fitVsDatCorrelation=0.890323769219502
cont.fitVsDatCorrelation=0.23647908914338

fstatistic=13995.4857252894,65,991
cont.fstatistic=3061.57194971692,65,991

residuals=-0.481453749264788,-0.0837323146981455,-0.00548779133593101,0.0733593122135405,0.616814377119275
cont.residuals=-0.593185157800069,-0.192231894423797,-0.0480821855313488,0.124012170596051,1.35317849249566

predictedValues:
Include	Exclude	Both
Lung	62.9362445159377	57.9516541990837	53.5663106365469
cerebhem	65.3657598945408	60.7306075591108	55.6737302285857
cortex	59.2116762670716	56.0120203410762	51.2930139182851
heart	60.5061245952046	58.638185972112	51.9135478227382
kidney	63.9027539085255	57.735825153201	53.0067532091821
liver	65.1308160682122	59.7783661373994	55.883191017063
stomach	68.7273987582831	55.934002528162	59.6748764096487
testicle	60.7388275601969	58.1387725130469	52.8001583572994


diffExp=4.98459031685402,4.63515233542996,3.19965592599543,1.86793862309257,6.16692875532454,5.35244993081279,12.7933962301211,2.60005504715006
diffExpScore=0.97652591370987
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=0,0,0,0,0,0,1,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	60.7195202500078	51.5024998796962	61.518001176016
cerebhem	61.9683963335961	55.9249366424543	60.2451589298691
cortex	60.3040340646272	50.3828600611378	60.8142457090717
heart	59.1007853724231	64.6023764732061	57.0811456836412
kidney	60.5145657541912	59.4380059823016	58.8151079576988
liver	57.8989424085981	49.4771547351398	60.2048036037056
stomach	61.3560425860806	54.8206033589148	52.1521836704754
testicle	58.9734627995269	59.4033254549341	56.2795967969451
cont.diffExp=9.2170203703116,6.04345969114177,9.92117400348948,-5.501591100783,1.07655977188959,8.42178767345824,6.53543922716575,-0.429862655407199
cont.diffExpScore=1.29938571849860

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=0.0968009771196943
cont.tran.correlation=-0.044175363235319

tran.covariance=0.000151848578554800
cont.tran.covariance=-2.73100536576142e-05

tran.mean=60.7149397481978
cont.tran.mean=57.8992195098022

weightedLogRatios:
wLogRatio
Lung	0.338375169787669
cerebhem	0.304737207355306
cortex	0.225173068204469
heart	0.128164119601548
kidney	0.416758664437063
liver	0.354465849170787
stomach	0.850093753230537
testicle	0.178707503522957

cont.weightedLogRatios:
wLogRatio
Lung	0.662482178384548
cerebhem	0.418185211532617
cortex	0.720703952263214
heart	-0.367042347790532
kidney	0.073486645370909
liver	0.625625060073457
stomach	0.457310690449861
testicle	-0.0296368298514681

varWeightedLogRatios=0.0501266813367483
cont.varWeightedLogRatios=0.150685816838113

