fitVsDatCorrelation=0.935662453201798
cont.fitVsDatCorrelation=0.265666746732140

fstatistic=7696.74856698994,56,784
cont.fstatistic=1019.18501810167,56,784

residuals=-0.643276747353282,-0.115579526842862,-0.0161038154931648,0.107763502263571,0.982301903774042
cont.residuals=-1.01569694717578,-0.415732791462002,-0.142324630608425,0.404025519618167,1.44744761783874

predictedValues:
Include	Exclude	Both
Lung	73.0897019147835	241.246653850113	105.591984580942
cerebhem	61.6941760595775	115.388738442757	72.1289223243036
cortex	58.3783885675434	176.190827337030	93.6579349623803
heart	84.9249966747889	118.166447724948	97.3188106905507
kidney	66.2011160896618	198.075480165711	70.8032171308288
liver	67.1536403675419	160.913373743210	65.3448978372175
stomach	65.0217936673942	151.984614176428	85.986868399511
testicle	62.9552060352607	225.969899484486	95.4425602693636


diffExp=-168.156951935329,-53.6945623831799,-117.812438769486,-33.241451050159,-131.874364076049,-93.7597333756684,-86.9628205090335,-163.014693449225
diffExpScore=0.99882286054111
diffExp1.5=-1,-1,-1,0,-1,-1,-1,-1
diffExp1.5Score=0.875
diffExp1.4=-1,-1,-1,0,-1,-1,-1,-1
diffExp1.4Score=0.875
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	76.1409444181803	87.5419357301119	110.904762202504
cerebhem	76.6846513705971	104.208636961188	83.8797077213802
cortex	88.3721940320586	100.748266181919	86.2675738461647
heart	78.5567037338382	126.180515876535	99.592796480338
kidney	74.4680034018057	91.2691098449139	69.2677051102068
liver	76.3339098998449	96.592858194	96.144884509649
stomach	81.38755919119	87.999662288272	81.2439682532095
testicle	82.848729193333	84.4384036182778	84.0088353440467
cont.diffExp=-11.4009913119315,-27.5239855905909,-12.3760721498601,-47.6238121426965,-16.8011064431082,-20.2589482941552,-6.61210309708196,-1.58967442494480
cont.diffExpScore=0.993112316451271

cont.diffExp1.5=0,0,0,-1,0,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,-1,0,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,-1,0,-1,0,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=0,-1,0,-1,-1,-1,0,0
cont.diffExp1.2Score=0.8

tran.correlation=-0.180739156390352
cont.tran.correlation=-0.0258573956000787

tran.covariance=-0.00643358411321887
cont.tran.covariance=-0.000177384010507444

tran.mean=120.459690893827
cont.tran.mean=88.360755246004

weightedLogRatios:
wLogRatio
Lung	-5.83781910978525
cerebhem	-2.77698527172962
cortex	-5.10253208159136
heart	-1.52178795594871
kidney	-5.19554432073167
liver	-4.0582469683642
stomach	-3.90502521367051
testicle	-6.11054179561258

cont.weightedLogRatios:
wLogRatio
Lung	-0.614268059255909
cerebhem	-1.37798843026062
cortex	-0.595976412599947
heart	-2.18027061223626
kidney	-0.897608339122194
liver	-1.04813623002291
stomach	-0.346676316736661
testicle	-0.0841300382207282

varWeightedLogRatios=2.47714587890605
cont.varWeightedLogRatios=0.433574634645012

