fitVsDatCorrelation=0.822622289752822
cont.fitVsDatCorrelation=0.249521031233893

fstatistic=6470.71016744714,68,1060
cont.fstatistic=2220.61128277506,68,1060

residuals=-1.00861815906392,-0.107254242757875,-0.00268521330245515,0.0931230448095388,1.09240565380746
cont.residuals=-0.698296335673875,-0.221778294454950,-0.0801359562556385,0.132311342807204,1.62739644774385

predictedValues:
Include	Exclude	Both
Lung	67.3486435951148	64.3518485145263	57.7293892375478
cerebhem	71.918770512152	130.132090742830	56.2552597564246
cortex	59.2268194874092	56.1610216528474	53.8507809040801
heart	60.3744191411281	59.9610438687618	55.3534048676654
kidney	65.6356802345688	59.3602632703685	58.6935801656138
liver	61.9708373996526	61.1284492216484	60.9508102706477
stomach	102.049891317685	63.6465714909007	90.7431456628624
testicle	67.5098383226948	64.7898069860783	60.7794138624482


diffExp=2.99679508058847,-58.2133202306775,3.06579783456182,0.413375272366288,6.27541696420031,0.842388178004207,38.4033198267847,2.72003133661649
diffExpScore=25.1168879905578
diffExp1.5=0,-1,0,0,0,0,1,0
diffExp1.5Score=2
diffExp1.4=0,-1,0,0,0,0,1,0
diffExp1.4Score=2
diffExp1.3=0,-1,0,0,0,0,1,0
diffExp1.3Score=2
diffExp1.2=0,-1,0,0,0,0,1,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	64.4229704057966	68.9160303760408	60.3109121148033
cerebhem	58.3871193201555	65.2698798517553	64.3170931669756
cortex	59.1769439813185	60.7466208019053	62.9541053231867
heart	61.0697338806157	67.8217758542287	63.7292402777813
kidney	61.0175216604959	60.9226535220434	67.9011480082896
liver	63.4895894602573	76.5189548393093	61.1769354030721
stomach	62.2055434853306	65.3987095696723	60.8845158203873
testicle	62.7972415701468	71.7443598469398	60.8681555053122
cont.diffExp=-4.49305997024422,-6.88276053159985,-1.56967682058685,-6.75204197361305,0.0948681384525543,-13.0293653790520,-3.19316608434173,-8.94711827679302
cont.diffExpScore=0.982297954152152

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

tran.correlation=0.127938902754927
cont.tran.correlation=0.671118967162698

tran.covariance=0.00958186066062129
cont.tran.covariance=0.00178389196993384

tran.mean=69.7228747348979
cont.tran.mean=64.3691030266258

weightedLogRatios:
wLogRatio
Lung	0.190585765831499
cerebhem	-2.71128005551268
cortex	0.215518714018515
heart	0.0281489590501024
kidney	0.415431093951992
liver	0.0563860681004133
stomach	2.07230929494466
testicle	0.172384311848767

cont.weightedLogRatios:
wLogRatio
Lung	-0.283102635614696
cerebhem	-0.459427174103745
cortex	-0.107168764437576
heart	-0.436713077960278
kidney	0.00639566850939644
liver	-0.792234494492445
stomach	-0.208016377320399
testicle	-0.560299143323654

varWeightedLogRatios=1.70148634441740
cont.varWeightedLogRatios=0.0671006185799325

