fitVsDatCorrelation=0.86835191872262
cont.fitVsDatCorrelation=0.237684773080667

fstatistic=9474.44735933143,63,945
cont.fstatistic=2458.82796425220,63,945

residuals=-0.853932113723354,-0.100747580227389,-0.00584544920251452,0.0928227281799572,0.811517384425326
cont.residuals=-0.613271100152636,-0.220382990360817,-0.0547998771473369,0.15569031396014,1.48869724870415

predictedValues:
Include	Exclude	Both
Lung	55.6424396819649	61.428992981525	79.5848803780596
cerebhem	68.7335127345093	51.5132449187628	70.6845456115083
cortex	81.096755314819	53.4479664231762	93.774111882073
heart	59.0938803521592	56.7815661049151	70.3547805852383
kidney	56.5507568854264	53.0582629889299	71.5479609906387
liver	61.2863546125933	58.8133754877725	73.7134485617173
stomach	57.9348492146044	68.6906429286825	83.1627523510343
testicle	58.4987759548738	53.129963472481	70.247289868204


diffExp=-5.78655329956012,17.2202678157465,27.6487888916428,2.31231424724407,3.49249389649652,2.47297912482076,-10.7557937140781,5.36881248239283
diffExpScore=1.74661910943956
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,1,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,1,1,0,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=0,1,1,0,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	66.9770957169917	59.763555386311	67.7040663901326
cerebhem	67.0884310476754	66.4855351167645	59.0316789388009
cortex	63.3206266097081	63.696813841613	63.2467368430251
heart	65.6590219118257	69.097542589195	66.4315502316808
kidney	66.7996558774247	68.3508412416943	60.7097629792271
liver	63.6848663209825	73.4180043455072	62.9452215714188
stomach	65.3424438479457	73.2030097292806	61.2367434441347
testicle	64.4031224361186	70.5584835421738	66.2010726781034
cont.diffExp=7.21354033068079,0.602895930910876,-0.376187231904886,-3.4385206773692,-1.55118536426954,-9.7331380245247,-7.86056588133495,-6.15536110605517
cont.diffExpScore=1.65622611702790

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=-0.422063071500094
cont.tran.correlation=-0.367656709275758

tran.covariance=-0.00542727907536782
cont.tran.covariance=-0.000576621248969553

tran.mean=59.7313337535747
cont.tran.mean=66.7405655975757

weightedLogRatios:
wLogRatio
Lung	-0.402511427782669
cerebhem	1.17840490211322
cortex	1.74577745647883
heart	0.162024346743574
kidney	0.255200013144618
liver	0.168662997579540
stomach	-0.705777277971679
testicle	0.387068486082038

cont.weightedLogRatios:
wLogRatio
Lung	0.472612574098358
cerebhem	0.0379277987942306
cortex	-0.0245891080654838
heart	-0.214895728614789
kidney	-0.0967174213957725
liver	-0.600897221168452
stomach	-0.481237160308817
testicle	-0.384361177655776

varWeightedLogRatios=0.627707431800057
cont.varWeightedLogRatios=0.116090221896745

