fitVsDatCorrelation=0.91707468705673
cont.fitVsDatCorrelation=0.256390086014654

fstatistic=4449.11150693669,61,899
cont.fstatistic=744.829133289808,61,899

residuals=-0.84385847415324,-0.140983757699239,-0.00492670969311029,0.130236749467557,1.2505999313986
cont.residuals=-1.06566960319509,-0.486333943139129,-0.171186090424412,0.402389031476803,2.0294375608349

predictedValues:
Include	Exclude	Both
Lung	80.564999444346	48.1110457097205	55.9744179969424
cerebhem	73.0717529954955	59.5953546665144	67.235314591449
cortex	80.1160663775825	46.3234749092151	57.4695230256426
heart	88.3248745401327	47.4032598324896	58.242580157817
kidney	150.190605170112	50.2829503584344	78.314444417026
liver	266.271793790468	52.7202390201729	117.106703966152
stomach	100.673754894331	45.685426255051	56.2631190137508
testicle	155.962521045457	52.6972304064916	92.4286410261544


diffExp=32.4539537346255,13.4763983289812,33.7925914683673,40.921614707643,99.9076548116777,213.551554770295,54.9883286392795,103.265290638965
diffExpScore=0.99831467506474
diffExp1.5=1,0,1,1,1,1,1,1
diffExp1.5Score=0.875
diffExp1.4=1,0,1,1,1,1,1,1
diffExp1.4Score=0.875
diffExp1.3=1,0,1,1,1,1,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	80.8929862923848	99.0323030009019	91.8752402108087
cerebhem	92.5027834297496	97.5507957145079	87.5542269843219
cortex	104.879412376517	80.5778447994466	94.2561738690236
heart	76.3844920389183	84.3324079818376	87.6127374610413
kidney	100.089298474221	76.8688397035872	88.5172696340302
liver	96.0421725736729	83.9908814363843	90.067402462634
stomach	87.1596868626665	98.1591661696139	100.971342817394
testicle	80.8177088588284	76.3048483649593	87.3627085738508
cont.diffExp=-18.1393167085170,-5.04801228475834,24.3015675770702,-7.94791594291932,23.2204587706337,12.0512911372885,-10.9994793069474,4.51286049386914
cont.diffExpScore=4.62806859404689

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=0,0,1,0,1,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,0,1,0,1,0,0,0
cont.diffExp1.2Score=1.5

tran.correlation=0.20212464398056
cont.tran.correlation=-0.284387273610125

tran.covariance=0.00718937154343118
cont.tran.covariance=-0.00324933136754527

tran.mean=87.3747093385008
cont.tran.mean=88.4741017548873

weightedLogRatios:
wLogRatio
Lung	2.12989593867228
cerebhem	0.854091250821916
cortex	2.25133168091585
heart	2.59502995064603
kidney	4.88554312855409
liver	7.73280801928243
stomach	3.33174358057572
testicle	4.89043000878781

cont.weightedLogRatios:
wLogRatio
Lung	-0.909279417272422
cerebhem	-0.241964152953806
cortex	1.19168371130358
heart	-0.434082978227421
kidney	1.18098836857958
liver	0.603054222138967
stomach	-0.538046396219666
testicle	0.250723162501109

varWeightedLogRatios=4.71398600199434
cont.varWeightedLogRatios=0.636077219134554

