fitVsDatCorrelation=0.802028180158554
cont.fitVsDatCorrelation=0.257409314619480

fstatistic=11760.2149548628,62,922
cont.fstatistic=4483.99306429861,62,922

residuals=-0.61616990072002,-0.0922478191508477,-0.00904206296676779,0.0881435353632739,0.767374357879354
cont.residuals=-0.513464648041917,-0.164869332070245,-0.0322007118998643,0.132305085068682,1.04325941447427

predictedValues:
Include	Exclude	Both
Lung	70.1912991510558	81.6986551899395	110.827050491690
cerebhem	58.050547894788	64.729837010252	93.2981182849223
cortex	59.1413825323888	70.2979624699887	76.7611584771103
heart	64.6429972669723	66.8230776953984	85.7004801708917
kidney	57.706796988373	55.9139362719918	67.2718706482282
liver	57.3162191278979	58.8316698240115	71.7822355995118
stomach	59.0460088900382	56.8256201365321	72.5310361482609
testicle	58.6087012558786	59.3041603207088	82.0547890274709


diffExp=-11.5073560388838,-6.67928911546396,-11.1565799375999,-2.18008042842608,1.79286071638118,-1.51545069611355,2.22038875350611,-0.695459064830196
diffExpScore=1.22871998826158
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	61.0332077609111	63.952319225402	58.511526682961
cerebhem	60.1605333069264	70.5851797654323	62.1285515777536
cortex	61.1925111349193	62.5195655520622	56.137365896476
heart	61.3740946284788	65.8766929214796	68.2823759137093
kidney	62.4979907394897	60.8006095731985	62.9842518045941
liver	59.3554527724899	68.0662348583409	54.8803316780887
stomach	63.4182250163834	58.0021406917717	74.9942916562026
testicle	61.0184011516702	68.5977273513212	55.2849575557125
cont.diffExp=-2.91911146449084,-10.4246464585059,-1.32705441714288,-4.50259829300083,1.69738116629117,-8.71078208585102,5.41608432461175,-7.57932619965102
cont.diffExpScore=1.45066122330478

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=0.843568733077562
cont.tran.correlation=-0.864690647270543

tran.covariance=0.00753804543232275
cont.tran.covariance=-0.00118907373005546

tran.mean=62.4455545016385
cont.tran.mean=63.0281804031423

weightedLogRatios:
wLogRatio
Lung	-0.656915517463447
cerebhem	-0.448240528240177
cortex	-0.719992686004328
heart	-0.138826461352798
kidney	0.127494884498238
liver	-0.105995078922828
stomach	0.155585956805171
testicle	-0.0480908929017418

cont.weightedLogRatios:
wLogRatio
Lung	-0.193175535865143
cerebhem	-0.667486842539159
cortex	-0.0884954924394621
heart	-0.293975955076881
kidney	0.113480167090715
liver	-0.568565322435288
stomach	0.366469263278514
testicle	-0.488206897840262

varWeightedLogRatios=0.114747907183616
cont.varWeightedLogRatios=0.124552642393456

