fitVsDatCorrelation=0.75209567369696
cont.fitVsDatCorrelation=0.250773236302604

fstatistic=11310.0924828994,65,991
cont.fstatistic=5234.05219871168,65,991

residuals=-0.500833814498936,-0.08194204516967,-0.00481116774487972,0.0768203690260344,1.01853858665090
cont.residuals=-0.56254020619125,-0.158075984156650,-0.0413950361882581,0.138210272504575,1.41986729832792

predictedValues:
Include	Exclude	Both
Lung	57.7616718545477	45.2171222615131	63.9482499620069
cerebhem	53.0933587517425	51.8163788426815	61.8885501388723
cortex	57.3361773937342	44.0715932296099	63.0471879027988
heart	56.7293968564616	46.7573353315895	60.9962061930727
kidney	58.3270367680553	44.9507641629627	64.6177938360362
liver	53.111267049663	46.6443465595251	56.3857471613502
stomach	61.3305761853508	46.0695540750872	72.1913030183269
testicle	52.7920374143407	48.4922395727314	59.6862503170357


diffExp=12.5445495930346,1.27697990906108,13.2645841641244,9.9720615248721,13.3762726050926,6.4669204901379,15.2610221102636,4.29979784160922
diffExpScore=0.987090475717972
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,0,0,0,0,0,0
diffExp1.4Score=0
diffExp1.3=0,0,1,0,0,0,1,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,1,1,1,0,1,0
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	53.4841933194587	54.4843896984549	62.7054001628759
cerebhem	55.9214546048659	55.313928188185	54.4730471814474
cortex	53.322002008503	50.9979793492423	54.0977391084021
heart	52.8411553267059	52.0540721586088	52.6478013366095
kidney	55.412998896892	53.9204381765892	54.8867406687893
liver	54.5215223975246	50.0189242896202	56.9938956922242
stomach	54.0445666655345	57.89078381328	54.3836609961477
testicle	56.9476337519541	51.3804198027894	50.884235555341
cont.diffExp=-1.00019637899621,0.607526416680969,2.32402265926069,0.787083168097048,1.49256072030281,4.5025981079044,-3.84621714774546,5.56721394916467
cont.diffExpScore=1.76022191588883

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=-0.650368400576598
cont.tran.correlation=0.00633822034079517

tran.covariance=-0.00183400449107001
cont.tran.covariance=1.60272819409529e-05

tran.mean=51.5313035193498
cont.tran.mean=53.909778903013

weightedLogRatios:
wLogRatio
Lung	0.96321404673953
cerebhem	0.0964054603669224
cortex	1.03072509392909
heart	0.762001749115466
kidney	1.02527230211506
liver	0.507335641387488
stomach	1.13684108507622
testicle	0.333359665973776

cont.weightedLogRatios:
wLogRatio
Lung	-0.0739020092365169
cerebhem	0.0438954022132464
cortex	0.176205231052734
heart	0.0594257721524354
kidney	0.109250166644197
liver	0.340940672536646
stomach	-0.276659334748016
testicle	0.410542621465032

varWeightedLogRatios=0.143838598423882
cont.varWeightedLogRatios=0.0481824031890241

