fitVsDatCorrelation=0.867046868968472
cont.fitVsDatCorrelation=0.348466571526318

fstatistic=7257.7286755323,54,738
cont.fstatistic=2040.77899690588,54,738

residuals=-0.851893208740171,-0.108938644329808,-0.000269298530139285,0.107179497940729,0.976426146340784
cont.residuals=-1.00982931905317,-0.251317948877683,-0.0395736442843257,0.194998308796507,1.53216986514301

predictedValues:
Include	Exclude	Both
Lung	95.7323845222826	102.978102086500	121.144600106375
cerebhem	109.311632969916	88.176184601441	117.633262661803
cortex	106.433235485967	82.9308783629163	178.964714543612
heart	95.8114311607934	83.6506479775624	163.103558092406
kidney	95.292092557845	104.724812133184	130.543022305551
liver	89.9452260470285	93.806127999724	125.939664582243
stomach	90.165722879453	81.04271831756	132.229076393478
testicle	100.354930475033	85.0656541970438	139.943170250390


diffExp=-7.24571756421757,21.1354483684753,23.5023571230507,12.1607831832310,-9.43271957533895,-3.86090195269539,9.12300456189304,15.2892762779894
diffExpScore=1.64987325448234
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=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	93.9491873148264	129.697235919079	123.218870427339
cerebhem	102.766738745121	103.738954705866	87.0896870010012
cortex	102.774080106438	108.689956112516	84.7991125845291
heart	90.6825289147846	99.5340488290296	101.170079690061
kidney	110.766864878329	92.5835835430018	98.6231375836424
liver	94.1634356573922	128.684349669597	79.7904471238536
stomach	92.3682334753139	98.5431848448973	129.491227706991
testicle	105.522048917634	96.2442507271514	93.4949155933756
cont.diffExp=-35.7480486042522,-0.9722159607456,-5.91587600607723,-8.85151991424496,18.1832813353269,-34.5209140122044,-6.17495136958341,9.27779819048308
cont.diffExpScore=1.82045270761227

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

tran.correlation=-0.235736245158655
cont.tran.correlation=-0.473316217058425

tran.covariance=-0.00156059906703764
cont.tran.covariance=-0.00441277334078516

tran.mean=94.0888613608907
cont.tran.mean=103.169292647561

weightedLogRatios:
wLogRatio
Lung	-0.335471488065088
cerebhem	0.985540482972507
cortex	1.13346663217266
heart	0.610053308207475
kidney	-0.434581412216705
liver	-0.189981489290772
stomach	0.474514612726221
testicle	0.748113130336433

cont.weightedLogRatios:
wLogRatio
Lung	-1.51679174822036
cerebhem	-0.0436633358463088
cortex	-0.260831421998362
heart	-0.424130804833148
kidney	0.828039234496002
liver	-1.46832753570599
stomach	-0.294965155808874
testicle	0.424528777208313

varWeightedLogRatios=0.375969301644819
cont.varWeightedLogRatios=0.672829668907813

