fitVsDatCorrelation=0.80979333406168
cont.fitVsDatCorrelation=0.277295508703673

fstatistic=7990.49876993055,50,646
cont.fstatistic=2971.62492150235,50,646

residuals=-0.597957865544092,-0.0864527826446878,-0.000304412771359097,0.0788212528356301,0.879564061389595
cont.residuals=-0.560944486344955,-0.152638972434479,-0.0498166508264044,0.0651358443623375,1.61617217609435

predictedValues:
Include	Exclude	Both
Lung	47.6946718288664	48.8829567301297	54.7415466521388
cerebhem	53.2427129010297	52.422918248383	49.9709855735985
cortex	47.4929770402631	43.0877557105624	45.7640225974068
heart	77.0675786130275	46.593181289489	96.1260833629181
kidney	47.9088693836880	44.0381776044529	44.7292436932579
liver	52.0067871973574	47.9448105007095	48.0145731469148
stomach	48.8428171906632	47.4936135814577	49.5017100038654
testicle	49.4534502410472	47.7915177109478	47.2676087176364


diffExp=-1.18828490126332,0.819794652646685,4.40522132970074,30.4743973235385,3.87069177923503,4.0619766966479,1.34920360920553,1.66193253009934
diffExpScore=1.02963237083863
diffExp1.5=0,0,0,1,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,0,0,1,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=0,0,0,1,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,0,1,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	50.605380012259	52.6590249693283	49.7496591788568
cerebhem	51.7083376790601	47.1442655868480	55.3279467069293
cortex	50.79735255884	47.9791958287232	51.0338874187835
heart	51.044344314914	52.9248398530604	56.3691168977269
kidney	48.5098047680314	46.5894380035308	57.8245944359913
liver	51.0865192381257	48.0646210313631	49.1598570002669
stomach	48.96341759756	51.500776492948	56.3174771644294
testicle	51.402002235993	54.6043315732361	50.4771769449625
cont.diffExp=-2.05364495706928,4.56407209221219,2.81815673011682,-1.88049553814643,1.92036676450054,3.02189820676254,-2.53735889538803,-3.20232933724309
cont.diffExpScore=6.025839710099

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

tran.correlation=0.0618253058304997
cont.tran.correlation=0.212808351974535

tran.covariance=0.00104130272910398
cont.tran.covariance=0.000302032041740385

tran.mean=50.1227997357547
cont.tran.mean=50.3489782339888

weightedLogRatios:
wLogRatio
Lung	-0.0954125873291965
cerebhem	0.0615578511219884
cortex	0.371062720618567
heart	2.05974859144925
kidney	0.322415783551111
liver	0.318033464879025
stomach	0.108535635621173
testicle	0.132767276409404

cont.weightedLogRatios:
wLogRatio
Lung	-0.156889229817345
cerebhem	0.3603319733502
cortex	0.222559673119988
heart	-0.142931612483280
kidney	0.155976819182145
liver	0.237984635049110
stomach	-0.197866745937168
testicle	-0.23992492877623

varWeightedLogRatios=0.469129673916086
cont.varWeightedLogRatios=0.0564202547892526

