fitVsDatCorrelation=0.940130161780552
cont.fitVsDatCorrelation=0.221760319269011

fstatistic=9059.2165750501,63,945
cont.fstatistic=1093.53320307349,63,945

residuals=-0.83068470958456,-0.0926903730281121,-0.0105448591579188,0.0842916856280089,0.846920500709974
cont.residuals=-0.671605240401723,-0.302794528017856,-0.147206541058606,0.111263777543714,2.1571566685059

predictedValues:
Include	Exclude	Both
Lung	73.8816406344532	51.7396762501323	70.7476798227845
cerebhem	83.5127384181564	59.0916942681848	59.3491693907361
cortex	76.1184909371926	50.5097413907082	60.8612500704935
heart	80.2411046683001	53.9560170155589	64.802551677991
kidney	76.4465283508703	50.6088307986644	72.9572141626974
liver	83.4805906593737	186.887695500424	294.67733382813
stomach	82.0348934349937	55.5140732039996	69.6863663494389
testicle	79.4165438506038	55.4490803408303	60.5081317771917


diffExp=22.1419643843209,24.4210441499716,25.6087495464845,26.2850876527412,25.8376975522060,-103.407104841051,26.5208202309940,23.9674635097736
diffExpScore=3.84369127474492
diffExp1.5=0,0,1,0,1,-1,0,0
diffExp1.5Score=1.5
diffExp1.4=1,1,1,1,1,-1,1,1
diffExp1.4Score=1.14285714285714
diffExp1.3=1,1,1,1,1,-1,1,1
diffExp1.3Score=1.14285714285714
diffExp1.2=1,1,1,1,1,-1,1,1
diffExp1.2Score=1.14285714285714

cont.predictedValues:
Include	Exclude	Both
Lung	67.0249218493703	66.130468917504	71.7444611247147
cerebhem	72.851698334503	67.5639058012605	71.5777464372869
cortex	59.6578693022167	73.5674538045113	70.8785868944093
heart	84.0594763135762	55.5679669474703	64.1480623031243
kidney	75.208074727323	66.53378434716	67.7668557360335
liver	66.936970262611	75.3594670164641	66.9105784002875
stomach	82.9917060434605	73.9733803579201	73.0073979175206
testicle	83.0206611098095	70.9385673735505	64.9238298267361
cont.diffExp=0.894452931866269,5.28779253324259,-13.9095845022947,28.4915093661058,8.67429038016309,-8.4224967538531,9.01832568554039,12.0820937362590
cont.diffExpScore=2.01270466334052

cont.diffExp1.5=0,0,0,1,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,1,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,-1,1,0,0,0,0
cont.diffExp1.2Score=2

tran.correlation=0.505849969326836
cont.tran.correlation=-0.407905057268429

tran.covariance=0.0110372161975702
cont.tran.covariance=-0.00508730703136266

tran.mean=74.9305837326529
cont.tran.mean=71.3366482817944

weightedLogRatios:
wLogRatio
Lung	1.46925423479996
cerebhem	1.47081969997432
cortex	1.69267968142677
heart	1.66152312506986
kidney	1.7036302516241
liver	-3.89050110694167
stomach	1.64477751667061
testicle	1.50704928960937

cont.weightedLogRatios:
wLogRatio
Lung	0.0564044804311758
cerebhem	0.320302260189797
cortex	-0.878841781712929
heart	1.74862253238905
kidney	0.52193318459878
liver	-0.505244004352973
stomach	0.501694909078609
testicle	0.682645661555351

