fitVsDatCorrelation=0.88662729321926
cont.fitVsDatCorrelation=0.240975130119222

fstatistic=10126.6742283653,59,853
cont.fstatistic=2288.37287828754,59,853

residuals=-0.496656561761438,-0.0918427285025008,-0.00133797312319590,0.0801084878314721,0.896714368513174
cont.residuals=-0.583857806153142,-0.22744865759833,-0.0735317681217369,0.147815730860331,1.45636506902575

predictedValues:
Include	Exclude	Both
Lung	48.947485263574	79.5280512711267	75.6486882961212
cerebhem	64.6445754396191	95.4714204820638	83.3360035235304
cortex	47.9747710491421	130.164136284521	100.345701865369
heart	48.872869238691	64.0636467905042	70.0353409255668
kidney	49.4586463140098	60.1635902991581	67.8832074066459
liver	50.337224973881	49.2359191431145	63.3359030432873
stomach	50.7091324535099	57.7212853082876	69.7338423017433
testicle	53.2627359863991	52.6367480326214	62.0937972857164


diffExp=-30.5805660075527,-30.8268450424446,-82.1893652353792,-15.1907775518132,-10.7049439851483,1.10130583076654,-7.01215285477774,0.625987953777738
diffExpScore=1.01396418521976
diffExp1.5=-1,0,-1,0,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,-1,-1,0,0,0,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,-1,-1,-1,0,0,0,0
diffExp1.3Score=0.8
diffExp1.2=-1,-1,-1,-1,-1,0,0,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	64.3996508785047	61.988261211544	58.1528454364831
cerebhem	63.9342316210525	62.0037714419857	62.4634773905393
cortex	61.9477406732932	56.4070771894841	64.9778593357238
heart	67.5641980674399	56.5652975577225	65.0359337186975
kidney	64.9565025209003	60.2318435790999	58.5944962803494
liver	60.2665344790678	56.7836074327893	65.4368576041038
stomach	59.9742176513273	61.0286489281969	68.4564542952157
testicle	65.1170869110253	60.64722541902	60.481999654727
cont.diffExp=2.41138966696075,1.93046017906681,5.54066348380908,10.9989005097174,4.72465894180042,3.48292704627851,-1.05443127686959,4.46986149200528
cont.diffExpScore=1.03309599812096

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=0.123742925198617
cont.tran.correlation=0.0562928761566114

tran.covariance=0.00461101936260496
cont.tran.covariance=0.000112287801186326

tran.mean=62.699514895639
cont.tran.mean=61.4884934726533

weightedLogRatios:
wLogRatio
Lung	-2.00620853861321
cerebhem	-1.70157044373200
cortex	-4.3615248962438
heart	-1.08926309912881
kidney	-0.783545937381005
liver	0.086443606158831
stomach	-0.516896885939487
testicle	0.0469271225404543

cont.weightedLogRatios:
wLogRatio
Lung	0.158225560807977
cerebhem	0.127008622070556
cortex	0.382229538754426
heart	0.732804932809325
kidney	0.312333924258187
liver	0.242225387688779
stomach	-0.0715032635311143
testicle	0.294453064662158

varWeightedLogRatios=2.09900809887059
cont.varWeightedLogRatios=0.0542100642998838

