fitVsDatCorrelation=0.869277079714879
cont.fitVsDatCorrelation=0.261234594880731

fstatistic=10928.1959933735,65,991
cont.fstatistic=2854.72111433160,65,991

residuals=-0.872173605992233,-0.0921516628098823,1.0487999932438e-05,0.0863080858085055,1.08777973007834
cont.residuals=-0.698738628835438,-0.208430452602715,-0.0558083091345552,0.148102010996336,1.24235430499161

predictedValues:
Include	Exclude	Both
Lung	64.8000962248711	59.620433469577	78.2851518385403
cerebhem	71.0471430500503	70.4815687844105	81.9119061511526
cortex	66.1172956028243	55.7887521963598	86.1346397475116
heart	71.7720690729425	54.0980793422305	91.4019250411914
kidney	67.3655073390211	61.1984120027335	91.6807358909076
liver	68.8433364818367	57.2058847617603	89.0584932692463
stomach	65.1723682316068	59.9415938564208	78.2660416702942
testicle	70.8599015198145	61.6771053580638	89.7882198481835


diffExp=5.17966275529403,0.565574265639853,10.3285434064645,17.6739897307119,6.16709533628757,11.6374517200764,5.23077437518602,9.18279616175067
diffExpScore=0.985067023919519
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=0,0,0,1,0,1,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	74.9511942094666	67.0707954824248	68.3476222822829
cerebhem	73.3592253878303	77.4465812855433	71.8681016701671
cortex	80.0215140876932	70.2525209554738	65.2561493001448
heart	85.9304641956603	72.1064788574885	69.0190565408974
kidney	73.1268935665818	69.2680181301127	69.8290401646645
liver	75.6116678071509	78.9722729661923	70.1548204718355
stomach	69.9224018921745	78.9835395245895	71.8339712039776
testicle	73.1876194856617	76.932939520931	69.2125385566791
cont.diffExp=7.88039872704182,-4.087355897713,9.7689931322194,13.8239853381718,3.85887543646908,-3.3606051590414,-9.06113763241501,-3.74532003526929
cont.diffExpScore=3.45734827659977

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=0.212572566869893
cont.tran.correlation=-0.377053897165508

tran.covariance=0.000612487903793682
cont.tran.covariance=-0.00156113859216090

tran.mean=64.1243467059077
cont.tran.mean=74.821507959686

weightedLogRatios:
wLogRatio
Lung	0.344036121862491
cerebhem	0.0340424228223567
cortex	0.697522492854408
heart	1.16814436018231
kidney	0.399613675964269
liver	0.76649159936762
stomach	0.345971465049555
testicle	0.58171975359826

cont.weightedLogRatios:
wLogRatio
Lung	0.473380415612616
cerebhem	-0.234365450825174
cortex	0.56209601502871
heart	0.765744586748702
kidney	0.231222881216177
liver	-0.189049929484738
stomach	-0.524982506431506
testicle	-0.215501121625943

varWeightedLogRatios=0.117732784921743
cont.varWeightedLogRatios=0.214041124070628

