fitVsDatCorrelation=0.929406343065397
cont.fitVsDatCorrelation=0.267729132402792

fstatistic=10616.3858113142,52,692
cont.fstatistic=1546.28753362023,52,692

residuals=-0.578714527087999,-0.0873768241989154,-0.00566413341672045,0.0709123628987101,0.656485312188832
cont.residuals=-0.655434492739277,-0.250642400062467,-0.0991126005208122,0.143106637165949,1.95581168572613

predictedValues:
Include	Exclude	Both
Lung	58.6534278864094	68.6898631564067	67.7566987705582
cerebhem	63.0868209666268	65.2408362715658	64.5634326502024
cortex	56.443416763406	68.9247346672097	57.3155743228316
heart	57.9121614101054	71.5626025478876	59.5994261537072
kidney	59.5296294368022	75.8538177503943	93.521281054466
liver	60.8991192121693	72.3305601465136	76.1002125944717
stomach	59.229321760831	70.9169249129229	75.4130314821224
testicle	57.6218329820615	65.2105757965925	58.1281258614164


diffExp=-10.0364352699973,-2.15401530493904,-12.4813179038038,-13.6504411377822,-16.3241883135921,-11.4314409343443,-11.6876031520919,-7.58874281453098
diffExpScore=0.988419785306802
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,-1,-1,-1,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	60.2352588605995	60.486047630488	74.6563414747799
cerebhem	59.8688047058232	60.7347313309543	58.2239085772066
cortex	64.685628267314	59.3801431253584	56.8313479040249
heart	71.7122915740245	58.9619803773313	58.9501731602728
kidney	63.8513625800516	71.1716793560715	60.7903299533162
liver	62.5475601042272	55.7113232708343	68.7715750094575
stomach	61.2334929754481	59.8291961309419	55.7714330632908
testicle	62.6736670186922	69.1933661155918	62.568675180635
cont.diffExp=-0.250788769888509,-0.865926625131152,5.30548514195558,12.7503111966932,-7.32031677601991,6.83623683339292,1.40429684450621,-6.51969909689959
cont.diffExpScore=3.34314446724924

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

tran.correlation=-0.0646589208408809
cont.tran.correlation=-0.0738251723080458

tran.covariance=-0.000111860228540958
cont.tran.covariance=-0.000326227382999496

tran.mean=64.506602854244
cont.tran.mean=62.6422833389845

weightedLogRatios:
wLogRatio
Lung	-0.655614344173344
cerebhem	-0.139710228069098
cortex	-0.825701392500803
heart	-0.88144944401828
kidney	-1.01965397274428
liver	-0.72169755027343
stomach	-0.751248773079452
testicle	-0.509202521378676

cont.weightedLogRatios:
wLogRatio
Lung	-0.017036255733581
cerebhem	-0.0588670116778459
cortex	0.353163787232960
heart	0.817293384433668
kidney	-0.45703070139462
liver	0.472009670373445
stomach	0.095193994284793
testicle	-0.414402544124009

varWeightedLogRatios=0.0721648120153815
cont.varWeightedLogRatios=0.190119097853434