varWeightedLogRatios=3.76794718905555
cont.varWeightedLogRatios=0.63320209916716

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31849021235849	0.0820726445357559	52.6179001150267	4.35165503686464e-283	***
df.mm.trans1	0.162011702304282	0.0679743069734295	2.38342558413448	0.0173490303945754	*  
df.mm.trans2	-0.369828099833048	0.0619769625085002	-5.96718659424985	3.40996163934139e-09	***
df.mm.exp2	0.431081888526465	0.078961384321096	5.45940135463524	6.10680593790595e-08	***
df.mm.exp3	0.156291210227797	0.078961384321096	1.97933726177139	0.0480679133795704	*  
df.mm.exp4	0.212290605886084	0.078961384321096	2.68853703251713	0.00730287735985257	** 
df.mm.exp5	-0.0187250979146052	0.0789613843210961	-0.237142472559241	0.812597661726498	   
df.mm.exp6	-0.0203284781822012	0.0789613843210961	-0.257448350950074	0.796888729255884	   
df.mm.exp7	0.190207013045345	0.0789613843210961	2.40886117537997	0.0161928454343628	*  
df.mm.exp8	0.297824530761397	0.0789613843210961	3.77177443534038	0.000172168013015085	***
df.mm.trans1:exp2	-0.308547074230328	0.066734549918228	-4.62349824204105	4.29743788134522e-06	***
df.mm.trans2:exp2	-0.298216430401084	0.0516923600899889	-5.76906200223655	1.07972469279027e-08	***
df.mm.trans1:exp3	-0.126464354614904	0.066734549918228	-1.89503570144498	0.0583926668751151	.  
df.mm.trans2:exp3	-0.180349913216251	0.0516923600899889	-3.48890847510712	0.00050737533508371	***
df.mm.trans1:exp4	-0.129719057105842	0.066734549918228	-1.94380657792390	0.0522153154439348	.  
df.mm.trans2:exp4	-0.170346310411884	0.0516923600899889	-3.29538659320906	0.00101942786957693	** 
df.mm.trans1:exp5	0.052852256712731	0.0667345499182279	0.791977420653808	0.428572594559897	   
df.mm.trans2:exp5	-0.00337373879946952	0.0516923600899889	-0.0652657141905753	0.947976243970194	   
df.mm.trans1:exp6	0.142478273994579	0.066734549918228	2.13500014863609	0.0330176612605153	*  
df.mm.trans2:exp6	1.30461143631476	0.0516923600899889	25.2379932748982	7.64029186902507e-108	***
df.mm.trans1:exp7	-0.0855266884293992	0.066734549918228	-1.28159534355439	0.200298995550766	   
df.mm.trans2:exp7	-0.119795372686560	0.0516923600899889	-2.31746765823836	0.0206911435284639	*  
df.mm.trans1:exp8	-0.22558218520484	0.066734549918228	-3.38029080111057	0.000753734361583223	***
df.mm.trans2:exp8	-0.228584321972918	0.0516923600899889	-4.4220136510499	1.09137578113166e-05	***
df.mm.trans1:probe2	-0.679614695931246	0.0516923600899889	-13.1472947791151	2.22358724746822e-36	***
df.mm.trans1:probe3	-0.60297708915508	0.0516923600899889	-11.6647235317827	1.80898773832311e-29	***
df.mm.trans1:probe4	-0.720381970848878	0.0516923600899889	-13.9359466194772	2.76822313808136e-40	***
df.mm.trans1:probe5	-0.550707783645889	0.0516923600899889	-10.6535623965938	4.1747484053848e-25	***
df.mm.trans1:probe6	-0.624738000080882	0.0516923600899889	-12.0856931080977	2.26540805402549e-31	***
df.mm.trans1:probe7	-0.335820423696587	0.0516923600899889	-6.49651946848573	1.32744276037405e-10	***
df.mm.trans1:probe8	-0.542598215811697	0.0516923600899889	-10.4966810350139	1.86442858053953e-24	***
df.mm.trans1:probe9	-0.69539380375827	0.0516923600899889	-13.4525450675436	7.13898563574027e-38	***
df.mm.trans1:probe10	-0.569123017055794	0.0516923600899889	-11.0098091103798	1.31051492784751e-26	***
df.mm.trans1:probe11	-0.610262121343028	0.0516923600899889	-11.8056540711364	4.22727191717068e-30	***
df.mm.trans1:probe12	-0.477734780606854	0.0516923600899889	-9.241883709221	1.56211645177709e-19	***
df.mm.trans2:probe2	-0.045672103308319	0.0516923600899889	-0.88353681721652	0.377171013504637	   
df.mm.trans2:probe3	-0.0105340305069162	0.0516923600899889	-0.203783121694927	0.8385668573336	   
df.mm.trans2:probe4	-0.0348307450022021	0.0516923600899889	-0.67380837209922	0.5005979225327	   
df.mm.trans2:probe5	0.000862080350469503	0.0516923600899889	0.0166771327323563	0.986697710301894	   
df.mm.trans2:probe6	0.0170590074962421	0.0516923600899889	0.330010227169834	0.74146537518965	   
df.mm.trans3:probe2	0.103197117543134	0.0516923600899889	1.99637078600170	0.0461801093880562	*  
