fitVsDatCorrelation=0.905335075950163
cont.fitVsDatCorrelation=0.23562866933938

fstatistic=10261.3830392642,62,922
cont.fstatistic=1947.59854229045,62,922

residuals=-0.542758944063693,-0.0873429503328444,-0.00270105630012322,0.0825764846101363,1.34601099235096
cont.residuals=-0.58319647312043,-0.224418262499262,-0.079571979239522,0.131620388984205,1.91720181396261

predictedValues:
Include	Exclude	Both
Lung	60.9204385283817	66.5227759091328	79.3435130710668
cerebhem	65.6879358193086	66.6559760616537	80.0933743054994
cortex	58.6143869678407	81.2728736105956	85.4135651561242
heart	60.6108407814107	65.8532164373215	73.7326834686876
kidney	60.2658549080317	56.1217015733388	71.0270846507155
liver	64.0473765424532	52.5843758492167	65.1908950116034
stomach	61.0900272861638	67.4995628943698	76.7259021299107
testicle	63.4250295369034	56.8396272682207	71.6170763337295


diffExp=-5.60233738075117,-0.968040242345026,-22.6584866427549,-5.24237565591076,4.14415333469295,11.4630006932365,-6.40953560820598,6.58540226868268
diffExpScore=3.20360775542977
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,-1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,-1,0,0,1,0,0
diffExp1.2Score=2

cont.predictedValues:
Include	Exclude	Both
Lung	62.825993148201	59.882351400603	56.2735241910577
cerebhem	61.1134405314307	75.9508757787608	58.3700621477108
cortex	56.3057531536761	62.1353598136234	60.5364505273783
heart	65.473950501033	60.1267900453878	65.4313569850805
kidney	60.1978258506969	80.05590689793	59.9716953735066
liver	59.4006986585975	56.8136149004018	61.2967953635915
stomach	57.5756068146216	65.9672519080352	64.0058695525559
testicle	58.9147287888837	58.394948266248	64.4213712460247
cont.diffExp=2.94364174759793,-14.8374352473301,-5.82960665994733,5.34716045564527,-19.8580810472331,2.58708375819577,-8.39164509341359,0.51978052263572
cont.diffExpScore=1.56583181027785

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,-1,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,-1,0,0,-1,0,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=-0.521853602647822
cont.tran.correlation=-0.0312941730060839

tran.covariance=-0.00266959300691584
cont.tran.covariance=-0.000183007684968058

tran.mean=63.0007499983965
cont.tran.mean=62.5709435286332

weightedLogRatios:
wLogRatio
Lung	-0.365411868765013
cerebhem	-0.0613299042002254
cortex	-1.38393672178514
heart	-0.343925310660862
kidney	0.289471043516033
liver	0.800849715371402
stomach	-0.415276533148815
testicle	0.448918720682887

cont.weightedLogRatios:
wLogRatio
Lung	0.197532768420002
cerebhem	-0.917544171224625
cortex	-0.401961037999746
heart	0.352634403291283
kidney	-1.20882884471226
liver	0.180882653994685
stomach	-0.56071850767273
testicle	0.0360819942183631

varWeightedLogRatios=0.448173065154728
cont.varWeightedLogRatios=0.328944081906719

