fitVsDatCorrelation=0.935104689689515
cont.fitVsDatCorrelation=0.25588954652771

fstatistic=7286.07533737599,45,531
cont.fstatistic=968.875596053797,45,531

residuals=-0.625264840462138,-0.113987391760388,0.00404059066314462,0.101452682203958,0.803350684468521
cont.residuals=-0.930083018952473,-0.433177159402151,-0.085525317482421,0.416413896024871,1.51840100504566

predictedValues:
Include	Exclude	Both
Lung	128.321231046437	227.290531296376	83.1463253904895
cerebhem	102.639107336325	191.463679606351	75.4772304170881
cortex	98.5918520622991	164.397427031612	66.7326467741706
heart	101.497622550060	174.451498506655	71.5055733604013
kidney	148.279102122692	240.789585595047	80.6934580587273
liver	109.118390099403	216.198000970218	83.8307191305587
stomach	98.3059501050666	181.995340773047	74.887723109229
testicle	111.609681926425	189.173965134123	75.9810822226812


diffExp=-98.9693002499382,-88.8245722700259,-65.8055749693132,-72.9538759565951,-92.5104834723547,-107.079610870815,-83.68939066798,-77.5642832076981
diffExpScore=0.998547350051143
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	87.8116736509225	85.4728622570104	113.975506163070
cerebhem	99.24804436631	85.6997748738874	94.0285743269595
cortex	93.2596971019968	95.60823813882	104.477943548387
heart	110.852771515772	77.3819485308694	103.071191795700
kidney	88.929831370542	88.3603432862543	84.9426755504127
liver	103.108386406245	76.2093051848402	106.521918947629
stomach	105.312321695901	84.3102790236196	89.5017017718924
testicle	87.0722888277803	76.1450428286073	90.4223409964047
cont.diffExp=2.33881139391207,13.5482694924225,-2.34854103682316,33.4708229849025,0.569488084287713,26.8990812214051,21.0020426722812,10.9272459991730
cont.diffExpScore=1.03442116876046

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=0,0,0,1,0,1,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,0,0,1,0,1,1,0
cont.diffExp1.2Score=0.75

tran.correlation=0.894229752395936
cont.tran.correlation=-0.343765367525782

tran.covariance=0.0173220485639427
cont.tran.covariance=-0.00242739531675144

tran.mean=155.257685385134
cont.tran.mean=90.2989255662111

weightedLogRatios:
wLogRatio
Lung	-2.93871750613213
cerebhem	-3.08183138763149
cortex	-2.47807719393647
heart	-2.64893519116086
kidney	-2.54122625249368
liver	-3.44226870874031
stomach	-3.01544963117866
testicle	-2.62712957191824

cont.weightedLogRatios:
wLogRatio
Lung	0.120445840198114
cerebhem	0.664031271510392
cortex	-0.113108672446907
heart	1.62775881983019
kidney	0.0288110183567239
liver	1.35569156106826
stomach	1.01108856497211
testicle	0.589992619279619

varWeightedLogRatios=0.109064513092812
cont.varWeightedLogRatios=0.40513383050904

