fitVsDatCorrelation=0.868338396613657
cont.fitVsDatCorrelation=0.202728495573971

fstatistic=10596.5101939960,59,853
cont.fstatistic=2707.59071420504,59,853

residuals=-0.60533637622144,-0.0842867665867006,-0.000176995548731769,0.0864814844697667,1.29725836654655
cont.residuals=-0.706732977309249,-0.223931119202981,-0.0458497082670713,0.170975787452193,1.33677976202726

predictedValues:
Include	Exclude	Both
Lung	56.7697264443065	64.7814487070695	76.4978193486041
cerebhem	57.3301957516236	96.1315517189985	80.5886066606871
cortex	65.400308398038	68.5497456663052	78.3710542367956
heart	71.9844756394968	73.3237156043384	83.944249522476
kidney	61.1680274395533	65.3113537062837	81.3451732430208
liver	60.9944141632668	67.776078551404	80.566721715545
stomach	62.2917576632145	74.0968119585424	79.4893536703374
testicle	57.6507906089457	69.6545765716191	78.367005385983


diffExp=-8.01172226276299,-38.8013559673749,-3.14943726826722,-1.33923996484170,-4.14332626673042,-6.78166438813724,-11.8050542953279,-12.0037859626734
diffExpScore=0.988510446799559
diffExp1.5=0,-1,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,-1,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,-1
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	70.0053343650216	81.7403726725701	65.321493693844
cerebhem	65.8744678167569	78.3596662317074	71.9147639124039
cortex	66.904449059485	65.2432264229778	62.6834857680633
heart	67.0994937463006	70.3564328978083	64.5623071037304
kidney	64.9603239044643	72.552539544999	69.8327890285558
liver	67.8900843386689	70.5734234588829	68.6131441137443
stomach	65.964433975949	70.1095010009956	66.7668775837445
testicle	66.6719499255875	71.622471239536	69.1797337259333
cont.diffExp=-11.7350383075485,-12.4851984149505,1.66122263650725,-3.25693915150771,-7.59221564053468,-2.683339120214,-4.14506702504652,-4.95052131394853
cont.diffExpScore=1.05028342236664

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=-0.143652836392509
cont.tran.correlation=0.408672125640066

tran.covariance=-0.00120747024948702
cont.tran.covariance=0.000606226926355937

tran.mean=67.0759361620629
cont.tran.mean=69.7455106626069

weightedLogRatios:
wLogRatio
Lung	-0.541927485729315
cerebhem	-2.22638662160448
cortex	-0.197727600566585
heart	-0.0790003561815818
kidney	-0.271759992698064
liver	-0.438944955185345
stomach	-0.732110292672583
testicle	-0.78475580016339

cont.weightedLogRatios:
wLogRatio
Lung	-0.67043799979192
cerebhem	-0.741880633778154
cortex	0.105367608025341
heart	-0.200486454411943
kidney	-0.467454402767415
liver	-0.164252099361625
stomach	-0.257152613105885
testicle	-0.303372679990677

varWeightedLogRatios=0.462731034810960
cont.varWeightedLogRatios=0.0776855976209389

