fitVsDatCorrelation=0.819750827636103
cont.fitVsDatCorrelation=0.267274125204518

fstatistic=5728.18234502179,52,692
cont.fstatistic=2014.83134643607,52,692

residuals=-0.48298164294705,-0.101484423687996,-0.0085617194720974,0.0694267376842916,2.78310139970923
cont.residuals=-0.691819657006925,-0.205106274500454,-0.0651588281354094,0.109323993336418,2.71576202799456

predictedValues:
Include	Exclude	Both
Lung	49.5961746485237	69.8082958115345	105.911769559655
cerebhem	61.7130697635738	88.5397946697035	104.203438435166
cortex	48.403991441583	76.3634617209191	132.275126214032
heart	49.2348108436068	60.9488897329012	94.3934744374182
kidney	48.8981065631417	55.5132772769294	86.5411266507528
liver	53.3049985420376	58.9373268953197	85.3797970958136
stomach	50.6118785757713	70.775412034598	123.156543896034
testicle	51.6440601077571	55.7112349030617	87.6087986246696


diffExp=-20.2121211630108,-26.8267249061297,-27.9594702793361,-11.7140788892944,-6.61517071378768,-5.63232835328208,-20.1635334588267,-4.06717479530459
diffExpScore=0.991947860954091
diffExp1.5=0,0,-1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=-1,-1,-1,0,0,0,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,-1,-1,0,0,0,-1,0
diffExp1.3Score=0.8
diffExp1.2=-1,-1,-1,-1,0,0,-1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	60.6890321355884	52.7373776337658	60.0641398738927
cerebhem	57.3255694229035	65.8628339341225	55.2232533573042
cortex	59.092982084762	55.2740860433762	55.6803918842953
heart	58.5440458602914	53.1548536560832	51.6872855101884
kidney	57.6836098853286	68.4328524569033	61.752900693926
liver	59.6883593354709	59.2801867967757	54.0238753766
stomach	64.7069004690443	52.7259002850312	62.7462171064642
testicle	57.0898862489557	58.0442139950318	69.6557504133438
cont.diffExp=7.95165450182263,-8.53726451121899,3.81889604138576,5.3891922042082,-10.7492425715748,0.408172538695254,11.9810001840131,-0.954327746076139
cont.diffExpScore=4.83016693716249

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=0,0,0,0,0,0,1,0
cont.diffExp1.2Score=0.5

tran.correlation=0.593950500342158
cont.tran.correlation=-0.60183935952043

tran.covariance=0.00699208294733576
cont.tran.covariance=-0.00257070160415811

tran.mean=59.3752989706851
cont.tran.mean=58.7707931402146

weightedLogRatios:
wLogRatio
Lung	-1.39293753327387
cerebhem	-1.55318585637141
cortex	-1.87271958023989
heart	-0.8544471509676
kidney	-0.501593558119531
liver	-0.404414967141628
stomach	-1.37210068876019
testicle	-0.301883170066594

cont.weightedLogRatios:
wLogRatio
Lung	0.566745848153293
cerebhem	-0.571714111718982
cortex	0.270285587316714
heart	0.388355598340886
kidney	-0.707513501910337
liver	0.0280356382973834
stomach	0.83286296663372
testicle	-0.0671894559403376

varWeightedLogRatios=0.352025304915938
cont.varWeightedLogRatios=0.286149008647854

