fitVsDatCorrelation=0.86185635688181
cont.fitVsDatCorrelation=0.246516414453266

fstatistic=12377.3089329996,52,692
cont.fstatistic=3379.80433966855,52,692

residuals=-0.56854650803291,-0.0758553872899772,-0.00524092735586499,0.0701983793121373,0.894525279846673
cont.residuals=-0.613966599973713,-0.188558498633153,-0.00891223936977137,0.171637109529471,1.02640297438912

predictedValues:
Include	Exclude	Both
Lung	60.7683455378257	96.0835930982042	60.7491564356306
cerebhem	60.4710030714214	67.8363706279058	67.8364633395333
cortex	61.333288801948	91.8447164286403	56.8003950759159
heart	59.92089519566	97.0966710182142	56.8430976774508
kidney	62.0715779155005	106.730268296370	57.5225904394328
liver	61.9134478559621	100.431368911056	53.7440143748523
stomach	61.663888718393	95.806061544993	61.0751387889498
testicle	57.1654230020941	98.990555587584	54.7599429966873


diffExp=-35.3152475603785,-7.36536755648439,-30.5114276266923,-37.1757758225542,-44.6586903808692,-38.5179210550943,-34.1421728266000,-41.8251325854899
diffExpScore=0.996303302707112
diffExp1.5=-1,0,0,-1,-1,-1,-1,-1
diffExp1.5Score=0.857142857142857
diffExp1.4=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.875
diffExp1.3=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.875
diffExp1.2=-1,0,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	61.7536205629713	62.4230841267577	62.0437205821588
cerebhem	67.4614966929578	62.9400874850655	59.7297123637964
cortex	67.9735234285713	67.1978346902909	76.84060678125
heart	68.4996029868876	64.9136237403018	66.3069714018696
kidney	68.025282972135	71.3617728175238	84.5577442519781
liver	67.4525503816382	64.5898967225291	72.4010046441767
stomach	67.9000591905114	63.1501651579482	71.5055759193918
testicle	67.001205036344	67.2974620051256	57.3261836533385
cont.diffExp=-0.669463563786472,4.52140920789228,0.775688738280408,3.58597924658582,-3.3364898453889,2.86265365910906,4.74989403256323,-0.296256968781506
cont.diffExpScore=1.57638003810024

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=0.0916479055400538
cont.tran.correlation=0.420934120639649

tran.covariance=0.000273433877290263
cont.tran.covariance=0.000648139317493686

tran.mean=77.5079672257358
cont.tran.mean=66.2463292498475

weightedLogRatios:
wLogRatio
Lung	-1.98660234347984
cerebhem	-0.47808525578684
cortex	-1.74359231627637
heart	-2.09211972269041
kidney	-2.38449169979498
liver	-2.11277345651438
stomach	-1.91320985259892
testicle	-2.37227228038967

cont.weightedLogRatios:
wLogRatio
Lung	-0.0445161833129729
cerebhem	0.289765079286483
cortex	0.0483579815134074
heart	0.225832721808939
kidney	-0.203206395053889
liver	0.181693960419334
stomach	0.303268439800508
testicle	-0.0185605769355871

varWeightedLogRatios=0.370318340715813
cont.varWeightedLogRatios=0.0327630688451922

