fitVsDatCorrelation=0.900977895970366
cont.fitVsDatCorrelation=0.261191469836159

fstatistic=6854.69932471361,58,830
cont.fstatistic=1373.37313785954,58,830

residuals=-0.613666066701688,-0.127475790361349,-0.0105362651982836,0.123427548487852,0.760313015470561
cont.residuals=-1.04961323993979,-0.367121440939939,-0.086415111545853,0.338245274455396,1.43552007490631

predictedValues:
Include	Exclude	Both
Lung	109.519782463235	152.274829909291	80.7138311712196
cerebhem	85.4901864422463	96.9128753736539	73.4988996939659
cortex	89.191915855829	127.040824236642	65.7510750899564
heart	96.4361820126294	128.811893346052	71.2380771586758
kidney	106.221432213201	155.600726712372	80.1881631480619
liver	105.539915461452	152.520717053887	75.3543055423039
stomach	103.303176406874	129.342980945858	72.3151318102833
testicle	100.397737635525	114.045311499865	79.1456703636136


diffExp=-42.7550474460558,-11.4226889314076,-37.8489083808133,-32.3757113334229,-49.3792944991714,-46.9808015924347,-26.0398045389840,-13.6475738643397
diffExpScore=0.996175174419673
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,-1,0,-1,-1,0,0
diffExp1.4Score=0.75
diffExp1.3=-1,0,-1,-1,-1,-1,0,0
diffExp1.3Score=0.833333333333333
diffExp1.2=-1,0,-1,-1,-1,-1,-1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	92.7383073740405	90.1385485806843	105.501095678075
cerebhem	98.1089089824776	116.489122236378	91.7111595780612
cortex	101.259522473630	94.4329211527115	93.9670896422702
heart	106.138103805709	116.909379938862	96.42974113343
kidney	110.180804272980	87.9723887198585	94.4694137443006
liver	93.8985628632147	127.490007509986	96.9492544049265
stomach	101.918411330208	100.220935809073	95.0242885375009
testicle	109.302563776430	100.921392946993	106.284954741945
cont.diffExp=2.59975879335613,-18.3802132539002,6.82660132091887,-10.7712761331535,22.2084155531213,-33.5914446467711,1.69747552113483,8.38117082943745
cont.diffExpScore=4.74165546547778

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,-1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,1,-1,0,0
cont.diffExp1.2Score=2

tran.correlation=0.820851829788797
cont.tran.correlation=-0.306444149209481

tran.covariance=0.0117811664048954
cont.tran.covariance=-0.00249228899165147

tran.mean=115.790655473038
cont.tran.mean=103.007492610827

weightedLogRatios:
wLogRatio
Lung	-1.60206280922619
cerebhem	-0.565741499846337
cortex	-1.65103199739602
heart	-1.36445874770052
kidney	-1.85401878372073
liver	-1.78331862626174
stomach	-1.06781297138199
testicle	-0.595585804813432

cont.weightedLogRatios:
wLogRatio
Lung	0.128394269101391
cerebhem	-0.802263889424004
cortex	0.319864859284148
heart	-0.455555893038177
kidney	1.03311142926049
liver	-1.43587725006323
stomach	0.0775242338800415
testicle	0.371304883091212

varWeightedLogRatios=0.263931014970421
cont.varWeightedLogRatios=0.59571873358868

