fitVsDatCorrelation=0.87413836972076
cont.fitVsDatCorrelation=0.359068205840906

fstatistic=9336.37925793104,37,347
cont.fstatistic=2521.41360921058,37,347

residuals=-0.368051423328489,-0.0852077744633354,-0.0128900236255641,0.0647522644770826,0.812627453757434
cont.residuals=-0.653169117789033,-0.170640628622465,-0.0406344842639689,0.123605619733390,0.874664263781253

predictedValues:
Include	Exclude	Both
Lung	57.0433311940569	50.2459919727534	86.9095008458985
cerebhem	55.0087033243753	43.3811316559449	68.8985639360965
cortex	48.6219478342177	46.1567498868075	69.296092103518
heart	55.2519698062422	49.5902022322338	86.2350687073629
kidney	60.1503663153287	59.814122850245	104.819016784504
liver	51.5035804656425	44.3034580587333	68.7882123547922
stomach	50.0216734166251	45.6386095833622	78.326732317098
testicle	49.9719437356097	45.8694178983841	65.5889326860545


diffExp=6.7973392213035,11.6275716684304,2.46519794741027,5.66176757400835,0.336243465083669,7.20012240690912,4.38306383326287,4.10252583722554
diffExpScore=0.977050446215883
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	51.5809064922941	54.3706241019864	62.1898304461114
cerebhem	54.2229139336779	52.8147194021379	56.3586575517718
cortex	56.3202453978726	57.668393791502	50.3730569401611
heart	62.2969782012143	60.3874576235007	50.326358181517
kidney	53.5016421611607	62.4898815656178	52.7691938733688
liver	55.8426567145803	55.0913513274525	63.0912435905476
stomach	59.6862279609633	52.4200698156	64.4011131671351
testicle	55.580496921749	53.4643731507451	53.19292618418
cont.diffExp=-2.78971760969221,1.40819453153997,-1.34814839362933,1.90952057771364,-8.9882394044571,0.751305387127886,7.2661581453633,2.11612377100393
cont.diffExpScore=20.055439093848

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=0.765450810793484
cont.tran.correlation=0.142447853337260

tran.covariance=0.00573831288420108
cont.tran.covariance=0.000528739933234517

tran.mean=50.7858250144102
cont.tran.mean=56.1086836601284

weightedLogRatios:
wLogRatio
Lung	0.505031035645213
cerebhem	0.923451008021965
cortex	0.200741839632519
heart	0.427885841038852
kidney	0.0229501143115167
liver	0.58222927250117
stomach	0.354576669870992
testicle	0.331398870056798

cont.weightedLogRatios:
wLogRatio
Lung	-0.209082588100103
cerebhem	0.104726953808753
cortex	-0.0956351196216903
heart	0.128148031130318
kidney	-0.630076535787457
liver	0.0543947226273839
stomach	0.522387956684813
testicle	0.155206243285417

varWeightedLogRatios=0.0721299468585633
cont.varWeightedLogRatios=0.111010230920747

