fitVsDatCorrelation=0.805457534558967
cont.fitVsDatCorrelation=0.279317382603845

fstatistic=5381.20358722587,43,485
cont.fstatistic=2043.04096871427,43,485

residuals=-0.721789675349172,-0.100115008805721,-0.0099803766219424,0.102752788287007,1.4962809121377
cont.residuals=-0.672409079417988,-0.213479772447626,-0.0595563190159283,0.145078875057035,1.67935726351539

predictedValues:
Include	Exclude	Both
Lung	80.8125821039378	77.1373438355682	95.3386208306354
cerebhem	100.725771395857	150.984175229532	101.154106178509
cortex	84.0740985449634	98.6205193397997	167.010222526554
heart	85.9698988338449	85.9059701610216	103.084907282452
kidney	76.908965391145	89.531333098011	153.091792106613
liver	74.9701854272415	74.0628714688817	107.449610450084
stomach	101.711326948341	86.4167911683943	98.1873963766232
testicle	81.2202202749357	89.9757473160805	126.923029111760


diffExp=3.6752382683696,-50.2584038336758,-14.5464207948363,0.0639286728232662,-12.6223677068660,0.907313958359751,15.2945357799464,-8.75552704114473
diffExpScore=1.57824284334666
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	108.155824688960	86.2339658014014	87.5020435467644
cerebhem	99.1838061704577	83.4005462634914	97.4332232687927
cortex	88.5487816050825	110.494450722541	89.4186690819858
heart	99.1457424457638	104.373453731352	76.8557065286438
kidney	99.1266394593807	95.0811058552583	107.258123835119
liver	96.4934405047227	87.0568026801735	108.392854103852
stomach	98.3160534121048	91.0992548786654	86.4499576552746
testicle	92.9613693942022	92.3047799281273	89.542641371492
cont.diffExp=21.9218588875588,15.7832599069663,-21.9456691174581,-5.22771128558865,4.04553360412244,9.4366378245492,7.2167985334394,0.656589466074863
cont.diffExpScore=2.62210836228071

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

tran.correlation=0.626608633683748
cont.tran.correlation=-0.585258254903348

tran.covariance=0.0158874525320604
cont.tran.covariance=-0.00330885329888533

tran.mean=89.9392375335972
cont.tran.mean=95.7485010963553

weightedLogRatios:
wLogRatio
Lung	0.203349263175103
cerebhem	-1.94889797587412
cortex	-0.719947065484888
heart	0.0033130231469351
kidney	-0.671478694188618
liver	0.0524914550257981
stomach	0.739930905162435
testicle	-0.455404172756319

cont.weightedLogRatios:
wLogRatio
Lung	1.03521771154459
cerebhem	0.781727299989945
cortex	-1.01722277380080
heart	-0.237513500077052
kidney	0.190654363560017
liver	0.464968299388712
stomach	0.346886751451106
testicle	0.0320994392509268

varWeightedLogRatios=0.655538142754877
cont.varWeightedLogRatios=0.404157887389694