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86562741984394	0.0744108754107837	51.9497640432775	3.77945350708427e-285	***
df.mm.trans1	0.373530230476182	0.0635131983467447	5.88114345048294	5.56573700266686e-09	***
df.mm.trans2	0.247179517185032	0.0556283997141211	4.44340513937678	9.85203218396646e-06	***
df.mm.exp2	0.214103034982496	0.0701852462359455	3.05054190823431	0.00234505185484411	** 
df.mm.exp3	-0.14185649792763	0.0701852462359455	-2.02117261868319	0.0435303506841919	*  
df.mm.exp4	-0.149919960233322	0.0701852462359455	-2.13606089988383	0.0329189571868805	*  
df.mm.exp5	-0.0930054055274692	0.0701852462359455	-1.32514182845221	0.185429553571906	   
df.mm.exp6	-0.109750664436199	0.0701852462359455	-1.56372842331056	0.118200615957018	   
df.mm.exp7	0.0113449238447084	0.0701852462359455	0.161642573804892	0.87162027750251	   
df.mm.exp8	-0.0137837705484538	0.0701852462359454	-0.196391282893219	0.844344184102442	   
df.mm.trans1:exp2	-0.122066479971179	0.0637736773550892	-1.91405741418231	0.0559006035029578	.  
df.mm.trans2:exp2	-0.0467501537898568	0.0438162613520118	-1.06695898616896	0.286250210351869	   
df.mm.trans1:exp3	0.161979780386435	0.0637736773550892	2.53991595128722	0.0112396739729923	*  
df.mm.trans2:exp3	0.0754304146910806	0.0438162613520118	1.72151644991083	0.0854692269169751	.  
df.mm.trans1:exp4	0.252108262168442	0.0637736773550892	3.9531711612726	8.26008049801525e-05	***
df.mm.trans2:exp4	0.0527202847003073	0.0438162613520118	1.20321275876922	0.229181357412552	   
df.mm.trans1:exp5	0.131831443594118	0.0637736773550892	2.06717644428885	0.0389767248272632	*  
df.mm.trans2:exp5	0.119128288278152	0.0438162613520118	2.71881453602573	0.0066660838399814	** 
df.mm.trans1:exp6	0.170277013493130	0.0637736773550892	2.67002030547862	0.00770879243590927	** 
df.mm.trans2:exp6	0.0684090792172774	0.0438162613520118	1.56127148018612	0.118778976715165	   
df.mm.trans1:exp7	-0.005616433178328	0.0637736773550892	-0.0880682032346344	0.92984026098863	   
df.mm.trans2:exp7	-0.00597263020837636	0.0438162613520119	-0.136310813019699	0.891603260947191	   
df.mm.trans1:exp8	0.103181391890685	0.0637736773550892	1.61793072267379	0.105995743425442	   
df.mm.trans2:exp8	0.0476982099466025	0.0438162613520118	1.08859607083781	0.276596710865416	   
df.mm.trans1:probe2	0.0771738060214285	0.0474823969777155	1.62531403074802	0.104413618743704	   
df.mm.trans1:probe3	-0.286625974520098	0.0474823969777155	-6.03646809689531	2.22382362187259e-09	***
df.mm.trans1:probe4	-0.170771719952515	0.0474823969777155	-3.59652694097693	0.000338410933306128	***
df.mm.trans1:probe5	-0.0805709307798211	0.0474823969777155	-1.69685896054563	0.0900373447323163	.  
df.mm.trans1:probe6	-0.259079358551605	0.0474823969777155	-5.45632434422374	6.14136038118751e-08	***
df.mm.trans1:probe7	-0.290639201635108	0.0474823969777155	-6.12098841116869	1.33800883929318e-09	***
df.mm.trans1:probe8	0.101548568430238	0.0474823969777155	2.13865716336724	0.0327073814272757	*  
df.mm.trans1:probe9	-0.453784991924644	0.0474823969777155	-9.55690994575558	9.21298623248752e-21	***
df.mm.trans1:probe10	-0.143388559047077	0.0474823969777155	-3.01982562325934	0.00259401465812336	** 
