fitVsDatCorrelation=0.827702654791129
cont.fitVsDatCorrelation=0.234834269411556

fstatistic=3847.63202350159,60,876
cont.fstatistic=1272.6363767038,60,876

residuals=-0.762136955080988,-0.128864397131117,-0.00809439028739342,0.130381754419390,1.554658809572
cont.residuals=-0.839435444088237,-0.344340485029305,-0.115938083088172,0.28893842627087,2.19988285422678

predictedValues:
Include	Exclude	Both
Lung	69.1747024336443	49.2274427673123	67.8681376319968
cerebhem	134.552516058073	54.6549021081784	136.943895091792
cortex	115.754318472671	46.1228428488792	128.991386671334
heart	63.3519629519259	48.6900134321893	59.4872797262844
kidney	72.305787503319	50.3391841066182	68.456901446734
liver	71.7408452230827	51.0287361940601	63.5452236919164
stomach	65.1955866068564	48.1187483329227	67.1195877829086
testicle	66.713691037917	50.8953351795663	63.6002152829835


diffExp=19.947259666332,79.8976139498948,69.6314756237918,14.6619495197367,21.9666033967007,20.7121090290226,17.0768382739337,15.8183558583507
diffExpScore=0.996164352954703
diffExp1.5=0,1,1,0,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,1,1,0,1,1,0,0
diffExp1.4Score=0.833333333333333
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	67.256759795243	61.5376314564145	73.2243958649045
cerebhem	82.8162267319485	68.92719726109	79.7244937054746
cortex	75.3428174006184	63.9125692481984	66.3863804826815
heart	84.098085563091	65.7557462127784	82.64269743087
kidney	76.0925698426146	66.3514603541737	72.1600985648732
liver	74.6417100712302	75.4033407694633	69.9159939969667
stomach	74.6554314470185	70.9066337245235	77.3678047694821
testicle	77.5286236536458	63.2587173088133	68.5259499400661
cont.diffExp=5.71912833882856,13.8890294708585,11.4302481524200,18.3423393503127,9.7411094884409,-0.761630698233063,3.748797722495,14.2699063448324
cont.diffExpScore=1.00676232417328

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=0,1,0,1,0,0,0,1
cont.diffExp1.2Score=0.75

tran.correlation=0.319531998612991
cont.tran.correlation=0.181859586240733

tran.covariance=0.00376431350426075
cont.tran.covariance=0.00101716887082251

tran.mean=66.116663453576
cont.tran.mean=71.7803450525541

weightedLogRatios:
wLogRatio
Lung	1.38337284737397
cerebhem	4.01042315419592
cortex	3.9487718104956
heart	1.05742639683747
kidney	1.48463711054697
liver	1.39768062246998
stomach	1.22263411282269
testicle	1.10017177483422

cont.weightedLogRatios:
wLogRatio
Lung	0.370056451134642
cerebhem	0.793924167106428
cortex	0.697582211449865
heart	1.06016381120670
kidney	0.584029194139812
liver	-0.0438345498706359
stomach	0.220869550874218
testicle	0.864295019776157

varWeightedLogRatios=1.59015306824351
cont.varWeightedLogRatios=0.133161099003888