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.46574604927328	0.102616032076727	43.5189897611146	5.9188435595938e-206	***
df.mm.trans1	0.185153677383748	0.0903955873290523	2.04826012922249	0.0408878320073282	*  
df.mm.trans2	0.220763254727817	0.0815666988640315	2.70653658665064	0.0069560182612534	** 
df.mm.exp2	0.00687976284404336	0.108622564064343	0.0633364062366275	0.949515776227472	   
df.mm.exp3	-0.500751426905875	0.108622564064343	-4.61001294914426	4.74436522061044e-06	***
df.mm.exp4	-0.504442277754649	0.108622564064343	-4.64399162457478	4.04547046640456e-06	***
df.mm.exp5	-0.0625080670376005	0.108622564064343	-0.575461162936398	0.565154861982896	   
df.mm.exp6	-0.194459997495586	0.108622564064343	-1.79023575046890	0.073825841312833	.  
df.mm.exp7	-0.386998228682771	0.108622564064343	-3.56277935451357	0.000390531605016256	***
df.mm.exp8	-0.288187973634659	0.108622564064343	-2.65311333899233	0.0081468180598963	** 
df.mm.trans1:exp2	0.125766420760845	0.102457388283725	1.22749977202788	0.220026198721693	   
df.mm.trans2:exp2	-0.162059216673698	0.0838287722321389	-1.93321710861902	0.0535916472154749	.  
df.mm.trans1:exp3	0.606712681150082	0.102457388283725	5.92160986448309	4.88480949872586e-09	***
df.mm.trans2:exp3	0.284242532406448	0.0838287722321389	3.39075146680334	0.000734108439685246	***
df.mm.trans1:exp4	0.505267641316676	0.102457388283725	4.93149054236564	1.00876748620755e-06	***
df.mm.trans2:exp4	0.29657508692743	0.0838287722321389	3.53786747712532	0.00042860865055419	***
df.mm.trans1:exp5	0.0578982625679125	0.102457388283725	0.565096022236879	0.572180189875455	   
df.mm.trans2:exp5	0.079327775438116	0.0838287722321389	0.946307256158318	0.344301623067617	   
df.mm.trans1:exp6	0.132104245598651	0.102457388283725	1.28935792539263	0.197677631143224	   
df.mm.trans2:exp6	0.101173817336406	0.0838287722321389	1.20691040370047	0.227853352976000	   
df.mm.trans1:exp7	0.327090933627511	0.102457388283725	3.19245824148601	0.00147050762991110	** 
df.mm.trans2:exp7	0.147458266447419	0.0838287722321389	1.75904122798169	0.0789848764711082	.  
df.mm.trans1:exp8	0.33534454297771	0.102457388283725	3.27301474881511	0.00111362560783128	** 
df.mm.trans2:exp8	0.0970949697691075	0.0838287722321389	1.15825351110036	0.247135275613151	   
df.mm.trans1:probe2	-0.266445336715307	0.0598222264503205	-4.45395219344731	9.73604320725325e-06	***
df.mm.trans1:probe3	1.12430862154547	0.0598222264503205	18.7941621076099	1.08281708371213e-64	***
df.mm.trans1:probe4	0.155343577566738	0.0598222264503205	2.59675352764984	0.00959831236259796	** 
df.mm.trans1:probe5	0.135886351534296	0.0598222264503205	2.27150274400341	0.0234036779339381	*  
df.mm.trans1:probe6	-0.0257036723853382	0.0598222264503205	-0.429667598658902	0.667562873971553	   
df.mm.trans1:probe7	-0.198517156153270	0.0598222264503205	-3.31845148421765	0.000949584370542594	***
df.mm.trans1:probe8	-0.130093181773991	0.0598222264503205	-2.17466298888135	0.0299723035246698	*  
df.mm.trans1:probe9	0.0121206181591372	0.0598222264503205	0.202610616126146	0.839495234531703	   
df.mm.trans1:probe10	-0.593641457433091	0.0598222264503205	-9.92342633596365	7.17475384360603e-22	***
df.mm.trans1:probe11	-0.542202152411225	0.0598222264503205	-9.06355688485613	1.11903256742418e-18	***
df.mm.trans1:probe12	-0.413114448443934	0.0598222264503205	-6.90570165901474	1.07866867980528e-11	***
df.mm.trans1:probe13	-0.540857031620489	0.0598222264503205	-9.04107158347984	1.34695318809101e-18	***