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.83110810981259	0.0941350266333424	51.3210468259582	7.1086781715634e-253	***
df.mm.trans1	-0.655914777478908	0.0820471143922862	-7.99436741117827	4.65920000188539e-15	***
df.mm.trans2	0.657731982256865	0.0732181956183544	8.98317660933976	1.93005688499919e-18	***
df.mm.exp2	-0.525883693722721	0.0957921365527605	-5.48984199170747	5.43602842931388e-08	***
df.mm.exp3	-0.419060624229349	0.0957921365527606	-4.3746871017804	1.3804135928058e-05	***
df.mm.exp4	-0.482054503283608	0.0957921365527605	-5.03229722846928	6.01395239751889e-07	***
df.mm.exp5	0.103516168379752	0.0957921365527605	1.08063325555681	0.280192585206716	   
df.mm.exp6	-0.0097549451626003	0.0957921365527605	-0.101834508694014	0.918914072657858	   
df.mm.exp7	-0.373617682898963	0.0957921365527605	-3.90029595689393	0.000104311885019095	***
df.mm.exp8	-0.113624228342701	0.0957921365527605	-1.18615402507615	0.235920820075650	   
df.mm.trans1:exp2	0.356385748771174	0.0894490957507912	3.98422975413949	7.40080645691398e-05	***
df.mm.trans2:exp2	-0.211629414187925	0.0697698135161486	-3.03325182514558	0.00249914071304445	** 
df.mm.trans1:exp3	0.194318906647010	0.0894490957507912	2.17239654594598	0.0301247134178696	*  
df.mm.trans2:exp3	0.104808408058649	0.0697698135161486	1.50220278336262	0.133447399970021	   
df.mm.trans1:exp4	0.632135498022642	0.0894490957507912	7.06698589534987	3.50173722905836e-12	***
df.mm.trans2:exp4	-0.231671162306911	0.0697698135161486	-3.32050711663848	0.000940094437656927	***
df.mm.trans1:exp5	-0.202506326371392	0.0894490957507912	-2.26392815569185	0.0238510029295016	*  
df.mm.trans2:exp5	-0.300687867441839	0.0697698135161486	-4.30971293010899	1.84268497798855e-05	***
df.mm.trans1:exp6	-0.0749494010178293	0.0894490957507911	-0.837900041232853	0.402342209960001	   
df.mm.trans2:exp6	-0.395198756010438	0.0697698135161486	-5.66432295134296	2.07248032388942e-08	***
df.mm.trans1:exp7	0.256652703636157	0.0894490957507912	2.86925990119791	0.00422495672797994	** 
df.mm.trans2:exp7	-0.088422893973166	0.0697698135161486	-1.26735173160095	0.205405856090079	   
df.mm.trans1:exp8	-0.0356397936232904	0.0894490957507911	-0.398436600439028	0.690416866312995	   
df.mm.trans2:exp8	0.0482061605513591	0.0697698135161486	0.690931480563604	0.48981311701585	   
df.mm.trans1:probe2	-0.133924567128074	0.0568439082967421	-2.35600561504231	0.0187179031227369	*  
df.mm.trans1:probe3	-0.0398354745001215	0.0568439082967421	-0.700787044623471	0.483643851292929	   
df.mm.trans1:probe4	-0.00570766378461637	0.0568439082967421	-0.10040941862795	0.920044963172413	   
df.mm.trans1:probe5	0.0612436486705679	0.0568439082967421	1.07740038476696	0.281632827814125	   
df.mm.trans1:probe6	-0.225020873503953	0.0568439082967421	-3.95857498624614	8.2248581297208e-05	***
df.mm.trans1:probe7	-0.129157112898954	0.0568439082967421	-2.27213639542016	0.0233483294021885	*  
df.mm.trans1:probe8	-0.0156999752755186	0.0568439082967421	-0.276194507836443	0.782471464640346	   
df.mm.trans1:probe9	0.0707229645290025	0.0568439082967421	1.24416083707347	0.213812209345799	   
df.mm.trans1:probe10	-0.0814712279543334	0.0568439082967421	-1.43324465884769	0.152186581004341	   
df.mm.trans1:probe11	-0.228905115290182	0.0568439082967421	-4.02690670203796	6.20072820057801e-05	***
df.mm.trans1:probe12	-0.215501910534148	0.0568439082967421	-3.79111706058571	0.000161483257695496	***
df.mm.trans1:probe13	-0.227935241016026	0.0568439082967421	-4.00984464027589	6.65650516202108e-05	***
df.mm.trans1:probe14	-0.134426635665757	0.0568439082967421	-2.36483802211506	0.0182806102474194	*  
