fitVsDatCorrelation=0.7583028726877
cont.fitVsDatCorrelation=0.219146557984486

fstatistic=15851.2790093793,63,945
cont.fstatistic=7067.96042944113,63,945

residuals=-0.389091949700966,-0.077037889422715,-0.00420282324321999,0.0663015125242285,0.71079132602397
cont.residuals=-0.471596361038548,-0.121844923057483,-0.0221922013289838,0.110464362562837,0.91667036856187

predictedValues:
Include	Exclude	Both
Lung	59.1255146725583	42.0985457855501	50.0012944698259
cerebhem	63.3366025735228	49.7749160769346	53.5175900800939
cortex	57.8270507064839	44.4775077031044	53.1283414615873
heart	57.4911691662689	46.0028620040177	54.5931115241845
kidney	56.9272541516891	43.2807727524455	50.4956811691656
liver	59.2840164138302	51.2169476758031	49.2409869515428
stomach	58.2898930963975	46.2342425241563	52.7671288738531
testicle	56.7456308370669	48.7947469443186	55.2351265940436


diffExp=17.0269688870082,13.5616864965882,13.3495430033796,11.4883071622512,13.6464813992437,8.06706873802712,12.0556505722412,7.95088389274836
diffExpScore=0.989811159017786
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,1,0,1,0,0,0
diffExp1.3Score=0.75
diffExp1.2=1,1,1,1,1,0,1,0
diffExp1.2Score=0.857142857142857

cont.predictedValues:
Include	Exclude	Both
Lung	52.9472002243809	51.0329229770177	51.7019100014299
cerebhem	51.6490286568029	52.1061217140599	53.2570000879668
cortex	54.0408931157678	52.5231291477688	50.8397177720628
heart	52.4553364765382	51.2348213890081	52.8271819262134
kidney	52.5439627655653	53.0739752207631	54.0623747023817
liver	51.4370372896459	55.1270447348165	53.0192880196632
stomach	52.4525685446636	53.4749388005246	51.2937544326033
testicle	50.8329236961936	50.6860077559367	53.5213036890388
cont.diffExp=1.91427724736315,-0.457093057257033,1.51776396799895,1.22051508753002,-0.530012455197799,-3.69000744517064,-1.02237025586096,0.146915940256896
cont.diffExpScore=5.52573412498297

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=0.414826433593208
cont.tran.correlation=-0.0165819156523350

tran.covariance=0.000982467368872506
cont.tran.covariance=-3.45587917377515e-06

tran.mean=52.5567295677592
cont.tran.mean=52.3511195318409

weightedLogRatios:
wLogRatio
Lung	1.32797390616194
cerebhem	0.970552549793911
cortex	1.03052699573263
heart	0.878372649248292
kidney	1.07015575203428
liver	0.586423405967618
stomach	0.915148113726141
testicle	0.598253828698494

cont.weightedLogRatios:
wLogRatio
Lung	0.145488194968326
cerebhem	-0.0347937599816699
cortex	0.113251630918655
heart	0.0929509422324053
kidney	-0.0398114444070561
liver	-0.275395685463919
stomach	-0.076627637771104
testicle	0.0113664230515923

varWeightedLogRatios=0.0600390969551979
cont.varWeightedLogRatios=0.018109541563631

