fitVsDatCorrelation=0.750408259138557
cont.fitVsDatCorrelation=0.269677090870874

fstatistic=11641.7662404819,53,715
cont.fstatistic=5477.91100266481,53,715

residuals=-0.499558547183754,-0.0832490038681514,-0.00824218297849716,0.0696155936509455,1.16166798942055
cont.residuals=-0.430572933961762,-0.127047352769336,-0.0269950129723867,0.0993824691898404,1.57993980259519

predictedValues:
Include	Exclude	Both
Lung	49.2188009031469	60.7559454618002	69.8920377464524
cerebhem	58.1645214202579	53.4944147933624	69.9627807919025
cortex	48.0968287246895	52.8043706449408	57.9003039871533
heart	48.3692768058006	58.4832360223807	64.6596143896139
kidney	49.0435742114858	53.2490524147962	66.3060690078311
liver	50.6314641414271	54.7174873902408	62.6540526778031
stomach	52.2259787675516	51.6622702378428	58.9352247571033
testicle	53.9234229773243	57.934127348657	64.8669895756131


diffExp=-11.5371445586533,4.67010662689548,-4.70754192025133,-10.1139592165801,-4.20547820331044,-4.08602324881370,0.563708529708791,-4.0107043713327
diffExpScore=1.27500567326109
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=-1,0,0,-1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	53.1881435511807	56.1901152087226	59.9731426421932
cerebhem	54.9447496069758	52.9292763515344	56.7595561489803
cortex	52.4605410442179	63.7057279176367	53.2703200646944
heart	52.9183124607837	52.0521035420831	56.9576890867924
kidney	53.9118901515879	55.1670258457118	51.5844302076052
liver	55.54021012798	64.5759861663034	53.7892354136553
stomach	54.1405180755788	53.6652311355191	52.1722530261273
testicle	54.6544220878719	61.4202614126969	56.3317235403645
cont.diffExp=-3.00197165754195,2.01547325544148,-11.2451868734188,0.86620891870053,-1.25513569412396,-9.03577603832342,0.475286940059689,-6.76583932482506
cont.diffExpScore=1.19739351153637

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,-1,0,0,0,0,0
cont.diffExp1.2Score=0.5

tran.correlation=-0.209338551770137
cont.tran.correlation=0.166142104340876

tran.covariance=-0.000776471548264114
cont.tran.covariance=0.000269459727749361

tran.mean=53.2984232666065
cont.tran.mean=55.716532167899

weightedLogRatios:
wLogRatio
Lung	-0.842687740980757
cerebhem	0.336586631445559
cortex	-0.366031803589978
heart	-0.754526996120041
kidney	-0.323640686487424
liver	-0.307598385400343
stomach	0.0428683189312267
testicle	-0.288648053439526

cont.weightedLogRatios:
wLogRatio
Lung	-0.219692126147600
cerebhem	0.149024231717138
cortex	-0.787955520640713
heart	0.0653650068558826
kidney	-0.0920311903058584
liver	-0.616880622781686
stomach	0.0351570127715082
testicle	-0.473769369406124

varWeightedLogRatios=0.146809717379908
cont.varWeightedLogRatios=0.119927797913359