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.36911630363388	0.0744140664726218	58.7135807884031	7.03769608765251e-302	***
df.mm.trans1	-0.484979569975155	0.0629173476910588	-7.70820112056432	3.54724680492580e-14	***
df.mm.trans2	-0.0015505595089553	0.0561936054816593	-0.0275931664406439	0.977993086056892	   
df.mm.exp2	0.364092766171178	0.0715931323387588	5.08558229368135	4.50734826845352e-07	***
df.mm.exp3	0.190092568851242	0.0715931323387588	2.65517882290408	0.00807444029144482	** 
df.mm.exp4	-0.140658192216385	0.0715931323387588	-1.96468833841254	0.049774564647915	*  
df.mm.exp5	-0.160342139647181	0.0715931323387588	-2.23963017693495	0.0253718782083414	*  
df.mm.exp6	-0.273841772215792	0.0715931323387588	-3.82497263732014	0.000140317727696141	***
df.mm.exp7	-0.203711417682166	0.0715931323387588	-2.84540445469350	0.00454176817979259	** 
df.mm.exp8	-0.130752342928961	0.0715931323387588	-1.82632521664616	0.0681506905387541	.  
df.mm.trans1:exp2	-0.0859365651367015	0.0634605291155879	-1.35417347340700	0.1760396659924	   
df.mm.trans2:exp2	-0.181375631042618	0.0468687068830504	-3.86986633736657	0.000117198661713488	***
df.mm.trans1:exp3	-0.210165293347263	0.0634605291155879	-3.31174820437543	0.00096612526112176	***
df.mm.trans2:exp3	0.302593866413497	0.0468687068830504	6.45620258242984	1.80013687327974e-10	***
df.mm.trans1:exp4	0.139132619348973	0.0634605291155879	2.19242765996412	0.0286181320528453	*  
df.mm.trans2:exp4	-0.0755745430934524	0.0468687068830504	-1.61247339897878	0.107228855041917	   
df.mm.trans1:exp5	0.170731037974283	0.0634605291155879	2.69035005465068	0.00727726010299213	** 
df.mm.trans2:exp5	-0.118700308919538	0.0468687068830504	-2.53261326828849	0.0114998512669250	*  
df.mm.trans1:exp6	0.301838640652361	0.0634605291155879	4.75632089519121	2.3142284987333e-06	***
df.mm.trans2:exp6	-0.205644612370915	0.0468687068830504	-4.38767412303591	1.28914662449440e-05	***
df.mm.trans1:exp7	0.239069445263207	0.0634605291155879	3.76721481202533	0.000176420944883533	***
df.mm.trans2:exp7	-0.116772386274149	0.0468687068830504	-2.49147872941164	0.012910253197015	*  
df.mm.trans1:exp8	0.215241298291302	0.0634605291155879	3.3917350090047	0.000726461394456673	***
df.mm.trans2:exp8	-0.281942955060488	0.0468687068830504	-6.01559065335447	2.65733472478329e-09	***
df.mm.trans1:probe2	0.237793149983388	0.0468687068830504	5.07360168004515	4.79183542252456e-07	***
df.mm.trans1:probe3	0.128193664013576	0.0468687068830504	2.73516537022139	0.0063642884007476	** 
df.mm.trans1:probe4	0.0564841303231254	0.0468687068830504	1.20515657630726	0.228477091182291	   
df.mm.trans1:probe5	-0.119573853949931	0.0468687068830504	-2.55125139783136	0.0109071739780925	*  
df.mm.trans1:probe6	-0.0611524789346127	0.0468687068830504	-1.30476138561288	0.192326001400477	   
df.mm.trans1:probe7	0.0284712262426102	0.0468687068830504	0.60746771430356	0.54370223801041	   
df.mm.trans1:probe8	-0.0717842262078467	0.0468687068830504	-1.53160244823837	0.125991334365094	   
df.mm.trans1:probe9	-0.0499954602253585	0.0468687068830504	-1.06671302773746	0.286403258358783	   
df.mm.trans1:probe10	0.00510068014547357	0.0468687068830504	0.108829120423593	0.913363621138675	   
df.mm.trans1:probe11	0.0309319730519645	0.0468687068830504	0.659970694927594	0.509450792833415	   
df.mm.trans1:probe12	-0.124887580236619	0.0468687068830504	-2.66462611286131	0.00785295844825833	** 
df.mm.trans1:probe13	0.041301432927185	0.0468687068830504	0.88121554175247	0.378449395724107	   
df.mm.trans1:probe14	-0.0340365157937005	0.0468687068830504	-0.726209832898323	0.467909359882661	   
df.mm.trans1:probe15	0.124159061699124	0.0468687068830504	2.64908229725503	0.00822032485598881	** 
df.mm.trans1:probe16	0.0205551305476622	0.0468687068830504	0.438568330868453	0.661085400667134	   