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.68967939229782	0.0741653487916017	49.7493701899186	2.47719027165468e-263	***
df.mm.trans1	0.643266495315504	0.0635114103003963	10.1283610657200	6.2179742574512e-23	***
df.mm.trans2	0.509807026529808	0.0558296463489483	9.13147511885345	4.17811506439701e-19	***
df.mm.exp2	0.0679404388932869	0.0709156373575891	0.958045946209298	0.338290737026077	   
df.mm.exp3	0.0879611325038841	0.0709156373575891	1.24036299723774	0.21515671059098	   
df.mm.exp4	0.0581294550034772	0.0709156373575891	0.819698689449294	0.412599739081698	   
df.mm.exp5	-0.070099430994752	0.0709156373575891	-0.988490460027576	0.323171837987949	   
df.mm.exp6	0.0113958768557961	0.0709156373575891	0.160696248111441	0.8723678460043	   
df.mm.exp7	0.0509039795969438	0.0709156373575891	0.717810365861377	0.473056051678500	   
df.mm.exp8	-0.0145676425185421	0.0709156373575891	-0.205422147517132	0.837287662318516	   
df.mm.trans1:exp2	0.00740611802772687	0.0647368237676793	0.114403481615118	0.90894286191443	   
df.mm.trans2:exp2	-0.0659401168454519	0.0457758470786045	-1.44050019942224	0.150065416016804	   
df.mm.trans1:exp3	-0.126549681088790	0.0647368237676793	-1.95483302583609	0.0509046403518343	.  
df.mm.trans2:exp3	0.112306786549431	0.0457758470786045	2.4534070632616	0.0143351621969497	*  
df.mm.trans1:exp4	-0.0632244135763815	0.0647368237676794	-0.976637559532957	0.329004789693570	   
df.mm.trans2:exp4	-0.0682455669950963	0.0457758470786045	-1.49086409865682	0.136339205365773	   
df.mm.trans1:exp5	0.0592963943161473	0.0647368237676793	0.915960822065413	0.359926946616645	   
df.mm.trans2:exp5	-0.0999223778624502	0.0457758470786045	-2.18286245344335	0.0292975663900443	*  
df.mm.trans1:exp6	0.0386584646920614	0.0647368237676794	0.597163444885631	0.550544877908607	   
df.mm.trans2:exp6	-0.246521222221526	0.0457758470786045	-5.3853994618212	9.17694389511028e-08	***
df.mm.trans1:exp7	-0.0481240727116878	0.0647368237676793	-0.74338019554976	0.457440909907532	   
df.mm.trans2:exp7	-0.0363272412763987	0.0457758470786045	-0.793589711491715	0.427638507569649	   
df.mm.trans1:exp8	0.0548574872704204	0.0647368237676793	0.847392319822287	0.396996393329399	   
df.mm.trans2:exp8	-0.142742995733276	0.0457758470786045	-3.11830375281016	0.00187539971366706	** 
df.mm.trans1:probe2	-0.294161487639529	0.0469062851284556	-6.27125953023033	5.49728248036465e-10	***
df.mm.trans1:probe3	-0.50210245477714	0.0469062851284556	-10.7043747634694	2.76276439043987e-25	***
df.mm.trans1:probe4	-0.469334193101995	0.0469062851284556	-10.0057847645938	1.91051964094936e-22	***
df.mm.trans1:probe5	-0.519385716134279	0.0469062851284556	-11.0728384205211	7.66863783151681e-27	***
df.mm.trans1:probe6	-0.356803076686279	0.0469062851284556	-7.60672212069562	6.92681063474583e-14	***
df.mm.trans1:probe7	0.192319301150978	0.0469062851284556	4.10007530172769	4.49459279343504e-05	***
df.mm.trans1:probe8	-0.460164536576912	0.0469062851284556	-9.81029589780397	1.11935394724131e-21	***
df.mm.trans1:probe9	-0.321825428885292	0.0469062851284556	-6.86102998785672	1.25297433141877e-11	***
df.mm.trans1:probe10	-0.41406028400612	0.0469062851284556	-8.82739451380964	5.30403223256256e-18	***
df.mm.trans1:probe11	-0.395871332522556	0.0469062851284556	-8.43962235419922	1.22268917592507e-16	***
df.mm.trans1:probe12	-0.462846594037567	0.0469062851284556	-9.86747496140516	6.69303942845916e-22	***
df.mm.trans1:probe13	-0.569712813366568	0.0469062851284556	-12.1457670716488	1.35415283517802e-31	***
df.mm.trans1:probe14	-0.481997250728572	0.0469062851284556	-10.2757498149468	1.58977348369860e-23	***
df.mm.trans1:probe15	-0.541737604652805	0.0469062851284556	-11.5493606703073	6.50755787736921e-29	***
df.mm.trans1:probe16	-0.366244051726144	0.0469062851284556	-7.8079952552875	1.57507593832004e-14	***
df.mm.trans1:probe17	-0.419173221597503	0.0469062851284556	-8.93639776523707	2.15030449498594e-18	***
df.mm.trans1:probe18	-0.445647222232203	0.0469062851284556	-9.50079975448432	1.73759504136047e-20	***