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89266984524023	0.0687628130920357	56.6101017424938	2.2111238827425e-266	***
df.mm.trans1	-0.0221230688832701	0.0600431018383874	-0.368453131265883	0.712644449605308	   
df.mm.trans2	0.183966462089245	0.0540900862001787	3.40111238514974	0.000708437398072457	***
df.mm.exp2	0.0387005314082180	0.0714332410458558	0.541772021563106	0.588144350616954	   
df.mm.exp3	0.0248986796830455	0.0714332410458558	0.348558728660541	0.727523224391395	   
df.mm.exp4	0.0222792820068916	0.0714332410458558	0.311889558428262	0.75521538357861	   
df.mm.exp5	-0.0827811438644525	0.0714332410458558	-1.15886025402812	0.246900041898397	   
df.mm.exp6	0.0329392976060870	0.0714332410458558	0.461120020928939	0.644852732547399	   
df.mm.exp7	0.0676799315190576	0.0714332410458558	0.947457101597997	0.343726002862861	   
df.mm.exp8	0.118343823482736	0.0714332410458558	1.65670522224753	0.0980178037335807	.  
df.mm.trans1:exp2	0.128299357176810	0.0666118407434498	1.92607433971003	0.0544912514023994	.  
df.mm.trans2:exp2	-0.16598822445757	0.0537041829162308	-3.09078763411191	0.00207374748055906	** 
df.mm.trans1:exp3	-0.0479581183066405	0.0666118407434498	-0.719963864853207	0.471782521916741	   
df.mm.trans2:exp3	-0.165169660146265	0.0537041829162308	-3.0755455381176	0.00218124976456185	** 
df.mm.trans1:exp4	-0.039690129262155	0.0666118407434498	-0.595841952709554	0.551469377538303	   
df.mm.trans2:exp4	-0.0604040776721801	0.0537041829162307	-1.12475554774570	0.261070073994014	   
df.mm.trans1:exp5	0.0792146337491248	0.0666118407434498	1.18919748899019	0.234756736394127	   
df.mm.trans2:exp5	-0.0491037918475291	0.0537041829162308	-0.914338309999474	0.360847337383173	   
df.mm.trans1:exp6	-0.00464177626854226	0.0666118407434499	-0.069683951332612	0.944464699784307	   
df.mm.trans2:exp6	-0.137620888037927	0.0537041829162308	-2.56257297969866	0.0105936355107803	*  
df.mm.trans1:exp7	-0.00837556557369317	0.0666118407434499	-0.125736888220084	0.89997554691313	   
df.mm.trans2:exp7	-0.229817144175131	0.0537041829162308	-4.27931553364485	2.13003164602146e-05	***
df.mm.trans1:exp8	-0.0270545590586105	0.0666118407434499	-0.406152401084501	0.684752150636284	   
df.mm.trans2:exp8	-0.165902138856338	0.0537041829162308	-3.08918467515122	0.00208481950098389	** 
df.mm.trans1:probe2	-0.107066629776931	0.0407912551622468	-2.62474467507986	0.00885660508873516	** 
df.mm.trans1:probe3	0.0844340067959	0.0407912551622467	2.06990460234834	0.0388198339635796	*  
df.mm.trans1:probe4	0.0628426699616309	0.0407912551622467	1.54059172025168	0.123858613209657	   
df.mm.trans1:probe5	-0.0433650069211672	0.0407912551622467	-1.06309567451856	0.288097479260663	   
df.mm.trans1:probe6	-0.0683552227633682	0.0407912551622467	-1.67573227377991	0.0942278440687558	.  
df.mm.trans1:probe7	-0.0618145568364704	0.0407912551622468	-1.51538746700987	0.130116251047267	   
df.mm.trans1:probe8	-0.0319445014500274	0.0407912551622467	-0.783121316639277	0.433815181916334	   
df.mm.trans1:probe9	0.268607856434506	0.0407912551622468	6.58493727065082	8.81479248799942e-11	***
df.mm.trans1:probe10	0.00558021158465512	0.0407912551622467	0.136799212538567	0.891228015708944	   
df.mm.trans1:probe11	-0.0723091699555192	0.0407912551622468	-1.77266351986254	0.0767102028180529	.  
df.mm.trans1:probe12	0.038791524351666	0.0407912551622467	0.95097648251747	0.341937655877469	   
