fitVsDatCorrelation=0.818231057732134
cont.fitVsDatCorrelation=0.265840677736430

fstatistic=11981.8919890015,59,853
cont.fstatistic=4251.81397764317,59,853

residuals=-0.348027329540662,-0.0865800434570265,-0.00617938174854521,0.0700947348497137,1.52620886984299
cont.residuals=-0.438567940649811,-0.153720406580140,-0.0458336117247629,0.113442354294961,1.92771205293084

predictedValues:
Include	Exclude	Both
Lung	54.6295127441662	55.4243156813478	56.7396965646934
cerebhem	60.1567908465811	63.1259340089773	62.056650569425
cortex	54.2607474574191	56.3170911401934	56.3616626152341
heart	54.1853088500291	59.263854039141	60.1603417735946
kidney	55.3210954756807	54.0918224144315	57.0560360084852
liver	56.0879454503594	55.3651199206308	48.7817766341002
stomach	54.7758030148564	53.909555311763	55.402652530306
testicle	56.0270185216056	56.3801653668625	54.3658927886445


diffExp=-0.794802937181657,-2.9691431623962,-2.05634368277421,-5.07854518911191,1.22927306124918,0.722825529728652,0.866247703093379,-0.353146845256894
diffExpScore=1.49150643747153
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	52.2765459591361	55.3631467913868	55.3657527441109
cerebhem	50.746661429543	55.3089478598543	57.7929044462302
cortex	50.5041412775983	55.0191532222554	53.1755927847563
heart	51.3903006959835	50.0056065020474	52.5000161040068
kidney	51.2337384386442	51.7254426374325	57.9706566858367
liver	51.4435814331164	59.7710852916825	54.5705980591467
stomach	54.8984016535957	54.0387609365349	57.5209086520724
testicle	52.4464597730223	55.4289373274061	52.1837756447227
cont.diffExp=-3.0866008322507,-4.56228643031127,-4.51501194465708,1.38469419393613,-0.491704198788298,-8.32750385856608,0.859640717060813,-2.9824775543838
cont.diffExpScore=1.15354216146233

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=0.706071247961303
cont.tran.correlation=-0.0160681976312302

tran.covariance=0.00123235060549408
cont.tran.covariance=-9.30141563830407e-06

tran.mean=56.2076300152528
cont.tran.mean=53.2250569518275

weightedLogRatios:
wLogRatio
Lung	-0.0578891934367288
cerebhem	-0.198540927664290
cortex	-0.149249143079718
heart	-0.361692195213773
kidney	0.0899282810357733
liver	0.0521495581841056
stomach	0.0636879494156874
testicle	-0.0253155483804615

cont.weightedLogRatios:
wLogRatio
Lung	-0.228618090020608
cerebhem	-0.341763367454221
cortex	-0.339495922947350
heart	0.107230358890329
kidney	-0.0376441717780308
liver	-0.60247142004214
stomach	0.0630926102098699
testicle	-0.220541549724376

varWeightedLogRatios=0.0240443355454363
cont.varWeightedLogRatios=0.0561068262139106

