fitVsDatCorrelation=0.704018798952865
cont.fitVsDatCorrelation=0.238924297192011

fstatistic=15964.8842294096,70,1106
cont.fstatistic=8532.13581260437,70,1106

residuals=-0.487440900174573,-0.0781168004859613,-0.00323067650062012,0.0731046195744653,0.643618966126833
cont.residuals=-0.413419200329448,-0.119518411342063,-0.0169761078279193,0.0884726028110709,0.763681240588342

predictedValues:
Include	Exclude	Both
Lung	50.6343849999068	48.9088250702292	52.1827566791681
cerebhem	54.3127429146199	58.5265688159435	48.8818953237885
cortex	48.4184620951114	43.4143887951555	49.484404286858
heart	50.6745434322444	42.0960484532736	51.5868508898044
kidney	49.0370844146869	47.2503385261551	51.5239968206492
liver	51.4390439830977	44.1100106592733	54.7167544604453
stomach	51.7126750206432	44.264770137193	54.093132928978
testicle	49.727020395789	48.9535248558115	52.3343833467177


diffExp=1.72555992967762,-4.21382590132368,5.00407329995591,8.57849497897078,1.78674588853176,7.32903332382437,7.4479048834502,0.773495539977496
diffExpScore=1.25237097530516
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=0,0,0,1,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	53.1224346368545	52.5129845597633	52.4476876648652
cerebhem	51.9400432293032	51.6411491758694	49.453046224733
cortex	52.0022897268807	51.3347817110984	51.0647281072784
heart	52.5071052382894	47.8340834860326	53.7078628206886
kidney	51.8993787437885	55.367860933243	52.2799882074078
liver	54.3504450028805	51.580943425993	51.4888637000273
stomach	52.2907112556928	53.0289507972692	54.5180523040768
testicle	52.1781585752569	50.3131319217552	52.4168801786926
cont.diffExp=0.609450077091189,0.298894053433735,0.667508015782346,4.67302175225672,-3.46848218945446,2.76950157688756,-0.73823954157644,1.86502665350173
cont.diffExpScore=1.9657095356045

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=0.631416394547876
cont.tran.correlation=-0.123418981532660

tran.covariance=0.00224101243979306
cont.tran.covariance=-7.80181106639312e-05

tran.mean=48.9675270355709
cont.tran.mean=52.1190282762482

weightedLogRatios:
wLogRatio
Lung	0.135477770040828
cerebhem	-0.301287830914676
cortex	0.417306920438525
heart	0.710848049110345
kidney	0.143792008631698
liver	0.593867914839591
stomach	0.601519029068521
testicle	0.0611203857109323

cont.weightedLogRatios:
wLogRatio
Lung	0.0457727461334361
cerebhem	0.0227801819754382
cortex	0.0509641117602904
heart	0.364856225392034
kidney	-0.257583002951956
liver	0.207596549226681
stomach	-0.0555699233090912
testicle	0.14327895973412

varWeightedLogRatios=0.118533386316314
cont.varWeightedLogRatios=0.0339029549866646

