fitVsDatCorrelation=0.870066365649864
cont.fitVsDatCorrelation=0.26003112466451

fstatistic=8947.9997032379,57,807
cont.fstatistic=2321.43129421852,57,807

residuals=-0.861389267313751,-0.0877455414319696,-0.0053312095834252,0.0862602314740853,1.21883144452174
cont.residuals=-0.687664286386577,-0.250815056851998,-0.0557390260467516,0.185676775238552,1.48270238326395

predictedValues:
Include	Exclude	Both
Lung	66.152748461207	54.3456724967115	64.1529582882475
cerebhem	62.8659289795321	66.2305278755919	67.4910544406228
cortex	67.9067546851757	56.4917383079373	73.4201200448556
heart	67.2194527647266	56.9627244048675	71.158681298055
kidney	69.0406301662244	55.4228465250006	69.643924414054
liver	66.5087544102809	52.3122141906963	63.7988877454407
stomach	67.7741705539834	57.3160420577314	67.5928649785953
testicle	72.6085311969143	53.1761981102056	80.5399159303446


diffExp=11.8070759644955,-3.36459889605979,11.4150163772384,10.2567283598590,13.6177836412239,14.1965402195846,10.4581284962520,19.4323330867087
diffExpScore=1.06450418631722
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=0,0,0,0,0,0,0,1
diffExp1.3Score=0.5
diffExp1.2=1,0,1,0,1,1,0,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	69.8970024523888	72.8792726120366	67.8959343277076
cerebhem	68.921717663244	60.7233834371943	66.7295662210602
cortex	62.9494400574576	58.3585425629615	71.2704986539174
heart	64.3710785566177	72.0534646402147	67.8779052454761
kidney	74.5284386393944	73.7263226135827	70.872549582196
liver	67.3201731552783	59.2616537051673	71.6791968554762
stomach	66.2462269288576	59.7536672752569	69.13490396322
testicle	66.6430122484502	70.7997793308244	65.0323779137008
cont.diffExp=-2.98227015964783,8.1983342260497,4.59089749449614,-7.68238608359705,0.802116025811713,8.05851945011106,6.49255965360071,-4.15676708237424
cont.diffExpScore=3.00005862733966

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=-0.681510743891908
cont.tran.correlation=0.482928847055525

tran.covariance=-0.00203161069497884
cont.tran.covariance=0.00262648856766399

tran.mean=62.0209334491742
cont.tran.mean=66.7770734924329

weightedLogRatios:
wLogRatio
Lung	0.804820619502018
cerebhem	-0.217259329057311
cortex	0.759374762880455
heart	0.682987822341398
kidney	0.906241627450498
liver	0.978970428476224
stomach	0.692589537144559
testicle	1.28617393083332

cont.weightedLogRatios:
wLogRatio
Lung	-0.178319859949601
cerebhem	0.528054830365519
cortex	0.310815478142361
heart	-0.475896388821934
kidney	0.0465922451535957
liver	0.528567787158341
stomach	0.427218628018659
testicle	-0.255915030364788

varWeightedLogRatios=0.187054944220524
cont.varWeightedLogRatios=0.150701999069223