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15155768128333	0.0668366953327981	62.1149453995533	0	***
df.mm.trans1	-0.0968568763202846	0.057718535208813	-1.67808964607084	0.0936959007000615	.  
df.mm.trans2	-0.0915296185461012	0.0509940649738604	-1.79490728172032	0.0730224061681589	.  
df.mm.exp2	0.136919741228090	0.0655946724062894	2.08736069874726	0.0371518108135296	*  
df.mm.exp3	0.0158913675314180	0.0655946724062894	0.242266131508178	0.808632190048171	   
df.mm.exp4	0.000277442403807846	0.0655946724062894	0.0042296484398822	0.996626227828545	   
df.mm.exp5	-0.0173151363835961	0.0655946724062894	-0.263971687766762	0.791865494511641	   
df.mm.exp6	0.176395300694328	0.0655946724062894	2.6891711510006	0.00730279080278274	** 
df.mm.exp7	-0.00118977618132994	0.0655946724062894	-0.0181383051044228	0.985532761573475	   
df.mm.exp8	0.0850958900059478	0.0655946724062894	1.29729880315385	0.194879147759173	   
df.mm.trans1:exp2	-0.0405396698189238	0.0606305197452603	-0.668634707227508	0.503909426178119	   
df.mm.trans2:exp2	-0.00680646623576131	0.0447786064457758	-0.152002636437638	0.879220799739765	   
df.mm.trans1:exp3	-0.0226645480868163	0.0606305197452603	-0.373814181076488	0.708635459816313	   
df.mm.trans2:exp3	8.82855197931995e-05	0.0447786064457758	0.00197160043156117	0.998427352464839	   
df.mm.trans1:exp4	-0.0084418882965926	0.0606305197452603	-0.139234965031823	0.8892973452673	   
df.mm.trans2:exp4	0.0667037250488597	0.0447786064457758	1.48963378593825	0.136690191665813	   
df.mm.trans1:exp5	0.0298951823400556	0.0606305197452603	0.493071516880616	0.622088961859257	   
df.mm.trans2:exp5	-0.00702025471456254	0.0447786064457758	-0.156776980611571	0.87545771382415	   
df.mm.trans1:exp6	-0.150048650531519	0.0606305197452603	-2.47480396278887	0.0135243295967244	*  
df.mm.trans2:exp6	-0.177463918221417	0.0447786064457758	-3.96314071176635	8.01595449297678e-05	***
df.mm.trans1:exp7	0.00386405863025285	0.0606305197452603	0.063731247010379	0.94919915510848	   
df.mm.trans2:exp7	-0.0265208917798745	0.0447786064457758	-0.592267019564123	0.553828752375244	   
df.mm.trans1:exp8	-0.0598361051926508	0.0606305197452603	-0.986897447754905	0.323972763762997	   
df.mm.trans2:exp8	-0.0679968799231661	0.0447786064457758	-1.51851264075192	0.129255746532299	   
df.mm.trans1:probe2	-0.191264386072874	0.0415108791721768	-4.60757251802731	4.69566519995259e-06	***
df.mm.trans1:probe3	0.093932444974161	0.0415108791721768	2.26283920859764	0.0238962923260584	*  
df.mm.trans1:probe4	-0.0570068647606533	0.0415108791721768	-1.37329938313768	0.170020146723295	   
df.mm.trans1:probe5	-0.0793405824192409	0.0415108791721768	-1.91132021295323	0.0562983189514382	.  
df.mm.trans1:probe6	0.172484666569112	0.0415108791721768	4.15516775382398	3.57809964834599e-05	***
df.mm.trans1:probe7	-0.274950781790631	0.0415108791721768	-6.62358367911708	6.20136301644741e-11	***
df.mm.trans1:probe8	0.190556712747758	0.0415108791721768	4.59052461783275	5.08592230918293e-06	***
df.mm.trans1:probe9	0.487077511442194	0.0415108791721768	11.733731521848	1.39913725838078e-29	***
df.mm.trans1:probe10	0.325724893160511	0.0415108791721768	7.84673559452897	1.27632668080029e-14	***
df.mm.trans1:probe11	-0.169198561560141	0.0415108791721768	-4.07600525294459	5.00961067966542e-05	***
df.mm.trans1:probe12	-0.147376760715319	0.0415108791721768	-3.55031653519158	0.000405820536378112	***
df.mm.trans1:probe13	-0.235506633147633	0.0415108791721768	-5.67337136298198	1.91758915585985e-08	***
df.mm.trans1:probe14	-0.289120992581506	0.0415108791721768	-6.96494505409784	6.56326813252836e-12	***
df.mm.trans1:probe15	-0.165683812194345	0.0415108791721768	-3.9913346934217	7.13547298342193e-05	***
df.mm.trans1:probe16	-0.220742485508071	0.0415108791721768	-5.31770200752642	1.34312016940417e-07	***
