fitVsDatCorrelation=0.958940456052492
cont.fitVsDatCorrelation=0.246516017416767

fstatistic=7546.80711342332,63,945
cont.fstatistic=632.573493402834,63,945

residuals=-0.97055269444712,-0.0964715438170874,-0.00318976604907058,0.0856776991426933,1.14288969650971
cont.residuals=-0.996515253229023,-0.37706039003914,-0.158390424716112,0.148357740935168,3.88392576850522

predictedValues:
Include	Exclude	Both
Lung	61.2902913628053	84.5005819434259	113.289513991495
cerebhem	61.6409964514779	75.7820337482938	80.5529529499211
cortex	58.9638581733426	78.6760167195841	83.4045675890217
heart	64.5781872458169	86.2827295177113	83.9738482845653
kidney	60.8603131372373	89.3633990113168	87.5496732593802
liver	62.4522136898091	89.170640268969	82.2380244120807
stomach	64.8139412070122	88.3679035168289	104.958795546351
testicle	64.2965623892232	85.7468672481432	95.3457497427896


diffExp=-23.2102905806206,-14.1410372968159,-19.7121585462416,-21.7045422718944,-28.5030858740794,-26.7184265791600,-23.5539623098167,-21.45030485892
diffExpScore=0.99444425333656
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,-1,-1,0,0
diffExp1.4Score=0.666666666666667
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	79.1771059222195	65.463642720473	82.098850841554
cerebhem	76.048564020174	98.483058812841	74.0038413690385
cortex	75.0826106008848	70.921597498184	79.3378611626847
heart	92.1820854925127	76.286154766606	76.265311970153
kidney	109.515825050309	83.2071747039533	66.8146913022771
liver	86.288443223805	63.1078574493091	88.3522825173242
stomach	85.5024701089648	85.5328085408355	86.6830434806564
testicle	95.7518220127802	62.7521307511694	77.8628283894859
cont.diffExp=13.7134632017465,-22.4344947926670,4.16101310270081,15.8959307259067,26.3086503463552,23.1805857744959,-0.0303384318707458,32.9996912616109
cont.diffExpScore=1.46341998637477

cont.diffExp1.5=0,0,0,0,0,0,0,1
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,0,0,0,1
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,0,1,1,0,1
cont.diffExp1.3Score=0.75
cont.diffExp1.2=1,-1,0,1,1,1,0,1
cont.diffExp1.2Score=1.2

tran.correlation=0.482944362959733
cont.tran.correlation=-0.0580540646152856

tran.covariance=0.00098409408977262
cont.tran.covariance=-0.00124369953287306

tran.mean=73.5491584769374
cont.tran.mean=81.5814594796888

weightedLogRatios:
wLogRatio
Lung	-1.37324267893364
cerebhem	-0.872522622555085
cortex	-1.21743208948814
heart	-1.24963211365961
kidney	-1.6520068174151
liver	-1.53588927624086
stomach	-1.34116808950672
testicle	-1.24008718056926

cont.weightedLogRatios:
wLogRatio
Lung	0.813374580894669
cerebhem	-1.15312776045438
cortex	0.244594630692032
heart	0.838320431481752
kidney	1.25243700098175
liver	1.34565414550861
stomach	-0.00157823896231894
testicle	1.83836816767570

varWeightedLogRatios=0.0544932259851234
cont.varWeightedLogRatios=0.880193848909656

