806510539634934	.  
df.mm.trans1:probe3	0.719342263917902	0.0408965243511138	17.5893251402502	1.60038079073019e-57	***
df.mm.trans1:probe4	-0.0429019435217039	0.0408965243511137	-1.04903642063499	0.294527587371679	   
df.mm.trans1:probe5	0.182686681509617	0.0408965243511137	4.46704663558144	9.26127909995685e-06	***
df.mm.trans1:probe6	0.189381471900651	0.0408965243511137	4.63074735336266	4.35232049927726e-06	***
df.mm.trans1:probe7	0.498170031592669	0.0408965243511137	12.1812315226514	4.60352548174982e-31	***
df.mm.trans1:probe8	0.0244303694672032	0.0408965243511137	0.59737031092076	0.550455584124381	   
df.mm.trans1:probe9	0.298584461052967	0.0408965243511137	7.3009740018368	7.8709941935146e-13	***
df.mm.trans1:probe10	0.25256626891533	0.0408965243511137	6.1757392082257	1.12275062022971e-09	***
df.mm.trans1:probe11	0.609986529224025	0.0408965243511137	14.9153635645669	7.92769595880662e-44	***
df.mm.trans1:probe12	0.679125065230522	0.0408965243511137	16.6059359812572	2.2611944470483e-52	***
df.mm.trans1:probe13	0.557240838811808	0.0408965243511137	13.6256282814564	1.26743876758092e-37	***
df.mm.trans1:probe14	0.604095272763697	0.0408965243511137	14.771310822829	4.0426199309427e-43	***
df.mm.trans1:probe15	0.65765370105154	0.0408965243511137	16.0809191364359	1.12944072940087e-49	***
df.mm.trans1:probe16	0.723655987678777	0.0408965243511137	17.6948041223721	4.41577948389547e-58	***
df.mm.trans1:probe17	0.188294381531226	0.0408965243511137	4.60416586785323	4.92793771407945e-06	***
df.mm.trans1:probe18	0.117245878390566	0.0408965243511137	2.86689101949011	0.00427125607352265	** 
df.mm.trans1:probe19	0.270147174859883	0.0408965243511138	6.60562674080948	7.89584939812125e-11	***
df.mm.trans1:probe20	0.225178766334787	0.0408965243511137	5.50606120954272	5.1751452472509e-08	***
df.mm.trans1:probe21	0.340612848804416	0.0408965243511137	8.32865027551272	4.37725436860126e-16	***
df.mm.trans1:probe22	0.179685631365097	0.0408965243511138	4.39366509052018	1.28934706749822e-05	***
df.mm.trans2:probe2	0.0524125643626248	0.0408965243511137	1.28158969971729	0.200415863826383	   
df.mm.trans2:probe3	0.06080329069945	0.0408965243511137	1.48675936804381	0.137533844500702	   
df.mm.trans2:probe4	0.178494548468684	0.0408965243511137	4.36454078435208	1.46836171273353e-05	***
df.mm.trans2:probe5	0.194188121942627	0.0408965243511137	4.7482793470526	2.49455362138154e-06	***
df.mm.trans2:probe6	0.0463779833910567	0.0408965243511138	1.13403239338586	0.257173655983368	   
df.mm.trans3:probe2	-0.127692356184517	0.0408965243511137	-3.12232783129013	0.00186902774762452	** 
df.mm.trans3:probe3	0.00572849530383155	0.0408965243511138	0.140072913156385	0.888643141020477	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14958217383446	0.162266148188977	25.5726916559443	4.66775893273246e-102	***
df.mm.trans1	0.0826116234028769	0.145738730716272	0.566847419329507	0.571001660888519	   
df.mm.trans2	0.0135078697230840	0.134026556028296	0.100785024426295	0.919750307781874	   
df.mm.exp2	0.199496791914414	0.183583189986314	1.08668332830084	0.277555156657243	   
df.mm.exp3	0.167734833891913	0.183583189986315	0.913672073703571	0.361207405226199	   
df.mm.exp4	0.111195255879592	0.183583189986315	0.605694104606642	0.544916500551513	   
df.mm.exp5	0.0576402007160067	0.183583189986315	0.313973195042006	0.753636022546045	   
df.mm.exp6	-0.035412483372883	0.183583189986315	-0.192896110888600	0.847096929167888	   
df.mm.exp7	-0.12008249128052	0.183583189986315	-0.654103958480467	0.513262143342963	   
df.mm.exp8	0.0916224502699979	0.183583189986315	0.499078648087703	0.617882595670288	   
df.mm.trans1:exp2	-0.207822226377742	0.175767522621279	-1.18236988994565	0.237465067826994	   
df.mm.trans2:exp2	-0.216004217081565	0.152985991655262	-1.41192154094937	0.158422565035167	   
df.mm.trans1:exp3	-0.143723317151688	0.175767522621279	-0.817689838306272	0.413815761178619	   