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.82961412295054	0.0572424459469268	66.9016506824538	0	***
df.mm.trans1	0.400884433929349	0.0490939433042151	8.1656596913642	1.01719997480118e-15	***
df.mm.trans2	-0.0942143560127646	0.0430404403368945	-2.18897286541011	0.0288428591340234	*  
df.mm.exp2	0.168337323948284	0.0546093305864845	3.0825743905007	0.00211170791742769	** 
df.mm.exp3	-0.0278970906355111	0.0546093305864845	-0.51084842710772	0.609576513597998	   
df.mm.exp4	-0.0271995991937664	0.0546093305864845	-0.498076041248857	0.618546336608154	   
df.mm.exp5	-0.0200319676155352	0.0546093305864845	-0.366823167403795	0.713833045396952	   
df.mm.exp6	0.214057018432245	0.0546093305864845	3.91978836095131	9.50222559652141e-05	***
df.mm.exp7	0.0256341288648351	0.0546093305864845	0.469409322354509	0.638885370590499	   
df.mm.exp8	0.00697536869828164	0.0546093305864845	0.127732177328832	0.898388100244344	   
df.mm.trans1:exp2	-0.0995364738017047	0.0500411806431826	-1.98909123490605	0.04697910062095	*  
df.mm.trans2:exp2	-0.000839358228797637	0.0350613056657823	-0.0239397310755828	0.980905736529878	   
df.mm.trans1:exp3	0.0056912105405154	0.0500411806431825	0.113730540873854	0.909475558311191	   
df.mm.trans2:exp3	0.0828675089725005	0.0350613056657823	2.36350322382244	0.0183046327229601	*  
df.mm.trans1:exp4	-0.000831596110057378	0.0500411806431826	-0.0166182352088584	0.986744684778904	   
df.mm.trans2:exp4	0.115890013045254	0.0350613056657823	3.30535360405462	0.000984257035987095	***
df.mm.trans1:exp5	-0.0178563741898434	0.0500411806431825	-0.356833591061087	0.721296049638578	   
df.mm.trans2:exp5	0.0477272585373273	0.0350613056657823	1.36125160290041	0.173758679681480	   
df.mm.trans1:exp6	-0.211379838065314	0.0500411806431825	-4.22411772361155	2.63087481496684e-05	***
df.mm.trans2:exp6	-0.0179997300460701	0.0350613056657823	-0.513378771961614	0.60780639105061	   
df.mm.trans1:exp7	-0.039867962400134	0.0500411806431825	-0.796703073103163	0.42582360690766	   
df.mm.trans2:exp7	0.0680733766801945	0.0350613056657823	1.94155281406505	0.0524882042941349	.  
df.mm.trans1:exp8	-0.0480592565513262	0.0500411806431825	-0.960394138060244	0.337102546302986	   
df.mm.trans2:exp8	0.140634095602264	0.0350613056657823	4.01109122811458	6.52097090494974e-05	***
df.mm.trans1:probe2	-0.491749439171209	0.036258280076224	-13.5624039016034	2.04431889957241e-38	***
df.mm.trans1:probe3	0.06962314552634	0.036258280076224	1.92019989309958	0.0551333903256623	.  
df.mm.trans1:probe4	-0.421509264477638	0.036258280076224	-11.6251864013273	2.71371836088557e-29	***
df.mm.trans1:probe5	-0.365690094336091	0.036258280076224	-10.0856988684328	8.65977184348125e-23	***
df.mm.trans1:probe6	-0.252378897961185	0.036258280076224	-6.9605865868602	6.33281018271135e-12	***
df.mm.trans1:probe7	-0.20610292583038	0.036258280076224	-5.68429956956314	1.74953370260668e-08	***
df.mm.trans1:probe8	-0.315181148568029	0.036258280076224	-8.69266683100907	1.54398313680682e-17	***
df.mm.trans1:probe9	-0.0542363992952139	0.036258280076224	-1.49583485982224	0.135030567005051	   
df.mm.trans1:probe10	-0.0198976049902369	0.036258280076224	-0.548774099279036	0.583290115835288	   
df.mm.trans1:probe11	-0.400460166923315	0.036258280076224	-11.0446542439809	9.29816248962054e-27	***
df.mm.trans1:probe12	-0.387080262693191	0.036258280076224	-10.6756377268710	3.37740305750159e-25	***
df.mm.trans1:probe13	-0.377206511296812	0.036258280076224	-10.4033205795705	4.50577636169108e-24	***
df.mm.trans1:probe14	-0.367960782921501	0.036258280076224	-10.1483242489151	4.86250970761343e-23	***
df.mm.trans1:probe15	-0.422247518051348	0.036258280076224	-11.6455473663858	2.20250075142235e-29	***
df.mm.trans1:probe16	-0.312876261070401	0.036258280076224	-8.62909824770112	2.58843605845616e-17	***
df.mm.trans1:probe17	-0.174364536032683	0.036258280076224	-4.80895772403228	1.76468751782559e-06	***
df.mm.trans1:probe18	-0.154204833098833	0.036258280076224	-4.25295498778915	2.31937128819136e-05	***
df.mm.trans1:probe19	-0.224120733036859	0.036258280076224	-6.18122902039758	9.44787350338661e-10	***