df.mm.trans1:probe11	0.0552973172400097	0.0474823969777155	1.16458563088047	0.244467032126333	   
df.mm.trans1:probe12	-0.0813914369311817	0.0474823969777155	-1.71413917813333	0.0868158556742172	.  
df.mm.trans1:probe13	-0.105473536331186	0.0474823969777155	-2.22131870007925	0.0265547142534392	*  
df.mm.trans1:probe14	-0.033725899215593	0.0474823969777155	-0.710282154277538	0.477696291079203	   
df.mm.trans1:probe15	-0.0268700579232112	0.0474823969777155	-0.56589514501178	0.57159317227788	   
df.mm.trans1:probe16	-0.175433064772529	0.0474823969777155	-3.69469689693347	0.000232193578586189	***
df.mm.trans1:probe17	-0.128882934532900	0.0474823969777155	-2.71433084124603	0.00675630303867463	** 
df.mm.trans1:probe18	-0.0481033208282327	0.0474823969777155	-1.01307692724124	0.31127069504952	   
df.mm.trans1:probe19	-0.190506616257794	0.0474823969777155	-4.01215246878128	6.46956800371964e-05	***
df.mm.trans1:probe20	-0.197334890887127	0.0474823969777155	-4.15595891209410	3.51911027721035e-05	***
df.mm.trans1:probe21	-0.139758554029581	0.0474823969777155	-2.94337613358425	0.00332224377081595	** 
df.mm.trans2:probe2	-0.115136180504878	0.0474823969777155	-2.42481820281554	0.0154939540261264	*  
df.mm.trans2:probe3	-0.0966298358713751	0.0474823969777155	-2.03506650931556	0.0421098897677291	*  
df.mm.trans2:probe4	-0.0964259658088504	0.0474823969777155	-2.03077291683706	0.0425445968880608	*  
df.mm.trans2:probe5	-0.100002314928921	0.0474823969777155	-2.10609238989881	0.0354473317696672	*  
df.mm.trans2:probe6	-0.162403002998684	0.0474823969777155	-3.42027811011528	0.000651085478694747	***
df.mm.trans3:probe2	0.0513927886864826	0.0474823969777155	1.08235455574415	0.279358302298112	   
df.mm.trans3:probe3	-0.555686177201051	0.0474823969777155	-11.7029933737727	1.00344201923327e-29	***
df.mm.trans3:probe4	-0.605562761038043	0.0474823969777155	-12.7534159937681	1.3287517487944e-34	***
df.mm.trans3:probe5	0.10745828884431	0.0474823969777155	2.26311845408189	0.0238440580201874	*  
df.mm.trans3:probe6	-0.0910462142548642	0.0474823969777155	-1.91747300157560	0.0554650900571569	.  
df.mm.trans3:probe7	-0.313561959151795	0.0474823969777155	-6.6037516871559	6.52703611405434e-11	***
df.mm.trans3:probe8	-0.088929268270767	0.0474823969777155	-1.87288919538968	0.0613779103068383	.  
df.mm.trans3:probe9	0.417684522918449	0.0474823969777155	8.79661831550915	6.15455869744447e-18	***
df.mm.trans3:probe10	0.529143610390831	0.0474823969777155	11.1439953345062	2.95393341478571e-27	***
df.mm.trans3:probe11	-0.539572225485414	0.0474823969777155	-11.3636265190792	3.24504591064676e-28	***
df.mm.trans3:probe12	-0.559687245990653	0.0474823969777155	-11.7872576284076	4.18314310465047e-30	***
df.mm.trans3:probe13	-0.216485414981719	0.0474823969777155	-4.5592773061419	5.7741611577526e-06	***
df.mm.trans3:probe14	-0.0458440057158263	0.0474823969777155	-0.965494765088246	0.334532603253528	   
df.mm.trans3:probe15	-0.292139893703960	0.0474823969777155	-6.152593640988	1.10473970955861e-09	***
df.mm.trans3:probe16	-0.171045015445618	0.0474823969777155	-3.60228266331823	0.000331098699527433	***