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15447378428409	0.0748355396937024	55.5147166879282	1.99782969081083e-285	***
df.mm.trans1	-0.129068898537059	0.0646261415405718	-1.99716237826191	0.0461248241162743	*  
df.mm.trans2	0.0529287601454287	0.0570969039461456	0.92699877729538	0.354189376507663	   
df.mm.exp2	0.352427521948655	0.0734448746472871	4.79853119283215	1.88648045645028e-06	***
df.mm.exp3	0.173871776496253	0.0734448746472871	2.36737794612977	0.018136921615122	*  
df.mm.exp4	0.268421455232836	0.0734448746472871	3.65473365598222	0.000273188109121661	***
df.mm.exp5	0.0213287776418517	0.0734448746472871	0.290405256245332	0.771576808888331	   
df.mm.exp6	0.0651456752266098	0.0734448746472871	0.887000972354661	0.375328389865996	   
df.mm.exp7	0.188818287257231	0.0734448746472871	2.5708844648999	0.0103124160625027	*  
df.mm.exp8	0.0637891373515794	0.0734448746472871	0.868530822033824	0.385348100668894	   
df.mm.trans1:exp2	-0.342603259067762	0.0678866249214402	-5.04669748222465	5.49531688697373e-07	***
df.mm.trans2:exp2	0.0422707848935474	0.0501375952748088	0.843095578514632	0.399411336898856	   
df.mm.trans1:exp3	-0.0323480009824791	0.0678866249214402	-0.476500356585895	0.63383998611488	   
df.mm.trans2:exp3	-0.117331357673312	0.0501375952748088	-2.34018717950488	0.0195037674866537	*  
df.mm.trans1:exp4	-0.0309741740879676	0.0678866249214402	-0.456263279015736	0.648316810300065	   
df.mm.trans2:exp4	-0.144556633985553	0.0501375952748088	-2.88319839021447	0.00403556104908242	** 
df.mm.trans1:exp5	0.0532926494007875	0.0678866249214402	0.785024288104746	0.432657300379096	   
df.mm.trans2:exp5	-0.0131821635313307	0.0501375952748088	-0.262919740348096	0.79267593088902	   
df.mm.trans1:exp6	0.00663341515523597	0.0678866249214402	0.0977131380873964	0.922183037596057	   
df.mm.trans2:exp6	-0.0199556432488134	0.0501375952748088	-0.398017558270091	0.690716835449973	   
df.mm.trans1:exp7	-0.0959923694718998	0.0678866249214402	-1.41401004370131	0.157723818053994	   
df.mm.trans2:exp7	-0.0544650563681943	0.0501375952748088	-1.08631170022547	0.277647995712244	   
df.mm.trans1:exp8	-0.0483883752898511	0.0678866249214402	-0.712782162109947	0.476175498008294	   
df.mm.trans2:exp8	0.00873999186477024	0.0501375952748088	0.174320124785912	0.861655247413451	   
df.mm.trans1:probe2	0.361122909212175	0.0464787947779565	7.76962722328259	2.25876021782969e-14	***
df.mm.trans1:probe3	-0.110203167365047	0.0464787947779565	-2.37104184588953	0.0179593208266663	*  
df.mm.trans1:probe4	0.382312745407982	0.0464787947779565	8.22553052923183	7.22090020093067e-16	***
df.mm.trans1:probe5	-0.00203277522720891	0.0464787947779564	-0.0437355408400779	0.965125444486956	   
df.mm.trans1:probe6	0.56057165932822	0.0464787947779565	12.0608045455190	4.85703871144901e-31	***
df.mm.trans1:probe7	-0.0804455062486319	0.0464787947779564	-1.73080017743457	0.0838490407397304	.  
df.mm.trans1:probe8	-0.235194788381871	0.0464787947779564	-5.06026004988876	5.12905869771182e-07	***
df.mm.trans1:probe9	-0.0638738988055245	0.0464787947779565	-1.37425893056543	0.169722269891010	   
df.mm.trans1:probe10	-0.318788919749017	0.0464787947779564	-6.85880348817069	1.33320800773589e-11	***
df.mm.trans1:probe11	0.236353706004864	0.0464787947779564	5.08519438023294	4.51629830535852e-07	***
df.mm.trans1:probe12	0.206386557727768	0.0464787947779564	4.44044555616686	1.01567211083748e-05	***
df.mm.trans1:probe13	0.0989331827646882	0.0464787947779564	2.12856601031336	0.0335761892653049	*  
df.mm.trans1:probe14	0.221084333706144	0.0464787947779564	4.75667096710085	2.31032446726143e-06	***
df.mm.trans1:probe15	0.332629013202181	0.0464787947779564	7.1565756984718	1.78337030188402e-12	***
df.mm.trans1:probe16	-0.0489549150122681	0.0464787947779564	-1.05327419194368	0.292513604951016	   
