fitVsDatCorrelation=0.870905268385998
cont.fitVsDatCorrelation=0.255671029188682

fstatistic=13278.2196073809,52,692
cont.fstatistic=3421.43657411183,52,692

residuals=-0.41656690566066,-0.0739832169623648,-0.00439544542784139,0.0797262977053977,0.627486016414729
cont.residuals=-0.486588535097669,-0.158048285962373,-0.0498476614256797,0.119784773388776,1.33480486563583

predictedValues:
Include	Exclude	Both
Lung	52.9711876540254	43.1302636788569	91.5435613286184
cerebhem	54.3374719796197	56.6842910100957	63.521204776134
cortex	55.7694923760941	42.1808166643959	100.764142479995
heart	68.3763739296155	45.8324973218118	174.899464698918
kidney	48.4341532269786	42.2176867778747	62.228446317926
liver	51.4690112770949	44.4270392473373	60.6841310459484
stomach	51.0200979582278	42.6163920299651	66.9647512239263
testicle	51.7186144021374	46.4245739543285	66.5689992337388


diffExp=9.84092397516854,-2.34681903047591,13.5886757116983,22.5438766078038,6.21646644910388,7.0419720297576,8.40370592826268,5.29404044780889
diffExpScore=1.05159948881053
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=0,0,1,1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	54.6105839880684	50.2099439838731	59.1887264585113
cerebhem	54.2067163035728	56.8687050898407	65.5942549207304
cortex	56.531656883505	52.8022511311675	58.9831419760109
heart	53.5097690108578	55.614510800131	59.6686051766779
kidney	53.1581866278314	59.6263760585313	52.7428095605395
liver	53.7545668602869	50.8593960929618	56.6037559183719
stomach	56.5769705300112	52.8048255127835	69.8400121527888
testicle	54.4068197099971	55.3781234516391	50.8150077722839
cont.diffExp=4.40064000419532,-2.66198878626788,3.72940575233751,-2.10474178927319,-6.46818943069984,2.8951707673251,3.77214501722773,-0.97130374164206
cont.diffExpScore=7.51950686494987

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=0.158386104036118
cont.tran.correlation=-0.46011887956092

tran.covariance=0.00197208295654156
cont.tran.covariance=-0.000622195864150582

tran.mean=49.8506227180287
cont.tran.mean=54.4324626271912

weightedLogRatios:
wLogRatio
Lung	0.794755493795464
cerebhem	-0.169823713249723
cortex	1.08398020823166
heart	1.61014086425157
kidney	0.5235734059335
liver	0.569018929860686
stomach	0.691526980818499
testicle	0.420273461656168

cont.weightedLogRatios:
wLogRatio
Lung	0.332548365117069
cerebhem	-0.192565694379950
cortex	0.273034044644419
heart	-0.154287037893470
kidney	-0.462827064762403
liver	0.219060307519336
stomach	0.276073828056637
testicle	-0.0708749357235546

varWeightedLogRatios=0.266748232792268
cont.varWeightedLogRatios=0.0833427805264145