df.mm.trans1:probe14	-0.365028397663857	0.0598222264503205	-6.10188585954747	1.69187668186207e-09	***
df.mm.trans1:probe15	-0.399053118556306	0.0598222264503205	-6.67064972728323	4.99652671331918e-11	***
df.mm.trans1:probe16	-0.442875610138035	0.0598222264503205	-7.40319503998772	3.63325223523281e-13	***
df.mm.trans1:probe17	0.0112054170383885	0.0598222264503205	0.187311935768457	0.851467549088915	   
df.mm.trans1:probe18	0.0343679534814955	0.0598222264503205	0.574501410609257	0.565803617841638	   
df.mm.trans1:probe19	0.116754813034861	0.0598222264503205	1.95169621665319	0.0513521810932027	.  
df.mm.trans1:probe20	-0.0543179396769516	0.0598222264503205	-0.907989269206823	0.364180286976844	   
df.mm.trans1:probe21	-0.00610937067090898	0.0598222264503205	-0.102125431188733	0.918684856540813	   
df.mm.trans1:probe22	-0.024291886553939	0.0598222264503205	-0.406067911466188	0.684810413555668	   
df.mm.trans2:probe2	-0.216588045268140	0.0598222264503205	-3.62052798967632	0.000314099780316927	***
df.mm.trans2:probe3	-0.0595676026084691	0.0598222264503205	-0.99574365821936	0.319701076519075	   
df.mm.trans2:probe4	-0.11276756147491	0.0598222264503205	-1.88504454224147	0.0598164757383405	.  
df.mm.trans2:probe5	-0.12431088634546	0.0598222264503205	-2.07800501120924	0.0380540089452759	*  
df.mm.trans2:probe6	-0.0586882385413728	0.0598222264503205	-0.981044037037146	0.326892519285533	   
df.mm.trans3:probe2	-0.193472729524738	0.0598222264503205	-3.23412786525771	0.00127447819237192	** 
df.mm.trans3:probe3	-0.322551396972095	0.0598222264503205	-5.39183203487016	9.39601808466831e-08	***
df.mm.trans3:probe4	0.334988049732894	0.0598222264503205	5.59972554701028	3.02982627021825e-08	***
df.mm.trans3:probe5	-0.192354458642058	0.0598222264503205	-3.21543463117006	0.00135922012409903	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.8200484171954	0.193053094127609	24.9674756003099	3.24867314502833e-100	***
df.mm.trans1	-0.291500074237021	0.170062586480714	-1.71407527234145	0.0869347889545498	.  
df.mm.trans2	0.124525420530483	0.153452664995878	0.81149076514134	0.417345535660776	   
df.mm.exp2	0.213406320689133	0.204353273658219	1.04430096405529	0.296688106028772	   
df.mm.exp3	0.286752847072396	0.204353273658219	1.40322120580261	0.160971471002563	   
df.mm.exp4	-0.102933206361018	0.204353273658219	-0.503702262842991	0.614620936201934	   
df.mm.exp5	0.0502389763890695	0.204353273658219	0.245843756205707	0.80587153827937	   
df.mm.exp6	0.428996019718277	0.204353273658219	2.09928626069271	0.0361307644710558	*  
df.mm.exp7	-0.341329783196656	0.204353273658219	-1.67029270970981	0.0952854847079065	.  
df.mm.exp8	0.0939074611380867	0.204353273658219	0.459534899818375	0.645985429387303	   
df.mm.trans1:exp2	-0.123698648719625	0.192754635158935	-0.64174150010779	0.521240304983325	   
df.mm.trans2:exp2	-0.436731407666370	0.157708337857310	-2.76923473799788	0.00575988554604992	** 
df.mm.trans1:exp3	-0.196973740520096	0.192754635158935	-1.02188847680722	0.307168541289634	   
df.mm.trans2:exp3	-0.463456237037592	0.157708337857310	-2.93869203958584	0.00339881979475207	** 
df.mm.trans1:exp4	0.0675438443187804	0.192754635158935	0.35041359323519	0.726128316806074	   
df.mm.trans2:exp4	-0.161769788478885	0.157708337857310	-1.02575292262131	0.305344150741703	   
df.mm.trans1:exp5	0.114434624120085	0.192754635158935	0.593680271427601	0.552907894219091	   
df.mm.trans2:exp5	-0.387329913760775	0.157708337857310	-2.45598881468918	0.0142791573173076	*  
