fitVsDatCorrelation=0.87537260283272
cont.fitVsDatCorrelation=0.278988230098047

fstatistic=9991.33751426548,53,715
cont.fstatistic=2522.23249397563,53,715

residuals=-0.494969437430216,-0.0828077250457794,-0.000145694002319082,0.0734264701998976,0.80647428533647
cont.residuals=-0.565325150260302,-0.216347091097119,-0.0798347131483106,0.192904769740117,1.24459948084101

predictedValues:
Include	Exclude	Both
Lung	55.8538615582688	53.7838095212998	65.613585232125
cerebhem	62.488999461291	76.9013578857555	67.5478100390848
cortex	53.2644159589012	48.154454582795	66.0635624851193
heart	55.582441682128	49.272373717394	64.1782019156445
kidney	55.911241607269	50.0384273512577	66.7458497465682
liver	56.4486564432627	50.0628468742533	66.3207787438189
stomach	56.3282671463524	52.2909903460193	82.2518039678997
testicle	56.0614635634707	54.779372416335	63.8565444501649


diffExp=2.07005203696895,-14.4123584244645,5.10996137610618,6.31006796473402,5.87281425601132,6.38580956900939,4.03727680033307,1.28209114713567
diffExpScore=2.57596094414775
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,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	56.5954250239794	61.4329361189732	57.5249403138194
cerebhem	62.5928752051689	56.2626678663306	60.1576518650489
cortex	63.0239235739592	54.9591263517755	56.5528962766995
heart	56.4443959603614	52.1051734510394	54.500171864478
kidney	62.1034590082386	58.7923920759971	63.132969050438
liver	57.8126818929268	55.8018401108927	58.3272876437434
stomach	59.4342550995093	58.0359242334274	55.5853990448898
testicle	55.8763189552942	55.930890567149	53.3451912828318
cont.diffExp=-4.83751109499378,6.33020733883834,8.06479722218366,4.33922250932196,3.31106693224154,2.01084178203416,1.39833086608184,-0.0545716118547972
cont.diffExpScore=1.40738377707078

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=0.94784533005028
cont.tran.correlation=0.0412752347060279

tran.covariance=0.00639250870549248
cont.tran.covariance=0.000146531453539698

tran.mean=55.4514362572533
cont.tran.mean=57.9502678434389

weightedLogRatios:
wLogRatio
Lung	0.151210334762196
cerebhem	-0.879681969924086
cortex	0.395839224044293
heart	0.47690770570189
kidney	0.440376535262154
liver	0.477004584921443
stomach	0.297044140322123
testicle	0.0928840558989737

cont.weightedLogRatios:
wLogRatio
Lung	-0.334382059326376
cerebhem	0.435366812288937
cortex	0.557975045625092
heart	0.319427941451310
kidney	0.224712927026167
liver	0.143003880741746
stomach	0.0969713981762468
testicle	-0.00392775965854873

varWeightedLogRatios=0.205110697889467
cont.varWeightedLogRatios=0.0765996336038597