df.mm.trans1:probe19	-0.403134666549361	0.0469062851284556	-8.59447013220835	3.54169983766697e-17	***
df.mm.trans1:probe20	-0.291872494439891	0.0469062851284556	-6.22246024473226	7.41760249700761e-10	***
df.mm.trans1:probe21	-0.29444551285491	0.0469062851284556	-6.27731469351183	5.29591557815603e-10	***
df.mm.trans2:probe2	-0.0186780010252543	0.0469062851284556	-0.398198257954206	0.690576236900764	   
df.mm.trans2:probe3	-0.0813858199724818	0.0469062851284556	-1.73507281059675	0.0830620641216767	.  
df.mm.trans2:probe4	-0.0582150416214511	0.0469062851284556	-1.24109256279890	0.214887231242510	   
df.mm.trans2:probe5	0.0691420916547718	0.0469062851284556	1.47404748564978	0.140810144254379	   
df.mm.trans2:probe6	0.0502960723718786	0.0469062851284556	1.07226722888286	0.28388059878386	   
df.mm.trans3:probe2	-0.78180493111855	0.0469062851284556	-16.66738111913	1.01649055880383e-54	***
df.mm.trans3:probe3	-0.459822909646525	0.0469062851284556	-9.80301271753396	1.19492917260579e-21	***
df.mm.trans3:probe4	-0.8257489776503	0.0469062851284556	-17.6042288445768	5.03465275204392e-60	***
df.mm.trans3:probe5	-0.339665449611948	0.0469062851284556	-7.24136325615542	9.36498743958918e-13	***
df.mm.trans3:probe6	-0.462889204854083	0.0469062851284556	-9.8683833858604	6.63845112728478e-22	***
df.mm.trans3:probe7	-0.527980169911383	0.0469062851284556	-11.2560644797489	1.24724966458182e-27	***
df.mm.trans3:probe8	-0.79665120450961	0.0469062851284556	-16.9838903747746	1.70388259699975e-56	***
df.mm.trans3:probe9	-0.648207384679532	0.0469062851284556	-13.8192010495902	1.29419159792200e-39	***
df.mm.trans3:probe10	-0.46672748286165	0.0469062851284556	-9.95021203626528	3.16691607599999e-22	***
df.mm.trans3:probe11	-0.769504990877793	0.0469062851284556	-16.4051574063147	2.91592922949073e-53	***
df.mm.trans3:probe12	-0.566671710426274	0.0469062851284556	-12.0809334799038	2.6784772807783e-31	***
df.mm.trans3:probe13	-0.830992883824864	0.0469062851284556	-17.7160242289310	1.14610875345604e-60	***
df.mm.trans3:probe14	0.911140201076475	0.0469062851284556	19.424693270449	1.01120721883419e-70	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.06562521160767	0.169713738282268	23.9557813807962	5.50003204235328e-99	***
df.mm.trans1	0.0585613642144652	0.145334162668695	0.402942867245619	0.687083668552257	   
df.mm.trans2	0.0284533733912743	0.127755860967917	0.222716775384729	0.823805272544114	   
df.mm.exp2	0.173488896681900	0.162277372313645	1.06908864870319	0.285309536479137	   
df.mm.exp3	-0.145660283512775	0.162277372313645	-0.897600703265315	0.369632786118051	   
df.mm.exp4	-0.105420304150619	0.162277372313645	-0.64963033753631	0.516092838307029	   
df.mm.exp5	0.183962198610237	0.162277372313645	1.13362815768720	0.257245231527038	   
df.mm.exp6	-0.194172136773737	0.162277372313645	-1.19654474314785	0.2317916380727	   
df.mm.exp7	-0.119243927336143	0.162277372313645	-0.73481549297996	0.462638720187737	   
df.mm.exp8	-0.224651479479642	0.162277372313645	-1.38436724896828	0.166580986596088	   
df.mm.trans1:exp2	-0.201125970204465	0.148138295648079	-1.35769059124499	0.174894072161198	   
df.mm.trans2:exp2	0.0642160359567438	0.104749593406174	0.6130432956216	0.539998898643722	   
df.mm.trans1:exp3	0.0360881094184746	0.148138295648079	0.243610939768109	0.807586346841577	   
df.mm.trans2:exp3	0.182593684262843	0.104749593406174	1.74314456338577	0.081641716589928	.  
df.mm.trans1:exp4	0.146703773939551	0.148138295648079	0.990316334461305	0.322279342627204	   
df.mm.trans2:exp4	0.109493976870443	0.104749593406174	1.04529261937917	0.296161713826582	   
df.mm.trans1:exp5	-0.226694853858014	0.148138295648079	-1.53029203465764	0.126287466335836	   
df.mm.trans2:exp5	0.106381200887857	0.104749593406174	1.01557626553600	0.310097635415237	   
df.mm.trans1:exp6	0.138109233528972	0.148138295648079	0.932299328305133	0.351425918705807	   
df.mm.trans2:exp6	0.141566305400792	0.104749593406174	1.35147355514649	0.17687527363069	   
df.mm.trans1:exp7	0.0319740210644322	0.148138295648079	0.215838996422575	0.829160989343793	   