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.5656770204893	0.124666188302106	28.6017970794819	1.83656096893126e-128	***
df.mm.trans1	0.618974864853542	0.107235411238808	5.7721125671362	1.07724678120614e-08	***
df.mm.trans2	0.316139231010767	0.0943267799546354	3.35153210109375	0.000837165069601624	***
df.mm.exp2	-0.0668604504384543	0.120400396458114	-0.55531752722853	0.578815642938543	   
df.mm.exp3	-0.0698108414462275	0.120400396458114	-0.579822355240451	0.562179698672227	   
df.mm.exp4	0.037414735381592	0.120400396458114	0.310752592867153	0.756060745614165	   
df.mm.exp5	0.33115785631124	0.120400396458114	2.75047147727996	0.0060705011733619	** 
df.mm.exp6	0.548750141812548	0.120400396458114	4.55771042251886	5.88584300984626e-06	***
df.mm.exp7	0.165943913462113	0.120400396458114	1.37826716808066	0.168463724460632	   
df.mm.exp8	0.250060398918666	0.120400396458114	2.07690677335651	0.0380936769563475	*  
df.mm.trans1:exp2	-0.0307619785425473	0.110752256493762	-0.277754869439432	0.781264399134441	   
df.mm.trans2:exp2	0.280926288352034	0.0794913868812061	3.53404688701503	0.000430107544237766	***
df.mm.trans1:exp3	0.0642229492747327	0.110752256493762	0.579879374993593	0.562141260183626	   
df.mm.trans2:exp3	0.0319479002430390	0.0794913868812061	0.401903923135506	0.687850262743704	   
df.mm.trans1:exp4	0.0545427322996527	0.110752256493762	0.492475133477076	0.62250374320321	   
df.mm.trans2:exp4	-0.0522355274910638	0.0794913868812061	-0.657121853580513	0.511270757209165	   
df.mm.trans1:exp5	0.291683027133117	0.110752256493762	2.6336531314786	0.00859228766201708	** 
df.mm.trans2:exp5	-0.287003587025104	0.0794913868812062	-3.61049917840795	0.000322510680549504	***
df.mm.trans1:exp6	0.6467031213447	0.110752256493762	5.83918686461368	7.32373872664181e-09	***
df.mm.trans2:exp6	-0.45726250914511	0.0794913868812061	-5.7523528911183	1.20603645320644e-08	***
df.mm.trans1:exp7	0.0568769204947988	0.110752256493762	0.513550895443854	0.60769220558406	   
df.mm.trans2:exp7	-0.217676358031682	0.0794913868812061	-2.7383640740472	0.0062966388887179	** 
df.mm.trans1:exp8	0.410491024629109	0.110752256493762	3.70638971723551	0.000223092077821870	***
df.mm.trans2:exp8	-0.159009289991967	0.0794913868812061	-2.00033357361841	0.0457646275632565	*  
df.mm.trans1:probe2	-0.0312274674459322	0.0783136715984509	-0.398748606833931	0.690173139536388	   
df.mm.trans1:probe3	-0.174306794264190	0.0783136715984509	-2.22575178390229	0.0262781091608621	*  
df.mm.trans1:probe4	-0.112231805210338	0.0783136715984509	-1.43310616038794	0.152175159004328	   
df.mm.trans1:probe5	0.0731793545715813	0.0783136715984509	0.934439071466404	0.350328366520013	   
df.mm.trans1:probe6	-0.328003006436532	0.0783136715984509	-4.18832369548896	3.08704546602383e-05	***
df.mm.trans1:probe7	-0.211581482579998	0.0783136715984509	-2.70171833680421	0.00702811577028792	** 
df.mm.trans1:probe8	0.277962711893947	0.0783136715984509	3.54935104204009	0.00040619200805133	***
df.mm.trans1:probe9	-0.420303712572256	0.0783136715984509	-5.36692641263636	1.01962495608645e-07	***
df.mm.trans1:probe10	0.105236212573324	0.0783136715984509	1.34377830109814	0.179359135802009	   
df.mm.trans1:probe11	0.660553062093218	0.0783136715984509	8.43470940144612	1.3135297857878e-16	***
df.mm.trans1:probe12	0.67307475726966	0.0783136715984509	8.59460096215146	3.66414095519698e-17	***
df.mm.trans1:probe13	0.704004847405194	0.0783136715984509	8.98955230977984	1.43836730644495e-18	***
df.mm.trans1:probe14	0.717976755409472	0.0783136715984509	9.16796187376911	3.20543271066434e-19	***
df.mm.trans1:probe15	0.5357141268085	0.0783136715984509	6.84062074825639	1.45660011528693e-11	***
df.mm.trans1:probe16	0.711735953151502	0.0783136715984509	9.08827205549614	6.28619023930852e-19	***
df.mm.trans1:probe17	0.749168538978186	0.0783136715984509	9.56625482737557	1.03109763889794e-20	***
df.mm.trans1:probe18	1.11526364445566	0.0783136715984509	14.2409827261594	1.19988007399426e-41	***
