fitVsDatCorrelation=0.860512856242664
cont.fitVsDatCorrelation=0.266730749017874

fstatistic=10328.6142676818,59,853
cont.fstatistic=2875.34787608646,59,853

residuals=-0.571865011532669,-0.0942651781079578,-0.00154111068685631,0.0906912763286115,0.970632736362323
cont.residuals=-0.762268948351545,-0.233143820571219,-0.0346940050639703,0.213825906421334,1.32954455830963

predictedValues:
Include	Exclude	Both
Lung	65.3194939542251	65.7874588294797	55.2933786323638
cerebhem	62.4949737039448	54.1635633304289	62.0600899403883
cortex	81.5768009317949	71.1084865036844	58.6804989095313
heart	69.2999557823884	71.0261613798937	51.3801283625274
kidney	68.6246231654801	64.8219424085518	54.5881162036358
liver	57.6218875936234	66.3281416910285	48.6928074114701
stomach	64.3070236551923	110.340288823303	53.9587540986921
testicle	58.2714616223557	64.748653356341	49.4583416810082


diffExp=-0.467964875254651,8.33141037351586,10.4683144281105,-1.72620559750523,3.80268075692831,-8.70625409740507,-46.0332651681107,-6.47719173398533
diffExpScore=2.05731697104552
diffExp1.5=0,0,0,0,0,0,-1,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,0,0,0,-1,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,0,0,0,-1,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,0,0,0,-1,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	59.9791570049808	79.0112172825517	69.2811771545673
cerebhem	65.6519110595442	59.787472740475	59.6488028738063
cortex	63.8881925951047	65.9470068512851	70.0120410368134
heart	68.5868832825343	69.7391813634624	71.1152550077933
kidney	68.0043950694262	65.2842598060509	66.2432750433802
liver	64.5812836997958	67.4052642124898	67.5118971613335
stomach	66.7605915753466	78.8938936619484	68.3364996059341
testicle	62.8466920891666	73.1449509785784	68.0922172573238
cont.diffExp=-19.0320602775709,5.86443831906924,-2.05881425618040,-1.15229808092813,2.72013526337525,-2.82398051269402,-12.1333020866018,-10.2982588894118
cont.diffExpScore=1.40509821716956

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

tran.correlation=0.077401075728562
cont.tran.correlation=-0.389933102745408

tran.covariance=0.00294465012415318
cont.tran.covariance=-0.00166904502388627

tran.mean=68.4900572957322
cont.tran.mean=67.4695220795463

weightedLogRatios:
wLogRatio
Lung	-0.0298601984073296
cerebhem	0.581403053640744
cortex	0.595069491409059
heart	-0.104585408830693
kidney	0.239438854483367
liver	-0.580331731579667
stomach	-2.39371207981783
testicle	-0.434019261775551

cont.weightedLogRatios:
wLogRatio
Lung	-1.16625145202859
cerebhem	0.387155799506286
cortex	-0.132354519954025
heart	-0.0705831569291251
kidney	0.171415546309505
liver	-0.179296678034629
stomach	-0.71549049000955
testicle	-0.639842679629831

varWeightedLogRatios=0.921573010652962
cont.varWeightedLogRatios=0.261123477354471