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95061149146156	0.058295512545397	67.7687066973649	0	***
df.mm.trans1	-0.108717271175166	0.0506079819276127	-2.1482237985832	0.0319126447323431	*  
df.mm.trans2	-0.0708048066116137	0.0438016086785886	-1.61648872604592	0.106273862640878	   
df.mm.exp2	0.314995970891383	0.055537529449699	5.67176779400458	1.80321057285056e-08	***
df.mm.exp3	-0.110822075006118	0.055537529449699	-1.99544481189951	0.0462393479134796	*  
df.mm.exp4	-0.137725888847992	0.055537529449699	-2.47987064265672	0.0132910365823068	*  
df.mm.exp5	-0.0538476361270653	0.055537529449699	-0.96957204723764	0.332471830660572	   
df.mm.exp6	-0.134922343797306	0.055537529449699	-2.42939045244183	0.0152826540718914	*  
df.mm.exp7	-0.114651858869902	0.055537529449699	-2.06440329640057	0.0392122343409015	*  
df.mm.exp8	-0.0200703838710918	0.055537529449699	-0.361384167966452	0.717881303788729	   
df.mm.trans1:exp2	-0.24486798629928	0.052267698984216	-4.68488169669046	3.14930760107279e-06	***
df.mm.trans2:exp2	-0.135473003944224	0.0355407128887388	-3.81176945910808	0.000145585509201751	***
df.mm.trans1:exp3	0.0660723732822603	0.052267698984216	1.26411482744272	0.206455160859573	   
df.mm.trans2:exp3	-0.00834485175715104	0.0355407128887388	-0.234796971666686	0.81440977941817	   
df.mm.trans1:exp4	0.138518680461966	0.052267698984216	2.65017751219155	0.00815975722579945	** 
df.mm.trans2:exp4	-0.0122780878178436	0.0355407128887389	-0.345465434423909	0.729810185637355	   
df.mm.trans1:exp5	0.0217935818200960	0.052267698984216	0.416960804543497	0.676787991626168	   
df.mm.trans2:exp5	0.0193496026342817	0.0355407128887389	0.544434848418944	0.586252015675408	   
df.mm.trans1:exp6	0.150688947506779	0.052267698984216	2.88302241030898	0.00401503913262164	** 
df.mm.trans2:exp6	0.0316512475846248	0.0355407128887389	0.890563103889048	0.373357280441377	   
df.mm.trans1:exp7	0.13572388418599	0.052267698984216	2.59670670076707	0.00953709075267641	** 
df.mm.trans2:exp7	0.0148831110584413	0.0355407128887389	0.418762310847091	0.675471158817937	   
df.mm.trans1:exp8	0.00198794821445193	0.052267698984216	0.0380339722828101	0.96966745939503	   
df.mm.trans2:exp8	0.0209839075852619	0.0355407128887389	0.590418871195764	0.555030477607961	   
df.mm.trans1:probe2	0.300294001241578	0.0369588443887563	8.12509173941959	1.18679595587346e-15	***
df.mm.trans1:probe3	-0.0520403319252181	0.0369588443887564	-1.40806166388281	0.159393826314686	   
df.mm.trans1:probe4	-0.0158773637409504	0.0369588443887563	-0.429595784271346	0.667573381415916	   
df.mm.trans1:probe5	0.187870002827504	0.0369588443887564	5.08322178181139	4.35465962618019e-07	***
df.mm.trans1:probe6	0.0434956707361727	0.0369588443887563	1.17686771476559	0.239501522948165	   
df.mm.trans1:probe7	0.43153935379842	0.0369588443887564	11.6762133918263	8.69824629975761e-30	***
df.mm.trans1:probe8	-0.0317310474936304	0.0369588443887564	-0.8585508561865	0.390774385322137	   
df.mm.trans1:probe9	0.232635450073212	0.0369588443887564	6.29444599582731	4.43946434104067e-10	***
df.mm.trans1:probe10	0.180246130441453	0.0369588443887564	4.87694172862958	1.23505891626732e-06	***
df.mm.trans1:probe11	0.060740470586011	0.0369588443887564	1.64346238608287	0.100571521582348	   
df.mm.trans1:probe12	0.183791411260661	0.0369588443887564	4.97286682796212	7.64236900074522e-07	***
df.mm.trans1:probe13	0.084610210750233	0.0369588443887564	2.28930888261141	0.0222491595335247	*  
