fitVsDatCorrelation=0.848381719871812
cont.fitVsDatCorrelation=0.227330334382515

fstatistic=7488.83518162801,53,715
cont.fstatistic=2203.60195540914,53,715

residuals=-0.514840232739334,-0.103371103085402,-0.0112114056622742,0.0787779000183725,1.18337291072578
cont.residuals=-0.657592721192416,-0.225294954456999,-0.0404349062211619,0.153617398669568,1.42499918025723

predictedValues:
Include	Exclude	Both
Lung	76.0382795135707	69.7830161396018	50.6604546784246
cerebhem	74.0373329399255	89.1070706002317	53.2231251113941
cortex	70.6563172101656	64.054492778989	56.1199287535227
heart	71.5169029087366	63.125695597277	51.5171949780145
kidney	97.417796201629	69.8789875398159	104.374469325781
liver	83.389200251619	69.9474299822373	71.6242832824283
stomach	73.8530537600992	66.820226660893	50.1769271368641
testicle	70.9122180139548	70.0871925797666	48.0785098546291


diffExp=6.25526337396894,-15.0697376603061,6.60182443117665,8.39120731145957,27.5388086618132,13.4417702693817,7.03282709920613,0.825025434188206
diffExpScore=1.52018996168761
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=0,0,0,0,1,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,1,0,0,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	74.5661188034668	69.7099746726466	75.826642674221
cerebhem	71.1638235479066	73.4842557906269	74.9950249447806
cortex	72.6963085770612	84.5193097574482	71.9305392388806
heart	73.9291819157789	70.5792364863911	72.8651950279988
kidney	73.421920601935	65.5040515283245	66.8948357885619
liver	73.685348250127	75.3799782415698	81.3251897488954
stomach	73.0062184630369	70.2815648937626	74.5728932703906
testicle	72.6915581885216	66.8424043101855	76.632778346789
cont.diffExp=4.85614413082013,-2.3204322427203,-11.8230011803869,3.34994542938777,7.9178690736104,-1.69462999144272,2.72465356927430,5.84915387833608
cont.diffExpScore=4.11126287125765

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=0.0484654819806999
cont.tran.correlation=-0.241025839961525

tran.covariance=0.00102905881845378
cont.tran.covariance=-0.000270628492528606

tran.mean=73.789075792407
cont.tran.mean=72.5913283767993

weightedLogRatios:
wLogRatio
Lung	0.368135583126362
cerebhem	-0.814666577878476
cortex	0.412853412369429
heart	0.525124757275518
kidney	1.46615481470672
liver	0.762094947181213
stomach	0.425508307662135
testicle	0.0498017857822413

cont.weightedLogRatios:
wLogRatio
Lung	0.2880935665879
cerebhem	-0.137363557776096
cortex	-0.65725229154486
heart	0.198466965333643
kidney	0.483733668959831
liver	-0.0980262509243695
stomach	0.162467926656268
testicle	0.356042522889008

varWeightedLogRatios=0.411905262578526
cont.varWeightedLogRatios=0.132225664508129

