fitVsDatCorrelation=0.871846838793608
cont.fitVsDatCorrelation=0.244326645596715

fstatistic=13707.0366119283,63,945
cont.fstatistic=3485.65810955334,63,945

residuals=-0.549583995703324,-0.0816147688193225,-0.00439472957992373,0.0705418509674185,0.898354425500235
cont.residuals=-0.583850791390046,-0.199099310508071,-0.0419034558173111,0.142484762039829,1.23173761836202

predictedValues:
Include	Exclude	Both
Lung	48.7968369665004	71.1960873546515	93.5382410614164
cerebhem	52.5814228497244	76.096247680267	70.8162481613047
cortex	47.5384912252134	62.1365644064336	62.5644549239075
heart	48.6491596622863	92.4581118073035	95.8684544876568
kidney	47.3776835830851	63.361997407049	68.0982199319028
liver	52.4174860659955	66.75869190747	63.8186206518833
stomach	49.3092025748899	71.447276664093	70.3001574354326
testicle	48.7670563999505	70.0534504420755	69.7360609087357


diffExp=-22.3992503881510,-23.5148248305426,-14.5980731812201,-43.8089521450172,-15.9843138239638,-14.3412058414745,-22.1380740892031,-21.2863940421250
diffExpScore=0.994415625608463
diffExp1.5=0,0,0,-1,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,-1,0,-1,0,0,-1,-1
diffExp1.4Score=0.833333333333333
diffExp1.3=-1,-1,-1,-1,-1,0,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	57.2303186800439	53.6641288217767	54.6198840974978
cerebhem	53.061059206605	54.6952758013066	56.7524837457677
cortex	57.2254429435873	49.5631658401423	61.4771060234943
heart	57.7294443246002	55.9758080139261	57.6080797500161
kidney	55.2887570022221	59.7165468726907	58.9999628707576
liver	58.207036764584	54.609804993727	53.1353592514118
stomach	53.4390400721317	55.2700729208465	58.1834280591235
testicle	53.859501665097	53.0232351092004	55.2674926730515
cont.diffExp=3.5661898582672,-1.63421659470156,7.66227710344504,1.75363631067405,-4.42778987046859,3.5972317708569,-1.83103284871481,0.836266555896664
cont.diffExpScore=2.40517853227530

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=0.145729691413833
cont.tran.correlation=-0.173030269892706

tran.covariance=0.000948413004572993
cont.tran.covariance=-0.000349842284173105

tran.mean=60.5591104373118
cont.tran.mean=55.1599149395305

weightedLogRatios:
wLogRatio
Lung	-1.54000858736823
cerebhem	-1.53294768215501
cortex	-1.06995765732703
heart	-2.70056583183752
kidney	-1.16387161745837
liver	-0.986763871695884
stomach	-1.51437521184234
testicle	-1.47350069091156

cont.weightedLogRatios:
wLogRatio
Lung	0.258315645741531
cerebhem	-0.120929953822847
cortex	0.571426643466951
heart	0.124635488985717
kidney	-0.312094184104642
liver	0.257219612572523
stomach	-0.134604649075041
testicle	0.062258884567794

varWeightedLogRatios=0.286606129014598
cont.varWeightedLogRatios=0.0780897244985361