df.mm.trans1:probe17	-0.234336654386474	0.0415108791721768	-5.64518649230491	2.24640668932269e-08	***
df.mm.trans1:probe18	-0.227832645627644	0.0415108791721768	-5.48850446367687	5.3471257540967e-08	***
df.mm.trans1:probe19	-0.151873352067801	0.0415108791721768	-3.65863973725702	0.000269122281479523	***
df.mm.trans1:probe20	-0.172939342033694	0.0415108791721768	-4.16612091775712	3.41380204456105e-05	***
df.mm.trans1:probe21	-0.209367683630489	0.0415108791721768	-5.04368222995433	5.58011096327801e-07	***
df.mm.trans1:probe22	-0.175284027807137	0.0415108791721768	-4.22260456301353	2.67439311632438e-05	***
df.mm.trans2:probe2	-0.184994863178607	0.0415108791721768	-4.45653927037523	9.43972629018723e-06	***
df.mm.trans2:probe3	-0.108093212002129	0.0415108791721768	-2.60397308266552	0.00937522996402572	** 
df.mm.trans2:probe4	-0.216394802751954	0.0415108791721768	-5.21296602402474	2.33297462586770e-07	***
df.mm.trans2:probe5	-0.0126932675730277	0.0415108791721768	-0.305781708943798	0.759845464781438	   
df.mm.trans2:probe6	-0.197978319481191	0.0415108791721768	-4.76931164623197	2.17352280366789e-06	***
df.mm.trans3:probe2	-0.0603663708615497	0.0415108791721768	-1.45423012148611	0.146250357435238	   
df.mm.trans3:probe3	-0.139092301419299	0.0415108791721768	-3.35074332784855	0.000841357563023552	***
df.mm.trans3:probe4	0.353904837730344	0.0415108791721768	8.52559244198214	6.85687590422372e-17	***
df.mm.trans3:probe5	-0.103952180595733	0.0415108791721768	-2.5042153447188	0.0124579843327446	*  
df.mm.trans3:probe6	-0.080544518927751	0.0415108791721768	-1.94032312815328	0.05266966278805	.  
df.mm.trans3:probe7	0.55415661243886	0.0415108791721768	13.3496717846027	4.69558275221616e-37	***
df.mm.trans3:probe8	0.0594418301356625	0.0415108791721768	1.43195787034798	0.152522193619694	   
df.mm.trans3:probe9	0.137959142024755	0.0415108791721768	3.3234454383039	0.00092700476421394	***
df.mm.trans3:probe10	0.0315219007693283	0.0415108791721768	0.759364807442004	0.447844154579098	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.76394774679883	0.112076684585276	33.5836821077174	3.15886629953357e-158	***
df.mm.trans1	0.0949080581388403	0.096786683319872	0.980590044863682	0.327072977252388	   
df.mm.trans2	0.247308460186069	0.0855105972451013	2.89213814607332	0.00392358644358324	** 
df.mm.exp2	-0.0735862247551553	0.109993969228258	-0.66900235777882	0.50367498942689	   
df.mm.exp3	-0.000363554801783934	0.109993969228258	-0.00330522486218759	0.997363589718352	   
df.mm.exp4	-0.0657296443156446	0.109993969228258	-0.597574983217884	0.550282161374635	   
df.mm.exp5	-0.134089662696477	0.109993969228258	-1.21906376901643	0.223156994622449	   
df.mm.exp6	0.0750117314581248	0.109993969228258	0.681962220151013	0.495447965894492	   
df.mm.exp7	-0.0134634413306187	0.109993969228258	-0.122401631881105	0.902609777123685	   
df.mm.exp8	0.0636222426273772	0.109993969228258	0.578415735642282	0.563136150500639	   
df.mm.trans1:exp2	0.0438842374115727	0.101669713080448	0.431635303001677	0.666115568397665	   
df.mm.trans2:exp2	0.0726067739543707	0.07508805942308	0.966954992740875	0.333840603358068	   
df.mm.trans1:exp3	-0.0341289252442009	0.101669713080448	-0.335684287976654	0.737191501222958	   
df.mm.trans2:exp3	-0.00586923220663026	0.07508805942308	-0.0781646542969018	0.937715409168797	   
df.mm.trans1:exp4	0.0486312780954227	0.101669713080448	0.478326107372235	0.632540684771787	   
df.mm.trans2:exp4	-0.0360493785252788	0.07508805942308	-0.48009468885273	0.631283150564421	   
df.mm.trans1:exp5	0.113940113144902	0.101669713080448	1.12068884324228	0.262735749322111	   
df.mm.trans2:exp5	0.0661252917819579	0.07508805942308	0.880636579104784	0.378762601335752	   
df.mm.trans1:exp6	-0.091073848909019	0.101669713080448	-0.895781508077583	0.370622181587076	   
df.mm.trans2:exp6	0.00159613698888075	0.07508805942308	0.0212568682843086	0.983045721419644	   