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.55926704939342	0.0951025613760934	47.9405284507881	1.09780420770769e-267	***
df.mm.trans1	-0.150407663875054	0.0820510212098054	-1.83309923090975	0.0670681396383695	.  
df.mm.trans2	-0.410947700877653	0.0711777949625329	-5.773537956521	1.01886840125854e-08	***
df.mm.exp2	0.795705735332799	0.0899040045036	8.85061505019986	3.58315460685963e-18	***
df.mm.exp3	-0.195101515611475	0.0899040045036	-2.17010929255840	0.0302200852547193	*  
df.mm.exp4	-0.137959632115463	0.0899040045036	-1.53452154747944	0.125199801289954	   
df.mm.exp5	-0.123067899105446	0.0899040045036	-1.36888117259023	0.171326424110068	   
df.mm.exp6	-0.188907898012660	0.0899040045036	-2.10121783846787	0.0358574759669769	*  
df.mm.exp7	-0.0477076908244830	0.0899040045036	-0.530651455270522	0.595771484719585	   
df.mm.exp8	-0.0423115454813934	0.0899040045036	-0.47063026519246	0.638001615708369	   
df.mm.trans1:exp2	-0.730051203541972	0.0836341450283327	-8.72910464134778	9.81869965813635e-18	***
df.mm.trans2:exp2	-0.0915213776766381	0.0564487455622284	-1.62131818457765	0.105246812247940	   
df.mm.trans1:exp3	0.0665932237837679	0.0836341450283327	0.796244449694656	0.426068251753824	   
df.mm.trans2:exp3	0.0589588074137486	0.0564487455622284	1.04446621136608	0.296507872083428	   
df.mm.trans1:exp4	0.0286423601794197	0.0836341450283327	0.342472086845827	0.732063542091523	   
df.mm.trans2:exp4	0.0672890550432761	0.0564487455622284	1.19203809354979	0.233513108001717	   
df.mm.trans1:exp5	0.0973045901953744	0.0836341450283328	1.16345531077541	0.244906647124275	   
df.mm.trans2:exp5	0.0423272735284341	0.0564487455622284	0.749835503107381	0.453520169676645	   
df.mm.trans1:exp6	0.105689044868307	0.0836341450283327	1.26370688470006	0.206613097309388	   
df.mm.trans2:exp6	0.137519613625645	0.0564487455622284	2.43618546800203	0.0150069103354482	*  
df.mm.trans1:exp7	0.463286752077315	0.0836341450283327	5.53944506660967	3.82649005851426e-08	***
df.mm.trans2:exp7	0.0366874897740521	0.0564487455622284	0.649925687606437	0.515881044125344	   
df.mm.trans1:exp8	0.0447021226561429	0.0836341450283327	0.534496079812847	0.593110447187186	   
df.mm.trans2:exp8	0.0490941772951671	0.0564487455622284	0.869712458730306	0.384654455238470	   
df.mm.trans1:probe2	-0.434554721891078	0.0605986952225508	-7.17102439739273	1.39444829404907e-12	***
df.mm.trans1:probe3	0.0560528892528242	0.0605986952225508	0.924985085024818	0.355184191542891	   
df.mm.trans1:probe4	-0.277684667805069	0.0605986952225508	-4.58235390688301	5.14400677896779e-06	***
df.mm.trans1:probe5	-0.598175964109964	0.0605986952225508	-9.871103031396	4.79072843832527e-22	***
df.mm.trans1:probe6	-0.486201165341668	0.0605986952225508	-8.02329428968854	2.71229401149429e-15	***
df.mm.trans1:probe7	-0.466102565000663	0.0605986952225508	-7.69162707693433	3.30820781724637e-14	***
df.mm.trans1:probe8	-0.472627723466155	0.0605986952225508	-7.79930527762047	1.48339792993290e-14	***
df.mm.trans1:probe9	0.164385323047473	0.0605986952225508	2.71268750001567	0.00678208899445827	** 
df.mm.trans1:probe10	-0.353026522852139	0.0605986952225508	-5.82564561094323	7.53929453272143e-09	***
df.mm.trans1:probe11	-0.332778192033266	0.0605986952225508	-5.49150754502431	4.98744892835216e-08	***