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.01090865458425	0.100837438582941	49.6929387041369	5.92324431931313e-251	***
df.mm.trans1	-0.415895754284629	0.086311653136016	-4.8185353793332	1.71925805969461e-06	***
df.mm.trans2	-0.0386086322443325	0.076576217334207	-0.50418568046826	0.614264709262994	   
df.mm.exp2	-0.605938686052291	0.0980778448412813	-6.17814030307127	1.01524475772870e-09	***
df.mm.exp3	-0.181459354416334	0.0980778448412813	-1.85015642125893	0.064646239834017	.  
df.mm.exp4	-0.169675078554218	0.0980778448412813	-1.73000414954878	0.084001269594423	.  
df.mm.exp5	-0.00243895568482951	0.0980778448412813	-0.0248675497384394	0.98016658806423	   
df.mm.exp6	0.0333063754467259	0.0980778448412813	0.339591224711610	0.734250297768746	   
df.mm.exp7	-0.111779854999972	0.0980778448412813	-1.13970545724026	0.254737825097212	   
df.mm.exp8	-0.356436794816887	0.0980778448412813	-3.63422335996174	0.000295971904650622	***
df.mm.trans1:exp2	0.358235082250304	0.0891422920478457	4.01868825695021	6.38420369954705e-05	***
df.mm.trans2:exp2	0.154064089190343	0.065739623979871	2.34354989979128	0.0193363338492973	*  
df.mm.trans1:exp3	-0.0238554342276444	0.0891422920478457	-0.267610734249915	0.789065452603674	   
df.mm.trans2:exp3	0.000280860177960683	0.065739623979871	0.00427231190197681	0.996592225266998	   
df.mm.trans1:exp4	0.0424513471285798	0.0891422920478457	0.476220053953680	0.63404294342161	   
df.mm.trans2:exp4	0.00234124787970619	0.0657396239798711	0.0356139530159628	0.971598745594618	   
df.mm.trans1:exp5	-0.0281403406549581	0.0891422920478457	-0.315678899526773	0.752325602508527	   
df.mm.trans2:exp5	0.0240452580812254	0.065739623979871	0.365765068698717	0.714633586391061	   
df.mm.trans1:exp6	-0.0703223431829625	0.0891422920478457	-0.788877440409745	0.430408912813901	   
df.mm.trans2:exp6	-0.0316929188172721	0.0657396239798711	-0.482097658894067	0.629863673091091	   
df.mm.trans1:exp7	0.0533427852894249	0.0891422920478457	0.598400423233385	0.549736041362014	   
df.mm.trans2:exp7	-0.0514394816097141	0.065739623979871	-0.782473012402146	0.434159815892625	   
df.mm.trans1:exp8	0.269471273606035	0.0891422920478457	3.02293409127736	0.002580354023948	** 
df.mm.trans2:exp8	0.0673456538134586	0.065739623979871	1.02443016458505	0.305930612386386	   
df.mm.trans1:probe2	-0.362803663294253	0.0630331191975433	-5.75576249300372	1.21344248214595e-08	***
df.mm.trans1:probe3	-0.266809391258499	0.0630331191975433	-4.23284448961393	2.56504786301911e-05	***
df.mm.trans1:probe4	-0.393431760453757	0.0630331191975433	-6.24166732445457	6.89936459855538e-10	***
df.mm.trans1:probe5	0.297109721896682	0.0630331191975433	4.71354941146973	2.85411533335461e-06	***
df.mm.trans1:probe6	-0.307884196342943	0.0630331191975433	-4.88448295534997	1.24422908420100e-06	***
df.mm.trans1:probe7	0.531901538428242	0.0630331191975433	8.4384454585102	1.42128125954171e-16	***
df.mm.trans1:probe8	0.779122869266428	0.0630331191975433	12.3605317202325	2.46154222636506e-32	***
df.mm.trans1:probe9	0.680832773815821	0.0630331191975433	10.8011912226986	1.54545086084792e-25	***
df.mm.trans1:probe10	0.772867045871382	0.0630331191975433	12.2612851102806	6.96334834649128e-32	***
df.mm.trans1:probe11	0.652499920389778	0.0630331191975433	10.3516996889345	1.05769683353247e-23	***
df.mm.trans1:probe12	0.53582203614669	0.0630331191975434	8.5006428837425	8.694565663837e-17	***
df.mm.trans1:probe13	0.933305210621834	0.0630331191975434	14.8065845781309	3.43175021614836e-44	***
df.mm.trans1:probe14	-0.159176712559354	0.0630331191975433	-2.52528693781598	0.0117455606340263	*  
df.mm.trans1:probe15	-0.194080995526104	0.0630331191975433	-3.07903207070972	0.00214510783531837	** 
df.mm.trans1:probe16	-0.211921549509012	0.0630331191975433	-3.36206667553383	0.000809003910072413	***