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.65020682739350	0.0731207977452319	49.9202270756342	1.91334986689082e-160	***
df.mm.trans1	0.42284185593765	0.0609196590225528	6.94097542110523	1.92166618437434e-11	***
df.mm.trans2	0.236342950901425	0.0609196590225527	3.87958427039011	0.000125196636037941	***
df.mm.exp2	0.0490060716491873	0.0839344925974103	0.583860938842434	0.559693207310394	   
df.mm.exp3	-0.0181448734735362	0.0839344925974103	-0.216178985683129	0.828975258404122	   
df.mm.exp4	-0.0372542538994018	0.0839344925974103	-0.443849158391782	0.657428286189011	   
df.mm.exp5	0.0399795199249099	0.0839344925974103	0.476318122475234	0.634147529851549	   
df.mm.exp6	0.00580706431864189	0.0839344925974102	0.069185672527924	0.94488167885346	   
df.mm.exp7	-0.123553114484450	0.0839344925974103	-1.47201836409579	0.141922276491881	   
df.mm.exp8	0.0579787679170935	0.0839344925974103	0.690762118443811	0.490176848069342	   
df.mm.trans1:exp2	-0.085325832092929	0.0709375935371805	-1.20282952717035	0.229862359656046	   
df.mm.trans2:exp2	-0.195912261569069	0.0709375935371805	-2.76175511178548	0.00605510615830407	** 
df.mm.trans1:exp3	-0.141591271452776	0.0709375935371805	-1.99599767052380	0.046715353067392	*  
df.mm.trans2:exp3	-0.066742698558507	0.0709375935371805	-0.940864994574776	0.347428559696507	   
df.mm.trans1:exp4	0.00534707084479719	0.0709375935371805	0.0753771107557326	0.939958107301947	   
df.mm.trans2:exp4	0.0241167504553029	0.0709375935371805	0.339971364304353	0.734083787923945	   
df.mm.trans1:exp5	0.0130568370890404	0.0709375935371805	0.184060896881101	0.85407319588029	   
df.mm.trans2:exp5	0.134331499190887	0.0709375935371805	1.89365740353851	0.0591020914605248	.  
df.mm.trans1:exp6	-0.107966910879493	0.0709375935371805	-1.52199849890459	0.128920188915680	   
df.mm.trans2:exp6	-0.131675112319322	0.0709375935371805	-1.85621058952764	0.0642710460678172	.  
df.mm.trans1:exp7	-0.00780168109375524	0.0709375935371805	-0.109979500357115	0.91248920947319	   
df.mm.trans2:exp7	0.0273763921230985	0.0709375935371805	0.385922199471705	0.69979078735607	   
df.mm.trans1:exp8	-0.190328220685061	0.0709375935371805	-2.68303745862628	0.0076450344262245	** 
df.mm.trans2:exp8	-0.149110931657027	0.0709375935371805	-2.10200155124904	0.036273474621103	*  
df.mm.trans1:probe2	-0.136184122300967	0.0388541201554464	-3.50501109679297	0.000516453100592907	***
df.mm.trans1:probe3	-0.0232912914590552	0.0388541201554464	-0.599454867743037	0.54926079197166	   
df.mm.trans1:probe4	-0.0221974191409711	0.0388541201554464	-0.571301551860248	0.568165081755981	   
df.mm.trans1:probe5	-0.0487523515661481	0.0388541201554464	-1.25475371392020	0.210412628856329	   
df.mm.trans1:probe6	-0.0619498946583587	0.0388541201554464	-1.59442278992579	0.111751754287607	   
df.mm.trans2:probe2	0.055677004625286	0.0388541201554464	1.43297556095814	0.15276513550076	   
df.mm.trans2:probe3	0.132068407406389	0.0388541201554464	3.39908372337384	0.000754777371328988	***
df.mm.trans2:probe4	0.135497660244865	0.0388541201554464	3.48734341950789	0.000550555240218788	***
df.mm.trans2:probe5	0.0181300271491119	0.0388541201554464	0.466617879302833	0.641066109175448	   
df.mm.trans2:probe6	-0.0375630621568691	0.0388541201554464	-0.966771657847042	0.334331504631263	   
df.mm.trans3:probe2	0.0556194363000188	0.0388541201554464	1.43149390791757	0.153188751082202	   
df.mm.trans3:probe3	0.294181565392260	0.0388541201554464	7.57143809241613	3.36907933041862e-13	***