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.64211423461441	0.127431898552806	28.5808677103338	1.85514789733579e-127	***
df.mm.trans1	0.53713387911992	0.109813735263627	4.89131781038533	1.19164179875188e-06	***
df.mm.trans2	0.241876932056164	0.0967914752149787	2.49894870926332	0.0126383421581721	*  
df.mm.exp2	0.0679023686603736	0.123992321841963	0.547633657081765	0.584082974033724	   
df.mm.exp3	-0.192487099213130	0.123992321841963	-1.55241144252842	0.120924993175824	   
df.mm.exp4	0.0328975328707012	0.123992321841963	0.265319113167599	0.79082606368518	   
df.mm.exp5	0.0579637833197386	0.123992321841963	0.467478812064004	0.640273612177639	   
df.mm.exp6	0.138177544354674	0.123992321841963	1.11440403971781	0.265411543862215	   
df.mm.exp7	-0.0709320664070784	0.123992321841963	-0.572068216429451	0.567422529598321	   
df.mm.exp8	0.0620448103691813	0.123992321841963	0.500392358554763	0.616924518076381	   
df.mm.trans1:exp2	0.597416984119749	0.114315272358495	5.2260469821236	2.16582505502383e-07	***
df.mm.trans2:exp2	0.0366852941771866	0.0831765780979985	0.441053178864416	0.659283328667029	   
df.mm.trans1:exp3	0.707321876084883	0.114315272358495	6.18746613196799	9.37657418170947e-10	***
df.mm.trans2:exp3	0.127344185155874	0.0831765780979985	1.53101014814336	0.126127910090766	   
df.mm.trans1:exp4	-0.120826864777820	0.114315272358495	-1.05696170148556	0.290820455161400	   
df.mm.trans2:exp4	-0.0438748346248458	0.0831765780979985	-0.527490257812151	0.597986749009487	   
df.mm.trans1:exp5	-0.0136948329212453	0.114315272358495	-0.119798804120399	0.904669997982475	   
df.mm.trans2:exp5	-0.0356312492198295	0.0831765780979985	-0.428380801838817	0.66847920653343	   
df.mm.trans1:exp6	-0.101752514583064	0.114315272358495	-0.890104292136626	0.373654269664139	   
df.mm.trans2:exp6	-0.102239863346238	0.0831765780979985	-1.22919054479230	0.219330440968137	   
df.mm.trans1:exp7	0.0116886189518897	0.114315272358495	0.102248970856965	0.918582458460844	   
df.mm.trans2:exp7	0.0481526979858966	0.0831765780979985	0.578921363285265	0.562791056131487	   
df.mm.trans1:exp8	-0.0982698395917005	0.114315272358495	-0.859638765356955	0.390223383506249	   
df.mm.trans2:exp8	-0.0287247855997344	0.0831765780979985	-0.345347046687721	0.729916389942156	   
df.mm.trans1:probe2	-0.0725866929346137	0.0796355106014359	-0.911486501265741	0.362289847453907	   
df.mm.trans1:probe3	0.293632641510527	0.0796355106014359	3.68720736883468	0.000240684821000258	***
df.mm.trans1:probe4	-0.094397803119838	0.0796355106014359	-1.18537323873373	0.236191360384007	   
df.mm.trans1:probe5	0.253630891116025	0.0796355106014359	3.18489690341047	0.00149924743810904	** 
df.mm.trans1:probe6	-0.185154838264847	0.0796355106014359	-2.32502858167785	0.0202980979615414	*  
df.mm.trans1:probe7	0.26307109830492	0.0796355106014358	3.30343958766778	0.000993780524722315	***
df.mm.trans1:probe8	0.661013769350644	0.0796355106014359	8.30049012505139	3.90164700656553e-16	***
df.mm.trans1:probe9	0.466112788471499	0.0796355106014359	5.85307716308024	6.81649755863261e-09	***
df.mm.trans1:probe10	0.244875911040187	0.0796355106014359	3.07495876137161	0.00217063059581653	** 
df.mm.trans1:probe11	0.480005176676085	0.0796355106014359	6.02752682880934	2.45084065724017e-09	***
df.mm.trans1:probe12	0.288081252102528	0.0796355106014359	3.61749739440151	0.000314482829347379	***
df.mm.trans1:probe13	0.140350824242197	0.0796355106014359	1.76241507315288	0.078347892822823	.  
df.mm.trans1:probe14	0.0462030093054651	0.0796355106014359	0.580180989065349	0.561941771911242	   
df.mm.trans1:probe15	-0.144813527885086	0.0796355106014359	-1.81845418948661	0.0693361178811276	.  
df.mm.trans1:probe16	0.0161208490457388	0.0796355106014359	0.202432921243154	0.839625297897073	   
df.mm.trans1:probe17	-0.0713821151601932	0.0796355106014359	-0.89636036262077	0.370306582941013	   