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.01903738439006	0.0820811684610716	48.9641833777763	7.25884433257717e-244	***
df.mm.trans1	0.126801256791432	0.0712766894350566	1.77900036879471	0.0756160385886293	.  
df.mm.trans2	-0.0728159590086008	0.06335473775325	-1.14933723334472	0.250757464189224	   
df.mm.exp2	0.0960895668510708	0.0823417902197999	1.16695989478214	0.243571273052036	   
df.mm.exp3	-0.0700293217435615	0.0823417902197999	-0.85047120734961	0.395315419959861	   
df.mm.exp4	-0.0406137746858518	0.0823417902197999	-0.493234050139535	0.621981410605162	   
df.mm.exp5	-0.0197696119797377	0.0823417902197999	-0.240092083581926	0.810319843975898	   
df.mm.exp6	-0.0272335087133235	0.0823417902197999	-0.330737389126803	0.740928708947849	   
df.mm.exp7	0.0251980569248841	0.0823417902198	0.306017841701296	0.759669987815312	   
df.mm.exp8	-0.156120815754223	0.0823417902198	-1.8960094908974	0.0583157678813327	.  
df.mm.trans1:exp2	-0.147051658162538	0.0765897521585717	-1.91999130455575	0.0552112211574439	.  
df.mm.trans2:exp2	0.101686948432102	0.0585506281207798	1.73673539799333	0.0828154733297678	.  
df.mm.trans1:exp3	0.096198392639825	0.0765897521585717	1.25602172521273	0.209471593381534	   
df.mm.trans2:exp3	0.108758737044261	0.0585506281207798	1.85751614517115	0.0636018353639893	.  
df.mm.trans1:exp4	0.0566100173704485	0.0765897521585717	0.73913305337825	0.460041134240827	   
df.mm.trans2:exp4	0.0876458832658276	0.0585506281207799	1.49692473127751	0.134803836815865	   
df.mm.trans1:exp5	0.0624983476456663	0.0765897521585717	0.816014491289506	0.414732637896131	   
df.mm.trans2:exp5	0.039396525349175	0.0585506281207798	0.672862556963638	0.501227359176293	   
df.mm.trans1:exp6	0.0326006543579513	0.0765897521585717	0.425652955377826	0.670474236891956	   
df.mm.trans2:exp6	-0.0109015940726975	0.0585506281207798	-0.186190898758752	0.852341852591235	   
df.mm.trans1:exp7	-0.000983338464808767	0.0765897521585717	-0.0128390344281681	0.989759387359486	   
df.mm.trans2:exp7	0.0280175061945734	0.0585506281207799	0.478517602523035	0.632411460737967	   
df.mm.trans1:exp8	0.249236801660547	0.0765897521585717	3.25417950360416	0.00118454527421506	** 
df.mm.trans2:exp8	0.134366720521757	0.0585506281207799	2.29488094038175	0.0219957799714927	*  
df.mm.trans1:probe2	-0.245224733465756	0.0501397623892232	-4.89082360546772	1.21208405260934e-06	***
df.mm.trans1:probe3	-0.164134116371323	0.0501397623892232	-3.27353199437182	0.00110733463356892	** 
df.mm.trans1:probe4	0.693818346403034	0.0501397623892232	13.8376871636743	3.122471610315e-39	***
df.mm.trans1:probe5	0.101160220400908	0.0501397623892232	2.01756481444057	0.043967168299507	*  
df.mm.trans1:probe6	0.160225268002610	0.0501397623892232	3.19557294186636	0.00144980822529100	** 
df.mm.trans1:probe7	0.558811279189326	0.0501397623892232	11.1450723450064	6.23225961119848e-27	***
df.mm.trans1:probe8	-0.30700095263831	0.0501397623892232	-6.12290401887296	1.43360713522980e-09	***
df.mm.trans1:probe9	-0.332044328069858	0.0501397623892232	-6.62237538128474	6.44895111522036e-11	***
df.mm.trans1:probe10	0.741391056874932	0.0501397623892232	14.7864892362211	5.87497014843106e-44	***
df.mm.trans1:probe11	0.283154250053389	0.0501397623892232	5.64729939993191	2.25840992093410e-08	***
df.mm.trans1:probe12	0.349078389215866	0.0501397623892232	6.96210697023357	6.94801124637956e-12	***
df.mm.trans1:probe13	0.141489667293367	0.0501397623892232	2.82190542099135	0.00489125145291666	** 
df.mm.trans1:probe14	0.10161797314426	0.0501397623892232	2.02669434999359	0.0430220493595716	*  
df.mm.trans1:probe15	0.457141053184584	0.0501397623892232	9.11733585085437	5.99784838189615e-19	***
