fitVsDatCorrelation=0.932056155061386
cont.fitVsDatCorrelation=0.255718968710602

fstatistic=7273.71823442255,64,968
cont.fstatistic=1008.63715048250,64,968

residuals=-0.96976713321854,-0.108168856392441,-0.00750103016750015,0.110412800547484,1.31718976796942
cont.residuals=-1.01970015512538,-0.420676033640764,-0.137824652231529,0.335114781037314,2.01066361277401

predictedValues:
Include	Exclude	Both
Lung	72.73388423006	268.825021023276	104.344057530672
cerebhem	74.7450781310638	174.911849801745	153.058869165065
cortex	93.5199005698862	276.11968900164	144.780008091477
heart	84.2906475538644	301.784601117824	121.929812730076
kidney	80.833123844705	247.285810979177	183.348335196234
liver	73.9079892540984	230.601063629206	101.424327080509
stomach	77.8772362759952	417.585792683572	120.652716692676
testicle	72.2122556811866	264.502311490579	103.654524160929


diffExp=-196.091136793216,-100.166771670681,-182.599788431754,-217.493953563960,-166.452687134472,-156.693074375107,-339.708556407577,-192.290055809392
diffExpScore=0.999355875967203
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	101.397159367276	68.9568670384804	87.7373652198116
cerebhem	95.3172375184095	81.6917422801199	96.3055645693116
cortex	99.2954787415994	87.0456827901266	96.8498708321397
heart	102.953708242176	83.7752521150878	110.268327633994
kidney	111.614872321875	93.0772835007216	99.9579089827588
liver	99.8772784048137	87.2940066610486	90.620514619346
stomach	115.299529313664	88.5918927068167	90.8542897197706
testicle	96.0994479647475	97.2998630135826	128.596592599719
cont.diffExp=32.4402923287958,13.6254952382896,12.2497959514727,19.1784561270878,18.5375888211536,12.5832717437652,26.7076366068471,-1.20041504883514
cont.diffExpScore=1.01036714106717

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=1,0,0,0,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=1,0,0,0,0,0,1,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=1,0,0,1,0,0,1,0
cont.diffExp1.2Score=0.75

tran.correlation=0.198292500939444
cont.tran.correlation=0.159214798502349

tran.covariance=0.00551570101953285
cont.tran.covariance=0.00107367273540149

tran.mean=175.733515954242
cont.tran.mean=94.349206373784

weightedLogRatios:
wLogRatio
Lung	-6.45839882703991
cerebhem	-4.02924732425418
cortex	-5.49937705110525
heart	-6.46903496901821
kidney	-5.5365209480511
liver	-5.54341561321868
stomach	-8.72394012678003
testicle	-6.39867533245782

cont.weightedLogRatios:
wLogRatio
Lung	1.70660720109014
cerebhem	0.691087327062125
cortex	0.596749796884599
heart	0.934071529783279
kidney	0.839874074308642
liver	0.610902025075242
stomach	1.21622750523322
testicle	-0.0567517699334665

varWeightedLogRatios=1.78749511120572
cont.varWeightedLogRatios=0.262184895131855