df.mm.trans1:probe18	-0.183921848955876	0.0796355106014359	-2.30954567336647	0.0211450878916582	*  
df.mm.trans1:probe19	0.229821235235022	0.0796355106014359	2.88591400368165	0.00399864247443378	** 
df.mm.trans1:probe20	-0.0424271375953666	0.0796355106014359	-0.532766567011898	0.594330296749651	   
df.mm.trans1:probe21	-0.335326024199239	0.0796355106014359	-4.21076001982956	2.80816428167269e-05	***
df.mm.trans1:probe22	-0.359134818930304	0.0796355106014358	-4.50973210591593	7.37376466690435e-06	***
df.mm.trans2:probe2	0.121318870602743	0.0796355106014359	1.52342679398298	0.128012865565416	   
df.mm.trans2:probe3	0.00516970083632937	0.0796355106014359	0.0649170300697006	0.948254872134195	   
df.mm.trans2:probe4	-0.0337894426703367	0.0796355106014359	-0.42430119949187	0.671450343145708	   
df.mm.trans2:probe5	0.105650584754442	0.0796355106014359	1.32667680481397	0.184961348173796	   
df.mm.trans2:probe6	0.0134716660228070	0.0796355106014359	0.169166568043128	0.865704679712988	   
df.mm.trans3:probe2	-0.235242118216072	0.0796355106014359	-2.95398518122681	0.00322091288067602	** 
df.mm.trans3:probe3	-0.206210117305845	0.0796355106014359	-2.58942418713050	0.00977318722426292	** 
df.mm.trans3:probe4	-0.667585495463883	0.0796355106014359	-8.3830126839401	2.0437271321105e-16	***
df.mm.trans3:probe5	0.0747273058948748	0.0796355106014359	0.93836663230395	0.348314673614363	   
df.mm.trans3:probe6	-0.161452653292092	0.0796355106014359	-2.02739521694209	0.0429243426649538	*  
df.mm.trans3:probe7	-0.542936513284722	0.0796355106014359	-6.81776897246306	1.72097740855763e-11	***
df.mm.trans3:probe8	-0.438383140394313	0.0796355106014359	-5.50487008978138	4.85389437491721e-08	***
df.mm.trans3:probe9	-0.0495603351941645	0.0796355106014359	-0.622339642451805	0.533880511606366	   
df.mm.trans3:probe10	0.278589312530669	0.0796355106014359	3.49830509563715	0.000491797213285028	***
df.mm.trans3:probe11	-0.291088377703003	0.0796355106014359	-3.65525850847944	0.000272217931943357	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.11373653126646	0.220733465450264	18.6366689929642	1.50012936115364e-65	***
df.mm.trans1	0.109720897287728	0.190215845593274	0.576823118733948	0.564207142928713	   
df.mm.trans2	-0.0436411361506115	0.167659102570713	-0.260296849270114	0.794695972755668	   
df.mm.exp2	0.236460220437990	0.214775540506128	1.10096438300544	0.271214584478248	   
df.mm.exp3	0.249434949209252	0.214775540506128	1.16137502725612	0.245805774268182	   
df.mm.exp4	0.168766630166111	0.214775540506128	0.78578142449743	0.432208035872399	   
df.mm.exp5	0.213391382234226	0.214775540506128	0.993555326325144	0.320713760920587	   
df.mm.exp6	0.353618843078182	0.214775540506128	1.64645770298081	0.100028237995838	   
df.mm.exp7	0.191038769016976	0.214775540506128	0.889481030133993	0.373988808118470	   
df.mm.exp8	0.236029872212853	0.214775540506128	1.09896067148353	0.272087159202840	   
df.mm.trans1:exp2	-0.0283537340556364	0.198013264403536	-0.143191084400556	0.886172200845095	   
df.mm.trans2:exp2	-0.123058266303680	0.144075812542789	-0.854121619249126	0.393271112335655	   
df.mm.trans1:exp3	-0.135903882662471	0.198013264403536	-0.686337266707087	0.492682073073951	   
df.mm.trans2:exp3	-0.211567786601446	0.144075812542789	-1.46844763786158	0.142341648265941	   
df.mm.trans1:exp4	0.0546996419060929	0.198013264403536	0.276242311699984	0.782427118958004	   
df.mm.trans2:exp4	-0.102468449337943	0.144075812542789	-0.711212017683477	0.477142190941303	   
df.mm.trans1:exp5	-0.0899582897244956	0.198013264403536	-0.45430436185915	0.649722320479032	   
df.mm.trans2:exp5	-0.138074493120935	0.144075812542789	-0.958346100459637	0.338152681642422	   
df.mm.trans1:exp6	-0.249436906711180	0.198013264403536	-1.25969796752023	0.208113845962220	   
df.mm.trans2:exp6	-0.15041614304065	0.144075812542789	-1.04400690432322	0.296770205721741	   
