fitVsDatCorrelation=0.8435675252832
cont.fitVsDatCorrelation=0.238459113816753

fstatistic=9735.88920760077,53,715
cont.fstatistic=2967.68844343750,53,715

residuals=-0.488217481804463,-0.0839800953463168,-0.0111881671128512,0.0751701798702064,0.987686698543676
cont.residuals=-0.716211334833164,-0.197198726983584,-0.0465779557468215,0.151543169085760,1.14755006498467

predictedValues:
Include	Exclude	Both
Lung	52.3892313296926	77.8017585151929	73.2608020670886
cerebhem	53.440922966767	78.94083287866	55.347322601363
cortex	51.4009483350435	71.2781027980251	58.3087297721335
heart	53.4247650002412	78.5629703557644	64.7270940373343
kidney	49.9998895323706	93.2235795266625	64.6150540198734
liver	50.358313977985	91.0724666698223	57.5208646527808
stomach	50.1641183139396	77.419377846485	59.2030027508213
testicle	49.2175025211747	85.0804019700783	61.962895005853


diffExp=-25.4125271855002,-25.499909911893,-19.8771544629816,-25.1382053555233,-43.2236899942919,-40.7141526918373,-27.2552595325453,-35.8628994489036
diffExpScore=0.995901367198126
diffExp1.5=0,0,0,0,-1,-1,-1,-1
diffExp1.5Score=0.8
diffExp1.4=-1,-1,0,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
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	57.5752503841096	59.1521330586101	56.0716993478071
cerebhem	59.7643602872891	55.5964850793902	59.012572163918
cortex	55.2471318908803	55.1361460745602	66.6453392390932
heart	59.463054168173	59.6770736176004	56.4246790046523
kidney	57.3871847556028	56.1516521793378	52.8720765371144
liver	55.264025147194	60.6316295836153	58.7635294511086
stomach	56.8911366431026	51.6190334080785	61.7272668836502
testicle	56.9937187483199	57.9558811537168	55.731522732363
cont.diffExp=-1.57688267450049,4.16787520789889,0.110985816320053,-0.214019449427411,1.23553257626495,-5.36760443642127,5.27210323502408,-0.962162405396867
cont.diffExpScore=5.15767964917619

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=-0.515240950766466
cont.tran.correlation=0.0345940975586789

tran.covariance=-0.00145393432883034
cont.tran.covariance=5.79126899478457e-05

tran.mean=66.485948908619
cont.tran.mean=57.1566185112238

weightedLogRatios:
wLogRatio
Lung	-1.64371517118144
cerebhem	-1.62822714298808
cortex	-1.34144440718845
heart	-1.60848005985703
kidney	-2.63116226973358
liver	-2.49759562728533
stomach	-1.79314468399839
testicle	-2.2823965687247

cont.weightedLogRatios:
wLogRatio
Lung	-0.109879024444273
cerebhem	0.293080852954864
cortex	0.00806541149473674
heart	-0.0146840679420640
kidney	0.0879071389653268
liver	-0.376197957599636
stomach	0.388268448913029
testicle	-0.0678229539705154

varWeightedLogRatios=0.225640105610717
cont.varWeightedLogRatios=0.0567727046882087