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95349222960567	0.093706290699861	42.1902542516447	1.53416446209619e-219	***
df.mm.trans1	0.100099510277206	0.0776095397608246	1.28978358312252	0.197441360629655	   
df.mm.trans2	0.547598759395396	0.0707620827666367	7.73859018821279	2.57594469349369e-14	***
df.mm.exp2	0.237840390315836	0.0901540150815116	2.63815638272788	0.00847285689964065	** 
df.mm.exp3	0.196126714009019	0.0901540150815116	2.17546288794452	0.0298426886618003	*  
df.mm.exp4	0.372567471566302	0.0901540150815116	4.13256659982864	3.90568059028347e-05	***
df.mm.exp5	0.306652894656054	0.0901540150815116	3.40143358428128	0.000698429402357567	***
df.mm.exp6	0.392902484941856	0.0901540150815116	4.35812519926726	1.45550138213974e-05	***
df.mm.exp7	0.177028434211355	0.0901540150815116	1.96362229736853	0.049866775154066	*  
df.mm.exp8	0.234962704206754	0.0901540150815116	2.60623671607211	0.00929839323370954	** 
df.mm.trans1:exp2	-0.232134665656965	0.0761940494269984	-3.04662460392493	0.00237853974510325	** 
df.mm.trans2:exp2	-0.346737568795795	0.0590196569021739	-5.874950601128	5.85641557207391e-09	***
df.mm.trans1:exp3	-0.234823482461732	0.0761940494269984	-3.08191366947515	0.00211635332133680	** 
df.mm.trans2:exp3	-0.267546769353385	0.0590196569021739	-4.53318069599842	6.55551657245065e-06	***
df.mm.trans1:exp4	-0.320312227856329	0.0761940494269984	-4.20390083300691	2.87260241333941e-05	***
df.mm.trans2:exp4	-0.351696436211122	0.0590196569021739	-5.95897120842408	3.57932055414221e-09	***
df.mm.trans1:exp5	-0.313693056320016	0.0761940494269984	-4.11702828080512	4.17344549075802e-05	***
df.mm.trans2:exp5	-0.250700124582639	0.0590196569021739	-4.24773944379546	2.37297357035913e-05	***
df.mm.trans1:exp6	-0.374122252929205	0.0761940494269984	-4.91012429110559	1.07197095178124e-06	***
df.mm.trans2:exp6	-0.339109065746563	0.0590196569021739	-5.74569700241806	1.23414114440827e-08	***
df.mm.trans1:exp7	-0.121129163234897	0.0761940494269984	-1.58974571040420	0.112226606313267	   
df.mm.trans2:exp7	-0.132278034130803	0.0590196569021739	-2.24125386479382	0.0252411327797129	*  
df.mm.trans1:exp8	-0.187077987782838	0.0761940494269984	-2.45528343997621	0.0142565622249092	*  
df.mm.trans2:exp8	-0.220321573621284	0.059019656902174	-3.73302023741802	0.000200501155050257	***
df.mm.trans1:probe2	0.425621229379501	0.0590196569021739	7.2115164966983	1.13318884716833e-12	***
df.mm.trans1:probe3	0.0105485541309526	0.0590196569021739	0.178729506144656	0.858188409058146	   
df.mm.trans1:probe4	0.029408199493377	0.0590196569021739	0.498278048991738	0.618404021319844	   
df.mm.trans1:probe5	0.257895123200641	0.0590196569021739	4.36964795691893	1.38222548153308e-05	***
df.mm.trans1:probe6	-0.1656822517216	0.0590196569021739	-2.80723847643203	0.00509963705276466	** 
df.mm.trans1:probe7	1.39730398498561	0.0590196569021739	23.6752305643129	1.15612231699066e-97	***
df.mm.trans1:probe8	0.706920551753663	0.0590196569021739	11.9777136780954	7.0428404624664e-31	***
df.mm.trans1:probe9	-0.115233540125084	0.0590196569021739	-1.95246035259212	0.0511785220323614	.  
df.mm.trans1:probe10	-0.163670134287588	0.0590196569021739	-2.77314614957648	0.00566099221877261	** 
df.mm.trans1:probe11	-0.129482216081761	0.0590196569021739	-2.19388290068137	0.0284867090058898	*  
df.mm.trans1:probe12	-0.0205598887913875	0.0590196569021739	-0.348356630155711	0.727649958124385	   
df.mm.trans2:probe2	-0.288601409095806	0.0590196569021739	-4.88992014260889	1.18504441403301e-06	***
df.mm.trans2:probe3	-0.305122200304915	0.0590196569021739	-5.16984029254287	2.85948139280149e-07	***
df.mm.trans2:probe4	-0.366664211814353	0.0590196569021739	-6.21257782677566	7.80280352848457e-10	***
df.mm.trans2:probe5	-0.4762535789652	0.0590196569021739	-8.06940609218719	2.13445821026887e-15	***
df.mm.trans2:probe6	-0.493335632535854	0.0590196569021739	-8.35883599515948	2.24777736726393e-16	***
df.mm.trans3:probe2	0.548152226036435	0.0590196569021739	9.28762135884671	1.05459984112976e-19	***
df.mm.trans3:probe3	0.00156198101984895	0.0590196569021739	0.0264654371413571	0.97889168865678	   