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	2.8556269992557	0.108974588914157	26.2045218771615	1.13535983190512e-105	***
df.mm.trans1	0.879544220614123	0.0978751171820549	8.9863925166813	2.40518237306531e-18	***
df.mm.trans2	1.29947659291135	0.0900094629087644	14.4371108427625	1.71560371615774e-41	***
df.mm.exp2	0.472542541381831	0.123290673277147	3.8327517306974	0.000138256396657972	***
df.mm.exp3	-0.156857608544769	0.123290673277147	-1.27225851214363	0.203708625474829	   
df.mm.exp4	-0.0278955692743118	0.123290673277147	-0.226258552515202	0.821067146182665	   
df.mm.exp5	-0.0413190318065598	0.123290673277147	-0.335135097475526	0.737624743948803	   
df.mm.exp6	0.118335050760315	0.123290673277147	0.959805373876967	0.337488406065941	   
df.mm.exp7	-0.116818480568403	0.123290673277147	-0.947504604065269	0.343712460980031	   
df.mm.exp8	0.00461540704987881	0.123290673277147	0.0374351678614308	0.97014882881472	   
df.mm.trans1:exp2	-0.253960512027162	0.118041832729070	-2.15144501026219	0.0317865232269158	*  
df.mm.trans2:exp2	-0.234843287022906	0.102742227730956	-2.28575233581537	0.0225702367226319	*  
df.mm.trans1:exp3	0.132526179933402	0.118041832729070	1.12270520432851	0.261952088420499	   
df.mm.trans2:exp3	0.246609086718976	0.102742227730956	2.40026999769516	0.0166461343750559	*  
df.mm.trans1:exp4	0.0205827733226509	0.118041832729070	0.174368466219027	0.861626894607204	   
df.mm.trans2:exp4	-0.107821644869678	0.102742227730956	-1.04943845632804	0.294342730617517	   
df.mm.trans1:exp5	0.0271440002126020	0.118041832729070	0.229952378619053	0.818196730162795	   
df.mm.trans2:exp5	-0.187811599711295	0.102742227730956	-1.82798839249530	0.0679816901954263	.  
df.mm.trans1:exp6	-0.0462186494441745	0.118041832729070	-0.39154466154601	0.695515220265961	   
df.mm.trans2:exp6	-0.287613281334109	0.102742227730956	-2.79936777395233	0.00526293992505852	** 
df.mm.trans1:exp7	0.137091077038217	0.118041832729070	1.16137706327272	0.24588903187929	   
df.mm.trans2:exp7	0.130577279428634	0.102742227730956	1.27092123961501	0.204183732709677	   
df.mm.trans1:exp8	0.0358460724134947	0.118041832729070	0.30367261829769	0.761468601686214	   
df.mm.trans2:exp8	-0.23018643050516	0.102742227730956	-2.24042670271794	0.025379789340661	*  
df.mm.trans1:probe2	0.0578478438813998	0.0590209163645351	0.980124461709643	0.32736721519694	   
df.mm.trans1:probe3	0.225725850203406	0.0590209163645351	3.82450602442767	0.000142866264367328	***
df.mm.trans1:probe4	0.0763312713774572	0.0590209163645351	1.29329187141058	0.196341688088121	   
df.mm.trans1:probe5	0.146622347550149	0.0590209163645351	2.4842438339072	0.0132181534884769	*  
df.mm.trans1:probe6	0.4174711722907	0.0590209163645351	7.07327500156458	3.71272912571724e-12	***
df.mm.trans1:probe7	0.0705848724905726	0.0590209163645351	1.19592979638971	0.232133581759794	   
df.mm.trans1:probe8	0.158927384714693	0.0590209163645351	2.69272987449226	0.00725847823272739	** 
df.mm.trans1:probe9	0.074450501122473	0.0590209163645351	1.26142570648411	0.207580565939395	   
df.mm.trans1:probe10	0.0187654414154783	0.0590209163645351	0.317945612697302	0.750622113341717	   
df.mm.trans1:probe11	0.210872005332058	0.0590209163645351	3.57283516287064	0.000377636657997227	***
df.mm.trans1:probe12	0.123993580017598	0.0590209163645351	2.10084132296029	0.036016078819164	*  
