fitVsDatCorrelation=0.912533066426388
cont.fitVsDatCorrelation=0.294007834307734

fstatistic=7843.72411294165,57,807
cont.fstatistic=1424.71214440476,57,807

residuals=-0.74418727991712,-0.0983683182232036,-0.00549855085487797,0.0992856249178775,1.01169899908999
cont.residuals=-0.965783299322806,-0.349367552270631,-0.0493468824611224,0.29564869213041,1.37163046191526

predictedValues:
Include	Exclude	Both
Lung	103.578721273228	46.6384777864930	79.6480897835489
cerebhem	124.220245283790	51.7915534973502	87.989939197444
cortex	154.208266671049	43.7160161180796	106.039897991610
heart	106.818012186401	46.6160993614627	80.0591115087544
kidney	99.4296965144505	45.9974538090091	77.3790170587207
liver	77.2741288170576	47.1854255003184	68.7403454987383
stomach	98.0099739721093	46.5336867996523	82.2205012689369
testicle	100.841168101071	48.9279541049662	80.3581186014453


diffExp=56.940243486735,72.42869178644,110.492250552969,60.2019128249387,53.4322427054414,30.0887033167392,51.4762871724569,51.9132139961049
diffExpScore=0.997950708581395
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
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	82.1850686032166	70.1582284987589	77.0694839068002
cerebhem	75.7779511002075	68.5963578821828	86.4936435140671
cortex	84.742365239758	69.5931162438037	85.7746012907637
heart	80.906554519394	109.377453716177	91.8440601544008
kidney	84.9208110788465	83.2795889269744	92.6856078390297
liver	78.1013308506401	88.306880618868	75.6879340338527
stomach	74.9133761760652	85.644113313209	84.2546509816176
testicle	84.0161293847188	65.0726363216553	84.4654278812716
cont.diffExp=12.0268401044577,7.1815932180247,15.1492489959543,-28.4708991967831,1.64122215187216,-10.2055497682279,-10.7307371371438,18.9434930630636
cont.diffExpScore=15.9672850272397

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,-1,0,0,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,1,-1,0,0,0,1
cont.diffExp1.2Score=1.5

tran.correlation=-0.20997525616106
cont.tran.correlation=-0.203811885336659

tran.covariance=-0.00182198549799898
cont.tran.covariance=-0.0019184807716524

tran.mean=77.6116799872805
cont.tran.mean=80.3494976546547

weightedLogRatios:
wLogRatio
Lung	3.38422172967156
cerebhem	3.83581201835369
cortex	5.55669038993934
heart	3.5294373116863
kidney	3.24843840952496
liver	2.02278023150211
stomach	3.13795224135986
testicle	3.07499926205697

cont.weightedLogRatios:
wLogRatio
Lung	0.685073320707468
cerebhem	0.425953920009571
cortex	0.854987646492674
heart	-1.37007617455725
kidney	0.0864928262173077
liver	-0.542752170095843
stomach	-0.586779253273697
testicle	1.09950211823889

varWeightedLogRatios=0.986955259273799
cont.varWeightedLogRatios=0.722992419907536

