fitVsDatCorrelation=0.873209752833157
cont.fitVsDatCorrelation=0.26067020031217

fstatistic=8442.13896619708,62,922
cont.fstatistic=2140.13958397129,62,922

residuals=-0.874149365060678,-0.101995733431721,-0.00150139134128613,0.0962544063168815,0.752440402672927
cont.residuals=-1.02665536309648,-0.253687210374373,0.00382838813491174,0.227577091407249,1.6781505342929

predictedValues:
Include	Exclude	Both
Lung	69.3950784737002	89.8534611894575	80.6226193385842
cerebhem	64.2346680470826	67.760693629994	90.9262958048198
cortex	94.8026112676117	75.5659963936802	111.531090701568
heart	75.4840982203803	84.9633727741117	82.9966298123833
kidney	67.6141745419707	96.6205488967802	83.0820238901653
liver	68.4416510410939	91.2377168999635	74.8158759655391
stomach	75.3273629745182	81.01179241667	76.4623627771988
testicle	79.1976835438034	81.431766035529	89.9385261188437


diffExp=-20.4583827157573,-3.52602558291142,19.2366148739315,-9.47927455373144,-29.0063743548095,-22.7960658588696,-5.68442944215191,-2.23408249172552
diffExpScore=1.49998958858222
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,-1,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,-1,-1,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=-1,0,1,0,-1,-1,0,0
diffExp1.2Score=1.33333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	85.2728459820304	85.5936670704017	86.1089133951395
cerebhem	80.9554999164755	79.985359719901	76.2007483644413
cortex	72.6524074793954	86.1408575079674	83.2007387464376
heart	73.6727938971543	108.658330850859	77.1820237040871
kidney	86.9819720653926	76.6286544976403	77.0700769429355
liver	83.894840388743	85.9600796420592	85.1327268253772
stomach	79.0848741213819	83.8916628664514	84.2925571038279
testicle	76.5554171378842	87.6413998869001	83.9440314250828
cont.diffExp=-0.320821088371289,0.970140196574505,-13.488450028572,-34.9855369537050,10.3533175677523,-2.06523925331624,-4.80678874506948,-11.0859827490159
cont.diffExpScore=1.38361085655471

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

tran.correlation=-0.288086285066151
cont.tran.correlation=-0.618709121782492

tran.covariance=-0.00313911519453706
cont.tran.covariance=-0.00438388747431219

tran.mean=78.9339172716467
cont.tran.mean=83.3481664394149

weightedLogRatios:
wLogRatio
Lung	-1.12879261715292
cerebhem	-0.223870934739248
cortex	1.00658757971225
heart	-0.518509603707254
kidney	-1.56793762748902
liver	-1.25623825573746
stomach	-0.317065993742478
testicle	-0.122007471879348

cont.weightedLogRatios:
wLogRatio
Lung	-0.0167022597032746
cerebhem	0.0529002044959127
cortex	-0.744341460114233
heart	-1.74622442585831
kidney	0.55790712846796
liver	-0.108017759249920
stomach	-0.259621682742092
testicle	-0.5958118089579

varWeightedLogRatios=0.661209101921545
cont.varWeightedLogRatios=0.475622507927359