df.mm.trans1:probe13	-0.00541448831304579	0.0407912551622467	-0.13273649686703	0.894439124138933	   
df.mm.trans1:probe14	0.00151914397542007	0.0407912551622468	0.0372419031818877	0.97030252300741	   
df.mm.trans1:probe15	0.0677367216739937	0.0407912551622468	1.66056968349151	0.0972383308201385	.  
df.mm.trans1:probe16	0.0767513338296694	0.0407912551622467	1.88156342638617	0.0603013383127039	.  
df.mm.trans1:probe17	0.248954919047338	0.0407912551622467	6.103144364083	1.70445533617692e-09	***
df.mm.trans1:probe18	0.0348970953212848	0.0407912551622468	0.855504327642825	0.392558594719772	   
df.mm.trans1:probe19	0.0155638492385267	0.0407912551622467	0.381548672052911	0.702909520338424	   
df.mm.trans1:probe20	0.153541808256568	0.0407912551622468	3.76408638679682	0.000180876187518967	***
df.mm.trans2:probe2	0.164783029376815	0.0407912551622467	4.03966557835478	5.93212437220816e-05	***
df.mm.trans2:probe3	0.0327050744974066	0.0407912551622467	0.801766809266411	0.422954239062256	   
df.mm.trans2:probe4	0.0400508568239381	0.0407912551622468	0.981849091542693	0.326506307799377	   
df.mm.trans2:probe5	0.080362580570925	0.0407912551622467	1.97009335092249	0.0492131795932317	*  
df.mm.trans2:probe6	0.0448421121677951	0.0407912551622468	1.09930699581163	0.272004187550022	   
df.mm.trans3:probe2	0.135184351550768	0.0407912551622467	3.31405226470903	0.000965891642378409	***
df.mm.trans3:probe3	0.536363389880061	0.0407912551622467	13.1489797935043	1.59393787728194e-35	***
df.mm.trans3:probe4	0.294818665840369	0.0407912551622468	7.22749679233284	1.26573137423792e-12	***
df.mm.trans3:probe5	0.00110004976519919	0.0407912551622467	0.0269677841690273	0.978492954094781	   
df.mm.trans3:probe6	0.187164513555944	0.0407912551622468	4.58834896870663	5.27574002103485e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87528822580658	0.100178138446549	38.6839712326483	1.78450427051404e-177	***
df.mm.trans1	0.0805873028713657	0.0874746959621249	0.921264166568338	0.357223258851045	   
df.mm.trans2	0.176408153934928	0.0788019555961839	2.23862660006715	0.0254871528161994	*  
df.mm.exp2	0.0277811872146504	0.104068591574336	0.266950736955120	0.789584065602879	   
df.mm.exp3	0.230276915323296	0.104068591574336	2.21274172965826	0.0272306281919802	*  
df.mm.exp4	-0.0299935591286953	0.104068591574336	-0.288209522920957	0.773269885072369	   
df.mm.exp5	0.145817058210545	0.104068591574336	1.40116298303498	0.161599221271226	   
df.mm.exp6	0.291196991827032	0.104068591574336	2.79812561524896	0.00527836357104714	** 
df.mm.exp7	0.111117855897932	0.104068591574336	1.06773671303662	0.285999660051078	   
df.mm.exp8	0.178832527367792	0.104068591574336	1.71841018180833	0.086154725437959	.  
df.mm.trans1:exp2	0.00471143458619029	0.0970444620298658	0.0485492369955158	0.961292083080224	   
df.mm.trans2:exp2	-0.087565428704052	0.078239746592975	-1.1191936645653	0.263433368154091	   
df.mm.trans1:exp3	-0.244051133405790	0.0970444620298658	-2.51483833596484	0.0121269989374877	*  
df.mm.trans2:exp3	-0.104743292037788	0.078239746592975	-1.33874784363364	0.181078265349734	   
df.mm.trans1:exp4	0.0249075034407855	0.0970444620298658	0.256660740033987	0.797514515162825	   
df.mm.trans2:exp4	-0.0465020882181770	0.078239746592975	-0.59435376829751	0.55246353598157	   
df.mm.trans1:exp5	-0.132301514049077	0.0970444620298658	-1.36330823296604	0.173214508186809	   
df.mm.trans2:exp5	-0.164192496748436	0.078239746592975	-2.09858165316679	0.036204012340193	*  