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.33882002709167	0.0919627040800596	47.1802136582939	3.19806840439964e-234	***
df.mm.trans1	0.644920416898024	0.079857502289714	8.07589015942829	2.43191493517919e-15	***
df.mm.trans2	-0.543423471358447	0.0709818477162045	-7.65580903910999	5.49021484660422e-14	***
df.mm.exp2	0.186920990979730	0.092254701405684	2.02614054494369	0.0430788868481624	*  
df.mm.exp3	0.0470634206513115	0.092254701405684	0.510146582604535	0.610088268152886	   
df.mm.exp4	0.0251675052039823	0.092254701405684	0.272804581452276	0.785073228925725	   
df.mm.exp5	-0.0258185428702347	0.092254701405684	-0.279861540678554	0.779655451234727	   
df.mm.exp6	-0.134031786460320	0.092254701405684	-1.45284505199278	0.146655491660321	   
df.mm.exp7	-0.0892986979739048	0.092254701405684	-0.967958235333932	0.33335522553619	   
df.mm.exp8	0.0122626839757640	0.092254701405684	0.132922049379788	0.894288165125713	   
df.mm.trans1:exp2	-0.00519674487251759	0.0858101906366537	-0.0605609291153096	0.951723884689256	   
df.mm.trans2:exp2	-0.0821198189054696	0.0655993839820467	-1.25183826311455	0.210991516666008	   
df.mm.trans1:exp3	0.350908733485863	0.0858101906366537	4.08935967724063	4.75914039119187e-05	***
df.mm.trans2:exp3	-0.111774788135925	0.0655993839820467	-1.70389996598924	0.0887847443026756	.  
df.mm.trans1:exp4	0.00562714491867632	0.0858101906366537	0.0655766509423496	0.947731120134946	   
df.mm.trans2:exp4	-0.0256474478758460	0.0655993839820467	-0.390970864648138	0.695921966658865	   
df.mm.trans1:exp5	-0.0150625460095479	0.0858101906366537	-0.175533300856157	0.860704640441159	   
df.mm.trans2:exp5	0.0119786818505376	0.0655993839820467	0.182603572219763	0.855154943057878	   
df.mm.trans1:exp6	-0.158940914999432	0.0858101906366537	-1.85223822275884	0.0643566214564986	.  
df.mm.trans2:exp6	0.145690945597255	0.0655993839820467	2.22091941651660	0.0266333120757709	*  
df.mm.trans1:exp7	0.0340360310775661	0.0858101906366537	0.396643228794175	0.691735433052306	   
df.mm.trans2:exp7	0.0870492915875332	0.0655993839820467	1.32698336940720	0.184889511076429	   
df.mm.trans1:exp8	-0.0390479136293357	0.0858101906366537	-0.455049841279067	0.649195774256026	   
df.mm.trans2:exp8	0.0356603037600511	0.0655993839820467	0.543607296218066	0.586861870963298	   
df.mm.trans1:probe2	-0.886436048557667	0.0561759562844367	-15.7796343344715	4.32495596668511e-49	***
df.mm.trans1:probe3	-0.271579662639696	0.0561759562844367	-4.83444663166216	1.59859164136624e-06	***
df.mm.trans1:probe4	-0.528008591436053	0.0561759562844367	-9.39919186711444	5.51922312581651e-20	***
df.mm.trans1:probe5	-0.601694717744283	0.0561759562844367	-10.7108940824739	4.02066238125701e-25	***
df.mm.trans1:probe6	-0.265054110911819	0.0561759562844366	-4.71828391438085	2.80270384749292e-06	***
df.mm.trans1:probe7	-0.129344335704474	0.0561759562844367	-2.30248569422766	0.0215614458715165	*  
df.mm.trans1:probe8	-0.203705900383945	0.0561759562844367	-3.62621152993849	0.000305664099180517	***
df.mm.trans1:probe9	-0.367672741422286	0.0561759562844367	-6.54501971556376	1.05687996380372e-10	***
df.mm.trans1:probe10	-0.571757580229503	0.0561759562844367	-10.1779768079873	5.65900731162893e-23	***
df.mm.trans1:probe11	-0.840176749035906	0.0561759562844366	-14.9561628249250	8.03595147265442e-45	***
df.mm.trans1:probe12	-1.07304511809832	0.0561759562844366	-19.1015015866424	2.14572538330853e-67	***
df.mm.trans1:probe13	-1.08440584463162	0.0561759562844366	-19.3037362664719	1.48202169721817e-68	***
df.mm.trans1:probe14	-0.810684731661786	0.0561759562844367	-14.431169227579	3.63139870330858e-42	***
df.mm.trans1:probe15	-1.09022185650321	0.0561759562844367	-19.4072683157020	3.75764319187175e-69	***
