fitVsDatCorrelation=0.924549785133895
cont.fitVsDatCorrelation=0.260666682489777

fstatistic=6284.7009960096,56,784
cont.fstatistic=967.296094802117,56,784

residuals=-0.903958158038568,-0.119900417937911,-0.00700523386483821,0.109771484162723,1.43326724706586
cont.residuals=-1.10723631361111,-0.357483450766202,-0.0557895985765566,0.230227239465568,2.63797431506714

predictedValues:
Include	Exclude	Both
Lung	79.1164056649771	92.1286978569564	57.8273436882223
cerebhem	90.1429189370215	85.089982032516	86.5253409224915
cortex	106.509699983653	76.9041550008132	66.0289129458101
heart	84.020003117726	76.5265209666616	57.3641151525264
kidney	83.5232909249965	92.9962917037772	54.5414226731555
liver	86.915202622432	89.0253158037289	55.3869815848286
stomach	91.9896659366245	80.0785410937886	57.8931383872157
testicle	101.74378023288	74.5812157323472	63.8702349271708


diffExp=-13.0122921919793,5.05293690450543,29.6055449828399,7.49348215106428,-9.47300077878073,-2.11011318129682,11.9111248428360,27.1625645005327
diffExpScore=1.83620693334906
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=0,0,1,0,0,0,0,1
diffExp1.3Score=0.666666666666667
diffExp1.2=0,0,1,0,0,0,0,1
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	95.3143543406229	86.1304659834898	80.3451863901981
cerebhem	94.645173967282	83.9444783260806	112.064305800269
cortex	84.3321194762234	82.4678355452748	104.384298742388
heart	92.6135280679468	107.467822354023	103.893027460930
kidney	96.9695226127054	82.2891914104094	87.1419589632127
liver	91.1517126801635	89.6236945468337	95.76646583795
stomach	92.2260445051026	87.0769261645474	102.339265201286
testicle	93.895388105496	80.8822120987079	82.886833008781
cont.diffExp=9.18388835713309,10.7006956412014,1.86428393094855,-14.8542942860764,14.6803312022959,1.52801813332979,5.14911834055525,13.0131760067881
cont.diffExpScore=1.67924857337414

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=-0.722493111385041
cont.tran.correlation=-0.0146391808536064

tran.covariance=-0.00650343034664335
cont.tran.covariance=-1.20096267334303e-05

tran.mean=86.9557304756812
cont.tran.mean=90.0644043865568

weightedLogRatios:
wLogRatio
Lung	-0.677136060156094
cerebhem	0.258008560336043
cortex	1.46730068924632
heart	0.409574842351231
kidney	-0.48118038499861
liver	-0.107391696519334
stomach	0.617398828549209
testicle	1.38736559314413

cont.weightedLogRatios:
wLogRatio
Lung	0.456588301823201
cerebhem	0.538724394693928
cortex	0.0988868723440813
heart	-0.684697268882259
kidney	0.737445315053544
liver	0.0761438570553874
stomach	0.258270308587763
testicle	0.66650753369514

varWeightedLogRatios=0.623199287078626
cont.varWeightedLogRatios=0.208739616402441