df.mm.trans2:exp3	-0.116634822439694	0.152985991655262	-0.762388903570456	0.446087707389405	   
df.mm.trans1:exp4	-0.0811232525220232	0.175767522621279	-0.461537212974304	0.644558272569708	   
df.mm.trans2:exp4	-0.0413637060401649	0.152985991655262	-0.270375774883845	0.786951792028624	   
df.mm.trans1:exp5	-0.0244572170143401	0.175767522621280	-0.139145256470601	0.889375850852785	   
df.mm.trans2:exp5	-0.0350718641941781	0.152985991655262	-0.229248860073469	0.81874323670991	   
df.mm.trans1:exp6	0.0777022886569687	0.175767522621279	0.442074209718432	0.658573628596858	   
df.mm.trans2:exp6	0.0362062718173299	0.152985991655262	0.236663967893982	0.812987527710931	   
df.mm.trans1:exp7	0.0741173802445022	0.175767522621279	0.421678471307811	0.673390677648515	   
df.mm.trans2:exp7	0.0842753251631607	0.152985991655262	0.55086955512284	0.581900895344617	   
df.mm.trans1:exp8	-0.0687825591211836	0.175767522621279	-0.391326896433462	0.695676082963207	   
df.mm.trans2:exp8	-0.105969921838053	0.152985991655262	-0.692677288237244	0.488744477291207	   
df.mm.trans1:probe2	-0.0429748702744409	0.0878837613106398	-0.488996711491888	0.624998968213322	   
df.mm.trans1:probe3	-0.0721535051042657	0.0878837613106398	-0.821010662586768	0.411922935611448	   
df.mm.trans1:probe4	-0.000718236587871074	0.0878837613106397	-0.00817257451387803	0.993481656981266	   
df.mm.trans1:probe5	-0.0353284006397398	0.0878837613106397	-0.401990084548904	0.687815450093724	   
df.mm.trans1:probe6	-0.0332886993061511	0.0878837613106397	-0.37878100356318	0.704966678804203	   
df.mm.trans1:probe7	-0.0059975563302638	0.0878837613106397	-0.0682441925655007	0.945610962036319	   
df.mm.trans1:probe8	-0.0126895056927672	0.0878837613106398	-0.144389651780082	0.885234828027631	   
df.mm.trans1:probe9	-0.0218447133713683	0.0878837613106397	-0.248563705576444	0.803772034062074	   
df.mm.trans1:probe10	0.000232840081428055	0.0878837613106397	0.00264940960600267	0.997886843020724	   
df.mm.trans1:probe11	0.0345509053208776	0.0878837613106398	0.393143224705093	0.694334785343562	   
df.mm.trans1:probe12	-0.0142638990983628	0.0878837613106397	-0.162304149090122	0.871113727569676	   
df.mm.trans1:probe13	-0.0687151552938684	0.0878837613106397	-0.781886827203302	0.434548526316659	   
df.mm.trans1:probe14	-0.0229802980454976	0.0878837613106397	-0.261485144727362	0.79379621016777	   
df.mm.trans1:probe15	-0.0990565159784402	0.0878837613106397	-1.12713104788845	0.260077756271444	   
df.mm.trans1:probe16	-0.0244184024958583	0.0878837613106398	-0.277848855484774	0.781211377049964	   
df.mm.trans1:probe17	-0.114968039526507	0.0878837613106397	-1.30818296590805	0.191245617745388	   
df.mm.trans1:probe18	0.0113468575364894	0.0878837613106397	0.129112106346724	0.89730645914844	   
df.mm.trans1:probe19	-0.0288593415153621	0.0878837613106398	-0.328380818992874	0.742723084076333	   
df.mm.trans1:probe20	-0.0788461264969628	0.0878837613106397	-0.89716376860872	0.369943548038539	   
df.mm.trans1:probe21	-0.00586515998675352	0.0878837613106398	-0.0667376987430265	0.946809798488156	   
df.mm.trans1:probe22	0.0611404630816469	0.0878837613106398	0.695696931604188	0.486852213606514	   
df.mm.trans2:probe2	0.0178159991122862	0.0878837613106397	0.202722310089945	0.83941167267767	   
df.mm.trans2:probe3	-0.00775919274951993	0.0878837613106398	-0.0882892656595997	0.929672314018894	   
df.mm.trans2:probe4	0.0805204844430453	0.0878837613106397	0.916215729074593	0.359872931763213	   
df.mm.trans2:probe5	-0.00393218952612751	0.0878837613106397	-0.0447430727529803	0.964325008845677	   
df.mm.trans2:probe6	-0.0249288855238393	0.0878837613106398	-0.283657471551814	0.776757733442942	   
df.mm.trans3:probe2	0.0532627718457275	0.0878837613106397	0.606059311201547	0.544674109876029	   
df.mm.trans3:probe3	-0.00967731638548965	0.0878837613106398	-0.110114954585109	0.912350134077314