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.45121546920445	0.0634749213052568	54.3713233232261	3.32982083803325e-293	***
df.mm.trans1	0.47884421746623	0.054904218557668	8.72144673115203	1.22068201757308e-17	***
df.mm.trans2	0.870872620064406	0.0480796900120606	18.1131080472015	3.57842922663804e-63	***
df.mm.exp2	0.419540356794671	0.0614326977063086	6.82926800317916	1.52596498890214e-11	***
df.mm.exp3	0.239944024299174	0.0614326977063086	3.90580315138161	0.000100596405087654	***
df.mm.exp4	0.233680235405024	0.0614326977063086	3.80384134393999	0.000151621013648027	***
df.mm.exp5	0.171331494777776	0.0614326977063086	2.78893001894301	0.00539450297881741	** 
df.mm.exp6	0.389546725805849	0.0614326977063086	6.34103238747801	3.5315989490881e-10	***
df.mm.exp7	0.299563467987478	0.0614326977063086	4.8762870453712	1.26774658822130e-06	***
df.mm.exp8	0.276862971877164	0.0614326977063086	4.50676890669466	7.40691690532658e-06	***
df.mm.trans1:exp2	-0.344842971693055	0.0571548785129889	-6.03348271687231	2.30113044946826e-09	***
df.mm.trans2:exp2	-0.352979264916003	0.0407460690207117	-8.66290352417996	1.96732334320123e-17	***
df.mm.trans1:exp3	-0.266069794322815	0.0571548785129888	-4.65524205886203	3.698253283655e-06	***
df.mm.trans2:exp3	-0.376047273842252	0.0407460690207117	-9.22904424598855	1.74376704710549e-19	***
df.mm.trans1:exp4	-0.236711194549144	0.0571548785129888	-4.14157462508383	3.75801358601941e-05	***
df.mm.trans2:exp4	0.0276375971976398	0.0407460690207117	0.67828867573928	0.497754731165328	   
df.mm.trans1:exp5	-0.200845681924436	0.0571548785129888	-3.51406016686384	0.000462238481055854	***
df.mm.trans2:exp5	-0.287905087059891	0.0407460690207117	-7.06583712194533	3.09692460615736e-12	***
df.mm.trans1:exp6	-0.317971981094166	0.0571548785129888	-5.56333928733493	3.44597331329299e-08	***
df.mm.trans2:exp6	-0.453900085184447	0.0407460690207117	-11.1397269992775	3.63020108526361e-27	***
df.mm.trans1:exp7	-0.289118234051672	0.0571548785129888	-5.05850491810541	5.07570467042413e-07	***
df.mm.trans2:exp7	-0.29604154362998	0.0407460690207117	-7.26552403078438	7.77112186158963e-13	***
df.mm.trans1:exp8	-0.277473455252668	0.0571548785129888	-4.85476415087839	1.40973982294263e-06	***
df.mm.trans2:exp8	-0.293042307484894	0.0407460690207117	-7.19191604313873	1.29865952535040e-12	***
df.mm.trans1:probe2	-0.0835263995800441	0.0398158342261665	-2.09781864937421	0.036186252024612	*  
df.mm.trans1:probe3	-0.0222832101405853	0.0398158342261665	-0.559656995104249	0.575846043367198	   
df.mm.trans1:probe4	-0.134306428028747	0.0398158342261665	-3.37319136064923	0.000773200389767261	***
df.mm.trans1:probe5	-0.196744173739989	0.0398158342261665	-4.941355055439	9.17384897237492e-07	***
df.mm.trans1:probe6	-0.079818193501961	0.0398158342261665	-2.00468469525386	0.0452816382455766	*  
df.mm.trans1:probe7	-0.146761071870895	0.0398158342261665	-3.68599766206695	0.000240772003986161	***
df.mm.trans1:probe8	-0.158611741169947	0.0398158342261665	-3.98363475869882	7.30848648386194e-05	***
df.mm.trans1:probe9	-0.115685645204074	0.0398158342261665	-2.90551855693749	0.00375198131648355	** 
df.mm.trans1:probe10	-0.0924864886156157	0.0398158342261665	-2.32285698424057	0.0203983857822124	*  
df.mm.trans1:probe11	0.119964374248467	0.0398158342261665	3.01298155821704	0.00265590853721333	** 
df.mm.trans1:probe12	0.107815460197399	0.0398158342261665	2.70785385495060	0.00689426955143071	** 
df.mm.trans1:probe13	0.113025633009088	0.0398158342261665	2.83871065885664	0.00462668081222419	** 
df.mm.trans1:probe14	0.0991999288885043	0.0398158342261665	2.49146930653311	0.0128921471549619	*  
df.mm.trans1:probe15	-0.0144461023553812	0.0398158342261665	-0.362823048572154	0.716818207070034	   
df.mm.trans1:probe16	0.077122334407356	0.0398158342261665	1.93697647948997	0.053045988571577	.  
df.mm.trans1:probe17	-0.0560607210974272	0.0398158342261665	-1.40800066573978	0.159459747273369	   
df.mm.trans1:probe18	-0.162429762642917	0.0398158342261665	-4.07952679630583	4.89337673094294e-05	***
df.mm.trans1:probe19	-0.151942296584038	0.0398158342261665	-3.81612741606662	0.000144379604085085	***
