fitVsDatCorrelation=0.868075784949885
cont.fitVsDatCorrelation=0.229372721435151

fstatistic=13371.2306714572,61,899
cont.fstatistic=3467.35920697687,61,899

residuals=-0.518788659390663,-0.0909474037858023,-0.0054173604701182,0.0850338342759664,0.663326374370367
cont.residuals=-0.612432652942374,-0.206302529171567,-0.00394699313516327,0.167014957542387,1.09427829403761

predictedValues:
Include	Exclude	Both
Lung	69.9622519119818	67.9251089277809	59.4825523317184
cerebhem	60.398117709483	52.1259407442224	56.4755425096497
cortex	61.8864817321182	56.496401292688	59.3641067483471
heart	64.0759265604808	61.91361198055	63.7773373640518
kidney	88.9098575870089	76.5768571039836	108.438400426752
liver	67.535844363762	66.988834974751	63.1355982112073
stomach	62.8970391771463	57.9483108765416	62.5074791931386
testicle	62.6865476525944	57.2542444108522	61.795507406436


diffExp=2.03714298420093,8.27217696526058,5.39008043943021,2.16231457993079,12.3330004830253,0.547009389011052,4.94872830060465,5.43230324174225
diffExpScore=0.976259863174607
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=0,0,0,0,0,0,0,0
diffExp1.3Score=0
diffExp1.2=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	68.0622453677311	68.3858195335563	73.4415058269287
cerebhem	69.0959652674662	63.6734553353821	64.758315932945
cortex	69.7777725579384	66.9274704545787	69.4350165714165
heart	67.6845503283544	62.2547757904841	69.3642754677043
kidney	68.0767220194556	60.5771032331404	72.3240961922258
liver	68.8713120915995	64.2460256959158	69.3009297748001
stomach	66.7308402544503	70.6638917507217	74.8308025025649
testicle	69.2192532436933	72.9574354994592	67.5636889337845
cont.diffExp=-0.323574165825178,5.42250993208409,2.85030210335975,5.42977453787022,7.4996187863152,4.62528639568369,-3.93305149627136,-3.73818225576586
cont.diffExpScore=1.79593625449797

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=0.913966997662706
cont.tran.correlation=0.0104014114762299

tran.covariance=0.0141733508091544
cont.tran.covariance=1.08096166663234e-05

tran.mean=64.7238360628716
cont.tran.mean=67.3252899014954

weightedLogRatios:
wLogRatio
Lung	0.125090828026800
cerebhem	0.593203533090509
cortex	0.371765534050036
heart	0.142220416213828
kidney	0.65897884717934
liver	0.0342264655161163
stomach	0.33602848754859
testicle	0.370994452924364

cont.weightedLogRatios:
wLogRatio
Lung	-0.0200279810773990
cerebhem	0.342821309832969
cortex	0.176185483319421
heart	0.348961399186155
kidney	0.485813992877418
liver	0.291808191197853
stomach	-0.242201488386244
testicle	-0.224252013659684

varWeightedLogRatios=0.0493840483908646
cont.varWeightedLogRatios=0.0761312939393637