df.mm.trans1:probe14	0.0662724862112947	0.0369588443887564	1.79314281350897	0.0732232403521934	.  
df.mm.trans1:probe15	0.0522517925942537	0.0369588443887564	1.41378318122278	0.157706976946121	   
df.mm.trans1:probe16	0.101509541387699	0.0369588443887564	2.7465561509434	0.00612020551012192	** 
df.mm.trans1:probe17	-0.0299272316586246	0.0369588443887563	-0.809744789199337	0.418261045767198	   
df.mm.trans1:probe18	0.142229465627356	0.0369588443887564	3.84832015122813	0.000125735861028618	***
df.mm.trans1:probe19	0.156498235980006	0.0369588443887564	4.23439202627277	2.48202944380497e-05	***
df.mm.trans1:probe20	0.0830361637341547	0.0369588443887564	2.24671969882846	0.0248545749070529	*  
df.mm.trans1:probe21	0.243589650926211	0.0369588443887564	6.59083515609908	6.75534566278517e-11	***
df.mm.trans1:probe22	0.0450525593087964	0.0369588443887563	1.21899264043284	0.223106907884425	   
df.mm.trans1:probe23	-0.0130682492375691	0.0369588443887564	-0.353589227523163	0.723714136756021	   
df.mm.trans1:probe24	-0.0209641158626978	0.0369588443887563	-0.567228662297556	0.570673943684479	   
df.mm.trans1:probe25	0.156257252265912	0.0369588443887563	4.22787170027018	2.55385695847597e-05	***
df.mm.trans1:probe26	0.0656649423633373	0.0369588443887563	1.77670442486329	0.0758917019127951	.  
df.mm.trans1:probe27	0.0255224054105454	0.0369588443887564	0.690562863440337	0.489985184539544	   
df.mm.trans1:probe28	0.459102324339883	0.0369588443887564	12.4219880770826	2.92847321471441e-33	***
df.mm.trans1:probe29	-0.00156159546813527	0.0369588443887564	-0.0422522807182342	0.966305212038045	   
df.mm.trans1:probe30	0.184995812312371	0.0369588443887564	5.00545445540638	6.48022521935568e-07	***
df.mm.trans1:probe31	0.235641440160209	0.0369588443887564	6.37577943946472	2.66846264686218e-10	***
df.mm.trans2:probe2	0.0963267661524884	0.0369588443887564	2.60632516372165	0.00927498106707088	** 
df.mm.trans2:probe3	0.0628013252501259	0.0369588443887564	1.69922318429500	0.0895583508641442	.  
df.mm.trans2:probe4	-0.033459405111535	0.0369588443887564	-0.905315240909266	0.365495571977144	   
df.mm.trans2:probe5	0.0201647404504912	0.0369588443887564	0.545599863415258	0.585451036043338	   
df.mm.trans2:probe6	0.0368875816278517	0.0369588443887564	0.998071834710114	0.318462835059768	   
df.mm.trans3:probe2	0.100241579532580	0.0369588443887564	2.71224875102088	0.00678649552567519	** 
df.mm.trans3:probe3	0.208750695694732	0.0369588443887564	5.64819325785625	2.06074077061271e-08	***
df.mm.trans3:probe4	0.090422903480599	0.0369588443887564	2.44658362500391	0.0145765959470884	*  
df.mm.trans3:probe5	0.165217165544795	0.0369588443887564	4.47030117627427	8.61508667360382e-06	***
df.mm.trans3:probe6	0.316027657957886	0.0369588443887564	8.55079922504364	4.01591966882804e-17	***
df.mm.trans3:probe7	0.236674209771197	0.0369588443887564	6.40372321389999	2.23732205141589e-10	***
df.mm.trans3:probe8	0.233580793257168	0.0369588443887564	6.32002426266955	3.78510700766355e-10	***
df.mm.trans3:probe9	0.265825960615144	0.0369588443887564	7.19248572328235	1.17017385447320e-12	***
df.mm.trans3:probe10	0.209435950752572	0.0369588443887564	5.66673428826921	1.85542493911593e-08	***
df.mm.trans3:probe11	0.204597326208850	0.0369588443887564	5.53581502865098	3.86759362489356e-08	***
df.mm.trans3:probe12	0.173178095090916	0.0369588443887564	4.6857010265072	3.13697894266674e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.03868688621509	0.0797080498892747	50.6684944848779	1.50737746083681e-290	***