df.mm.trans1:probe13	0.288831810649434	0.0590209163645351	4.89371952250795	1.23208074960553e-06	***
df.mm.trans1:probe14	0.22713140840832	0.0590209163645351	3.84832060223315	0.000129932733549934	***
df.mm.trans1:probe15	0.263897226812354	0.0590209163645351	4.47124922938212	9.08613438832119e-06	***
df.mm.trans1:probe16	0.490863089331677	0.0590209163645351	8.31676496345675	4.79512037189717e-16	***
df.mm.trans1:probe17	0.0436679853078556	0.0590209163645351	0.739873048363836	0.459627954497832	   
df.mm.trans1:probe18	0.160978001572755	0.0590209163645351	2.72747377520361	0.0065439179329929	** 
df.mm.trans1:probe19	0.332151941852773	0.0590209163645351	5.62769882801004	2.65398195538870e-08	***
df.mm.trans1:probe20	0.31360920920923	0.0590209163645351	5.31352660253974	1.45155643705617e-07	***
df.mm.trans1:probe21	0.189212248949872	0.0590209163645351	3.20585074927042	0.00140856559311998	** 
df.mm.trans1:probe22	0.326626979138440	0.0590209163645351	5.53408857837908	4.44199889082743e-08	***
df.mm.trans2:probe2	0.201943252094878	0.0590209163645351	3.42155399363171	0.000659258153408365	***
df.mm.trans2:probe3	0.0491601042988412	0.0590209163645351	0.832926821996631	0.405173376103938	   
df.mm.trans2:probe4	0.124668774853236	0.0590209163645351	2.11228124760442	0.0350200080476215	*  
df.mm.trans2:probe5	0.0942079983660178	0.0590209163645351	1.59617986586576	0.110905185452598	   
df.mm.trans2:probe6	0.345863225751401	0.0590209163645351	5.8600111122508	7.16032122468881e-09	***
df.mm.trans3:probe2	-0.264116211696306	0.0590209163645351	-4.47495952223151	8.93414653021855e-06	***
df.mm.trans3:probe3	-0.852008072063711	0.0590209163645351	-14.4356971145855	1.74285507107556e-41	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.97111952106163	0.183353280593745	21.6582954403767	8.16202132875638e-80	***
df.mm.trans1	0.0930337109747155	0.164678059377343	0.564942964026182	0.572295650713325	   
df.mm.trans2	-0.00698475559135551	0.151443840928856	-0.0461210937897222	0.963227039736852	   
df.mm.exp2	0.249262486518919	0.207440556897019	1.20160922361321	0.229926034713099	   
df.mm.exp3	0.0961138485891034	0.207440556897019	0.463332002318224	0.643272095799743	   
df.mm.exp4	0.12210252911827	0.207440556897019	0.58861454550995	0.556311944594009	   
df.mm.exp5	0.182010674854283	0.207440556897019	0.877411233255799	0.38056782315036	   
df.mm.exp6	0.206311601946685	0.207440556897019	0.994557694178893	0.320299067374174	   
df.mm.exp7	0.0202018993603710	0.207440556897019	0.097386449701829	0.922447704131834	   
df.mm.exp8	-0.113407710560439	0.207440556897019	-0.54669979803775	0.584761226884419	   
df.mm.trans1:exp2	-0.306278716631009	0.198609212421277	-1.54211737158169	0.123502219474314	   
df.mm.trans2:exp2	-0.0270126379844912	0.172867130747516	-0.156262430386173	0.87587174337887	   
df.mm.trans1:exp3	-0.122764669724035	0.198609212421277	-0.618121728732471	0.536698477994959	   
df.mm.trans2:exp3	-0.0491341138607518	0.172867130747516	-0.284230516514533	0.776318757967438	   
df.mm.trans1:exp4	-0.158086129539642	0.198609212421277	-0.795965744047763	0.426324904631937	   
df.mm.trans2:exp4	-0.114217565694688	0.172867130747516	-0.660724599296497	0.509008835431512	   
df.mm.trans1:exp5	-0.232800591184170	0.198609212421277	-1.17215404233298	0.24153859405036	   
df.mm.trans2:exp5	0.0785178763861772	0.172867130747516	0.454209403757952	0.649820543318911	   