df.mm.trans3:probe17	0.232234329297974	0.0474823969777155	4.89095631391495	1.17032378449719e-06	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.32401263308648	0.145303111793629	29.7585686893463	1.47350805959363e-139	***
df.mm.trans1	0.0684696869959467	0.124023072014693	0.552072174021259	0.581023381776156	   
df.mm.trans2	-0.109983952897568	0.108626320251438	-1.01249819236247	0.311547048182628	   
df.mm.exp2	0.0721450828783062	0.137051669178549	0.526407911050806	0.598722611584659	   
df.mm.exp3	0.158092340584906	0.137051669178549	1.15352364208673	0.248973648578043	   
df.mm.exp4	0.199320571516161	0.137051669178549	1.45434617988117	0.146166988703745	   
df.mm.exp5	-0.0138495636538015	0.137051669178549	-0.101053593413434	0.91952835728578	   
df.mm.exp6	0.146023809100598	0.137051669178549	1.06546538233227	0.286924899369951	   
df.mm.exp7	0.0442889591867037	0.137051669178549	0.323155197249036	0.746645883541759	   
df.mm.exp8	0.100799268623397	0.137051669178549	0.735483699159308	0.462218990294071	   
df.mm.trans1:exp2	-0.0936139718762478	0.124531712858661	-0.751727971352132	0.45239315454623	   
df.mm.trans2:exp2	0.0716946315659392	0.085560599663199	0.837939797618975	0.402266562160273	   
df.mm.trans1:exp3	-0.0926339738058375	0.124531712858662	-0.74385850543125	0.457138408136475	   
df.mm.trans2:exp3	-0.111744858049525	0.085560599663199	-1.30603173060261	0.191844832282253	   
df.mm.trans1:exp4	-0.0626193160215068	0.124531712858661	-0.502838309889604	0.61518968397918	   
df.mm.trans2:exp4	-0.126925382456979	0.085560599663199	-1.48345597104986	0.138271259796496	   
df.mm.trans1:exp5	-0.0107913940386690	0.124531712858662	-0.0866557906492202	0.93096262332713	   
df.mm.trans2:exp5	0.0460841539605435	0.085560599663199	0.538614200250457	0.590274047669766	   
df.mm.trans1:exp6	-0.137250359587110	0.124531712858662	-1.10213178985889	0.27067197107275	   
df.mm.trans2:exp6	0.0173242961269770	0.085560599663199	0.20247983528835	0.839583181368035	   
df.mm.trans1:exp7	-0.113740034261492	0.124531712858661	-0.913341924322383	0.361284885861454	   
df.mm.trans2:exp7	0.119201800600254	0.085560599663199	1.39318566103417	0.163876063659586	   
df.mm.trans1:exp8	-0.124610152507058	0.124531712858661	-1.00062987689317	0.317250040641868	   
df.mm.trans2:exp8	0.0363861479288465	0.085560599663199	0.425267565585995	0.670733961930893	   
df.mm.trans1:probe2	-0.0641347435705487	0.0927195117406536	-0.691707089117773	0.489283310733207	   
df.mm.trans1:probe3	-0.178089944265400	0.0927195117406536	-1.92073859020674	0.0550513540989061	.  
df.mm.trans1:probe4	-0.0454624663283045	0.0927195117406536	-0.490322538102529	0.62401421915936	   
df.mm.trans1:probe5	-0.161127850671607	0.0927195117406536	-1.73779873994913	0.0825569219249259	.  
df.mm.trans1:probe6	-0.0180304307605834	0.0927195117406536	-0.194462097805438	0.84585392675978	   
df.mm.trans1:probe7	-0.139953256592482	0.0927195117406536	-1.50942616030967	0.131508661586724	   
df.mm.trans1:probe8	-0.190194631963427	0.0927195117406536	-2.05129026666385	0.0405011341867901	*  
df.mm.trans1:probe9	-0.12539848044556	0.0927195117406536	-1.35244974969576	0.176540000837323	   
df.mm.trans1:probe10	-0.0410088834426705	0.0927195117406536	-0.442289682859599	0.658376103825596	   