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.0218107045518	0.0797708709938496	50.4170338676869	1.30676843008966e-237	***
df.mm.trans1	-0.0794624580730863	0.0708380772710176	-1.12174781041943	0.262346258058309	   
df.mm.trans2	0.324899073589283	0.0644364430929917	5.04216337826723	5.8397434787453e-07	***
df.mm.exp2	0.31480774562555	0.0868699007314771	3.62389899118971	0.00031075799790021	***
df.mm.exp3	0.121654578393438	0.0868699007314772	1.40042267078771	0.161820582149127	   
df.mm.exp4	0.153155608252343	0.0868699007314772	1.76304573808322	0.0783199069041569	.  
df.mm.exp5	0.259734677308943	0.0868699007314771	2.98992717986183	0.00288621952368366	** 
df.mm.exp6	0.359832120689579	0.0868699007314772	4.14219560123423	3.85125221766154e-05	***
df.mm.exp7	0.164725453124214	0.0868699007314771	1.89623162611173	0.0583323018059552	.  
df.mm.exp8	0.194470889560579	0.0868699007314771	2.23864523756861	0.025485933198173	*  
df.mm.trans1:exp2	-0.294932006693395	0.0824870462677166	-3.57549482055855	0.000373088450090226	***
df.mm.trans2:exp2	-0.300273158617354	0.0694603090427024	-4.32294590616858	1.75804182164873e-05	***
df.mm.trans1:exp3	-0.140699017236199	0.0824870462677167	-1.70571045518531	0.0884963090610858	.  
df.mm.trans2:exp3	-0.209229445726371	0.0694603090427024	-3.01221587709525	0.00268497745124258	** 
df.mm.trans1:exp4	-0.133582266973846	0.0824870462677167	-1.61943326883468	0.105795033768897	   
df.mm.trans2:exp4	-0.143419168845309	0.0694603090427024	-2.06476433551625	0.0393053287203786	*  
df.mm.trans1:exp5	-0.306414942467626	0.0824870462677166	-3.71470377873804	0.000219302883016452	***
df.mm.trans2:exp5	-0.0788980223551297	0.0694603090427024	-1.13587203170411	0.256390709669518	   
df.mm.trans1:exp6	-0.399369452694382	0.0824870462677167	-4.84160205468147	1.57926960510643e-06	***
df.mm.trans2:exp6	-0.202340628001761	0.0694603090427024	-2.91303955871212	0.00369091050071961	** 
df.mm.trans1:exp7	-0.208126517854221	0.0824870462677167	-2.52314184191702	0.0118468758105403	*  
df.mm.trans2:exp7	-0.169652378041549	0.0694603090427024	-2.44243626870781	0.0148296301151414	*  
df.mm.trans1:exp8	-0.256922648255109	0.0824870462677166	-3.11470297313412	0.00191485123988181	** 
df.mm.trans2:exp8	-0.105038208578599	0.0694603090427024	-1.51220474003398	0.130923632426157	   
df.mm.trans1:probe2	0.171965449496256	0.0451800159428007	3.80622817207435	0.000153193488367951	***
df.mm.trans1:probe3	-0.0508537136857274	0.0451800159428007	-1.1255798083407	0.260721092183834	   
df.mm.trans1:probe4	0.197978495451073	0.0451800159428007	4.38199259827	1.35225253430098e-05	***
df.mm.trans1:probe5	-0.0904240893612358	0.0451800159428007	-2.00141782764564	0.0457248162406618	*  
df.mm.trans1:probe6	0.00500237199933662	0.0451800159428007	0.110720899383253	0.911868743663582	   
df.mm.trans1:probe7	-0.000691547782351395	0.0451800159428007	-0.0153064970855016	0.987791929134129	   
df.mm.trans1:probe8	0.108528111145760	0.0451800159428007	2.40212644641736	0.0165541383839901	*  
df.mm.trans1:probe9	0.179131102595484	0.0451800159428007	3.96483044234136	8.08327420529984e-05	***
df.mm.trans1:probe10	-0.113313983143126	0.0451800159428007	-2.50805540411018	0.0123601737600938	*  
df.mm.trans1:probe11	0.0818790801717468	0.0451800159428007	1.81228533153703	0.0703614548289422	.  
df.mm.trans1:probe12	0.125758225151457	0.0451800159428007	2.78349227035844	0.00551972852378132	** 
df.mm.trans1:probe13	0.146732835006108	0.0451800159428007	3.24773756591578	0.00121760067216281	** 
