fitVsDatCorrelation=0.904255577148413
cont.fitVsDatCorrelation=0.241809978399268

fstatistic=7219.9290842492,52,692
cont.fstatistic=1387.36994149618,52,692

residuals=-0.819882402861446,-0.093650075469896,-0.00613967445259646,0.0878166218199753,1.34202683986580
cont.residuals=-0.714722578248167,-0.297007302613925,-0.101607881326655,0.192482058897104,1.83792394104213

predictedValues:
Include	Exclude	Both
Lung	79.8819485009186	73.9463247987836	51.9072258420424
cerebhem	69.9673273893945	58.0992417305066	62.668360470305
cortex	74.2297109673365	62.8344222094627	48.3563256758312
heart	94.2896699374352	67.429292723277	52.5281275688627
kidney	89.8825757168735	80.6941655697942	51.444286499714
liver	86.6844810174663	81.2559363428782	52.5773350061622
stomach	73.3806407151068	69.5162960789704	49.0362624414946
testicle	82.7570518970683	66.8082823498567	49.3008916449736


diffExp=5.93562370213503,11.8680856588879,11.3952887578738,26.8603772141581,9.18841014707937,5.42854467458812,3.86434463613641,15.9487695472116
diffExpScore=0.989069777314366
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=0,0,0,1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,1,0,1,0,0,0,1
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	73.0789018619482	73.8269966876247	74.1620458185308
cerebhem	76.2333269998803	67.3343049395608	76.4434197773522
cortex	80.8869727791078	65.5284668739575	79.4933455084587
heart	75.5854354942	76.2219357166696	68.4301217731732
kidney	77.580521681653	72.347238540878	69.8534333034576
liver	80.5618138415106	78.35015633826	71.0226385381324
stomach	82.5694120890845	80.0330501497104	67.3604726346615
testicle	87.8246763923006	66.6221035712003	69.2072843100614
cont.diffExp=-0.74809482567656,8.89902206031951,15.3585059051503,-0.636500222469664,5.23328314077504,2.21165750325054,2.53636193937413,21.2025728211003
cont.diffExpScore=1.03213390224059

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

tran.correlation=0.607103047210192
cont.tran.correlation=-0.193496863327105

tran.covariance=0.0078355534348093
cont.tran.covariance=-0.000909572206684524

tran.mean=75.7285854965706
cont.tran.mean=75.9115821223467

weightedLogRatios:
wLogRatio
Lung	0.335243337242547
cerebhem	0.772330640762844
cortex	0.703950216807036
heart	1.46815212047979
kidney	0.479294418989644
liver	0.286488566010270
stomach	0.230927352589479
testicle	0.922451841852555

cont.weightedLogRatios:
wLogRatio
Lung	-0.0437602036426594
cerebhem	0.530245638687626
cortex	0.90286742154434
heart	-0.0363054326174465
kidney	0.301453279438336
liver	0.121788958138273
stomach	0.137217169633465
testicle	1.19839192878504

varWeightedLogRatios=0.171064677126830
cont.varWeightedLogRatios=0.207107459740878

