fitVsDatCorrelation=0.709471686640594
cont.fitVsDatCorrelation=0.270672406751433

fstatistic=11054.9661200173,51,669
cont.fstatistic=5918.41012744192,51,669

residuals=-0.429179411619442,-0.083605928866508,-0.00845005096043349,0.0657593226829473,0.873280562436434
cont.residuals=-0.45231939784272,-0.123326458932866,-0.0248676373144166,0.100038507359163,0.923078720259102

predictedValues:
Include	Exclude	Both
Lung	54.7401937751324	47.4523586293234	52.1078895529236
cerebhem	58.406296966547	64.3554329873672	62.4789250409438
cortex	50.8801658716368	45.1071640200755	54.9781974105594
heart	53.3470183276478	47.4658669504745	49.8457261133599
kidney	55.6890127998788	44.5035810167342	53.0576458049796
liver	55.1096219137902	50.5570559791105	53.0444066032469
stomach	53.729864501972	49.142273210464	51.965774820988
testicle	55.0856228484926	52.573285820205	54.7338807154598


diffExp=7.28783514580897,-5.94913602082022,5.77300185156131,5.88115137717328,11.1854317831446,4.55256593467971,4.58759129150805,2.51233702828763
diffExpScore=1.29590121408354
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=0,0,0,0,1,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	51.2769929448181	49.8313645448131	54.1140482843889
cerebhem	55.1596005682132	51.0393377455009	57.674868176015
cortex	54.3141401635147	57.4210641909429	51.5874525923734
heart	56.3029494583167	56.3374888297927	54.9516415081262
kidney	55.5470581447133	53.9857000593803	54.3559163103245
liver	51.3456847844582	49.5909986331149	55.0623632261442
stomach	55.6628863653095	50.771010556745	55.7033051456352
testicle	53.8932889379978	51.6733397306059	55.628596020172
cont.diffExp=1.44562840000493,4.12026282271234,-3.10692402742817,-0.0345393714760647,1.56135808533294,1.75468615134339,4.89187580856452,2.21994920739185
cont.diffExpScore=1.38137550535149

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=0.754890835662096
cont.tran.correlation=0.571241723948391

tran.covariance=0.00340965562793328
cont.tran.covariance=0.00117701639456355

tran.mean=52.3840509761782
cont.tran.mean=53.3845566036398

weightedLogRatios:
wLogRatio
Lung	0.561653058474124
cerebhem	-0.399234815005562
cortex	0.465982752874772
heart	0.457699121510331
kidney	0.876152648748414
liver	0.341974089979638
stomach	0.351583946182946
testicle	0.186047940467257

cont.weightedLogRatios:
wLogRatio
Lung	0.112186667829446
cerebhem	0.308317321170817
cortex	-0.223763471824474
heart	-0.00247211503588217
kidney	0.114130250630518
liver	0.136346068944278
stomach	0.365498032639811
testicle	0.166824668230787

varWeightedLogRatios=0.133537959620775
cont.varWeightedLogRatios=0.0330606106475796