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.53853789318971	0.0790656623783748	44.754420398779	1.63427930208986e-209	***
df.mm.trans1	0.405811445582753	0.070211838372372	5.77981512790638	1.11675363033292e-08	***
df.mm.trans2	0.385354525450076	0.0638667973782892	6.0337223920527	2.57081421529279e-09	***
df.mm.exp2	0.440750040897828	0.0861019336570547	5.11893313166859	3.95369645616355e-07	***
df.mm.exp3	-0.164863589553640	0.0861019336570547	-1.91474897892879	0.0559232833003153	.  
df.mm.exp4	-0.0703610934523082	0.0861019336570547	-0.817183662013416	0.414095682864888	   
df.mm.exp5	-0.0882637818248797	0.0861019336570547	-1.0251080094953	0.305658910895641	   
df.mm.exp6	-0.0718209917335646	0.0861019336570547	-0.834139126533775	0.404481177752753	   
df.mm.exp7	-0.245693126607237	0.0861019336570547	-2.85351462123762	0.00444886595214682	** 
df.mm.exp8	0.0491949034578212	0.0861019336570547	0.571356546459982	0.567937527427966	   
df.mm.trans1:exp2	-0.328498172989559	0.0817578255011847	-4.0179416584997	6.49293259641319e-05	***
df.mm.trans2:exp2	-0.0831989903408923	0.06884625020443	-1.20847526326915	0.227264018872893	   
df.mm.trans1:exp3	0.117393415014003	0.0817578255011847	1.43586762850367	0.1514773508541	   
df.mm.trans2:exp3	0.0543047546074164	0.0688462502044301	0.788783041141172	0.430500279587811	   
df.mm.trans1:exp4	0.0654897830270104	0.0817578255011847	0.801021585708165	0.423385245113422	   
df.mm.trans2:exp4	-0.0172478370203331	0.06884625020443	-0.250526890994323	0.80225188240939	   
df.mm.trans1:exp5	0.0892905791420466	0.0817578255011847	1.09213495582454	0.275141642347265	   
df.mm.trans2:exp5	0.0160825554862154	0.06884625020443	0.233601037652165	0.815361586543786	   
df.mm.trans1:exp6	0.0824138164946424	0.0817578255011847	1.00802358660393	0.31378414414499	   
df.mm.trans2:exp6	0.000127661751243971	0.0688462502044301	0.00185430217135858	0.99852099900648	   
df.mm.trans1:exp7	0.254150951781369	0.0817578255011847	3.10858257571547	0.001954412667916	** 
df.mm.trans2:exp7	0.217544730508664	0.0688462502044301	3.15986317138105	0.00164495944671651	** 
df.mm.trans1:exp8	-0.0454849156952759	0.0817578255011847	-0.556337150804199	0.578154460228621	   
df.mm.trans2:exp8	-0.0308536797372876	0.06884625020443	-0.448153380113973	0.654178269602848	   
df.mm.trans1:probe2	0.0741285239931685	0.0447806052795700	1.65537119318456	0.0982880397680667	.  
df.mm.trans1:probe3	-0.154612854349382	0.0447806052795699	-3.45267450906745	0.000587736829092587	***
df.mm.trans1:probe4	0.277545864569877	0.0447806052795700	6.19790337439902	9.66383198735883e-10	***
df.mm.trans1:probe5	-0.0284082016013519	0.0447806052795699	-0.634386280042365	0.526031677263055	   
df.mm.trans1:probe6	-0.026124698859087	0.04478060527957	-0.583393160855862	0.559812630042972	   
df.mm.trans1:probe7	0.125589231299303	0.04478060527957	2.80454519351036	0.005175525398547	** 
df.mm.trans1:probe8	-0.122419354625158	0.0447806052795700	-2.73375837286882	0.00641647842294117	** 
df.mm.trans1:probe9	0.0245619674702081	0.0447806052795699	0.548495656029329	0.583522828214793	   
df.mm.trans1:probe10	-0.00381015364141590	0.0447806052795699	-0.08508490712952	0.932217707461186	   
df.mm.trans1:probe11	0.673537229782913	0.0447806052795700	15.0408246064998	1.26207510153829e-44	***
df.mm.trans1:probe12	0.333796169085287	0.0447806052795700	7.45403433029462	2.62267950029221e-13	***
df.mm.trans1:probe13	0.397360156249919	0.0447806052795700	8.87348783628891	5.63261388298134e-18	***