df.mm.trans1:probe16	-0.746231145740376	0.0561759562844367	-13.2838174033384	1.46278361256937e-36	***
df.mm.trans1:probe17	-0.363005108035193	0.0561759562844367	-6.46193019300255	1.78671420060646e-10	***
df.mm.trans1:probe18	-0.0450292136516128	0.0561759562844367	-0.8015744925394	0.423035090856263	   
df.mm.trans1:probe19	-0.39155877807938	0.0561759562844367	-6.97022007238816	6.5803574628185e-12	***
df.mm.trans1:probe20	-0.0336126425189496	0.0561759562844367	-0.598345711264053	0.549777173693154	   
df.mm.trans1:probe21	0.00422232868006934	0.0561759562844367	0.0751625599160316	0.940104000662254	   
df.mm.trans1:probe22	-0.00325330297174619	0.0561759562844367	-0.0579127296965572	0.95383247966972	   
df.mm.trans2:probe2	0.0691569710323979	0.0561759562844366	1.23107777075007	0.218652438495682	   
df.mm.trans2:probe3	0.0698583801430467	0.0561759562844366	1.24356370168995	0.214021307865909	   
df.mm.trans2:probe4	0.221138847157603	0.0561759562844367	3.93653907799819	8.9797715285959e-05	***
df.mm.trans2:probe5	0.146015836397368	0.0561759562844366	2.59925858062911	0.00951316916386297	** 
df.mm.trans2:probe6	0.152240840987024	0.0561759562844367	2.71007119516009	0.00686963963926333	** 
df.mm.trans3:probe2	-0.316012930710114	0.0561759562844367	-5.62541257170666	2.55199165350164e-08	***
df.mm.trans3:probe3	0.150177987479232	0.0561759562844367	2.6733499064766	0.00766151225045333	** 
df.mm.trans3:probe4	-0.340502952131006	0.0561759562844367	-6.06136458820446	2.07050280544804e-09	***
df.mm.trans3:probe5	-0.305351910530380	0.0561759562844367	-5.43563351168044	7.2372080935736e-08	***
df.mm.trans3:probe6	0.506678283225372	0.0561759562844367	9.01948656930554	1.35477578543978e-18	***
df.mm.trans3:probe7	-0.189323799167478	0.0561759562844366	-3.37019272460395	0.0007868354471727	***
df.mm.trans3:probe8	-0.00725603170498203	0.0561759562844367	-0.129166144822572	0.897258369026974	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07928456098684	0.214908552813155	18.9814900690967	1.04286683317867e-66	***
df.mm.trans1	0.339375594283343	0.186619787010775	1.81854025084569	0.0693521550481792	.  
df.mm.trans2	0.116525492858579	0.165878182044463	0.702476307748205	0.482584742768757	   
df.mm.exp2	-0.219043450225450	0.215590924251696	-1.01601424543143	0.309927109822848	   
df.mm.exp3	-0.0844609593275493	0.215590924251696	-0.391764911351016	0.695335351452547	   
df.mm.exp4	0.252988145258999	0.215590924251696	1.1734638001906	0.240956082396231	   
df.mm.exp5	0.0196901375148596	0.215590924251696	0.0913310130433504	0.92725224098933	   
df.mm.exp6	0.197187035619903	0.215590924251696	0.914635142014111	0.360656401040296	   
df.mm.exp7	0.0176698489678184	0.215590924251696	0.0819600780002657	0.934698782854367	   
df.mm.exp8	-0.144848721750406	0.215590924251696	-0.671868364835706	0.501859792580667	   
df.mm.trans1:exp2	0.137877179576742	0.200530683289714	0.687561510861387	0.49192650858001	   
df.mm.trans2:exp2	0.196529793153803	0.153299849303506	1.28199599703917	0.200212219845789	   
df.mm.trans1:exp3	0.115102977464104	0.200530683289714	0.573991847909931	0.566133257928203	   
df.mm.trans2:exp3	0.0763735189359053	0.153299849303506	0.498196960289891	0.618480966245916	   
df.mm.trans1:exp4	-0.268666942858741	0.200530683289714	-1.33977972074522	0.180694111706809	   
df.mm.trans2:exp4	0.191063534846026	0.153299849303506	1.24633869970579	0.213001737651587	   
df.mm.trans1:exp5	0.0130554119684498	0.200530683289714	0.0651043109925884	0.948107071759888	   
df.mm.trans2:exp5	0.151760252446710	0.153299849303506	0.989956957793555	0.322491879909957	   
df.mm.trans1:exp6	-0.248153576977821	0.200530683289714	-1.23748432362993	0.216267266257986	   