df.mm.trans1:probe15	-0.119423512850438	0.0568439082967421	-2.10090256685047	0.0359682614563420	*  
df.mm.trans1:probe16	0.310777481002765	0.0568439082967421	5.46720819019716	6.1485595022487e-08	***
df.mm.trans1:probe17	1.14227399999967	0.0568439082967421	20.0949236994166	9.11012732667913e-73	***
df.mm.trans1:probe18	0.864861867503393	0.0568439082967421	15.2146798736737	5.36410171470995e-46	***
df.mm.trans1:probe19	0.774889749883319	0.0568439082967421	13.6318872699281	3.96862114872579e-38	***
df.mm.trans1:probe20	0.611451768725371	0.0568439082967421	10.7566806549157	2.88914922663801e-25	***
df.mm.trans1:probe21	1.01265669421973	0.0568439082967421	17.8146915749240	7.22082971298989e-60	***
df.mm.trans1:probe22	0.0864614226265966	0.0568439082967421	1.52103233604632	0.128654915008189	   
df.mm.trans2:probe2	0.0389466961233617	0.0568439082967421	0.685151624692101	0.493450763155989	   
df.mm.trans2:probe3	0.0348227623011849	0.0568439082967421	0.612603238317107	0.540316359397765	   
df.mm.trans2:probe4	-0.0424864289118252	0.0568439082967421	-0.747422726284642	0.455032539991124	   
df.mm.trans2:probe5	0.207166317210254	0.0568439082967421	3.64447701464832	0.000285596733108123	***
df.mm.trans2:probe6	-0.277712232695631	0.0568439082967421	-4.88552319882529	1.25091371082212e-06	***
df.mm.trans3:probe2	-0.0967427538918879	0.0568439082967421	-1.70190187111804	0.0891702158782392	.  
df.mm.trans3:probe3	-0.540212128783731	0.0568439082967421	-9.50343044612033	2.40412669737506e-20	***
df.mm.trans3:probe4	-0.145683753264043	0.0568439082967421	-2.56287362409232	0.0105665722194185	*  
df.mm.trans3:probe5	-0.504964952653098	0.0568439082967421	-8.88336090504247	4.37859175299326e-18	***
df.mm.trans3:probe6	0.0847111724819893	0.0568439082967421	1.49024187499163	0.136562837517605	   
df.mm.trans3:probe7	-0.0105075324106596	0.0568439082967421	-0.184848873441411	0.853395378790719	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.94997974010442	0.25716417673296	15.3597588524396	9.64587829617357e-47	***
df.mm.trans1	0.320204939487756	0.224141633360242	1.42858305566606	0.153522371578476	   
df.mm.trans2	0.563485023037615	0.200022219905526	2.81711213536056	0.00496760959298271	** 
df.mm.exp2	0.460680604706999	0.261691177185651	1.76039792270177	0.0787302431115483	.  
df.mm.exp3	0.540696310908965	0.261691177185651	2.06616178934218	0.0391411549895493	*  
df.mm.exp4	0.504412150678488	0.261691177185650	1.92750919653912	0.054277178132686	.  
df.mm.exp5	0.49017089645722	0.261691177185651	1.87308911874197	0.0614279693551623	.  
df.mm.exp6	0.243733543707815	0.261691177185651	0.931378529184819	0.351944503662396	   
df.mm.exp7	0.383066557328431	0.261691177185651	1.46381151037688	0.143646274466612	   
df.mm.exp8	0.326084482640099	0.261691177185650	1.24606601623702	0.213112373976046	   
df.mm.trans1:exp2	-0.453565183450979	0.244362846550811	-1.85611352074616	0.0638123943058909	.  
df.mm.trans2:exp2	-0.286403534559169	0.190601705819642	-1.50262839111304	0.133337566451309	   
df.mm.trans1:exp3	-0.391725092477854	0.244362846550811	-1.60304685432776	0.109327083286004	   
df.mm.trans2:exp3	-0.40018926338226	0.190601705819642	-2.09961008303326	0.0360822444079468	*  
df.mm.trans1:exp4	-0.473177600677551	0.244362846550811	-1.93637292803087	0.0531811182394905	.  
df.mm.trans2:exp4	-0.138816547366108	0.190601705819642	-0.728306951761826	0.466643179269865	   
df.mm.trans1:exp5	-0.512387502449625	0.244362846550811	-2.09683063396090	0.0363284069796706	*  
df.mm.trans2:exp5	-0.448476447002124	0.190601705819642	-2.35295085672789	0.0188712640152374	*  
df.mm.trans1:exp6	-0.241202430160632	0.244362846550811	-0.987066706601317	0.323914460716924	   