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.15868723552516	0.0751005824103195	55.3748999282556	1.07663127943646e-284	***
df.mm.trans1	-0.151741588588578	0.0643982181337409	-2.35630104350783	0.0186832577730930	*  
df.mm.trans2	-0.0244552308458983	0.0569559271245546	-0.429371130987265	0.667761587087686	   
df.mm.exp2	-0.354075136199940	0.072858266781918	-4.85977984158984	1.39845212973479e-06	***
df.mm.exp3	0.240577573787952	0.0728582667819179	3.30199419247863	0.000999912099568072	***
df.mm.exp4	0.209174477668462	0.0728582667819179	2.87097795360093	0.0041933299209607	** 
df.mm.exp5	0.0474127851417718	0.0728582667819179	0.650753678833584	0.515380752747517	   
df.mm.exp6	0.00991884826944595	0.0728582667819179	0.136138954542186	0.891743554707996	   
df.mm.exp7	0.525951461753518	0.0728582667819179	7.21883027121433	1.16036117058568e-12	***
df.mm.exp8	-0.0185719456109746	0.0728582667819179	-0.254905125132400	0.79885782084316	   
df.mm.trans1:exp2	0.309870748078888	0.0665101936953096	4.65899632616376	3.68493141412273e-06	***
df.mm.trans2:exp2	0.159654330430021	0.048572177854612	3.28695021474854	0.00105417708028476	** 
df.mm.trans1:exp3	-0.0183231754384893	0.0665101936953096	-0.275494242618353	0.783003270003449	   
df.mm.trans2:exp3	-0.162800108862547	0.0485721778546119	-3.35171524220814	0.000838448241786556	***
df.mm.trans1:exp4	-0.150020730426182	0.0665101936953096	-2.25560507481670	0.0243480157357546	*  
df.mm.trans2:exp4	-0.132555423225268	0.0485721778546119	-2.72904014355786	0.00648265452721517	** 
df.mm.trans1:exp5	0.00194810256683967	0.0665101936953096	0.0292902855728272	0.97663992593255	   
df.mm.trans2:exp5	-0.0621978465545768	0.0485721778546119	-1.28052414575171	0.200708938986497	   
df.mm.trans1:exp6	-0.135306880694387	0.0665101936953096	-2.03437808818062	0.0422223719464398	*  
df.mm.trans2:exp6	-0.00173380610941753	0.0485721778546119	-0.0356954574820018	0.971533543996669	   
df.mm.trans1:exp7	-0.541573124783354	0.0665101936953096	-8.14270857884371	1.36611449693485e-15	***
df.mm.trans2:exp7	-0.00881156120597633	0.0485721778546119	-0.181411696884406	0.856087535444485	   
df.mm.trans1:exp8	-0.095606110824353	0.0665101936953096	-1.43746552990561	0.150952481040982	   
df.mm.trans2:exp8	0.00265562351986296	0.0485721778546119	0.0546737584592537	0.956411178254179	   
df.mm.trans1:probe2	0.0299763052071698	0.0470298089799842	0.637389473980803	0.524042221326169	   
df.mm.trans1:probe3	0.266192986180972	0.0470298089799842	5.66009073722291	2.06623399654661e-08	***
df.mm.trans1:probe4	-0.0411055964797512	0.0470298089799842	-0.874032818148287	0.38234638434655	   
df.mm.trans1:probe5	-0.188383937614492	0.0470298089799842	-4.00562838123939	6.72489632657467e-05	***
df.mm.trans1:probe6	0.192985758994110	0.0470298089799842	4.10347741527602	4.4602658062923e-05	***
df.mm.trans1:probe7	-0.201729223256952	0.0470298089799842	-4.28939065737664	1.99652657585516e-05	***
df.mm.trans1:probe8	0.168443739414016	0.0470298089799842	3.58163775416791	0.00036076642830517	***
df.mm.trans1:probe9	0.441910541405665	0.0470298089799842	9.39639243684237	5.03204579172135e-20	***
df.mm.trans1:probe10	0.267904937719958	0.0470298089799842	5.69649215105207	1.68325196658907e-08	***
df.mm.trans1:probe11	0.587426990554369	0.0470298089799842	12.4905246968870	5.32999327309332e-33	***
df.mm.trans1:probe12	0.471247448170568	0.0470298089799842	10.0201863114335	2.03702287021112e-22	***
df.mm.trans1:probe13	0.594133703913615	0.0470298089799842	12.633130280552	1.16437919849406e-33	***
df.mm.trans1:probe14	0.474145855065392	0.0470298089799842	10.081815455963	1.16448753235936e-22	***
df.mm.trans1:probe15	0.499942105786031	0.0470298089799842	10.6303239717347	7.15845037981007e-25	***
df.mm.trans1:probe16	0.457141428169376	0.0470298089799842	9.72024845697193	2.98180473735055e-21	***
