fitVsDatCorrelation=0.885103594539738
cont.fitVsDatCorrelation=0.271490584752489

fstatistic=9000.50174350515,53,715
cont.fstatistic=2094.21784752486,53,715

residuals=-0.842007787536023,-0.0990936823189467,0.0076365632740031,0.113527014513244,0.769496095497825
cont.residuals=-1.11545350300300,-0.247026109113219,-0.0113810363758055,0.221752690161065,1.16055669027503

predictedValues:
Include	Exclude	Both
Lung	81.923846724429	93.711094703253	75.5201027759929
cerebhem	83.6594251058409	74.0140924573761	82.4470131216445
cortex	126.308837605478	90.6845572992753	123.516695186914
heart	142.322167815106	95.100110832774	139.295474644935
kidney	80.4499292463077	95.7905617393025	66.5163206831218
liver	84.1509995472235	98.5712883352644	62.9024486348999
stomach	94.9410368888974	108.965490228169	83.8995347694735
testicle	128.374987986293	91.7101936110837	118.194551657761


diffExp=-11.7872479788239,9.64533264846474,35.6242803062028,47.2220569823323,-15.3406324929948,-14.4202887880409,-14.0244533392711,36.6647943752098
diffExpScore=2.47679769060416
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,1,1,0,0,0,1
diffExp1.3Score=0.75
diffExp1.2=0,0,1,1,0,0,0,1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	105.008293898159	107.955298731940	101.601980077005
cerebhem	98.342384701086	128.003544937308	100.358737856127
cortex	116.768818532733	102.238479564283	92.4844382627111
heart	98.5602806312212	100.472684691775	93.7402981313819
kidney	98.6057443078568	106.487858119325	114.697067475104
liver	99.475818952372	109.913721861154	108.191966148715
stomach	94.5621402748018	120.980111944793	93.7912872275811
testicle	108.409985536934	103.840931365258	106.045453334420
cont.diffExp=-2.94700483378114,-29.6611602362223,14.5303389684506,-1.9124040605536,-7.88211381146873,-10.4379029087823,-26.4179716699909,4.56905417167515
cont.diffExpScore=1.60822914532833

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,-1,0,0,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,-1,0,0,0,0,-1,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.0304785649263763
cont.tran.correlation=-0.539554821757186

tran.covariance=0.00185886482649190
cont.tran.covariance=-0.00319991793928072

tran.mean=98.1674137578796
cont.tran.mean=106.226631128187

weightedLogRatios:
wLogRatio
Lung	-0.60129005826491
cerebhem	0.534768141700039
cortex	1.54838485619658
heart	1.91765019559295
kidney	-0.781000489700176
liver	-0.713602511149145
stomach	-0.636816736039383
testicle	1.57627230105112

cont.weightedLogRatios:
wLogRatio
Lung	-0.129197088414492
cerebhem	-1.24427306168966
cortex	0.623743097703058
heart	-0.0884060526114714
kidney	-0.356021441881968
liver	-0.463962776314111
stomach	-1.15114469283481
testicle	0.200848549776028

varWeightedLogRatios=1.38863754818743
cont.varWeightedLogRatios=0.402683969867133