df.mm.trans1	-0.0226027854670344	0.0691968107346229	-0.326644902085422	0.743998280478262	   
df.mm.trans2	-0.068961122170143	0.0598903870527699	-1.15145561021973	0.249793700784884	   
df.mm.exp2	0.01954164611709	0.0759370314251266	0.257340137615966	0.79696407856423	   
df.mm.exp3	-0.0172812807031966	0.0759370314251266	-0.227573825034705	0.820019631587358	   
df.mm.exp4	-0.128716038133388	0.0759370314251266	-1.6950364758504	0.09034998616696	.  
df.mm.exp5	0.0328489010599504	0.0759370314251266	0.432580790208255	0.665403680600954	   
df.mm.exp6	0.0233959931805951	0.0759370314251266	0.308097284572723	0.758066321210357	   
df.mm.exp7	-0.0447186790932519	0.0759370314251266	-0.58889158891264	0.556054251741447	   
df.mm.exp8	-0.0601421294524034	0.0759370314251266	-0.792000007423297	0.428530503119898	   
df.mm.trans1:exp2	-0.0420509444797422	0.0714661588228963	-0.58840359090728	0.556381562953599	   
df.mm.trans2:exp2	-0.036283290752602	0.0485951798404708	-0.746643820883337	0.455437271249062	   
df.mm.trans1:exp3	-0.00403030536556354	0.0714661588228963	-0.0563945989534884	0.955037645472607	   
df.mm.trans2:exp3	-0.00541065476373273	0.0485951798404708	-0.111341387798027	0.91136583853944	   
df.mm.trans1:exp4	0.117065199555802	0.0714661588228963	1.63805081291561	0.101695532393188	   
df.mm.trans2:exp4	0.0353940032111345	0.0485951798404708	0.728343908332608	0.46655726759586	   
df.mm.trans1:exp5	-0.0561414181043074	0.0714661588228963	-0.785566469906884	0.432289767436292	   
df.mm.trans2:exp5	0.0200899328297189	0.0485951798404708	0.413414106001264	0.67938338433309	   
df.mm.trans1:exp6	-0.000542528811402428	0.0714661588228964	-0.00759140858188411	0.993944359495763	   
df.mm.trans2:exp6	-0.0413041663215109	0.0485951798404708	-0.849964265120636	0.395528907126796	   
df.mm.trans1:exp7	0.0289380924468091	0.0714661588228963	0.404920215713874	0.685614460088337	   
df.mm.trans2:exp7	0.0544962210020485	0.0485951798404708	1.12143264375088	0.262347167410925	   
df.mm.trans1:exp8	0.0422067817911913	0.0714661588228963	0.590584165797772	0.55491973199459	   
df.mm.trans2:exp8	0.0173477805221399	0.0485951798404708	0.356985622423656	0.721170668270551	   
df.mm.trans1:probe2	-0.0675245624505871	0.0505342055290248	-1.33621498040181	0.181753861750236	   
df.mm.trans1:probe3	-0.0878093932082762	0.0505342055290248	-1.73762290886006	0.0825556342349965	.  
df.mm.trans1:probe4	-0.111354139379380	0.0505342055290248	-2.2035399233777	0.0277622390062249	*  
df.mm.trans1:probe5	-0.0893661467944692	0.0505342055290248	-1.76842884653922	0.0772648557044502	.  
df.mm.trans1:probe6	-0.0952056435887136	0.0505342055290248	-1.88398417650063	0.0598292385462018	.  
df.mm.trans1:probe7	-0.0981852801309587	0.0505342055290248	-1.94294694263206	0.0522760580554772	.  
df.mm.trans1:probe8	-0.061690370431078	0.0505342055290248	-1.22076462438191	0.222435340995222	   
df.mm.trans1:probe9	-0.0884660175201288	0.0505342055290248	-1.75061656939115	0.0802892262106537	.  
df.mm.trans1:probe10	0.00125812772113861	0.0505342055290248	0.0248965568562465	0.980141964682957	   
df.mm.trans1:probe11	-0.0582190491270787	0.0505342055290248	-1.15207211665057	0.249540402072145	   
df.mm.trans1:probe12	-0.0539277081278839	0.0505342055290248	-1.06715258631918	0.286135818838106	   
df.mm.trans1:probe13	0.0125177178884447	0.0505342055290248	0.247707820028060	0.804406459066684	   