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.98309442794457	0.0813458669065443	48.964926915348	5.1155076811308e-227	***
df.mm.trans1	-0.0422180267301145	0.0730604844219748	-0.577850353226188	0.563553132826378	   
df.mm.trans2	0.221165189117233	0.0671890379496967	3.29168560625641	0.00104647000208660	** 
df.mm.exp2	0.0696249656405193	0.0920323425777852	0.756527147852101	0.449590677602151	   
df.mm.exp3	0.132357054268316	0.0920323425777853	1.43815804923632	0.150841289634307	   
df.mm.exp4	0.156529758951922	0.0920323425777853	1.70081250316566	0.0894274727309626	.  
df.mm.exp5	-0.208231141694998	0.0920323425777853	-2.26258656318568	0.0239703941739233	*  
df.mm.exp6	-0.0269099477739499	0.0920323425777853	-0.292396640357228	0.770071021312387	   
df.mm.exp7	-0.0653785779970236	0.0920323425777853	-0.710386980987319	0.4777035262439	   
df.mm.exp8	0.0835492350390946	0.0920323425777853	0.907824713561748	0.364286869101791	   
df.mm.trans1:exp2	0.00324090273188512	0.0881142595742894	0.0367806839385934	0.970670482998767	   
df.mm.trans2:exp2	-0.121141005615794	0.0766936188148211	-1.57954478466184	0.114668115420384	   
df.mm.trans1:exp3	-0.170764411016984	0.0881142595742894	-1.93798837829435	0.0530310811119368	.  
df.mm.trans2:exp3	-0.128943582985857	0.0766936188148211	-1.68128176735532	0.0931594206181576	.  
df.mm.trans1:exp4	-0.169248374680968	0.0881142595742894	-1.92078303215241	0.0551696614072229	.  
df.mm.trans2:exp4	-0.115558768253709	0.0766936188148211	-1.50675858095481	0.132328892703359	   
df.mm.trans1:exp5	0.223059284148931	0.0881142595742895	2.53147771117419	0.0115785708018826	*  
df.mm.trans2:exp5	0.307437543089049	0.0766936188148211	4.00864567143931	6.77006015392582e-05	***
df.mm.trans1:exp6	0.0644826396532004	0.0881142595742894	0.731807087351563	0.464534074882446	   
df.mm.trans2:exp6	0.078555036890149	0.0766936188148211	1.02427083379417	0.306065287693422	   
df.mm.trans1:exp7	0.0751492774028067	0.0881142595742894	0.852861702134012	0.394031090665865	   
df.mm.trans2:exp7	0.097286062288195	0.0766936188148211	1.26850269672494	0.205045045585366	   
df.mm.trans1:exp8	-0.101293714206786	0.0881142595742894	-1.14957232457233	0.250717208545439	   
df.mm.trans2:exp8	-0.135529209824063	0.0766936188148211	-1.76715106052437	0.0776436480863974	.  
df.mm.trans1:probe2	-0.0920279802819863	0.0440571297871447	-2.08883285694291	0.0370875958668421	*  
df.mm.trans1:probe3	0.199038421409566	0.0440571297871447	4.51773464070831	7.3487570426053e-06	***
df.mm.trans1:probe4	-0.0597670680374197	0.0440571297871447	-1.35658106477147	0.175356754940094	   
df.mm.trans1:probe5	0.0539451132302503	0.0440571297871447	1.22443548844144	0.221204659501898	   
df.mm.trans1:probe6	0.210754504093700	0.0440571297871447	4.78366396340224	2.10471755650939e-06	***
df.mm.trans1:probe7	-0.020238890309297	0.0440571297871447	-0.459378320990907	0.646106784973227	   
df.mm.trans1:probe8	0.0273855377221385	0.0440571297871447	0.621591507536861	0.534415221813058	   
df.mm.trans1:probe9	0.0301681427524329	0.0440571297871447	0.684750525015716	0.49373063693809	   
df.mm.trans1:probe10	0.127530771779598	0.0440571297871447	2.8946681818753	0.00391511567304459	** 
df.mm.trans1:probe11	-0.0148335058191754	0.0440571297871447	-0.336687975155013	0.736454207835793	   
df.mm.trans1:probe12	-0.117722626662547	0.0440571297871447	-2.67204484793507	0.00771650296568706	** 