df.mm.trans3:probe4	0.272101889729512	0.0388541201554464	7.00316693933345	1.30392156799343e-11	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.69596932700103	0.140514115062327	26.3031890095998	1.24106150383145e-84	***
df.mm.trans1	0.254408613647841	0.117067540855855	2.1731780798325	0.0304428875426024	*  
df.mm.trans2	0.279002995907003	0.117067540855855	2.38326519774203	0.0176973195738391	*  
df.mm.exp2	0.119373550296934	0.161294478646461	0.74009694131308	0.459741611396728	   
df.mm.exp3	0.357522781005341	0.161294478646461	2.21658412616212	0.0272994923173638	*  
df.mm.exp4	0.505381293443545	0.161294478646461	3.13328328213443	0.00187582686760293	** 
df.mm.exp5	0.340005329697616	0.161294478646461	2.1079787265556	0.0357495297075318	*  
df.mm.exp6	0.0781646897132085	0.161294478646461	0.484608589017709	0.628259770150582	   
df.mm.exp7	0.0744758219961374	0.161294478646461	0.461738198487127	0.644558406085086	   
df.mm.exp8	0.21413835760029	0.161294478646461	1.32762360743703	0.185175205947153	   
df.mm.trans1:exp2	-0.0694215396785142	0.136318714892274	-0.509259053192912	0.610894339892226	   
df.mm.trans2:exp2	-0.148407631765189	0.136318714892274	-1.08868127081794	0.277049991573927	   
df.mm.trans1:exp3	-0.269620286749208	0.136318714892274	-1.97786699326116	0.0487344927248092	*  
df.mm.trans2:exp3	-0.298637535367665	0.136318714892274	-2.19073027209553	0.0291360352558735	*  
df.mm.trans1:exp4	-0.316619947610551	0.136318714892274	-2.32264475102160	0.0207761136303218	*  
df.mm.trans2:exp4	-0.400423875191314	0.136318714892274	-2.93740940492102	0.00353083999491557	** 
df.mm.trans1:exp5	-0.303444556456297	0.136318714892274	-2.22599337659612	0.0266564535200538	*  
df.mm.trans2:exp5	-0.200824690896926	0.136318714892274	-1.47319970743289	0.141603698883123	   
df.mm.trans1:exp6	0.00122177008527927	0.136318714892274	0.008962599788626	0.992854126190734	   
df.mm.trans2:exp6	-0.0649959589636321	0.136318714892274	-0.476794099878326	0.63380886339963	   
df.mm.trans1:exp7	0.0714739095782264	0.136318714892274	0.52431472549245	0.60039441161147	   
df.mm.trans2:exp7	-0.111010302136474	0.136318714892274	-0.814343813497654	0.416006514399117	   
df.mm.trans1:exp8	-0.139457567459014	0.136318714892274	-1.02302583742240	0.307008295943798	   
df.mm.trans2:exp8	-0.230946857824275	0.136318714892274	-1.6941683906482	0.0911305674150192	.  
df.mm.trans1:probe2	-0.0274584410524418	0.0746648351566141	-0.367755999123898	0.713279391772111	   
df.mm.trans1:probe3	0.0191942580472658	0.0746648351566141	0.257072261754877	0.797275354170949	   
df.mm.trans1:probe4	-0.0352323941348969	0.0746648351566141	-0.471873996118719	0.63731331131249	   
df.mm.trans1:probe5	0.0111243170576807	0.0746648351566141	0.148990043764869	0.881648038426153	   
df.mm.trans1:probe6	-0.0398913984992674	0.0746648351566141	-0.534272906591071	0.593494858251523	   
df.mm.trans2:probe2	-0.0501210874897041	0.0746648351566141	-0.671281030549013	0.502488125348696	   
df.mm.trans2:probe3	0.0226369014966631	0.0746648351566141	0.3031802246557	0.76193420124455	   
df.mm.trans2:probe4	0.0248195739481579	0.0746648351566141	0.332413162047399	0.73977801681563	   
df.mm.trans2:probe5	-0.00845149455943505	0.0746648351566141	-0.113192435792666	0.909943449542786	   
df.mm.trans2:probe6	0.219632976375465	0.0746648351566141	2.94158523104982	0.00348473808766614	** 
df.mm.trans3:probe2	-0.142466378191179	0.0746648351566141	-1.90807865432699	0.0572061305601502	.  
df.mm.trans3:probe3	-0.0878490133149163	0.0746648351566141	-1.17657814593239	0.240170412026084	   
df.mm.trans3:probe4	-0.166442399809439	0.0746648351566141	-2.22919396340078	0.0264407368669636	*