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.52212816639775	0.0778602836214819	45.236518576282	1.09928839092359e-238	***
df.mm.trans1	0.589567714990198	0.066776817211955	8.82892805625735	5.04736013402184e-18	***
df.mm.trans2	0.60994462458273	0.0585429367384324	10.4187568742569	3.8957436071474e-24	***
df.mm.exp2	0.153844886557566	0.0742787611099841	2.07118272112493	0.0386126738181641	*  
df.mm.exp3	0.0734587110794888	0.0742787611099841	0.988959831609455	0.32293593995772	   
df.mm.exp4	0.104784362790610	0.0742787611099841	1.41069077115404	0.158664904125042	   
df.mm.exp5	-0.0238428156511322	0.074278761109984	-0.320991024821057	0.748288165667709	   
df.mm.exp6	0.129737255709082	0.0742787611099841	1.74662654263957	0.0810270288893492	.  
df.mm.exp7	0.108128512314400	0.074278761109984	1.45571238263243	0.145804265558270	   
df.mm.exp8	0.0297211480295759	0.0742787611099841	0.400129829650335	0.689151324883149	   
df.mm.trans1:exp2	0.057445792737243	0.0680652346904331	0.84398140987116	0.398893386478622	   
df.mm.trans2:exp2	-0.329887851163817	0.0476898420065476	-6.91736095746606	8.47259061477295e-12	***
df.mm.trans1:exp3	0.303238027071988	0.068065234690433	4.45510881511159	9.38806078634935e-06	***
df.mm.trans2:exp3	-0.212632042769984	0.0476898420065476	-4.45864431131457	9.23770783021152e-06	***
df.mm.trans1:exp4	-0.0446032048016828	0.0680652346904331	-0.655300830218867	0.512433570811043	   
df.mm.trans2:exp4	-0.183454552243252	0.0476898420065476	-3.84682658873277	0.000127685238686249	***
df.mm.trans1:exp5	0.0400351887604277	0.068065234690433	0.58818850684217	0.556546309564039	   
df.mm.trans2:exp5	-0.122648495007145	0.0476898420065476	-2.57179495353133	0.0102691179922813	*  
df.mm.trans1:exp6	-0.0331262513877648	0.068065234690433	-0.486683863479294	0.62659523503485	   
df.mm.trans2:exp6	-0.173249874489696	0.0476898420065476	-3.63284647631899	0.000295399615484235	***
df.mm.trans1:exp7	-0.0677556362546787	0.068065234690433	-0.995451445408769	0.319771567295658	   
df.mm.trans2:exp7	0.00360255362742865	0.0476898420065476	0.075541320244551	0.939800011116198	   
df.mm.trans1:exp8	0.0203384684200802	0.068065234690433	0.298808466797793	0.765151885517029	   
df.mm.trans2:exp8	-0.174862017177771	0.0476898420065476	-3.66665121586612	0.000259452605680459	***
df.mm.trans1:probe2	-0.313796042017073	0.0493179479608434	-6.36271489369784	3.08497282423555e-10	***
df.mm.trans1:probe3	0.428664178758833	0.0493179479608434	8.69184944797736	1.55430604412884e-17	***
df.mm.trans1:probe4	-0.330799774880164	0.0493179479608434	-6.70749267878718	3.40480484112907e-11	***
df.mm.trans1:probe5	-0.425551458912694	0.0493179479608434	-8.62873409190841	2.59608536078486e-17	***
df.mm.trans1:probe6	-0.122288546614951	0.0493179479608434	-2.47959519143098	0.0133265403218379	*  
df.mm.trans1:probe7	-0.24518743894214	0.0493179479608434	-4.97156611497318	7.88440768084903e-07	***
df.mm.trans1:probe8	0.0300239272336082	0.0493179479608434	0.608782978104565	0.542814606116274	   
df.mm.trans1:probe9	0.00305876006515752	0.0493179479608434	0.0620212355061094	0.950559035908223	   
df.mm.trans1:probe10	-0.0428968636241345	0.0493179479608434	-0.86980228086929	0.384629338120785	   
df.mm.trans1:probe11	-0.242627265073095	0.0493179479608434	-4.91965450926166	1.02231563882720e-06	***
df.mm.trans1:probe12	-0.0396209975959991	0.0493179479608434	-0.80337887593086	0.421957802020535	   
df.mm.trans1:probe13	-0.329285334509600	0.0493179479608434	-6.67678498649296	4.16029345790075e-11	***
df.mm.trans1:probe14	-0.199249983141486	0.0493179479608434	-4.04011098149669	5.77639029462979e-05	***
df.mm.trans1:probe15	-0.286440980914606	0.0493179479608434	-5.80804743015725	8.62959180618046e-09	***
df.mm.trans1:probe16	-0.186771696636671	0.0493179479608434	-3.78709383417495	0.000162044878249240	***
df.mm.trans1:probe17	-0.208268542208075	0.0493179479608434	-4.22297664074411	2.64398591914957e-05	***
df.mm.trans1:probe18	-0.218815190309722	0.0493179479608434	-4.43682674071219	1.02036474510207e-05	***
df.mm.trans1:probe19	-0.158878674097282	0.0493179479608434	-3.22151834507441	0.00131870962674445	** 