df.mm.trans1:exp6	-0.22293757861122	0.198609212421277	-1.12249364414345	0.262041917207286	   
df.mm.trans2:exp6	-0.0893609270300074	0.172867130747516	-0.516934171600991	0.605367284478255	   
df.mm.trans1:exp7	0.0439029577223454	0.198609212421277	0.221051970284345	0.825117174466376	   
df.mm.trans2:exp7	-0.0204195551929295	0.172867130747516	-0.118122832863777	0.906004610171316	   
df.mm.trans1:exp8	0.0522716960029589	0.198609212421277	0.263188677734059	0.792483500752899	   
df.mm.trans2:exp8	0.209288283796245	0.172867130747516	1.21068871156266	0.226428065530318	   
df.mm.trans1:probe2	-0.0115252115774594	0.0993046062106387	-0.116059184133039	0.907639307806576	   
df.mm.trans1:probe3	0.000654177196151678	0.0993046062106387	0.0065875815947961	0.994745806956947	   
df.mm.trans1:probe4	-0.0351886976617442	0.0993046062106387	-0.354351112244523	0.723183718059872	   
df.mm.trans1:probe5	-0.00333751916088696	0.0993046062106387	-0.0336089058528425	0.973198712020652	   
df.mm.trans1:probe6	0.07151058704781	0.0993046062106387	0.720113494998673	0.471698273372046	   
df.mm.trans1:probe7	0.174872886403404	0.0993046062106387	1.76097457183884	0.0786843407417437	.  
df.mm.trans1:probe8	0.07476483628528	0.0993046062106387	0.752883870529566	0.451775744267871	   
df.mm.trans1:probe9	0.148507177544661	0.0993046062106387	1.49547118921812	0.135247400105962	   
df.mm.trans1:probe10	-0.0520636230852532	0.0993046062106387	-0.524282055706652	0.600250263320513	   
df.mm.trans1:probe11	0.117672399660883	0.0993046062106387	1.18496416381012	0.236438401231763	   
df.mm.trans1:probe12	0.213692486458885	0.0993046062106387	2.15188896681805	0.0317513911782137	*  
df.mm.trans1:probe13	-0.0912738521061613	0.0993046062106387	-0.919130094656002	0.358347792713044	   
df.mm.trans1:probe14	0.06246161984981	0.0993046062106387	0.628990156985471	0.529563071674908	   
df.mm.trans1:probe15	0.0556487396315595	0.0993046062106387	0.560384273751823	0.575398728561903	   
df.mm.trans1:probe16	0.0195156108791769	0.0993046062106387	0.196522715550391	0.844258754982605	   
df.mm.trans1:probe17	0.0342804217178209	0.0993046062106387	0.34520474956728	0.730045350017527	   
df.mm.trans1:probe18	0.0331587226736286	0.0993046062106387	0.333909210649246	0.738549230274059	   
df.mm.trans1:probe19	0.0702536945300251	0.0993046062106387	0.707456554240872	0.479520983531436	   
df.mm.trans1:probe20	0.094488710051282	0.0993046062106387	0.95150379883546	0.341680901673197	   
df.mm.trans1:probe21	0.0532091700247797	0.0993046062106387	0.5358177435588	0.59225668895923	   
df.mm.trans1:probe22	0.0089416638611498	0.0993046062106387	0.0900427905849936	0.92827926589437	   
df.mm.trans2:probe2	-0.0306164263008426	0.0993046062106387	-0.308308219216951	0.757940595633536	   
df.mm.trans2:probe3	0.120398814165993	0.0993046062106387	1.21241922968417	0.225765705082428	   
df.mm.trans2:probe4	-0.107447833015564	0.0993046062106387	-1.08200250839979	0.279628310603847	   
df.mm.trans2:probe5	-0.0229447328119173	0.0993046062106387	-0.231054063728407	0.81734109823244	   
df.mm.trans2:probe6	0.0513174031599286	0.0993046062106387	0.516767601404886	0.605483509664388	   
df.mm.trans3:probe2	0.0238487660132241	0.0993046062106387	0.240157701875758	0.810279127303052	   
df.mm.trans3:probe3	0.0908847720986125	0.0993046062106387	0.915212048732497	0.360399119772155