df.mm.trans1:probe16	0.135958786433998	0.0501397623892232	2.71159614556171	0.00683840889415686	** 
df.mm.trans1:probe17	-0.277729848117433	0.0501397623892232	-5.53911376686393	4.11537311566204e-08	***
df.mm.trans1:probe18	-0.292270696641763	0.0501397623892232	-5.82912009779652	8.05316949976457e-09	***
df.mm.trans1:probe19	-0.206652179624739	0.0501397623892232	-4.12152291469884	4.15279762641586e-05	***
df.mm.trans1:probe20	-0.101625240819467	0.0501397623892232	-2.02683929833122	0.0430071836764965	*  
df.mm.trans1:probe21	-0.190339227008322	0.0501397623892232	-3.79617329517366	0.000157954000524538	***
df.mm.trans1:probe22	-0.222991044281400	0.0501397623892232	-4.44738933045540	9.91000156120338e-06	***
df.mm.trans2:probe2	0.122583751243825	0.0501397623892232	2.44484108824122	0.0147044819247783	*  
df.mm.trans2:probe3	0.0663234882249161	0.0501397623892232	1.32277228819041	0.186285823755424	   
df.mm.trans2:probe4	0.269841847285070	0.0501397623892232	5.3817934993459	9.6723756739217e-08	***
df.mm.trans2:probe5	0.0738701352857216	0.0501397623892232	1.47328451045071	0.141064262353317	   
df.mm.trans2:probe6	0.155390647921844	0.0501397623892232	3.09915006608095	0.00200810116746659	** 
df.mm.trans3:probe2	0.387577905324388	0.0501397623892232	7.72995097814209	3.19917709583003e-14	***
df.mm.trans3:probe3	0.266577584913644	0.0501397623892232	5.31669023168208	1.36894474355205e-07	***
df.mm.trans3:probe4	0.173125473120258	0.0501397623892232	3.4528578690965	0.000583561194709883	***
df.mm.trans3:probe5	-0.257727433837394	0.0501397623892232	-5.14018059831869	3.44599817635307e-07	***
df.mm.trans3:probe6	-0.165122907126937	0.0501397623892232	-3.29325268526657	0.00103347276714818	** 
df.mm.trans3:probe7	0.506445490146529	0.0501397623892232	10.1006759109689	1.14167607097859e-22	***
df.mm.trans3:probe8	-0.204895928565120	0.0501397623892232	-4.08649580296296	4.81702876869425e-05	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.4126853353513	0.160787243692210	27.4442501408779	1.20959081802384e-117	***
df.mm.trans1	-0.153794296730983	0.139622554705757	-1.10150037760799	0.271007356498506	   
df.mm.trans2	-0.125747136196511	0.124104393847890	-1.01323677830967	0.311250764248938	   
df.mm.exp2	-0.17919882078697	0.161297770710987	-1.11098138552738	0.266907255030761	   
df.mm.exp3	-0.375395744445971	0.161297770710987	-2.32734614242502	0.0201933523461452	*  
df.mm.exp4	-0.0934886126236904	0.161297770710987	-0.57960263313994	0.562344382227341	   
df.mm.exp5	0.0328066041531677	0.161297770710987	0.203391553451476	0.838880274517005	   
df.mm.exp6	-0.298629056704788	0.161297770710987	-1.85141465618809	0.0644750636849426	.  
df.mm.exp7	-0.270301530380617	0.161297770710987	-1.67579210294817	0.0941662780383296	.  
df.mm.exp8	-0.0335300668880689	0.161297770710987	-0.207876815285612	0.835377600874313	   
df.mm.trans1:exp2	0.165147389752512	0.150030212477868	1.10076088692385	0.271328960351869	   
df.mm.trans2:exp2	-0.00327659799835132	0.114693714630204	-0.0285682437692053	0.977216002713894	   
df.mm.trans1:exp3	0.270704844910793	0.150030212477868	1.80433554308754	0.0715514467683178	.  
df.mm.trans2:exp3	0.153197222293390	0.114693714630204	1.33570721627885	0.182021564438479	   
df.mm.trans1:exp4	0.0111302891772452	0.150030212477868	0.074186985363945	0.940879986130853	   
df.mm.trans2:exp4	0.0820927482458885	0.114693714630204	0.71575629502081	0.474348886984178	   
df.mm.trans1:exp5	0.0313514101469684	0.150030212477868	0.208967311511294	0.834526491429157	   
df.mm.trans2:exp5	-0.0212509820646657	0.114693714630204	-0.185284626391108	0.853052352236856	   
df.mm.trans1:exp6	0.261066233196904	0.150030212477868	1.74009107155944	0.0822242341464821	.  