df.mm.trans2:exp7	0.216020535683773	0.104749593406174	2.06225655546114	0.0394630104464686	*  
df.mm.trans1:exp8	0.160373711730262	0.148138295648079	1.08259455145379	0.279271471282363	   
df.mm.trans2:exp8	0.199499035954427	0.104749593406174	1.90453279547210	0.0571518151892855	.  
df.mm.trans1:probe2	0.0469791059648142	0.107336392638736	0.437681058678139	0.661720010008806	   
df.mm.trans1:probe3	0.0521784844891832	0.107336392638736	0.48612109282265	0.626996826970843	   
df.mm.trans1:probe4	-0.051385465662936	0.107336392638736	-0.478732929248752	0.632242143207363	   
df.mm.trans1:probe5	0.108588677979064	0.107336392638736	1.01166692218308	0.311962748563128	   
df.mm.trans1:probe6	0.0150421350520492	0.107336392638736	0.140140120999565	0.888579889303986	   
df.mm.trans1:probe7	-0.00636414566675387	0.107336392638736	-0.0592915926304121	0.952732705690795	   
df.mm.trans1:probe8	0.0796815413520734	0.107336392638736	0.742353449684668	0.458062291273675	   
df.mm.trans1:probe9	-0.0152132462038278	0.107336392638736	-0.141734278838971	0.88732086044315	   
df.mm.trans1:probe10	-0.0562944983003488	0.107336392638736	-0.524467954590391	0.60007916828955	   
df.mm.trans1:probe11	-0.0387128257917186	0.107336392638736	-0.360668221094548	0.718430097964047	   
df.mm.trans1:probe12	0.122883819865384	0.107336392638736	1.14484767788850	0.252569359543761	   
df.mm.trans1:probe13	0.0764536566544809	0.107336392638736	0.712280846923956	0.476471081579929	   
df.mm.trans1:probe14	-0.107962256907154	0.107336392638736	-1.00583086736039	0.314760831216241	   
df.mm.trans1:probe15	0.00620208625386992	0.107336392638736	0.0577817653584131	0.953934983019008	   
df.mm.trans1:probe16	0.000276275518832339	0.107336392638736	0.00257392215296638	0.997946866306435	   
df.mm.trans1:probe17	0.098077018588325	0.107336392638736	0.913734998701	0.361094986655590	   
df.mm.trans1:probe18	0.0335254986895367	0.107336392638736	0.312340464080754	0.754852415933024	   
df.mm.trans1:probe19	-0.0112373369438103	0.107336392638736	-0.104692701772008	0.916642423136544	   
df.mm.trans1:probe20	0.073535654084027	0.107336392638736	0.685095262438412	0.493456004309762	   
df.mm.trans1:probe21	0.140126868780796	0.107336392638736	1.30549262310710	0.192050821882906	   
df.mm.trans2:probe2	-0.0918479804504984	0.107336392638736	-0.855702135990662	0.392384874466557	   
df.mm.trans2:probe3	0.0340104134705297	0.107336392638736	0.316858174887611	0.751422949267743	   
df.mm.trans2:probe4	-0.0247067323968238	0.107336392638736	-0.230180387000519	0.81800266062597	   
df.mm.trans2:probe5	-0.0135237533978758	0.107336392638736	-0.125994111274012	0.899764054312449	   
df.mm.trans2:probe6	0.0621328993474397	0.107336392638736	0.578861445032551	0.562824047896341	   
df.mm.trans3:probe2	-0.0848490121749593	0.107336392638736	-0.790496215580279	0.429441305224993	   
df.mm.trans3:probe3	-0.191395714525070	0.107336392638736	-1.78313906234258	0.074892566792794	.  
df.mm.trans3:probe4	-0.0840972456477796	0.107336392638736	-0.78349237924203	0.433539239291105	   
df.mm.trans3:probe5	-0.0490315581859289	0.107336392638736	-0.456802739318392	0.647920434389272	   
df.mm.trans3:probe6	-0.185056812720577	0.107336392638736	-1.72408265427204	0.0850281201085677	.  
df.mm.trans3:probe7	-0.122897958423036	0.107336392638736	-1.14497939982646	0.252514817476893	   
df.mm.trans3:probe8	-0.155886829781440	0.107336392638736	-1.45232037288706	0.146752754567150	   
df.mm.trans3:probe9	-0.185783919029600	0.107336392638736	-1.73085674357342	0.0838118800459218	.  
df.mm.trans3:probe10	-0.132543080681089	0.107336392638736	-1.23483822609162	0.217205313069648	   
df.mm.trans3:probe11	-0.106017059799113	0.107336392638736	-0.98770842947868	0.323554591092464	   
df.mm.trans3:probe12	-0.111419917084965	0.107336392638736	-1.03804417444858	0.299521640083266	   
df.mm.trans3:probe13	-0.177809798531184	0.107336392638736	-1.65656581295444	0.0979475256553535	.  
df.mm.trans3:probe14	-0.127032121388560	0.107336392638736	-1.18349534827498	0.236917938399767