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30826677757343	0.103507915153385	41.6225828835326	1.43101499385626e-190	***
df.mm.trans1	-0.125035961922814	0.0929652447038656	-1.34497534343191	0.1790738160768	   
df.mm.trans2	-0.0344747705266308	0.0854941683432386	-0.403241194045223	0.686895355890553	   
df.mm.exp2	-0.562107092245485	0.117105837938305	-4.79999205967531	1.94527088602839e-06	***
df.mm.exp3	-0.165360893075592	0.117105837938305	-1.41206361686861	0.158380746393612	   
df.mm.exp4	0.0616709994261715	0.117105837938305	0.526626174338652	0.598621967831636	   
df.mm.exp5	0.214239588700505	0.117105837938305	1.82945267693122	0.067761943159873	.  
df.mm.exp6	0.163162235348464	0.117105837938305	1.39328865427208	0.163979880234818	   
df.mm.exp7	-0.0897701066733905	0.117105837938305	-0.766572429298397	0.443597197849771	   
df.mm.exp8	-0.0146372607707521	0.117105837938305	-0.124991725676935	0.900566401689321	   
df.mm.trans1:exp2	0.429585572651468	0.112120303718653	3.83146993366553	0.000138963664427826	***
df.mm.trans2:exp2	0.320920215447689	0.0975881982819205	3.28851460624972	0.00105814452923327	** 
df.mm.trans1:exp3	0.0919754793519705	0.112120303718653	0.820328489144724	0.412311344804004	   
df.mm.trans2:exp3	0.00252445127850828	0.0975881982819205	0.025868407481153	0.979369756578921	   
df.mm.trans1:exp4	0.104150738439963	0.112120303718653	0.928919517568479	0.353254701275054	   
df.mm.trans2:exp4	-0.153930955246354	0.0975881982819205	-1.57735215893284	0.115171519516856	   
df.mm.trans1:exp5	-0.0962853856087737	0.112120303718653	-0.8587685050371	0.39076561086791	   
df.mm.trans2:exp5	-0.126912803270733	0.0975881982819205	-1.30049335375675	0.193864864152099	   
df.mm.trans1:exp6	-0.0814372650229324	0.112120303718653	-0.72633824848785	0.467877038555306	   
df.mm.trans2:exp6	-0.0688978435864426	0.0975881982819205	-0.706005898248117	0.480422080870184	   
df.mm.trans1:exp7	0.00488035592933084	0.112120303718653	0.0435278514904602	0.96529331991352	   
df.mm.trans2:exp7	0.0279918182549005	0.0975881982819205	0.286836100550145	0.774323680610814	   
df.mm.trans1:exp8	0.0499965895960119	0.112120303718653	0.445919141652253	0.655795137866796	   
df.mm.trans2:exp8	-0.0868751685595855	0.0975881982819205	-0.890222076942272	0.373656036820429	   
df.mm.trans1:probe2	0.646208193369906	0.0560601518593267	11.5270503546165	3.03250364678855e-28	***
df.mm.trans1:probe3	0.0331656870948013	0.0560601518593267	0.591608941374703	0.554305697338245	   
df.mm.trans1:probe4	-0.178092415830715	0.0560601518593267	-3.17680937214740	0.00155523039691104	** 
df.mm.trans1:probe5	0.210779930121267	0.0560601518593267	3.75988867547457	0.000184352339021898	***
df.mm.trans1:probe6	0.166002388172819	0.0560601518593267	2.96114767204651	0.00316986796811218	** 
df.mm.trans1:probe7	0.0169468763097752	0.0560601518593267	0.302298080681275	0.762515677519006	   
df.mm.trans1:probe8	-0.179731837222129	0.0560601518593267	-3.20605334200904	0.00140758891674792	** 
df.mm.trans1:probe9	-0.0926989734163976	0.0560601518593267	-1.65356265264871	0.0986699373741294	.  
df.mm.trans1:probe10	-0.128508666233296	0.0560601518593267	-2.29233532145555	0.0221855862937157	*  
df.mm.trans1:probe11	-0.136501343537712	0.0560601518593267	-2.43490855822579	0.0151472032844716	*  
df.mm.trans1:probe12	-0.209124193257780	0.0560601518593267	-3.73035367050986	0.000206880411112864	***