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14851043329435	0.0877132216910881	47.2962952826511	8.84331916677117e-249	***
df.mm.trans1	-0.0411686054001777	0.0753306459974092	-0.546505407660961	0.584850795658973	   
df.mm.trans2	0.345775587875214	0.0661451618412336	5.22752652272845	2.12566580455765e-07	***
df.mm.exp2	-0.479740726476031	0.084161265954737	-5.70025558710157	1.60968806817598e-08	***
df.mm.exp3	-0.185716876543708	0.084161265954737	-2.20667874273171	0.0275823276745150	*  
df.mm.exp4	-0.00087456963391485	0.084161265954737	-0.0103915931395947	0.991711105488792	   
df.mm.exp5	0.0165638873991076	0.084161265954737	0.196811290933003	0.84401858930075	   
df.mm.exp6	0.0762030018630784	0.084161265954737	0.9054402996274	0.365468715074564	   
df.mm.exp7	0.0314227175661229	0.084161265954737	0.373363176155435	0.708963988693164	   
df.mm.exp8	-0.0756307374871577	0.084161265954737	-0.89864068261322	0.369078698786642	   
df.mm.trans1:exp2	0.402467842523796	0.0772608642951396	5.20920709592831	2.33995133035348e-07	***
df.mm.trans2:exp2	0.197542878986231	0.0547622229008282	3.60728379021377	0.000326041409704306	***
df.mm.trans1:exp3	0.497697880808734	0.0772608642951396	6.44178505313515	1.89895639460157e-10	***
df.mm.trans2:exp3	0.0125431410426686	0.0547622229008282	0.229047331869337	0.818882924101967	   
df.mm.trans1:exp4	0.0849806344742638	0.0772608642951396	1.09991824773839	0.271654843289775	   
df.mm.trans2:exp4	-0.0550853096347609	0.0547622229008282	-1.00589981043169	0.314727680475753	   
df.mm.trans1:exp5	-0.0425621933905715	0.0772608642951396	-0.550889428676104	0.581842931687826	   
df.mm.trans2:exp5	0.0560474183447498	0.0547622229008282	1.02346865002630	0.306354767049818	   
df.mm.trans1:exp6	-0.090037378843524	0.0772608642951396	-1.16536851697099	0.244171253906144	   
df.mm.trans2:exp6	-0.0609147621279573	0.0547622229008282	-1.11235006362454	0.266277609746849	   
df.mm.trans1:exp7	0.0506047877131645	0.0772608642951396	0.654986042090497	0.512640110908093	   
df.mm.trans2:exp7	-0.135008122450586	0.0547622229008282	-2.46535139187244	0.0138691026588228	*  
df.mm.trans1:exp8	0.207761838061906	0.0772608642951396	2.68909544252893	0.00729392630503033	** 
df.mm.trans2:exp8	-0.0227839538452121	0.0547622229008282	-0.41605239229373	0.677468488114184	   
df.mm.trans1:probe2	-0.0852575224581956	0.0553458509966916	-1.54045011365517	0.123793790132956	   
df.mm.trans1:probe3	-0.258248179556096	0.0553458509966916	-4.66608020123374	3.52434822717062e-06	***
df.mm.trans1:probe4	-0.364164969435945	0.0553458509966916	-6.57980612598609	7.88842337412335e-11	***
df.mm.trans1:probe5	-0.252272350965365	0.0553458509966916	-4.55810772483098	5.85630718797794e-06	***
df.mm.trans1:probe6	-0.0670290667556353	0.0553458509966916	-1.21109469903430	0.226169443616533	   
df.mm.trans1:probe7	-0.0355355172087700	0.0553458509966916	-0.642062893041326	0.520991948628646	   
df.mm.trans1:probe8	-0.305127313769759	0.0553458509966916	-5.51310185451839	4.5776419774093e-08	***
df.mm.trans1:probe9	-0.0220655780568039	0.0553458509966916	-0.398685315329652	0.690217402056659	   
df.mm.trans1:probe10	-0.291750138536366	0.0553458509966916	-5.27140035399953	1.68684124318124e-07	***
df.mm.trans1:probe11	0.694317030603508	0.0553458509966916	12.5450601644017	1.91735717195151e-33	***
df.mm.trans1:probe12	0.203735209724036	0.0553458509966916	3.68112886612249	0.000245684561575761	***
df.mm.trans1:probe13	0.432394827924968	0.0553458509966916	7.8125969722792	1.52205026556143e-14	***
df.mm.trans1:probe14	0.342294931457247	0.0553458509966916	6.1846538682314	9.34238254605529e-10	***
df.mm.trans1:probe15	0.443339459583513	0.0553458509966916	8.0103467848026	3.43821844807042e-15	***
df.mm.trans1:probe16	0.166384610229851	0.0553458509966916	3.00627070021558	0.00271641664453378	** 
df.mm.trans1:probe17	0.52504921292819	0.0553458509966916	9.48669509046985	1.96561624760329e-20	***
df.mm.trans1:probe18	0.716766639066187	0.0553458509966916	12.9506842185701	2.30149507022455e-35	***