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53585438299989	0.113342379187264	40.019050381021	8.87568031706662e-156	***
df.mm.trans1	-0.189591228396691	0.0985759631770407	-1.92330079551126	0.0550273772804068	.  
df.mm.trans2	-0.294185549532978	0.0925436650806147	-3.1788837115616	0.00157291919417808	** 
df.mm.exp2	0.832646355477672	0.125742079951619	6.6218592518753	9.43202014346003e-11	***
df.mm.exp3	-0.275362348681699	0.125742079951619	-2.18989815332821	0.0290071949116887	*  
df.mm.exp4	0.0914123503071732	0.125742079951619	0.726982966580044	0.467587135052516	   
df.mm.exp5	-0.374111788424976	0.125742079951619	-2.97523143063102	0.00307375149773384	** 
df.mm.exp6	-0.235302351671525	0.125742079951619	-1.87130952312909	0.061903985860708	.  
df.mm.exp7	0.31415760365335	0.125742079951619	2.49842855927170	0.0128044194545706	*  
df.mm.exp8	-0.127161646446928	0.125742079951619	-1.01128951020895	0.312382131993317	   
df.mm.trans1:exp2	-0.612377338506902	0.114786289361866	-5.33493452842933	1.46932316973036e-07	***
df.mm.trans2:exp2	-0.161058843109852	0.102667978359238	-1.56873492284325	0.117361880526573	   
df.mm.trans1:exp3	0.314928211703988	0.114786289361866	2.74360477592554	0.00630197095140092	** 
df.mm.trans2:exp3	0.521054176382772	0.102667978359238	5.07513817560127	5.5268364715714e-07	***
df.mm.trans1:exp4	-0.0295478013948809	0.114786289361866	-0.257415772904121	0.796967005008799	   
df.mm.trans2:exp4	0.0162534584095848	0.102667978359238	0.158310883971179	0.874277725683376	   
df.mm.trans1:exp5	0.324601570689239	0.114786289361866	2.82787754960808	0.00487963100404479	** 
df.mm.trans2:exp5	0.523112923768181	0.102667978359238	5.09519065367973	4.99941579951646e-07	***
df.mm.trans1:exp6	0.160260186023768	0.114786289361866	1.39616139623212	0.163304593341579	   
df.mm.trans2:exp6	0.194629179451189	0.102667978359238	1.89571454081015	0.0585920003171695	.  
df.mm.trans1:exp7	-0.0841516032230427	0.114786289361866	-0.733115459092443	0.463842197408896	   
df.mm.trans2:exp7	-0.200563123649359	0.102667978359238	-1.95351196015163	0.0513334858730393	.  
df.mm.trans1:exp8	0.132193208266687	0.114786289361867	1.15164632467511	0.25003382664849	   
df.mm.trans2:exp8	0.281114287045906	0.102667978359238	2.73809119005227	0.00640693893179571	** 
df.mm.trans1:probe2	0.176344958377047	0.0628710399758095	2.80486784447813	0.00523583601785324	** 
df.mm.trans1:probe3	0.13309852193823	0.0628710399758095	2.11700843487624	0.0347676274635048	*  
df.mm.trans1:probe4	0.179380988441880	0.0628710399758095	2.85315764636467	0.00451374594743994	** 
df.mm.trans1:probe5	0.200488190419847	0.0628710399758095	3.18887981647810	0.00152061748376134	** 
df.mm.trans1:probe6	0.111030789033868	0.0628710399758095	1.76600846870973	0.0780235030950457	.  
df.mm.trans1:probe7	-0.196617880397365	0.0628710399758095	-3.12732031270703	0.00187009108177382	** 
df.mm.trans1:probe8	-0.0245305960000783	0.0628710399758095	-0.390173218218067	0.696579762191071	   
df.mm.trans1:probe9	0.00498730913852213	0.0628710399758095	0.0793260162459705	0.936806032461673	   
df.mm.trans1:probe10	0.0743394367941349	0.0628710399758095	1.18241143812378	0.237621835426637	   
df.mm.trans1:probe11	-0.146492907354296	0.0628710399758095	-2.33005382781421	0.0202126960507614	*  
df.mm.trans1:probe12	0.221883476152658	0.0628710399758095	3.52918412416957	0.000456551342160327	***
df.mm.trans2:probe2	0.151619319336401	0.0628710399758095	2.41159235467934	0.0162535595195784	*  
df.mm.trans2:probe3	0.204850123414449	0.0628710399758095	3.25825886597817	0.00119962160598888	** 
df.mm.trans2:probe4	0.235772307009868	0.0628710399758095	3.75009395582742	0.00019811042351808	***
df.mm.trans2:probe5	0.198057065981446	0.0628710399758095	3.15021138599984	0.00173229635389259	** 
df.mm.trans2:probe6	0.248888041096688	0.0628710399758095	3.95870723933389	8.66368125531076e-05	***
df.mm.trans3:probe2	0.261037890435085	0.0628710399758095	4.15195757117304	3.89464553044556e-05	***
df.mm.trans3:probe3	1.10279605193721	0.0628710399758095	17.5406045829929	1.05934940438902e-53	***