df.mm.trans1:probe12	-0.328621855408152	0.0605986952225508	-5.42291965530407	7.26080619453763e-08	***
df.mm.trans1:probe13	-0.326282806220344	0.0605986952225508	-5.38432065281372	8.95314004340513e-08	***
df.mm.trans1:probe14	-0.355848942424037	0.0605986952225508	-5.87222119415557	5.74840626241452e-09	***
df.mm.trans1:probe15	-0.224718598813966	0.0605986952225508	-3.70830754670013	0.000219460117054757	***
df.mm.trans1:probe16	-0.262401252063530	0.0605986952225508	-4.3301468967253	1.63133362749997e-05	***
df.mm.trans1:probe17	-0.180051903673746	0.0605986952225508	-2.97121749919696	0.00303313104306393	** 
df.mm.trans1:probe18	-0.266914889346259	0.0605986952225508	-4.4046309638517	1.16711599598365e-05	***
df.mm.trans1:probe19	-0.314610167731821	0.0605986952225508	-5.19169870863398	2.49697774208128e-07	***
df.mm.trans1:probe20	-0.383822837357997	0.0605986952225508	-6.33384656135572	3.52702351081869e-10	***
df.mm.trans1:probe21	-0.304662716438701	0.0605986952225508	-5.02754581298849	5.82956767765046e-07	***
df.mm.trans1:probe22	-0.381448533301494	0.0605986952225508	-6.29466578282934	4.50228999784682e-10	***
df.mm.trans1:probe23	-0.0337800964492276	0.0605986952225508	-0.557439336361436	0.577344987221593	   
df.mm.trans1:probe24	-0.266559306839194	0.0605986952225508	-4.3987631393753	1.19853312716169e-05	***
df.mm.trans1:probe25	-0.502594794593375	0.0605986952225508	-8.29382204926325	3.29835992043343e-16	***
df.mm.trans1:probe26	-0.324008063853381	0.0605986952225508	-5.34678284183265	1.09625322670388e-07	***
df.mm.trans1:probe27	-0.501001448061791	0.0605986952225508	-8.26752863608442	4.05852606792972e-16	***
df.mm.trans2:probe2	0.0522806819656962	0.0605986952225508	0.862736099740986	0.3884777253387	   
df.mm.trans2:probe3	0.0637940950252367	0.0605986952225508	1.05273050502079	0.292704436897547	   
df.mm.trans2:probe4	0.0955580538961335	0.0605986952225508	1.57689952803758	0.115117017911720	   
df.mm.trans2:probe5	0.091720245786149	0.0605986952225508	1.51356799761617	0.130433535188814	   
df.mm.trans2:probe6	0.0175731444917824	0.0605986952225508	0.289992126517648	0.771878977910272	   
df.mm.trans3:probe2	-0.102398873608968	0.0605986952225508	-1.68978677235384	0.0913628013721606	.  
df.mm.trans3:probe3	-0.180815087039986	0.0605986952225508	-2.98381155528079	0.00291204781221679	** 
df.mm.trans3:probe4	0.532949526025416	0.0605986952225508	8.79473599337644	5.70456747891572e-18	***
df.mm.trans3:probe5	-0.184789677719644	0.0605986952225508	-3.04940027241507	0.0023499071126659	** 
df.mm.trans3:probe6	-0.210489923520591	0.0605986952225508	-3.47350586918677	0.000534454129641485	***
df.mm.trans3:probe7	-0.252930757144926	0.0605986952225508	-4.17386473778072	3.24027415351715e-05	***
df.mm.trans3:probe8	-0.148649032849431	0.0605986952225508	-2.45300715309978	0.0143269666563573	*  
df.mm.trans3:probe9	0.0937563053489283	0.0605986952225508	1.54716706365715	0.122121453964205	   
df.mm.trans3:probe10	0.79390792734896	0.0605986952225508	13.1010729592990	1.86502527856635e-36	***
df.mm.trans3:probe11	-0.195323911603130	0.0605986952225508	-3.22323625757610	0.00130616218217834	** 
df.mm.trans3:probe12	-0.179429257859567	0.0605986952225508	-2.96094259456590	0.00313530452949258	** 
df.mm.trans3:probe13	0.103360144265324	0.0605986952225508	1.70564966598258	0.0883662874011071	.  