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	6.01112847711637	0.0941966864310167	63.8146489528431	3.41320697895644e-251	***
df.mm.trans1	-0.908062626057377	0.0773009809497956	-11.7471035283127	1.74504649008190e-28	***
df.mm.trans2	-0.525414074310781	0.0743283789020872	-7.06882192335862	4.94067446061207e-12	***
df.mm.exp2	-0.298077673860389	0.098409026398204	-3.02896680081197	0.00257329745163820	** 
df.mm.exp3	-0.367582634518997	0.0984090263982041	-3.73525323816947	0.000207818880480342	***
df.mm.exp4	-0.348257061422936	0.0984090263982041	-3.53887315187676	0.000437084511755582	***
df.mm.exp5	0.232198481041021	0.0984090263982041	2.35952421784409	0.0186593443392256	*  
df.mm.exp6	-0.220335215805168	0.0984090263982041	-2.23897363757669	0.0255710081450107	*  
df.mm.exp7	-0.384088122887827	0.0984090263982041	-3.90297655556153	0.000107243117145787	***
df.mm.exp8	-0.232973345438774	0.0984090263982041	-2.36739813374503	0.0182711748977877	*  
df.mm.trans1:exp2	0.074759959456365	0.0812009285206001	0.920678627922326	0.357636314066719	   
df.mm.trans2:exp2	0.126546729169102	0.07458781556838	1.69661396040064	0.0903557253678419	.  
df.mm.trans1:exp3	0.104034518789459	0.0812009285206001	1.28119863510016	0.200683135046414	   
df.mm.trans2:exp3	0.0436403936655745	0.07458781556838	0.585087434630207	0.558737464725419	   
df.mm.trans1:exp4	0.113755698839772	0.0812009285206002	1.40091623226836	0.161823245488412	   
df.mm.trans2:exp4	0.083674746217066	0.07458781556838	1.12182862012302	0.262442346453953	   
df.mm.trans1:exp5	-0.0876388956980136	0.0812009285206002	-1.07928440345088	0.280950958115378	   
df.mm.trans2:exp5	-0.17450409042537	0.07458781556838	-2.33957904646489	0.0196751481218306	*  
df.mm.trans1:exp6	0.05823191866505	0.0812009285206002	0.717133655069928	0.473606956974013	   
df.mm.trans2:exp6	0.170300802067587	0.07458781556838	2.28322549426937	0.0228113203644625	*  
df.mm.trans1:exp7	0.11763594063223	0.0812009285206002	1.44870191481107	0.148011089545252	   
df.mm.trans2:exp7	0.161840136795102	0.07458781556838	2.16979322375692	0.0304652425615072	*  
df.mm.trans1:exp8	0.0934444095974944	0.0812009285206002	1.15078006249384	0.25034067343767	   
df.mm.trans2:exp8	0.0494113148950279	0.07458781556838	0.662458265046373	0.507964992431848	   
df.mm.trans1:probe2	-0.953644229252127	0.0555944753013716	-17.1535790936514	8.34571005967187e-53	***
df.mm.trans1:probe3	0.0255802639396218	0.0555944753013716	0.460122409663083	0.645616708717581	   
df.mm.trans1:probe4	-0.839842751922807	0.0555944753013716	-15.1065865334660	3.75829614000068e-43	***
df.mm.trans1:probe5	-0.563935295993027	0.0555944753013716	-10.1437290834385	3.21249896353355e-22	***
df.mm.trans1:probe6	-0.434676459608795	0.0555944753013716	-7.81869884107119	2.89012851398924e-14	***
df.mm.trans1:probe7	-0.8601548423193	0.0555944753013716	-15.4719482045022	7.64624612646084e-45	***
df.mm.trans1:probe8	-0.846850726411355	0.0555944753013716	-15.2326417655831	9.84298155195409e-44	***
df.mm.trans2:probe2	-0.292480549842391	0.0555944753013716	-5.26096430008352	2.08220830386263e-07	***
df.mm.trans2:probe3	-0.0180613968460445	0.0555944753013716	-0.324877548499840	0.745401703042153	   
df.mm.trans2:probe4	-0.0719656206253712	0.0555944753013716	-1.29447432024951	0.196064272000317	   
df.mm.trans2:probe5	-0.273055703823562	0.0555944753013716	-4.91156184752814	1.20488311851976e-06	***
df.mm.trans2:probe6	-0.296202010652329	0.0555944753013716	-5.32790369990273	1.47043131942348e-07	***
df.mm.trans3:probe2	-0.600773316426714	0.0555944753013716	-10.8063492490933	9.69792156613173e-25	***
df.mm.trans3:probe3	-0.511085965690794	0.0555944753013716	-9.19310710138467	8.61861195211618e-19	***
df.mm.trans3:probe4	0.351185685566285	0.0555944753013716	6.31691698972866	5.64851316499193e-10	***
df.mm.trans3:probe5	-0.150578391251771	0.0555944753013716	-2.70851357865151	0.00697660357978596	** 
df.mm.trans3:probe6	-0.341429167648836	0.0555944753013716	-6.14142261075377	1.60400072363702e-09	***
df.mm.trans3:probe7	-0.422663712692194	0.0555944753013716	-7.60262077123635	1.32493656784870e-13	***
df.mm.trans3:probe8	0.527909559746465	0.0555944753013716	9.49571979742095	7.3968920452427e-20	***
df.mm.trans3:probe9	-0.103140673981009	0.0555944753013716	-1.85523243850930	0.0641170296892584	.  