df.mm.trans1:probe17	-0.172516228815691	0.0464787947779564	-3.71171906758457	0.000219212877572312	***
df.mm.trans1:probe18	-0.252263282751464	0.0464787947779564	-5.42749191231407	7.45199645090798e-08	***
df.mm.trans1:probe19	-0.182744503327929	0.0464787947779564	-3.93178231494504	9.11580888002428e-05	***
df.mm.trans1:probe20	-0.19118653223461	0.0464787947779565	-4.11341415258221	4.27605159003522e-05	***
df.mm.trans1:probe21	-0.189581042838088	0.0464787947779565	-4.07887174664867	4.94942076827131e-05	***
df.mm.trans1:probe22	-0.116462538354885	0.0464787947779564	-2.50571338846591	0.0124057245539627	*  
df.mm.trans2:probe2	-0.133513776043947	0.0464787947779564	-2.87257397016818	0.00417241151745983	** 
df.mm.trans2:probe3	-0.0513915094713749	0.0464787947779564	-1.10569797940949	0.269169017999288	   
df.mm.trans2:probe4	-0.174800872534434	0.0464787947779564	-3.76087360632976	0.000180878935952172	***
df.mm.trans2:probe5	-0.0676909123685756	0.0464787947779564	-1.45638269434386	0.145654829049948	   
df.mm.trans2:probe6	-0.154735208677789	0.0464787947779564	-3.32915707941668	0.000908441617458491	***
df.mm.trans3:probe2	-0.102865211451259	0.0464787947779564	-2.21316434607820	0.0271503216944121	*  
df.mm.trans3:probe3	0.726619317352276	0.0464787947779564	15.6333510974964	1.24319911787396e-48	***
df.mm.trans3:probe4	0.456478881326535	0.0464787947779564	9.82122887452818	1.21664756228581e-21	***
df.mm.trans3:probe5	0.279974608035816	0.0464787947779564	6.02370628096836	2.53256751867996e-09	***
df.mm.trans3:probe6	0.706258525348524	0.0464787947779564	15.1952848330629	2.49858917067789e-46	***
df.mm.trans3:probe7	0.157150049450065	0.0464787947779564	3.38111283222424	0.000754744200598984	***
df.mm.trans3:probe8	0.169639257049725	0.0464787947779564	3.64982047964334	0.000278384128582729	***
df.mm.trans3:probe9	-0.193461199645497	0.0464787947779565	-4.16235404918997	3.46947809692048e-05	***
df.mm.trans3:probe10	0.389491665401221	0.0464787947779564	8.37998634134003	2.16789686913286e-16	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43229244775554	0.147753397818714	29.9979053828173	1.48148245946099e-135	***
df.mm.trans1	-0.165406491754667	0.127596220186492	-1.29632752061865	0.195213272303702	   
df.mm.trans2	-0.00686873802273112	0.112730683810137	-0.0609305096942334	0.951428824023263	   
df.mm.exp2	-0.199219667950423	0.145007703905413	-1.37385575100459	0.169847383041604	   
df.mm.exp3	-0.229508675369982	0.145007703905413	-1.58273435954608	0.113852699153911	   
df.mm.exp4	-0.180678385793966	0.145007703905413	-1.24599163305020	0.213109568159353	   
df.mm.exp5	-0.260814338045772	0.145007703905413	-1.79862401114839	0.072431598242135	.  
df.mm.exp6	-0.226738842129864	0.145007703905413	-1.56363307619686	0.118274659434413	   
df.mm.exp7	-0.234831422130254	0.145007703905413	-1.61944100765454	0.105722039989066	   
df.mm.exp8	-0.238313108580214	0.145007703905413	-1.64345136266459	0.10065809145012	   
df.mm.trans1:exp2	0.138399151735825	0.134033636152576	1.03257029883364	0.302097698535041	   
df.mm.trans2:exp2	0.156980964226569	0.0989904006924118	1.58582007071927	0.113150750600542	   
df.mm.trans1:exp3	0.184202699063762	0.134033636152576	1.37430203605069	0.169708897657326	   
df.mm.trans2:exp3	0.00408286920534189	0.0989904006924118	0.0412451023208645	0.967110149593054	   
df.mm.trans1:exp4	0.138283440618136	0.134033636152576	1.03170699973194	0.302501825850315	   
df.mm.trans2:exp4	0.0307045691379954	0.0989904006924118	0.310177238633494	0.756501944452169	   
df.mm.trans1:exp5	0.186019575740326	0.134033636152576	1.38785741460130	0.165542869033817	   
df.mm.trans2:exp5	0.141577283460173	0.0989904006924118	1.43021224755003	0.153022293799688	   
df.mm.trans1:exp6	0.196057388366286	0.134033636152576	1.46274766539278	0.143904782093697	   
df.mm.trans2:exp6	0.0798444401296274	0.0989904006924117	0.806587705182892	0.420128882065301	   