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.39022632214224	0.0966590560451953	45.41971028653	6.63692461322901e-213	***
df.mm.trans1	-0.258276743206559	0.0858351124384876	-3.00898706682127	0.00271331126933241	** 
df.mm.trans2	-0.0342343573397271	0.0780781967989134	-0.438462448459151	0.661183531446875	   
df.mm.exp2	0.168433237799306	0.105261011931182	1.60014838076443	0.110007404755103	   
df.mm.exp3	-0.261411224277871	0.105261011931182	-2.48345726002308	0.0132395089015935	*  
df.mm.exp4	-0.178335832076399	0.105261011931182	-1.69422494430313	0.0906580329641481	.  
df.mm.exp5	-0.473693125788251	0.105261011931182	-4.50017643852733	7.92381987566745e-06	***
df.mm.exp6	-0.25165333125078	0.105261011931182	-2.39075538638472	0.0170714870619514	*  
df.mm.exp7	-0.0629540442899974	0.105261011931182	-0.598075613515436	0.549978870921926	   
df.mm.exp8	-0.0131344054329618	0.105261011931182	-0.12477939544747	0.900733272612402	   
df.mm.trans1:exp2	-0.195100663917264	0.099950269175431	-1.95197737361594	0.0513308838422868	.  
df.mm.trans2:exp2	0.0760147909330066	0.0841656587301499	0.903156846627002	0.366746824868928	   
df.mm.trans1:exp3	0.188001853689746	0.099950269175431	1.88095395080696	0.0603843338728025	.  
df.mm.trans2:exp3	0.175754736653031	0.08416565873015	2.08820009615242	0.0371330505674488	*  
df.mm.trans1:exp4	0.117032766861876	0.099950269175431	1.17090997180270	0.242025048564	   
df.mm.trans2:exp4	0.078073080595444	0.0841656587301499	0.927612066172502	0.353921855204154	   
df.mm.trans1:exp5	0.721465140995848	0.099950269175431	7.21824110077728	1.34865257713212e-12	***
df.mm.trans2:exp5	0.475067464016138	0.0841656587301499	5.64443350392218	2.39022891051507e-08	***
df.mm.trans1:exp6	0.343935247551244	0.099950269175431	3.44106374488672	0.000613110905834357	***
df.mm.trans2:exp6	0.254006632525858	0.08416565873015	3.0179367257167	0.00263543968530216	** 
df.mm.trans1:exp7	0.0337945119667359	0.099950269175431	0.338113266182609	0.73537708559214	   
df.mm.trans2:exp7	0.0195692149458245	0.08416565873015	0.232508308508187	0.816209771234863	   
df.mm.trans1:exp8	-0.0566597397507397	0.099950269175431	-0.566879311263199	0.570974091868117	   
df.mm.trans2:exp8	0.0174838222597360	0.0841656587301499	0.207731068983755	0.835498109933461	   
df.mm.trans1:probe2	0.504269204441171	0.0547450170560968	9.21123476725653	3.51822690075118e-19	***
df.mm.trans1:probe3	1.03573434240324	0.0547450170560968	18.9192441266743	4.96394600282925e-65	***
df.mm.trans1:probe4	0.370187233474351	0.0547450170560968	6.76202608714182	2.82930211969153e-11	***
df.mm.trans1:probe5	0.443798718074011	0.0547450170560968	8.10665046682246	2.25664193371058e-15	***
df.mm.trans1:probe6	0.402121273867515	0.0547450170560968	7.34534932111655	5.60900053164972e-13	***
df.mm.trans1:probe7	-0.316259235756471	0.0547450170560968	-5.77695017306147	1.13506492666326e-08	***
df.mm.trans1:probe8	0.321340398905119	0.0547450170560968	5.86976525326211	6.67828785132659e-09	***
df.mm.trans1:probe9	0.376517856153511	0.0547450170560968	6.87766442318753	1.32883591745103e-11	***
df.mm.trans1:probe10	0.45278706643694	0.0547450170560968	8.27083615615595	6.4850625658521e-16	***
df.mm.trans1:probe11	-0.147054964175384	0.0547450170560968	-2.68617989514365	0.00739519052607806	** 
df.mm.trans1:probe12	-0.0136743726910551	0.0547450170560968	-0.249782965215684	0.802826939378225	   
df.mm.trans1:probe13	0.130159974506116	0.0547450170560968	2.3775675213096	0.0176892535819836	*  
