fitVsDatCorrelation=0.907846234128805
cont.fitVsDatCorrelation=0.264525444599285

fstatistic=9730.7769717465,44,508
cont.fstatistic=1830.17507924858,44,508

residuals=-0.579334255803118,-0.08885771992438,-0.00310856693842358,0.086863161473374,0.630611862780191
cont.residuals=-0.686777142935928,-0.23523076133771,-0.0537555101335853,0.151586124727012,1.61265280274017

predictedValues:
Include	Exclude	Both
Lung	58.8966972151229	108.102991467607	123.465077596762
cerebhem	57.3578051986933	75.9264362659341	61.5834905201573
cortex	68.8641979062576	79.3381831995418	90.1941656740763
heart	55.3115655528122	79.4782265772307	77.9401112243403
kidney	57.7652754813618	85.5861070198002	86.3433559687993
liver	55.929472447842	87.0688831882909	98.4698411899525
stomach	74.4202181416605	101.100842112778	104.453027067747
testicle	128.632651517375	111.687280168097	172.144696811886


diffExp=-49.2062942524838,-18.5686310672408,-10.4739852932842,-24.1666610244185,-27.8208315384383,-31.1394107404488,-26.6806239711172,16.9453713492775
diffExpScore=1.19110184696499
diffExp1.5=-1,0,0,0,0,-1,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=-1,0,0,-1,-1,-1,0,0
diffExp1.4Score=0.8
diffExp1.3=-1,-1,0,-1,-1,-1,-1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=-1,-1,0,-1,-1,-1,-1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	78.2415116421458	81.3978789920988	83.4588612222137
cerebhem	78.7305692807637	86.452339412391	87.7502528249907
cortex	78.4717167509588	72.7313352678547	77.477979379942
heart	82.0907200863043	79.203622789893	72.0671967347984
kidney	81.0639432025183	76.3325760814098	71.9076367242794
liver	86.2300936673446	81.9903796222525	73.8242707538484
stomach	71.8010560619867	85.9234644614736	80.3877161048878
testicle	72.5006945239674	76.6433694508436	88.365128395503
cont.diffExp=-3.15636734995297,-7.72177013162737,5.74038148310407,2.88709729641131,4.73136712110848,4.23971404509213,-14.1224083994869,-4.14267492687621
cont.diffExpScore=3.72602984385182

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=0.645007663210599
cont.tran.correlation=-0.094657868472933

tran.covariance=0.02709635243134
cont.tran.covariance=-0.000343707295117962

tran.mean=80.3416770912753
cont.tran.mean=79.3628294558879

weightedLogRatios:
wLogRatio
Lung	-2.65962799386844
cerebhem	-1.17498075124653
cortex	-0.609221867521682
heart	-1.52041381819924
kidney	-1.67198623186144
liver	-1.87904640186193
stomach	-1.36739857116069
testicle	0.67610695545753

cont.weightedLogRatios:
wLogRatio
Lung	-0.173207233415959
cerebhem	-0.41287051437899
cortex	0.328534145984916
heart	0.157172159940366
kidney	0.262514591076486
liver	0.223439978751836
stomach	-0.783532292269066
testicle	-0.239570241142340

varWeightedLogRatios=0.966929653343845
cont.varWeightedLogRatios=0.153207542050076