df.mm.trans1:probe14	0.566726199444746	0.0447806052795700	12.6556172232737	2.87112528087492e-33	***
df.mm.trans1:probe15	0.473124338697065	0.04478060527957	10.5653850755992	2.40864418188388e-24	***
df.mm.trans1:probe16	0.550400744016423	0.0447806052795699	12.2910519091963	1.23090176494910e-31	***
df.mm.trans1:probe17	-0.185098257913843	0.0447806052795700	-4.13344698577109	3.99731060302321e-05	***
df.mm.trans1:probe18	-0.22370134886099	0.0447806052795700	-4.99549631954279	7.38381341677228e-07	***
df.mm.trans1:probe19	-0.165616123353532	0.04478060527957	-3.69838956663433	0.000233601873245270	***
df.mm.trans1:probe20	-0.170916152292388	0.0447806052795699	-3.81674502221087	0.000146936564041555	***
df.mm.trans1:probe21	-0.217847112165450	0.0447806052795700	-4.86476479729133	1.41037000117274e-06	***
df.mm.trans1:probe22	-0.160093692221836	0.0447806052795700	-3.57506762631623	0.000373687374135337	***
df.mm.trans2:probe2	0.0849352907763407	0.04478060527957	1.89669814077056	0.0582705663818453	.  
df.mm.trans2:probe3	-0.0043382928667959	0.0447806052795700	-0.0968788349266718	0.922849765487534	   
df.mm.trans2:probe4	0.159925111791094	0.04478060527957	3.57130304051642	0.000379004380673868	***
df.mm.trans2:probe5	0.128035400531076	0.04478060527957	2.85917083370664	0.00437119690195967	** 
df.mm.trans2:probe6	0.242243139479701	0.04478060527957	5.40955482775081	8.62532743231608e-08	***
df.mm.trans3:probe2	0.217620210126963	0.04478060527957	4.85969782606415	1.44576050977628e-06	***
df.mm.trans3:probe3	-0.405739618519576	0.04478060527957	-9.060610413515	1.22385164821825e-18	***
df.mm.trans3:probe4	-0.395564982647198	0.04478060527957	-8.83339964204692	7.78656231518509e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.18055191924547	0.157051402349751	26.6190040757195	5.09158342376988e-109	***
df.mm.trans1	-0.0691843911194915	0.139464684747282	-0.496071039380742	0.61999674386003	   
df.mm.trans2	-0.107440699242942	0.126861266827142	-0.846914916822787	0.397325924725446	   
df.mm.exp2	-0.0319416319580773	0.171027839634771	-0.186762763455870	0.851899622439322	   
df.mm.exp3	0.0132720639973438	0.171027839634771	0.0776017753933288	0.938166540548169	   
df.mm.exp4	-0.113339265388395	0.171027839634771	-0.662694831615898	0.507739582555111	   
df.mm.exp5	-0.0440846377694546	0.171027839634771	-0.257762934172572	0.796664041540618	   
df.mm.exp6	-0.088710696059836	0.171027839634771	-0.518691554832694	0.604136339492748	   
df.mm.exp7	0.0263567239728385	0.171027839634771	0.154107799228027	0.877568225687072	   
df.mm.exp8	-0.0311820125249798	0.171027839634771	-0.182321267646067	0.855382288567048	   
df.mm.trans1:exp2	0.13266493699565	0.162398957547204	0.816907565167644	0.414253355039792	   
df.mm.trans2:exp2	-0.0559732560077352	0.136752160367467	-0.409304363874978	0.682438894248616	   
df.mm.trans1:exp3	0.0943141776034828	0.162398957547204	0.580756053043437	0.561587876784988	   
df.mm.trans2:exp3	-0.124628422491254	0.136752160367467	-0.91134518208973	0.36242067320502	   
df.mm.trans1:exp4	0.110667124683123	0.162398957547204	0.681452186359974	0.49580606494999	   
df.mm.trans2:exp4	-0.0513426025053152	0.136752160367467	-0.375442715986002	0.707442592420311	   
df.mm.trans1:exp5	0.136958173796234	0.162398957547204	0.843343922059504	0.399318165877914	   
df.mm.trans2:exp5	0.000150987632545067	0.136752160367467	0.00110409687232251	0.999119366287339	   