df.mm.trans2:probe2	0.12743245356985	0.0468687068830504	2.71892403363777	0.00668249126486866	** 
df.mm.trans2:probe3	-0.035729309009512	0.0468687068830504	-0.762327603760562	0.446075243013556	   
df.mm.trans2:probe4	0.111369038946096	0.0468687068830504	2.37619184211739	0.0177122650167737	*  
df.mm.trans2:probe5	0.0986300546004136	0.0468687068830504	2.10439035253354	0.0356364811002882	*  
df.mm.trans2:probe6	-0.113732885602180	0.0468687068830504	-2.42662734190582	0.0154461629004831	*  
df.mm.trans3:probe2	1.27788183706265	0.0468687068830504	27.2651396218653	3.25798057832514e-118	***
df.mm.trans3:probe3	0.450799433625356	0.0468687068830504	9.6183458773509	7.31411415398864e-21	***
df.mm.trans3:probe4	0.445908660041838	0.0468687068830504	9.51399536484964	1.81913712996099e-20	***
df.mm.trans3:probe5	0.409722173282954	0.0468687068830504	8.74191332620541	1.20356909701406e-17	***
df.mm.trans3:probe6	0.431088244006746	0.0468687068830504	9.19778403706386	2.74215807659202e-19	***
df.mm.trans3:probe7	0.270130060588374	0.0468687068830504	5.76354840048005	1.15037649361665e-08	***
df.mm.trans3:probe8	0.257370876075998	0.0468687068830504	5.49131591614433	5.2655521738284e-08	***
df.mm.trans3:probe9	0.534504626653843	0.0468687068830504	11.4042964314670	3.8641277557644e-28	***
df.mm.trans3:probe10	0.740477648411256	0.0468687068830504	15.7989775621277	1.63831943929449e-49	***
df.mm.trans3:probe11	0.440153701079917	0.0468687068830504	9.39120642219156	5.26175288791973e-20	***
df.mm.trans3:probe12	0.468224260887766	0.0468687068830504	9.99012543819712	2.67325301226212e-22	***
df.mm.trans3:probe13	0.440420442351861	0.0468687068830504	9.39689766672728	5.01020539190328e-20	***
df.mm.trans3:probe14	0.426707289047426	0.0468687068830504	9.10431111556312	6.02985123051553e-19	***
df.mm.trans3:probe15	0.331975042435284	0.0468687068830504	7.08308516519663	2.9498825753102e-12	***
df.mm.trans3:probe16	0.170858533593212	0.0468687068830504	3.64547146605833	0.000283060776095001	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20280730792573	0.156159088616142	26.9136260026257	5.48231324577009e-116	***
df.mm.trans1	-0.0303555365997174	0.132033043472977	-0.229908633484846	0.81821788780873	   
df.mm.trans2	-0.103703184951607	0.117923164719126	-0.879413177204086	0.379424959395025	   
df.mm.exp2	-0.0785103205251494	0.150239313978476	-0.522568417321162	0.601410314399563	   
df.mm.exp3	-0.244139249613597	0.150239313978476	-1.62500242545419	0.104531443487828	   
df.mm.exp4	-0.155444468705566	0.150239313978476	-1.03464575675469	0.301127609077936	   
df.mm.exp5	-0.0277001758784462	0.150239313978476	-0.184373684523178	0.85376413550011	   
df.mm.exp6	-0.272039456505272	0.150239313978476	-1.81070752588930	0.0705378510804506	.  
df.mm.exp7	-0.249918100170269	0.150239313978476	-1.66346672886215	0.0965863641489627	.  
df.mm.exp8	-0.0500633542566642	0.150239313978476	-0.333224060540083	0.739047044351999	   
df.mm.trans1:exp2	0.0712570326347152	0.133172918233603	0.535071496366258	0.592739874230993	   
df.mm.trans2:exp2	0.0787605016084124	0.0983547183806577	0.800780103945693	0.423481939252253	   
df.mm.trans1:exp3	0.205322174904555	0.133172918233603	1.54177123718497	0.123500070018068	   
df.mm.trans2:exp3	0.149788850507220	0.0983547183806577	1.52294524323175	0.128143027993716	   
df.mm.trans1:exp4	0.203414485117882	0.133172918233603	1.52744632929847	0.127020766662816	   
df.mm.trans2:exp4	0.0638951167054464	0.0983547183806577	0.649639567449689	0.51609996371347	   
df.mm.trans1:exp5	0.0363098178246530	0.133172918233603	0.272651664514558	0.785186982283001	   
df.mm.trans2:exp5	-0.00104368037169959	0.0983547183806577	-0.0106113909823856	0.991535975069253	   
df.mm.trans1:exp6	0.20570821046015	0.133172918233603	1.54466999138151	0.122797002723496	   
df.mm.trans2:exp6	0.184342107222317	0.0983547183806577	1.87425789283302	0.0612364854409142	.  