df.mm.trans2:exp6	0.0328778938600536	0.153299849303506	0.214467881145540	0.830236391654704	   
df.mm.trans1:exp7	-0.110311025658527	0.200530683289714	-0.550095495855646	0.582406091808923	   
df.mm.trans2:exp7	0.181777545583003	0.153299849303506	1.18576467236518	0.236064262595048	   
df.mm.trans1:exp8	0.166883880246186	0.200530683289714	0.832211198348546	0.405535939676300	   
df.mm.trans2:exp8	0.0695997513019728	0.153299849303506	0.454010565686712	0.649943296756856	   
df.mm.trans1:probe2	-0.102298109653592	0.131278147905190	-0.779247051287409	0.436062648020172	   
df.mm.trans1:probe3	-0.0238813160749475	0.131278147905190	-0.181913871089914	0.855696001791442	   
df.mm.trans1:probe4	-0.0790839457335096	0.131278147905190	-0.602415154353216	0.547067052920619	   
df.mm.trans1:probe5	0.00923973463136771	0.131278147905190	0.0703828838143015	0.943906351382855	   
df.mm.trans1:probe6	-0.129884840174744	0.131278147905190	-0.989386598206335	0.322770578598716	   
df.mm.trans1:probe7	-0.0291608021830043	0.131278147905190	-0.222129902411972	0.82426896180541	   
df.mm.trans1:probe8	-0.0853878496551857	0.131278147905190	-0.650434600256954	0.515596660917849	   
df.mm.trans1:probe9	-0.173596661044507	0.131278147905190	-1.32235763388344	0.186423736182063	   
df.mm.trans1:probe10	-0.0908011367821636	0.131278147905190	-0.691669849331973	0.489343621622697	   
df.mm.trans1:probe11	-0.0930173600772803	0.131278147905190	-0.708551739657828	0.478807310171612	   
df.mm.trans1:probe12	0.0251177709396936	0.131278147905190	0.191332459670545	0.84831325896369	   
df.mm.trans1:probe13	0.107299344298469	0.131278147905190	0.817343526022021	0.413973374012473	   
df.mm.trans1:probe14	-0.134681708809337	0.131278147905190	-1.02592633243581	0.305233721902886	   
df.mm.trans1:probe15	0.178330759167408	0.131278147905190	1.35841921913919	0.174710303183399	   
df.mm.trans1:probe16	0.157127768880322	0.131278147905190	1.19690726436665	0.231694188604236	   
df.mm.trans1:probe17	0.074658096090197	0.131278147905190	0.568701625377251	0.56971686777052	   
df.mm.trans1:probe18	0.0919111272868674	0.131278147905190	0.700125106527599	0.484050957445757	   
df.mm.trans1:probe19	-0.00691770372625209	0.131278147905190	-0.0526950131201431	0.957987953429289	   
df.mm.trans1:probe20	-0.0191776120294103	0.131278147905190	-0.146083810104180	0.883891708810061	   
df.mm.trans1:probe21	0.120314373155911	0.131278147905190	0.916484388877899	0.359686682211273	   
df.mm.trans1:probe22	-0.0867054346273893	0.131278147905190	-0.660471190452872	0.509139978034876	   
df.mm.trans2:probe2	0.246509917969079	0.131278147905190	1.87776809699592	0.0607733237102764	.  
df.mm.trans2:probe3	0.213546895116585	0.131278147905190	1.62667510567569	0.104196496955715	   
df.mm.trans2:probe4	0.185239666474085	0.131278147905190	1.41104722628983	0.158616072442815	   
df.mm.trans2:probe5	0.137842876385242	0.131278147905190	1.05000625454278	0.294029570708250	   
df.mm.trans2:probe6	-0.0139367336145528	0.131278147905190	-0.106161869564293	0.915480306791865	   
df.mm.trans3:probe2	-0.297886818419081	0.131278147905190	-2.26912721707664	0.0235237972209176	*  
df.mm.trans3:probe3	-0.194914073188892	0.131278147905190	-1.48474118731214	0.138002885551264	   
df.mm.trans3:probe4	-0.284387945959485	0.131278147905190	-2.16630071719835	0.0305803085356799	*  
df.mm.trans3:probe5	-0.229957013452022	0.131278147905190	-1.75167777060732	0.0802090755106062	.  
df.mm.trans3:probe6	-0.26432894134313	0.131278147905190	-2.01350297487461	0.0443932744267094	*  
df.mm.trans3:probe7	0.0241403120129113	0.131278147905190	0.183886750370256	0.854148493475557	   
df.mm.trans3:probe8	-0.276491477911981	0.131278147905190	-2.10615005104783	0.0354995927128694	*