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.84403037422521	0.0728870178760732	52.7395753899693	1.66254598543949e-240	***
df.mm.trans1	0.0950314380694916	0.0645605567942159	1.47197364440955	0.141498245106471	   
df.mm.trans2	0.0103353982437378	0.0589554302043322	0.175308673143705	0.860890142421582	   
df.mm.exp2	0.188007660674304	0.0795600900955483	2.36309009263960	0.0184083395702808	*  
df.mm.exp3	-0.177430542266282	0.0795600900955483	-2.23014506460709	0.0260687807978468	*  
df.mm.exp4	0.0188880849439468	0.0795600900955483	0.237406530350368	0.812414107535868	   
df.mm.exp5	-0.0650345569885336	0.0795600900955483	-0.817426889668298	0.413975527918595	   
df.mm.exp6	0.0522893172282017	0.0795600900955483	0.657230492894169	0.511258761508589	   
df.mm.exp7	0.0190951766689995	0.0795600900955483	0.2400094902616	0.810396420578469	   
df.mm.exp8	0.0596058544010551	0.0795600900955483	0.749192897211038	0.454004290826264	   
df.mm.trans1:exp2	-0.123182195407809	0.0751772188138264	-1.63855749589333	0.101775626534263	   
df.mm.trans2:exp2	0.116687467013443	0.0637899854568019	1.82924429560284	0.0678079711573888	.  
df.mm.trans1:exp3	0.104305477823937	0.0751772188138264	1.38746124783155	0.165763147246414	   
df.mm.trans2:exp3	0.126745392113178	0.0637899854568018	1.98691677393483	0.0473390697963132	*  
df.mm.trans1:exp4	-0.0446682411740048	0.0751772188138264	-0.594172568216763	0.552597563784963	   
df.mm.trans2:exp4	-0.0186034542281971	0.0637899854568019	-0.291635969109842	0.770655375439168	   
df.mm.trans1:exp5	0.0822191842047802	0.0751772188138264	1.09367153377133	0.274492696825217	   
df.mm.trans2:exp5	0.000877983710569449	0.0637899854568018	0.0137636606166655	0.989022637850487	   
df.mm.trans1:exp6	-0.0455632334548204	0.0751772188138264	-0.606077667859143	0.544668758964715	   
df.mm.trans2:exp6	0.0110869711552050	0.0637899854568018	0.173804259019827	0.862071874227401	   
df.mm.trans1:exp7	-0.0377244372880018	0.0751772188138264	-0.501806769167997	0.61596855153464	   
df.mm.trans2:exp7	0.0158982177346793	0.0637899854568019	0.249227486428029	0.803261316649539	   
df.mm.trans1:exp8	-0.0533153441540779	0.0751772188138264	-0.709195484952847	0.478450248643148	   
df.mm.trans2:exp8	0.0428760306916646	0.0637899854568018	0.672143603492432	0.501724336738989	   
df.mm.trans1:probe2	0.00580625005697564	0.0411762585548345	0.141009656067791	0.887904761746681	   
df.mm.trans1:probe3	-0.0454350778279524	0.0411762585548345	-1.10342900065693	0.270237654453315	   
df.mm.trans1:probe4	0.259304223546736	0.0411762585548345	6.29742071396362	5.48121277238727e-10	***
df.mm.trans1:probe5	0.278096025459005	0.0411762585548345	6.75379539616657	3.13106215392910e-11	***
df.mm.trans1:probe6	0.255700499533502	0.0411762585548345	6.20990125154246	9.30916759274348e-10	***
df.mm.trans1:probe7	-0.00301480698726578	0.0411762585548345	-0.0732171181422654	0.941655229452754	   
df.mm.trans1:probe8	0.237494804204185	0.0411762585548345	5.76776065965082	1.22789944851782e-08	***
df.mm.trans1:probe9	-0.0157579968407562	0.0411762585548345	-0.382696179638839	0.702066602048918	   
df.mm.trans1:probe10	0.00523598834512946	0.0411762585548345	0.127160371750549	0.898851679603228	   
df.mm.trans1:probe11	-0.0492839960948099	0.0411762585548345	-1.19690321132937	0.231768242168963	   