df.mm.trans3:probe3	0.0205009304718279	0.0516923600899889	0.396594979144671	0.69175569557575	   
df.mm.trans3:probe4	0.0580236599878645	0.0516923600899889	1.12248037982506	0.261943352710904	   
df.mm.trans3:probe5	0.323552638646811	0.0516923600899889	6.259196486358	5.86161959319692e-10	***
df.mm.trans3:probe6	-0.000595566499328023	0.0516923600899889	-0.0115213640524679	0.990809916651356	   
df.mm.trans3:probe7	0.0406503932083642	0.0516923600899889	0.78639073815933	0.431835756493099	   
df.mm.trans3:probe8	0.106892253099834	0.0516923600899889	2.06785399068161	0.0389254274914707	*  
df.mm.trans3:probe9	-0.0540212010790771	0.0516923600899889	-1.04505193775316	0.296266206989652	   
df.mm.trans3:probe10	0.283639235219648	0.0516923600899889	5.487062976538	5.24906454173767e-08	***
df.mm.trans3:probe11	-0.0395004513439476	0.0516923600899889	-0.764144861545944	0.444971671822243	   
df.mm.trans3:probe12	-0.0256097954333877	0.0516923600899889	-0.495427088041729	0.620413859554942	   
df.mm.trans3:probe13	0.311188991947083	0.0516923600899889	6.02001903966752	2.49323745601655e-09	***
df.mm.trans3:probe14	-0.0937681844041859	0.0516923600899889	-1.81396601433847	0.07000000035528	.  
df.mm.trans3:probe15	0.133677215894271	0.0516923600899889	2.58601494808049	0.00985788470323367	** 
df.mm.trans3:probe16	0.0139724947173928	0.0516923600899889	0.270300963103034	0.786987743879245	   
df.mm.trans3:probe17	0.517299972529228	0.0516923600899889	10.0072809914015	1.77678784950825e-22	***
df.mm.trans3:probe18	0.0193346555084451	0.0516923600899889	0.374033135163229	0.70846356800496	   
df.mm.trans3:probe19	0.651176367168338	0.0516923600899889	12.5971490958187	9.53197773740187e-34	***
df.mm.trans3:probe20	0.0252563884597971	0.0516923600899889	0.488590352923128	0.62524510389583	   
df.mm.trans3:probe21	0.394982045439671	0.0516923600899889	7.64101396709427	5.27994184188554e-14	***
df.mm.trans3:probe22	0.465239039534263	0.0516923600899889	9.00015086802672	1.21366041165800e-18	***
df.mm.trans3:probe23	-0.00799873776267498	0.0516923600899889	-0.154737329631504	0.87706144126594	   
df.mm.trans3:probe24	0.31561278830594	0.0516923600899889	6.10559834676737	1.49369098002413e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.00252429390898	0.234816518528453	17.0453267895801	5.19535118407559e-57	***
df.mm.trans1	0.228120698316443	0.194480026849034	1.17297751348791	0.241100400190082	   
df.mm.trans2	0.190118319738671	0.177321136019617	1.07216953379792	0.28391761899882	   
df.mm.exp2	0.107132070179663	0.225914949729555	0.474214169128303	0.635456740877593	   
df.mm.exp3	0.0022769632812269	0.225914949729555	0.0100788517269560	0.99196050323998	   
df.mm.exp4	0.164354160105564	0.225914949729555	0.727504577728539	0.467097109436899	   
df.mm.exp5	0.178311778025370	0.225914949729555	0.789287199624586	0.430142146692773	   
df.mm.exp6	0.199080458958023	0.225914949729555	0.881218614334041	0.378423573680401	   
df.mm.exp7	0.308301746018816	0.225914949729555	1.36468058615814	0.172678313709167	   
df.mm.exp8	0.384105570323812	0.225914949729555	1.70022201179527	0.0894182150817257	.  
df.mm.trans1:exp2	-0.0237707431801571	0.190932980970712	-0.124497837195573	0.900947589558056	   
df.mm.trans2:exp2	-0.0856877600827804	0.147896051108261	-0.579378282521256	0.562472038906613	   
df.mm.trans1:exp3	-0.118715416970598	0.190932980970712	-0.621764853652015	0.534246466884338	   
df.mm.trans2:exp3	0.104296168404359	0.147896051108261	0.705199142389629	0.480860092753698	   
df.mm.trans1:exp4	0.0621059214106433	0.190932980970712	0.325276026671211	0.745044174548583	   
df.mm.trans2:exp4	-0.338376851421604	0.147896051108261	-2.28793702662087	0.0223613060795758	*  
df.mm.trans1:exp5	-0.0631176942700204	0.190932980970712	-0.330575126147023	0.741038714182759	   
df.mm.trans2:exp5	-0.172231516579448	0.147896051108261	-1.16454438971716	0.244497364896918	   
df.mm.trans1:exp6	-0.20039354282105	0.190932980970712	-1.04954912347903	0.294193791546305	   
df.mm.trans2:exp6	-0.0684404937193593	0.147896051108261	-0.462760791829798	0.643642376349908	   
df.mm.trans1:exp7	-0.0946255885316012	0.190932980970713	-0.495595826611621	0.620294824867788	   