df.mm.trans1:exp6	0.109990705606371	0.162398957547204	0.677287017525345	0.498442943077599	   
df.mm.trans2:exp6	-0.00742856840661297	0.136752160367467	-0.0543213970927525	0.956694276568523	   
df.mm.trans1:exp7	0.0225858693764971	0.162398957547204	0.139076443085739	0.889428905496218	   
df.mm.trans2:exp7	-0.0832406320378587	0.136752160367467	-0.608697016662716	0.542918614179494	   
df.mm.trans1:exp8	0.018394518666795	0.162398957547204	0.113267467627976	0.909850302864576	   
df.mm.trans2:exp8	-0.0626472660875843	0.136752160367467	-0.458108054156107	0.647014005425085	   
df.mm.trans1:probe2	-0.0597173080191201	0.0889495723639274	-0.671361384119906	0.50220717413778	   
df.mm.trans1:probe3	-0.137127802815684	0.0889495723639274	-1.5416353240535	0.123604675590164	   
df.mm.trans1:probe4	-0.147336082635223	0.0889495723639274	-1.65640012334645	0.0980795555010136	.  
df.mm.trans1:probe5	-0.185556816386068	0.0889495723639274	-2.08609003342796	0.037324345522058	*  
df.mm.trans1:probe6	-0.0548776050521345	0.0889495723639274	-0.61695187052287	0.537462873212038	   
df.mm.trans1:probe7	-0.0451844067267232	0.0889495723639274	-0.507977784781878	0.61162559280949	   
df.mm.trans1:probe8	0.00763244699932991	0.0889495723639274	0.0858064496150986	0.931644301087896	   
df.mm.trans1:probe9	-0.0908673411485783	0.0889495723639274	-1.02156017992762	0.30733462600748	   
df.mm.trans1:probe10	-0.118076366420054	0.0889495723639274	-1.32745288461824	0.184782596125116	   
df.mm.trans1:probe11	0.0113249660711519	0.0889495723639274	0.127318949042464	0.898723760768814	   
df.mm.trans1:probe12	-0.093571716416192	0.0889495723639274	-1.05196364557385	0.293171618895139	   
df.mm.trans1:probe13	0.0408027628589513	0.0889495723639274	0.45871792044161	0.646576146842823	   
df.mm.trans1:probe14	-0.212452876467795	0.0889495723639274	-2.3884642817457	0.0171774294158675	*  
df.mm.trans1:probe15	-0.0731460626348311	0.0889495723639274	-0.822331807684943	0.411162224844328	   
df.mm.trans1:probe16	-0.0933064055839158	0.0889495723639274	-1.04898093497474	0.294541329878134	   
df.mm.trans1:probe17	-0.132302790079471	0.0889495723639274	-1.48739096280495	0.137352434407995	   
df.mm.trans1:probe18	-0.105466250111450	0.0889495723639274	-1.18568585894878	0.236140268689251	   
df.mm.trans1:probe19	-0.120787434375011	0.0889495723639274	-1.35793159163062	0.174913753110846	   
df.mm.trans1:probe20	-0.0212968897727643	0.0889495723639274	-0.239426556044928	0.81084345632329	   
df.mm.trans1:probe21	-0.216118339711269	0.0889495723639274	-2.42967261075798	0.0153576752141743	*  
df.mm.trans1:probe22	-0.113991461441709	0.0889495723639274	-1.28152905530928	0.200423334913306	   
df.mm.trans2:probe2	0.0818021921666902	0.0889495723639274	0.919646829014595	0.358067497379842	   
df.mm.trans2:probe3	0.163343133847366	0.0889495723639274	1.83635659516232	0.0667199419729545	.  
df.mm.trans2:probe4	-0.0740948694697564	0.0889495723639274	-0.832998602473381	0.405123677632700	   
df.mm.trans2:probe5	0.0750587757107266	0.0889495723639274	0.843835149691691	0.399043755552451	   
df.mm.trans2:probe6	0.202239669796726	0.0889495723639274	2.27364409318669	0.0232830732299889	*  
df.mm.trans3:probe2	0.129592458310210	0.0889495723639274	1.45692053223141	0.145577327821893	   
df.mm.trans3:probe3	0.0529961507371641	0.0889495723639274	0.595799949665144	0.551497424930446	   
df.mm.trans3:probe4	0.0105784688263701	0.0889495723639274	0.118926584414475	0.905366924360548