df.mm.trans1:exp7	-0.0866730196755597	0.198013264403536	-0.437713200358773	0.661702098111162	   
df.mm.trans2:exp7	-0.0493236566171489	0.144075812542789	-0.342345156668823	0.732173277019266	   
df.mm.trans1:exp8	-0.093900197407245	0.198013264403536	-0.474211652891513	0.635467161806255	   
df.mm.trans2:exp8	-0.208445812319859	0.144075812542789	-1.44677866909793	0.148316644154025	   
df.mm.trans1:probe2	0.0192125109422154	0.137942088500487	0.139279542241726	0.889261268653765	   
df.mm.trans1:probe3	-0.0606626234957473	0.137942088500487	-0.439768776558239	0.660213055751495	   
df.mm.trans1:probe4	0.00523911936828148	0.137942088500487	0.0379805715951807	0.969711825898626	   
df.mm.trans1:probe5	-0.243492174421278	0.137942088500487	-1.76517680041084	0.0778824308306068	.  
df.mm.trans1:probe6	0.00489577373740781	0.137942088500487	0.0354915152483757	0.971695897553035	   
df.mm.trans1:probe7	0.00110881775283878	0.137942088500487	0.00803828450687019	0.993588276133304	   
df.mm.trans1:probe8	-0.125777740198000	0.137942088500487	-0.91181554205304	0.362116677408119	   
df.mm.trans1:probe9	0.104601257271107	0.137942088500487	0.758298343951329	0.448476355145131	   
df.mm.trans1:probe10	-0.113876128728126	0.137942088500487	-0.825535773497616	0.409292240123749	   
df.mm.trans1:probe11	-0.0298060366684363	0.137942088500487	-0.216076449127643	0.828978460492693	   
df.mm.trans1:probe12	-0.129767579944739	0.137942088500487	-0.94073956219882	0.347097675463959	   
df.mm.trans1:probe13	-0.119405473726419	0.137942088500487	-0.865620312295023	0.386935412606404	   
df.mm.trans1:probe14	-0.0171187885724774	0.137942088500487	-0.124101271472463	0.901263561148025	   
df.mm.trans1:probe15	-0.128725845631778	0.137942088500487	-0.933187593656912	0.350980255284354	   
df.mm.trans1:probe16	0.140917152992048	0.137942088500487	1.02156748911012	0.307267683498836	   
df.mm.trans1:probe17	0.0801305866162345	0.137942088500487	0.580900198679766	0.561457133783681	   
df.mm.trans1:probe18	0.0565811852213026	0.137942088500487	0.410180720303528	0.68177367793804	   
df.mm.trans1:probe19	0.0642402646944876	0.137942088500487	0.465704596710242	0.641542723355626	   
df.mm.trans1:probe20	-0.0788276259178156	0.137942088500487	-0.571454490610656	0.567838186406332	   
df.mm.trans1:probe21	0.0130670202879683	0.137942088500487	0.0947283054071069	0.924552297897718	   
df.mm.trans1:probe22	0.0644497035818744	0.137942088500487	0.46722290696394	0.640456598329715	   
df.mm.trans2:probe2	0.179801753470051	0.137942088500487	1.30345825139088	0.192760822652565	   
df.mm.trans2:probe3	0.187012451946427	0.137942088500487	1.35573162607123	0.175533970330657	   
df.mm.trans2:probe4	0.0739321475799741	0.137942088500487	0.535965116837515	0.592118716370227	   
df.mm.trans2:probe5	0.252097894704067	0.137942088500487	1.82756327270757	0.0679551650757426	.  
df.mm.trans2:probe6	0.149565017126571	0.137942088500487	1.08425947984718	0.278548084013447	   
df.mm.trans3:probe2	0.209304758575446	0.137942088500487	1.51733789774183	0.129542173750074	   
df.mm.trans3:probe3	0.116499571358099	0.137942088500487	0.844554208396577	0.398590372533391	   
df.mm.trans3:probe4	0.00580752745162786	0.137942088500487	0.0421011999655738	0.966427617427127	   
df.mm.trans3:probe5	0.277091310158277	0.137942088500487	2.00875101406993	0.0448699865492053	*  
df.mm.trans3:probe6	0.146156919648704	0.137942088500487	1.05955275316995	0.289640175063277	   
df.mm.trans3:probe7	0.146527897260664	0.137942088500487	1.06224212532600	0.288418529726693	   
df.mm.trans3:probe8	0.0388512819606904	0.137942088500487	0.28164922238765	0.778279012420068	   
df.mm.trans3:probe9	0.183097013992987	0.137942088500487	1.32734697570090	0.184739752280256	   
df.mm.trans3:probe10	0.0391698493693678	0.137942088500487	0.283958651019190	0.776509170295266	   
df.mm.trans3:probe11	0.0883297189289416	0.137942088500487	0.640339144412983	0.522119588930938