df.mm.trans1:probe14	0.309601080759592	0.0547450170560968	5.65532896706099	2.24952918207966e-08	***
df.mm.trans1:probe15	0.260132268112439	0.0547450170560968	4.7517067689628	2.43882932704456e-06	***
df.mm.trans1:probe16	0.504760440690891	0.0547450170560968	9.22020793552163	3.26478529560304e-19	***
df.mm.trans1:probe17	0.136203202394195	0.0547450170560968	2.48795615963784	0.0130746561939162	*  
df.mm.trans1:probe18	0.0631600564267122	0.0547450170560968	1.15371333909701	0.249003215496075	   
df.mm.trans1:probe19	-0.0350929363383383	0.0547450170560968	-0.641025215178558	0.521711706308184	   
df.mm.trans1:probe20	0.145106991719248	0.0547450170560968	2.65059725108054	0.00821272807687947	** 
df.mm.trans1:probe21	0.185595764453061	0.0547450170560968	3.39018552616181	0.000736816890998981	***
df.mm.trans1:probe22	0.0520757574434595	0.0547450170560968	0.951241962169779	0.341802996762728	   
df.mm.trans2:probe2	-0.282691020939634	0.0547450170560968	-5.16377628762016	3.14076097239824e-07	***
df.mm.trans2:probe3	-0.211393914892023	0.0547450170560968	-3.86142750993043	0.000122944353706170	***
df.mm.trans2:probe4	-0.184846961250770	0.0547450170560968	-3.37650751047092	0.000773833181426502	***
df.mm.trans2:probe5	-0.194902609123836	0.0547450170560968	-3.56018902915165	0.000395117871279139	***
df.mm.trans2:probe6	-0.232178558033125	0.0547450170560968	-4.24109024927718	2.51661306109895e-05	***
df.mm.trans3:probe2	-0.234312023269557	0.0547450170560968	-4.28006119770607	2.12308595137127e-05	***
df.mm.trans3:probe3	-0.179401839682587	0.0547450170560968	-3.27704418282956	0.00109968167862096	** 
df.mm.trans3:probe4	-0.276564544909804	0.0547450170560968	-5.05186699688868	5.56038977594362e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.27780705240819	0.177806895644983	24.0587241394139	3.69653576932195e-94	***
df.mm.trans1	0.00329369242594218	0.157895964480445	0.0208598898444304	0.9833632426743	   
df.mm.trans2	-0.085437843294667	0.143626912556249	-0.594859568962794	0.552125544867381	   
df.mm.exp2	0.0170540608102074	0.193630421501135	0.0880753172874102	0.92984147579069	   
df.mm.exp3	0.219989781349597	0.193630421501135	1.13613232695622	0.256281845052499	   
df.mm.exp4	0.0436526124612614	0.193630421501135	0.225442944981688	0.82169912684172	   
df.mm.exp5	0.0476328304792921	0.193630421501135	0.24599869230266	0.805753844600291	   
df.mm.exp6	-0.00369006085206179	0.193630421501135	-0.0190572370986661	0.984800761713912	   
df.mm.exp7	0.00369714006184852	0.193630421501135	0.0190937975199669	0.984771606228061	   
df.mm.exp8	-0.0780418987403476	0.193630421501135	-0.403045648175125	0.68703513034888	   
df.mm.trans1:exp2	-0.0637557002614652	0.183861169435113	-0.346760006244632	0.728873653765435	   
df.mm.trans2:exp2	0.0356736993590503	0.154824960133333	0.230413102178929	0.817836689141964	   
df.mm.trans1:exp3	-0.245385406713009	0.183861169435113	-1.33462333274024	0.182424496950210	   
df.mm.trans2:exp3	-0.0273531714267762	0.154824960133333	-0.176671587082729	0.859816390339	   
df.mm.trans1:exp4	-0.0522312111204581	0.183861169435113	-0.284079619861719	0.776431610847512	   
df.mm.trans2:exp4	-0.0312600281512135	0.154824960133333	-0.201905610854302	0.840047946395917	   
df.mm.trans1:exp5	-0.0630965261489613	0.183861169435113	-0.343174833178839	0.731567811837717	   
df.mm.trans2:exp5	-0.10986425053049	0.154824960133333	-0.709602963474858	0.478181718582786	   