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.71689214474454	0.106933886677898	53.4619316883595	3.77919689923111e-291	***
df.mm.trans1	-1.48287757802855	0.095005267968207	-15.6083721433719	3.71151961021355e-49	***
df.mm.trans2	-0.116078651553925	0.084572140295255	-1.37254007228239	0.170213292422688	   
df.mm.exp2	-0.785631478831876	0.112177068706367	-7.00349445650369	4.66699405697547e-12	***
df.mm.exp3	-0.0493810862256305	0.112177068706367	-0.440206601893741	0.659885753165399	   
df.mm.exp4	0.107364501348652	0.112177068706367	0.9570984746418	0.338756522434021	   
df.mm.exp5	-0.541630550277787	0.112177068706367	-4.82835357104535	1.59923206989992e-06	***
df.mm.exp6	-0.108977186013694	0.112177068706367	-0.971474716449859	0.331554617678370	   
df.mm.exp7	0.363533035644485	0.112177068706367	3.24070721259496	0.00123299797796571	** 
df.mm.exp8	-0.016778081203327	0.112177068706367	-0.149567834110954	0.881136742949799	   
df.mm.trans1:exp2	0.812907485633364	0.10768215481343	7.54913836040713	1.01048918246683e-13	***
df.mm.trans2:exp2	0.355852921948655	0.0855162982088346	4.16122925573376	3.44696082867838e-05	***
df.mm.trans1:exp3	0.300747981291637	0.10768215481343	2.79292313394650	0.00532638972762354	** 
df.mm.trans2:exp3	0.0761548252634163	0.0855162982088346	0.890529955792085	0.373402666671887	   
df.mm.trans1:exp4	0.0400990561820685	0.10768215481343	0.372383485931759	0.709688858974146	   
df.mm.trans2:exp4	0.00828833173599453	0.0855162982088346	0.0969210771466517	0.922809133063037	   
df.mm.trans1:exp5	0.647210021470974	0.10768215481343	6.01037398065009	2.61872215493551e-09	***
df.mm.trans2:exp5	0.458114658936902	0.0855162982088346	5.35704501401786	1.05726643322571e-07	***
df.mm.trans1:exp6	0.124990758209956	0.10768215481343	1.16073789966884	0.246034674258622	   
df.mm.trans2:exp6	-0.044394282200542	0.0855162982088346	-0.519132412538826	0.603787072140411	   
df.mm.trans1:exp7	-0.295206701625499	0.10768215481343	-2.74146354274739	0.00622934468095895	** 
df.mm.trans2:exp7	0.0768962905126991	0.0855162982088346	0.89920041118846	0.368769543016809	   
df.mm.trans1:exp8	0.00958050007267521	0.10768215481343	0.0889701742064354	0.92912401702772	   
df.mm.trans2:exp8	0.000567383077405099	0.0855162982088347	0.00663479464487019	0.994707605680033	   
df.mm.trans1:probe2	0.334888607233454	0.0628728328704697	5.32644374277503	1.24569114369115e-07	***
df.mm.trans1:probe3	-0.310468381646224	0.0628728328704697	-4.93803710556274	9.29089105766716e-07	***
df.mm.trans1:probe4	-0.110967686889700	0.0628728328704697	-1.76495446162439	0.0778867063071378	.  
df.mm.trans1:probe5	-0.224420069154765	0.0628728328704697	-3.56942830327232	0.000375330641039188	***
df.mm.trans1:probe6	-0.142424800912765	0.0628728328704697	-2.26528365925848	0.0237155637247791	*  
df.mm.trans1:probe7	-0.287263106909410	0.0628728328704697	-4.56895440835675	5.53452195026618e-06	***
df.mm.trans1:probe8	-0.0908714735489556	0.0628728328704697	-1.44532176140637	0.148691173384109	   
df.mm.trans1:probe9	-0.0578846445303366	0.0628728328704697	-0.920662262023253	0.357456163684077	   
df.mm.trans1:probe10	-0.212089255316026	0.0628728328704697	-3.37330521996633	0.00077215918268049	***
df.mm.trans1:probe11	0.232431204238325	0.0628728328704697	3.69684637428663	0.000230553477090835	***
df.mm.trans1:probe12	-0.0074523179167541	0.0628728328704697	-0.118530016487524	0.905672304492359	   
df.mm.trans1:probe13	0.0867032804517925	0.0628728328704697	1.37902614680045	0.168205236198560	   
df.mm.trans1:probe14	0.0114983783311472	0.0628728328704697	0.182883096023939	0.854928041959108	   
df.mm.trans1:probe15	0.0405536121810592	0.0628728328704697	0.645010099427325	0.519073476448922	   
df.mm.trans1:probe16	0.0879700406606992	0.0628728328704697	1.39917412091061	0.162081117189980	   
df.mm.trans1:probe17	0.652606002458689	0.0628728328704697	10.3797772847803	5.2722621201868e-24	***
df.mm.trans1:probe18	0.387217844981619	0.0628728328704697	6.15874658899756	1.07341259038311e-09	***
df.mm.trans1:probe19	0.605994878926321	0.0628728328704697	9.6384217357406	4.6835816944954e-21	***
df.mm.trans1:probe20	0.450909785521494	0.0628728328704697	7.17177459540365	1.46953457553372e-12	***