df.mm.trans1:probe17	-0.22779730477935	0.0630331191975433	-3.61393038579357	0.000319749398881626	***
df.mm.trans1:probe18	0.244314845485151	0.0630331191975433	3.87597581391265	0.000114577805463003	***
df.mm.trans1:probe19	-0.170009261666319	0.0630331191975433	-2.69714181735980	0.00713557281529438	** 
df.mm.trans2:probe2	0.0608366347902432	0.0630331191975433	0.965153487003929	0.334749060822681	   
df.mm.trans2:probe3	0.0334656077952675	0.0630331191975433	0.530921017733353	0.595615544426049	   
df.mm.trans2:probe4	0.311963804657844	0.0630331191975433	4.94920461860949	9.0250452026114e-07	***
df.mm.trans2:probe5	0.138521745555707	0.0630331191975433	2.19760258288322	0.0282530048726454	*  
df.mm.trans2:probe6	0.416177440132179	0.0630331191975433	6.60252015813933	7.20850662537788e-11	***
df.mm.trans3:probe2	-0.360997346924544	0.0630331191975433	-5.72710586942703	1.42795401188967e-08	***
df.mm.trans3:probe3	0.27379309371117	0.0630331191975433	4.34363866482814	1.57481341505303e-05	***
df.mm.trans3:probe4	0.113883168315448	0.0630331191975433	1.80671954307930	0.0711680676558766	.  
df.mm.trans3:probe5	-0.00395777159339690	0.0630331191975433	-0.0627887631737437	0.949949831087595	   
df.mm.trans3:probe6	-0.558787193826808	0.0630331191975433	-8.86497766476685	4.60856987290189e-18	***
df.mm.trans3:probe7	-0.345309872231	0.0630331191975433	-5.47822916947537	5.69809003073017e-08	***
df.mm.trans3:probe8	-0.436970827028339	0.0630331191975433	-6.93240049978947	8.31381453313025e-12	***
df.mm.trans3:probe9	-0.47485843797578	0.0630331191975433	-7.5334751638673	1.29152383171501e-13	***
df.mm.trans3:probe10	-0.335182070252692	0.0630331191975433	-5.3175548746405	1.35314175401299e-07	***
df.mm.trans3:probe11	0.242593833465702	0.0630331191975433	3.84867251619616	0.000127876207576965	***
df.mm.trans3:probe12	-0.100138385504134	0.0630331191975433	-1.58866301999596	0.112517320841274	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54768817124690	0.224348752932737	20.2706193450978	1.57817482767030e-74	***
df.mm.trans1	-0.0139697732972879	0.192030975962375	-0.0727474993410699	0.942024576236792	   
df.mm.trans2	-0.0177717553725925	0.170371035844042	-0.104312069739723	0.916946900321945	   
df.mm.exp2	0.452823802237201	0.218209054986790	2.07518337066541	0.0382770722976912	*  
df.mm.exp3	0.250223688933009	0.218209054986790	1.14671542364801	0.251829715695376	   
df.mm.exp4	0.484917340095869	0.218209054986790	2.22226039210530	0.0265345546453117	*  
df.mm.exp5	0.258461369959689	0.218209054986790	1.18446674898682	0.236567415189211	   
df.mm.exp6	0.443657174679757	0.218209054986790	2.03317490516888	0.0423525885971258	*  
df.mm.exp7	0.305008979225376	0.218209054986790	1.39778332867003	0.162551593769434	   
df.mm.exp8	0.269929833535938	0.218209054986790	1.23702397937739	0.216428056678848	   
df.mm.trans1:exp2	-0.39652725196604	0.198328739162196	-1.99934338130267	0.0458967944308245	*  
df.mm.trans2:exp2	-0.196373820251265	0.146261179036396	-1.34262434875079	0.17976071444703	   
df.mm.trans1:exp3	-0.162318565738898	0.198328739162196	-0.81843189456346	0.413345521246744	   
df.mm.trans2:exp3	-0.203681850851964	0.146261179036396	-1.39258996949066	0.164116748352889	   
df.mm.trans1:exp4	-0.349957855378241	0.198328739162196	-1.76453426193589	0.0780098351691875	.  
df.mm.trans2:exp4	-0.224866151137737	0.146261179036396	-1.53742881480382	0.124569445672729	   
df.mm.trans1:exp5	-0.086120305806533	0.198328739162196	-0.434230087733793	0.664234221204448	   
df.mm.trans2:exp5	-0.282786284561817	0.146261179036396	-1.93343364537932	0.0535225654642976	.  
df.mm.trans1:exp6	-0.431223721046223	0.198328739162196	-2.17428761392751	0.0299653960983524	*  