df.mm.trans1:probe19	0.653985083298526	0.0783136715984509	8.35084181280376	2.54583107647325e-16	***
df.mm.trans1:probe20	0.571734279376478	0.0783136715984509	7.30056793030998	6.30526142481258e-13	***
df.mm.trans1:probe21	0.426159528347448	0.0783136715984509	5.44170027594361	6.80766345383466e-08	***
df.mm.trans1:probe22	0.251927685591085	0.0783136715984509	3.21690556002572	0.00134213621273818	** 
df.mm.trans2:probe2	-0.0722162449429352	0.0783136715984509	-0.92214096809584	0.356702453759719	   
df.mm.trans2:probe3	0.0198447095966592	0.0783136715984509	0.253400321956706	0.800016814921561	   
df.mm.trans2:probe4	-0.0954051770593406	0.0783136715984509	-1.21824421090261	0.223450796859392	   
df.mm.trans2:probe5	0.0233924021834504	0.0783136715984509	0.298701385160354	0.765236929624628	   
df.mm.trans2:probe6	-0.0250959736833988	0.0783136715984509	-0.320454566503753	0.748698235567075	   
df.mm.trans3:probe2	-0.539609712713809	0.0783136715984509	-6.89036411778302	1.04602812013291e-11	***
df.mm.trans3:probe3	-0.629632210368931	0.0783136715984509	-8.03987602059237	2.82185057600826e-15	***
df.mm.trans3:probe4	-0.391404137181985	0.0783136715984509	-4.99790303778488	6.96554463334183e-07	***
df.mm.trans3:probe5	-0.550957194951447	0.0783136715984509	-7.03526196264237	3.94067026706072e-12	***
df.mm.trans3:probe6	-0.217913585726653	0.0783136715984509	-2.78257399096282	0.0055059464539624	** 
df.mm.trans3:probe7	-0.058541102762348	0.0783136715984509	-0.74752085513899	0.454944695591114	   
df.mm.trans3:probe8	-0.639586336468318	0.0783136715984509	-8.16698187447732	1.06539100840841e-15	***
df.mm.trans3:probe9	-0.770812452199652	0.0783136715984509	-9.8426294728199	8.87189414073943e-22	***
df.mm.trans3:probe10	-0.606079398693198	0.0783136715984509	-7.73912634055567	2.68693391720743e-14	***
df.mm.trans3:probe11	-0.680401225971557	0.0783136715984509	-8.68815383168698	1.72020880756469e-17	***
df.mm.trans3:probe12	-0.625818860678353	0.0783136715984509	-7.99118273865648	4.08432348377265e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.51694596331662	0.302218079235199	14.9459819701962	2.81994718599788e-45	***
df.mm.trans1	-0.191063110376645	0.259962067116813	-0.734965345120106	0.462552274553208	   
df.mm.trans2	0.0521306512509553	0.228668724427903	0.227974557436217	0.819717859680571	   
df.mm.exp2	0.167211978782557	0.291876867756235	0.572885340547803	0.566865583803225	   
df.mm.exp3	0.0278769984326965	0.291876867756235	0.0955094476893536	0.92393147115474	   
df.mm.exp4	-0.170522115015122	0.291876867756235	-0.584226205817502	0.559214699603789	   
df.mm.exp5	-0.00317595092260375	0.291876867756235	-0.0108811326742624	0.991320697676937	   
df.mm.exp6	0.0267956829123542	0.291876867756235	0.0918047501274851	0.926873622122958	   
df.mm.exp7	-0.0286461610134336	0.291876867756235	-0.0981446773553763	0.921839288859167	   
df.mm.exp8	-0.211277568050474	0.291876867756235	-0.72385855609128	0.469340887737254	   
df.mm.trans1:exp2	-0.0331003678755876	0.268487668423753	-0.123284499693838	0.901909370795032	   
df.mm.trans2:exp2	-0.182284844606367	0.192704490176303	-0.945929409530605	0.344438761109710	   
df.mm.trans1:exp3	0.231807113898598	0.268487668423753	0.863380859387321	0.388158375785493	   
df.mm.trans2:exp3	-0.234099354975916	0.192704490176303	-1.21481006883515	0.224757509784153	   
df.mm.trans1:exp4	0.113174682508699	0.268487668423753	0.421526557152993	0.673471390386449	   
df.mm.trans2:exp4	0.00984225261592467	0.192704490176303	0.0510743294404822	0.959277637026187	   
df.mm.trans1:exp5	0.216111598888123	0.268487668423753	0.804921880237103	0.421077581522356	   
df.mm.trans2:exp5	-0.250169549949716	0.192704490176303	-1.29820301395592	0.194550524856263	   
df.mm.trans1:exp6	0.144864585443839	0.268487668423753	0.539557687302049	0.589635711358989	   
df.mm.trans2:exp6	-0.191533534141275	0.192704490176303	-0.9939235664205	0.320527521061239	   