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29349817215006	0.0949381423287701	45.2241645647721	6.5505612723459e-212	***
df.mm.trans1	-0.283748800099142	0.0843069077529667	-3.36566489819047	0.000804397552069223	***
df.mm.trans2	0.250026670656203	0.0766880959090168	3.2603061491165	0.00116566444616295	** 
df.mm.exp2	-0.302754037336877	0.103386949358582	-2.92835835872109	0.00351590117887663	** 
df.mm.exp3	-0.0918670726967228	0.103386949358582	-0.888575137061987	0.374530288450941	   
df.mm.exp4	-0.0451818070703656	0.103386949358582	-0.437016541746089	0.662231303921835	   
df.mm.exp5	0.130743988850759	0.103386949358582	1.26460824757769	0.206423839633958	   
df.mm.exp6	0.260199909201574	0.103386949358582	2.51675778051164	0.0120617278578519	*  
df.mm.exp7	0.193059371458571	0.103386949358582	1.86734759712247	0.0622620270716792	.  
df.mm.exp8	-0.0203507226995459	0.103386949358582	-0.196840344219486	0.84400841642528	   
df.mm.trans1:exp2	0.323718014260772	0.0981707588406315	3.29749935809592	0.00102374789320042	** 
df.mm.trans2:exp2	0.0667929621366671	0.0826671769273382	0.807974393456982	0.419374049577369	   
df.mm.trans1:exp3	0.524806955454441	0.0981707588406315	5.34585819293098	1.21169903605865e-07	***
df.mm.trans2:exp3	0.0590375651262881	0.0826671769273382	0.71415968611315	0.475361511828687	   
df.mm.trans1:exp4	0.597484965015857	0.0981707588406315	6.08618057018183	1.88521452301737e-09	***
df.mm.trans2:exp4	0.0598953531685957	0.0826671769273382	0.724536090318431	0.468973696006864	   
df.mm.trans1:exp5	-0.148899112160929	0.0981707588406315	-1.51673587857917	0.129775361459963	   
df.mm.trans2:exp5	-0.108796418080416	0.0826671769273382	-1.31607757908613	0.188569775232883	   
df.mm.trans1:exp6	-0.23337722774866	0.0981707588406315	-2.37725806039169	0.0177039825994320	*  
df.mm.trans2:exp6	-0.209636472238758	0.0826671769273382	-2.53590941448287	0.0114273669550917	*  
df.mm.trans1:exp7	-0.045593454151329	0.0981707588406315	-0.464430087836486	0.642481018408814	   
df.mm.trans2:exp7	-0.0422447316566078	0.0826671769273382	-0.511021825430661	0.609493526651185	   
df.mm.trans1:exp8	0.469516180091286	0.0981707588406315	4.78264796601491	2.10169314702229e-06	***
df.mm.trans2:exp8	-0.00123233049534309	0.0826671769273382	-0.0149071317195974	0.988110428799732	   
df.mm.trans1:probe2	0.870631947909681	0.0537703391044136	16.1916767201161	1.81434289849665e-50	***
df.mm.trans1:probe3	0.252108312358031	0.0537703391044135	4.68861302638386	3.29465444235471e-06	***
df.mm.trans1:probe4	-0.139851787661031	0.0537703391044136	-2.60090953470576	0.00948988969322079	** 
df.mm.trans1:probe5	-0.241771885151456	0.0537703391044136	-4.49638014523161	8.0625752806939e-06	***
df.mm.trans1:probe6	0.901296392743188	0.0537703391044136	16.7619622222023	1.95123423074863e-53	***
df.mm.trans1:probe7	1.03714950095502	0.0537703391044136	19.2885058608435	4.59215488932170e-67	***
df.mm.trans1:probe8	0.562068525835845	0.0537703391044136	10.4531333667879	6.75287930433582e-24	***
df.mm.trans1:probe9	0.348523058800649	0.0537703391044136	6.48169724434641	1.68977179773831e-10	***
df.mm.trans1:probe10	0.688521648766688	0.0537703391044136	12.8048597095452	6.04384407258039e-34	***
df.mm.trans1:probe11	0.515058149052255	0.0537703391044136	9.57885253526286	1.57206575516834e-20	***
df.mm.trans1:probe12	0.570069076659843	0.0537703391044136	10.6019245211167	1.71909192218929e-24	***