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.4430525010097	0.0671637719879715	66.1525159993309	0	***
df.mm.trans1	-0.144654449687959	0.0593359827005978	-2.43788748587628	0.0149482483538978	*  
df.mm.trans2	-0.361375158362298	0.0524460946699504	-6.89041120480897	9.87950962293931e-12	***
df.mm.exp2	0.0461269811789137	0.0688439035641215	0.670022744075673	0.502999382942141	   
df.mm.exp3	-0.0516804879059606	0.0688439035641215	-0.750690841605534	0.45301694004375	   
df.mm.exp4	0.00373992258083673	0.0688439035641215	0.0543246734600596	0.956687441326467	   
df.mm.exp5	0.0220100012548274	0.0688439035641215	0.319708792142026	0.74925648052967	   
df.mm.exp6	0.022966990701799	0.0688439035641215	0.333609651875818	0.738744708402807	   
df.mm.exp7	-0.0554016938220748	0.0688439035641215	-0.804743644010155	0.421160647172176	   
df.mm.exp8	-0.0179092500294207	0.0688439035641214	-0.260142860910552	0.794807616094505	   
df.mm.trans1:exp2	-0.00825063100011274	0.0658273624944768	-0.125337408145511	0.900281859098917	   
df.mm.trans2:exp2	0.000711718286921563	0.0507233893924334	0.0140313629559568	0.988807783667466	   
df.mm.trans1:exp3	-0.00932297758269676	0.0658273624944768	-0.141627694463362	0.887402875555885	   
df.mm.trans2:exp3	0.0176376898948995	0.0507233893924334	0.347723015085632	0.728122051086623	   
df.mm.trans1:exp4	-0.0431175518607412	0.0658273624944768	-0.65500956178153	0.512613601506634	   
df.mm.trans2:exp4	0.0080370848675831	0.0507233893924334	0.1584492866871	0.874135060924587	   
df.mm.trans1:exp5	-0.00676976581154637	0.0658273624944768	-0.102841213061125	0.918109801472722	   
df.mm.trans2:exp5	-0.0257412485340039	0.0507233893924334	-0.507482816947635	0.611929006325191	   
df.mm.trans1:exp6	0.0113085894423372	0.0658273624944768	0.171791623024332	0.863636424678421	   
df.mm.trans2:exp6	0.00806771986232024	0.0507233893924334	0.159053248589175	0.873659329270006	   
df.mm.trans1:exp7	0.143427408814524	0.0658273624944768	2.17884179738415	0.0295784172390730	*  
df.mm.trans2:exp7	0.0199650486059828	0.0507233893924334	0.393606358824299	0.69395635367254	   
df.mm.trans1:exp8	-0.0176298153088594	0.0658273624944768	-0.267818953103864	0.788894412267735	   
df.mm.trans2:exp8	0.0211329176703735	0.0507233893924335	0.416630629843636	0.677038824250281	   
df.mm.trans1:probe2	-0.209122934082194	0.0403108623061727	-5.1877564040641	2.58070950301281e-07	***
df.mm.trans1:probe3	0.676595345163678	0.0403108623061727	16.7844423675370	7.75369453456392e-56	***
df.mm.trans1:probe4	-0.320007914744686	0.0403108623061727	-7.93850333228133	5.51795238781236e-15	***
df.mm.trans1:probe5	-0.495051919499967	0.0403108623061727	-12.2808566023695	2.26727381956623e-32	***
df.mm.trans1:probe6	-0.30732962288377	0.0403108623061727	-7.62399029198414	5.7466812752214e-14	***
df.mm.trans1:probe7	-0.371820751851525	0.0403108623061727	-9.22383522901192	1.68089356195094e-19	***
df.mm.trans1:probe8	-0.308514645034842	0.0403108623061727	-7.65338738456111	4.63224204295496e-14	***
df.mm.trans1:probe9	-0.475869269043521	0.0403108623061727	-11.8049885767552	3.47766622816053e-30	***
df.mm.trans1:probe10	-0.0372771160450323	0.0403108623061727	-0.924741221408308	0.355325648987005	   
df.mm.trans1:probe11	-0.396725044669827	0.0403108623061727	-9.8416412344788	7.20496966924015e-22	***
df.mm.trans1:probe12	-0.480283943458397	0.0403108623061727	-11.9145043291434	1.10628420270816e-30	***
df.mm.trans1:probe13	-0.318846308886528	0.0403108623061727	-7.9096871325847	6.86238861202296e-15	***
df.mm.trans1:probe14	-0.491434375432533	0.0403108623061727	-12.1911154293834	5.92276609449107e-32	***
df.mm.trans1:probe15	-0.399614400801473	0.0403108623061727	-9.91331809690116	3.75757843593792e-22	***
df.mm.trans1:probe16	-0.436056114491988	0.0403108623061727	-10.8173353172159	7.42162438107308e-26	***
df.mm.trans1:probe17	-0.0761639631975299	0.0403108623061727	-1.88941537938441	0.0591279027518793	.  
df.mm.trans1:probe18	0.0380336743569888	0.0403108623061727	0.94350932183271	0.34565040873259	   
df.mm.trans1:probe19	-0.0269158427472021	0.0403108623061727	-0.667706945655701	0.504476135541031	   
df.mm.trans1:probe20	0.110232075678947	0.0403108623061727	2.73455017760976	0.00635797740777053	** 