df.mm.trans1:exp6	-0.247925232522619	0.0970444620298658	-2.55475920353208	0.0108321591382342	*  
df.mm.trans2:exp6	-0.152095236783515	0.078239746592975	-1.94396382154401	0.0522917464254974	.  
df.mm.trans1:exp7	-0.09337050850442	0.0970444620298658	-0.962141543694526	0.336303775728482	   
df.mm.trans2:exp7	-0.157093384431041	0.078239746592975	-2.00784628365791	0.0450353608286015	*  
df.mm.trans1:exp8	-0.151637905427889	0.0970444620298658	-1.56256114214144	0.118598327440899	   
df.mm.trans2:exp8	-0.0898336116788666	0.078239746592975	-1.14818382715637	0.251276680176088	   
df.mm.trans1:probe2	0.0216215891763134	0.059427353584017	0.363832273731411	0.716090817291235	   
df.mm.trans1:probe3	0.0105620222454724	0.059427353584017	0.17772997800651	0.858985379932456	   
df.mm.trans1:probe4	0.0288178796994799	0.059427353584017	0.484926182330126	0.627877239362928	   
df.mm.trans1:probe5	0.054845311973527	0.059427353584017	0.922896758240933	0.356372332865794	   
df.mm.trans1:probe6	0.0397082044865803	0.059427353584017	0.668180595160473	0.504233953823974	   
df.mm.trans1:probe7	0.0185946520674027	0.059427353584017	0.312897192050021	0.754450013752026	   
df.mm.trans1:probe8	0.00911766559895912	0.059427353584017	0.153425401756596	0.878106104266787	   
df.mm.trans1:probe9	0.00321063077195107	0.059427353584017	0.0540261441629224	0.956929426172579	   
df.mm.trans1:probe10	-0.0265572073438194	0.059427353584017	-0.446885242942435	0.655093248017879	   
df.mm.trans1:probe11	0.0707977402622752	0.059427353584017	1.19133254288672	0.233918372733888	   
df.mm.trans1:probe12	0.0026726640063191	0.059427353584017	0.044973633270419	0.964140882523871	   
df.mm.trans1:probe13	0.0225049393984423	0.059427353584017	0.378696644578416	0.705025564724823	   
df.mm.trans1:probe14	0.0409249425335313	0.059427353584017	0.688654972254024	0.491263801564329	   
df.mm.trans1:probe15	0.0639478803411751	0.059427353584017	1.07606811484155	0.282259748242760	   
df.mm.trans1:probe16	0.0799359150982278	0.059427353584017	1.34510305906886	0.179018425101582	   
df.mm.trans1:probe17	-0.0112926544232061	0.059427353584017	-0.190024521405632	0.849343850245268	   
df.mm.trans1:probe18	0.0601922908876691	0.059427353584017	1.01287180494367	0.311464024631183	   
df.mm.trans1:probe19	-0.0107108513724536	0.059427353584017	-0.180234365599182	0.85701965003962	   
df.mm.trans1:probe20	-0.0119342068526276	0.059427353584017	-0.200820096014461	0.840896359214692	   
df.mm.trans2:probe2	0.0329211838486571	0.059427353584017	0.55397358056865	0.579770132455613	   
df.mm.trans2:probe3	-0.0829123830807884	0.059427353584017	-1.39518888323992	0.163392085518002	   
df.mm.trans2:probe4	-0.0453100119274458	0.059427353584017	-0.76244370975375	0.446046674128555	   
df.mm.trans2:probe5	-0.0789604952450312	0.059427353584017	-1.32868940787341	0.184374343767660	   
df.mm.trans2:probe6	-0.101204584924149	0.059427353584017	-1.70299666434023	0.0890032862315181	.  
df.mm.trans3:probe2	-0.0513878174349922	0.059427353584017	-0.864716571340188	0.387484385530654	   
df.mm.trans3:probe3	-0.0275437490342629	0.059427353584017	-0.463486044272899	0.643157069349097	   
df.mm.trans3:probe4	-0.0686573832226897	0.059427353584017	-1.15531618155642	0.248346907665448	   
df.mm.trans3:probe5	-0.0405973056012289	0.059427353584017	-0.683141737816634	0.494738577458582	   
df.mm.trans3:probe6	-0.0421348041096046	0.059427353584017	-0.709013637129835	0.478547127377195