df.mm.trans1:exp6	-0.426718145625407	0.192754635158935	-2.21378928332156	0.0271482904825906	*  
df.mm.trans2:exp6	-0.436836295453658	0.157708337857310	-2.76989981245566	0.00574826073567682	** 
df.mm.trans1:exp7	0.324358833542588	0.192754635158935	1.68275503867982	0.0928454930387002	.  
df.mm.trans2:exp7	0.0666218803615548	0.157708337857310	0.422437274190489	0.67282892040356	   
df.mm.trans1:exp8	0.0222583887877339	0.192754635158935	0.115475245352107	0.908099852546596	   
df.mm.trans2:exp8	-0.39222100221354	0.157708337857310	-2.48700232050134	0.0131023578951811	*  
df.mm.trans1:probe2	-0.0291581152036450	0.112544459965103	-0.259080857580073	0.795645101215431	   
df.mm.trans1:probe3	0.0738975156636545	0.112544459965103	0.656607314891985	0.511638128026391	   
df.mm.trans1:probe4	0.0711413887742862	0.112544459965103	0.632118087343841	0.527505487604456	   
df.mm.trans1:probe5	-0.0155365480197436	0.112544459965103	-0.138048092501054	0.890240065543878	   
df.mm.trans1:probe6	-0.0272506294549445	0.112544459965103	-0.242132126835868	0.808745053596238	   
df.mm.trans1:probe7	-0.169822825000072	0.112544459965103	-1.50893988964654	0.131742123261077	   
df.mm.trans1:probe8	0.12987082280164	0.112544459965103	1.15395127260738	0.248893701388503	   
df.mm.trans1:probe9	-0.0243762567027712	0.112544459965103	-0.216592240171837	0.82858599048036	   
df.mm.trans1:probe10	0.0332399678050819	0.112544459965103	0.295349658396236	0.767809858833079	   
df.mm.trans1:probe11	0.0630393846207317	0.112544459965103	0.560128722820107	0.575561619853767	   
df.mm.trans1:probe12	0.0159360898057871	0.112544459965103	0.141598172053324	0.887436054183298	   
df.mm.trans1:probe13	-0.0926561394661774	0.112544459965103	-0.823284766703823	0.410612027139611	   
df.mm.trans1:probe14	-0.107978791662810	0.112544459965103	-0.959432314094287	0.337655216610422	   
df.mm.trans1:probe15	0.165501410336811	0.112544459965103	1.47054248950263	0.141841098617560	   
df.mm.trans1:probe16	0.0607818969713969	0.112544459965103	0.540070093101375	0.589311622768115	   
df.mm.trans1:probe17	0.247038306123956	0.112544459965103	2.19502857982132	0.0284722990921237	*  
df.mm.trans1:probe18	0.114308012576835	0.112544459965103	1.01566983050325	0.310119495592067	   
df.mm.trans1:probe19	-0.0958371582023534	0.112544459965103	-0.851549318661	0.394740505636269	   
df.mm.trans1:probe20	-0.0148572239999418	0.112544459965103	-0.132012042214683	0.895010748509093	   
df.mm.trans1:probe21	-0.0770473274674351	0.112544459965103	-0.684594581477628	0.493814719273931	   
df.mm.trans1:probe22	0.0633210019399119	0.112544459965103	0.562630998980725	0.573857042848666	   
df.mm.trans2:probe2	-0.0292249518726731	0.112544459965103	-0.259674726607911	0.795187111733486	   
df.mm.trans2:probe3	-0.142188443956698	0.112544459965103	-1.26339798512327	0.206845153909185	   
df.mm.trans2:probe4	-0.314714736986924	0.112544459965103	-2.79635920848089	0.00530263122242754	** 
df.mm.trans2:probe5	-0.171574338255970	0.112544459965103	-1.52450274592966	0.127811566268814	   
df.mm.trans2:probe6	-0.215379166614547	0.112544459965103	-1.91372517742171	0.0560418321507498	.  
df.mm.trans3:probe2	0.284325950096728	0.112544459965103	2.52634336852201	0.0117337003296305	*  
df.mm.trans3:probe3	0.2361813848508	0.112544459965103	2.09856073701037	0.0361949383623282	*  
df.mm.trans3:probe4	0.086940114541557	0.112544459965103	0.772495728075065	0.44006819288673	   
df.mm.trans3:probe5	0.196994775183151	0.112544459965103	1.75037292145908	0.0804695163267174	.