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.58917644936122	0.0729914065491223	49.1725891998772	4.16469215140673e-268	***
df.mm.trans1	0.623519718498145	0.064484419324553	9.6693081061945	3.39338815194808e-21	***
df.mm.trans2	0.201401473969223	0.0569967127315853	3.53356297788054	0.000428917736591233	***
df.mm.exp2	0.0846958506497532	0.074817318991216	1.13203536015100	0.257893407940413	   
df.mm.exp3	-0.0188633405727957	0.074817318991216	-0.252125321077201	0.800996477591606	   
df.mm.exp4	0.0627249163955312	0.074817318991216	0.838374286078007	0.402022690460388	   
df.mm.exp5	-0.00658342315803024	0.074817318991216	-0.0879933048496855	0.92989977490091	   
df.mm.exp6	0.0729973027935338	0.074817318991216	0.975673865059293	0.32946420705597	   
df.mm.exp7	-0.0426158710835738	0.074817318991216	-0.569599013412619	0.569078785359196	   
df.mm.exp8	0.0489353649078157	0.074817318991216	0.654064668015717	0.513221890217933	   
df.mm.trans1:exp2	-0.168969439828372	0.071539040105599	-2.3619193041863	0.0183731611436349	*  
df.mm.trans2:exp2	0.0515346163082963	0.0551245325732392	0.934876250239886	0.350079817671757	   
df.mm.trans1:exp3	0.0114696941766031	0.071539040105599	0.160327761732232	0.872655564865254	   
df.mm.trans2:exp3	-0.00679705518066542	0.0551245325732392	-0.123303633851856	0.90189166561932	   
df.mm.trans1:exp4	-0.0807578159563251	0.071539040105599	-1.12886356648228	0.259228583821822	   
df.mm.trans2:exp4	-0.0292295936834683	0.0551245325732392	-0.530246558456227	0.596059780148197	   
df.mm.trans1:exp5	0.0163237224524408	0.071539040105599	0.228179221140588	0.819553986480212	   
df.mm.trans2:exp5	0.00067535801673766	0.0551245325732392	0.0122514964791832	0.990227430536582	   
df.mm.trans1:exp6	-0.156933650576967	0.071539040105599	-2.19367844949159	0.0284901748827644	*  
df.mm.trans2:exp6	-0.0419213974815095	0.0551245325732392	-0.760485314334633	0.447145438257568	   
df.mm.trans1:exp7	0.102568946498417	0.071539040105599	1.43374787174967	0.151959657042628	   
df.mm.trans2:exp7	0.0612923444912677	0.0551245325732392	1.11188869329339	0.266455674532512	   
df.mm.trans1:exp8	-0.138900431130510	0.071539040105599	-1.94160322706984	0.0524682993718463	.  
df.mm.trans2:exp8	0.0209925852203867	0.0551245325732393	0.380821101611984	0.703417618755247	   
df.mm.trans1:probe2	-0.0218109292502719	0.0438085362368048	-0.497869390850541	0.618686561552608	   
df.mm.trans1:probe3	-0.0094550324681458	0.0438085362368048	-0.215826258540963	0.82916759248241	   
df.mm.trans1:probe4	-0.358333893279996	0.0438085362368048	-8.17954499422305	8.6739023087988e-16	***
df.mm.trans1:probe5	-0.0712608378638084	0.0438085362368048	-1.62664275013919	0.104130900000449	   
df.mm.trans1:probe6	0.0218338576365021	0.0438085362368048	0.498392767986593	0.618317825258188	   
df.mm.trans1:probe7	-0.269846945979081	0.0438085362368048	-6.15968870816493	1.05810995109150e-09	***
df.mm.trans1:probe8	-0.366524781516865	0.0438085362368048	-8.36651513612859	1.99955225393634e-16	***
df.mm.trans1:probe9	-0.0024439098991333	0.0438085362368048	-0.0557861574265543	0.955523407340504	   
df.mm.trans1:probe10	-0.137613280024281	0.0438085362368048	-3.14124350743928	0.00173220355693148	** 