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.53382682423717	0.0763389851838773	59.3907138445261	7.33787821774526e-274	***
df.mm.trans1	-0.694343164602217	0.068563572433541	-10.1269980538900	1.43152071659114e-22	***
df.mm.trans2	0.0886061788844503	0.0630535166396786	1.40525356247445	0.160394656127559	   
df.mm.exp2	-0.463371803146182	0.0863677025478704	-5.36510512004591	1.10455459162162e-07	***
df.mm.exp3	0.0313443491967049	0.0863677025478705	0.362917482716781	0.716777349331884	   
df.mm.exp4	0.0629031711411689	0.0863677025478704	0.728318217175037	0.466665193405159	   
df.mm.exp5	0.180880849986524	0.0863677025478705	2.09431123730852	0.0365954311141569	*  
df.mm.exp6	0.185445306357746	0.0863677025478705	2.14716034914742	0.0321273057411394	*  
df.mm.exp7	0.0063851553791666	0.0863677025478704	0.073929897297286	0.941087530973522	   
df.mm.exp8	0.0724803121430716	0.0863677025478705	0.83920620793286	0.401643414247036	   
df.mm.trans1:exp2	0.458466744417698	0.0826907796539673	5.54435145413097	4.19964577448134e-08	***
df.mm.trans2:exp2	0.115251720185023	0.0719730854565587	1.60131692915383	0.109763114253153	   
df.mm.trans1:exp3	-0.0220906271056451	0.0826907796539673	-0.267147403834948	0.789435264978513	   
df.mm.trans2:exp3	-0.0764636371172515	0.0719730854565588	-1.06239209604823	0.288428330101551	   
df.mm.trans1:exp4	-0.0769469129906979	0.0826907796539673	-0.930538003302115	0.352417100677624	   
df.mm.trans2:exp4	-0.0524146545017235	0.0719730854565587	-0.728253543240963	0.466704749664785	   
df.mm.trans1:exp5	-0.159661669130183	0.0826907796539674	-1.93082795685701	0.0539125183549966	.  
df.mm.trans2:exp5	-0.0757946292925604	0.0719730854565587	-1.05309684601903	0.292664178535471	   
df.mm.trans1:exp6	-0.166776919749251	0.0826907796539673	-2.01687443808313	0.0440943497889038	*  
df.mm.trans2:exp6	-0.141189282560632	0.0719730854565588	-1.96169556529364	0.0501984318871702	.  
df.mm.trans1:exp7	0.0082443114121639	0.0826907796539673	0.0997004919612988	0.920610985448086	   
df.mm.trans2:exp7	-0.00927777350640834	0.0719730854565587	-0.128906152175568	0.897469364186787	   
df.mm.trans1:exp8	-0.133600110301599	0.0826907796539673	-1.61565909597987	0.106623831332398	   
df.mm.trans2:exp8	-0.0426744386790379	0.0719730854565587	-0.592922179288746	0.553426947562154	   
df.mm.trans1:probe2	0.435468971496579	0.0413453898269837	10.5324674242731	3.67631203158582e-24	***
df.mm.trans1:probe3	0.0761582625787722	0.0413453898269837	1.84200131858639	0.0659026639487423	.  
df.mm.trans1:probe4	0.593011987763697	0.0413453898269837	14.342880554404	4.8959624938768e-41	***
df.mm.trans1:probe5	0.0201109339283713	0.0413453898269837	0.486412971616150	0.626828400465219	   
df.mm.trans1:probe6	-0.00934527650102527	0.0413453898269837	-0.226029468826683	0.821245243156824	   
df.mm.trans1:probe7	0.198473509880986	0.0413453898269837	4.80037824559232	1.94164423791143e-06	***
df.mm.trans1:probe8	0.475287347762727	0.0413453898269837	11.4955343207947	4.12114720843689e-28	***
df.mm.trans1:probe9	0.465982784979806	0.0413453898269837	11.2704895740440	3.62391456438367e-27	***
df.mm.trans1:probe10	0.139295841075344	0.0413453898269837	3.36907794697908	0.000796024337167157	***
df.mm.trans1:probe11	0.456521630886732	0.0413453898269837	11.0416574325969	3.20826276803021e-26	***
df.mm.trans1:probe12	0.444523964642537	0.0413453898269837	10.7514759566355	4.87581981082515e-25	***