df.mm.trans2:exp6	0.0917872321100046	0.114693714630204	0.800281274400658	0.423783369395293	   
df.mm.trans1:exp7	0.216657276658321	0.150030212477868	1.44409098061020	0.149101492853985	   
df.mm.trans2:exp7	0.0717278240237814	0.114693714630204	0.625385830906662	0.531894663724113	   
df.mm.trans1:exp8	-0.0141424994169956	0.150030212477868	-0.0942643430507826	0.924922572951971	   
df.mm.trans2:exp8	0.00458167850990822	0.114693714630204	0.0399470757807479	0.968145200286222	   
df.mm.trans1:probe2	-0.0632690892490973	0.0982178293157342	-0.644171121372581	0.51964751607121	   
df.mm.trans1:probe3	0.0248880627973926	0.0982178293157341	0.253396587674389	0.800026303078424	   
df.mm.trans1:probe4	-0.071457012189492	0.0982178293157342	-0.727536056206088	0.467108650836601	   
df.mm.trans1:probe5	-0.0517808433722422	0.0982178293157342	-0.527204110831913	0.59819675044782	   
df.mm.trans1:probe6	-0.0365938846767469	0.0982178293157342	-0.372578837586718	0.70955975284867	   
df.mm.trans1:probe7	0.0572337516865657	0.0982178293157341	0.582722628725385	0.560242819370947	   
df.mm.trans1:probe8	0.0273988477017497	0.0982178293157341	0.278960020727729	0.780346976251102	   
df.mm.trans1:probe9	-0.0712236945120593	0.0982178293157341	-0.72516054374508	0.468563759275769	   
df.mm.trans1:probe10	-0.0233738845355647	0.0982178293157342	-0.237980056150765	0.811956972039963	   
df.mm.trans1:probe11	-0.0841937009657595	0.0982178293157341	-0.857214026743635	0.391581099195488	   
df.mm.trans1:probe12	-0.0175588178659305	0.0982178293157342	-0.178774240769315	0.858159843270154	   
df.mm.trans1:probe13	-0.0240221481670159	0.0982178293157342	-0.244580320440533	0.806843576073494	   
df.mm.trans1:probe14	-0.0121973982743769	0.0982178293157341	-0.124187210808404	0.90119797338873	   
df.mm.trans1:probe15	0.113426291774658	0.0982178293157341	1.15484421275525	0.24849606777057	   
df.mm.trans1:probe16	0.00713665425334094	0.0982178293157341	0.07266149438509	0.942093495154735	   
df.mm.trans1:probe17	0.026475869579808	0.0982178293157342	0.269562764360204	0.787565559575177	   
df.mm.trans1:probe18	-0.0439144236895574	0.0982178293157342	-0.447112545609094	0.654913773937625	   
df.mm.trans1:probe19	-0.0468868081345787	0.0982178293157341	-0.477375731689762	0.633223850506936	   
df.mm.trans1:probe20	-0.0864542272058627	0.0982178293157342	-0.880229463511601	0.37899706515579	   
df.mm.trans1:probe21	0.0736098359149522	0.0982178293157341	0.749454925116739	0.453801465209392	   
df.mm.trans1:probe22	-0.0532915909930721	0.0982178293157342	-0.542585713452893	0.587564883402058	   
df.mm.trans2:probe2	-0.0259785596047723	0.0982178293157341	-0.264499427301135	0.791462642181018	   
df.mm.trans2:probe3	0.0388600091856743	0.0982178293157341	0.395651272853462	0.692466877737193	   
df.mm.trans2:probe4	-0.0401748269184371	0.0982178293157341	-0.409038024952576	0.682620285918134	   
df.mm.trans2:probe5	0.136759038762416	0.0982178293157341	1.39240542898567	0.164183198008359	   
df.mm.trans2:probe6	-0.083340637717977	0.0982178293157342	-0.848528605229785	0.396395263822288	   
df.mm.trans3:probe2	0.0114604491438441	0.0982178293157342	0.116683999470228	0.907139501522812	   
df.mm.trans3:probe3	0.0606054478273631	0.0982178293157342	0.617051387203223	0.537374880458986	   
df.mm.trans3:probe4	0.198093649683741	0.0982178293157341	2.01688075437855	0.0440386862387246	*  
df.mm.trans3:probe5	0.0513230779101739	0.0982178293157341	0.522543394287295	0.60143545694919	   
df.mm.trans3:probe6	-0.0267584048883085	0.0982178293157342	-0.272439383711995	0.785353885595315	   
df.mm.trans3:probe7	0.241750041747874	0.0982178293157341	2.46136616368029	0.0140491255470712	*  
df.mm.trans3:probe8	0.142183762533491	0.0982178293157341	1.44763698733784	0.14810695968584