df.mm.trans3:probe14	0.74081848470389	0.0605986952225508	12.2249906864035	3.02560699665224e-32	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.37745393765536	0.161970305508207	27.0262745008748	8.4986067727779e-123	***
df.mm.trans1	-0.230890762765883	0.139742071930707	-1.65226377121682	0.098776944997501	.  
df.mm.trans2	-0.123483524213936	0.121223750745161	-1.01864134259899	0.308605702864259	   
df.mm.exp2	-0.217045466922207	0.1531165813534	-1.41751771757010	0.156625487022849	   
df.mm.exp3	-0.254008525863571	0.1531165813534	-1.65892239506907	0.097427258615815	.  
df.mm.exp4	-0.124589791670411	0.1531165813534	-0.813692354996175	0.416003953676339	   
df.mm.exp5	-0.296132808082762	0.1531165813534	-1.93403487372327	0.0533747648458077	.  
df.mm.exp6	0.075798172633133	0.1531165813534	0.495035690864776	0.62067752154048	   
df.mm.exp7	-0.0968782544648048	0.1531165813534	-0.632709100533047	0.52706008571903	   
df.mm.exp8	0.00546423761123317	0.1531165813534	0.0356867790734007	0.97153883163648	   
df.mm.trans1:exp2	0.118670520009410	0.142438308970319	0.833136259951933	0.404955566093961	   
df.mm.trans2:exp2	0.162687326340330	0.0961385312022471	1.69221772275763	0.0908983557295407	.  
df.mm.trans1:exp3	0.169070279285593	0.142438308970319	1.18697196356651	0.235504619957422	   
df.mm.trans2:exp3	0.127831169437999	0.096138531202247	1.32965594376598	0.183917717527174	   
df.mm.trans1:exp4	0.0711359284498841	0.142438308970319	0.499415704694354	0.617590196018669	   
df.mm.trans2:exp4	0.108584300402073	0.096138531202247	1.12945661894547	0.258960786178930	   
df.mm.trans1:exp5	0.241823618512708	0.142438308970319	1.69774283520242	0.089849825367259	.  
df.mm.trans2:exp5	0.172849080207935	0.0961385312022471	1.79791679825347	0.0724746711335106	.  
df.mm.trans1:exp6	-0.0903924785165373	0.142438308970319	-0.634607916718338	0.525821103266815	   
df.mm.trans2:exp6	0.0288515007461525	0.0961385312022471	0.300103406879157	0.764157122225701	   
df.mm.trans1:exp7	0.0618521209632833	0.142438308970319	0.434237961756284	0.664204021703176	   
df.mm.trans2:exp7	0.0444919689435901	0.0961385312022471	0.46279018815039	0.643609753346716	   
df.mm.trans1:exp8	-0.0310233419777754	0.142438308970319	-0.217801953716257	0.82762534118727	   
df.mm.trans2:exp8	0.0347561930945479	0.0961385312022471	0.361521989777762	0.717781316858723	   
df.mm.trans1:probe2	0.0355793505265314	0.103206359918952	0.344739903184959	0.730358303440733	   
df.mm.trans1:probe3	-0.0359694780373096	0.103206359918952	-0.348519975566977	0.727518922621048	   
df.mm.trans1:probe4	0.0102545903641744	0.103206359918952	0.0993600624247131	0.920871178358343	   
df.mm.trans1:probe5	-0.017460911288531	0.103206359918952	-0.169184450476145	0.865683839437747	   
df.mm.trans1:probe6	-0.00735115428320696	0.103206359918952	-0.0712277255876465	0.943229951559666	   
df.mm.trans1:probe7	0.0270848482896836	0.103206359918952	0.262433907280070	0.79303792722164	   
df.mm.trans1:probe8	0.0343530228540936	0.103206359918952	0.332857615374393	0.739307583843086	   
df.mm.trans1:probe9	0.162921307182464	0.103206359918952	1.57859755261601	0.114726749419435	   
df.mm.trans1:probe10	-0.00459268164867048	0.103206359918952	-0.0444999867476882	0.964514243851868	   
df.mm.trans1:probe11	0.0161427647155188	0.103206359918952	0.156412499464140	0.87573766894973	   