df.mm.trans2:exp7	-0.196226034929422	0.147896051108261	-1.32678346351372	0.184900829683219	   
df.mm.trans1:exp8	-0.170080582600162	0.190932980970713	-0.890786818157158	0.373270279600013	   
df.mm.trans2:exp8	-0.313920908590974	0.147896051108262	-2.12257802854506	0.0340487317854664	*  
df.mm.trans1:probe2	-0.229750685188911	0.147896051108261	-1.55346057901662	0.120647959066632	   
df.mm.trans1:probe3	-0.0791034104504057	0.147896051108261	-0.534858164620644	0.592873761739341	   
df.mm.trans1:probe4	-0.0180734980800285	0.147896051108261	-0.122204061194295	0.902763392081984	   
df.mm.trans1:probe5	0.0991643128732778	0.147896051108261	0.670500071707043	0.502702885291213	   
df.mm.trans1:probe6	0.0282727608468451	0.147896051108261	0.191166435039899	0.84843626283565	   
df.mm.trans1:probe7	-0.195738045529158	0.147896051108261	-1.32348392037780	0.185994594056385	   
df.mm.trans1:probe8	-0.0340934827838877	0.147896051108261	-0.230523279887513	0.817735070045608	   
df.mm.trans1:probe9	-0.144384224964964	0.147896051108261	-0.976254767338398	0.329188067792603	   
df.mm.trans1:probe10	-0.220322414926757	0.147896051108261	-1.48971127542465	0.136633831511384	   
df.mm.trans1:probe11	-0.0947795825329995	0.147896051108261	-0.640852692298186	0.521773767741112	   
df.mm.trans1:probe12	-0.0320887967303855	0.147896051108261	-0.216968583609418	0.828279660230414	   
df.mm.trans2:probe2	0.00965530834808678	0.147896051108261	0.0652844229155179	0.947961352308706	   
df.mm.trans2:probe3	0.0173534354384703	0.147896051108261	0.117335353502896	0.906619243088628	   
df.mm.trans2:probe4	-0.0354691661754843	0.147896051108261	-0.239824971050244	0.81051795690974	   
df.mm.trans2:probe5	-0.0074132555593936	0.147896051108261	-0.0501247700925227	0.96003355309166	   
df.mm.trans2:probe6	-0.0145169563226129	0.147896051108261	-0.098156483650712	0.921828851796236	   
df.mm.trans3:probe2	-0.116918102712779	0.147896051108261	-0.790542423794627	0.429409397303508	   
df.mm.trans3:probe3	-0.0105997286346946	0.147896051108261	-0.0716701261140198	0.942879595771752	   
df.mm.trans3:probe4	-0.137122566478390	0.147896051108261	-0.92715502172546	0.354082781157308	   
df.mm.trans3:probe5	-0.03994373206132	0.147896051108261	-0.27007977401696	0.787157850154812	   
df.mm.trans3:probe6	-0.117618651113275	0.147896051108261	-0.795279185832869	0.426650818139495	   
df.mm.trans3:probe7	-0.111463662199600	0.147896051108261	-0.753662192900659	0.451239821783964	   
df.mm.trans3:probe8	0.00715428392296085	0.147896051108261	0.0483737318836447	0.961428615034686	   
df.mm.trans3:probe9	-0.225060254005661	0.147896051108261	-1.52174620160017	0.128407213001708	   
df.mm.trans3:probe10	-0.186475651438078	0.147896051108261	-1.26085618947037	0.20767186443499	   
df.mm.trans3:probe11	-0.192906519561876	0.147896051108261	-1.30433854126820	0.192435763693804	   
df.mm.trans3:probe12	-0.113498162175675	0.147896051108261	-0.767418476187664	0.443024440246267	   
df.mm.trans3:probe13	-0.215246518348080	0.147896051108261	-1.45539057152051	0.145893267642561	   
df.mm.trans3:probe14	-0.242145386179201	0.147896051108261	-1.63726742103444	0.101907533257883	   
df.mm.trans3:probe15	-0.261741755372495	0.147896051108261	-1.76976838401789	0.0770881608662127	.  
df.mm.trans3:probe16	-0.229331082580634	0.147896051108261	-1.55062343356796	0.121326768633927	   
df.mm.trans3:probe17	-0.09437379074836	0.147896051108261	-0.638108928812963	0.523557368960478	   
df.mm.trans3:probe18	-0.157575873457263	0.147896051108261	-1.06545017447366	0.286944403472368	   
df.mm.trans3:probe19	-0.249138959870454	0.147896051108261	-1.68455450976227	0.0924047549162535	.  
df.mm.trans3:probe20	-0.16993259408628	0.147896051108261	-1.14900021206035	0.250846625309993	   
df.mm.trans3:probe21	-0.108229197977143	0.147896051108261	-0.731792344461705	0.464476754785316	   
df.mm.trans3:probe22	-0.222969467280659	0.147896051108261	-1.50760933513663	0.131988758264071	   
df.mm.trans3:probe23	-0.151135864291587	0.147896051108261	-1.02190601546862	0.307086914463773	   
df.mm.trans3:probe24	-0.197390535484148	0.147896051108261	-1.33465724071062	0.182310025981157