df.mm.trans3:probe4	0.657595572835313	0.0628710399758095	10.4594352676261	3.16817406381852e-23	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.74772138749672	0.18363381299093	25.8542874548448	2.72186682844676e-93	***
df.mm.trans1	0.0176874445747429	0.159709723029068	0.110747450056774	0.911862430791154	   
df.mm.trans2	-0.327537558824986	0.149936380449816	-2.18451024255994	0.0294028296825740	*  
df.mm.exp2	-0.227512633246386	0.203723424199312	-1.11677208519625	0.264644731626878	   
df.mm.exp3	0.0262144225079497	0.203723424199312	0.128676526084222	0.897666951384576	   
df.mm.exp4	0.233661563957206	0.203723424199312	1.1469548230674	0.251965792608416	   
df.mm.exp5	-0.193084804729421	0.203723424199312	-0.947779105364521	0.343713907932195	   
df.mm.exp6	-0.318701318734351	0.203723424199312	-1.56438229912408	0.118380033930877	   
df.mm.exp7	-0.0284037684386403	0.203723424199312	-0.139423183908649	0.889173629320762	   
df.mm.exp8	-0.106409996104484	0.203723424199312	-0.52232577830804	0.601682010099724	   
df.mm.trans1:exp2	0.140914381510099	0.185973191543595	0.757713412027273	0.448990555146888	   
df.mm.trans2:exp2	0.194103357634240	0.166339479313627	1.16691093680933	0.243819757008385	   
df.mm.trans1:exp3	-0.226233826226208	0.185973191543595	-1.21648622765704	0.224391431899127	   
df.mm.trans2:exp3	0.221686742605935	0.166339479313627	1.33273678335828	0.183243955968423	   
df.mm.trans1:exp4	-0.320643658902202	0.185973191543595	-1.72413914199584	0.0853199304953319	.  
df.mm.trans2:exp4	-0.0427503303231961	0.166339479313627	-0.257006517632486	0.797282735048922	   
df.mm.trans1:exp5	0.105910015408551	0.185973191543595	0.569490766542682	0.569286795209476	   
df.mm.trans2:exp5	0.290750943063739	0.166339479313627	1.74793707581313	0.0811077079894847	.  
df.mm.trans1:exp6	0.204603342272724	0.185973191543595	1.10017653928772	0.271800969018619	   
df.mm.trans2:exp6	0.32819799383206	0.166339479313627	1.97306132727069	0.0490559503772813	*  
df.mm.trans1:exp7	-0.0669819157960225	0.185973191543595	-0.360169738659999	0.718876995291634	   
df.mm.trans2:exp7	0.0832892586352994	0.166339479313627	0.500718524423542	0.616796381830368	   
df.mm.trans1:exp8	-0.0449789883475616	0.185973191543595	-0.241857377260840	0.80899290513557	   
df.mm.trans2:exp8	0.174441788265326	0.166339479313627	1.04870947645822	0.294834154172889	   
df.mm.trans1:probe2	-0.181257855257252	0.101861712099656	-1.77945031082846	0.0757920872905702	.  
df.mm.trans1:probe3	-0.0609126142679366	0.101861712099656	-0.597993230354729	0.550123576899687	   
df.mm.trans1:probe4	-0.192512334758327	0.101861712099656	-1.88993814054474	0.0593622704047532	.  
df.mm.trans1:probe5	-0.0806883187590781	0.101861712099656	-0.792135897736896	0.428668708115821	   
df.mm.trans1:probe6	-0.136235930714016	0.101861712099656	-1.33745965884345	0.181699506330468	   
df.mm.trans1:probe7	-0.129242472321626	0.101861712099656	-1.26880325941491	0.205119846031896	   
df.mm.trans1:probe8	-0.0960602876612348	0.101861712099656	-0.943046073751978	0.346126851086018	   
df.mm.trans1:probe9	-0.163053023436438	0.101861712099656	-1.60072926397424	0.110088027211533	   
df.mm.trans1:probe10	-0.00328865057196407	0.101861712099656	-0.0322854437077067	0.974257699252395	   
df.mm.trans1:probe11	-0.122864136273357	0.101861712099656	-1.20618565838706	0.228333968060792	   
df.mm.trans1:probe12	-0.143257553891807	0.101861712099656	-1.40639255848804	0.16024797596519	   
df.mm.trans2:probe2	0.184333909174799	0.101861712099656	1.80964864398173	0.0709691977737374	.  
df.mm.trans2:probe3	0.0275456742722303	0.101861712099656	0.270422258809877	0.786950453855329	   
df.mm.trans2:probe4	0.131834969694975	0.101861712099656	1.29425440607159	0.196193471966363	   
df.mm.trans2:probe5	0.0266401561533789	0.101861712099656	0.261532577886730	0.793792856515707	   
df.mm.trans2:probe6	-0.00155164723919498	0.101861712099656	-0.0152328800214641	0.987852654335233	   
df.mm.trans3:probe2	0.0860738114499261	0.101861712099656	0.84500652576619	0.398523844457338	   
df.mm.trans3:probe3	0.0177593051331983	0.101861712099656	0.174347208260387	0.861665352109324	   
df.mm.trans3:probe4	0.03133038207501	0.101861712099656	0.307577611147534	0.758535792729896