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.85001990089628	0.0803412029804107	47.9208644888603	1.62515310855814e-252	***
df.mm.trans1	0.420266823146274	0.072690128108605	5.78162171510224	1.01218574890144e-08	***
df.mm.trans2	0.574943460604991	0.066130101833641	8.69412634584076	1.57989507996674e-17	***
df.mm.exp2	-0.250554556720874	0.090365686526555	-2.77267363699213	0.00567189120244108	** 
df.mm.exp3	0.0456839247558219	0.0903656865265551	0.505545041617064	0.613296902951709	   
df.mm.exp4	-0.0262211698973514	0.090365686526555	-0.290167329051897	0.77175347600419	   
df.mm.exp5	-0.0758445765879399	0.090365686526555	-0.83930725813334	0.401514472509441	   
df.mm.exp6	-0.096664096524809	0.090365686526555	-1.06969913293807	0.285034714687745	   
df.mm.exp7	-0.112001339075486	0.090365686526555	-1.23942331852448	0.215504158635069	   
df.mm.exp8	-0.200115569632128	0.090365686526555	-2.21450837507135	0.0270377201125788	*  
df.mm.trans1:exp2	0.0606443435126544	0.0876675930542215	0.69175326252138	0.489266444657066	   
df.mm.trans2:exp2	0.0177392698941514	0.0748604591497324	0.236964481591946	0.812736983711937	   
df.mm.trans1:exp3	-0.216977392956850	0.0876675930542215	-2.47500114235658	0.0135024144282338	*  
df.mm.trans2:exp3	-0.195978651103457	0.0748604591497324	-2.61791943743586	0.00899179385985087	** 
df.mm.trans1:exp4	-0.0561234079874139	0.0876675930542215	-0.640184200708034	0.522211906856627	   
df.mm.trans2:exp4	-0.174767876127631	0.0748604591497324	-2.3345819423585	0.0197791532136184	*  
df.mm.trans1:exp5	-0.120004817710059	0.0876675930542215	-1.36886178266395	0.171375878406341	   
df.mm.trans2:exp5	-0.303379308518641	0.0748604591497324	-4.05259748556758	5.49217466273009e-05	***
df.mm.trans1:exp6	-0.105976623107040	0.0876675930542215	-1.20884604464383	0.227031911544531	   
df.mm.trans2:exp6	-0.231693132502700	0.0748604591497324	-3.09500015274123	0.00202752815002031	** 
df.mm.trans1:exp7	-0.0609060688513492	0.0876675930542215	-0.694738691111086	0.487394107101809	   
df.mm.trans2:exp7	-0.251048919539087	0.0748604591497324	-3.35355837234382	0.000830293370340442	***
df.mm.trans1:exp8	0.0197743811544446	0.0876675930542215	0.225560899592787	0.821592941581865	   
df.mm.trans2:exp8	-0.120242511033218	0.0748604591497324	-1.60622192809042	0.108567563812884	   
df.mm.trans1:probe2	0.317764910504429	0.0438337965271108	7.24931298861816	8.85942953613188e-13	***
df.mm.trans1:probe3	-0.00660026951202026	0.0438337965271108	-0.150574899619705	0.880343997618926	   
df.mm.trans1:probe4	-0.132445594507353	0.0438337965271108	-3.02154056916874	0.00258436892399361	** 
df.mm.trans1:probe5	-0.133162545239342	0.0438337965271108	-3.03789668679469	0.00244946947993779	** 
df.mm.trans1:probe6	0.0661624260483997	0.0438337965271107	1.50939300928403	0.131540976635104	   
df.mm.trans1:probe7	0.0153893257406566	0.0438337965271107	0.351083569298827	0.725605860602341	   
df.mm.trans1:probe8	0.0172271268097187	0.0438337965271107	0.393010146841010	0.694402836856631	   
df.mm.trans1:probe9	0.426750850120297	0.0438337965271107	9.73565796100633	2.18252854942742e-21	***
df.mm.trans1:probe10	-0.114139186992288	0.0438337965271107	-2.60390830900751	0.00936495539936456	** 
df.mm.trans1:probe11	-0.100131802461474	0.0438337965271108	-2.28435158244949	0.0225773930114006	*  
df.mm.trans1:probe12	-0.201139562900184	0.0438337965271108	-4.58868678590916	5.07718453081213e-06	***
df.mm.trans1:probe13	-0.223321604340345	0.0438337965271107	-5.09473561575308	4.23572607431014e-07	***
df.mm.trans1:probe14	-0.277328302708311	0.0438337965271107	-6.32681457415597	3.89888159568569e-10	***
df.mm.trans1:probe15	-0.210908480680534	0.0438337965271108	-4.81154947530244	1.74898221494908e-06	***
df.mm.trans1:probe16	-0.234493338411528	0.0438337965271107	-5.34960138044387	1.112381052945e-07	***
df.mm.trans1:probe17	0.0211805759182851	0.0438337965271108	0.483201949098458	0.62906707154009	   
df.mm.trans1:probe18	0.0093578003775719	0.0438337965271107	0.213483684256831	0.83099691058104	   