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.76926116718057	0.0782538467752663	48.1671038869864	5.49954384795944e-216	***
df.mm.trans1	0.0615209835149416	0.0661867510510394	0.92950601952799	0.352974099834627	   
df.mm.trans2	0.138121477169536	0.0605720214745621	2.28028508554138	0.0229157395663149	*  
df.mm.exp2	0.271136731860033	0.0789192197525605	3.43562357446186	0.00062906712568213	***
df.mm.exp3	0.0486969303829167	0.0789192197525605	0.61704779311806	0.537420615315679	   
df.mm.exp4	-0.131149345507342	0.0789192197525605	-1.66181756381451	0.0970345063319731	.  
df.mm.exp5	0.102104538533975	0.0789192197525605	1.29378545370961	0.196201915320754	   
df.mm.exp6	0.198294694372346	0.0789192197525605	2.51262867263602	0.0122260077096427	*  
df.mm.exp7	0.0955698313604717	0.0789192197525605	1.21098297297055	0.226344763691095	   
df.mm.exp8	0.160429207339166	0.0789192197525605	2.03282809741617	0.0424783091599323	*  
df.mm.trans1:exp2	-0.161095473518196	0.0695259694646919	-2.31705468846438	0.0208125325960828	*  
df.mm.trans2:exp2	-0.20122166760243	0.0566310910860778	-3.55320132004129	0.000408306220832521	***
df.mm.trans1:exp3	-0.0529347720116895	0.0695259694646919	-0.761366902457534	0.446715793027	   
df.mm.trans2:exp3	-0.174886866479522	0.0566310910860778	-3.08817759159361	0.00210022298360125	** 
df.mm.trans1:exp4	0.611012336827186	0.0695259694646918	8.78826058135706	1.36486004097411e-17	***
df.mm.trans2:exp4	0.0831747490697512	0.0566310910860778	1.46871175311328	0.142397752530211	   
df.mm.trans1:exp5	-0.0976235766258466	0.0695259694646919	-1.40413110924579	0.160760382995259	   
df.mm.trans2:exp5	-0.206476410600563	0.0566310910860778	-3.64599033217856	0.000287802131533419	***
df.mm.trans1:exp6	-0.111740151236485	0.0695259694646919	-1.60717142237379	0.108505398451176	   
df.mm.trans2:exp6	-0.217672928808083	0.0566310910860778	-3.84370007064187	0.000133172874514707	***
df.mm.trans1:exp7	-0.0717821916992828	0.0695259694646919	-1.03245150340171	0.302247019803196	   
df.mm.trans2:exp7	-0.124403382878750	0.0566310910860778	-2.19673293402840	0.0283940102292615	*  
df.mm.trans1:exp8	-0.124217069342278	0.0695259694646919	-1.78662836776927	0.074466400259556	.  
df.mm.trans2:exp8	-0.183009839937256	0.0566310910860778	-3.23161423217302	0.00129347752170405	** 
df.mm.trans1:probe2	-0.0690402273927198	0.0476011772602839	-1.45038907368208	0.147435402243188	   
df.mm.trans1:probe3	-0.0261393377498498	0.0476011772602839	-0.549132169713357	0.583104505486684	   
df.mm.trans1:probe4	-0.140559187567525	0.0476011772602839	-2.95285107758881	0.0032628754223951	** 
df.mm.trans1:probe5	-0.0649020827557105	0.0476011772602839	-1.36345541205473	0.173213987029572	   
df.mm.trans1:probe6	-0.044107039631555	0.0476011772602839	-0.926595562760499	0.354482595853164	   
df.mm.trans1:probe7	-0.0315932391798121	0.0476011772602839	-0.663707097138792	0.507114585246087	   
df.mm.trans1:probe8	0.126011192246271	0.0476011772602839	2.64722848254867	0.00831336958552926	** 
df.mm.trans1:probe9	0.113059498039051	0.0476011772602839	2.37514079580090	0.0178331900427416	*  
df.mm.trans1:probe10	0.181587982252537	0.0476011772602839	3.81477922824494	0.000149385738916832	***
df.mm.trans1:probe11	0.14539997020668	0.0476011772602839	3.05454567670944	0.00234679807894720	** 
df.mm.trans1:probe12	0.406703674227685	0.0476011772602839	8.54398352384909	9.31874234314808e-17	***
df.mm.trans1:probe13	0.186442200256689	0.0476011772602839	3.91675607595208	9.93010460627919e-05	***
df.mm.trans2:probe2	-0.0635055915753291	0.0476011772602839	-1.33411808762795	0.182635268768735	   
df.mm.trans2:probe3	-0.0509693928587319	0.0476011772602839	-1.07075908186116	0.284677654923487	   
df.mm.trans2:probe4	0.00588449170023651	0.0476011772602839	0.123620717783092	0.901654040164732	   
df.mm.trans2:probe5	-0.120609338373865	0.0476011772602839	-2.53374696416375	0.0115202610195999	*  
df.mm.trans2:probe6	-0.058061636848251	0.0476011772602839	-1.21975211938077	0.223003982583805	   
df.mm.trans3:probe2	0.337122932803796	0.0476011772602839	7.08223939421506	3.71580444263384e-12	***
df.mm.trans3:probe3	0.0693492876584738	0.0476011772602839	1.45688177582817	0.145634899740782	   
df.mm.trans3:probe4	0.0239365515136828	0.0476011772602839	0.502856292456748	0.615236701317962	   
df.mm.trans3:probe5	0.0787404985641598	0.0476011772602839	1.65417124315236	0.0985784975313247	.  
df.mm.trans3:probe6	-0.0153821819222834	0.0476011772602839	-0.323147090211097	0.746688421206193	   
df.mm.trans3:probe7	-0.0147833694406778	0.0476011772602839	-0.310567307187428	0.75622975869355	   
df.mm.trans3:probe8	-0.124473524361678	0.0476011772602839	-2.6149253343264	0.00913295351346595	** 
df.mm.trans3:probe9	0.0635408684462895	0.0476011772602839	1.33485918003345	0.182392680497369	   