df.mm.trans1:probe13	0.459516274497542	0.0537703391044136	8.54590620314358	7.67873773613994e-17	***
df.mm.trans1:probe14	0.501563340327169	0.0537703391044136	9.3278812944291	1.32543568716859e-19	***
df.mm.trans1:probe15	0.600165085282807	0.0537703391044135	11.1616384660952	8.85813210550983e-27	***
df.mm.trans1:probe16	0.494285049909355	0.0537703391044136	9.19252246019002	4.11108103633361e-19	***
df.mm.trans1:probe17	0.615240285451741	0.0537703391044136	11.4420012166381	5.8999662788201e-28	***
df.mm.trans1:probe18	0.334347897703174	0.0537703391044136	6.21807307285013	8.55612940829358e-10	***
df.mm.trans1:probe19	0.347029809898608	0.0537703391044136	6.45392637797449	2.0100401090148e-10	***
df.mm.trans1:probe20	0.696811137989064	0.0537703391044135	12.9590244286161	1.19438043853060e-34	***
df.mm.trans1:probe21	0.569710866724942	0.0537703391044136	10.5952626710918	1.82823029531529e-24	***
df.mm.trans1:probe22	0.314586687748672	0.0537703391044135	5.850561722101	7.4573447723391e-09	***
df.mm.trans2:probe2	0.0755606013923505	0.0537703391044136	1.40524688240525	0.160382210157838	   
df.mm.trans2:probe3	0.0694027964762282	0.0537703391044136	1.29072640478348	0.197215763104364	   
df.mm.trans2:probe4	-0.0773003524327294	0.0537703391044136	-1.43760210034429	0.150984473983896	   
df.mm.trans2:probe5	-0.148921161284547	0.0537703391044136	-2.76957824266955	0.00575844122078495	** 
df.mm.trans2:probe6	0.0481755766504672	0.0537703391044136	0.895950768636921	0.370580592842347	   
df.mm.trans3:probe2	0.132936741323815	0.0537703391044136	2.47230617358899	0.0136560810058336	*  
df.mm.trans3:probe3	0.0731675531434533	0.0537703391044136	1.36074189529237	0.174024030105169	   
df.mm.trans3:probe4	0.0523870656932113	0.0537703391044136	0.97427441533303	0.33024979760363	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71064355758206	0.19633334079773	23.9930902130125	8.85875624691074e-94	***
df.mm.trans1	-0.0103983817740149	0.174347806323676	-0.0596415979832313	0.952457752357464	   
df.mm.trans2	0.0008017616757387	0.158592002117481	0.00505549879586474	0.995967722900556	   
df.mm.exp2	0.117068370891025	0.21380558608534	0.547545894541304	0.584174621866866	   
df.mm.exp3	0.145770240588993	0.21380558608534	0.681788737413104	0.495593328032814	   
df.mm.exp4	-0.0546674828138512	0.21380558608534	-0.255687813470088	0.79826544374606	   
df.mm.exp5	-0.197827521359352	0.21380558608534	-0.925268254124986	0.355138563980704	   
df.mm.exp6	-0.0989903667607213	0.21380558608534	-0.462992424908905	0.643510679293586	   
df.mm.exp7	0.089117836981467	0.21380558608534	0.416817159051662	0.676937255967086	   
df.mm.exp8	-0.0497809362876664	0.21380558608534	-0.232832720599715	0.815957937528848	   
df.mm.trans1:exp2	-0.182652596192091	0.203018434730722	-0.899684781997042	0.368590924872118	   
df.mm.trans2:exp2	0.0532723466542943	0.170956821171577	0.311612875632664	0.755425586137908	   
df.mm.trans1:exp3	-0.0396135069975628	0.203018434730722	-0.195122709177149	0.845352317354354	   
df.mm.trans2:exp3	-0.200179362125237	0.170956821171578	-1.17093521483025	0.242014907654496	   
df.mm.trans1:exp4	-0.00870350663575557	0.203018434730722	-0.042870523789131	0.965816714670268	   
df.mm.trans2:exp4	-0.0171638615549493	0.170956821171577	-0.100398810865365	0.920055858249881	   
df.mm.trans1:exp5	0.134917703427053	0.203018434730722	0.66455887912841	0.506546943279029	   
df.mm.trans2:exp5	0.184141250926628	0.170956821171577	1.07712140214527	0.281789311487274	   