df.mm.trans1:probe19	0.705928866660555	0.0553458509966916	12.7548651605836	1.97013269732923e-34	***
df.mm.trans1:probe20	0.85024246738596	0.0553458509966916	15.3623524089779	1.36151251065927e-47	***
df.mm.trans1:probe21	0.727703952025193	0.0553458509966916	13.1483017953540	2.57739522426016e-36	***
df.mm.trans1:probe22	0.50988769003369	0.0553458509966916	9.21275363647709	2.09337665853301e-19	***
df.mm.trans2:probe2	0.128242809736883	0.0553458509966916	2.31711695506406	0.0207156420010698	*  
df.mm.trans2:probe3	-0.109676221773254	0.0553458509966916	-1.98165209854321	0.0478148332075005	*  
df.mm.trans2:probe4	-0.16856742131779	0.0553458509966916	-3.04571017126227	0.00238733216270748	** 
df.mm.trans2:probe5	0.0517120728334989	0.0553458509966916	0.93434416315308	0.350371021621055	   
df.mm.trans2:probe6	0.172276910974963	0.0553458509966916	3.11273397865471	0.00191077316982138	** 
df.mm.trans3:probe2	-0.0976958953930148	0.0553458509966916	-1.76518914487113	0.0778629737285902	.  
df.mm.trans3:probe3	0.0772018873794564	0.0553458509966916	1.39489927409502	0.163382154631665	   
df.mm.trans3:probe4	-0.0338889412795627	0.0553458509966916	-0.612312227010268	0.54048217683422	   
df.mm.trans3:probe5	-0.122082151711628	0.0553458509966916	-2.20580494315511	0.0276436906183653	*  
df.mm.trans3:probe6	-0.0311407352066497	0.0553458509966916	-0.56265708532535	0.573805190768483	   
df.mm.trans3:probe7	0.130368273014441	0.0553458509966916	2.35552025430476	0.0187053985783371	*  
df.mm.trans3:probe8	0.0153497957621280	0.0553458509966916	0.27734320614287	0.781578757039589	   
df.mm.trans3:probe9	-0.139405449913327	0.0553458509966916	-2.51880578946486	0.0119435605961425	*  
df.mm.trans3:probe10	0.254748447712087	0.0553458509966916	4.60284634032125	4.75105891686553e-06	***
df.mm.trans3:probe11	0.0460714794759201	0.0553458509966916	0.832428784565515	0.405382518784244	   
df.mm.trans3:probe12	-0.353595701526215	0.0553458509966916	-6.38883846139346	2.64851879868573e-10	***
df.mm.trans3:probe13	-0.569327201060629	0.0553458509966916	-10.2867187116639	1.43545105651544e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.37099271354093	0.173759589834804	25.1554041863042	9.85601883676398e-107	***
df.mm.trans1	0.0134748471742291	0.149229750066637	0.0902959843343036	0.928071622817431	   
df.mm.trans2	0.102918185803506	0.131033337614336	0.785435124200373	0.432400278026405	   
df.mm.exp2	0.00251751460397767	0.166723177764188	0.0150999677293724	0.987955693455004	   
df.mm.exp3	-0.119440301119902	0.166723177764188	-0.716398899790871	0.473926488363691	   
df.mm.exp4	0.201820951837848	0.166723177764188	1.21051526574968	0.226391460058211	   
df.mm.exp5	0.0201024317219091	0.166723177764188	0.120573707816089	0.904054964611983	   
df.mm.exp6	-0.000618854069630686	0.166723177764188	-0.00371186584810653	0.997039169296719	   
df.mm.exp7	-0.0741001362319669	0.166723177764188	-0.444450119207621	0.656821487584055	   
df.mm.exp8	-0.0587363469357537	0.166723177764188	-0.352298628921467	0.724694827135508	   
df.mm.trans1:exp2	-0.0544739629655478	0.153053505861248	-0.355914506231119	0.72198600996721	   
df.mm.trans2:exp2	-0.0702851977669393	0.108483774808796	-0.647886726755386	0.517219514139393	   
df.mm.trans1:exp3	-0.0407292396822406	0.153053505861248	-0.266111118808112	0.790213128003268	   
df.mm.trans2:exp3	0.125812837925256	0.108483774808796	1.15973875491522	0.246455422433058	   
df.mm.trans1:exp4	-0.348043435692815	0.153053505861248	-2.27399845390237	0.0231951906789396	*  
df.mm.trans2:exp4	0.0367761303685512	0.108483774808796	0.339001204865608	0.734686015036146	   
df.mm.trans1:exp5	-0.000257620499281208	0.153053505861248	-0.00168320547661780	0.998657361075733	   
df.mm.trans2:exp5	-0.130742642910458	0.108483774808796	-1.20518154111887	0.228442454827577	   
df.mm.trans1:exp6	-0.0156730998229198	0.153053505861248	-0.102402749513809	0.918459267993739	   
df.mm.trans2:exp6	0.00489055475647547	0.108483774808796	0.0450809788384957	0.964052524358954	   
df.mm.trans1:exp7	-0.00123430036174955	0.153053505861248	-0.00806450237649909	0.993567272355226	   