df.mm.trans1:probe17	0.401056851846678	0.0470298089799842	8.52771594325138	6.74191864951052e-17	***
df.mm.trans1:probe18	0.328070731870608	0.0470298089799842	6.97580404824171	6.1010490043476e-12	***
df.mm.trans1:probe19	0.407954507233224	0.0470298089799842	8.67438154823995	2.07982907944529e-17	***
df.mm.trans1:probe20	0.35772083241222	0.0470298089799842	7.60625739654749	7.45294198581621e-14	***
df.mm.trans2:probe2	0.515199329537483	0.0470298089799842	10.9547399981308	3.20395346478087e-26	***
df.mm.trans2:probe3	-0.0321484922500406	0.0470298089799842	-0.683576925939098	0.494427997526563	   
df.mm.trans2:probe4	0.273406425247075	0.0470298089799842	5.81347088531524	8.6431931804351e-09	***
df.mm.trans2:probe5	0.090013659328321	0.0470298089799842	1.91397033669923	0.0559583197543136	.  
df.mm.trans2:probe6	0.0930790415528346	0.0470298089799842	1.97914989602549	0.0481203175546462	*  
df.mm.trans3:probe2	0.126215894853669	0.0470298089799842	2.68374245167328	0.00742139835897369	** 
df.mm.trans3:probe3	0.0463475040061734	0.0470298089799842	0.985492074311823	0.324661867004514	   
df.mm.trans3:probe4	0.0805162765776258	0.0470298089799842	1.71202644288633	0.087255266545554	.  
df.mm.trans3:probe5	0.0986100037612625	0.0470298089799842	2.09675535367857	0.0363091986197126	*  
df.mm.trans3:probe6	-0.0389216195507939	0.0470298089799842	-0.827594676545654	0.408131551090655	   
df.mm.trans3:probe7	0.109311171869131	0.0470298089799842	2.32429546791598	0.0203436789045258	*  
df.mm.trans3:probe8	-0.0102482970469946	0.0470298089799842	-0.217910667069821	0.827550832828928	   
df.mm.trans3:probe9	-0.0190598634768534	0.0470298089799842	-0.405271973036617	0.685379226673198	   
df.mm.trans3:probe10	-0.0422606901728989	0.0470298089799842	-0.898593702366194	0.369122690259095	   
df.mm.trans3:probe11	-0.0247426256719728	0.0470298089799842	-0.526105170499485	0.59895188966089	   
df.mm.trans3:probe12	0.036185243273169	0.0470298089799842	0.769410806847406	0.441862473853591	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.1741684480901	0.142080268727062	29.3789453348284	1.25892944985685e-131	***
df.mm.trans1	-0.104288631074684	0.121832825316793	-0.85599780521801	0.392239380549975	   
df.mm.trans2	0.200174062456269	0.107753004993257	1.85771211177632	0.0635543160756754	.  
df.mm.exp2	-0.0387249125149928	0.137838107124193	-0.280944894869322	0.778820795646906	   
df.mm.exp3	-0.128094880720153	0.137838107124193	-0.92931398575242	0.352989293438745	   
df.mm.exp4	-0.0168519539948820	0.137838107124193	-0.122259035229629	0.90272267058205	   
df.mm.exp5	-0.0204243481196186	0.137838107124193	-0.148176353736606	0.882238612269844	   
df.mm.exp6	-0.0590697714327979	0.137838107124193	-0.428544563366470	0.668362888862272	   
df.mm.exp7	0.119359069802571	0.137838107124193	0.865936657814288	0.386768385919879	   
df.mm.exp8	-0.0131352150952979	0.137838107124193	-0.095294511578449	0.924103283428582	   
df.mm.trans1:exp2	0.129094504037759	0.125828400926223	1.02595680377001	0.305202793437564	   
df.mm.trans2:exp2	-0.240068767724541	0.0918920714161286	-2.61250795661578	0.00914618694986316	** 
df.mm.trans1:exp3	0.191232326990247	0.125828400926223	1.51978667441202	0.128935158058983	   
df.mm.trans2:exp3	-0.0526434595728718	0.0918920714161286	-0.572883587904756	0.566874493772476	   
df.mm.trans1:exp4	0.15095614603036	0.125828400926223	1.19969851733926	0.23058956258265	   
df.mm.trans2:exp4	-0.107975576539628	0.0918920714161286	-1.17502603734621	0.240312191700392	   
df.mm.trans1:exp5	0.145999565960751	0.125828400926223	1.16030693298212	0.246248505168503	   
df.mm.trans2:exp5	-0.170414522277237	0.0918920714161286	-1.85450735467180	0.0640115478495275	.  
df.mm.trans1:exp6	0.132997295613070	0.125828400926223	1.05697358175163	0.290822890656642	   
df.mm.trans2:exp6	-0.0997969429616679	0.0918920714161286	-1.08602343405388	0.277775434521572	   