df.mm.trans1:probe21	0.0597947036517565	0.0403108623061727	1.48333973105309	0.138302120680289	   
df.mm.trans1:probe22	-0.0660721483239649	0.0403108623061727	-1.63906561517161	0.101516992803401	   
df.mm.trans1:probe23	-0.476948889512962	0.0403108623061727	-11.8317709477509	2.62999802278684e-30	***
df.mm.trans1:probe24	-0.410801821449511	0.0403108623061727	-10.190846782918	2.91610928053243e-23	***
df.mm.trans1:probe25	-0.0180924217245487	0.0403108623061727	-0.448822493231018	0.653657835480965	   
df.mm.trans1:probe26	-0.184123064893087	0.0403108623061727	-4.5675794155585	5.55471901154324e-06	***
df.mm.trans1:probe27	0.721859834510788	0.0403108623061727	17.9073280305455	2.40079257158850e-62	***
df.mm.trans1:probe28	-0.319377000014033	0.0403108623061727	-7.92285209848083	6.21224043404075e-15	***
df.mm.trans1:probe29	-0.324420208676154	0.0403108623061727	-8.04796003151925	2.39556838042509e-15	***
df.mm.trans1:probe30	-0.335754724170163	0.0403108623061727	-8.32913773017327	2.68722007967722e-16	***
df.mm.trans1:probe31	-0.263563537261468	0.0403108623061727	-6.53827584385634	9.94793966238198e-11	***
df.mm.trans1:probe32	0.00519084182848847	0.0403108623061727	0.128770299902357	0.897565535202731	   
df.mm.trans2:probe2	-0.0170904514737129	0.0403108623061727	-0.423966407463723	0.671682321550801	   
df.mm.trans2:probe3	-0.0875572884660228	0.0403108623061727	-2.17205198442543	0.0300882627341326	*  
df.mm.trans2:probe4	-0.100041023576312	0.0403108623061727	-2.48173861468086	0.0132391385790923	*  
df.mm.trans2:probe5	0.0828184018230037	0.0403108623061727	2.05449343142237	0.0401897535957887	*  
df.mm.trans2:probe6	-0.142948374153215	0.0403108623061727	-3.54615024276784	0.000409187025615525	***
df.mm.trans3:probe2	0.319517338420897	0.0403108623061727	7.9263335027187	6.05071730885642e-15	***
df.mm.trans3:probe3	-0.0971130491275396	0.0403108623061727	-2.4091037395811	0.0161732988536836	*  
df.mm.trans3:probe4	0.0931760219991165	0.0403108623061727	2.31143708341979	0.0210131139092728	*  
df.mm.trans3:probe5	-0.0209291965326004	0.0403108623061727	-0.519194959751471	0.603740721204127	   
df.mm.trans3:probe6	-0.0312664471151018	0.0403108623061727	-0.775633298975944	0.438150584377963	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.90179407242898	0.143322567158135	27.2238639719865	2.65661774732313e-122	***
df.mm.trans1	0.195947777389255	0.126618638497901	1.5475429187505	0.12205165216777	   
df.mm.trans2	-0.0186114153371264	0.111916122383092	-0.166297892929305	0.867956445995521	   
df.mm.exp2	0.123646927725533	0.146907844809015	0.841663206524319	0.400179552497541	   
df.mm.exp3	-0.0173397713082078	0.146907844809015	-0.118031622686658	0.906066520165786	   
df.mm.exp4	0.274455801022959	0.146907844809015	1.86821746231292	0.0620266802232252	.  
df.mm.exp5	0.184853439637689	0.146907844809015	1.25829522499635	0.20858137942337	   
df.mm.exp6	-0.066107727936532	0.146907844809015	-0.44999453924652	0.652812792953821	   
df.mm.exp7	0.238027957670288	0.146907844809015	1.62025355405447	0.105495957986284	   
df.mm.exp8	0.202539954720815	0.146907844809015	1.37868712854731	0.168302360970028	   
df.mm.trans1:exp2	-0.103287641230395	0.140470767241114	-0.735296341430988	0.462333010751421	   
df.mm.trans2:exp2	-0.0412669010969257	0.108239995573615	-0.381253721216756	0.703096711384962	   
df.mm.trans1:exp3	0.0104735406793389	0.140470767241114	0.0745602867062111	0.940579620076545	   
df.mm.trans2:exp3	-0.00463953745110751	0.108239995573615	-0.0428634297933996	0.9658190352286	   
df.mm.trans1:exp4	-0.301476819860925	0.140470767241114	-2.14618903122704	0.0321001786724327	*  
df.mm.trans2:exp4	-0.0478349512450369	0.108239995573615	-0.441934157439097	0.658633272625493	   
df.mm.trans1:exp5	-0.188234579345619	0.140470767241114	-1.34002670479133	0.180543825905637	   
df.mm.trans2:exp5	-0.0415499343669406	0.108239995573615	-0.383868588932843	0.701158191585512	   
df.mm.trans1:exp6	0.0185416144861286	0.140470767241114	0.131996249826859	0.895014051576638	   
df.mm.trans2:exp6	0.0259884226306638	0.108239995573615	0.240099997167765	0.810302391567677	   
df.mm.trans1:exp7	-0.227599529738114	0.140470767241114	-1.62026259419120	0.105494016555293	   
df.mm.trans2:exp7	-0.175592208518817	0.108239995573615	-1.62224885162154	0.105068142161399	   