df.mm.trans1:probe20	-0.0908625238974966	0.0493179479608434	-1.84238249267058	0.0657323463851288	.  
df.mm.trans1:probe21	-0.145129542099133	0.0493179479608434	-2.94273277984639	0.00333280725956585	** 
df.mm.trans1:probe22	-0.214264046595816	0.0493179479608434	-4.34454504810162	1.54662594453247e-05	***
df.mm.trans2:probe2	-0.125089594924315	0.0493179479608434	-2.53639091033616	0.0113601531185143	*  
df.mm.trans2:probe3	-0.0623579153014535	0.0493179479608434	-1.26440612149888	0.206396052264987	   
df.mm.trans2:probe4	-0.0696884066478211	0.0493179479608434	-1.41304351720293	0.157972205857182	   
df.mm.trans2:probe5	-0.0522579310094246	0.0493179479608434	-1.05961284218304	0.289591521500713	   
df.mm.trans2:probe6	0.0255764702471144	0.0493179479608434	0.518603699152713	0.604158538885864	   
df.mm.trans3:probe2	-0.474553244619712	0.0493179479608434	-9.62232339829892	5.67292603998785e-21	***
df.mm.trans3:probe3	-0.372002519233503	0.0493179479608434	-7.54294399127999	1.07770585628922e-13	***
df.mm.trans3:probe4	-0.499226340756208	0.0493179479608434	-10.1226097475218	6.16489002753526e-23	***
df.mm.trans3:probe5	-0.75844879609251	0.0493179479608434	-15.3787581895072	8.4549113584846e-48	***
df.mm.trans3:probe6	-0.25498557448229	0.0493179479608434	-5.17023892974499	2.85355595383958e-07	***
df.mm.trans3:probe7	-0.430553225952973	0.0493179479608434	-8.73015289068426	1.13678853199633e-17	***
df.mm.trans3:probe8	0.60913488563016	0.0493179479608434	12.3511806718680	1.35066405453597e-32	***
df.mm.trans3:probe9	-0.144062233456682	0.0493179479608434	-2.92109139599769	0.00357105275765798	** 
df.mm.trans3:probe10	-0.554903364944767	0.0493179479608434	-11.2515501534115	1.19160484732956e-27	***
df.mm.trans3:probe11	-0.515486405606057	0.0493179479608434	-10.4523084783522	2.83802491836643e-24	***
df.mm.trans3:probe12	-0.318587340693369	0.0493179479608434	-6.45986611094028	1.67494594206175e-10	***
df.mm.trans3:probe13	-0.289962882209031	0.0493179479608434	-5.87945959226143	5.70458834298982e-09	***
df.mm.trans3:probe14	-0.823792223843092	0.0493179479608434	-16.7037003343519	4.42770647003081e-55	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11160023517855	0.152493627199359	26.9624397470940	3.23635043529545e-119	***
df.mm.trans1	0.151119422495567	0.130786051576495	1.15547048537649	0.248189874753262	   
df.mm.trans2	0.00498856617425362	0.114659545982993	0.0435076393464312	0.96530606605388	   
df.mm.exp2	0.245321295016036	0.145479019323930	1.68630016999079	0.0920680760577115	.  
df.mm.exp3	0.075701642679986	0.145479019323929	0.520361238560631	0.602933706200609	   
df.mm.exp4	0.144221588530328	0.145479019323929	0.991356617610943	0.32176524007975	   
df.mm.exp5	0.240646778467845	0.145479019323929	1.65416827516559	0.09842541480949	.  
df.mm.exp6	0.228250807549381	0.145479019323930	1.56896031201000	0.116991945969048	   
df.mm.exp7	0.278530455215814	0.145479019323929	1.91457473737589	0.055848466739778	.  
df.mm.exp8	0.149306873793067	0.145479019323930	1.02631207226256	0.305007137243713	   
df.mm.trans1:exp2	-0.243660385474787	0.133309487730354	-1.82777977489225	0.0678977909913895	.  
df.mm.trans2:exp2	-0.138732921526646	0.0934031659003146	-1.48531283912485	0.137794471955079	   
df.mm.trans1:exp3	-0.131841217770254	0.133309487730354	-0.988986005534208	0.322923140411397	   
df.mm.trans2:exp3	-0.0119631330088789	0.0934031659003146	-0.128080594416324	0.898112443675763	   
df.mm.trans1:exp4	-0.164097278798875	0.133309487730354	-1.23094973653185	0.218647941002437	   
df.mm.trans2:exp4	0.000901545031283265	0.0934031659003146	0.0096521892228519	0.992300824035095	   
df.mm.trans1:exp5	-0.243299555449035	0.133309487730354	-1.82507306562575	0.0683055563968789	.  
df.mm.trans2:exp5	-0.106388940992501	0.0934031659003146	-1.13902928200577	0.254979590497506	   
df.mm.trans1:exp6	-0.278654556550813	0.133309487730354	-2.09028300457091	0.0368591433305305	*  
df.mm.trans2:exp6	-0.0224776446680784	0.0934031659003146	-0.240651849981915	0.809877158151272	   
df.mm.trans1:exp7	-0.303239353697920	0.133309487730354	-2.27470196503406	0.0231471697276068	*  