df.mm.trans1:probe13	-0.153020159999919	0.0560601518593267	-2.72957091489686	0.00650288841250317	** 
df.mm.trans1:probe14	-0.219228281679269	0.0560601518593267	-3.91059022154248	0.000101141308790998	***
df.mm.trans1:probe15	0.0597455488331933	0.0560601518593267	1.06574004621169	0.286912864660116	   
df.mm.trans1:probe16	0.0742295183383537	0.0560601518593267	1.32410483875641	0.185905414287493	   
df.mm.trans1:probe17	0.854706817733204	0.0560601518593267	15.2462451382212	1.82404099767366e-45	***
df.mm.trans1:probe18	0.795663067191922	0.0560601518593267	14.1930237575614	2.57527723918746e-40	***
df.mm.trans1:probe19	0.74735831448666	0.0560601518593267	13.3313644308711	2.98296251448622e-36	***
df.mm.trans1:probe20	0.86733247065579	0.0560601518593267	15.4714613123456	1.36775084509130e-46	***
df.mm.trans1:probe21	0.774180288150151	0.0560601518593267	13.8098143239573	1.72071462241760e-38	***
df.mm.trans1:probe22	0.983563906975739	0.0560601518593267	17.5447956231696	2.75377246105126e-57	***
df.mm.trans2:probe2	0.0255989060278083	0.0560601518593267	0.456632834175055	0.648078266547459	   
df.mm.trans2:probe3	0.165673370351895	0.0560601518593267	2.95527865796053	0.00323002387204653	** 
df.mm.trans2:probe4	0.0357716931872725	0.0560601518593267	0.638094832083855	0.523623045464434	   
df.mm.trans2:probe5	-0.0131818691160167	0.0560601518593267	-0.235137948771426	0.814171228779054	   
df.mm.trans2:probe6	0.0520652412719319	0.0560601518593267	0.928738855409822	0.353348275971379	   
df.mm.trans3:probe2	-0.285520033270418	0.0560601518593267	-5.09310131707958	4.54707335009573e-07	***
df.mm.trans3:probe3	-0.312374081928462	0.0560601518593267	-5.57212336335284	3.60647731564796e-08	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.26762376370762	0.235218136571253	18.1432598094529	1.7942581875092e-60	***
df.mm.trans1	0.0471079268321177	0.211260284710881	0.222985247305650	0.823612791445811	   
df.mm.trans2	0.0159531633083735	0.194282523569387	0.0821132184989142	0.934580426245735	   
df.mm.exp2	-0.080093959667287	0.266118943084154	-0.300970531218287	0.76352737305391	   
df.mm.exp3	-0.08714754540292	0.266118943084154	-0.327475918823868	0.74340699655085	   
df.mm.exp4	0.146088128563508	0.266118943084154	0.548958021817	0.583211337961158	   
df.mm.exp5	0.0993827627587016	0.266118943084154	0.373452417956109	0.708926129622583	   
df.mm.exp6	0.200202377941984	0.266118943084154	0.752304122441499	0.452124004133644	   
df.mm.exp7	0.299008917086424	0.266118943084154	1.12359125442592	0.261576101836462	   
df.mm.exp8	0.150261128774868	0.266118943084154	0.564638980725815	0.572502322515465	   
df.mm.trans1:exp2	0.122352984070757	0.254789489996235	0.480212052987605	0.631228376348706	   
df.mm.trans2:exp2	-0.0119606753983812	0.221765785903462	-0.0539338173814957	0.957003459820356	   
df.mm.trans1:exp3	0.188660622935133	0.254789489996235	0.740456849055747	0.459273989643996	   
df.mm.trans2:exp3	-0.0320922710983098	0.221765785903462	-0.14471245403148	0.884980041808057	   
df.mm.trans1:exp4	-0.112364221008274	0.254789489996235	-0.441008069092389	0.659344900864486	   
df.mm.trans2:exp4	-0.114163310490221	0.221765785903462	-0.514792261687826	0.606862575805098	   
df.mm.trans1:exp5	-0.0396060812119877	0.254789489996236	-0.155446291024692	0.876514823837792	   
df.mm.trans2:exp5	-0.119629951959477	0.221765785903462	-0.539442779561829	0.589754891573218	   