df.mm.trans1:exp7	-0.0122432284439554	0.125828400926223	-0.0973009936853125	0.922510223975445	   
df.mm.trans2:exp7	-0.120845071636091	0.0918920714161286	-1.31507615155229	0.188837722068872	   
df.mm.trans1:exp8	0.05983639829115	0.125828400926223	0.475539686197186	0.634524104929056	   
df.mm.trans2:exp8	-0.0640115159756314	0.0918920714161286	-0.696594548247351	0.486246345499774	   
df.mm.trans1:probe2	0.0312048100471176	0.0889741155607922	0.350717844739874	0.725886631142722	   
df.mm.trans1:probe3	0.120396969309116	0.0889741155607922	1.35316848670279	0.176360313488666	   
df.mm.trans1:probe4	-0.142040393959751	0.0889741155607922	-1.59642378083209	0.110764591088118	   
df.mm.trans1:probe5	0.105249661517285	0.0889741155607922	1.18292450398534	0.237168664682537	   
df.mm.trans1:probe6	0.0601694180771373	0.0889741155607922	0.676257557581745	0.499060449819383	   
df.mm.trans1:probe7	0.0337635058869983	0.0889741155607922	0.379475600001094	0.704429169769609	   
df.mm.trans1:probe8	-0.0125958456081293	0.0889741155607922	-0.141567528137136	0.88745504423009	   
df.mm.trans1:probe9	0.064480738885131	0.0889741155607922	0.724713457152312	0.46882657520875	   
df.mm.trans1:probe10	0.169465055275575	0.0889741155607922	1.90465568786449	0.057160969047425	.  
df.mm.trans1:probe11	0.0247458400177968	0.0889741155607922	0.278124034859206	0.780984546658618	   
df.mm.trans1:probe12	0.0318533798180112	0.0889741155607922	0.358007265565311	0.720426447999807	   
df.mm.trans1:probe13	0.111127576103877	0.0889741155607922	1.24898770168666	0.212012217178521	   
df.mm.trans1:probe14	-0.0492075793550554	0.0889741155607922	-0.553054998579154	0.580370659985017	   
df.mm.trans1:probe15	0.156487787502784	0.0889741155607922	1.75880127064442	0.0789698007044914	.  
df.mm.trans1:probe16	-0.026331122075399	0.0889741155607922	-0.295941374740703	0.767346882847791	   
df.mm.trans1:probe17	0.0327682733995612	0.0889741155607922	0.368289959310380	0.712748415385982	   
df.mm.trans1:probe18	0.00358254629097728	0.0889741155607922	0.0402650396511048	0.96789124756179	   
df.mm.trans1:probe19	0.0201720653330075	0.0889741155607922	0.226718357421882	0.820697071454818	   
df.mm.trans1:probe20	0.0364609648883865	0.0889741155607922	0.409792945494067	0.682060731703324	   
df.mm.trans2:probe2	0.0354505789090406	0.0889741155607922	0.39843699131596	0.69040780450824	   
df.mm.trans2:probe3	0.00656689261557709	0.0889741155607922	0.0738067759840806	0.941181439010203	   
df.mm.trans2:probe4	-0.0927020752835495	0.0889741155607922	-1.04189937375899	0.297753538726926	   
df.mm.trans2:probe5	-0.0335915166320456	0.0889741155607922	-0.377542574268063	0.705864348542034	   
df.mm.trans2:probe6	-0.00127206994211956	0.0889741155607922	-0.0142970788088409	0.988596313341613	   
df.mm.trans3:probe2	-0.126197135387493	0.0889741155607922	-1.41835785151770	0.156451548378314	   
df.mm.trans3:probe3	0.0220471814626268	0.0889741155607922	0.247793207312782	0.804354045709548	   
df.mm.trans3:probe4	-0.0257711930570789	0.0889741155607922	-0.289648207174035	0.772155771924454	   
df.mm.trans3:probe5	-0.0828339969978342	0.0889741155607922	-0.930989833118794	0.352122231147910	   
df.mm.trans3:probe6	0.0175473481560251	0.0889741155607922	0.197218573575319	0.84370347925767	   
df.mm.trans3:probe7	-0.0926002733331329	0.0889741155607922	-1.04075519885177	0.298284071893749	   
df.mm.trans3:probe8	0.00962161914055104	0.0889741155607922	0.108139531142369	0.913910441552547	   
df.mm.trans3:probe9	0.00324714588403051	0.0889741155607922	0.0364953994042444	0.970895885758527	   
df.mm.trans3:probe10	-0.0515912514193032	0.0889741155607922	-0.579845622450196	0.562171846784445	   
df.mm.trans3:probe11	-0.0270581435642395	0.0889741155607922	-0.304112531984113	0.761116329793175	   
df.mm.trans3:probe12	-0.0289787900935002	0.0889741155607922	-0.325699108227721	0.744731897507157