df.mm.trans2:exp6	-0.0969671009221366	0.146261179036396	-0.662972236111995	0.507532404528551	   
df.mm.trans1:exp7	-0.210618002434949	0.198328739162196	-1.06196410729311	0.288560873337027	   
df.mm.trans2:exp7	-0.198979787488149	0.146261179036396	-1.36044156623839	0.174059567618140	   
df.mm.trans1:exp8	-0.105591609795027	0.198328739162196	-0.532407003851682	0.59458662774797	   
df.mm.trans2:exp8	-0.156935822670167	0.146261179036396	-1.07298343760181	0.283590406160542	   
df.mm.trans1:probe2	0.0859884418913936	0.140239596365767	0.613153803346112	0.539942623596648	   
df.mm.trans1:probe3	0.0442405602670526	0.140239596365767	0.315464115795558	0.752488592593837	   
df.mm.trans1:probe4	-0.0049979165847847	0.140239596365767	-0.0356384125047632	0.971579248016289	   
df.mm.trans1:probe5	0.116383914779836	0.140239596365767	0.829893395273962	0.406837683909463	   
df.mm.trans1:probe6	-0.203196202288663	0.140239596365767	-1.44892175643956	0.147737120485231	   
df.mm.trans1:probe7	0.0207531834130755	0.140239596365767	0.147983764577787	0.882391523152483	   
df.mm.trans1:probe8	-0.0657803664604101	0.140239596365767	-0.469057015030509	0.639152062446813	   
df.mm.trans1:probe9	-0.0654766891951477	0.140239596365767	-0.466891597608241	0.64069996836226	   
df.mm.trans1:probe10	0.0637468942812422	0.140239596365767	0.454557029064604	0.649546809726792	   
df.mm.trans1:probe11	-0.108169296922742	0.140239596365767	-0.771317799864596	0.440738108643487	   
df.mm.trans1:probe12	-0.0035158794202576	0.140239596365767	-0.0250705186792440	0.980004741571876	   
df.mm.trans1:probe13	-0.0330874852873981	0.140239596365767	-0.235935400164022	0.813541003901549	   
df.mm.trans1:probe14	0.191206292428785	0.140239596365767	1.36342586105346	0.173118030837079	   
df.mm.trans1:probe15	0.189253621808729	0.140239596365767	1.34950204302589	0.177543733062341	   
df.mm.trans1:probe16	-0.00486034784022748	0.140239596365767	-0.0346574574241566	0.972361217439547	   
df.mm.trans1:probe17	-0.114559039300891	0.140239596365767	-0.816880840145198	0.414230929341962	   
df.mm.trans1:probe18	-0.0539566782771096	0.140239596365767	-0.384746388861404	0.700523884988978	   
df.mm.trans1:probe19	-0.176012891463993	0.140239596365767	-1.25508697989207	0.209800446428233	   
df.mm.trans2:probe2	-0.096234858611977	0.140239596365767	-0.686217452886711	0.492767637102708	   
df.mm.trans2:probe3	-0.0225717409801867	0.140239596365767	-0.160951268865008	0.872170948157865	   
df.mm.trans2:probe4	-0.151547113236164	0.140239596365767	-1.08062998727481	0.280175627823835	   
df.mm.trans2:probe5	-0.100512026226042	0.140239596365767	-0.716716454059744	0.473750693320979	   
df.mm.trans2:probe6	-0.143367271303427	0.140239596365767	-1.02230236693995	0.306935680444662	   
df.mm.trans3:probe2	-0.0200976793552777	0.140239596365767	-0.143309592127318	0.886080464654731	   
df.mm.trans3:probe3	0.210063119657106	0.140239596365767	1.49788736634145	0.134542711732748	   
df.mm.trans3:probe4	0.155721997115460	0.140239596365767	1.11039963855367	0.267148434052701	   
df.mm.trans3:probe5	0.00830273598486034	0.140239596365767	0.0592039352652264	0.952803926254779	   
df.mm.trans3:probe6	0.161189027475435	0.140239596365767	1.14938313894621	0.250729120953582	   
df.mm.trans3:probe7	0.179437286913788	0.140239596365767	1.2795051580567	0.201076775658812	   
df.mm.trans3:probe8	0.395690356117354	0.140239596365767	2.82153091117955	0.00489363747440502	** 
df.mm.trans3:probe9	0.167318386973028	0.140239596365767	1.19308947907005	0.233175264848091	   
df.mm.trans3:probe10	0.0283768889440502	0.140239596365767	0.202345768808681	0.8396960082675	   
df.mm.trans3:probe11	0.22500512415594	0.140239596365767	1.60443362635679	0.10899891540579	   
df.mm.trans3:probe12	0.202289106330314	0.140239596365767	1.44245356926665	0.149551701976109