df.mm.trans1:probe13	0.491919682587823	0.0413453898269837	11.8978121780043	7.88222947831419e-30	***
df.mm.trans1:probe14	0.466773022806783	0.0413453898269837	11.2896026560656	3.01632100338753e-27	***
df.mm.trans1:probe15	0.471519245413701	0.0413453898269837	11.4043971380327	9.97431744897277e-28	***
df.mm.trans1:probe16	0.502040435045032	0.0413453898269837	12.1425976909614	6.79722732348247e-31	***
df.mm.trans1:probe17	0.299265299109249	0.0413453898269837	7.23817819499518	1.21219837462013e-12	***
df.mm.trans1:probe18	0.186795174007275	0.0413453898269837	4.51792025154313	7.34250475052334e-06	***
df.mm.trans1:probe19	0.246527654106176	0.0413453898269837	5.96263948986356	3.9566419173398e-09	***
df.mm.trans1:probe20	0.223881943832658	0.0413453898269837	5.41491916679291	8.46571573712724e-08	***
df.mm.trans1:probe21	0.30220465265498	0.0413453898269837	7.30927085025932	7.43277126611819e-13	***
df.mm.trans1:probe22	0.203216960926797	0.0413453898269837	4.9151056932149	1.10896866341655e-06	***
df.mm.trans2:probe2	-0.171863496639395	0.0413453898269837	-4.15677533477335	3.63263166934685e-05	***
df.mm.trans2:probe3	-0.123403139964564	0.0413453898269837	-2.98468923575188	0.00293871899964134	** 
df.mm.trans2:probe4	-0.0877872466255366	0.0413453898269837	-2.12326566499666	0.0340858729473194	*  
df.mm.trans2:probe5	-0.234998963006936	0.0413453898269837	-5.6838008781711	1.94223545354982e-08	***
df.mm.trans2:probe6	0.103122984273696	0.0413453898269837	2.4941833830865	0.0128569707233555	*  
df.mm.trans3:probe2	0.144529870091916	0.0413453898269837	3.49567075547538	0.000503045967283986	***
df.mm.trans3:probe3	0.391460203222884	0.0413453898269837	9.4680496389322	4.39412684187391e-20	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12637189041344	0.145879324168529	28.2862010359083	1.43951971176205e-117	***
df.mm.trans1	0.0302607192820972	0.131020966352816	0.230960892172101	0.817413452186553	   
df.mm.trans2	-0.0704437215298032	0.120491572840400	-0.584636085903792	0.558982993400913	   
df.mm.exp2	0.134662262176167	0.165043614967163	0.815919247787676	0.414827079245337	   
df.mm.exp3	-0.0442223854767552	0.165043614967163	-0.267943631055061	0.788822554954346	   
df.mm.exp4	0.0763419849901123	0.165043614967163	0.462556427919378	0.643827754556202	   
df.mm.exp5	-0.0790413332337186	0.165043614967163	-0.478911790979884	0.632152671576858	   
df.mm.exp6	-0.0319862252169337	0.165043614967163	-0.193804681406776	0.84638569540663	   
df.mm.exp7	-0.0354715052030919	0.165043614967163	-0.214922008404562	0.829891447572041	   
df.mm.exp8	0.235827208402246	0.165043614967163	1.42887810866943	0.153490493763232	   
df.mm.trans1:exp2	-0.0462578528002782	0.158017230931662	-0.292739295123350	0.769809186558447	   
df.mm.trans2:exp2	-0.126414125459280	0.137536345805970	-0.9191325007109	0.358346535267597	   
df.mm.trans1:exp3	0.140188047099524	0.158017230931662	0.887169369270567	0.375295946319604	   
df.mm.trans2:exp3	0.117928265722025	0.137536345805970	0.857433466266385	0.391502220665483	   
df.mm.trans1:exp4	0.0273333580614246	0.158017230931662	0.172977072818378	0.862720030366195	   
df.mm.trans2:exp4	-0.0372196092471116	0.137536345805970	-0.270616534335	0.78676667084354	   
df.mm.trans1:exp5	0.175768171331967	0.158017230931662	1.11233547313572	0.266380207859928	   
df.mm.trans2:exp5	0.212868519597752	0.137536345805970	1.54772557283191	0.122145567595433	   
