fitVsDatCorrelation=0.842871002902255
cont.fitVsDatCorrelation=0.280385654507195

fstatistic=7463.33958418736,44,508
cont.fstatistic=2337.62851990956,44,508

residuals=-0.590074494807502,-0.108527426595779,-0.000979566503879112,0.0934404095384586,0.64958371470099
cont.residuals=-0.655865677219681,-0.254289044779526,0.0143570618960077,0.217893581909138,0.816766859860646

predictedValues:
Include	Exclude	Both
Lung	96.7203479863683	52.1741311369256	74.8818353050117
cerebhem	87.9582233906212	58.0204459157993	75.0761696375656
cortex	90.7390599516364	50.473142049622	95.6386993263094
heart	82.3922725280623	53.5879402036224	93.6855647145785
kidney	88.318166112938	51.6188875665476	68.381402151996
liver	93.396702404531	51.0180077608375	66.0719745705393
stomach	77.2969079433146	48.3523367810879	80.0599970795166
testicle	84.4771214999816	53.8917946666857	77.2091397146053


diffExp=44.5462168494426,29.9377774748219,40.2659179020144,28.8043323244399,36.6992785463904,42.3786946436935,28.9445711622267,30.5853268332959
diffExpScore=0.996468454131303
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	82.1795267845493	85.4928897406131	78.5636993101773
cerebhem	77.900653511079	84.6352747388669	79.8685772161974
cortex	81.4241891311909	81.4847343378498	76.5566729766637
heart	71.0519096902054	79.6500391000018	78.1555303342274
kidney	76.0115870130242	66.1027342856256	83.9208561902936
liver	74.9035246165172	68.9015442193965	73.4086471805017
stomach	71.8434828633117	78.6595705816536	78.6763602893443
testicle	77.1787786187758	81.89680391509	94.9105398379991
cont.diffExp=-3.31336295606378,-6.73462122778784,-0.0605452066589436,-8.59812940979636,9.90885272739855,6.00198039712075,-6.81608771834189,-4.71802529631424
cont.diffExpScore=3.01055378442263

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=0.129469542558728
cont.tran.correlation=0.398688763319483

tran.covariance=0.000640347898767722
cont.tran.covariance=0.00182999752938524

tran.mean=70.0272179936614
cont.tran.mean=77.4573276967344

weightedLogRatios:
wLogRatio
Lung	2.6314080689868
cerebhem	1.77611663718450
cortex	2.47212637087789
heart	1.80515871826108
kidney	2.26231284003016
liver	2.56051683498048
stomach	1.92960988995654
testicle	1.89318298742029

cont.weightedLogRatios:
wLogRatio
Lung	-0.175052055894524
cerebhem	-0.364576262138671
cortex	-0.00327055857349967
heart	-0.49354151206953
kidney	0.595164759221391
liver	0.357011283497507
stomach	-0.391544968760721
testicle	-0.259639536428862

varWeightedLogRatios=0.126823122287107
cont.varWeightedLogRatios=0.148848666556846