df.mm.trans1:exp7	0.175375695396355	0.134033636152576	1.30844540542583	0.191074720672272	   
df.mm.trans2:exp7	0.0813417045817209	0.0989904006924118	0.821713055132186	0.411469837372176	   
df.mm.trans1:exp8	0.189525987843443	0.134033636152576	1.41401810234931	0.157721452664549	   
df.mm.trans2:exp8	0.106173940066329	0.0989904006924117	1.07256804017026	0.283768315305843	   
df.mm.trans1:probe2	0.00985767835960123	0.0917665574815072	0.107421250509345	0.914480056620677	   
df.mm.trans1:probe3	-0.0124842978122601	0.0917665574815072	-0.136044090079067	0.891818525119948	   
df.mm.trans1:probe4	-0.0532923564960376	0.0917665574815072	-0.580738320785075	0.56157022393814	   
df.mm.trans1:probe5	-0.103680422214689	0.0917665574815072	-1.12982795759318	0.258866329727543	   
df.mm.trans1:probe6	-0.0466586499881752	0.0917665574815072	-0.508449387976419	0.611269693392893	   
df.mm.trans1:probe7	0.0187357596678911	0.0917665574815072	0.204167620341068	0.838271214483788	   
df.mm.trans1:probe8	-0.0601055124403076	0.0917665574815072	-0.654982752866371	0.51265543999361	   
df.mm.trans1:probe9	-0.0269465684093325	0.0917665574815072	-0.293642576869713	0.76910246727186	   
df.mm.trans1:probe10	-0.0397830069203996	0.0917665574815072	-0.433524020212011	0.664743730568406	   
df.mm.trans1:probe11	0.0648302439213578	0.0917665574815072	0.706469172437055	0.480089338108231	   
df.mm.trans1:probe12	-0.0465255788000273	0.0917665574815072	-0.50699928249355	0.612286358929275	   
df.mm.trans1:probe13	0.0189024490601001	0.0917665574815072	0.205984070655689	0.836852505249445	   
df.mm.trans1:probe14	-0.0699463763406945	0.0917665574815072	-0.76222077258145	0.44613895628251	   
df.mm.trans1:probe15	-0.0887632937151928	0.0917665574815072	-0.96727278598285	0.333681847081942	   
df.mm.trans1:probe16	0.0287211132238305	0.0917665574815072	0.312980175044905	0.754372229663702	   
df.mm.trans1:probe17	-0.0341092388277342	0.0917665574815072	-0.371695743676643	0.710211704494644	   
df.mm.trans1:probe18	0.0180868475271268	0.0917665574815072	0.197096284567193	0.84379914352618	   
df.mm.trans1:probe19	-0.0495174480974427	0.0917665574815072	-0.539602328521711	0.589612135446179	   
df.mm.trans1:probe20	-0.0633679353594486	0.0917665574815072	-0.69053408015462	0.490046235006726	   
df.mm.trans1:probe21	-0.0176434745502600	0.0917665574815072	-0.192264753462235	0.847580587714504	   
df.mm.trans1:probe22	-0.0323743042802997	0.0917665574815072	-0.352789787138128	0.724333196275203	   
df.mm.trans2:probe2	-0.0447889740404095	0.0917665574815072	-0.48807512529208	0.625622061517716	   
df.mm.trans2:probe3	-0.182252835567735	0.0917665574815072	-1.9860485188677	0.0473476381203867	*  
df.mm.trans2:probe4	-0.0985041352930476	0.0917665574815072	-1.07342084084279	0.283385904052303	   
df.mm.trans2:probe5	-0.0542225655369102	0.0917665574815072	-0.590875009644304	0.55476069582782	   
df.mm.trans2:probe6	0.0297577526222799	0.0917665574815072	0.324276658501401	0.745808089453788	   
df.mm.trans3:probe2	-0.0409646133412428	0.0917665574815072	-0.446400240626854	0.655421458194148	   
df.mm.trans3:probe3	-0.0598156536650848	0.0917665574815072	-0.651824099178383	0.514690237700947	   
df.mm.trans3:probe4	-0.0403208892462452	0.0917665574815072	-0.439385440108404	0.660493560906935	   
df.mm.trans3:probe5	-0.132896654608621	0.0917665574815072	-1.44820355318878	0.147927589175296	   
df.mm.trans3:probe6	-0.0667520939238745	0.0917665574815072	-0.727411987066491	0.467173211978091	   
df.mm.trans3:probe7	-0.161714672592127	0.0917665574815072	-1.76223972033293	0.0783869263944927	.  
df.mm.trans3:probe8	-0.0650792544050397	0.0917665574815072	-0.7091826934682	0.478404897293707	   
df.mm.trans3:probe9	-0.115259996973739	0.0917665574815072	-1.25601308512598	0.209455127921303	   
df.mm.trans3:probe10	-0.124156999340519	0.0917665574815072	-1.35296564181934	0.176425085322430