df.mm.trans1:probe19	-0.00804151234857845	0.0438337965271107	-0.183454616886878	0.854481660143816	   
df.mm.trans1:probe20	0.030590124287725	0.0438337965271108	0.697866183432351	0.485436836187053	   
df.mm.trans1:probe21	-0.0673845666919974	0.0438337965271107	-1.53727425025393	0.124569252832316	   
df.mm.trans1:probe22	0.0809872142228473	0.0438337965271107	1.84759753065782	0.064980750740938	.  
df.mm.trans1:probe23	0.0422452765640328	0.0438337965271107	0.963760383792094	0.335418772291559	   
df.mm.trans1:probe24	0.206994732159684	0.0438337965271107	4.72226338030428	2.69480561310516e-06	***
df.mm.trans1:probe25	0.0248551956371292	0.0438337965271107	0.567032691812505	0.570829989110877	   
df.mm.trans1:probe26	-0.131006352178451	0.0438337965271107	-2.98870649037723	0.00287592362911340	** 
df.mm.trans1:probe27	-0.137593979199887	0.0438337965271107	-3.13899297120627	0.001749213802014	** 
df.mm.trans1:probe28	0.0662834422311034	0.0438337965271107	1.51215380557118	0.130837406064581	   
df.mm.trans1:probe29	-0.221005149717586	0.0438337965271107	-5.04188930066544	5.5488682751394e-07	***
df.mm.trans1:probe30	0.45419600227787	0.0438337965271107	10.3617764889919	7.12112583790332e-24	***
df.mm.trans1:probe31	-0.143712779901087	0.0438337965271107	-3.27858390756097	0.00108232886452512	** 
df.mm.trans1:probe32	-0.104752729444769	0.0438337965271107	-2.38977085591891	0.0170588899693328	*  
df.mm.trans2:probe2	-0.0985931328629694	0.0438337965271107	-2.24924922489866	0.0247319357509378	*  
df.mm.trans2:probe3	0.0898134795735105	0.0438337965271108	2.04895506867542	0.0407491173017351	*  
df.mm.trans2:probe4	0.0101260375255463	0.0438337965271107	0.231009821822837	0.817358423489584	   
df.mm.trans2:probe5	-0.143703393324846	0.0438337965271107	-3.27836976740008	0.00108314078901682	** 
df.mm.trans2:probe6	-0.0549753719394096	0.0438337965271107	-1.25417774172054	0.210095279543088	   
df.mm.trans3:probe2	-0.226309521378431	0.0438337965271107	-5.16290030315901	2.97898839506932e-07	***
df.mm.trans3:probe3	-0.185468384980219	0.0438337965271107	-4.23117319681651	2.5570198259871e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.10109797581018	0.129977727272013	31.5523133223243	9.04171940554967e-149	***
df.mm.trans1	-0.0825070396345603	0.117599653679217	-0.701592539206106	0.483110355028023	   
df.mm.trans2	0.0715464192243511	0.106986701987761	0.66874123507925	0.503827953663272	   
df.mm.exp2	0.0242989704785634	0.14619555249827	0.166208684623637	0.868029165204279	   
df.mm.exp3	0.0213705949461659	0.14619555249827	0.146178146879118	0.883812709677484	   
df.mm.exp4	-0.119211280907225	0.14619555249827	-0.815423443942566	0.41504056009604	   
df.mm.exp5	-0.100482627956860	0.14619555249827	-0.687316585489484	0.492056097177751	   
df.mm.exp6	0.098538110707643	0.14619555249827	0.674015789288864	0.50047021689496	   
df.mm.exp7	-0.307512927107747	0.14619555249827	-2.10343558236073	0.0356981254554334	*  
df.mm.exp8	0.126601898819987	0.14619555249827	0.865976403909314	0.38672838272779	   
df.mm.trans1:exp2	-0.0387005316372124	0.141830518810800	-0.272864627174060	0.785018380327785	   
df.mm.trans2:exp2	0.074383440050897	0.121110861947064	0.614176456636968	0.539250242661452	   
df.mm.trans1:exp3	-0.0187638855620531	0.141830518810800	-0.132297940664547	0.89477750156384	   
df.mm.trans2:exp3	-0.0440288319575221	0.121110861947064	-0.363541562248697	0.716283710240999	   
df.mm.trans1:exp4	0.124781009872068	0.141830518810800	0.879789560937336	0.379202567674604	   
df.mm.trans2:exp4	0.148858192839186	0.121110861947064	1.22910687320721	0.219345328770923	   
df.mm.trans1:exp5	0.124198930444062	0.141830518810800	0.875685511732083	0.381429094892418	   
df.mm.trans2:exp5	0.0499446491565513	0.121110861947064	0.41238785979726	0.680151026725414	   
df.mm.trans1:exp6	-0.126412224590026	0.141830518810800	-0.891290715495855	0.373005757677955	   
df.mm.trans2:exp6	-0.0361946311653809	0.121110861947064	-0.298855367582149	0.765117748162813	   
