fitVsDatCorrelation=0.889017226567975
cont.fitVsDatCorrelation=0.222235848703232

fstatistic=11187.4819091861,49,623
cont.fstatistic=2457.38370763937,49,623

residuals=-0.638554907247706,-0.0806152758148132,-0.00420087107276781,0.0788163409533144,0.771576004999683
cont.residuals=-0.451894127417812,-0.182726498394504,-0.0659114649561268,0.0878297838069796,1.45847993931228

predictedValues:
Include	Exclude	Both
Lung	48.9929131557561	54.2618862122763	108.576116397451
cerebhem	55.2059548778818	72.7231147659308	83.5155673512643
cortex	49.4814463113881	61.1743223417008	164.420914481327
heart	49.669150556561	54.7525496797128	111.570415596739
kidney	46.737473554079	55.3645793341586	76.5580902850572
liver	51.4344377679393	56.4504416212143	79.8318110525625
stomach	48.6966067078672	57.8242755176474	107.621821493412
testicle	50.3439071591742	60.724385887659	114.782597957873


diffExp=-5.26897305652012,-17.5171598880490,-11.6928760303127,-5.08339912315179,-8.62710578007957,-5.016003853275,-9.12766880978018,-10.3804787284848
diffExpScore=0.986433994343628
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,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,-1,0,0,0,0,-1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	55.3858898380009	56.8574798616248	56.9381560000343
cerebhem	52.9472858899469	62.148982134793	54.614103310941
cortex	57.8393946908258	50.7409793410574	53.5440353766074
heart	56.1255249291998	56.9081575740822	58.9390868622487
kidney	52.3522068705029	57.2739918225184	54.0133904523994
liver	53.6313229612918	51.0427956022362	53.3829481388258
stomach	55.3537616655112	54.0552752827339	52.9144708662958
testicle	57.2585871496852	60.6356266060939	47.8946180086768
cont.diffExp=-1.47159002362390,-9.20169624484616,7.09841534976842,-0.782632644882341,-4.92178495201546,2.58852735905567,1.29848638277721,-3.37703945640873
cont.diffExpScore=3.14660493962086

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=0.824625962518354
cont.tran.correlation=-0.230620848104000

tran.covariance=0.00375865736900899
cont.tran.covariance=-0.000615805094980997

tran.mean=54.6148403406842
cont.tran.mean=55.6598288887565

weightedLogRatios:
wLogRatio
Lung	-0.402737645628203
cerebhem	-1.14337931249595
cortex	-0.850144495341596
heart	-0.385287535647036
kidney	-0.665589242230386
liver	-0.370995658655970
stomach	-0.68230258366876
testicle	-0.752235285215302

cont.weightedLogRatios:
wLogRatio
Lung	-0.105611133201576
cerebhem	-0.648868752799915
cortex	0.522723986599807
heart	-0.0558699548391076
kidney	-0.359672534793926
liver	0.195768051763136
stomach	0.0949944442687603
testicle	-0.233588338804620

varWeightedLogRatios=0.071989641975958
cont.varWeightedLogRatios=0.128041754365660