df.mm.trans1:probe13	-0.108159500561681	0.0440571297871447	-2.45498290706266	0.0143341608941469	*  
df.mm.trans1:probe14	-0.149144993107753	0.0440571297871447	-3.38526349374832	0.000751255987815418	***
df.mm.trans1:probe15	-0.0278320906766426	0.0440571297871447	-0.631727277993574	0.527773730005368	   
df.mm.trans1:probe16	-0.165861556248950	0.0440571297871447	-3.76469273078580	0.000180914141799233	***
df.mm.trans1:probe17	-0.0509062709276836	0.0440571297871447	-1.15546044814153	0.248300718767024	   
df.mm.trans1:probe18	-0.0551928116176373	0.0440571297871447	-1.25275549915060	0.210717869904352	   
df.mm.trans1:probe19	0.663910784061175	0.0440571297871447	15.0693153927357	1.37781741784434e-44	***
df.mm.trans1:probe20	0.364368034877252	0.0440571297871447	8.27035343967345	6.83907955559605e-16	***
df.mm.trans1:probe21	0.83592765690924	0.0440571297871447	18.9737202797344	5.92051515390234e-65	***
df.mm.trans1:probe22	1.61789879399539	0.0440571297871447	36.7227461664438	1.18787229448493e-164	***
df.mm.trans2:probe2	-0.283643273512071	0.0440571297871447	-6.43807880546124	2.26213678292003e-10	***
df.mm.trans2:probe3	0.411009698021039	0.0440571297871447	9.32901666556058	1.41803017283478e-19	***
df.mm.trans2:probe4	-0.212645191101187	0.0440571297871447	-4.82657840237322	1.71023588210209e-06	***
df.mm.trans2:probe5	0.175488896437625	0.0440571297871447	3.98321219029639	7.51911567580588e-05	***
df.mm.trans2:probe6	0.137868040894515	0.0440571297871447	3.12930146744927	0.00182584003469627	** 
df.mm.trans3:probe2	0.256105099336389	0.0440571297871447	5.81302278595363	9.36701675777285e-09	***
df.mm.trans3:probe3	-0.094459881188831	0.0440571297871447	-2.14403166173556	0.0323781253326576	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86685667908389	0.212368409457825	18.2082480579666	8.04665483369443e-61	***
df.mm.trans1	0.225647954075935	0.190737888241311	1.18302638325564	0.237204967171556	   
df.mm.trans2	0.236238329686817	0.175409392818587	1.34678266591539	0.178491140653737	   
df.mm.exp2	0.246600096569363	0.240267427899887	1.02635675057926	0.305082083530337	   
df.mm.exp3	0.325635727321262	0.240267427899887	1.35530533692210	0.175762498361283	   
df.mm.exp4	0.385087307752529	0.240267427899887	1.60274453811103	0.109447388058504	   
df.mm.exp5	0.426447043336319	0.240267427899887	1.77488495658266	0.0763564146809868	.  
df.mm.exp6	0.0375450128041853	0.240267427899887	0.156263431678427	0.87587095445684	   
df.mm.exp7	0.297151168883754	0.240267427899887	1.23675177897010	0.216598808666100	   
df.mm.exp8	0.350806484745606	0.240267427899887	1.46006675899397	0.144725553356636	   
df.mm.trans1:exp2	-0.252702392879129	0.230038548582243	-1.09852194093801	0.272358699073641	   
df.mm.trans2:exp2	-0.242497102817465	0.200222856583239	-1.21113596597125	0.226256744713065	   
df.mm.trans1:exp3	-0.254354555499231	0.230038548582243	-1.10570405293744	0.269238894617433	   
df.mm.trans2:exp3	-0.344088568421742	0.200222856583239	-1.71852791581112	0.0861476420321665	.  
df.mm.trans1:exp4	-0.210683020460425	0.230038548582243	-0.91585963204381	0.360059563312988	   
df.mm.trans2:exp4	-0.410607192708758	0.200222856583239	-2.05075084690971	0.0406673156891139	*  
df.mm.trans1:exp5	-0.368146996976756	0.230038548582243	-1.60037089107757	0.109972734634059	   
df.mm.trans2:exp5	-0.263764786662114	0.200222856583239	-1.31735602599625	0.188155322861187	   