df.mm.trans3:probe4	0.104786918529742	0.0590196569021739	1.77545794113016	0.0761440889310832	.  
df.mm.trans3:probe5	0.114649208531369	0.0590196569021739	1.94255972584528	0.0523661387869848	.  
df.mm.trans3:probe6	0.127666092681528	0.0590196569021739	2.16311140020921	0.0307827557774632	*  
df.mm.trans3:probe7	-0.353626435727064	0.0590196569021739	-5.99167216971806	2.95023545878472e-09	***
df.mm.trans3:probe8	-0.0319192542653603	0.0590196569021739	-0.540824124380578	0.58875626439543	   
df.mm.trans3:probe9	0.189004267256583	0.0590196569021739	3.20239522181331	0.00140845070435351	** 
df.mm.trans3:probe10	-0.390984077592883	0.0590196569021739	-6.62464165525303	5.83596621619555e-11	***
df.mm.trans3:probe11	-0.0668208028921535	0.0590196569021739	-1.13217877567316	0.257846477236837	   
df.mm.trans3:probe12	-0.279944504706812	0.0590196569021739	-4.7432418180747	2.42753031298689e-06	***
df.mm.trans3:probe13	-0.0296105576805121	0.0590196569021739	-0.501706706455311	0.615990708078437	   
df.mm.trans3:probe14	2.63466553638751	0.0590196569021739	44.6404753039231	5.63836423071895e-235	***
df.mm.trans3:probe15	-0.304322779474648	0.0590196569021739	-5.15629529970105	3.06804186521916e-07	***
df.mm.trans3:probe16	-0.478013077552073	0.0590196569021739	-8.09921816970891	1.69799570052101e-15	***
df.mm.trans3:probe17	-0.140524127160458	0.0590196569021739	-2.38097160397559	0.0174643224814578	*  
df.mm.trans3:probe18	-0.39754163727383	0.0590196569021739	-6.73574971695891	2.82943427732670e-11	***
df.mm.trans3:probe19	-0.024379664128841	0.0590196569021739	-0.413077022274980	0.6796438926318	   
df.mm.trans3:probe20	-0.0928125144453346	0.0590196569021739	-1.57256953559003	0.116153252281636	   
df.mm.trans3:probe21	-0.280280278575921	0.0590196569021739	-4.74893100514783	2.36178830611320e-06	***
df.mm.trans3:probe22	-0.0235526353168544	0.0590196569021739	-0.399064253387534	0.689936022505549	   
df.mm.trans3:probe23	2.66118572689512	0.0590196569021739	45.0898203509736	8.96533266429224e-238	***
df.mm.trans3:probe24	-0.397192883009916	0.0590196569021739	-6.7298405964689	2.94127489643395e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17377894966627	0.320211564613574	13.0344416345584	7.82153958546322e-36	***
df.mm.trans1	0.217681933545692	0.265206017335077	0.820803146674684	0.41196540806197	   
df.mm.trans2	-0.0180277902160039	0.241807002163768	-0.0745544589473645	0.940584988174256	   
df.mm.exp2	0.47188158921411	0.308072787961603	1.53172109856364	0.125925952632398	   
df.mm.exp3	0.0611904448374853	0.308072787961603	0.198623335875779	0.84260012042115	   
df.mm.exp4	0.378780980447740	0.308072787961603	1.22951781283243	0.219183754329005	   
df.mm.exp5	0.770221565664463	0.308072787961603	2.50012852728966	0.0125833239255182	*  
df.mm.exp6	-0.0240491593279326	0.308072787961603	-0.0780632378700385	0.937794275265387	   
df.mm.exp7	0.289928919264343	0.308072787961603	0.94110525367297	0.346891427016421	   
df.mm.exp8	0.200745435582755	0.308072787961603	0.651616901677714	0.514806776359023	   
df.mm.trans1:exp2	-0.512196643129994	0.260369027511825	-1.96719497716268	0.0494529273074166	*  
df.mm.trans2:exp2	-0.0634919631374484	0.201680981484304	-0.314813834552812	0.752972502907106	   
df.mm.trans1:exp3	-0.114288653194782	0.260369027511825	-0.438948727070048	0.660799029391677	   
df.mm.trans2:exp3	0.0188896459086375	0.201680981484304	0.093661017363244	0.925398291259927	   
df.mm.trans1:exp4	-0.226702359672952	0.260369027511825	-0.870696341417398	0.384141100719739	   
df.mm.trans2:exp4	-0.225784432026739	0.201680981484304	-1.11951275903678	0.263205912240326	   
df.mm.trans1:exp5	-0.445839696236602	0.260369027511825	-1.71233767893669	0.0871625170688954	.  
df.mm.trans2:exp5	-0.530382902694055	0.201680981484304	-2.62981119384989	0.00868209666093666	** 
df.mm.trans1:exp6	0.110057644079348	0.260369027511825	0.422698679374793	0.672611282170735	   
df.mm.trans2:exp6	-0.0126004706777943	0.201680981484304	-0.0624772379877323	0.950195999615492	   
df.mm.trans1:exp7	-0.213070843969535	0.260369027511825	-0.818341743661725	0.413368376169357	   