df.mm.trans1:exp7	0.062399857182405	0.101669713080448	0.613750696168779	0.539543716313771	   
df.mm.trans2:exp7	-0.0107491264997118	0.07508805942308	-0.143153606343006	0.886202677846042	   
df.mm.trans1:exp8	-0.0603772257029764	0.101669713080448	-0.593856556427987	0.552765505656988	   
df.mm.trans2:exp8	-0.0624346028374823	0.07508805942308	-0.831485103186615	0.405932333862038	   
df.mm.trans1:probe2	0.0186554940594232	0.0696084940865494	0.268005999903223	0.788759494553354	   
df.mm.trans1:probe3	0.141281563039936	0.0696084940865493	2.02965981226759	0.0427011001587349	*  
df.mm.trans1:probe4	0.188265647510003	0.0696084940865494	2.70463612207899	0.00697419220882064	** 
df.mm.trans1:probe5	0.138699196323767	0.0696084940865494	1.99256136975628	0.0466277846791603	*  
df.mm.trans1:probe6	0.115679656008666	0.0696084940865494	1.66186120712269	0.0969080293429585	.  
df.mm.trans1:probe7	0.109864126965019	0.0696084940865494	1.57831495145429	0.114864019433890	   
df.mm.trans1:probe8	0.141881476218919	0.0696084940865494	2.0382782026933	0.0418300977719	*  
df.mm.trans1:probe9	0.135231434349783	0.0696084940865494	1.94274328333608	0.0523759286075479	.  
df.mm.trans1:probe10	0.148506914304660	0.0696084940865494	2.13345966255225	0.0331718018614080	*  
df.mm.trans1:probe11	0.292230589415774	0.0696084940865493	4.19820301028811	2.97287275905575e-05	***
df.mm.trans1:probe12	0.0929034131728782	0.0696084940865494	1.33465627136488	0.182345005002461	   
df.mm.trans1:probe13	0.193994237734840	0.0696084940865493	2.78693340921344	0.00543913514133335	** 
df.mm.trans1:probe14	0.212138302408897	0.0696084940865494	3.04759218243006	0.00237794580070544	** 
df.mm.trans1:probe15	0.136063863909183	0.0696084940865493	1.95470201869336	0.0509445654673542	.  
df.mm.trans1:probe16	0.0847888426714199	0.0696084940865493	1.21808184164990	0.223529685349513	   
df.mm.trans1:probe17	0.15779200975368	0.0696084940865494	2.26684992721557	0.0236490011139956	*  
df.mm.trans1:probe18	0.179128038157796	0.0696084940865494	2.57336465194999	0.0102393771084205	*  
df.mm.trans1:probe19	0.153007990130762	0.0696084940865493	2.19812240070179	0.0282083738587157	*  
df.mm.trans1:probe20	0.121774530573523	0.0696084940865494	1.74942055810188	0.0805779661685474	.  
df.mm.trans1:probe21	0.151558093043109	0.0696084940865493	2.17729308803414	0.0297321675632987	*  
df.mm.trans1:probe22	0.212699013027508	0.0696084940865494	3.05564738641011	0.00231566685524319	** 
df.mm.trans2:probe2	0.0300210640165556	0.0696084940865494	0.431284492079777	0.666370497424502	   
df.mm.trans2:probe3	0.00387466811985981	0.0696084940865494	0.0556637256804054	0.955622732364677	   
df.mm.trans2:probe4	-0.0435972448701288	0.0696084940865494	-0.626320759301604	0.531272137362024	   
df.mm.trans2:probe5	0.037168227422964	0.0696084940865493	0.533961090678818	0.593507574114241	   
df.mm.trans2:probe6	0.0150604059350151	0.0696084940865494	0.216358738005299	0.828759856873824	   
df.mm.trans3:probe2	-0.102253748875368	0.0696084940865493	-1.46898378160902	0.142205872116992	   
df.mm.trans3:probe3	-0.184529576590985	0.0696084940865493	-2.65096349249484	0.00817505940732453	** 
df.mm.trans3:probe4	-0.0901780362544986	0.0696084940865493	-1.29550333530235	0.195497124784917	   
df.mm.trans3:probe5	-0.101130834341545	0.0696084940865493	-1.45285192085612	0.146632627811622	   
df.mm.trans3:probe6	-0.0371812314185402	0.0696084940865493	-0.534147906896392	0.59337838330147	   
df.mm.trans3:probe7	-0.0574840870735015	0.0696084940865493	-0.825820006995516	0.409137110103071	   
df.mm.trans3:probe8	-0.122767442492821	0.0696084940865494	-1.76368479312561	0.0781430117028365	.  
df.mm.trans3:probe9	-0.098118225463404	0.0696084940865493	-1.40957259241101	0.159030400291113	   
df.mm.trans3:probe10	-0.128387257866505	0.0696084940865493	-1.84441941391336	0.065468618537953	.