df.mm.trans2:exp6	-0.145346681001098	0.190601705819642	-0.762567577116194	0.445950692948802	   
df.mm.trans1:exp7	-0.316430286073034	0.244362846550811	-1.29491979054696	0.195729109824962	   
df.mm.trans2:exp7	-0.377851524530401	0.190601705819642	-1.98241418095148	0.0477816103105536	*  
df.mm.trans1:exp8	-0.241654232112802	0.244362846550810	-0.988915604494543	0.323009534916604	   
df.mm.trans2:exp8	-0.362180109393651	0.190601705819642	-1.90019343130311	0.057774358724275	.  
df.mm.trans1:probe2	0.301379701682097	0.155289878828566	1.94075559821132	0.0526460534509034	.  
df.mm.trans1:probe3	-0.0420903442887973	0.155289878828566	-0.271043706172657	0.786428762550048	   
df.mm.trans1:probe4	-0.0287162465453549	0.155289878828566	-0.184920271443167	0.85333939584678	   
df.mm.trans1:probe5	0.0160159980923269	0.155289878828566	0.103136136193447	0.917881300275026	   
df.mm.trans1:probe6	0.025726049920868	0.155289878828566	0.165664691832676	0.86846354314501	   
df.mm.trans1:probe7	0.298583489792707	0.155289878828566	1.92274919682519	0.0548735366926378	.  
df.mm.trans1:probe8	0.00750656096003768	0.155289878828566	0.0483390225857838	0.961458366512431	   
df.mm.trans1:probe9	0.133690948640657	0.155289878828566	0.860912183389924	0.389549617875563	   
df.mm.trans1:probe10	0.163419522821169	0.155289878828566	1.05235140920921	0.292962587978964	   
df.mm.trans1:probe11	0.164403974851335	0.155289878828566	1.05869085668378	0.290066663039801	   
df.mm.trans1:probe12	0.142489437412802	0.155289878828566	0.917570665182277	0.359125859962512	   
df.mm.trans1:probe13	0.242828369652576	0.155289878828566	1.56371021398407	0.118289170427216	   
df.mm.trans1:probe14	0.0408240336959376	0.155289878828566	0.262889210835213	0.792705040790822	   
df.mm.trans1:probe15	0.0212606161347302	0.155289878828566	0.136909219680705	0.891137700888168	   
df.mm.trans1:probe16	-0.00259101321787615	0.155289878828566	-0.0166850102364787	0.986692150633241	   
df.mm.trans1:probe17	0.0585404527933367	0.155289878828566	0.376975326627456	0.706293952362184	   
df.mm.trans1:probe18	-0.0147166633917386	0.155289878828566	-0.0947689798121696	0.924522541960758	   
df.mm.trans1:probe19	0.198302666520780	0.155289878828566	1.27698384477265	0.201986020917896	   
df.mm.trans1:probe20	0.127328530775429	0.155289878828566	0.819940950021569	0.4124989741093	   
df.mm.trans1:probe21	-0.0631871807166078	0.155289878828566	-0.406898255013542	0.684193776998321	   
df.mm.trans1:probe22	0.0186438691619318	0.155289878828566	0.1200584951355	0.90446756958511	   
df.mm.trans2:probe2	-0.184531074322168	0.155289878828566	-1.18830071678968	0.235074791379994	   
df.mm.trans2:probe3	-0.0507051140581616	0.155289878828566	-0.326519116639521	0.744118773203048	   
df.mm.trans2:probe4	-0.13740769978628	0.155289878828566	-0.884846461487505	0.376510734054607	   
df.mm.trans2:probe5	-0.120308614263374	0.155289878828566	-0.77473570828264	0.438729432520405	   
df.mm.trans2:probe6	-0.0445561459024456	0.155289878828566	-0.286922407555188	0.774247544361095	   
df.mm.trans3:probe2	-0.148842516687084	0.155289878828566	-0.95848176204323	0.338115363107467	   
df.mm.trans3:probe3	0.0156510588410656	0.155289878828566	0.100786084444974	0.919746041445429	   
df.mm.trans3:probe4	-0.167253616791235	0.155289878828566	-1.07704132460478	0.281793098920164	   
df.mm.trans3:probe5	-0.123932069894588	0.155289878828566	-0.798069203411542	0.42507199468579	   
df.mm.trans3:probe6	-0.287344320675117	0.155289878828566	-1.85037378380812	0.0646357354568875	.  
df.mm.trans3:probe7	-0.163263609282499	0.155289878828566	-1.05134739310819	0.293423010408092