df.mm.trans1:probe11	-0.172397336119761	0.0438085362368048	-3.93524529529761	8.89124089110214e-05	***
df.mm.trans1:probe12	-0.262167483221033	0.0438085362368048	-5.98439267187335	3.03178379386599e-09	***
df.mm.trans1:probe13	-0.228931687728985	0.0438085362368048	-5.22573241186391	2.11479375004796e-07	***
df.mm.trans1:probe14	0.00251852934015736	0.0438085362368048	0.0574894656727077	0.954166886648115	   
df.mm.trans1:probe15	-0.227188475993741	0.0438085362368048	-5.18594081221261	2.60530903829607e-07	***
df.mm.trans1:probe16	-0.246381243741221	0.0438085362368048	-5.62404647371507	2.42564730755108e-08	***
df.mm.trans1:probe17	-0.36823748717994	0.0438085362368048	-8.40561038582644	1.46603787105037e-16	***
df.mm.trans1:probe18	-0.263901373642757	0.0438085362368048	-6.02397149761525	2.39603746310699e-09	***
df.mm.trans1:probe19	-0.418069600237129	0.0438085362368048	-9.54310817365079	1.04083297433606e-20	***
df.mm.trans1:probe20	-0.359620259312108	0.0438085362368048	-8.20890835905129	6.9013716764295e-16	***
df.mm.trans1:probe21	-0.365293570369769	0.0438085362368048	-8.33841076988268	2.49747223728095e-16	***
df.mm.trans1:probe22	-0.351249552761594	0.0438085362368048	-8.01783357615357	3.01693685017283e-15	***
df.mm.trans1:probe23	-0.137697907966315	0.0438085362368048	-3.14317527574982	0.00172092523390797	** 
df.mm.trans1:probe24	0.0336963579157060	0.0438085362368048	0.769173334930937	0.441973696653883	   
df.mm.trans1:probe25	-0.279754405029117	0.0438085362368048	-6.3858423280093	2.61589082879554e-10	***
df.mm.trans1:probe26	-0.0232592460725919	0.0438085362368048	-0.530929541833246	0.595586568373581	   
df.mm.trans1:probe27	-0.375693472498432	0.0438085362368048	-8.5758051916558	3.74309285915159e-17	***
df.mm.trans1:probe28	-0.0872865467409367	0.0438085362368048	-1.99245522080705	0.0465950968855898	*  
df.mm.trans1:probe29	-0.338681878469131	0.0438085362368048	-7.73095628300392	2.61374821240871e-14	***
df.mm.trans1:probe30	-0.0227829232167310	0.0438085362368048	-0.520056709806031	0.603140199793989	   
df.mm.trans1:probe31	-0.127223946232563	0.0438085362368048	-2.90409032488237	0.00376498248572385	** 
df.mm.trans1:probe32	-0.107028434129167	0.0438085362368048	-2.44309541753758	0.0147355621190056	*  
df.mm.trans2:probe2	0.0313906181750354	0.0438085362368048	0.716541132654033	0.473826103153973	   
df.mm.trans2:probe3	0.177642344337845	0.0438085362368048	4.05497100787866	5.40737885686431e-05	***
df.mm.trans2:probe4	-0.00156506527931447	0.0438085362368048	-0.0357251214889854	0.971508733806727	   
df.mm.trans2:probe5	0.0467724951547668	0.0438085362368048	1.06765710915198	0.285935223430168	   
df.mm.trans2:probe6	-0.00346555792000663	0.0438085362368048	-0.0791069096961773	0.936963558010744	   
df.mm.trans3:probe2	-0.345550646081271	0.0438085362368048	-7.88774690424293	8.0980376883705e-15	***
df.mm.trans3:probe3	-0.494571089141032	0.0438085362368048	-11.2893771768053	6.8718172339526e-28	***
df.mm.trans3:probe4	-0.4079374767105	0.0438085362368048	-9.3118262273228	7.86907773644192e-20	***
df.mm.trans3:probe5	-0.216712186813033	0.0438085362368048	-4.94680273364092	8.85882944906386e-07	***
