fitVsDatCorrelation=0.888771908892766
cont.fitVsDatCorrelation=0.216988183877383

fstatistic=7030.75988234425,53,715
cont.fstatistic=1539.51892474956,53,715

residuals=-0.857422852685931,-0.0960592707630427,-0.000997788421912175,0.0888702594969048,2.84230868852627
cont.residuals=-0.666757136180569,-0.301484062303384,-0.0644112861234576,0.247797769579303,2.4622897481612

predictedValues:
Include	Exclude	Both
Lung	67.9966558836973	44.7717312563289	95.0148451482004
cerebhem	62.2363155727044	43.3993531207572	86.5712416847697
cortex	92.4430501216685	45.3459069539925	145.301953992590
heart	71.8815604165185	49.6774135625517	105.95705965742
kidney	75.9689576257084	44.1346818184851	110.622459125885
liver	58.2139383135334	47.4829055801066	73.1677075791378
stomach	64.6590258520605	44.8194040148128	100.414661639293
testicle	65.0026230205145	44.3274856639600	96.6789793661802


diffExp=23.2249246273684,18.8369624519472,47.0971431676760,22.2041468539669,31.8342758072233,10.7310327334267,19.8396218372477,20.6751373565544
diffExpScore=0.994883425104602
diffExp1.5=1,0,1,0,1,0,0,0
diffExp1.5Score=0.75
diffExp1.4=1,1,1,1,1,0,1,1
diffExp1.4Score=0.875
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	69.5009853006338	67.5137224207306	63.5061799688075
cerebhem	63.7768835995735	72.0422814093162	63.4534125439999
cortex	65.6706272955314	57.8357246356928	81.4317871620077
heart	74.7675463945919	55.7676732623669	72.022289939764
kidney	71.5573410828481	64.8649384712947	69.7703195691671
liver	74.8004116694654	61.4365366409919	71.2715924910981
stomach	71.3771122306678	63.7731830512267	66.59178523117
testicle	71.8650013915363	65.8732384995626	68.1081982165763
cont.diffExp=1.98726287990313,-8.26539780974276,7.83490265983861,18.999873132225,6.69240261155335,13.3638750284735,7.60392917944104,5.99176289197369
cont.diffExpScore=1.28131111176512

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=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,1,0,1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.000875126338223952
cont.tran.correlation=-0.487526775866621

tran.covariance=1.23392704695822e-05
cont.tran.covariance=-0.00220358701584862

tran.mean=57.6475630485875
cont.tran.mean=67.0264504597519

weightedLogRatios:
wLogRatio
Lung	1.67592150428518
cerebhem	1.42420119386539
cortex	2.97050288246914
heart	1.51123515508781
kidney	2.20424066821329
liver	0.807327903551136
stomach	1.46077297888415
testicle	1.52478902379457

cont.weightedLogRatios:
wLogRatio
Lung	0.122620750917799
cerebhem	-0.513811074233404
cortex	0.523569248765154
heart	1.22195222287745
kidney	0.414507441821942
liver	0.82986901069229
stomach	0.474419720215091
testicle	0.368361346924418

varWeightedLogRatios=0.409349986128651
cont.varWeightedLogRatios=0.255301753914043