df.mm.trans1:exp7	0.345846102497993	0.141830518810800	2.43844629066997	0.0149384056537705	*  
df.mm.trans2:exp7	0.209855051854742	0.121110861947064	1.73275169940138	0.0834741909765314	.  
df.mm.trans1:exp8	-0.126844527482493	0.141830518810800	-0.894338739969652	0.371374074483267	   
df.mm.trans2:exp8	-0.0564802872235567	0.121110861947064	-0.466351954858049	0.641073757059594	   
df.mm.trans1:probe2	0.0582238405492531	0.0709152594054	0.821034020568211	0.411839122434716	   
df.mm.trans1:probe3	-0.00252762697729531	0.0709152594054	-0.035642920839444	0.971574799221897	   
df.mm.trans1:probe4	0.0388092179552859	0.0709152594054	0.547261876790522	0.584331267802403	   
df.mm.trans1:probe5	0.122950759756086	0.0709152594054	1.73377014745466	0.083293155783574	.  
df.mm.trans1:probe6	0.202234285777436	0.0709152594053999	2.85177389849661	0.0044447091442571	** 
df.mm.trans1:probe7	0.128343037984282	0.0709152594054	1.80980848212916	0.0706510014432535	.  
df.mm.trans1:probe8	0.128774372309844	0.0709152594053999	1.81589087298803	0.0697118049237557	.  
df.mm.trans1:probe9	0.179206682619380	0.0709152594053999	2.52705389674897	0.0116686077620570	*  
df.mm.trans1:probe10	0.147875466038873	0.0709152594053999	2.08524184045518	0.0373219131538966	*  
df.mm.trans1:probe11	0.114829002351173	0.0709152594054	1.61924250597085	0.105737074680037	   
df.mm.trans1:probe12	0.160806190649846	0.0709152594054	2.26758235107861	0.0235853911310341	*  
df.mm.trans1:probe13	0.0824656413437708	0.0709152594053999	1.16287583286329	0.245180769458741	   
df.mm.trans1:probe14	0.0894574581339586	0.0709152594053999	1.26146980049186	0.207458693058920	   
df.mm.trans1:probe15	0.127442329330733	0.0709152594054	1.79710728550235	0.0726457531053288	.  
df.mm.trans1:probe16	0.11027955264193	0.0709152594054	1.55508918061622	0.120268001120905	   
df.mm.trans1:probe17	0.125593270850583	0.0709152594054	1.77103308799318	0.0768855261603018	.  
df.mm.trans1:probe18	0.115917376283728	0.0709152594054	1.63459003401602	0.102476372834466	   
df.mm.trans1:probe19	0.117205492733200	0.0709152594054	1.65275419868627	0.0987213483257992	.  
df.mm.trans1:probe20	0.1395275617146	0.0709152594054	1.9675252249585	0.0494221090742742	*  
df.mm.trans1:probe21	0.0901715163595174	0.0709152594054	1.27153897645689	0.203857626829672	   
df.mm.trans1:probe22	0.0921363248453583	0.0709152594054	1.29924540385087	0.194184511522784	   
df.mm.trans1:probe23	0.082570875856947	0.0709152594054	1.16435978024018	0.244579430854798	   
df.mm.trans1:probe24	0.0238770541879189	0.0709152594054	0.336698397328301	0.736420892424873	   
df.mm.trans1:probe25	0.0627788137250514	0.0709152594053999	0.885265234188384	0.37624440797145	   
df.mm.trans1:probe26	0.0965243232971548	0.0709152594053999	1.36112205054988	0.173807688582995	   
df.mm.trans1:probe27	0.100629500896098	0.0709152594054	1.41901054497779	0.156233899880007	   
df.mm.trans1:probe28	0.129165925252727	0.0709152594053999	1.82141229314733	0.0688681422492962	.  
df.mm.trans1:probe29	0.138792180567950	0.0709152594053999	1.95715536728871	0.0506306205906518	.  
df.mm.trans1:probe30	0.0208470543608766	0.0709152594053999	0.293971347431738	0.768845962099809	   
df.mm.trans1:probe31	0.146791448130445	0.0709152594053999	2.06995573817597	0.0387344767213949	*  
df.mm.trans1:probe32	0.077251999284623	0.0709152594054	1.08935650708119	0.276281343210604	   
df.mm.trans2:probe2	-0.0548025378094655	0.0709152594053999	-0.77278907627168	0.439845277504541	   
df.mm.trans2:probe3	-0.0811332600659945	0.0709152594054	-1.14408747491399	0.252884297261259	   
df.mm.trans2:probe4	-0.068545071893315	0.0709152594053999	-0.966577186180264	0.334008906328618	   
df.mm.trans2:probe5	0.0483087739480443	0.0709152594053999	0.681218321036921	0.495904412556878	   
df.mm.trans2:probe6	0.0256126828843041	0.0709152594054	0.361173083184884	0.718052803898927	   
df.mm.trans3:probe2	-0.0306164037420863	0.0709152594053999	-0.431732239278179	0.66603696961493	   
df.mm.trans3:probe3	-0.0321243460255669	0.0709152594054	-0.452996242203984	0.650658027263454