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.84512886973924	0.106889006422032	35.9730995585966	4.04361957561646e-168	***
df.mm.trans1	-0.0959320764056924	0.0931633511014485	-1.02971903942387	0.303459478883004	   
df.mm.trans2	0.624135823824072	0.0831382372912387	7.5072053986144	1.64388992150226e-13	***
df.mm.exp2	-0.351976630609022	0.108770631563634	-3.23595280774943	0.00126326025799871	** 
df.mm.exp3	-0.0159417627394397	0.108770631563635	-0.146563116443001	0.883514545706417	   
df.mm.exp4	-0.117371681997372	0.108770631563634	-1.07907511715334	0.280886108171214	   
df.mm.exp5	0.122079673738165	0.108770631563634	1.12235878364597	0.262053526310113	   
df.mm.exp6	0.102864152028269	0.108770631563634	0.945697846464097	0.344594086482249	   
df.mm.exp7	0.00944028249616866	0.108770631563634	0.0867907298179636	0.930860017439312	   
df.mm.exp8	-0.059152084220785	0.108770631563634	-0.543824039361021	0.586717172752779	   
df.mm.trans1:exp2	0.482452772231614	0.101568197429763	4.7500377523707	2.41847624951484e-06	***
df.mm.trans2:exp2	0.272499450048045	0.0792226476340117	3.43966603220486	0.000613210733505136	***
df.mm.trans1:exp3	0.313257566023352	0.101568197429763	3.08420917128098	0.00211255081216633	** 
df.mm.trans2:exp3	-0.164684820252495	0.0792226476340118	-2.07875935948640	0.0379641201688247	*  
df.mm.trans1:exp4	0.177506327468773	0.101568197429763	1.74765656928709	0.0809149967156045	.  
df.mm.trans2:exp4	-0.0681774472105925	0.0792226476340117	-0.860580266460606	0.389732352526685	   
df.mm.trans1:exp5	-0.0678744049192887	0.101568197429763	-0.668264344911958	0.504161557345945	   
df.mm.trans2:exp5	-0.112706544796674	0.0792226476340117	-1.42265561884966	0.155233765696412	   
df.mm.trans1:exp6	-0.00885144856311508	0.101568197429763	-0.0871478355145184	0.930576255700956	   
df.mm.trans2:exp6	-0.137129864774501	0.0792226476340117	-1.73094271486615	0.0838554236803921	.  
df.mm.trans1:exp7	0.141315704091145	0.101568197429763	1.39133811239358	0.164517590874877	   
df.mm.trans2:exp7	-0.149618855028525	0.0792226476340118	-1.8885869066094	0.0593161199084792	.  
df.mm.trans1:exp8	0.310689521365935	0.101568197429763	3.05892522687316	0.00229692163164957	** 
df.mm.trans2:exp8	-0.152145729374725	0.0792226476340117	-1.92048276494872	0.0551594055944208	.  
df.mm.trans1:probe2	0.570662525070466	0.0645454630044347	8.84124922972909	6.17282017213259e-18	***
df.mm.trans1:probe3	0.107375371708270	0.0645454630044347	1.66356187887122	0.0965996518656687	.  
df.mm.trans1:probe4	1.11431594251761	0.0645454630044347	17.2640475511199	7.59529877577539e-57	***
df.mm.trans1:probe5	-0.0591096792368614	0.0645454630044347	-0.915783642806934	0.36006197340139	   
df.mm.trans1:probe6	1.58811244553614	0.0645454630044347	24.6045557908079	1.65906671207675e-99	***
df.mm.trans1:probe7	0.577415758843279	0.0645454630044347	8.94587678151146	2.62351029557517e-18	***
df.mm.trans1:probe8	0.76337798366606	0.0645454630044347	11.8269812955499	8.17694528146212e-30	***
df.mm.trans1:probe9	2.81774822179331	0.0645454630044347	43.6552484192376	4.48869317744308e-212	***
df.mm.trans1:probe10	0.862061037248894	0.0645454630044347	13.3558734746339	8.2869827511406e-37	***
df.mm.trans1:probe11	1.04519582436059	0.0645454630044347	16.1931726214247	4.29881348673052e-51	***
df.mm.trans1:probe12	1.07259573537333	0.0645454630044347	16.6176782293690	2.36349332346421e-53	***
df.mm.trans1:probe13	0.659730193833415	0.0645454630044347	10.2211706776058	4.15812727584908e-23	***
df.mm.trans1:probe14	0.900588310791742	0.0645454630044347	13.9527748174936	1.10625659414456e-39	***