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.16671839636941	0.0984837912279876	32.154716597359	5.15226708088142e-141	***
df.mm.trans1	0.780516506257649	0.087455512595429	8.92472621901302	3.71738091201592e-18	***
df.mm.trans2	0.61002976375141	0.079552161459207	7.66829904507651	5.70494001821532e-14	***
df.mm.exp2	-0.0265867844803235	0.107248135328677	-0.247899736427534	0.804283158790915	   
df.mm.exp3	-0.104903643220698	0.107248135328677	-0.978139553654766	0.328336137514303	   
df.mm.exp4	0.0505339069802898	0.107248135328677	0.471186812017026	0.637651063897219	   
df.mm.exp5	-0.0555547441798555	0.107248135328677	-0.518001958818214	0.604617143934956	   
df.mm.exp6	0.164738086200415	0.107248135328677	1.5360461577775	0.124969435653690	   
df.mm.exp7	-0.104541674583020	0.107248135328677	-0.974764496022583	0.330006755095550	   
df.mm.exp8	-0.0723657703648145	0.107248135328677	-0.674750848982498	0.500052189584684	   
df.mm.trans1:exp2	-0.0619330602843061	0.101837136067745	-0.608157914447896	0.54327587327819	   
df.mm.trans2:exp2	-0.00454562109086537	0.0857545428445983	-0.0530073502823378	0.957740857427439	   
df.mm.trans1:exp3	0.412037897987553	0.101837136067745	4.04604758045685	5.77631167570257e-05	***
df.mm.trans2:exp3	0.117646619596124	0.0857545428445984	1.37189955999555	0.170524996989117	   
df.mm.trans1:exp4	0.00502733754624384	0.101837136067745	0.0493664466653847	0.960641053840897	   
df.mm.trans2:exp4	0.0534395258294572	0.0857545428445983	0.623168453317961	0.533372550183328	   
df.mm.trans1:exp5	0.166421022947620	0.101837136067745	1.63418797281291	0.102659576254476	   
df.mm.trans2:exp5	0.0412237118325876	0.0857545428445984	0.480717527784993	0.630864327289702	   
df.mm.trans1:exp6	-0.320071795998802	0.101837136067745	-3.14297719238571	0.00174147951733169	** 
df.mm.trans2:exp6	-0.105945263971936	0.0857545428445984	-1.23544783118869	0.217069657421139	   
df.mm.trans1:exp7	0.0542108552843618	0.101837136067745	0.53232894578162	0.594663565292099	   
df.mm.trans2:exp7	0.105605904048112	0.0857545428445984	1.23149049070774	0.218544356365372	   
df.mm.trans1:exp8	0.0273348678221093	0.101837136067745	0.268417483813817	0.788455406674521	   
df.mm.trans2:exp8	0.0623937573871032	0.0857545428445983	0.727585446991084	0.467105569713567	   
df.mm.trans1:probe2	0.127347622167015	0.0557784966160267	2.28309527672756	0.0227173839606466	*  
df.mm.trans1:probe3	-0.0891143644882315	0.0557784966160267	-1.59764729949044	0.110563308884657	   
df.mm.trans1:probe4	-0.0204061395663480	0.0557784966160267	-0.365842408891399	0.714590883761998	   
df.mm.trans1:probe5	0.251596268242847	0.0557784966160267	4.51063193715689	7.55339559864729e-06	***
df.mm.trans1:probe6	0.000527318570093471	0.0557784966160267	0.00945379675116519	0.992459710970292	   
df.mm.trans1:probe7	0.34031816692074	0.0557784966160267	6.10124308769837	1.72384039238649e-09	***
df.mm.trans1:probe8	0.31161617927482	0.0557784966160267	5.58667225149421	3.29146826238495e-08	***
df.mm.trans1:probe9	-0.139681200923163	0.0557784966160267	-2.50421236493184	0.0124940434755692	*  
df.mm.trans1:probe10	0.183165873503499	0.0557784966160267	3.2838080015744	0.00107401375600326	** 
df.mm.trans1:probe11	0.428353951228381	0.0557784966160267	7.67955354152201	5.2605168195401e-14	***
df.mm.trans1:probe12	0.242772395338956	0.0557784966160267	4.35243705132779	1.54266821188320e-05	***