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.46699021555958	0.0753550941495736	46.008703919577	1.56277668045529e-202	***
df.mm.trans1	0.524051814264165	0.0674319169852235	7.77156927600029	3.21191399146321e-14	***
df.mm.trans2	0.522422664356553	0.0623467946501005	8.37930269372254	3.53908938996334e-16	***
df.mm.exp2	0.674650790482293	0.0854991902899485	7.89072724775976	1.35521253065999e-14	***
df.mm.exp3	-0.285150649004089	0.0854991902899485	-3.33512689461823	0.000903234167476	***
df.mm.exp4	-0.00449422389425449	0.0854991902899485	-0.0525645199564287	0.95809573667431	   
df.mm.exp5	0.322390232087721	0.0854991902899485	3.77068169879057	0.000178300955199921	***
df.mm.exp6	0.395702730979413	0.0854991902899485	4.62814594661645	4.49115539563816e-06	***
df.mm.exp7	0.0663483335690747	0.0854991902899485	0.7760112504466	0.438036830798596	   
df.mm.exp8	0.0841368106147396	0.0854991902899485	0.984065583889289	0.325465274576062	   
df.mm.trans1:exp2	-0.555255622971598	0.0813259518306834	-6.82753303800996	2.05483153419460e-11	***
df.mm.trans2:exp2	-0.381813578324207	0.0714526047566819	-5.34359215628877	1.27972036872564e-07	***
df.mm.trans1:exp3	0.295072768146842	0.0813259518306834	3.62827315887024	0.000308712750697154	***
df.mm.trans2:exp3	0.405056111789734	0.0714526047566819	5.66887817692685	2.19972026151843e-08	***
df.mm.trans1:exp4	0.0182025929506	0.0813259518306834	0.223822685635415	0.822968653573444	   
df.mm.trans2:exp4	0.0134960918903707	0.0714526047566819	0.188881734071544	0.85024702334324	   
df.mm.trans1:exp5	-0.369519615800503	0.0813259518306834	-4.54368633237548	6.64036623508747e-06	***
df.mm.trans2:exp5	-0.302272274121598	0.0714526047566819	-4.23038845331011	2.68344311923953e-05	***
df.mm.trans1:exp6	-0.347070445556279	0.0813259518306834	-4.26764689184164	2.28302301067562e-05	***
df.mm.trans2:exp6	-0.356161686295094	0.0714526047566819	-4.98458646130445	8.05788810014848e-07	***
df.mm.trans1:exp7	-0.0724146415410542	0.0813259518306834	-0.890424764923967	0.373581572297109	   
df.mm.trans2:exp7	-0.00276172351639201	0.0714526047566819	-0.0386511244173187	0.969180923324348	   
df.mm.trans1:exp8	-0.0569348666971276	0.0813259518306834	-0.70008238963699	0.484137190375971	   
df.mm.trans2:exp8	0.0283864821068698	0.0714526047566819	0.397277079030702	0.691299216650551	   
df.mm.trans1:probe2	-0.0355403983690960	0.0406629759153417	-0.874023545229188	0.382442216810931	   
df.mm.trans1:probe3	-0.155626855431588	0.0406629759153417	-3.82723723309368	0.000142666281306267	***
df.mm.trans1:probe4	-0.110617677060059	0.0406629759153417	-2.72035370186284	0.00670321500972707	** 
df.mm.trans1:probe5	0.236036706181217	0.0406629759153417	5.80470811267315	1.02733806081787e-08	***
df.mm.trans1:probe6	-0.104652616247680	0.0406629759153417	-2.57365856511735	0.0102929856531642	*  
df.mm.trans1:probe7	-0.214222241050191	0.0406629759153417	-5.26823815099492	1.90008964868609e-07	***
df.mm.trans1:probe8	-0.177513457993295	0.0406629759153417	-4.36548122702256	1.48498019769326e-05	***
df.mm.trans1:probe9	-0.140964478874510	0.0406629759153417	-3.46665426475404	0.000563236351119898	***
df.mm.trans1:probe10	-0.175021243187803	0.0406629759153417	-4.30419169399181	1.94608279010630e-05	***
df.mm.trans1:probe11	-0.145958897742403	0.0406629759153417	-3.58947899057564	0.000357396967129559	***
df.mm.trans1:probe12	-0.130800723257211	0.0406629759153417	-3.21670316332802	0.00136390674874447	** 
df.mm.trans1:probe13	-0.127754457958960	0.0406629759153417	-3.14178820126048	0.00175883252826747	** 
df.mm.trans1:probe14	-0.0669166832088977	0.0406629759153417	-1.64564156219690	0.100341951488086	   
df.mm.trans1:probe15	-0.196352075352867	0.0406629759153417	-4.82876796232677	1.73071297834851e-06	***
df.mm.trans1:probe16	-0.155107782551366	0.0406629759153417	-3.81447198735017	0.000150066749304622	***
df.mm.trans1:probe17	-0.195422783497989	0.0406629759153417	-4.80591444917482	1.93267740982242e-06	***
df.mm.trans1:probe18	-0.144100528009486	0.0406629759153417	-3.54377722647492	0.000423964992584161	***
df.mm.trans1:probe19	-0.145523982629192	0.0406629759153417	-3.57878338595201	0.000372034225092991	***
df.mm.trans2:probe2	0.0908587644561874	0.0406629759153417	2.23443470161532	0.0258083640286115	*  
df.mm.trans2:probe3	0.0245226252515579	0.0406629759153417	0.6030701073776	0.546681524952588	   
df.mm.trans2:probe4	0.0222927705892572	0.0406629759153417	0.548232638842509	0.583728558636475	   
df.mm.trans2:probe5	0.0088844618262317	0.0406629759153417	0.218490202112327	0.827118743229754	   
df.mm.trans2:probe6	-0.106875919390709	0.0406629759153417	-2.62833491609713	0.00879190289794592	** 
df.mm.trans3:probe2	0.297798977088492	0.0406629759153417	7.32359032719333	7.49660558103754e-13	***