df.mm.trans2:exp7	-0.0225238068629675	0.201680981484304	-0.11168037113465	0.91110055954539	   
df.mm.trans1:exp8	-0.0106729692428673	0.260369027511825	-0.0409917006829186	0.96731117086237	   
df.mm.trans2:exp8	-0.243047817514723	0.201680981484304	-1.20511024751056	0.228462435351450	   
df.mm.trans1:probe2	-0.209967131112314	0.201680981484304	-1.04108542891366	0.298102175110209	   
df.mm.trans1:probe3	-0.165586802907338	0.201680981484304	-0.82103330561303	0.411834365196483	   
df.mm.trans1:probe4	0.29045647130605	0.201680981484304	1.44017779548864	0.150148305015883	   
df.mm.trans1:probe5	-0.0816485837412586	0.201680981484304	-0.404840273685464	0.685686543180448	   
df.mm.trans1:probe6	0.172235474447524	0.201680981484304	0.853999584789447	0.393321645385095	   
df.mm.trans1:probe7	0.00180269639075125	0.201680981484304	0.00893835589991687	0.99287020533169	   
df.mm.trans1:probe8	-0.197383335067631	0.201680981484304	-0.97869086918834	0.327983220587499	   
df.mm.trans1:probe9	0.00492150663061416	0.201680981484304	0.0244024329631557	0.980536759410128	   
df.mm.trans1:probe10	-0.205449615459654	0.201680981484304	-1.01868611481169	0.308612729290577	   
df.mm.trans1:probe11	-0.208368254416753	0.201680981484304	-1.03315767745294	0.301794440675520	   
df.mm.trans1:probe12	-0.112865367519279	0.201680981484304	-0.559623255939295	0.575869051900189	   
df.mm.trans2:probe2	0.323393305266835	0.201680981484304	1.60348934682274	0.109160809461064	   
df.mm.trans2:probe3	0.180443025640528	0.201680981484304	0.89469529706038	0.371177809198131	   
df.mm.trans2:probe4	0.00814030793736221	0.201680981484304	0.0403622983062275	0.967812812831465	   
df.mm.trans2:probe5	-0.00285137321841922	0.201680981484304	-0.0141380371983222	0.988722838356233	   
df.mm.trans2:probe6	0.263187417704702	0.201680981484304	1.30496894534989	0.192221091929406	   
df.mm.trans3:probe2	0.0393859711593431	0.201680981484304	0.195288474250153	0.845209099266644	   
df.mm.trans3:probe3	-0.0599260335706971	0.201680981484304	-0.297132794226118	0.766430427653215	   
df.mm.trans3:probe4	0.104534059106311	0.201680981484304	0.518313915060188	0.604360597408962	   
df.mm.trans3:probe5	-0.0554509740954547	0.201680981484304	-0.274943991681091	0.783419367273576	   
df.mm.trans3:probe6	0.0342189396666964	0.201680981484304	0.169668649045917	0.865307044274849	   
df.mm.trans3:probe7	0.185250932861035	0.201680981484304	0.918534467145344	0.358573501905067	   
df.mm.trans3:probe8	0.164778231235792	0.201680981484304	0.817024143888433	0.414120554136534	   
df.mm.trans3:probe9	-0.138281123726405	0.201680981484304	-0.685642853920595	0.493106512591016	   
df.mm.trans3:probe10	0.041516851586862	0.201680981484304	0.205854073504165	0.836949238552733	   
df.mm.trans3:probe11	0.0131494273125287	0.201680981484304	0.0651991437950834	0.948029232431564	   
df.mm.trans3:probe12	-0.0495756592144602	0.201680981484304	-0.245812266727384	0.805880937407336	   
df.mm.trans3:probe13	0.235395432054087	0.201680981484304	1.16716722777555	0.243437267367495	   
df.mm.trans3:probe14	0.172670319527616	0.201680981484304	0.85615568833918	0.392128703425964	   
df.mm.trans3:probe15	-0.125306889320593	0.201680981484304	-0.62131237362282	0.534543953046455	   
df.mm.trans3:probe16	0.238273723904127	0.201680981484304	1.18143873631769	0.237725707226974	   
df.mm.trans3:probe17	-0.214479613114375	0.201680981484304	-1.06345978453634	0.287845162693258	   
df.mm.trans3:probe18	0.0589089891323093	0.201680981484304	0.292089956617422	0.770281956644663	   
df.mm.trans3:probe19	-0.0767308991596352	0.201680981484304	-0.380456791686166	0.70369186477245	   
df.mm.trans3:probe20	0.074114269047491	0.201680981484304	0.367482687271924	0.713341286262936	   
df.mm.trans3:probe21	0.110510561274803	0.201680981484304	0.547947359545174	0.583857450352639	   
df.mm.trans3:probe22	-0.0141622720994769	0.201680981484304	-0.0702211581639842	0.944032490870053	   
df.mm.trans3:probe23	0.153925672010140	0.201680981484304	0.763213620229824	0.445526490303904	   
df.mm.trans3:probe24	-0.0649376842181934	0.201680981484304	-0.321982190587699	0.747537397296027