df.mm.trans1:probe20	-0.197776515108902	0.036258280076224	-5.45465793449458	6.266953778052e-08	***
df.mm.trans1:probe21	-0.304080229089405	0.036258280076224	-8.38650450187246	1.80632130039351e-16	***
df.mm.trans1:probe22	-0.0505952118113065	0.036258280076224	-1.39541124689154	0.163219671212086	   
df.mm.trans2:probe2	0.03737044611171	0.036258280076224	1.03067343605786	0.302957697001485	   
df.mm.trans2:probe3	0.0219198401416475	0.036258280076224	0.604547157106362	0.545625045047583	   
df.mm.trans2:probe4	0.000523876060343457	0.036258280076224	0.0144484531324193	0.988475253108979	   
df.mm.trans2:probe5	0.0115188239363507	0.036258280076224	0.317688095302239	0.750791724910308	   
df.mm.trans2:probe6	0.0209356386110046	0.036258280076224	0.577402970217909	0.563804803295511	   
df.mm.trans3:probe2	-0.353256648061239	0.0362582800762240	-9.7427855739049	1.94163630618447e-21	***
df.mm.trans3:probe3	-0.165169769116318	0.036258280076224	-4.55536690568581	5.91357975817518e-06	***
df.mm.trans3:probe4	-0.223878868055334	0.036258280076224	-6.17455840665042	9.8393322486163e-10	***
df.mm.trans3:probe5	-0.186827765816634	0.036258280076224	-5.1526924449774	3.12594842136883e-07	***
df.mm.trans3:probe6	-0.32519014575223	0.036258280076224	-8.96871404458784	1.57932557164530e-18	***
df.mm.trans3:probe7	-0.315583189012178	0.036258280076224	-8.70375506915227	1.41046362515971e-17	***
df.mm.trans3:probe8	-0.242777075273216	0.036258280076224	-6.69576920810466	3.67586749531344e-11	***
df.mm.trans3:probe9	-0.281732112632627	0.036258280076224	-7.77014552373569	2.03900537049069e-14	***
df.mm.trans3:probe10	-0.269492478705288	0.036258280076224	-7.43257755576786	2.38303974996903e-13	***
df.mm.trans3:probe11	-0.0726806821817096	0.036258280076224	-2.00452647033771	0.0452985985922886	*  
df.mm.trans3:probe12	-0.397017910625179	0.036258280076224	-10.9497171346944	2.3639497990033e-26	***
df.mm.trans3:probe13	-0.193764635312704	0.036258280076224	-5.34401066198843	1.13987314809574e-07	***
df.mm.trans3:probe14	-0.111923008741488	0.036258280076224	-3.08682619545653	0.00208203765333778	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.95069535039999	0.0856738568851902	46.1131959507115	4.14044771086765e-244	***
df.mm.trans1	0.0320463360988786	0.0734781227985032	0.436134387738216	0.662838843078851	   
df.mm.trans2	-0.00808487195106534	0.0644179413492836	-0.125506524761913	0.900149254480962	   
df.mm.exp2	-0.0336468657636435	0.0817329150749488	-0.411668490384681	0.680675774318612	   
df.mm.exp3	0.0660453296013112	0.0817329150749488	0.808062817051709	0.419257783352998	   
df.mm.exp4	-0.0269158306424126	0.0817329150749488	-0.329314458168179	0.741990990074575	   
df.mm.exp5	-0.0130730056420334	0.0817329150749488	-0.159947869595077	0.87295632034357	   
df.mm.exp6	0.0230716932789795	0.0817329150749488	0.28228154174879	0.777789448455786	   
df.mm.exp7	0.0452819735873474	0.0817329150749488	0.554023719156769	0.57969368399913	   
df.mm.exp8	-0.082157077224293	0.0817329150749488	-1.00518961239735	0.315062866753341	   
df.mm.trans1:exp2	0.00882306008350996	0.0748958378327322	0.11780441128404	0.9062476678214	   
df.mm.trans2:exp2	0.0544583339141794	0.0524756976073872	1.03778199046778	0.299637027245705	   
df.mm.trans1:exp3	-0.0455994845186696	0.0748958378327322	-0.608838699695284	0.542777683408958	   
df.mm.trans2:exp3	-0.0372626748405375	0.0524756976073872	-0.710093939471362	0.477821069883447	   
df.mm.trans1:exp4	0.0175827095689173	0.0748958378327323	0.234762172074041	0.814444186225163	   
df.mm.trans2:exp4	0.0308642637587137	0.0524756976073872	0.588163000511856	0.556563420949381	   
df.mm.trans1:exp5	0.00542801602682433	0.0748958378327322	0.0724742012893551	0.942239871730384	   
df.mm.trans2:exp5	0.0522887318940267	0.0524756976073872	0.99643709904041	0.319292887366838	   
df.mm.trans1:exp6	-0.0520084052776874	0.0748958378327322	-0.694409820126985	0.487595914509122	   
df.mm.trans2:exp6	0.0540977594787445	0.0524756976073872	1.03091072525597	0.302846456542075	   
df.mm.trans1:exp7	-0.0546678634559984	0.0748958378327322	-0.729918578093621	0.465620848256646	   
df.mm.trans2:exp7	0.00146016391379573	0.0524756976073872	0.0278255264888594	0.97780718157014	   