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12492574404222	0.0796733428025215	51.7729719746574	9.26414460337476e-205	***
df.mm.trans1	-0.185450893535211	0.0686554688480261	-2.70118166326589	0.00714018303226352	** 
df.mm.trans2	0.65361132816037	0.0641207814239751	10.1934398434512	2.51869192627563e-22	***
df.mm.exp2	0.315768967545558	0.0862068406769792	3.66292239764079	0.000275521581521319	***
df.mm.exp3	0.160980123529557	0.0862068406769792	1.86737064327362	0.0624255140033988	.  
df.mm.exp4	0.089613337853576	0.0862068406769792	1.03951539286031	0.299059604367023	   
df.mm.exp5	0.104667866666083	0.0862068406769792	1.21414804027302	0.225255323925085	   
df.mm.exp6	-0.041870346363643	0.0862068406769792	-0.4856963326209	0.627391899733576	   
df.mm.exp7	0.334197572560331	0.0862068406769792	3.87669435436782	0.000119776172871092	***
df.mm.exp8	0.481417051887328	0.0862068406769792	5.58444142143219	3.82430834979389e-08	***
df.mm.trans1:exp2	-0.34224505024655	0.0778164093780125	-4.39810899760242	1.33099273077321e-05	***
df.mm.trans2:exp2	-0.669088437301811	0.0684044425272715	-9.7813594056418	8.123831559571e-21	***
df.mm.trans1:exp3	-0.00462871920252922	0.0778164093780125	-0.0594825595208855	0.952591148194841	   
df.mm.trans2:exp3	-0.470345005040505	0.0684044425272715	-6.87594237542375	1.81389715375173e-11	***
df.mm.trans1:exp4	-0.152416323827770	0.0778164093780125	-1.95866559567622	0.0506986779337846	.  
df.mm.trans2:exp4	-0.397214630649943	0.0684044425272715	-5.8068542915408	1.12304544474012e-08	***
df.mm.trans1:exp5	-0.124065056380323	0.0778164093780125	-1.59433026236980	0.111484021640288	   
df.mm.trans2:exp5	-0.338229295274321	0.0684044425272715	-4.94455159311438	1.03866805329759e-06	***
df.mm.trans1:exp6	-0.00982319185122992	0.0778164093780125	-0.126235480790579	0.899595471765438	   
df.mm.trans2:exp6	-0.174514484887157	0.0684044425272715	-2.55121565850905	0.0110267422208211	*  
df.mm.trans1:exp7	-0.100254933061729	0.0778164093780125	-1.28835208233157	0.198209906740185	   
df.mm.trans2:exp7	-0.401163514478470	0.0684044425272715	-5.86458276183646	8.11850499066043e-09	***
df.mm.trans1:exp8	0.299758612873969	0.0778164093780125	3.85212598820665	0.000132076074192471	***
df.mm.trans2:exp8	-0.448798624659424	0.0684044425272715	-6.56095727233646	1.31986550561258e-10	***
df.mm.trans1:probe2	0.067503256764793	0.0454349944044177	1.48571068731615	0.137975962087079	   
df.mm.trans1:probe3	0.184328871835386	0.0454349944044177	4.0569801812821	5.753270901967e-05	***
df.mm.trans1:probe4	0.0273414918365449	0.0454349944044177	0.601771656295977	0.547594607689376	   
df.mm.trans1:probe5	0.195476346998022	0.0454349944044177	4.30233016555661	2.02784619329979e-05	***
df.mm.trans1:probe6	0.0762640689951138	0.0454349944044177	1.67853149306647	0.0938583708287645	.  
df.mm.trans1:probe7	0.326732451670902	0.0454349944044177	7.19120704104529	2.31238627285722e-12	***
df.mm.trans1:probe8	0.341108664468555	0.0454349944044177	7.5076198190395	2.72244476138273e-13	***
df.mm.trans1:probe9	0.353253992567768	0.0454349944044177	7.77493201437305	4.22853356030124e-14	***
df.mm.trans1:probe10	0.256279795989189	0.0454349944044177	5.6405816562458	2.81741535680422e-08	***
df.mm.trans1:probe11	0.275705131844201	0.0454349944044177	6.0681229404398	2.53223210594837e-09	***
df.mm.trans1:probe12	0.213278717276490	0.0454349944044177	4.69415084280836	3.44831500596519e-06	***
df.mm.trans2:probe2	-0.341884797916721	0.0454349944044177	-7.52470210238388	2.42059869454445e-13	***
df.mm.trans2:probe3	-0.160465240455742	0.0454349944044177	-3.53175437917827	0.000450412595870636	***
df.mm.trans2:probe4	-0.112869293130851	0.0454349944044177	-2.48419295766164	0.0133057083877430	*  
df.mm.trans2:probe5	-0.154939742152613	0.0454349944044177	-3.41014110783181	0.000700973364439604	***
df.mm.trans2:probe6	-0.279820349028254	0.0454349944044177	-6.15869667634529	1.49217516088564e-09	***
df.mm.trans3:probe2	0.173795082278740	0.0454349944044177	3.8251370899661	0.000146959977221137	***
df.mm.trans3:probe3	-0.126389569598325	0.0454349944044177	-2.78176703343086	0.00560737941041422	** 
df.mm.trans3:probe4	0.341740631282103	0.0454349944044177	7.52152907162844	2.47405587595147e-13	***