df.mm.trans1:probe12	0.0943225228994538	0.103206359918952	0.913921612713845	0.360965867189188	   
df.mm.trans1:probe13	0.117195894530899	0.103206359918952	1.13554915242562	0.256401984942534	   
df.mm.trans1:probe14	-0.00217143675640783	0.103206359918952	-0.0210397572214839	0.983217900686441	   
df.mm.trans1:probe15	-0.102772939294442	0.103206359918952	-0.99580044655339	0.319574373434111	   
df.mm.trans1:probe16	-0.0630476207486292	0.103206359918952	-0.610888910316582	0.541404093010468	   
df.mm.trans1:probe17	0.038878387261159	0.103206359918952	0.376705343466142	0.706467985011759	   
df.mm.trans1:probe18	-0.000203537819684589	0.103206359918952	-0.00197214415705028	0.99842682872772	   
df.mm.trans1:probe19	-0.0337179917193057	0.103206359918952	-0.326704592098632	0.743955823810445	   
df.mm.trans1:probe20	-0.0451312449524179	0.103206359918952	-0.43729131603769	0.661989167089291	   
df.mm.trans1:probe21	0.0070873840541572	0.103206359918952	0.0686719700193181	0.945263681743377	   
df.mm.trans1:probe22	0.0610450621118787	0.103206359918952	0.591485468141863	0.55432130389836	   
df.mm.trans1:probe23	0.161493433175443	0.103206359918952	1.56476241679548	0.117937099544937	   
df.mm.trans1:probe24	0.121716037037513	0.103206359918952	1.17934628382492	0.23852494474586	   
df.mm.trans1:probe25	0.258808351969511	0.103206359918952	2.50767832692437	0.0123013098704246	*  
df.mm.trans1:probe26	-0.00350433392030861	0.103206359918952	-0.0339546315077925	0.972919721150805	   
df.mm.trans1:probe27	-0.055769430787884	0.103206359918952	-0.540368159788602	0.589056676447377	   
df.mm.trans2:probe2	-0.0115075957693589	0.103206359918952	-0.111500839467606	0.91124030892145	   
df.mm.trans2:probe3	-0.184278748955599	0.103206359918952	-1.78553675471467	0.0744601179660673	.  
df.mm.trans2:probe4	-0.115735421960425	0.103206359918952	-1.12139815851768	0.262372393767091	   
df.mm.trans2:probe5	0.0587362425529071	0.103206359918952	0.569114564248104	0.569398961589954	   
df.mm.trans2:probe6	-0.168846495913377	0.103206359918952	-1.63600863402190	0.102134642943039	   
df.mm.trans3:probe2	-0.0465151060625598	0.103206359918952	-0.450699996580524	0.652297957985436	   
df.mm.trans3:probe3	0.144614479768150	0.103206359918952	1.40121674557379	0.161441968972090	   
df.mm.trans3:probe4	0.00423748988348321	0.103206359918952	0.0410584181712341	0.96725705609937	   
df.mm.trans3:probe5	0.206487234656906	0.103206359918952	2.00072199832512	0.0456770170709902	*  
df.mm.trans3:probe6	0.071022579549318	0.103206359918952	0.68816088083227	0.491502040211437	   
df.mm.trans3:probe7	0.0228076682497675	0.103206359918952	0.220990918269751	0.825142064057351	   
df.mm.trans3:probe8	0.0931943753741716	0.103206359918952	0.902990624292511	0.366736021507304	   
df.mm.trans3:probe9	0.0425652510402303	0.103206359918952	0.412428566162561	0.680108725743091	   
df.mm.trans3:probe10	0.077951372458899	0.103206359918952	0.755296209653306	0.450239061223138	   
df.mm.trans3:probe11	0.175628825288827	0.103206359918952	1.70172483000805	0.0891002035928014	.  
df.mm.trans3:probe12	0.172326440056268	0.103206359918952	1.66972694504095	0.0952686539771661	.  
df.mm.trans3:probe13	0.00693033768543892	0.103206359918952	0.0671502966569245	0.94647471653772	   
df.mm.trans3:probe14	0.0988166583074897	0.103206359918952	0.957466752873475	0.338550040061555