df.mm.trans1:probe20	-0.121997637554152	0.0398158342261665	-3.06404825932233	0.00224557556483143	** 
df.mm.trans1:probe21	-0.126629012932280	0.0398158342261665	-3.18036819756148	0.00151879219936547	** 
df.mm.trans1:probe22	-0.178000958718543	0.0398158342261665	-4.47060728923677	8.7458658865766e-06	***
df.mm.trans1:probe23	0.0278952382856505	0.0398158342261665	0.700606651293471	0.483720984060783	   
df.mm.trans1:probe24	-0.106085122092919	0.0398158342261665	-2.66439531293811	0.00784407500159987	** 
df.mm.trans1:probe25	-0.123398921416261	0.0398158342261665	-3.09924239475471	0.00199757248135678	** 
df.mm.trans2:probe2	-0.265267448500582	0.0398158342261665	-6.66236068278211	4.56967653949393e-11	***
df.mm.trans2:probe3	-0.256667553044901	0.0398158342261665	-6.4463688387627	1.82415619641511e-10	***
df.mm.trans2:probe4	-0.0990904599618463	0.0398158342261665	-2.48871992481637	0.0129915964940922	*  
df.mm.trans2:probe5	-0.169067747351790	0.0398158342261665	-4.24624400411736	2.38855929364570e-05	***
df.mm.trans2:probe6	-0.172960620305875	0.0398158342261665	-4.34401598427912	1.55028403626169e-05	***
df.mm.trans3:probe2	-0.122606426136387	0.0398158342261665	-3.07933837176294	0.00213454948195637	** 
df.mm.trans3:probe3	-0.462044973742679	0.0398158342261665	-11.6045533824086	3.35201255837595e-29	***
df.mm.trans3:probe4	-0.243290705115448	0.0398158342261665	-6.11040079515802	1.45108548452883e-09	***
df.mm.trans3:probe5	-0.411150045170905	0.0398158342261665	-10.3262948814646	9.2883652965147e-24	***
df.mm.trans3:probe6	-0.192554976185556	0.0398158342261665	-4.83614069447302	1.5448607129017e-06	***
df.mm.trans3:probe7	-0.320841999639968	0.0398158342261665	-8.058150880815	2.32657071079215e-15	***
df.mm.trans3:probe8	-0.288118652390820	0.0398158342261665	-7.23628320216061	9.53474499878015e-13	***
df.mm.trans3:probe9	-0.507559580662903	0.0398158342261665	-12.7476816831164	1.84803563950475e-34	***
df.mm.trans3:probe10	-0.0445590281649045	0.0398158342261665	-1.11912833250699	0.263369771844139	   
df.mm.trans3:probe11	-0.295455159007018	0.0398158342261665	-7.420544231944	2.59683129810280e-13	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05057019974649	0.125671445127759	32.2314285128865	2.35085687316686e-154	***
df.mm.trans1	-0.0122849213762020	0.108702655282867	-0.11301399532729	0.910043462371569	   
df.mm.trans2	-0.0860782118435885	0.095191045547778	-0.90426795239248	0.36608379793281	   
df.mm.exp2	-0.0949093677292836	0.121628128717494	-0.780324162922296	0.435395499290433	   
df.mm.exp3	-0.197848902786395	0.121628128717494	-1.62667061371913	0.104140450748239	   
df.mm.exp4	-0.00240648696226647	0.121628128717494	-0.0197856119932259	0.98421857222592	   
df.mm.exp5	-0.00478868379206775	0.121628128717494	-0.0393715158044604	0.968602505283513	   
df.mm.exp6	0.061946496483056	0.121628128717494	0.509310610434034	0.610653425284092	   
df.mm.exp7	-0.102258018306013	0.121628128717494	-0.84074316841237	0.400704505899596	   
df.mm.exp8	-0.084506445968687	0.121628128717494	-0.694793604569635	0.487355444417634	   
df.mm.trans1:exp2	0.0192688738564858	0.113158646456391	0.170281940089409	0.864824861701588	   
df.mm.trans2:exp2	0.113941921657694	0.0806715041438605	1.41241846011081	0.158156011719129	   
df.mm.trans1:exp3	0.197763704166591	0.113158646456391	1.74766763618726	0.0808463797110011	.  
df.mm.trans2:exp3	0.118352050281685	0.0806715041438605	1.46708619775614	0.142685316213071	   
df.mm.trans1:exp4	0.0110900257560089	0.113158646456391	0.0980042277218535	0.921949718944857	   
df.mm.trans2:exp4	0.0445812984443474	0.0806715041438605	0.552627584144782	0.580649136661264	   
df.mm.trans1:exp5	-0.0297255423464477	0.113158646456391	-0.262689094269992	0.792847479750653	   
df.mm.trans2:exp5	0.111653046670706	0.0806715041438605	1.38404567828061	0.166671242684753	   
df.mm.trans1:exp6	-0.045024047260759	0.113158646456391	-0.397884286094835	0.690805348193757	   
df.mm.trans2:exp6	-0.0444778373777958	0.0806715041438605	-0.551345085849385	0.581527471784021	   
df.mm.trans1:exp7	0.0337157798365364	0.113158646456391	0.297951423884605	0.765805731456608	   