df.mm.trans3:probe6	-0.0713694806619228	0.0438085362368048	-1.62912269600013	0.103604858084242	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.90313347971735	0.107212815788233	36.4054749520507	6.57548229221159e-185	***
df.mm.trans1	0.0692298815148301	0.0947173988982082	0.730909867882149	0.465006978162733	   
df.mm.trans2	0.0743952007134257	0.0837191438215928	0.888628303126982	0.374418512299011	   
df.mm.exp2	0.20041399997501	0.109894791976319	1.82368969785389	0.0685000801301835	.  
df.mm.exp3	0.0784898385465847	0.109894791976319	0.71422709971095	0.475254951578426	   
df.mm.exp4	0.117096073401145	0.109894791976319	1.06552886897841	0.286896199392764	   
df.mm.exp5	0.158199174442440	0.109894791976319	1.43955115249256	0.150310221291780	   
df.mm.exp6	0.0291999602882415	0.109894791976319	0.265708317592827	0.790519122127948	   
df.mm.exp7	0.213450632658346	0.109894791976319	1.94231800087798	0.0523816390035485	.  
df.mm.exp8	0.212983319677032	0.109894791976319	1.93806563392856	0.0528989723276448	.  
df.mm.trans1:exp2	-0.155852048415214	0.105079519509559	-1.48318196678694	0.138344014461706	   
df.mm.trans2:exp2	-0.185303490278541	0.080969207686247	-2.28856741437542	0.0223142375090504	*  
df.mm.trans1:exp3	-0.0815269551694498	0.105079519509559	-0.77585961136826	0.438016995207892	   
df.mm.trans2:exp3	-0.144618060299974	0.080969207686247	-1.78608713648730	0.0743908131829306	.  
df.mm.trans1:exp4	-0.129191887637238	0.105079519509559	-1.22946781865981	0.219188277739716	   
df.mm.trans2:exp4	-0.162727278974093	0.080969207686247	-2.00974276053998	0.0447290932217523	*  
df.mm.trans1:exp5	-0.122771129358865	0.105079519509559	-1.16836401547970	0.242940952688094	   
df.mm.trans2:exp5	-0.168603814540692	0.080969207686247	-2.08232017279984	0.0375692772895431	*  
df.mm.trans1:exp6	-0.00999058847213893	0.105079519509559	-0.0950764575130182	0.924273318247298	   
df.mm.trans2:exp6	-0.114712773765078	0.080969207686247	-1.41674566224714	0.156871570761831	   
df.mm.trans1:exp7	-0.203027776127649	0.105079519509559	-1.93213460696477	0.0536276631545411	.  
df.mm.trans2:exp7	-0.152806668047988	0.080969207686247	-1.88721950497662	0.05942285843243	.  
df.mm.trans1:exp8	-0.150237338492344	0.105079519509559	-1.42974900526336	0.153104241485695	   
df.mm.trans2:exp8	-0.271640390569734	0.080969207686247	-3.35486042573532	0.000824161737231873	***
df.mm.trans1:probe2	-0.050150588903692	0.0643478013038123	-0.779367560158125	0.435949302367769	   
df.mm.trans1:probe3	-0.00183788801513407	0.0643478013038122	-0.028561784208549	0.977219842033146	   
df.mm.trans1:probe4	0.0387972029122325	0.0643478013038122	0.60292973693157	0.546693382851394	   
df.mm.trans1:probe5	0.0078825535126687	0.0643478013038122	0.122499189606370	0.902528524484605	   
df.mm.trans1:probe6	0.0487807184425359	0.0643478013038123	0.7580790245221	0.448583909133902	   
df.mm.trans1:probe7	0.025749956974833	0.0643478013038123	0.40016840440681	0.689118727021737	   
df.mm.trans1:probe8	-0.0128033492471424	0.0643478013038122	-0.198971044662312	0.842326208331626	   
df.mm.trans1:probe9	-0.0891274949563518	0.0643478013038123	-1.38508998210435	0.166336658037217	   
df.mm.trans1:probe10	0.0548534630799869	0.0643478013038122	0.852452795100197	0.394168793894948	   