df.mm.trans1:probe21	0.699812241534407	0.0628728328704697	11.1305982184095	3.65661934477108e-27	***
df.mm.trans1:probe22	0.723475719075281	0.0628728328704697	11.5069686865515	8.25489009385633e-29	***
df.mm.trans1:probe23	-0.0032931444306461	0.0628728328704697	-0.0523778598847394	0.958238421779895	   
df.mm.trans1:probe24	0.350186314178907	0.0628728328704697	5.56975561925703	3.30488718217325e-08	***
df.mm.trans1:probe25	-0.302793471012882	0.0628728328704697	-4.81596672503521	1.69923494388141e-06	***
df.mm.trans1:probe26	-0.0478275985178634	0.0628728328704697	-0.760703730598518	0.447019298669669	   
df.mm.trans1:probe27	-0.254688298644744	0.0628728328704697	-4.05084814882529	5.51170105044844e-05	***
df.mm.trans1:probe28	-0.0593809315249628	0.0628728328704697	-0.94446088738039	0.345169851226052	   
df.mm.trans1:probe29	-0.171317058071893	0.0628728328704697	-2.72481849871215	0.00654967614129747	** 
df.mm.trans1:probe30	-0.0589929496603455	0.0628728328704697	-0.938289988966817	0.348329452850682	   
df.mm.trans1:probe31	-0.136502996499593	0.0628728328704697	-2.17109664488661	0.0301662329951246	*  
df.mm.trans1:probe32	-0.232276413740666	0.0628728328704697	-3.69438441272720	0.000232767034489478	***
df.mm.trans2:probe2	-0.116333256697712	0.0628728328704697	-1.85029449742437	0.0645756760104193	.  
df.mm.trans2:probe3	0.152868056525876	0.0628728328704697	2.43138490102417	0.0152218056701378	*  
df.mm.trans2:probe4	0.300203811347853	0.0628728328704697	4.77477787530795	2.07690614578114e-06	***
df.mm.trans2:probe5	-0.0843686971891963	0.0628728328704697	-1.34189431805963	0.179944937021126	   
df.mm.trans2:probe6	-0.326650765793636	0.0628728328704697	-5.19541987978497	2.4905665146862e-07	***
df.mm.trans3:probe2	0.417449394835286	0.0628728328704697	6.63958304050516	5.23447719241598e-11	***
df.mm.trans3:probe3	0.460598399761674	0.0628728328704697	7.3258731749943	4.99577247566731e-13	***
df.mm.trans3:probe4	0.374791233369682	0.0628728328704697	5.9610998305393	3.50655500754786e-09	***
df.mm.trans3:probe5	1.39594981834127	0.0628728328704697	22.2027504505356	1.32539540658160e-88	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42185090018046	0.285328479757307	15.4974046192009	1.48485885712026e-48	***
df.mm.trans1	0.356606230379204	0.253499704541339	1.40673233140219	0.159827736043292	   
df.mm.trans2	-0.283366263931362	0.225661303165318	-1.25571491415065	0.209522360523227	   
df.mm.exp2	0.0144590884261638	0.299318704967958	0.0483066650569387	0.961481807437014	   
df.mm.exp3	0.113192648432015	0.299318704967958	0.378167640556014	0.705388975550195	   
df.mm.exp4	-0.0186780463446620	0.299318704967958	-0.0624018680912755	0.950255689487834	   
df.mm.exp5	0.265556867334523	0.299318704967958	0.887204384246387	0.375189245660043	   
df.mm.exp6	0.188365002060696	0.299318704967958	0.629312498465006	0.529292978157183	   
df.mm.exp7	0.344138278139443	0.299318704967959	1.14973863119006	0.250535513252307	   
df.mm.exp8	-0.0916775669500635	0.299318704967959	-0.306287463591283	0.759451647020257	   
df.mm.trans1:exp2	-0.0762934943837632	0.287325061160968	-0.265530246736888	0.790657550060664	   
df.mm.trans2:exp2	0.15501264090193	0.228180571383296	0.679341978864366	0.497083607386911	   
df.mm.trans1:exp3	-0.13413768635585	0.287325061160968	-0.46684993579694	0.640712301067424	   
df.mm.trans2:exp3	0.119759228206150	0.228180571383296	0.524844106928716	0.599811742418627	   
df.mm.trans1:exp4	0.0339124223710852	0.287325061160968	0.118028069789869	0.906069902778705	   
df.mm.trans2:exp4	0.213334495647746	0.228180571383296	0.934937161189714	0.350053850234327	   
df.mm.trans1:exp5	-0.169547638368564	0.287325061160968	-0.590089975735108	0.555268047233243	   
df.mm.trans2:exp5	0.0343920923621997	0.228180571383296	0.150723140685050	0.880225519632238	   
df.mm.trans1:exp6	-0.203467862305871	0.287325061160968	-0.708145197929262	0.479025570814398	   
df.mm.trans2:exp6	0.0474356124596916	0.228180571383296	0.207886290108414	0.83536148254916	   
df.mm.trans1:exp7	-0.215650009783265	0.287325061160968	-0.750543683561424	0.453109720523834	   
df.mm.trans2:exp7	-0.0935791228997605	0.228180571383296	-0.410109950783526	0.681816040455972	   