df.mm.trans1:probe13	0.541318488044824	0.0557784966160267	9.70478806144962	5.30766355746176e-21	***
df.mm.trans1:probe14	0.31578181794378	0.0557784966160267	5.66135405401098	2.17521782627410e-08	***
df.mm.trans1:probe15	0.440256849216709	0.0557784966160267	7.89294936088707	1.10975404661604e-14	***
df.mm.trans1:probe16	0.393488539467477	0.0557784966160267	7.05448449384019	4.09849702426107e-12	***
df.mm.trans1:probe17	0.951488792723815	0.0557784966160267	17.0583441729124	5.37205620316692e-55	***
df.mm.trans1:probe18	0.273926563840523	0.0557784966160267	4.91097072275369	1.12391763592755e-06	***
df.mm.trans1:probe19	0.80144741965941	0.0557784966160267	14.3683940636925	2.58829940057419e-41	***
df.mm.trans1:probe20	0.717027579434101	0.0557784966160267	12.8549104571614	3.57490473953045e-34	***
df.mm.trans1:probe21	0.602071172923954	0.0557784966160267	10.7939655862106	2.88131802776887e-25	***
df.mm.trans1:probe22	0.404510908606597	0.0557784966160267	7.25209414285952	1.06896006110209e-12	***
df.mm.trans2:probe2	0.0300360668433944	0.0557784966160267	0.538488282503552	0.59040758944614	   
df.mm.trans2:probe3	0.104171710829369	0.0557784966160267	1.86759624495577	0.0622272841166416	.  
df.mm.trans2:probe4	0.0247364554921735	0.0557784966160267	0.44347655445891	0.657555234062591	   
df.mm.trans2:probe5	0.0705898297831236	0.0557784966160267	1.26553840755258	0.206090625057301	   
df.mm.trans2:probe6	0.0187537509916303	0.0557784966160267	0.336218294313831	0.736804903733183	   
df.mm.trans3:probe2	-0.270308429756818	0.0557784966160267	-4.84610461299438	1.5449812119882e-06	***
df.mm.trans3:probe3	0.468396983412644	0.0557784966160267	8.39744725708618	2.44562701181026e-16	***
df.mm.trans3:probe4	-0.211014642728179	0.0557784966160267	-3.78308229030950	0.000167860264821417	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.22189064454716	0.20974668135169	20.1285217832274	9.80732668587652e-72	***
df.mm.trans1	0.0269166986014616	0.186259112327808	0.144512116830501	0.88513681229818	   
df.mm.trans2	-0.0876682859357694	0.169426883879757	-0.517440231020172	0.6050089223924	   
df.mm.exp2	-0.0201965670810564	0.228412616795709	-0.0884214163139689	0.929566499571447	   
df.mm.exp3	-0.460041870318971	0.228412616795709	-2.01408257027426	0.0443749304145759	*  
df.mm.exp4	-0.24393194349733	0.228412616795709	-1.06794426209609	0.285906086995932	   
df.mm.exp5	-0.104936936619158	0.228412616795709	-0.459418302242969	0.646073453158593	   
df.mm.exp6	-0.136204297991439	0.228412616795709	-0.596308119499632	0.55115814340229	   
df.mm.exp7	-0.0778057687513499	0.228412616795709	-0.340636913331889	0.733476997451249	   
df.mm.exp8	-0.0611105243457182	0.228412616795709	-0.267544434291803	0.78912716318344	   
df.mm.trans1:exp2	-0.0657535637074172	0.216888495682727	-0.303167595406278	0.761850329635437	   
df.mm.trans2:exp2	0.0851188831954149	0.182636457717666	0.466056362782716	0.641317102144927	   
df.mm.trans1:exp3	0.403352693257385	0.216888495682727	1.85972378105027	0.0633351145133061	.  
df.mm.trans2:exp3	0.305317656011797	0.182636457717666	1.67172348734326	0.095016399851369	.  
df.mm.trans1:exp4	0.316974933140344	0.216888495682727	1.46146494373785	0.144327217534861	   
df.mm.trans2:exp4	0.0527954404524317	0.182636457717666	0.289073940177087	0.772608588970483	   
df.mm.trans1:exp5	0.13409510863935	0.216888495682727	0.618267502926988	0.536595911322932	   
df.mm.trans2:exp5	0.0649133027373548	0.182636457717666	0.355423575054784	0.722377115950867	   