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.27465945113683	0.0701718307612576	46.6662963700926	6.37480933242091e-216	***
df.mm.trans1	0.615324624541018	0.0630245658834064	9.76325050265877	3.49899616637443e-21	***
df.mm.trans2	0.451392119092868	0.0579596478507567	7.7880410911947	2.49185861470513e-14	***
df.mm.exp2	0.6641792931535	0.079390363807296	8.36599382219309	3.2846965703323e-16	***
df.mm.exp3	-0.0667481601474802	0.079390363807296	-0.840758965527577	0.400773391045229	   
df.mm.exp4	-0.331348700513943	0.079390363807296	-4.17366396403252	3.37956676397954e-05	***
df.mm.exp5	0.275074108051491	0.079390363807296	3.46482992217012	0.000563296535556783	***
df.mm.exp6	0.411987826855412	0.079390363807296	5.18939336082939	2.77541460886757e-07	***
df.mm.exp7	0.263134099889364	0.079390363807296	3.31443373314259	0.000966157389141284	***
df.mm.exp8	0.368249579389256	0.079390363807296	4.63846695907713	4.19763351817141e-06	***
df.mm.trans1:exp2	-0.638713349074575	0.0760104864037428	-8.40296358165554	2.46946696268008e-16	***
df.mm.trans2:exp2	-0.390907099526724	0.06615863650608	-5.90863294909047	5.41207162089568e-09	***
df.mm.trans1:exp3	0.118227011848426	0.0760104864037429	1.55540396387471	0.120307092095593	   
df.mm.trans2:exp3	0.0444887721989972	0.06615863650608	0.67245600194479	0.501517909138254	   
df.mm.trans1:exp4	0.586627918525925	0.0760104864037428	7.71772351790975	4.14826333782309e-14	***
df.mm.trans2:exp4	0.392117164310148	0.06615863650608	5.92692330160269	4.86866998210874e-09	***
df.mm.trans1:exp5	-0.364617034556779	0.0760104864037429	-4.79693068427496	1.974251743359e-06	***
df.mm.trans2:exp5	-0.296459781080383	0.06615863650608	-4.48104430104358	8.69014746366241e-06	***
df.mm.trans1:exp6	-0.44075605963126	0.0760104864037429	-5.7986217492427	1.01671041277221e-08	***
df.mm.trans2:exp6	-0.382364474945074	0.06615863650608	-5.7795096020447	1.13323860802242e-08	***
df.mm.trans1:exp7	-0.300662603743796	0.0760104864037428	-3.95554110977233	8.42302377672105e-05	***
df.mm.trans2:exp7	-0.275120055415284	0.06615863650608	-4.15849040948721	3.60613222497546e-05	***
df.mm.trans1:exp8	-0.392179952775919	0.0760104864037429	-5.15955062690675	3.23702413772148e-07	***
df.mm.trans2:exp8	-0.294645573491410	0.06615863650608	-4.45362221853427	9.84266269872264e-06	***
df.mm.trans1:probe2	0.0295725406703062	0.0380052432018714	0.778117390624933	0.436765770986063	   
df.mm.trans1:probe3	0.172994701969415	0.0380052432018714	4.55186409544926	6.28091809023396e-06	***
df.mm.trans1:probe4	-0.0163977729316304	0.0380052432018714	-0.431460807776725	0.666267726373317	   
df.mm.trans1:probe5	0.378851534229353	0.0380052432018714	9.96840178648555	5.82903434282131e-22	***
df.mm.trans1:probe6	0.110305342615589	0.0380052432018714	2.90237170775840	0.00382125125596597	** 
df.mm.trans1:probe7	0.18742430776916	0.0380052432018714	4.93153817681479	1.02252187328593e-06	***
df.mm.trans1:probe8	0.163750948946571	0.0380052432018714	4.30864099663249	1.88060891550993e-05	***
df.mm.trans1:probe9	0.125728472791597	0.0380052432018714	3.30818756043129	0.00098762013686737	***
df.mm.trans1:probe10	0.200196938878368	0.0380052432018714	5.26761367674948	1.84764017092875e-07	***
df.mm.trans1:probe11	-0.091593003075348	0.0380052432018714	-2.41000965547927	0.0162120068200553	*  
df.mm.trans1:probe12	0.0446572083780808	0.0380052432018714	1.1750275650356	0.240387838460223	   