df.mm.trans1:probe15	0.796258719927872	0.0645454630044347	12.3364010863655	4.40684498662319e-32	***
df.mm.trans1:probe16	1.06724558440239	0.0645454630044347	16.5347885773019	6.56154521464886e-53	***
df.mm.trans1:probe17	0.678797487476221	0.0645454630044347	10.5165794136388	2.74420958931444e-24	***
df.mm.trans1:probe18	0.792315376952288	0.0645454630044347	12.2753070482716	8.30918816252683e-32	***
df.mm.trans1:probe19	0.559368816394146	0.0645454630044347	8.66627630133683	2.53596565033199e-17	***
df.mm.trans1:probe20	0.713416009212773	0.0645454630044347	11.0529226378585	1.70820936139679e-26	***
df.mm.trans1:probe21	0.847709735038844	0.0645454630044347	13.1335293850259	9.31890246326327e-36	***
df.mm.trans1:probe22	0.554799056343159	0.0645454630044347	8.5954772112339	4.46332920739788e-17	***
df.mm.trans2:probe2	-0.0319611613505036	0.0645454630044347	-0.495172857437055	0.620616857262124	   
df.mm.trans2:probe3	0.217771997386015	0.0645454630044347	3.37393191169846	0.00077742497133187	***
df.mm.trans2:probe4	0.0986968526861215	0.0645454630044347	1.52910596798013	0.126641526160890	   
df.mm.trans2:probe5	0.198973332272705	0.0645454630044347	3.082685025577	0.00212326773356785	** 
df.mm.trans2:probe6	0.217502323112528	0.0645454630044347	3.36975386012157	0.000789135177838713	***
df.mm.trans3:probe2	-0.165390265214927	0.0645454630044347	-2.56238405484152	0.0105813538826518	*  
df.mm.trans3:probe3	-0.284478934622845	0.0645454630044347	-4.40741953626235	1.19178497173129e-05	***
df.mm.trans3:probe4	-0.50748905194489	0.0645454630044347	-7.86250540816513	1.24485508800974e-14	***
df.mm.trans3:probe5	-0.512039308395458	0.0645454630044347	-7.93300232985048	7.37330317201374e-15	***
df.mm.trans3:probe6	-0.206709516212131	0.0645454630044347	-3.20254138076179	0.00141720235241443	** 
df.mm.trans3:probe7	-0.534989077680071	0.0645454630044347	-8.28856208907067	4.95655268794048e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.67582522679435	0.270806136420311	17.2663193257081	7.38179772627996e-57	***
df.mm.trans1	-0.194530737971285	0.236031824153545	-0.824171650026048	0.410092690564939	   
df.mm.trans2	-0.257839999626639	0.210632932078548	-1.22412007031496	0.221274662023144	   
df.mm.exp2	-0.365493801950215	0.275573283686857	-1.32630346839259	0.185125430647411	   
df.mm.exp3	-0.427619550061019	0.275573283686858	-1.55174530832581	0.121126736783031	   
df.mm.exp4	-0.064446522078169	0.275573283686857	-0.233863461711338	0.815152032525976	   
df.mm.exp5	-0.109613436990954	0.275573283686857	-0.397765108157260	0.690911618534779	   
df.mm.exp6	-0.180478989173155	0.275573283686857	-0.654921938580371	0.512710112422389	   
df.mm.exp7	-0.263970350225186	0.275573283686857	-0.957895289026434	0.338410853757844	   
df.mm.exp8	-0.109012457594666	0.275573283686857	-0.395584274847704	0.692519357473657	   
df.mm.trans1:exp2	0.358448268120452	0.257325725533734	1.39297486629824	0.164022237418123	   
df.mm.trans2:exp2	0.339786216925605	0.200712681695695	1.69289859541991	0.0908720512099667	.  
df.mm.trans1:exp3	0.305201934464768	0.257325725533734	1.18605294449955	0.235960709888407	   
df.mm.trans2:exp3	0.384164702182392	0.200712681695695	1.91400313590963	0.0559835609527451	.  
df.mm.trans1:exp4	0.0357013225169923	0.257325725533734	0.138739810965041	0.889691386013311	   
df.mm.trans2:exp4	0.285774804878460	0.200712681695695	1.42380044182624	0.154902101815524	   
df.mm.trans1:exp5	0.126829744264695	0.257325725533734	0.492876271898699	0.622238024332291	   
df.mm.trans2:exp5	0.0639900114416098	0.200712681695695	0.318813992723323	0.749952484081567	   
df.mm.trans1:exp6	0.135823857820921	0.257325725533734	0.527828523709398	0.597767708270999	   