df.mm.trans1:exp7	0.103260954119809	0.268487668423753	0.384602222984904	0.700623107842042	   
df.mm.trans2:exp7	0.0197903769243959	0.192704490176303	0.102698058079964	0.9182255176906	   
df.mm.trans1:exp8	0.210346554317294	0.268487668423753	0.78344959212542	0.433569489675011	   
df.mm.trans2:exp8	-0.0494320420825561	0.192704490176303	-0.256517334065913	0.797610087066736	   
df.mm.trans1:probe2	0.110176556902995	0.189849451007401	0.580336452480444	0.561833178410478	   
df.mm.trans1:probe3	0.0731902539801648	0.189849451007401	0.385517332769699	0.699945368534807	   
df.mm.trans1:probe4	-0.122765283957996	0.189849451007401	-0.646645451469913	0.518026487031723	   
df.mm.trans1:probe5	-0.0345245465630112	0.189849451007401	-0.181852232807696	0.855739686547342	   
df.mm.trans1:probe6	0.0262922498514837	0.189849451007401	0.138489996741991	0.889884189283041	   
df.mm.trans1:probe7	-0.0459173079698973	0.189849451007401	-0.241861684225294	0.80894249871591	   
df.mm.trans1:probe8	0.057543473332306	0.189849451007401	0.303100551657970	0.7618833572895	   
df.mm.trans1:probe9	0.29725058397494	0.189849451007401	1.56571737446506	0.117766706521601	   
df.mm.trans1:probe10	0.089639057249172	0.189849451007401	0.472158632924767	0.636928149421123	   
df.mm.trans1:probe11	0.0871805941035937	0.189849451007401	0.459209092472936	0.64619503298429	   
df.mm.trans1:probe12	0.34775846469995	0.189849451007401	1.83175912732238	0.067318046564899	.  
df.mm.trans1:probe13	-0.0259659066505289	0.189849451007401	-0.136771038908702	0.8912423985559	   
df.mm.trans1:probe14	-0.0467246861618523	0.189849451007401	-0.246114412835625	0.805649846292303	   
df.mm.trans1:probe15	-0.0647319278590411	0.189849451007401	-0.340964524867221	0.733209955969082	   
df.mm.trans1:probe16	0.202146115848014	0.189849451007401	1.06477061047774	0.287265641128256	   
df.mm.trans1:probe17	0.433881658683178	0.189849451007401	2.28539854279728	0.0225215204378134	*  
df.mm.trans1:probe18	0.355573763302030	0.189849451007401	1.87292489609659	0.0614030902554006	.  
df.mm.trans1:probe19	0.329321555703487	0.189849451007401	1.7346458151762	0.0831463055491364	.  
df.mm.trans1:probe20	0.0792934486408392	0.189849451007401	0.417664882463885	0.676291882616476	   
df.mm.trans1:probe21	0.152794367645331	0.189849451007401	0.804818590912727	0.421137161109399	   
df.mm.trans1:probe22	-0.0151072588165225	0.189849451007401	-0.0795749407562603	0.936593035715558	   
df.mm.trans2:probe2	-0.0829616594753328	0.189849451007401	-0.436986565065699	0.662226033049991	   
df.mm.trans2:probe3	0.0615257657333667	0.189849451007401	0.324076605999605	0.745955419864809	   
df.mm.trans2:probe4	0.184134551689596	0.189849451007401	0.969897730609806	0.332358336512646	   
df.mm.trans2:probe5	0.0378748031811792	0.189849451007401	0.199499145139496	0.841917425493698	   
df.mm.trans2:probe6	0.27407709397339	0.189849451007401	1.44365491982753	0.149184440507837	   
df.mm.trans3:probe2	0.155619072724563	0.189849451007401	0.819697249050758	0.412605978446042	   
df.mm.trans3:probe3	0.0302818717526052	0.189849451007401	0.159504657990424	0.873307116426434	   
df.mm.trans3:probe4	0.27684361426086	0.189849451007401	1.45822709937712	0.14512720962127	   
df.mm.trans3:probe5	0.242833250418025	0.189849451007401	1.27908323742563	0.201197796539948	   
df.mm.trans3:probe6	0.131224094234263	0.189849451007401	0.691200809578048	0.489617795671041	   
df.mm.trans3:probe7	-0.135140405535864	0.189849451007401	-0.71182931959385	0.4767551467991	   
df.mm.trans3:probe8	0.123545568091076	0.189849451007401	0.650755466689551	0.515370633771896	   
df.mm.trans3:probe9	0.258508390311819	0.189849451007401	1.36164939608775	0.173649689592568	   
df.mm.trans3:probe10	0.0968869339537388	0.189849451007401	0.510335602445128	0.609941640733569	   
df.mm.trans3:probe11	0.304191408754955	0.189849451007401	1.60227699970066	0.109445649002657	   
df.mm.trans3:probe12	0.224223015570781	0.189849451007401	1.18105696055997	0.237892452313898