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.87206229525613	0.0669817264184084	57.8077410407263	4.53669255758535e-305	***
df.mm.trans1	0.208381267131557	0.0576163679646411	3.6167025880465	0.000314991168077056	***
df.mm.trans2	0.321345444402619	0.0506807070537833	6.34058723887971	3.61907957933349e-10	***
df.mm.exp2	-0.359865565716858	0.0646897649319488	-5.56294440233974	3.49943800801333e-08	***
df.mm.exp3	-0.304889636750879	0.0646897649319488	-4.71310472486044	2.82604137565759e-06	***
df.mm.exp4	-0.250267688269429	0.0646897649319488	-3.86873701786831	0.000117296581652457	***
df.mm.exp5	-0.240942914600163	0.0646897649319488	-3.72459097437788	0.000207824147090485	***
df.mm.exp6	-0.108778883806186	0.0646897649319488	-1.68154705648748	0.0930040183564578	.  
df.mm.exp7	-0.314914270294342	0.0646897649319488	-4.86806947939323	1.33081035109937e-06	***
df.mm.exp8	-0.318860580370641	0.0646897649319488	-4.92907310308006	9.83601772516256e-07	***
df.mm.trans1:exp2	0.212867668224750	0.059505928958937	3.57725140921071	0.000365763298106977	***
df.mm.trans2:exp2	0.0951225345647671	0.0427098189269566	2.22718187420668	0.0261820556398605	*  
df.mm.trans1:exp3	0.182235565575354	0.059505928958937	3.06247744995476	0.00226048230581283	** 
df.mm.trans2:exp3	0.120660819981019	0.0427098189269566	2.82513068452423	0.00483077023462054	** 
df.mm.trans1:exp4	0.162380582779559	0.059505928958937	2.72881350850958	0.00648034921818036	** 
df.mm.trans2:exp4	0.157601987581179	0.0427098189269566	3.69006452241611	0.000237677720061096	***
df.mm.trans1:exp5	0.480610096769203	0.059505928958937	8.0766758065546	2.13122819437208e-15	***
df.mm.trans2:exp5	0.360832060158446	0.0427098189269566	8.44845680042684	1.17789059848795e-16	***
df.mm.trans1:exp6	0.0734815301536972	0.059505928958937	1.23486065068246	0.217205015955225	   
df.mm.trans2:exp6	0.094899088183921	0.0427098189269566	2.22195014093175	0.0265349351031784	*  
df.mm.trans1:exp7	0.208457522790359	0.059505928958937	3.50313870293847	0.000482475749979813	***
df.mm.trans2:exp7	0.156059932750521	0.0427098189269566	3.65395912863547	0.000273178985165339	***
df.mm.trans1:exp8	0.209051615797858	0.059505928958937	3.51312246452144	0.000464941744569127	***
df.mm.trans2:exp8	0.14795659927873	0.0427098189269566	3.46422913971536	0.00055687704853456	***
df.mm.trans1:probe2	0.0227120023406056	0.0420770458876693	0.539771789142197	0.589488093785845	   
df.mm.trans1:probe3	0.456599095955733	0.0420770458876693	10.8515007725278	7.25581780170822e-26	***
df.mm.trans1:probe4	0.00425754105011613	0.0420770458876693	0.101184409701248	0.91942663424987	   
df.mm.trans1:probe5	-0.00202574153302958	0.0420770458876693	-0.0481436253494979	0.961612471522312	   
df.mm.trans1:probe6	0.0669379992211269	0.0420770458876693	1.59084360151679	0.111996336903456	   
df.mm.trans1:probe7	0.321493310068003	0.0420770458876693	7.64058653086711	5.53456279001586e-14	***
df.mm.trans1:probe8	0.154442859974375	0.0420770458876693	3.67047773236463	0.000256359904628685	***
df.mm.trans1:probe9	0.555383085072564	0.0420770458876693	13.1991938444357	1.72942445803084e-36	***
df.mm.trans1:probe10	-0.0226780792605981	0.0420770458876693	-0.538965575699883	0.590044045548816	   
df.mm.trans1:probe11	0.361833894027569	0.0420770458876693	8.5993179034844	3.52764316621057e-17	***
df.mm.trans1:probe12	0.243183530738618	0.0420770458876693	5.77948203369199	1.03272771457752e-08	***
df.mm.trans1:probe13	0.329968433638208	0.0420770458876693	7.84200569876284	1.25311649520605e-14	***
df.mm.trans1:probe14	0.311792810421864	0.0420770458876693	7.4100451646306	2.91048700794211e-13	***
df.mm.trans1:probe15	0.289012533819020	0.0420770458876693	6.8686507743576	1.20898865376685e-11	***
df.mm.trans1:probe16	0.359335428955541	0.0420770458876693	8.53993956502647	5.68187099101683e-17	***
df.mm.trans1:probe17	0.292750508356855	0.0420770458876693	6.95748720426797	6.66935486372037e-12	***
df.mm.trans1:probe18	0.246332055083408	0.0420770458876693	5.8543096333575	6.70988544209255e-09	***
df.mm.trans1:probe19	0.431834140151546	0.0420770458876693	10.2629386412818	1.91652411020897e-23	***
df.mm.trans1:probe20	0.551475325570262	0.0420770458876693	13.1063223174579	4.84536101289346e-36	***
df.mm.trans1:probe21	0.355926778519619	0.0420770458876693	8.45892982767414	1.08393070997934e-16	***
df.mm.trans1:probe22	0.364849865345087	0.0420770458876693	8.6709952575831	1.9771067046049e-17	***
df.mm.trans2:probe2	-0.0054553768914204	0.0420770458876693	-0.129652088836852	0.896870692095574	   
df.mm.trans2:probe3	0.159347708594563	0.0420770458876693	3.78704600650827	0.000162579003836414	***