df.mm.trans1:exp8	0.0414061313241484	0.0748958378327322	0.552849564439379	0.58049717385291	   
df.mm.trans2:exp8	0.0753359955699387	0.0524756976073872	1.43563590394906	0.151436880118092	   
df.mm.trans1:probe2	-0.0231632241726222	0.0542671901377815	-0.426836622898882	0.66959555248136	   
df.mm.trans1:probe3	0.0489367540362326	0.0542671901377815	0.901774238024574	0.367406581605102	   
df.mm.trans1:probe4	0.0263387799635258	0.0542671901377815	0.485353671281911	0.627537989085707	   
df.mm.trans1:probe5	-0.0111825936951451	0.0542671901377815	-0.206065463620893	0.836784160611184	   
df.mm.trans1:probe6	0.00442025752720728	0.0542671901377815	0.0814535913133605	0.935098478679443	   
df.mm.trans1:probe7	-0.0328742609654245	0.0542671901377816	-0.605785206161559	0.544802860549701	   
df.mm.trans1:probe8	-0.0565074418427192	0.0542671901377815	-1.04128188135870	0.298011064870562	   
df.mm.trans1:probe9	-0.0341774372013887	0.0542671901377815	-0.62979927861778	0.52897815650635	   
df.mm.trans1:probe10	-0.0173634174576769	0.0542671901377816	-0.319961608728813	0.74906816026603	   
df.mm.trans1:probe11	-0.0732199386329528	0.0542671901377815	-1.34924875319786	0.177580252786603	   
df.mm.trans1:probe12	-0.0157077241151459	0.0542671901377815	-0.289451583457054	0.772299316531744	   
df.mm.trans1:probe13	-0.0406367679064569	0.0542671901377816	-0.74882756603543	0.454147482563524	   
df.mm.trans1:probe14	0.00928185218934268	0.0542671901377815	0.171039852363399	0.86422904446917	   
df.mm.trans1:probe15	-0.0617937060955056	0.0542671901377815	-1.13869367362885	0.255119520896788	   
df.mm.trans1:probe16	-0.0560995885854534	0.0542671901377816	-1.03376623044273	0.301509937888157	   
df.mm.trans1:probe17	-0.0911204005723403	0.0542671901377816	-1.67910666354736	0.0934618320284899	.  
df.mm.trans1:probe18	0.0415403828515400	0.0542671901377815	0.765478786465102	0.444177628890548	   
df.mm.trans1:probe19	-0.00216388178053764	0.0542671901377816	-0.0398745867446546	0.968201534142015	   
df.mm.trans1:probe20	0.0210654341921844	0.0542671901377815	0.388179932270317	0.697970311054524	   
df.mm.trans1:probe21	-0.0585769738588751	0.0542671901377815	-1.07941785285273	0.280676916644066	   
df.mm.trans1:probe22	-0.0610698046231746	0.0542671901377815	-1.12535409458499	0.260724745483088	   
df.mm.trans2:probe2	-0.0598711082868583	0.0542671901377815	-1.10326530883299	0.270192833850320	   
df.mm.trans2:probe3	-0.0834898238926124	0.0542671901377815	-1.53849542754353	0.124262276450747	   
df.mm.trans2:probe4	-0.00870163736840105	0.0542671901377816	-0.160348036194762	0.872641189486816	   
df.mm.trans2:probe5	-0.0672839368677983	0.0542671901377815	-1.23986402643969	0.215333479860619	   
df.mm.trans2:probe6	0.0165563977662869	0.0542671901377815	0.305090381946275	0.760364498619567	   
df.mm.trans3:probe2	-0.0454112672092422	0.0542671901377816	-0.836808891227745	0.402911556915474	   
df.mm.trans3:probe3	-0.0493455709095543	0.0542671901377815	-0.909307645821877	0.363419605459486	   
df.mm.trans3:probe4	-0.00578267797916153	0.0542671901377815	-0.106559377120496	0.9151611635978	   
df.mm.trans3:probe5	-0.047696849884413	0.0542671901377816	-0.878926101817935	0.379664772540586	   
df.mm.trans3:probe6	-0.0880207921659244	0.0542671901377815	-1.62198912349145	0.105139233965819	   
df.mm.trans3:probe7	-0.00828620073275218	0.0542671901377816	-0.152692643781886	0.87867325741063	   
df.mm.trans3:probe8	-0.0350870431665314	0.0542671901377816	-0.646560897615065	0.518073167728556	   
df.mm.trans3:probe9	-0.0275011759000552	0.0542671901377816	-0.506773537200491	0.612431947396198	   
df.mm.trans3:probe10	-0.0481064389535573	0.0542671901377816	-0.886473739130727	0.375587831298789	   
df.mm.trans3:probe11	-0.0139979348932487	0.0542671901377816	-0.257944714987246	0.796505736539742	   
df.mm.trans3:probe12	0.0096529247189548	0.0542671901377816	0.177877732280712	0.858857120405141	   
df.mm.trans3:probe13	-0.0564212254649098	0.0542671901377816	-1.03969314279327	0.298748420230243	   
df.mm.trans3:probe14	0.00773499560618696	0.0542671901377816	0.142535399134324	0.886687503701222