df.mm.trans1:exp6	0.209686757101414	0.216888495682727	0.966795202490373	0.333973316651177	   
df.mm.trans2:exp6	0.0418781431277308	0.182636457717666	0.229297828325543	0.81870301530861	   
df.mm.trans1:exp7	0.104442100256275	0.216888495682727	0.481547441820323	0.630274816075305	   
df.mm.trans2:exp7	0.020807670049927	0.182636457717666	0.113929443824919	0.909325707460035	   
df.mm.trans1:exp8	0.0945589732543223	0.216888495682727	0.435979662990734	0.662983083688233	   
df.mm.trans2:exp8	0.0365119184663672	0.182636457717666	0.199915826898100	0.841603256098462	   
df.mm.trans1:probe2	-0.119297099078213	0.118794721548791	-1.00422895498110	0.315607998195201	   
df.mm.trans1:probe3	0.0775177715919703	0.118794721548791	0.652535487952067	0.514265519856281	   
df.mm.trans1:probe4	0.000385374957475209	0.118794721548791	0.00324404108575589	0.997412539113065	   
df.mm.trans1:probe5	0.0310529442976664	0.118794721548791	0.26140003438547	0.793859308400032	   
df.mm.trans1:probe6	-0.0328244616414267	0.118794721548791	-0.276312459118355	0.782387936021017	   
df.mm.trans1:probe7	0.0391119072498273	0.118794721548791	0.329239437071818	0.742071135115385	   
df.mm.trans1:probe8	0.0660072526256291	0.118794721548791	0.555641292517519	0.578629909543299	   
df.mm.trans1:probe9	-0.0319132302508795	0.118794721548791	-0.268641820400851	0.788282819261889	   
df.mm.trans1:probe10	-0.0273161759163486	0.118794721548791	-0.229944357461450	0.818200775142447	   
df.mm.trans1:probe11	-0.121940865909163	0.118794721548791	-1.02648387335190	0.305010698195844	   
df.mm.trans1:probe12	0.0271394376899171	0.118794721548791	0.228456595849424	0.819356617540271	   
df.mm.trans1:probe13	-0.0283246839481655	0.118794721548791	-0.238433859508918	0.811612925864659	   
df.mm.trans1:probe14	0.00299954301902155	0.118794721548791	0.0252498004954672	0.979862759764616	   
df.mm.trans1:probe15	-0.0392655811025468	0.118794721548791	-0.330533045497478	0.741094058238803	   
df.mm.trans1:probe16	-0.0210491256271600	0.118794721548791	-0.177189064907356	0.859410065904083	   
df.mm.trans1:probe17	0.145550886267780	0.118794721548791	1.22523024903931	0.220891936123972	   
df.mm.trans1:probe18	0.0255880873329764	0.118794721548791	0.215397510927848	0.829518837182256	   
df.mm.trans1:probe19	-0.0416713702658146	0.118794721548791	-0.350784695839364	0.725853205562416	   
df.mm.trans1:probe20	-0.0814465163215984	0.118794721548791	-0.685607199206631	0.493183068388187	   
df.mm.trans1:probe21	-0.0218260463089074	0.118794721548791	-0.183729091868304	0.854278076317655	   
df.mm.trans1:probe22	-0.0426048046236744	0.118794721548791	-0.358642236525432	0.719968615100093	   
df.mm.trans2:probe2	0.170522884029165	0.118794721548791	1.43544159038353	0.151598603822757	   
df.mm.trans2:probe3	0.190080303235621	0.118794721548791	1.60007364601255	0.110023983574826	   
df.mm.trans2:probe4	0.112783043349617	0.118794721548791	0.94939439967705	0.342740840112028	   
df.mm.trans2:probe5	0.198650293710693	0.118794721548791	1.67221481830826	0.094919467744832	.  
df.mm.trans2:probe6	0.109048612948678	0.118794721548791	0.917958403596999	0.35895018424577	   
df.mm.trans3:probe2	-0.0798514008393465	0.118794721548791	-0.672179704605394	0.501686444540699	   
df.mm.trans3:probe3	0.0537560408783375	0.118794721548791	0.452512032331832	0.651037417737221	   
df.mm.trans3:probe4	-0.0139119748641648	0.118794721548791	-0.117109368857360	0.906806272720847