df.mm.trans1:probe14	-0.106792095389932	0.0505342055290248	-2.11326356617193	0.0348017216741186	*  
df.mm.trans1:probe15	-0.0472461239835751	0.0505342055290248	-0.934933546277657	0.35002662572817	   
df.mm.trans1:probe16	-0.0648296025523516	0.0505342055290248	-1.28288555986333	0.199800945305046	   
df.mm.trans1:probe17	-0.0249736173348216	0.0505342055290248	-0.494192341076339	0.621268483867283	   
df.mm.trans1:probe18	-0.0446403348001882	0.0505342055290248	-0.883368687265671	0.377229072487178	   
df.mm.trans1:probe19	-0.00256476400790135	0.0505342055290248	-0.0507530291819519	0.959531484108727	   
df.mm.trans1:probe20	-0.034392797073735	0.0505342055290248	-0.680584501402345	0.496276883949889	   
df.mm.trans1:probe21	-0.0775050128190726	0.0505342055290248	-1.53371388760741	0.12538604384597	   
df.mm.trans1:probe22	-0.0830821178336665	0.0505342055290248	-1.64407685772259	0.100444522016359	   
df.mm.trans1:probe23	-0.0300343356404035	0.0505342055290248	-0.594336753214671	0.552408452721507	   
df.mm.trans1:probe24	-0.0630839822045763	0.0505342055290248	-1.24834221779431	0.212169952940160	   
df.mm.trans1:probe25	-0.105586045756757	0.0505342055290248	-2.08939756055158	0.0369000197560485	*  
df.mm.trans1:probe26	-0.096958061359816	0.0505342055290248	-1.91866202990224	0.0552842934527075	.  
df.mm.trans1:probe27	-0.0631822806600379	0.0505342055290248	-1.25028740431565	0.211459042027900	   
df.mm.trans1:probe28	-0.0697778488908724	0.0505342055290248	-1.38080431185952	0.167618090510045	   
df.mm.trans1:probe29	-0.0550836208549644	0.0505342055290248	-1.09002645392984	0.275938919432353	   
df.mm.trans1:probe30	-0.0345138052336394	0.0505342055290248	-0.682979080650948	0.494763095309328	   
df.mm.trans1:probe31	-0.0676259343510594	0.0505342055290248	-1.33822098602535	0.181099459782872	   
df.mm.trans2:probe2	-0.00423303965110877	0.0505342055290248	-0.083765829635483	0.933257774978388	   
df.mm.trans2:probe3	-0.095783965711708	0.0505342055290248	-1.89542834816496	0.0582962076050052	.  
df.mm.trans2:probe4	-0.0130092624160912	0.0505342055290248	-0.257434786594582	0.79689103875803	   
df.mm.trans2:probe5	-0.0385343420550025	0.0505342055290248	-0.762539781749809	0.445900503222967	   
df.mm.trans2:probe6	-0.00441479159292717	0.0505342055290248	-0.0873624418690324	0.93039923636492	   
df.mm.trans3:probe2	0.00519549658869816	0.0505342055290248	0.102811482525714	0.918131229349531	   
df.mm.trans3:probe3	0.0185071150502591	0.0505342055290248	0.366229464904309	0.714263899513812	   
df.mm.trans3:probe4	0.0412793624550058	0.0505342055290248	0.816859828365098	0.414184450176463	   
df.mm.trans3:probe5	-0.0753770579882701	0.0505342055290248	-1.49160469031173	0.136087965803477	   
df.mm.trans3:probe6	0.0111753264313394	0.0505342055290248	0.221143803773086	0.825021283265306	   
df.mm.trans3:probe7	0.054532075824237	0.0505342055290248	1.07911216280853	0.280773006474824	   
df.mm.trans3:probe8	-0.0279570577804547	0.0505342055290248	-0.553230381041556	0.580217473305547	   
df.mm.trans3:probe9	0.0628341038310389	0.0505342055290248	1.24339748044420	0.213984895912677	   
df.mm.trans3:probe10	0.0205007725388737	0.0505342055290248	0.405681108949045	0.685055390938395	   
df.mm.trans3:probe11	0.0438978314811396	0.0505342055290248	0.868675603417303	0.385213046408964	   
df.mm.trans3:probe12	-0.00226874251159338	0.0505342055290248	-0.0448951851096246	0.964198958942809