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03748650007987	0.0904976109175925	44.6142882573599	7.57960990820617e-178	***
df.mm.trans1	0.384499159690065	0.0779828696603517	4.93055925441986	1.11223622607087e-06	***
df.mm.trans2	-0.112909882427664	0.0728321082676506	-1.55027617781887	0.121697981102684	   
df.mm.exp2	0.00865497612150322	0.0979187373292093	0.0883893763090982	0.929602037817788	   
df.mm.exp3	-0.341647536549486	0.0979187373292092	-3.48909254620844	0.000526785312523739	***
df.mm.exp4	-0.357627682073196	0.0979187373292093	-3.65229058122786	0.000286879325951086	***
df.mm.exp5	-0.0107666897963657	0.0979187373292093	-0.109955357779659	0.91248820237098	   
df.mm.exp6	0.0677907682498852	0.0979187373292092	0.692316609659377	0.489054849779416	   
df.mm.exp7	-0.367107074083336	0.0979187373292092	-3.74909934601278	0.000197870499883512	***
df.mm.exp8	-0.133558122405781	0.0979187373292092	-1.36396900173201	0.173181342403362	   
df.mm.trans1:exp2	-0.103616812736752	0.0883883980662174	-1.17228974620771	0.241630203034495	   
df.mm.trans2:exp2	0.0975536881328545	0.077697739383315	1.25555375107610	0.209855006269944	   
df.mm.trans1:exp3	0.277811646756399	0.0883883980662174	3.14307819617086	0.00176948681053764	** 
df.mm.trans2:exp3	0.308502090789265	0.077697739383315	3.97054139847361	8.20576427171978e-05	***
df.mm.trans1:exp4	0.197295530417746	0.0883883980662175	2.23214284605475	0.0260407269573181	*  
df.mm.trans2:exp4	0.384364928670822	0.077697739383315	4.94692550544606	1.02666197651050e-06	***
df.mm.trans1:exp5	-0.080111296184254	0.0883883980662174	-0.90635533550724	0.365177498281849	   
df.mm.trans2:exp5	6.75334570506558e-05	0.077697739383315	0.000869181749516359	0.99930683459562	   
df.mm.trans1:exp6	-0.102758533973717	0.0883883980662174	-1.16257943601075	0.245546032910121	   
df.mm.trans2:exp6	-0.0901989044612039	0.077697739383315	-1.16089483654364	0.246229891595696	   
df.mm.trans1:exp7	0.142937223969332	0.0883883980662174	1.61714916319954	0.106466835787150	   
df.mm.trans2:exp7	0.291034825381366	0.077697739383315	3.74573092719688	0.000200471838360395	***
df.mm.trans1:exp8	-0.00178493554441186	0.0883883980662174	-0.0201942289198935	0.98389636072302	   
df.mm.trans2:exp8	0.165949556240548	0.077697739383315	2.13583506492835	0.0331703223379093	*  
df.mm.trans1:probe2	0.232029321968371	0.051607705928009	4.49602085184805	8.58501866889673e-06	***
df.mm.trans1:probe3	0.256012575279672	0.051607705928009	4.96074318119857	9.59393978896115e-07	***
df.mm.trans1:probe4	0.297389510084140	0.051607705928009	5.76250202826277	1.43841920176010e-08	***
df.mm.trans1:probe5	-0.0176768074384777	0.051607705928009	-0.342522635343183	0.732099286539657	   
df.mm.trans1:probe6	0.0252560638534573	0.051607705928009	0.489385517129723	0.624780027371944	   
df.mm.trans1:probe7	0.309002657785012	0.051607705928009	5.987529424696	4.03216998065109e-09	***
df.mm.trans1:probe8	0.354488563644194	0.051607705928009	6.86890760342444	1.89763505520343e-11	***
df.mm.trans1:probe9	0.200351484265233	0.051607705928009	3.88220093612989	0.000117172056511412	***
df.mm.trans1:probe10	0.366980904269569	0.051607705928009	7.1109710782629	3.93287480912187e-12	***
df.mm.trans1:probe11	0.261603991062100	0.051607705928009	5.06908777202824	5.60851446239071e-07	***
df.mm.trans1:probe12	0.261810190096044	0.051607705928009	5.07308328064921	5.49754920310253e-07	***
df.mm.trans2:probe2	0.113780461186545	0.051607705928009	2.20471844544426	0.0279212994978435	*  
df.mm.trans2:probe3	-0.00491440593775929	0.051607705928009	-0.0952262040985646	0.924172707255285	   
df.mm.trans2:probe4	0.0182490792205332	0.051607705928009	0.353611517744851	0.723776744979321	   
df.mm.trans2:probe5	0.0739456181035863	0.051607705928009	1.43284063443428	0.152518476336778	   
df.mm.trans2:probe6	0.129051252841574	0.051607705928009	2.50061983033302	0.0127115394390428	*  
df.mm.trans3:probe2	0.216891061055497	0.051607705928009	4.20268750868432	3.11646271326921e-05	***
df.mm.trans3:probe3	-0.0243531228154164	0.051607705928009	-0.471889272687071	0.637208446917302	   
df.mm.trans3:probe4	0.018407127063996	0.051607705928009	0.356674003097005	0.721483968578584	   