df.mm.trans3:probe10	-0.319921663856500	0.0555944753013716	-5.75455856219955	1.46943147347565e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.13934063134543	0.256963336113935	16.1086818607853	7.95398638466013e-48	***
df.mm.trans1	0.317695720750718	0.210872788654683	1.50657523323678	0.132514216712969	   
df.mm.trans2	0.256675112982418	0.202763695139194	1.26588299155929	0.206110081907844	   
df.mm.exp2	0.317463888598003	0.268454365913664	1.18256183883446	0.237512081292810	   
df.mm.exp3	0.259261243157480	0.268454365913664	0.965755361344576	0.334606236715907	   
df.mm.exp4	0.234126729401279	0.268454365913664	0.8721285966218	0.383532394097686	   
df.mm.exp5	0.339884480078763	0.268454365913664	1.26607916739216	0.206039899672883	   
df.mm.exp6	0.113503717927117	0.268454365913664	0.422804514803905	0.672609058433355	   
df.mm.exp7	0.409766772493997	0.268454365913664	1.52639265559860	0.127507497642937	   
df.mm.exp8	0.107477480622991	0.268454365913664	0.400356612779233	0.689054894074209	   
df.mm.trans1:exp2	-0.195036122445448	0.221511629323428	-0.880478027456863	0.378998724828941	   
df.mm.trans2:exp2	-0.314812614964562	0.203471423975598	-1.54720800008908	0.122408853575449	   
df.mm.trans1:exp3	-0.199067648773073	0.221511629323428	-0.898678093701415	0.369231468286996	   
df.mm.trans2:exp3	-0.147201178870307	0.203471423975598	-0.723448904982161	0.469722695139012	   
df.mm.trans1:exp4	-0.00111824035676582	0.221511629323428	-0.00504822415049408	0.995974012949309	   
df.mm.trans2:exp4	-0.333572124179433	0.203471423975598	-1.63940526714669	0.101721161579741	   
df.mm.trans1:exp5	-0.327231281976253	0.221511629323428	-1.47726457060394	0.140197562706963	   
df.mm.trans2:exp5	-0.30666014150548	0.203471423975598	-1.50714107914366	0.132369172899513	   
df.mm.trans1:exp6	0.0470825631314550	0.221511629323428	0.212551202278910	0.831758605190941	   
df.mm.trans2:exp6	-0.228219072461075	0.203471423975598	-1.12162714548283	0.262527958453667	   
df.mm.trans1:exp7	-0.228030794146886	0.221511629323429	-1.02943035019592	0.303746163143128	   
df.mm.trans2:exp7	-0.42346190612646	0.203471423975598	-2.08118613342602	0.0378954498685447	*  
df.mm.trans1:exp8	-0.115933250016145	0.221511629323429	-0.523373198825922	0.600932908893128	   
df.mm.trans2:exp8	-0.223036425916018	0.203471423975598	-1.09615601816777	0.273507390306188	   
df.mm.trans1:probe2	-0.168975295667123	0.151658645162706	-1.11418175657471	0.265705274798787	   
df.mm.trans1:probe3	0.0271442709986835	0.151658645162706	0.178982681597623	0.858019570175675	   
df.mm.trans1:probe4	0.0793185196758334	0.151658645162706	0.523006911941862	0.601187604303626	   
df.mm.trans1:probe5	0.0430478752857905	0.151658645162706	0.283847157144302	0.776638155527644	   
df.mm.trans1:probe6	0.0798030927064667	0.151658645162706	0.526202067945751	0.598967520200191	   
df.mm.trans1:probe7	0.0729203530521363	0.151658645162706	0.480818966659661	0.630843214452966	   
df.mm.trans1:probe8	0.19358693006845	0.151658645162706	1.27646485210758	0.202349220533277	   
df.mm.trans2:probe2	0.190292211538921	0.151658645162706	1.25474028424009	0.210125034228415	   
df.mm.trans2:probe3	0.344766809232774	0.151658645162706	2.27330798625356	0.0234061651468020	*  
df.mm.trans2:probe4	0.157640364011058	0.151658645162706	1.03944199054353	0.299072284395801	   
df.mm.trans2:probe5	0.0511240294730002	0.151658645162706	0.337099341868393	0.736175211906542	   
df.mm.trans2:probe6	0.09110747742198	0.151658645162706	0.60074041492482	0.548269220185757	   
df.mm.trans3:probe2	0.0753105203970856	0.151658645162706	0.496579145331869	0.619691368814732	   
df.mm.trans3:probe3	-0.0389584201822866	0.151658645162706	-0.256882290755600	0.797369144698995	   
df.mm.trans3:probe4	-0.0804081560563882	0.151658645162706	-0.530191707634754	0.596200651340712	   
df.mm.trans3:probe5	-0.0192065593535354	0.151658645162706	-0.126643353123258	0.899270621653964	   
df.mm.trans3:probe6	-0.0513757921719757	0.151658645162706	-0.338759403506853	0.734924900107	   
df.mm.trans3:probe7	0.132614940192562	0.151658645162706	0.874430468835373	0.382279184439852	   
df.mm.trans3:probe8	0.134916587117814	0.151658645162706	0.889606965518316	0.374079810115699	   
df.mm.trans3:probe9	-0.059138010840423	0.151658645162706	-0.389941574230584	0.696736110054214	   
df.mm.trans3:probe10	0.128965911016228	0.151658645162706	0.850369663251756	0.395502866809625