df.mm.trans1:probe11	-0.0126709272243639	0.0643478013038122	-0.196913134056272	0.843935891694007	   
df.mm.trans1:probe12	0.0178172182417739	0.0643478013038123	0.276889309048051	0.781922843412041	   
df.mm.trans1:probe13	0.00459875010229832	0.0643478013038122	0.0714670899256641	0.94304043585214	   
df.mm.trans1:probe14	0.0619544497793152	0.0643478013038122	0.962806009280767	0.335879752913318	   
df.mm.trans1:probe15	0.0273361776243175	0.0643478013038123	0.424819140210437	0.67106074116404	   
df.mm.trans1:probe16	-0.0113472949792846	0.0643478013038122	-0.176343165568460	0.860060381431075	   
df.mm.trans1:probe17	0.0476790816172191	0.0643478013038123	0.74095898618364	0.458893839102694	   
df.mm.trans1:probe18	0.0817274201935149	0.0643478013038122	1.27008877595749	0.204351134234363	   
df.mm.trans1:probe19	-0.054763256205295	0.0643478013038122	-0.851050930967094	0.394946649337207	   
df.mm.trans1:probe20	0.0378984616415599	0.0643478013038122	0.588962806399955	0.55602044652462	   
df.mm.trans1:probe21	-0.0169023914813864	0.0643478013038122	-0.262672401215129	0.792857686154172	   
df.mm.trans1:probe22	-0.00575199192175368	0.0643478013038122	-0.0893890980765011	0.928790748899532	   
df.mm.trans1:probe23	-0.0185930535071951	0.0643478013038122	-0.288946213086748	0.77268296940948	   
df.mm.trans1:probe24	0.113974344536716	0.0643478013038123	1.77122360402955	0.0768308310355739	.  
df.mm.trans1:probe25	-0.0180152931397004	0.0643478013038122	-0.279967501214887	0.779560864725525	   
df.mm.trans1:probe26	-0.0235000074055352	0.0643478013038123	-0.365202958444253	0.71503783761821	   
df.mm.trans1:probe27	-0.0265928968591566	0.0643478013038122	-0.413268150897660	0.6794995678596	   
df.mm.trans1:probe28	0.0953226137970343	0.0643478013038122	1.48136551468134	0.138827073076574	   
df.mm.trans1:probe29	-0.09049170147864	0.0643478013038123	-1.40629049703488	0.159951360206661	   
df.mm.trans1:probe30	0.0388244479257913	0.0643478013038123	0.603353139332379	0.546411852722336	   
df.mm.trans1:probe31	-0.0195317954584021	0.0643478013038123	-0.303534776055277	0.761546061857424	   
df.mm.trans1:probe32	0.0157493580165088	0.0643478013038122	0.244753631008301	0.806697867462724	   
df.mm.trans2:probe2	0.0403013248522465	0.0643478013038122	0.626304613920955	0.531259391084825	   
df.mm.trans2:probe3	0.0568836425083332	0.0643478013038122	0.884002892962298	0.376909060816416	   
df.mm.trans2:probe4	0.0302769562623792	0.0643478013038122	0.47052044745755	0.638086766816888	   
df.mm.trans2:probe5	0.0785035368743634	0.0643478013038123	1.21998786724221	0.222759718505510	   
df.mm.trans2:probe6	0.0386611714097946	0.0643478013038122	0.600815733038949	0.548100108757581	   
df.mm.trans3:probe2	0.115626799947364	0.0643478013038122	1.79690366422068	0.0726553214335842	.  
df.mm.trans3:probe3	0.107985524619672	0.0643478013038122	1.67815406947361	0.0936323170912463	.  
df.mm.trans3:probe4	0.144190901332835	0.0643478013038123	2.24080541077155	0.0252595245517734	*  
df.mm.trans3:probe5	0.111285138509019	0.0643478013038122	1.72943187263845	0.084043223286396	.  
df.mm.trans3:probe6	0.0710456894960845	0.0643478013038123	1.10408884307715	0.269822609436408