df.mm.trans1:exp6	0.000124340000286095	0.230038548582243	0.000540518104693402	0.999568884746093	   
df.mm.trans2:exp6	-0.119774316974006	0.200222856583239	-0.598205015241162	0.54989887780266	   
df.mm.trans1:exp7	-0.280714734443879	0.230038548582243	-1.22029432099081	0.222769000192082	   
df.mm.trans2:exp7	-0.308070118220621	0.200222856583239	-1.53863611516574	0.124350264888776	   
df.mm.trans1:exp8	-0.311122985298511	0.230038548582243	-1.35248195233365	0.176662966106262	   
df.mm.trans2:exp8	-0.216314212861549	0.200222856583239	-1.08036722956063	0.280355062280340	   
df.mm.trans1:probe2	-0.0631003699585857	0.115019274291122	-0.54860692129629	0.583452183073603	   
df.mm.trans1:probe3	-0.167828055941062	0.115019274291122	-1.45912984563159	0.144983131726383	   
df.mm.trans1:probe4	-0.102461699754227	0.115019274291122	-0.890821998188669	0.373334283433721	   
df.mm.trans1:probe5	0.170991137972448	0.115019274291122	1.48663029762871	0.137567942592148	   
df.mm.trans1:probe6	-0.00920784144298907	0.115019274291122	-0.0800547690788189	0.936216841696876	   
df.mm.trans1:probe7	0.205034987918146	0.115019274291122	1.78261416777147	0.0750874400142852	.  
df.mm.trans1:probe8	-0.0338822083682738	0.115019274291122	-0.294578526747749	0.768404214044942	   
df.mm.trans1:probe9	0.0120116130118759	0.115019274291122	0.104431305847694	0.91685733996457	   
df.mm.trans1:probe10	0.0317508459873844	0.115019274291122	0.276048046582357	0.782593581554605	   
df.mm.trans1:probe11	-0.100791254298597	0.115019274291122	-0.876298819652497	0.3811716910856	   
df.mm.trans1:probe12	0.0692646910836501	0.115019274291122	0.602200731229937	0.547237790939066	   
df.mm.trans1:probe13	-0.0399133054521674	0.115019274291122	-0.347014060888127	0.728686258711595	   
df.mm.trans1:probe14	-0.0327399303892966	0.115019274291122	-0.284647339248808	0.775999499964883	   
df.mm.trans1:probe15	0.163708825880178	0.115019274291122	1.42331645621255	0.155095120685948	   
df.mm.trans1:probe16	-0.0695909617834896	0.115019274291122	-0.605037392318701	0.545352501015214	   
df.mm.trans1:probe17	0.055902358953992	0.115019274291122	0.486026009975505	0.627102589463292	   
df.mm.trans1:probe18	0.168111704932494	0.115019274291122	1.46159594527602	0.144305900966226	   
df.mm.trans1:probe19	-0.0173074819959971	0.115019274291122	-0.150474623515627	0.880434019754467	   
df.mm.trans1:probe20	-0.0424171940604059	0.115019274291122	-0.368783356718502	0.712402009773726	   
df.mm.trans1:probe21	0.0503428158783701	0.115019274291122	0.437690258338346	0.661747406776665	   
df.mm.trans1:probe22	-0.104047601644522	0.115019274291122	-0.904610138481401	0.36598678351178	   
df.mm.trans2:probe2	0.0468176737898928	0.115019274291122	0.407041985601423	0.684103022699144	   
df.mm.trans2:probe3	-0.0796590782468393	0.115019274291122	-0.692571560182312	0.488810803820484	   
df.mm.trans2:probe4	0.00508084954624748	0.115019274291122	0.0441738967452316	0.964778532474339	   
df.mm.trans2:probe5	0.0399234321693594	0.115019274291122	0.347102104542152	0.728620145162922	   
df.mm.trans2:probe6	-0.0183034693319759	0.115019274291122	-0.159133931636959	0.873609787405048	   
df.mm.trans3:probe2	-0.00997451857054829	0.115019274291122	-0.0867204095315547	0.93091883940194	   
df.mm.trans3:probe3	-0.0375679605546123	0.115019274291122	-0.326623174995221	0.744051675657785