df.mm.trans1:exp6	-0.00819219480008134	0.183861169435113	-0.0445564162636987	0.964473323430938	   
df.mm.trans2:exp6	0.0818883438673936	0.154824960133333	0.52890918749065	0.597032596457683	   
df.mm.trans1:exp7	-0.0248387506235688	0.183861169435113	-0.135095141077816	0.892574673241752	   
df.mm.trans2:exp7	0.00446897339438993	0.154824960133333	0.0288646829977628	0.976980567390703	   
df.mm.trans1:exp8	0.0525809255780176	0.183861169435113	0.285981677042329	0.77497499017022	   
df.mm.trans2:exp8	0.0360361567747398	0.154824960133333	0.232754180874363	0.816018904379505	   
df.mm.trans1:probe2	0.0246003474212916	0.100704909948891	0.244281509548806	0.807082883835964	   
df.mm.trans1:probe3	0.127946800439884	0.100704909948891	1.27051203863664	0.204315545431479	   
df.mm.trans1:probe4	0.120223138060397	0.100704909948891	1.19381605247859	0.232945860055344	   
df.mm.trans1:probe5	0.0141824358839136	0.100704909948891	0.140831622719403	0.888042626814722	   
df.mm.trans1:probe6	0.0458683610597808	0.100704909948891	0.455472936553537	0.648907316388317	   
df.mm.trans1:probe7	0.137956441149859	0.100704909948891	1.36990779515988	0.171145709092573	   
df.mm.trans1:probe8	0.0674721880721356	0.100704909948891	0.669998991175094	0.503074753688679	   
df.mm.trans1:probe9	0.105530435256359	0.100704909948891	1.04791747800497	0.295030725035976	   
df.mm.trans1:probe10	0.0988735222690384	0.100704909948891	0.981814315897983	0.326523430934691	   
df.mm.trans1:probe11	-0.00471962767513247	0.100704909948891	-0.0468659142590738	0.962633181430775	   
df.mm.trans1:probe12	0.0652844886734947	0.100704909948891	0.64827513084146	0.517015145641058	   
df.mm.trans1:probe13	-0.0490183079203165	0.100704909948891	-0.486751916517217	0.62658332017667	   
df.mm.trans1:probe14	-0.0709206575949665	0.100704909948891	-0.704242301899279	0.481511191225682	   
df.mm.trans1:probe15	0.00840758580693843	0.100704909948891	0.0834873474511361	0.933487405527726	   
df.mm.trans1:probe16	-0.00874045002098477	0.100704909948891	-0.0867926899038057	0.930860598194599	   
df.mm.trans1:probe17	-0.065025791133632	0.100704909948891	-0.645706263643287	0.518676767223962	   
df.mm.trans1:probe18	-0.0707527990650937	0.100704909948891	-0.702575466290588	0.482549022859615	   
df.mm.trans1:probe19	-0.0419539130556879	0.100704909948891	-0.416602458380431	0.677094243974953	   
df.mm.trans1:probe20	0.10114793393775	0.100704909948891	1.00439922928369	0.315526008233675	   
df.mm.trans1:probe21	0.0891453177685297	0.100704909948891	0.88521322161722	0.376339236627839	   
df.mm.trans1:probe22	0.0997152301685326	0.100704909948891	0.990172477381086	0.322424784823492	   
df.mm.trans2:probe2	0.0938614232778873	0.100704909948891	0.932044160761608	0.351628311854293	   
df.mm.trans2:probe3	0.0813311143187792	0.100704909948891	0.8076181624119	0.419579019610077	   
df.mm.trans2:probe4	0.0408367773565586	0.100704909948891	0.405509298179044	0.685224495195115	   
df.mm.trans2:probe5	0.114979112957690	0.100704909948891	1.14174287049204	0.253943136430643	   
df.mm.trans2:probe6	0.188733642690948	0.100704909948891	1.87412552959665	0.0613207000410264	.  
df.mm.trans3:probe2	0.148990481357735	0.100704909948891	1.47947584118143	0.139453622805384	   
df.mm.trans3:probe3	0.156598473638958	0.100704909948891	1.55502322298321	0.120383036508145	   
df.mm.trans3:probe4	0.225558308280356	0.100704909948891	2.23979454819859	0.0254108212549353	*