df.mm.trans1:probe12	0.0922663563632737	0.0411762585548345	2.24076590738331	0.0253685839830698	*  
df.mm.trans1:probe13	0.229444277340810	0.0411762585548345	5.57224685762207	3.64757355108552e-08	***
df.mm.trans1:probe14	0.0201897152523045	0.0411762585548345	0.490324180994197	0.624065286529518	   
df.mm.trans1:probe15	-0.0580783887367261	0.0411762585548345	-1.41048241814839	0.158862071781284	   
df.mm.trans1:probe16	-0.0528739873718725	0.0411762585548345	-1.28408916272614	0.199555277688418	   
df.mm.trans1:probe17	0.0900727084461058	0.0411762585548345	2.18749132649233	0.0290520544039436	*  
df.mm.trans1:probe18	0.139695475870864	0.0411762585548345	3.39262188391476	0.000733066913916078	***
df.mm.trans1:probe19	0.123684527002669	0.0411762585548345	3.00378255197610	0.00276602330287191	** 
df.mm.trans1:probe20	0.0123277519567362	0.0411762585548345	0.299389803479092	0.764735627923081	   
df.mm.trans2:probe2	-0.0704202679623783	0.0411762585548345	-1.71021531421072	0.087689713235888	.  
df.mm.trans2:probe3	-0.0429462225741092	0.0411762585548345	-1.04298506181463	0.297331950994898	   
df.mm.trans2:probe4	0.0116217461831811	0.0411762585548345	0.282243860687450	0.777843822269245	   
df.mm.trans2:probe5	-0.0372094618385915	0.0411762585548345	-0.903663012243806	0.366499389730012	   
df.mm.trans2:probe6	0.192558796488459	0.0411762585548345	4.67645199556022	3.53215261057983e-06	***
df.mm.trans3:probe2	-0.105309616121954	0.0411762585548345	-2.55753241838893	0.0107611285697480	*  
df.mm.trans3:probe3	-0.14475167713214	0.0411762585548345	-3.51541597543094	0.000468697337024092	***
df.mm.trans3:probe4	0.265737924112499	0.0411762585548345	6.45366853228337	2.09635511808044e-10	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79826816401849	0.0995641788418953	38.1489428045203	5.24048191121796e-170	***
df.mm.trans1	0.131517945900034	0.0881901744659216	1.49129930512673	0.136354286444934	   
df.mm.trans2	0.105387243804723	0.0805335321379902	1.30861320752885	0.191114772912462	   
df.mm.exp2	0.0332130146839214	0.108679642407908	0.305604747568663	0.760000670833423	   
df.mm.exp3	0.247124390870909	0.108679642407908	2.27387931534937	0.0232892128001012	*  
df.mm.exp4	0.200860616392793	0.108679642407908	1.84818989042033	0.0650160269068123	.  
df.mm.exp5	0.155603348164661	0.108679642407908	1.43176168707506	0.152679047123705	   
df.mm.exp6	-0.0208691263497162	0.108679642407908	-0.192024245639196	0.847781483592104	   
df.mm.exp7	0.0718067194794032	0.108679642407909	0.660719136431184	0.509019894926481	   
df.mm.exp8	0.058457608492108	0.108679642407909	0.537889223748993	0.590832415595836	   
df.mm.trans1:exp2	0.0397756259131439	0.102692609423841	0.387327054364534	0.698637287395173	   
df.mm.trans2:exp2	-0.00926094685751698	0.0871375686016074	-0.106279610575985	0.91539236846798	   
df.mm.trans1:exp3	-0.189581960634865	0.102692609423841	-1.84611104634033	0.065317660280018	.  
df.mm.trans2:exp3	-0.105357778815734	0.0871375686016074	-1.20909706922658	0.22705268755693	   
df.mm.trans1:exp4	-0.107355865343560	0.102692609423841	-1.04540984931517	0.296211160487351	   
df.mm.trans2:exp4	-0.0781450225693317	0.0871375686016074	-0.896800585825505	0.370147929171729	   
df.mm.trans1:exp5	-0.0756149631437966	0.102692609423841	-0.736323320324958	0.46179206223433	   