df.mm.trans1:exp6	0.0448656192411697	0.203018434730722	0.220992833979230	0.825161105059965	   
df.mm.trans2:exp6	0.116968837262011	0.170956821171578	0.684201054163364	0.494069911707855	   
df.mm.trans1:exp7	-0.193899986084332	0.203018434730722	-0.95508561250369	0.339857194634619	   
df.mm.trans2:exp7	0.0247910901998704	0.170956821171578	0.145013752770878	0.884740880895525	   
df.mm.trans1:exp8	0.0816618020057536	0.203018434730722	0.402238358866610	0.687628831818096	   
df.mm.trans2:exp8	0.0109239176236979	0.170956821171577	0.0638986941195772	0.949068751739579	   
df.mm.trans1:probe2	-0.0844720426884449	0.111197776291407	-0.759655862785204	0.447710704242637	   
df.mm.trans1:probe3	-0.0461376461171286	0.111197776291407	-0.414915186758946	0.678328457050393	   
df.mm.trans1:probe4	-0.0341431593945137	0.111197776291407	-0.307048940484543	0.758895524814028	   
df.mm.trans1:probe5	-0.0263432854840218	0.111197776291407	-0.236904786791654	0.812798513625126	   
df.mm.trans1:probe6	-0.0797437696994485	0.111197776291407	-0.717134571922289	0.47352526684685	   
df.mm.trans1:probe7	0.0729531254670286	0.111197776291407	0.656066406182857	0.511992460712046	   
df.mm.trans1:probe8	0.0103973967325395	0.111197776291407	0.0935036390052608	0.925529653228353	   
df.mm.trans1:probe9	-0.0795629356612507	0.111197776291407	-0.715508334022316	0.474528575671881	   
df.mm.trans1:probe10	-0.0294442633279942	0.111197776291407	-0.264791835862185	0.791246140404692	   
df.mm.trans1:probe11	-0.165791820694681	0.111197776291407	-1.49096345470259	0.136412113202394	   
df.mm.trans1:probe12	-0.0104108034072095	0.111197776291407	-0.0936242050373987	0.925433909666734	   
df.mm.trans1:probe13	-0.110885473383171	0.111197776291407	-0.997191464446037	0.319008934255882	   
df.mm.trans1:probe14	-0.0406355014701556	0.111197776291407	-0.365434479226145	0.714895185811677	   
df.mm.trans1:probe15	-0.152123522668778	0.111197776291407	-1.36804464749476	0.171727872325092	   
df.mm.trans1:probe16	0.000863284894921346	0.111197776291407	0.00776350862142248	0.993807844113226	   
df.mm.trans1:probe17	0.0325583655625113	0.111197776291407	0.292796912387782	0.769762338781739	   
df.mm.trans1:probe18	-0.161704146917661	0.111197776291407	-1.45420306332292	0.146328828750766	   
df.mm.trans1:probe19	-0.0286219092670541	0.111197776291407	-0.257396417641006	0.796946825756516	   
df.mm.trans1:probe20	-0.0318742489535300	0.111197776291407	-0.286644661580280	0.774467453958893	   
df.mm.trans1:probe21	-0.155033166847071	0.111197776291407	-1.39421103566665	0.16368696921215	   
df.mm.trans1:probe22	-0.0811962975325776	0.111197776291407	-0.730197133796931	0.465508866261059	   
df.mm.trans2:probe2	0.137336274570318	0.111197776291407	1.23506313840676	0.217212697234689	   
df.mm.trans2:probe3	-0.0185077356993876	0.111197776291407	-0.166439800476638	0.867857909459165	   
df.mm.trans2:probe4	-0.180918518291338	0.111197776291407	-1.62699762823691	0.104178185656306	   
df.mm.trans2:probe5	-0.0192728187880805	0.111197776291407	-0.173320181669584	0.862448815302068	   
df.mm.trans2:probe6	-0.215917986234267	0.111197776291407	-1.94174733915927	0.0525601586941533	.  
df.mm.trans3:probe2	-0.0888303342819061	0.111197776291407	-0.798849916288935	0.424642715808035	   
df.mm.trans3:probe3	-0.159398972040666	0.111197776291407	-1.43347265886813	0.152159935842196	   
df.mm.trans3:probe4	-0.0717063480070649	0.111197776291407	-0.644854154449546	0.519228547484935