df.mm.trans1:exp6	0.120258012208028	0.158017230931662	0.761043662763815	0.446890239855856	   
df.mm.trans2:exp6	0.0661090813960315	0.137536345805970	0.480666263223944	0.630905635673924	   
df.mm.trans1:exp7	0.130355805110029	0.158017230931662	0.824946775370366	0.409686081871369	   
df.mm.trans2:exp7	0.0470518244299075	0.137536345805970	0.342104657166668	0.732376005093761	   
df.mm.trans1:exp8	-0.154269209943932	0.158017230931662	-0.976280934897846	0.329266423373565	   
df.mm.trans2:exp8	-0.160639829019019	0.137536345805970	-1.16798092953293	0.243216705354844	   
df.mm.trans1:probe2	-0.104378401101580	0.079008615465831	-1.32110150881863	0.186904186063923	   
df.mm.trans1:probe3	0.0406054959123333	0.079008615465831	0.513937570895594	0.607459706274407	   
df.mm.trans1:probe4	-0.00523360590636618	0.079008615465831	-0.066240952021613	0.947205125794034	   
df.mm.trans1:probe5	0.00665903127116652	0.079008615465831	0.0842823435381723	0.932856326306503	   
df.mm.trans1:probe6	-0.066916142559872	0.079008615465831	-0.846947414093231	0.39731723828677	   
df.mm.trans1:probe7	-0.0221479782468239	0.079008615465831	-0.280323583905888	0.779313038305127	   
df.mm.trans1:probe8	-0.0788828308963126	0.079008615465831	-0.998407963881195	0.318430558952040	   
df.mm.trans1:probe9	-0.0732507491757219	0.079008615465831	-0.927123564232065	0.354185620474638	   
df.mm.trans1:probe10	-0.0531231578970294	0.079008615465831	-0.672371710146011	0.501571523071956	   
df.mm.trans1:probe11	-0.0462087773458182	0.079008615465831	-0.584857449701827	0.558834210871835	   
df.mm.trans1:probe12	-0.0264003420952745	0.079008615465831	-0.334145104804322	0.738371304333731	   
df.mm.trans1:probe13	0.0304838551240153	0.079008615465831	0.385829506621322	0.69974152445478	   
df.mm.trans1:probe14	-0.0844551708448316	0.079008615465831	-1.06893622102967	0.285471136206929	   
df.mm.trans1:probe15	-0.0177598830754366	0.079008615465831	-0.224784132347152	0.822213568821616	   
df.mm.trans1:probe16	-0.074450681257352	0.079008615465831	-0.942310921643094	0.346362323954302	   
df.mm.trans1:probe17	0.00231686659799515	0.079008615465831	0.0293242272926188	0.976614459523205	   
df.mm.trans1:probe18	-0.0482617203132542	0.079008615465831	-0.610841235841249	0.541505268282506	   
df.mm.trans1:probe19	-0.0393743660107798	0.079008615465831	-0.498355347434332	0.618391955998592	   
df.mm.trans1:probe20	-0.0487321413247497	0.079008615465831	-0.616795282861589	0.537572630883616	   
df.mm.trans1:probe21	-0.0487485770476765	0.079008615465831	-0.617003307300821	0.537435491476536	   
df.mm.trans1:probe22	-0.0787408064952563	0.079008615465831	-0.9966103826906	0.319302020087066	   
df.mm.trans2:probe2	0.125589525890651	0.079008615465831	1.58956748134595	0.112389067084701	   
df.mm.trans2:probe3	0.0994371477476787	0.079008615465831	1.25856081848039	0.208613443554004	   
df.mm.trans2:probe4	0.146808647801247	0.079008615465831	1.85813467222112	0.0635743648243693	.  
df.mm.trans2:probe5	0.125099363292480	0.079008615465831	1.58336356807292	0.113795506658613	   
df.mm.trans2:probe6	0.205128099361494	0.079008615465831	2.59627507901599	0.0096240840020637	** 
df.mm.trans3:probe2	0.0844758946075783	0.079008615465831	1.06919851853513	0.285353037532303	   
df.mm.trans3:probe3	0.0404753625223899	0.079008615465831	0.512290492419708	0.608611179540146