df.mm.trans2:exp6	0.220235529240295	0.200712681695695	1.09726763341341	0.272861312219815	   
df.mm.trans1:exp7	0.231032497272097	0.257325725533734	0.89782122169441	0.369556497881323	   
df.mm.trans2:exp7	0.274899093964501	0.200712681695695	1.36961497221826	0.171199276086504	   
df.mm.trans1:exp8	0.0940133056557553	0.257325725533734	0.365347481137989	0.714950581587441	   
df.mm.trans2:exp8	0.0461431892916819	0.200712681695695	0.229896730499773	0.81823198440912	   
df.mm.trans1:probe2	-0.0447948503242454	0.163527644654841	-0.273928303797161	0.784211873324492	   
df.mm.trans1:probe3	-0.143535574840905	0.163527644654841	-0.877745014574548	0.380351008804244	   
df.mm.trans1:probe4	0.141712637595188	0.163527644654841	0.866597436135658	0.386427752728625	   
df.mm.trans1:probe5	0.0192484666617628	0.163527644654841	0.117707722766941	0.906329395978061	   
df.mm.trans1:probe6	0.108957203809760	0.163527644654841	0.666292259267461	0.505420321624906	   
df.mm.trans1:probe7	0.353131125406498	0.163527644654841	2.15945827478806	0.0311172327252477	*  
df.mm.trans1:probe8	0.164325092369159	0.163527644654841	1.00487653152469	0.315266344579801	   
df.mm.trans1:probe9	0.218653086414518	0.163527644654841	1.33710166789249	0.181577272202935	   
df.mm.trans1:probe10	0.165379610587432	0.163527644654841	1.01132509390996	0.312172870551464	   
df.mm.trans1:probe11	0.0959091732772712	0.163527644654841	0.586501282273755	0.557707525798208	   
df.mm.trans1:probe12	0.129798627712975	0.163527644654841	0.793741192731918	0.427586281044581	   
df.mm.trans1:probe13	0.0277631329082098	0.163527644654841	0.169776388370355	0.865229794506796	   
df.mm.trans1:probe14	0.247629757316279	0.163527644654841	1.51429905224253	0.130353035815105	   
df.mm.trans1:probe15	0.0907636444926786	0.163527644654841	0.55503547846148	0.579028710794622	   
df.mm.trans1:probe16	0.137061209948854	0.163527644654841	0.838153146754792	0.402200148261524	   
df.mm.trans1:probe17	-0.183637497147387	0.163527644654841	-1.12297524699871	0.261791773422355	   
df.mm.trans1:probe18	0.325578962345409	0.163527644654841	1.99097200374047	0.0468306567721515	*  
df.mm.trans1:probe19	0.0444303157699225	0.163527644654841	0.271699111570413	0.785924913222422	   
df.mm.trans1:probe20	0.000169607661433365	0.163527644654841	0.00103718036049108	0.999172713795314	   
df.mm.trans1:probe21	-0.0597661384799171	0.163527644654841	-0.365480335793168	0.714851464450317	   
df.mm.trans1:probe22	0.361914468341887	0.163527644654841	2.21316994509266	0.0271732019215107	*  
df.mm.trans2:probe2	0.096184974846081	0.163527644654841	0.588187856855025	0.556575599981336	   
df.mm.trans2:probe3	0.229050317543182	0.163527644654841	1.40068254530688	0.161704681499153	   
df.mm.trans2:probe4	0.151502947469697	0.163527644654841	0.926466884479838	0.354488502116852	   
df.mm.trans2:probe5	0.0810110192061765	0.163527644654841	0.495396477929876	0.62045910127289	   
df.mm.trans2:probe6	-0.0653357029742393	0.163527644654841	-0.399539191750385	0.68960476991669	   
df.mm.trans3:probe2	0.354300991215909	0.163527644654841	2.16661220776301	0.0305650206921823	*  
df.mm.trans3:probe3	0.210426333931107	0.163527644654841	1.28679364504549	0.198546033373982	   
df.mm.trans3:probe4	0.156160454727958	0.163527644654841	0.954948351745462	0.33989816266571	   
df.mm.trans3:probe5	0.134859987543127	0.163527644654841	0.824692288742843	0.409797147065706	   
df.mm.trans3:probe6	0.068731447477164	0.163527644654841	0.420304760227153	0.67437800231456	   
df.mm.trans3:probe7	0.215664637278101	0.163527644654841	1.31882678144913	0.187612119205263