df.mm.trans2:exp7	0.0540150772918412	0.108483774808796	0.497909271566585	0.618666726700918	   
df.mm.trans1:exp8	-0.0491048357769659	0.153053505861248	-0.320834439568358	0.748408563899494	   
df.mm.trans2:exp8	0.0823785370974162	0.108483774808796	0.759362745651221	0.447829689936975	   
df.mm.trans1:probe2	0.05576590976015	0.109639940055017	0.508627692902485	0.611134894897174	   
df.mm.trans1:probe3	0.0751076184556371	0.109639940055017	0.685038850057273	0.493491583928846	   
df.mm.trans1:probe4	0.0519307062803903	0.109639940055017	0.473647707708812	0.635863278652552	   
df.mm.trans1:probe5	0.0654203058305625	0.109639940055017	0.59668315941923	0.550865415849457	   
df.mm.trans1:probe6	0.0353828863699916	0.109639940055017	0.322718950340875	0.746981267172654	   
df.mm.trans1:probe7	0.0270888766416604	0.109639940055017	0.247071246373058	0.80490808921693	   
df.mm.trans1:probe8	0.153299486414354	0.109639940055017	1.39820841143683	0.162386726586034	   
df.mm.trans1:probe9	0.0797266120288862	0.109639940055017	0.727167599588979	0.467307883903075	   
df.mm.trans1:probe10	0.160963526464636	0.109639940055017	1.46811031074867	0.142415317557655	   
df.mm.trans1:probe11	0.147244331080215	0.109639940055017	1.34298076965683	0.17960869817948	   
df.mm.trans1:probe12	0.00649567426856284	0.109639940055017	0.0592455109452206	0.95276939901431	   
df.mm.trans1:probe13	0.312970915806741	0.109639940055017	2.85453380993910	0.00440661280137095	** 
df.mm.trans1:probe14	0.176437528966505	0.109639940055017	1.60924503313272	0.107905088809778	   
df.mm.trans1:probe15	0.0209664548858146	0.109639940055017	0.191230083446723	0.848387437273995	   
df.mm.trans1:probe16	0.154378517913167	0.109639940055017	1.40805000290679	0.159453344559377	   
df.mm.trans1:probe17	0.151484551628655	0.109639940055017	1.38165481988262	0.167412404290528	   
df.mm.trans1:probe18	0.206308950746635	0.109639940055017	1.88169521657081	0.0601922314419151	.  
df.mm.trans1:probe19	0.0611993672295296	0.109639940055017	0.558184975282	0.576853593784359	   
df.mm.trans1:probe20	0.0261931081526780	0.109639940055017	0.238901153535239	0.811235321683914	   
df.mm.trans1:probe21	0.115394287629581	0.109639940055017	1.05248404524552	0.292853278118932	   
df.mm.trans1:probe22	0.0648381496236529	0.109639940055017	0.591373450141594	0.554415180970857	   
df.mm.trans2:probe2	-0.0983469302246744	0.109639940055017	-0.896999124364026	0.369953535751812	   
df.mm.trans2:probe3	-0.14116464362735	0.109639940055017	-1.28752937621558	0.198232925473023	   
df.mm.trans2:probe4	-0.0127827708554145	0.109639940055017	-0.116588634114540	0.907211434668847	   
df.mm.trans2:probe5	0.0103113557739871	0.109639940055017	0.09404744082141	0.925091904782018	   
df.mm.trans2:probe6	-0.219709444430834	0.109639940055017	-2.00391795472147	0.0453709901108101	*  
df.mm.trans3:probe2	-0.0532632977462947	0.109639940055017	-0.485801959756338	0.627223012007598	   
df.mm.trans3:probe3	-0.0502641011784345	0.109639940055017	-0.458446996169573	0.646739372425409	   
df.mm.trans3:probe4	0.0716999294152346	0.109639940055017	0.65395812310054	0.513301855560251	   
df.mm.trans3:probe5	-0.190018771110952	0.109639940055017	-1.73311633530264	0.0834093381302618	.  
df.mm.trans3:probe6	-0.0437508358630139	0.109639940055017	-0.399041041440372	0.689955368322328	   
df.mm.trans3:probe7	0.0892362121393	0.109639940055017	0.81390241635048	0.415911001676632	   
df.mm.trans3:probe8	-0.0838120673962352	0.109639940055017	-0.764430073148331	0.444806585780394	   
df.mm.trans3:probe9	-0.00597479539418097	0.109639940055017	-0.0544946977459386	0.95655283847821	   
df.mm.trans3:probe10	-0.0573160925315922	0.109639940055017	-0.522766543860122	0.60126231709377	   
df.mm.trans3:probe11	-0.0545964680100861	0.109639940055017	-0.4979614908827	0.618629933893707	   
df.mm.trans3:probe12	-0.066804088767799	0.109639940055017	-0.609304316787085	0.542472854063663	   
df.mm.trans3:probe13	0.0318177594724596	0.109639940055017	0.290202269870757	0.771726755107901