df.mm.trans3:probe5	-0.176767242923528	0.051607705928009	-3.42521024224778	0.00066405427679353	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.48523555746546	0.161429084094462	27.7845568078729	5.22000484752588e-104	***
df.mm.trans1	-0.0467724222580942	0.139105365287396	-0.336237370582082	0.73683071337319	   
df.mm.trans2	-0.0631159193448565	0.129917468661374	-0.485815494984485	0.627307461586163	   
df.mm.exp2	-0.080026683083105	0.174666843936182	-0.458167567923449	0.647027981559551	   
df.mm.exp3	-0.0313728679789809	0.174666843936182	-0.179615474076142	0.85752607577647	   
df.mm.exp4	-0.211077225242829	0.174666843936182	-1.20845616996406	0.227434060206908	   
df.mm.exp5	-0.401207930033002	0.174666843936182	-2.29698963461887	0.0220252299467498	*  
df.mm.exp6	-0.240591865190677	0.174666843936182	-1.37743294473553	0.168984993159295	   
df.mm.exp7	-0.219153186107714	0.174666843936182	-1.25469254020406	0.210167336021714	   
df.mm.exp8	-0.294779930072490	0.174666843936182	-1.68766964255789	0.092088474104647	.  
df.mm.trans1:exp2	0.026554819868365	0.157666683128233	0.168423787077245	0.866316930568174	   
df.mm.trans2:exp2	0.0699446103241079	0.138596751645523	0.504662695868942	0.614014600450331	   
df.mm.trans1:exp3	0.0221390554582295	0.157666683128233	0.14041682756923	0.888386320766849	   
df.mm.trans2:exp3	-0.0166446496128645	0.138596751645523	-0.120094081681186	0.904456101266288	   
df.mm.trans1:exp4	0.0655817523829435	0.157666683128233	0.415951874433768	0.677620878148143	   
df.mm.trans2:exp4	0.140286540940315	0.138596751645523	1.01219212769946	0.311928051976406	   
df.mm.trans1:exp5	0.323187514237387	0.157666683128233	2.04981488685554	0.0408953512524073	*  
df.mm.trans2:exp5	0.143984830373434	0.138596751645523	1.03887593802842	0.299356648293896	   
df.mm.trans1:exp6	0.147886607078397	0.157666683128233	0.93796992582205	0.348705589935666	   
df.mm.trans2:exp6	0.0248372438760809	0.138596751645523	0.179205093778857	0.85784811572565	   
df.mm.trans1:exp7	0.0847368846045632	0.157666683128233	0.537443186622029	0.591196856057823	   
df.mm.trans2:exp7	0.13584928236989	0.138596751645523	0.980176524752472	0.327465550436407	   
df.mm.trans1:exp8	0.231998255797982	0.157666683128233	1.47144755756227	0.141789298696658	   
df.mm.trans2:exp8	0.251806684378123	0.138596751645523	1.81682962543124	0.0698323348283102	.  
df.mm.trans1:probe2	0.0252704536499647	0.0920575097585841	0.274507247874020	0.78380642963854	   
df.mm.trans1:probe3	-0.0461815138232793	0.0920575097585841	-0.501659386011938	0.616124474178713	   
df.mm.trans1:probe4	-0.0427983479601495	0.0920575097585841	-0.464908817025204	0.642195963394068	   
df.mm.trans1:probe5	-0.0799000241768767	0.0920575097585841	-0.867935971616116	0.385839203612083	   
df.mm.trans1:probe6	-0.0754443759209315	0.0920575097585841	-0.819535267886133	0.412865413477451	   
df.mm.trans1:probe7	-0.0433632451282464	0.0920575097585841	-0.471045167764848	0.637810688223847	   
df.mm.trans1:probe8	-0.0333666835387132	0.0920575097585841	-0.362454770134621	0.717162957574075	   
df.mm.trans1:probe9	-0.0609024034412449	0.0920575097585841	-0.661569095242291	0.508547374508924	   
df.mm.trans1:probe10	-0.129988602190264	0.0920575097585841	-1.41203691617503	0.158551344582825	   
df.mm.trans1:probe11	-0.0459533852675681	0.0920575097585841	-0.499181276878751	0.617867785449769	   
df.mm.trans1:probe12	0.0301603169696469	0.0920575097585841	0.327624732069558	0.743330393197772	   
df.mm.trans2:probe2	-0.0769717534947554	0.0920575097585841	-0.836126826552333	0.403476667514135	   
df.mm.trans2:probe3	0.128788801841165	0.0920575097585841	1.39900375514075	0.162422107668539	   
df.mm.trans2:probe4	0.102031206581403	0.0920575097585841	1.10834202281784	0.268238594109705	   
df.mm.trans2:probe5	0.124130597098389	0.0920575097585841	1.34840272590379	0.178129748354120	   
df.mm.trans2:probe6	0.0114704557647980	0.0920575097585841	0.124600978180690	0.900888742845297	   
df.mm.trans3:probe2	0.0170022626822334	0.0920575097585841	0.184691751132753	0.853544524433462	   
df.mm.trans3:probe3	0.0253768430929828	0.0920575097585841	0.275662932437910	0.782919064056262	   
df.mm.trans3:probe4	-0.0362011436652395	0.0920575097585841	-0.393244872256213	0.694303608191376	   
df.mm.trans3:probe5	-0.063365281840563	0.0920575097585841	-0.688322788729948	0.491563730690455