df.mm.trans1:probe14	0.283381204891077	0.0451800159428007	6.27226881127812	6.1586696020257e-10	***
df.mm.trans1:probe15	0.0302538120140376	0.0451800159428007	0.669628183671734	0.503311023317951	   
df.mm.trans1:probe16	0.221165263880752	0.0451800159428007	4.89520110308844	1.21471906331469e-06	***
df.mm.trans1:probe17	-0.123369985484718	0.0451800159428007	-2.73063173861886	0.00647701728333069	** 
df.mm.trans1:probe18	-0.121555461495605	0.0451800159428007	-2.69046964590491	0.00730174165736703	** 
df.mm.trans1:probe19	-0.158002792420579	0.0451800159428007	-3.49718319313157	0.000499266900119114	***
df.mm.trans1:probe20	-0.188796784753552	0.0451800159428007	-4.1787675549423	3.29385692860233e-05	***
df.mm.trans1:probe21	-0.156735288474043	0.0451800159428007	-3.46912866680867	0.000553452213240385	***
df.mm.trans1:probe22	-0.122859121621765	0.0451800159428007	-2.71932444152539	0.00670027441857435	** 
df.mm.trans2:probe2	-0.112978380020565	0.0451800159428007	-2.50062727210187	0.0126200861534342	*  
df.mm.trans2:probe3	-0.0112412938863984	0.0451800159428007	-0.248811197867443	0.803578279736812	   
df.mm.trans2:probe4	0.300259096456757	0.0451800159428007	6.6458386565621	5.98039242454295e-11	***
df.mm.trans2:probe5	-0.0402819693915622	0.0451800159428007	-0.891588206665539	0.372913630257532	   
df.mm.trans2:probe6	-0.0612148950495666	0.0451800159428007	-1.35491087756734	0.175873879738115	   
df.mm.trans3:probe2	0.316838512645550	0.0451800159428007	7.0128021434671	5.4202985702125e-12	***
df.mm.trans3:probe3	-0.186527392764822	0.0451800159428007	-4.12853755963636	4.0815655719112e-05	***
df.mm.trans3:probe4	-0.0231975780829227	0.0451800159428007	-0.513447762220615	0.607796759197289	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15972523623111	0.144257632582688	28.8353909720982	6.80306926879533e-122	***
df.mm.trans1	-0.110005437451100	0.128103569592645	-0.858722655433451	0.390781332459254	   
df.mm.trans2	-0.0814605233816818	0.116526855189544	-0.699070812896968	0.484735113361613	   
df.mm.exp2	-0.0757952548673654	0.157095517023780	-0.482478789359038	0.629613534604618	   
df.mm.exp3	-0.284337406888703	0.15709551702378	-1.80996512361115	0.0707209304368968	.  
df.mm.exp4	0.0348222388028466	0.15709551702378	0.221662842215768	0.824639650277938	   
df.mm.exp5	0.00342754681027282	0.157095517023780	0.0218182343787314	0.982599035964649	   
df.mm.exp6	-0.0631567896743394	0.157095517023780	-0.402027956436079	0.687783598941983	   
df.mm.exp7	-0.244269961763333	0.157095517023780	-1.55491363720046	0.120409137017200	   
df.mm.exp8	-0.0244970377739857	0.157095517023780	-0.155937217293588	0.876126523935406	   
df.mm.trans1:exp2	0.113111961807130	0.149169563589658	0.758277755094079	0.448534583142722	   
df.mm.trans2:exp2	0.0138025845017108	0.125612013710302	0.109882678368198	0.912533253098453	   
df.mm.trans1:exp3	0.243061039930683	0.149169563589658	1.62942784091871	0.103662933713407	   
df.mm.trans2:exp3	0.214030264953417	0.125612013710302	1.70389964010158	0.0888343370399107	.  
df.mm.trans1:exp4	-0.00255985199451007	0.149169563589658	-0.0171606856848614	0.986313213210252	   
df.mm.trans2:exp4	-0.0259869702944791	0.125612013710302	-0.206882841273548	0.836160258929978	   
df.mm.trans1:exp5	-0.00669932504011257	0.149169563589658	-0.044910804046738	0.96419094481163	   
df.mm.trans2:exp5	-0.055484093298841	0.125612013710302	-0.441710085364953	0.658832566166848	   