df.mm.trans3:probe10	-0.0808577646306589	0.0476011772602839	-1.69865052262316	0.0898665578674633	.  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05219598367214	0.128145808759981	31.6217597975598	2.55972156014495e-133	***
df.mm.trans1	-0.142635334574809	0.108385147723009	-1.31600443023181	0.188639343137389	   
df.mm.trans2	-0.0924952648718298	0.0991906596282245	-0.932499745626356	0.351426698014885	   
df.mm.exp2	-0.195338646738326	0.129235400669082	-1.51149488241621	0.131151413877529	   
df.mm.exp3	-0.114770056700532	0.129235400669082	-0.888069802131154	0.374833737056984	   
df.mm.exp4	-0.111245813801606	0.129235400669082	-0.860799852251471	0.389667654455787	   
df.mm.exp5	-0.315166314015217	0.129235400669082	-2.43869955432897	0.0150085024238796	*  
df.mm.exp6	-0.0699022682298403	0.129235400669082	-0.540891024192596	0.588769151910553	   
df.mm.exp7	-0.179226511854024	0.129235400669082	-1.38682211627872	0.165974354506512	   
df.mm.exp8	0.0373771253145824	0.129235400669082	0.289217390289906	0.772507815812407	   
df.mm.trans1:exp2	0.216899790643718	0.113853336979859	1.90508066252013	0.0572133591357412	.  
df.mm.trans2:exp2	0.0847133894695104	0.0927371275309536	0.913478686745338	0.361331635009168	   
df.mm.trans1:exp3	0.118556399949140	0.113853336979859	1.04130808190640	0.29812213557305	   
df.mm.trans2:exp3	0.0216999151073905	0.0927371275309536	0.233993824104025	0.81506390863733	   
df.mm.trans1:exp4	0.119882670109470	0.113853336979859	1.05295701724296	0.292754376513825	   
df.mm.trans2:exp4	0.116280966583220	0.0927371275309536	1.25387716526381	0.210340209173440	   
df.mm.trans1:exp5	0.272874356698306	0.113853336979859	2.39671812822296	0.0168258568844600	*  
df.mm.trans2:exp5	0.192702538860159	0.0927371275309536	2.07794379652140	0.0381088669389881	*  
df.mm.trans1:exp6	0.0793650242538498	0.113853336979859	0.697081230635248	0.486002734065614	   
df.mm.trans2:exp6	-0.0213889930988671	0.0927371275309536	-0.230641099938403	0.81766665728812	   
df.mm.trans1:exp7	0.146242056499319	0.113853336979859	1.28447756015433	0.199435331409090	   
df.mm.trans2:exp7	0.156985758630620	0.0927371275309536	1.69280376490227	0.0909749783793241	.  
df.mm.trans1:exp8	-0.0217578945988362	0.113853336979859	-0.191104583984966	0.848503701747798	   
df.mm.trans2:exp8	-0.00110155117965547	0.0927371275309536	-0.0118782111219457	0.99052644888336	   
df.mm.trans1:probe2	-0.0511967001803679	0.0779500511388824	-0.656788538716306	0.511550796020053	   
df.mm.trans1:probe3	0.0473141336738601	0.0779500511388824	0.606980149244049	0.544077353592863	   
df.mm.trans1:probe4	0.0114308010205256	0.0779500511388824	0.146642636579667	0.883459848512839	   
df.mm.trans1:probe5	0.0219700519849161	0.0779500511388824	0.281847820032502	0.77815046688723	   
df.mm.trans1:probe6	0.0547747646083932	0.0779500511388824	0.702690553862522	0.482501747695305	   
df.mm.trans1:probe7	0.0238660851091422	0.0779500511388824	0.306171513173486	0.759572707042216	   
df.mm.trans1:probe8	0.0551617162796449	0.0779500511388824	0.707654651584053	0.479414964842168	   
df.mm.trans1:probe9	0.0704169436952157	0.0779500511388824	0.903359813962854	0.366671687216895	   
df.mm.trans1:probe10	-0.0219555864184280	0.0779500511388824	-0.281662245215337	0.778292708601846	   
df.mm.trans1:probe11	0.0410898402102067	0.0779500511388824	0.527130381697859	0.598284019790983	   
df.mm.trans1:probe12	0.0272603019078163	0.0779500511388824	0.349714997097911	0.72666657547842	   
df.mm.trans1:probe13	0.053304303739958	0.0779500511388824	0.683826411415518	0.49432999725599	   
df.mm.trans2:probe2	-0.00367539886473224	0.0779500511388824	-0.047150692155209	0.962407698201943	   
df.mm.trans2:probe3	-0.045410006021494	0.0779500511388824	-0.582552613603649	0.560397784805789	   
df.mm.trans2:probe4	0.0036975940400704	0.0779500511388824	0.0474354280214962	0.96218085397247	   
df.mm.trans2:probe5	0.0718365994494173	0.0779500511388824	0.921572191420724	0.357095804236553	   
df.mm.trans2:probe6	0.0397419235900969	0.0779500511388824	0.509838326074852	0.610338864730763	   
df.mm.trans3:probe2	0.0331711023864051	0.0779500511388824	0.425543048423467	0.67058257396563	   
df.mm.trans3:probe3	0.0703798900414014	0.0779500511388824	0.902884462718398	0.366923752265353	   
df.mm.trans3:probe4	0.0106319479462434	0.0779500511388824	0.136394367815110	0.891552019564238	   
df.mm.trans3:probe5	-0.00785877099537159	0.0779500511388824	-0.100818035146247	0.919726191186955	   
df.mm.trans3:probe6	0.207001344003082	0.0779500511388824	2.65556392816563	0.00811288526819571	** 
df.mm.trans3:probe7	0.0711114214167513	0.0779500511388824	0.912269079722004	0.361967398755362	   
df.mm.trans3:probe8	0.191389498852265	0.0779500511388824	2.45528381387806	0.0143397726458288	*  
df.mm.trans3:probe9	0.131974511932026	0.0779500511388824	1.69306511033442	0.090925198679759	.  
df.mm.trans3:probe10	0.191581341211037	0.0779500511388824	2.45774490730865	0.0142428116491517	*