df.mm.trans1:probe11	-0.199631979147257	0.0927195117406536	-2.15307409842331	0.0315536101335191	*  
df.mm.trans1:probe12	-0.160088708827415	0.0927195117406536	-1.72659137027383	0.0845527191227854	.  
df.mm.trans1:probe13	-0.156089099255000	0.0927195117406536	-1.68345471546052	0.0926020462118751	.  
df.mm.trans1:probe14	-0.254908645505755	0.0927195117406536	-2.74924490778987	0.00608190439499734	** 
df.mm.trans1:probe15	-0.104823402379656	0.0927195117406536	-1.13054308000303	0.258520991500695	   
df.mm.trans1:probe16	-0.211215905165317	0.0927195117406536	-2.27800924746142	0.0229382292539407	*  
df.mm.trans1:probe17	-0.194390577089787	0.0927195117406536	-2.09654444291637	0.0362869625547522	*  
df.mm.trans1:probe18	-0.127360937580193	0.0927195117406536	-1.3736152745976	0.169871797588309	   
df.mm.trans1:probe19	-0.172745820888916	0.0927195117406536	-1.86310106304383	0.0627437112582862	.  
df.mm.trans1:probe20	-0.154069878713577	0.0927195117406536	-1.66167698493201	0.0968938305246342	.  
df.mm.trans1:probe21	-0.175790522738853	0.0927195117406536	-1.89593883141401	0.0582588255898249	.  
df.mm.trans2:probe2	-0.0581893921284409	0.0927195117406536	-0.62758518715244	0.530420290853847	   
df.mm.trans2:probe3	-0.149719315055089	0.0927195117406535	-1.61475521434874	0.106682033369668	   
df.mm.trans2:probe4	-0.0043956243719282	0.0927195117406536	-0.0474077601295317	0.962197796145033	   
df.mm.trans2:probe5	-0.0368656546934935	0.0927195117406536	-0.397604064143594	0.69100769074748	   
df.mm.trans2:probe6	0.0587306839048715	0.0927195117406536	0.63342313610481	0.526603512797705	   
df.mm.trans3:probe2	0.00581572871481652	0.0927195117406536	0.0627238927992172	0.949999019543632	   
df.mm.trans3:probe3	-0.114619090381727	0.0927195117406536	-1.23619169503749	0.216680232935927	   
df.mm.trans3:probe4	-0.0173425462744543	0.0927195117406536	-0.187043114754134	0.851665113773268	   
df.mm.trans3:probe5	-0.166913778918105	0.0927195117406536	-1.80020122824827	0.0721328837001186	.  
df.mm.trans3:probe6	0.0514386456727518	0.0927195117406536	0.55477692566621	0.579172437279162	   
df.mm.trans3:probe7	-0.153580235774829	0.0927195117406536	-1.65639607987162	0.0979581925925152	.  
df.mm.trans3:probe8	-0.0313278264717026	0.0927195117406536	-0.337877388303445	0.735527146954405	   
df.mm.trans3:probe9	-0.168889436416089	0.0927195117406536	-1.82150912192561	0.0688308659761831	.  
df.mm.trans3:probe10	-0.126811424491803	0.0927195117406536	-1.36768865701654	0.171719624298467	   
df.mm.trans3:probe11	-0.0556243445148452	0.0927195117406536	-0.599920593525476	0.548696302639279	   
df.mm.trans3:probe12	-0.0117593571830833	0.0927195117406536	-0.126827212118799	0.899102885174629	   
df.mm.trans3:probe13	-0.0492252046742296	0.0927195117406536	-0.530904485475697	0.595603925885432	   
df.mm.trans3:probe14	-0.0900132668188014	0.0927195117406536	-0.970812562846299	0.331878508562816	   
df.mm.trans3:probe15	-0.0221437149899399	0.0927195117406536	-0.238824758394741	0.811290849228264	   
df.mm.trans3:probe16	-0.0568529649470085	0.0927195117406536	-0.613171530778035	0.539903622871278	   
df.mm.trans3:probe17	0.0236917533960373	0.0927195117406536	0.255520687622964	0.798373990517234