df.mm.trans1:exp6	-0.102717319251454	0.254789489996235	-0.40314582541443	0.686965475793684	   
df.mm.trans2:exp6	-0.140738887249031	0.221765785903462	-0.634628496346578	0.525880490719863	   
df.mm.trans1:exp7	-0.176909323711324	0.254789489996235	-0.694335247948954	0.487705023117012	   
df.mm.trans2:exp7	-0.218293714344685	0.221765785903462	-0.984343520148375	0.325290667239119	   
df.mm.trans1:exp8	0.0335416798051967	0.254789489996235	0.131644675789776	0.895303611383608	   
df.mm.trans2:exp8	-0.252949194326973	0.221765785903462	-1.14061415423696	0.254425120872499	   
df.mm.trans1:probe2	-0.081966182276345	0.127394744998118	-0.643403166100423	0.520175683125774	   
df.mm.trans1:probe3	-0.135060336855100	0.127394744998118	-1.06017196280032	0.289436260887857	   
df.mm.trans1:probe4	0.0794918532226247	0.127394744998118	0.623980629843085	0.532845942921909	   
df.mm.trans1:probe5	-0.0125125845327263	0.127394744998118	-0.098219000578958	0.921786837457716	   
df.mm.trans1:probe6	-0.0988278949810942	0.127394744998118	-0.775761158614152	0.438155056093343	   
df.mm.trans1:probe7	-0.113068987455619	0.127394744998118	-0.887548285114037	0.375092151465219	   
df.mm.trans1:probe8	-0.023754835247209	0.127394744998118	-0.186466366784281	0.852133701361967	   
df.mm.trans1:probe9	-0.094041209010533	0.127394744998118	-0.738187505394531	0.460650776455998	   
df.mm.trans1:probe10	0.0869213039217717	0.127394744998118	0.682298974914986	0.495278238077656	   
df.mm.trans1:probe11	-0.00345023792456202	0.127394744998118	-0.0270830474570438	0.97840130419065	   
df.mm.trans1:probe12	-0.166009900609421	0.127394744998118	-1.30311419526664	0.192969200765878	   
df.mm.trans1:probe13	-0.0288096299655031	0.127394744998118	-0.226144571080454	0.821155757834663	   
df.mm.trans1:probe14	0.0577939035510955	0.127394744998118	0.453660027750355	0.650215773597555	   
df.mm.trans1:probe15	-0.035677902574477	0.127394744998118	-0.280057882882093	0.779516791852032	   
df.mm.trans1:probe16	-0.0372642245128467	0.127394744998118	-0.292509903084286	0.769984470306509	   
df.mm.trans1:probe17	-0.0755048775940904	0.127394744998118	-0.592684396795221	0.553586008378026	   
df.mm.trans1:probe18	0.0402168293021657	0.127394744998118	0.315686720851475	0.75233549059227	   
df.mm.trans1:probe19	-0.173191310851604	0.127394744998118	-1.35948551766529	0.174435610727887	   
df.mm.trans1:probe20	0.0231093181558591	0.127394744998118	0.181399304627522	0.85610728298403	   
df.mm.trans1:probe21	0.0463003953767142	0.127394744998118	0.363440386629749	0.716386935762447	   
df.mm.trans1:probe22	0.165506869300203	0.127394744998118	1.29916559197672	0.194319787687118	   
df.mm.trans2:probe2	0.167771134663700	0.127394744998118	1.31693920864773	0.188294938073779	   
df.mm.trans2:probe3	0.044505310025673	0.127394744998118	0.349349653522447	0.726933106802033	   
df.mm.trans2:probe4	0.0124125256393958	0.127394744998118	0.0974335765543485	0.922410294090397	   
df.mm.trans2:probe5	0.0489119920229975	0.127394744998118	0.383940420962578	0.701140545681245	   
df.mm.trans2:probe6	-0.110273044084741	0.127394744998118	-0.865601199534335	0.387008886893044	   
df.mm.trans3:probe2	-0.0477140395362277	0.127394744998118	-0.374536952343935	0.708119613915265	   
df.mm.trans3:probe3	-0.0255829997275223	0.127394744998118	-0.200816758398475	0.840900865898407