df.mm.trans1:exp8	-0.231717627091912	0.140470767241114	-1.64957899528075	0.0993459804203144	.  
df.mm.trans2:exp8	-0.0598200936743879	0.108239995573615	-0.552661641913168	0.580619754615815	   
df.mm.trans1:probe2	0.0833917491198433	0.0860204258794977	0.969441249182644	0.332561618897657	   
df.mm.trans1:probe3	0.0077631660354477	0.0860204258794977	0.0902479376970625	0.928108427498967	   
df.mm.trans1:probe4	-0.0620434251741817	0.0860204258794977	-0.721263868898947	0.470917303400219	   
df.mm.trans1:probe5	0.0224530692816223	0.0860204258794977	0.261020205980796	0.794131156806411	   
df.mm.trans1:probe6	0.00995427252198645	0.0860204258794977	0.115719870254199	0.907898001676078	   
df.mm.trans1:probe7	0.0258518467592667	0.0860204258794977	0.300531489991476	0.763834814902351	   
df.mm.trans1:probe8	0.169965846667865	0.0860204258794977	1.97587776310202	0.0484456648989505	*  
df.mm.trans1:probe9	-0.0520732658372639	0.0860204258794977	-0.605359312103512	0.545078880426155	   
df.mm.trans1:probe10	-0.0278968312828457	0.0860204258794977	-0.324304733412099	0.745775773678902	   
df.mm.trans1:probe11	0.0429718579416818	0.0860204258794977	0.499554117552025	0.617499960372933	   
df.mm.trans1:probe12	-0.0509280257443024	0.0860204258794977	-0.592045728948671	0.553955010680715	   
df.mm.trans1:probe13	-0.0503654594623797	0.0860204258794977	-0.58550581385094	0.558340961246197	   
df.mm.trans1:probe14	-0.0523047477572291	0.0860204258794977	-0.608050323193011	0.543293420041074	   
df.mm.trans1:probe15	-0.0419608380702822	0.0860204258794977	-0.487800864053653	0.625798797663231	   
df.mm.trans1:probe16	-0.0442333993198065	0.0860204258794977	-0.514219720113582	0.607213028198935	   
df.mm.trans1:probe17	0.0147727737465754	0.0860204258794977	0.171735649940514	0.863680419030588	   
df.mm.trans1:probe18	-0.00318355589010979	0.0860204258794977	-0.0370093016578352	0.970485042658004	   
df.mm.trans1:probe19	0.0581116025230858	0.0860204258794977	0.675555856983222	0.49948027731639	   
df.mm.trans1:probe20	0.0478249824764887	0.0860204258794977	0.55597239827067	0.578355223195085	   
df.mm.trans1:probe21	-0.0314024499748181	0.0860204258794977	-0.365058062125947	0.715145958718055	   
df.mm.trans1:probe22	0.0891706858993942	0.0860204258794977	1.03662223230921	0.3001648211472	   
df.mm.trans1:probe23	-0.0750103667730006	0.0860204258794977	-0.87200645667669	0.383416067730113	   
df.mm.trans1:probe24	0.056631547541985	0.0860204258794977	0.658350001909055	0.510466166969726	   
df.mm.trans1:probe25	0.0189917841315185	0.0860204258794977	0.220782261158801	0.825307454045596	   
df.mm.trans1:probe26	0.0196141437934133	0.0860204258794977	0.228017283021708	0.819679841509555	   
df.mm.trans1:probe27	0.00647116248557196	0.0860204258794977	0.0752282079449029	0.940048324120421	   
df.mm.trans1:probe28	0.0312807001222995	0.0860204258794977	0.363642702328855	0.716202395741744	   
df.mm.trans1:probe29	0.0893384869713248	0.0860204258794977	1.03857294425019	0.299256724340657	   
df.mm.trans1:probe30	-0.00310463840357981	0.0860204258794977	-0.0360918743639908	0.971216369904504	   
df.mm.trans1:probe31	0.0423267766702809	0.0860204258794977	0.49205495366385	0.622789474022145	   
df.mm.trans1:probe32	-0.0184909267548096	0.0860204258794977	-0.214959721086626	0.82984294968909	   
df.mm.trans2:probe2	0.135866560359867	0.0860204258794977	1.57946858517298	0.114547728496787	   
df.mm.trans2:probe3	0.0695898540847252	0.0860204258794977	0.80899220590015	0.418713807531308	   
df.mm.trans2:probe4	0.139740845264430	0.0860204258794977	1.62450771239133	0.104585480870447	   
df.mm.trans2:probe5	0.220642034656043	0.0860204258794977	2.56499584139622	0.0104639549984280	*  
df.mm.trans2:probe6	0.135532994671733	0.0860204258794977	1.57559083538594	0.115439289029296	   
df.mm.trans3:probe2	-0.0360935331533977	0.0860204258794977	-0.419592588438932	0.674874046571093	   
df.mm.trans3:probe3	0.0197488653765558	0.0860204258794977	0.229583441079693	0.81846285067816	   
df.mm.trans3:probe4	0.117828271991677	0.0860204258794977	1.36977085136428	0.171068719422131	   
df.mm.trans3:probe5	0.0942348426151222	0.0860204258794977	1.09549379291765	0.273566524640439	   
df.mm.trans3:probe6	0.0454779504791353	0.0860204258794977	0.528687808903008	0.59714041736981