df.mm.trans1:probe13	-0.00314292579584261	0.0380052432018714	-0.0826971631032175	0.934116255121694	   
df.mm.trans1:probe14	0.171729652282289	0.0380052432018714	4.51857790700397	7.320392663766e-06	***
df.mm.trans1:probe15	-0.0172881544800398	0.0380052432018714	-0.454888668603192	0.649332005344889	   
df.mm.trans1:probe16	0.195751655429917	0.0380052432018714	5.15064867208317	3.3885440064807e-07	***
df.mm.trans1:probe17	-0.0282046854444869	0.0380052432018714	-0.742126166504784	0.458262708741374	   
df.mm.trans1:probe18	0.140306034331590	0.0380052432018714	3.69175467675158	0.000240241990045115	***
df.mm.trans1:probe19	0.079472092247112	0.0380052432018714	2.09108232316742	0.036884829867974	*  
df.mm.trans1:probe20	0.118363648188548	0.0380052432018714	3.11440312484882	0.00191924589962423	** 
df.mm.trans1:probe21	0.0783530096250932	0.0380052432018714	2.06163684334047	0.0396152453237952	*  
df.mm.trans1:probe22	-0.0467300244998922	0.0380052432018714	-1.22956783230350	0.219276868292096	   
df.mm.trans2:probe2	0.155181034839240	0.0380052432018714	4.08314805446629	4.96205088422756e-05	***
df.mm.trans2:probe3	-0.00430180351576461	0.0380052432018714	-0.113189737871557	0.909912959679128	   
df.mm.trans2:probe4	0.154037468378429	0.0380052432018714	4.0530583519814	5.6287681107404e-05	***
df.mm.trans2:probe5	0.0232002007582987	0.0380052432018714	0.610447369986998	0.541765922679389	   
df.mm.trans2:probe6	0.0154432854937121	0.0380052432018714	0.406346182595975	0.684613887148407	   
df.mm.trans3:probe2	0.342447916978676	0.0380052432018714	9.01054402308924	1.97530878665922e-18	***
df.mm.trans3:probe3	0.183868315965656	0.0380052432018714	4.83797235526182	1.61810739548790e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.89415964342964	0.138039395088344	28.2104948441525	3.89146297138169e-117	***
df.mm.trans1	0.106821340181300	0.123979563535261	0.861604422013631	0.389203691300538	   
df.mm.trans2	0.0345387928358292	0.114016046639461	0.302929226664446	0.76203483784153	   
df.mm.exp2	0.0143521825300894	0.156173747740571	0.0918988161437388	0.926805028492849	   
df.mm.exp3	0.0883932190747929	0.156173747740571	0.565992814756728	0.571582152742775	   
df.mm.exp4	0.0737926707487092	0.156173747740571	0.47250368142084	0.636716292851104	   
df.mm.exp5	0.260232985749943	0.156173747740571	1.66630428938820	0.096105360751167	.  
df.mm.exp6	0.0417084602675687	0.156173747740571	0.267064477039079	0.789499086031246	   
df.mm.exp7	-0.0797121973993028	0.156173747740571	-0.510407149425122	0.609929017712225	   
df.mm.exp8	0.246772689002244	0.156173747740571	1.58011632923206	0.114537180222055	   
df.mm.trans1:exp2	-0.0217750746219655	0.149524979606723	-0.145628340356493	0.884257202232266	   
df.mm.trans2:exp2	0.110179914467377	0.130144789783809	0.846594893659617	0.397513630366300	   
df.mm.trans1:exp3	-0.0538201489085599	0.149524979606723	-0.359940854365046	0.719001187379805	   
df.mm.trans2:exp3	-0.0380524886205637	0.130144789783809	-0.292385801104868	0.770079304401303	   
df.mm.trans1:exp4	-0.0941561450824869	0.149524979606723	-0.629701775115665	0.529097567447968	   
df.mm.trans2:exp4	0.0284383876617446	0.130144789783809	0.218513454968003	0.827093501781097	   
df.mm.trans1:exp5	-0.287188573978026	0.149524979606723	-1.92067288511514	0.0551835811286908	.  
df.mm.trans2:exp5	-0.0883480526670822	0.130144789783809	-0.678844330332718	0.497463467277489	   