df.mm.trans1:exp8	0.0380160618910771	0.287325061160968	0.132310289041509	0.894766365926604	   
df.mm.trans2:exp8	0.435993954517352	0.228180571383296	1.91074091836229	0.0563330606480057	.  
df.mm.trans1:probe2	-0.0648173933373239	0.167761692558719	-0.386365876194512	0.699310626877038	   
df.mm.trans1:probe3	-0.154175136685821	0.167761692558719	-0.919012763488048	0.358317835418085	   
df.mm.trans1:probe4	-0.198431150798001	0.167761692558719	-1.18281562239572	0.237172657048554	   
df.mm.trans1:probe5	-0.096533833960855	0.167761692558719	-0.575422389274397	0.565139396361958	   
df.mm.trans1:probe6	-0.190623790285013	0.167761692558719	-1.13627722382624	0.256121784813254	   
df.mm.trans1:probe7	-0.323921898970813	0.167761692558719	-1.93084543932719	0.0537939306277612	.  
df.mm.trans1:probe8	-0.256359841454618	0.167761692558719	-1.52811906904723	0.126809658751284	   
df.mm.trans1:probe9	-0.342762962387491	0.167761692558719	-2.04315393555962	0.0413075708531816	*  
df.mm.trans1:probe10	-0.0779141592584464	0.167761692558719	-0.464433554943869	0.642441589715153	   
df.mm.trans1:probe11	-0.249556966677488	0.167761692558719	-1.48756824559420	0.137190453233904	   
df.mm.trans1:probe12	-0.0560428475698719	0.167761692558719	-0.33406224457503	0.738404947278519	   
df.mm.trans1:probe13	-0.274075500518930	0.167761692558719	-1.63371921407505	0.102642994001294	   
df.mm.trans1:probe14	-0.114726057525552	0.167761692558719	-0.683863257313024	0.494225186107555	   
df.mm.trans1:probe15	-0.085591046199939	0.167761692558719	-0.510194221901885	0.61003171072173	   
df.mm.trans1:probe16	-0.360616804917189	0.167761692558719	-2.14957776961488	0.0318359017080222	*  
df.mm.trans1:probe17	-0.214124690183596	0.167761692558719	-1.27636224288002	0.202133631114728	   
df.mm.trans1:probe18	-0.418604908075286	0.167761692558719	-2.49523536446658	0.0127529770857303	*  
df.mm.trans1:probe19	-0.171674225001647	0.167761692558719	-1.02332196571967	0.306411303797821	   
df.mm.trans1:probe20	-0.299273990533417	0.167761692558719	-1.78392329004827	0.074749307586713	.  
df.mm.trans1:probe21	-0.122817573871759	0.167761692558719	-0.732095462310448	0.464287510848233	   
df.mm.trans1:probe22	-0.3683291157772	0.167761692558719	-2.19554959275509	0.0283609132137065	*  
df.mm.trans1:probe23	-0.313797615370227	0.167761692558719	-1.87049624132990	0.061716517346203	.  
df.mm.trans1:probe24	-0.123655961726892	0.167761692558719	-0.737092955137004	0.461244436042234	   
df.mm.trans1:probe25	-0.0442428399559958	0.167761692558719	-0.263724329918227	0.792048476388051	   
df.mm.trans1:probe26	-0.141707050777803	0.167761692558719	-0.84469254343153	0.398491239155046	   
df.mm.trans1:probe27	-0.106109871550496	0.167761692558719	-0.63250358250502	0.52720723821164	   
df.mm.trans1:probe28	-0.248382755917107	0.167761692558719	-1.48056896737716	0.139046863416093	   
df.mm.trans1:probe29	-0.179177935289283	0.167761692558719	-1.06805035497939	0.285764076097765	   
df.mm.trans1:probe30	0.0307598333152274	0.167761692558719	0.183354333436169	0.8545584021142	   
df.mm.trans1:probe31	-0.0991628159107803	0.167761692558719	-0.591093320521142	0.554595886920429	   
df.mm.trans1:probe32	-0.231795088029673	0.167761692558719	-1.38169259319163	0.167384903683461	   
df.mm.trans2:probe2	0.219084898888693	0.167761692558719	1.30592923537660	0.191886873981480	   
df.mm.trans2:probe3	0.138756814642996	0.167761692558719	0.82710666855265	0.408380443656529	   
df.mm.trans2:probe4	0.297883215641503	0.167761692558719	1.77563310847761	0.0761075281840094	.  
df.mm.trans2:probe5	0.109310005155984	0.167761692558719	0.651579055318149	0.514827423321106	   
df.mm.trans2:probe6	0.279927198145855	0.167761692558719	1.66860022616829	0.0955200154115751	.  
df.mm.trans3:probe2	0.312263944639152	0.167761692558719	1.86135428104276	0.0629971104674198	.  
df.mm.trans3:probe3	-0.308141867625279	0.167761692558719	-1.83678325442159	0.0665483632429399	.  
df.mm.trans3:probe4	-0.0349276647847545	0.167761692558719	-0.208198094881102	0.8351180925739	   
df.mm.trans3:probe5	-0.0729094159708003	0.167761692558719	-0.43460109908751	0.663948858970127