df.mm.trans2:exp7	0.131744817344071	0.0806715041438605	1.63310227994674	0.102780599180494	   
df.mm.trans1:exp8	0.0238014760830236	0.113158646456391	0.210337228558104	0.833449822783403	   
df.mm.trans2:exp8	0.072491875576182	0.0806715041438605	0.898605726340594	0.369091606004418	   
df.mm.trans1:probe2	0.0344380436073545	0.0788297696677134	0.43686596767337	0.662308357290435	   
df.mm.trans1:probe3	-0.00530908860865671	0.0788297696677134	-0.0673487773849372	0.946318292526245	   
df.mm.trans1:probe4	-0.0611029764874783	0.0788297696677134	-0.775125650436912	0.438459326878365	   
df.mm.trans1:probe5	0.0256592737260052	0.0788297696677134	0.325502330327302	0.74487297502289	   
df.mm.trans1:probe6	0.129494182719469	0.0788297696677134	1.64270659758767	0.100776327398037	   
df.mm.trans1:probe7	0.0250299093007229	0.0788297696677134	0.317518488335436	0.750920355068276	   
df.mm.trans1:probe8	-0.0380478840312803	0.0788297696677134	-0.482658825360791	0.629449787648379	   
df.mm.trans1:probe9	-0.0212553698252596	0.0788297696677135	-0.269636330473324	0.787498912665032	   
df.mm.trans1:probe10	0.0485889416077471	0.0788297696677134	0.61637807407736	0.537793467852346	   
df.mm.trans1:probe11	0.0991939890784981	0.0788297696677134	1.25833158585424	0.208582662902717	   
df.mm.trans1:probe12	-0.0186809851554575	0.0788297696677134	-0.236978811865142	0.812724588669818	   
df.mm.trans1:probe13	0.076018491086176	0.0788297696677134	0.964337348778417	0.335123525026818	   
df.mm.trans1:probe14	0.0113857981742503	0.0788297696677134	0.144435258687729	0.885187572692274	   
df.mm.trans1:probe15	0.00829717112171141	0.0788297696677134	0.105254285997359	0.916196372104109	   
df.mm.trans1:probe16	0.117827962654012	0.0788297696677134	1.49471402936587	0.135322926828018	   
df.mm.trans1:probe17	-0.100987283123539	0.0788297696677134	-1.2810805302264	0.200479666853416	   
df.mm.trans1:probe18	0.00809453351735782	0.0788297696677134	0.102683713925313	0.918235784713673	   
df.mm.trans1:probe19	-0.0530781398007562	0.0788297696677134	-0.673326080039222	0.500904497402009	   
df.mm.trans1:probe20	-0.0241575727348012	0.0788297696677134	-0.306452407975201	0.75932771617065	   
df.mm.trans1:probe21	-0.00392481373833551	0.0788297696677134	-0.0497884714731446	0.960301474706458	   
df.mm.trans1:probe22	0.0982903294624696	0.0788297696677134	1.24686815497225	0.212754794679355	   
df.mm.trans1:probe23	0.0332006372768176	0.0788297696677134	0.421168771858225	0.673727617694327	   
df.mm.trans1:probe24	0.0386291644854897	0.0788297696677134	0.490032695114054	0.624224506682105	   
df.mm.trans1:probe25	-0.110857362322749	0.0788297696677134	-1.40628804054661	0.159967344603815	   
df.mm.trans2:probe2	0.0598952812084742	0.0788297696677134	0.75980535603424	0.447560442651457	   
df.mm.trans2:probe3	0.0268470042917467	0.0788297696677134	0.340569361104482	0.733503492623292	   
df.mm.trans2:probe4	0.099505397227229	0.0788297696677134	1.26228197350656	0.207158764311274	   
df.mm.trans2:probe5	0.113311293506999	0.0788297696677134	1.43741753888961	0.150930412325313	   
df.mm.trans2:probe6	0.0107385941867593	0.0788297696677134	0.136225111807697	0.891672337550195	   
df.mm.trans3:probe2	0.098200315202645	0.0788297696677134	1.24572627341908	0.213173666444455	   
df.mm.trans3:probe3	0.0125606930927757	0.0788297696677134	0.159339462055034	0.873435479434205	   
df.mm.trans3:probe4	0.0697767980969332	0.0788297696677134	0.88515795988063	0.37629660860045	   
df.mm.trans3:probe5	0.0100216488450758	0.0788297696677134	0.127130256593664	0.898864349789055	   
df.mm.trans3:probe6	0.0723569015456143	0.0788297696677134	0.917888024417884	0.358911694250405	   
df.mm.trans3:probe7	0.0493774332901709	0.0788297696677134	0.626380534895748	0.531216645233748	   
df.mm.trans3:probe8	0.0815939500360291	0.0788297696677134	1.03506518387618	0.300903266556400	   
df.mm.trans3:probe9	0.0288536729799681	0.0788297696677134	0.366025082929879	0.714428281974845	   
df.mm.trans3:probe10	0.10244029873718	0.0788297696677134	1.29951285116005	0.194084907750671	   
df.mm.trans3:probe11	0.00491849108541498	0.0788297696677134	0.0623938279427634	0.95026240390654