df.mm.trans1:exp6	-0.0575075419270409	0.149524979606723	-0.384601570107523	0.700650795265884	   
df.mm.trans2:exp6	-0.0288566686627396	0.130144789783809	-0.221727421517796	0.824591497403515	   
df.mm.trans1:exp7	0.115086508814177	0.149524979606723	0.769680819331157	0.441751896796668	   
df.mm.trans2:exp7	0.130101681813936	0.130144789783809	0.999668769145927	0.317820257300686	   
df.mm.trans1:exp8	-0.250510890581906	0.149524979606723	-1.67537819594287	0.0943118120645651	.  
df.mm.trans2:exp8	-0.148801151189593	0.130144789783809	-1.143350812866	0.253288345729449	   
df.mm.trans1:probe2	0.0533162305728939	0.0747624898033614	0.713141452526863	0.475998645030017	   
df.mm.trans1:probe3	-0.00960971241474052	0.0747624898033614	-0.128536548742769	0.89776172290416	   
df.mm.trans1:probe4	0.0146932298356177	0.0747624898033614	0.196532109541342	0.84425140587631	   
df.mm.trans1:probe5	-0.0813934962599651	0.0747624898033614	-1.08869429675288	0.276667720464927	   
df.mm.trans1:probe6	-0.0437084670925729	0.0747624898033613	-0.58463097212966	0.558986430686839	   
df.mm.trans1:probe7	-0.051560506912218	0.0747624898033614	-0.68965743446789	0.490640838289009	   
df.mm.trans1:probe8	-0.0219365906026975	0.0747624898033614	-0.293417068644913	0.769291353066049	   
df.mm.trans1:probe9	0.0200280612665487	0.0747624898033614	0.267889169010102	0.788864460233455	   
df.mm.trans1:probe10	-0.0188516687000545	0.0747624898033614	-0.252154104948052	0.800996823408321	   
df.mm.trans1:probe11	-0.0483915928523016	0.0747624898033614	-0.647271017586227	0.517671210893866	   
df.mm.trans1:probe12	0.00260120453338826	0.0747624898033614	0.0347929093885167	0.972254908109062	   
df.mm.trans1:probe13	0.0227985129593513	0.0747624898033613	0.304945876191596	0.76049906761918	   
df.mm.trans1:probe14	-0.0196539529876602	0.0747624898033614	-0.262885212080999	0.792717302455214	   
df.mm.trans1:probe15	0.0535404397795726	0.0747624898033614	0.716140405708712	0.474146248985591	   
df.mm.trans1:probe16	-0.0560179115342573	0.0747624898033614	-0.749278303619828	0.453944106661613	   
df.mm.trans1:probe17	0.113302115751628	0.0747624898033613	1.51549414752817	0.130103940845327	   
df.mm.trans1:probe18	0.0636614235780711	0.0747624898033614	0.851515562757634	0.394777591418041	   
df.mm.trans1:probe19	-0.0803040259981905	0.0747624898033614	-1.07412187862395	0.283142451104754	   
df.mm.trans1:probe20	0.0449178973585461	0.0747624898033614	0.600807938268083	0.548164644747831	   
df.mm.trans1:probe21	-0.0117734992227246	0.0747624898033614	-0.157478693576029	0.874913536172153	   
df.mm.trans1:probe22	0.0355104661802613	0.0747624898033614	0.474977040942057	0.634953206946537	   
df.mm.trans2:probe2	-0.00817510955195143	0.0747624898033614	-0.10934774341322	0.912958380624414	   
df.mm.trans2:probe3	-0.0692373464781551	0.0747624898033614	-0.92609738734306	0.354718228326304	   
df.mm.trans2:probe4	0.0110582493426809	0.0747624898033614	0.147911731829238	0.882455516273463	   
df.mm.trans2:probe5	-0.0518930854306427	0.0747624898033614	-0.694105902132617	0.4878487396762	   
df.mm.trans2:probe6	0.00587921541098154	0.0747624898033614	0.0786385716479603	0.937342840227622	   
df.mm.trans3:probe2	0.0698710227318695	0.0747624898033614	0.93457324542886	0.350334268417407	   
df.mm.trans3:probe3	0.0657629248623852	0.0747624898033614	0.879624595640853	0.379368063841461