df.mm.trans2:exp5	-0.0755287463051571	0.0871375686016074	-0.866775921307539	0.386375563116722	   
df.mm.trans1:exp6	0.0222078528655151	0.102692609423841	0.216255609728029	0.82885443084431	   
df.mm.trans2:exp6	0.0160338685199414	0.0871375686016074	0.184006379535883	0.854064208894436	   
df.mm.trans1:exp7	0.0102647213866791	0.102692609423841	0.0999557947185251	0.920409365581856	   
df.mm.trans2:exp7	-0.0531257820344436	0.0871375686016074	-0.609677121900594	0.542282720980947	   
df.mm.trans1:exp8	-0.00869381879960381	0.102692609423841	-0.0846586609141657	0.932558089040345	   
df.mm.trans2:exp8	-0.0221602284249027	0.0871375686016074	-0.254313137037586	0.79933179816087	   
df.mm.trans1:probe2	0.0301622981721126	0.0562470586705053	0.536246674671524	0.591966333475762	   
df.mm.trans1:probe3	-0.0297173399042515	0.0562470586705053	-0.528335891807878	0.597441436579096	   
df.mm.trans1:probe4	0.0224291753012932	0.0562470586705053	0.398761745617367	0.6901960095977	   
df.mm.trans1:probe5	0.0218482683351139	0.0562470586705053	0.388433970620594	0.697818488445015	   
df.mm.trans1:probe6	-0.0136917070363117	0.0562470586705053	-0.243420853639968	0.807754011512921	   
df.mm.trans1:probe7	-0.0427824422731239	0.0562470586705053	-0.760616524390067	0.447154135073713	   
df.mm.trans1:probe8	-0.0283016316128177	0.0562470586705053	-0.50316642828576	0.615012881262576	   
df.mm.trans1:probe9	-0.0321355111861623	0.0562470586705053	-0.571327851548856	0.567969306198066	   
df.mm.trans1:probe10	-0.0108144096664959	0.0562470586705053	-0.192266225507837	0.847592018933377	   
df.mm.trans1:probe11	0.0371837183826391	0.0562470586705053	0.661078450349927	0.508789594951432	   
df.mm.trans1:probe12	-0.0070780427050872	0.0562470586705053	-0.125838450443254	0.89989761142473	   
df.mm.trans1:probe13	-0.00335277150387067	0.0562470586705053	-0.0596079436528614	0.952485692542347	   
df.mm.trans1:probe14	0.103886623394580	0.0562470586705053	1.84696988340576	0.0651929059547637	.  
df.mm.trans1:probe15	0.0376649962368034	0.0562470586705053	0.6696349485125	0.503321572901481	   
df.mm.trans1:probe16	0.0643680652754333	0.0562470586705053	1.14438100048041	0.252874935658385	   
df.mm.trans1:probe17	-0.0132961857809242	0.0562470586705053	-0.236388996957389	0.813203189824203	   
df.mm.trans1:probe18	0.0254289567512124	0.0562470586705053	0.452093982374705	0.651347823442352	   
df.mm.trans1:probe19	0.00177142316473353	0.0562470586705053	0.0314936141836413	0.974885277892422	   
df.mm.trans1:probe20	0.0153719819237476	0.0562470586705053	0.273293969268624	0.784711596365968	   
df.mm.trans2:probe2	-0.00188464314367483	0.0562470586705053	-0.0335065190646688	0.9732806614298	   
df.mm.trans2:probe3	-0.000406656076684733	0.0562470586705053	-0.00722981941272556	0.99423364428482	   
df.mm.trans2:probe4	0.063481781219245	0.0562470586705053	1.12862401554401	0.259461033274104	   
df.mm.trans2:probe5	-0.0324614832791995	0.0562470586705053	-0.577123214021883	0.564050375640664	   
df.mm.trans2:probe6	0.0211628822506757	0.0562470586705053	0.376248691947568	0.706851307629326	   
df.mm.trans3:probe2	-0.087342966182648	0.0562470586705053	-1.55284504198348	0.120933074515416	   
df.mm.trans3:probe3	-0.00584820325742254	0.0562470586705053	-0.103973494715186	0.91722154409732	   
df.mm.trans3:probe4	-0.0831269467213414	0.0562470586705053	-1.4778896654543	0.139907932665173