df.mm.trans1:exp7	0.178724652276777	0.133172918233603	1.34204952964438	0.179937040379131	   
df.mm.trans2:exp7	0.234316476829778	0.0983547183806577	2.38236132122217	0.0174202346844270	*  
df.mm.trans1:exp8	0.0611421288286631	0.133172918233603	0.459118337569293	0.646266159321687	   
df.mm.trans2:exp8	0.0281922093597653	0.0983547183806577	0.286638097530352	0.774459046501247	   
df.mm.trans1:probe2	-0.0625941061692159	0.0983547183806577	-0.636411828530288	0.524678764275383	   
df.mm.trans1:probe3	-0.0152019036513402	0.0983547183806577	-0.154562016969080	0.877203178526024	   
df.mm.trans1:probe4	-0.129782586705183	0.0983547183806577	-1.31953594948939	0.187344050495396	   
df.mm.trans1:probe5	-0.0212572558841936	0.0983547183806577	-0.216128481014227	0.828939272344632	   
df.mm.trans1:probe6	0.0219826757047957	0.0983547183806577	0.223504027734767	0.823196763445497	   
df.mm.trans1:probe7	-0.0397158730886096	0.0983547183806577	-0.403802417845366	0.686459227722486	   
df.mm.trans1:probe8	-0.062483529456321	0.0983547183806577	-0.635287564085069	0.525411260618256	   
df.mm.trans1:probe9	0.0132231956895788	0.0983547183806577	0.134443938300973	0.89308325409502	   
df.mm.trans1:probe10	0.0798060875528677	0.0983547183806577	0.8114108694206	0.417356110208565	   
df.mm.trans1:probe11	0.0689450182051355	0.0983547183806577	0.70098333196686	0.483504591106891	   
df.mm.trans1:probe12	-0.138566908826685	0.0983547183806577	-1.40884861558340	0.159244347446921	   
df.mm.trans1:probe13	0.0108950232652215	0.0983547183806577	0.110772756453381	0.911822607013743	   
df.mm.trans1:probe14	0.0264528857952833	0.0983547183806577	0.268953907151703	0.788030190507333	   
df.mm.trans1:probe15	-0.0349540358520592	0.0983547183806577	-0.35538748346346	0.722387184411516	   
df.mm.trans1:probe16	0.0482574135283768	0.0983547183806577	0.490646654506278	0.62380257284259	   
df.mm.trans2:probe2	0.0235078197222633	0.0983547183806577	0.239010594603932	0.811154779634056	   
df.mm.trans2:probe3	0.0749755414835219	0.0983547183806577	0.762297353070013	0.44609328375892	   
df.mm.trans2:probe4	0.0606987181793404	0.0983547183806577	0.617140887378895	0.537306494374148	   
df.mm.trans2:probe5	0.215698241767272	0.0983547183806577	2.19306450487169	0.0285720550566155	*  
df.mm.trans2:probe6	0.237619673483697	0.0983547183806577	2.41594584780416	0.0159035338752195	*  
df.mm.trans3:probe2	-0.0379253123682185	0.0983547183806577	-0.385597284935919	0.699891085086853	   
df.mm.trans3:probe3	-0.0675893793612826	0.0983547183806577	-0.687200171726328	0.492143385622274	   
df.mm.trans3:probe4	-0.0583175730206068	0.0983547183806577	-0.592931116887581	0.553384413384085	   
df.mm.trans3:probe5	-0.0837592193556277	0.0983547183806577	-0.851603468899766	0.394673226334859	   
df.mm.trans3:probe6	0.0221728420139919	0.0983547183806577	0.225437501922149	0.821692938922043	   
df.mm.trans3:probe7	0.0925088781082203	0.0983547183806577	0.94056370280262	0.347194764194051	   
df.mm.trans3:probe8	-0.0930725097113824	0.0983547183806577	-0.946294303351754	0.344266478460703	   
df.mm.trans3:probe9	0.0619427383520004	0.0983547183806577	0.629789189292031	0.529001171568032	   
df.mm.trans3:probe10	-0.0170220179445989	0.0983547183806577	-0.173067629340560	0.862639326728448	   
df.mm.trans3:probe11	0.0248979498934286	0.0983547183806577	0.253144437840463	0.800217595218101	   
df.mm.trans3:probe12	-0.0936560103965692	0.0983547183806577	-0.952226918429035	0.341251651093277	   
df.mm.trans3:probe13	-0.0431447278382359	0.0983547183806577	-0.438664545520377	0.661015700472085	   
df.mm.trans3:probe14	0.0597048536901012	0.0983547183806577	0.607035988441635	0.543988570217157	   
df.mm.trans3:probe15	0.0496497184300502	0.0983547183806577	0.504802608837668	0.613827866636771	   
df.mm.trans3:probe16	0.0928293549922906	0.0983547183806577	0.94382208114325	0.345527819970045