df.mm.trans2:exp7	-0.0756899520446006	0.0934031659003146	-0.81035745753395	0.417938779604944	   
df.mm.trans1:exp8	-0.188495462800184	0.133309487730354	-1.41396884804970	0.157700398332259	   
df.mm.trans2:exp8	0.0167390124542608	0.0934031659003146	0.179212474148099	0.857809285368401	   
df.mm.trans1:probe2	-0.182774145518526	0.0965919005271038	-1.89223055474759	0.0587657242571028	.  
df.mm.trans1:probe3	-0.103291580979810	0.0965919005271038	-1.06936068569048	0.285180241459782	   
df.mm.trans1:probe4	-0.00681243720180381	0.0965919005271038	-0.0705280376991053	0.943788307255068	   
df.mm.trans1:probe5	-0.136083172149725	0.0965919005271038	-1.40884661557664	0.159209470679110	   
df.mm.trans1:probe6	-0.204119220848724	0.0965919005271038	-2.11321259582678	0.0348442036636086	*  
df.mm.trans1:probe7	-0.109344100625608	0.0965919005271038	-1.13202142238547	0.257912590461702	   
df.mm.trans1:probe8	-0.122801407377222	0.0965919005271038	-1.27134269754599	0.203919582952602	   
df.mm.trans1:probe9	-0.170519758592330	0.0965919005271038	-1.76536290995208	0.077825708824025	.  
df.mm.trans1:probe10	-0.121656960725153	0.0965919005271038	-1.25949443029145	0.208162785220505	   
df.mm.trans1:probe11	-0.101782588269547	0.0965919005271038	-1.05373833327761	0.292272077454341	   
df.mm.trans1:probe12	0.0254443373605743	0.0965919005271038	0.263421024140990	0.792283513475247	   
df.mm.trans1:probe13	-0.160472449334251	0.0965919005271038	-1.66134477589270	0.0969759077079517	.  
df.mm.trans1:probe14	-0.231194947122122	0.0965919005271038	-2.39352312005962	0.0168816368227485	*  
df.mm.trans1:probe15	0.0609318002920262	0.0965919005271038	0.630816869318444	0.528312798950385	   
df.mm.trans1:probe16	-0.106517136096041	0.0965919005271038	-1.10275432530859	0.270414620096308	   
df.mm.trans1:probe17	0.0190332619263503	0.0965919005271038	0.197048218561654	0.843832175969693	   
df.mm.trans1:probe18	-0.0983374114664147	0.0965919005271038	-1.01807098659189	0.308904790470820	   
df.mm.trans1:probe19	-0.0937965737984012	0.0965919005271038	-0.97106044385245	0.331766656209288	   
df.mm.trans1:probe20	-0.148168029339712	0.0965919005271038	-1.53395914700048	0.125374409016244	   
df.mm.trans1:probe21	-0.108668281683534	0.0965919005271038	-1.12502478044773	0.260864192514825	   
df.mm.trans1:probe22	-0.000351459619577694	0.0965919005271038	-0.00363860341974609	0.997097588860488	   
df.mm.trans2:probe2	-0.0637291586300367	0.0965919005271038	-0.659777458381764	0.509557412133602	   
df.mm.trans2:probe3	-0.180827546385934	0.0965919005271038	-1.87207773528789	0.0615045242914012	.  
df.mm.trans2:probe4	-0.131310377437907	0.0965919005271038	-1.35943465985599	0.174333188141165	   
df.mm.trans2:probe5	-0.0804887505173237	0.0965919005271038	-0.83328674638448	0.404893586590325	   
df.mm.trans2:probe6	-0.0674995246644691	0.0965919005271038	-0.69881143549431	0.484841822831565	   
df.mm.trans3:probe2	-0.0279716776419658	0.0965919005271038	-0.289586160840855	0.772196378168132	   
df.mm.trans3:probe3	-0.0553524146907945	0.0965919005271038	-0.573054411278123	0.566744181237507	   
df.mm.trans3:probe4	-0.0516340225505335	0.0965919005271038	-0.534558511311669	0.593080920224402	   
df.mm.trans3:probe5	-0.0964393021150181	0.0965919005271038	-0.998420173831833	0.318331236415524	   
df.mm.trans3:probe6	-0.0225571819620301	0.0965919005271038	-0.233530780934376	0.815399847329069	   
df.mm.trans3:probe7	-0.154509553229551	0.0965919005271038	-1.59961189692292	0.110018958605545	   
df.mm.trans3:probe8	0.046465368530589	0.0965919005271038	0.481048289525588	0.63059353606766	   
df.mm.trans3:probe9	-0.0780863761939508	0.0965919005271038	-0.80841536161761	0.419054974636761	   
df.mm.trans3:probe10	-0.0137765791225293	0.0965919005271038	-0.142626649308589	0.88661545273902	   
df.mm.trans3:probe11	-0.170657104467724	0.0965919005271038	-1.76678482912588	0.0775870299563955	.  
df.mm.trans3:probe12	-0.0115820462091577	0.0965919005271038	-0.119907012347353	0.904582296134928	   
df.mm.trans3:probe13	-0.082609693604764	0.0965919005271038	-0.85524452002664	0.392632571706533	   
df.mm.trans3:probe14	-0.0171571491619627	0.0965919005271038	-0.177625132835526	0.859055450912856