df.mm.trans3:probe3	0.224161322284288	0.0406629759153417	5.5126639710527	5.18108220414732e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.02349221273781	0.160460573660946	25.0746468178499	1.82372026780941e-96	***
df.mm.trans1	-0.0587645322400901	0.143589019489898	-0.409255056193376	0.682493153900533	   
df.mm.trans2	0.0278923579093651	0.132760798037341	0.210094834632736	0.833662346496187	   
df.mm.exp2	0.0856321489093708	0.182061336082202	0.470347800099122	0.638271164639501	   
df.mm.exp3	-0.00900739110421228	0.182061336082202	-0.0494744864453008	0.960557025980092	   
df.mm.exp4	-0.0203820402482816	0.182061336082202	-0.111951503196038	0.910897919249555	   
df.mm.exp5	0.00370177880729477	0.182061336082202	0.0203325916801104	0.983784566847783	   
df.mm.exp6	-0.0756006327380042	0.182061336082202	-0.415248148590263	0.678103224359469	   
df.mm.exp7	0.0221679525371319	0.182061336082202	0.12176090220014	0.903127642041408	   
df.mm.exp8	0.270550151670562	0.182061336082203	1.48603848292317	0.137774797321317	   
df.mm.trans1:exp2	-0.130660201403640	0.173174873332008	-0.754498611083954	0.450834975987438	   
df.mm.trans2:exp2	0.00335450792953762	0.152150641946891	0.0220472808173135	0.982417299410012	   
df.mm.trans1:exp3	0.0523526384175548	0.173174873332008	0.302310822639871	0.762516027917143	   
df.mm.trans2:exp3	-0.10480653717306	0.152150641946891	-0.688834012344447	0.491184167877071	   
df.mm.trans1:exp4	0.0336478738780370	0.173174873332008	0.194299976842067	0.846004342736827	   
df.mm.trans2:exp4	0.0212729545340314	0.152150641946891	0.139815082354084	0.888851284079186	   
df.mm.trans1:exp5	-0.060032551552457	0.173174873332008	-0.346658556159955	0.728964903392354	   
df.mm.trans2:exp5	0.00359706355089848	0.152150641946891	0.0236414615467351	0.981146169928969	   
df.mm.trans1:exp6	0.0434090484876251	0.173174873332008	0.250665975105996	0.802155013948371	   
df.mm.trans2:exp6	-0.0322827403804145	0.152150641946891	-0.212176169402447	0.832038995520393	   
df.mm.trans1:exp7	-0.0227481995029646	0.173174873332008	-0.131359700545888	0.89553317477023	   
df.mm.trans2:exp7	-0.0727085965899727	0.152150641946891	-0.47787242734968	0.632908631250114	   
df.mm.trans1:exp8	-0.237297391850926	0.173174873332008	-1.37027611041461	0.171094284489954	   
df.mm.trans2:exp8	-0.206215316975234	0.152150641946891	-1.35533648978763	0.175801538444070	   
df.mm.trans1:probe2	-0.000613104642950588	0.086587436666004	-0.00708075751584532	0.99435268681459	   
df.mm.trans1:probe3	0.0992448228266667	0.086587436666004	1.14618040039095	0.252160562140635	   
df.mm.trans1:probe4	0.138251416176517	0.086587436666004	1.59666831008981	0.110846743191929	   
df.mm.trans1:probe5	0.0230013214193063	0.086587436666004	0.265642711055530	0.790602267953769	   
df.mm.trans1:probe6	0.0306612087832875	0.086587436666004	0.354106900075559	0.723378584532769	   
df.mm.trans1:probe7	0.0948948260879397	0.086587436666004	1.09594220295469	0.273527536485405	   
df.mm.trans1:probe8	-0.0103150266723987	0.086587436666004	-0.119128444836486	0.905211997821763	   
df.mm.trans1:probe9	0.128163316689698	0.086587436666004	1.48016065175905	0.139335879816785	   
df.mm.trans1:probe10	0.0149584539275045	0.086587436666004	0.172755477046909	0.862899743034965	   
df.mm.trans1:probe11	0.00553514532985306	0.086587436666004	0.0639255017007135	0.949050051504952	   
df.mm.trans1:probe12	0.0704266009054726	0.086587436666004	0.813358191640792	0.416323453556083	   
df.mm.trans1:probe13	0.0385934631485628	0.086587436666004	0.445716660921958	0.655956776144464	   
df.mm.trans1:probe14	0.079775256083279	0.086587436666004	0.921325993180722	0.357236883593967	   
df.mm.trans1:probe15	0.055008571108946	0.086587436666004	0.635295063891683	0.525469255793358	   
df.mm.trans1:probe16	0.0727685457549565	0.086587436666004	0.840405358523876	0.401003558073997	   
df.mm.trans1:probe17	0.0406375857182846	0.086587436666004	0.469324272469655	0.639002072346455	   
df.mm.trans1:probe18	0.0314335126616376	0.086587436666004	0.363026252675511	0.716708408173514	   
df.mm.trans1:probe19	0.178712149193501	0.086587436666004	2.0639501072523	0.0394359580394375	*  
df.mm.trans2:probe2	-7.1349695065101e-06	0.086587436666004	-8.24019024149197e-05	0.99993427931107	   
df.mm.trans2:probe3	-0.0374415373461738	0.086587436666004	-0.432413047294586	0.665590870983599	   
df.mm.trans2:probe4	-0.0692334792942455	0.086587436666004	-0.79957880681122	0.424259620730781	   
df.mm.trans2:probe5	0.0066214773444412	0.086587436666004	0.0764715713895355	0.939068481341932	   
df.mm.trans2:probe6	0.00252958927551827	0.086587436666004	0.0292142760303175	0.976703051355173	   
df.mm.trans3:probe2	0.102680898271563	0.086587436666004	1.18586370292536	0.236128209971652	   
df.mm.trans3:probe3	0.0453560755855636	0.086587436666004	0.523818204256546	0.600591276832119