df.mm.trans1:exp6	0.022186152144878	0.149169563589658	0.148731092395688	0.881807762914448	   
df.mm.trans2:exp6	0.0878608352825492	0.125612013710302	0.699462039396822	0.484490812466752	   
df.mm.trans1:exp7	0.232316725459452	0.149169563589658	1.5574003159151	0.119817965856871	   
df.mm.trans2:exp7	0.108047779107440	0.125612013710302	0.860170742558353	0.389983253317058	   
df.mm.trans1:exp8	0.0143453076073058	0.149169563589658	0.0961677922901715	0.923414257656837	   
df.mm.trans2:exp8	0.00406643747359099	0.125612013710302	0.0323729980395776	0.974183630224963	   
df.mm.trans1:probe2	0.00659388628520159	0.0817035348712572	0.0807050306402505	0.935699120129714	   
df.mm.trans1:probe3	0.103364567489840	0.0817035348712572	1.26511744752189	0.206241378563301	   
df.mm.trans1:probe4	0.080655342185974	0.0817035348712572	0.987170779245539	0.323892859882271	   
df.mm.trans1:probe5	-0.0246966141441117	0.0817035348712572	-0.302271060646603	0.762533343659777	   
df.mm.trans1:probe6	0.0232555999651308	0.0817035348712572	0.284633951294462	0.776007014859258	   
df.mm.trans1:probe7	-0.0495887502007678	0.0817035348712572	-0.606935186817882	0.54408660039813	   
df.mm.trans1:probe8	-0.0240037952007706	0.0817035348712572	-0.293791391505816	0.769002573332602	   
df.mm.trans1:probe9	-0.00111686189832613	0.0817035348712572	-0.0136696888339777	0.989097319260646	   
df.mm.trans1:probe10	-0.0296271439890441	0.0817035348712572	-0.362617652170479	0.716997683042493	   
df.mm.trans1:probe11	-0.075418966755369	0.0817035348712572	-0.92308082966311	0.356276473051408	   
df.mm.trans1:probe12	-0.0180098382465052	0.0817035348712572	-0.220429119436311	0.825599893365139	   
df.mm.trans1:probe13	0.0201070316639805	0.0817035348712572	0.246097450932371	0.805677425940334	   
df.mm.trans1:probe14	0.045557665862277	0.0817035348712572	0.557597243914399	0.577293963022119	   
df.mm.trans1:probe15	-0.00665873078258459	0.0817035348712572	-0.0814986865999979	0.935068177021047	   
df.mm.trans1:probe16	-0.066167239676954	0.0817035348712572	-0.809845495439282	0.418298414673173	   
df.mm.trans1:probe17	0.00427980191014047	0.0817035348712572	0.052382089916725	0.958238875726815	   
df.mm.trans1:probe18	-0.00608440891375757	0.0817035348712572	-0.0744693472974573	0.940657759946706	   
df.mm.trans1:probe19	0.089852535934981	0.0817035348712572	1.09973865973565	0.271816139623192	   
df.mm.trans1:probe20	-0.0168572676467025	0.0817035348712572	-0.206322378502534	0.836597834872373	   
df.mm.trans1:probe21	0.0105944458414740	0.0817035348712572	0.12966936936288	0.896864486716233	   
df.mm.trans1:probe22	0.0216666298457405	0.0817035348712572	0.265185954070179	0.790942649105978	   
df.mm.trans2:probe2	-0.0413095322472732	0.0817035348712572	-0.505602754059122	0.613291374492836	   
df.mm.trans2:probe3	-0.0206280710224951	0.0817035348712572	-0.252474645742065	0.800746770887234	   
df.mm.trans2:probe4	0.126825260545478	0.0817035348712572	1.55226160955362	0.121042136296320	   
df.mm.trans2:probe5	-0.0213385122781317	0.0817035348712572	-0.261170000927811	0.794036618951174	   
df.mm.trans2:probe6	-0.0250697572191332	0.0817035348712572	-0.306838097747378	0.759055945811143	   
df.mm.trans3:probe2	0.189517583299106	0.0817035348712572	2.31957630227058	0.0206446194927495	*  
df.mm.trans3:probe3	-0.0404893333063944	0.0817035348712572	-0.495564033676079	0.62035430736147	   
df.mm.trans3:probe4	0.084459517567407	0.0817035348712572	1.03373149889405	0.301611213244139