df.mm.trans3:probe5	0.725214680623072	0.0454349944044177	15.9615884216453	8.83707036784726e-47	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.37580671181783	0.183245122283177	23.8795262722229	4.64378774753082e-85	***
df.mm.trans1	-0.075386357495081	0.157904505345636	-0.477417394330001	0.633270265662601	   
df.mm.trans2	0.0765444819034302	0.147474927242005	0.519033867891464	0.603963158824144	   
df.mm.exp2	0.0163343053619226	0.198271874956394	0.082383370639507	0.934374309208794	   
df.mm.exp3	-0.0352789137130174	0.198271874956394	-0.177932012398513	0.858847298514245	   
df.mm.exp4	0.167452314844509	0.198271874956394	0.844559092818065	0.398754614336081	   
df.mm.exp5	0.1201598560782	0.198271874956394	0.606035808682532	0.54476166439933	   
df.mm.exp6	0.227137868668434	0.198271874956394	1.14558793938066	0.252505071190621	   
df.mm.exp7	0.00569902477161639	0.198271874956394	0.0287434855441287	0.977080462514557	   
df.mm.exp8	-0.193513909554786	0.198271874956394	-0.976002822373805	0.329527597913273	   
df.mm.trans1:exp2	-0.0101031435127119	0.178974258522769	-0.0564502604793672	0.955005314172569	   
df.mm.trans2:exp2	0.0439097512124231	0.157327156043787	0.279098359854685	0.78028292704057	   
df.mm.trans1:exp3	0.0382168312934969	0.178974258522769	0.213532558307177	0.830997267564953	   
df.mm.trans2:exp3	-0.0772979891054238	0.157327156043787	-0.491320068633989	0.623412285883418	   
df.mm.trans1:exp4	-0.119427682729715	0.178974258522769	-0.667289719289554	0.504890086202418	   
df.mm.trans2:exp4	-0.194779490834538	0.157327156043787	-1.23805384736204	0.216267949208616	   
df.mm.trans1:exp5	-0.0847219367018027	0.178974258522769	-0.473374983648972	0.636149024319846	   
df.mm.trans2:exp5	-0.184409277633045	0.157327156043787	-1.17213888733691	0.241690700107595	   
df.mm.trans1:exp6	-0.129918983574203	0.178974258522769	-0.725908768370034	0.468228996129182	   
df.mm.trans2:exp6	-0.219885166027648	0.157327156043787	-1.39763008216108	0.162834209627175	   
df.mm.trans1:exp7	-0.0916001865608838	0.178974258522769	-0.511806487239786	0.609008856523442	   
df.mm.trans2:exp7	0.048408711059664	0.157327156043787	0.307694566386180	0.758440864199288	   
df.mm.trans1:exp8	0.117309704890965	0.178974258522769	0.65545573904999	0.51247108640534	   
df.mm.trans2:exp8	0.133327790840965	0.157327156043787	0.847455672584948	0.397140267030344	   
df.mm.trans1:probe2	0.0302712073579088	0.104498453469050	0.289680912520627	0.772178554093338	   
df.mm.trans1:probe3	0.205986018242093	0.104498453469050	1.97118724157100	0.0492447929408071	*  
df.mm.trans1:probe4	0.0499398370939887	0.104498453469050	0.477900250540832	0.632926775818912	   
df.mm.trans1:probe5	0.137780454810008	0.104498453469050	1.31849276459210	0.187932871463181	   
df.mm.trans1:probe6	0.0172296478389098	0.104498453469050	0.16487945291949	0.869104452465164	   
df.mm.trans1:probe7	-0.0115387447410562	0.104498453469050	-0.110420244108911	0.912119707467025	   
df.mm.trans1:probe8	0.0195177093965046	0.104498453469050	0.186775102870639	0.851911534685177	   
df.mm.trans1:probe9	0.117870428686469	0.104498453469050	1.12796337910761	0.259867828085364	   
df.mm.trans1:probe10	0.140190591189554	0.104498453469050	1.34155661194617	0.180339197933373	   
df.mm.trans1:probe11	0.081313729125947	0.104498453469050	0.778133325676728	0.436852679027047	   
df.mm.trans1:probe12	0.220898981544351	0.104498453469050	2.11389713637988	0.0350107984535413	*  
df.mm.trans2:probe2	-0.0216678248317671	0.104498453469050	-0.207350674698593	0.83581909289362	   
df.mm.trans2:probe3	-0.163363425450830	0.104498453469050	-1.56330950389819	0.118602522758109	   
df.mm.trans2:probe4	-0.158845901216149	0.104498453469050	-1.52007896713222	0.129113264868369	   
df.mm.trans2:probe5	-0.164528968375615	0.104498453469050	-1.57446318977672	0.116002950066086	   
df.mm.trans2:probe6	-0.0746156343896882	0.104498453469050	-0.714035776728383	0.475532995704498	   
df.mm.trans3:probe2	0.0720154122409813	0.104498453469050	0.689152899878181	0.49104169278599	   
df.mm.trans3:probe3	0.0982151279971959	0.104498453469050	0.93987159366224	0.347730107567881	   
df.mm.trans3:probe4	0.0866698754087756	0.104498453469050	0.82938907258034	0.407273829861378	   
df.mm.trans3:probe5	-0.0199554443945204	0.104498453469050	-0.190964016519447	0.848630079737703