df.mm.trans2:probe4	0.0292337930886533	0.0420770458876693	0.694768191823568	0.487380102100656	   
df.mm.trans2:probe5	0.192484641645098	0.0420770458876693	4.57457593764932	5.44101320642426e-06	***
df.mm.trans2:probe6	0.0743535809673635	0.0420770458876693	1.76708177579436	0.0775537749552691	.  
df.mm.trans3:probe2	0.106486755851168	0.0420770458876693	2.53075646364172	0.0115512636770364	*  
df.mm.trans3:probe3	-0.0253180919494278	0.0420770458876693	-0.601707924482582	0.547520278710496	   
df.mm.trans3:probe4	-0.151770888396224	0.0420770458876693	-3.60697585095203	0.000326856115554329	***
df.mm.trans3:probe5	-0.507333238220027	0.0420770458876693	-12.0572446928529	3.87959414899674e-31	***
df.mm.trans3:probe6	-0.268104992588068	0.0420770458876693	-6.37176367618138	2.98053315331739e-10	***
df.mm.trans3:probe7	-0.59414232892878	0.0420770458876693	-14.1203432036301	4.88964691796043e-41	***
df.mm.trans3:probe8	-0.670649368040517	0.0420770458876693	-15.9386039084329	1.47167054565804e-50	***
df.mm.trans3:probe9	-0.444633154372296	0.0420770458876693	-10.5671190786375	1.10589491136900e-24	***
df.mm.trans3:probe10	-0.568305875940121	0.0420770458876693	-13.5063159485411	5.53927403539205e-38	***
df.mm.trans3:probe11	-0.197395414786594	0.0420770458876693	-4.69128501353374	3.13677952675752e-06	***
df.mm.trans3:probe12	-0.472105106994787	0.0420770458876693	-11.2200154985961	1.96201000540231e-27	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.05651285615811	0.131329051417754	30.8881607867132	2.31928602110524e-143	***
df.mm.trans1	0.152098601312266	0.112966675473045	1.34640238526413	0.178512146697687	   
df.mm.trans2	0.134470846138158	0.0993681342427333	1.35325924314607	0.176312999880747	   
df.mm.exp2	0.0695030819501016	0.126835271636347	0.547979131147175	0.583842263419138	   
df.mm.exp3	0.0594348193903491	0.126835271636347	0.468598510678924	0.639470242025149	   
df.mm.exp4	-0.0423776699397406	0.126835271636347	-0.334115813314475	0.738370091949565	   
df.mm.exp5	-0.105703928535100	0.126835271636347	-0.833395373159026	0.404843144270303	   
df.mm.exp6	0.00740236768376128	0.126835271636347	0.0583620596089769	0.953473204908325	   
df.mm.exp7	-0.0057265577580511	0.126835271636347	-0.0451495682878329	0.96399811853314	   
df.mm.exp8	0.164985657324670	0.126835271636347	1.30078688046417	0.193664732030004	   
df.mm.trans1:exp2	-0.0544294021754824	0.116671480742272	-0.466518482744873	0.640957446232234	   
df.mm.trans2:exp2	-0.140900806326085	0.0837398542232817	-1.68260152388600	0.092799494394625	.  
df.mm.trans1:exp3	-0.0345419645975383	0.116671480742272	-0.296061765718409	0.767251286835845	   
df.mm.trans2:exp3	-0.0809908032619794	0.0837398542232817	-0.967171533951178	0.333718400066938	   
df.mm.trans1:exp4	0.0368129564290801	0.116671480742272	0.315526606801195	0.752435082912197	   
df.mm.trans2:exp4	-0.0515525645069261	0.0837398542232817	-0.615627588381845	0.538296157486515	   
df.mm.trans1:exp5	0.105916603158053	0.116671480742272	0.907819138697855	0.364217064484287	   
df.mm.trans2:exp5	-0.0155445705982138	0.0837398542232818	-0.185629300915263	0.852777347226527	   
df.mm.trans1:exp6	0.00441469387363054	0.116671480742272	0.0378386718463154	0.96982471128374	   
df.mm.trans2:exp6	-0.0698479890690948	0.0837398542232817	-0.834106886344153	0.404442328511112	   
df.mm.trans1:exp7	-0.0140288830837682	0.116671480742272	-0.120242607657977	0.904317833148045	   
df.mm.trans2:exp7	0.0384957889379511	0.0837398542232817	0.459706901749637	0.645837763002555	   
df.mm.trans1:exp8	-0.148129266866359	0.116671480742272	-1.26962704102109	0.204546062986383	   
df.mm.trans2:exp8	-0.100274947955232	0.0837398542232817	-1.19745787576680	0.231443772346459	   
df.mm.trans1:probe2	0.117354636972745	0.0824991952039362	1.42249432473429	0.155229671644593	   
df.mm.trans1:probe3	0.0471771866828525	0.0824991952039362	0.571850265523579	0.567566371467015	   
df.mm.trans1:probe4	0.100363847695259	0.0824991952039362	1.21654335472197	0.224097301507074	   
df.mm.trans1:probe5	0.0141013253391054	0.0824991952039362	0.170926823034422	0.86431976723534	   
df.mm.trans1:probe6	-0.0338106185116987	0.0824991952039362	-0.409829676860721	0.682028530797406	   
df.mm.trans1:probe7	0.102637697597526	0.0824991952039362	1.24410544059016	0.213785033601065	   
df.mm.trans1:probe8	-0.0124980478037006	0.0824991952039362	-0.151492966359316	0.879620846578536	   
df.mm.trans1:probe9	-0.0761773705168755	0.0824991952039362	-0.9233710744519	0.356061616671979	   
df.mm.trans1:probe10	-0.0321973249396270	0.0824991952039362	-0.390274412496219	0.696426094420709	   
df.mm.trans1:probe11	0.0652198471259805	0.0824991952039362	0.790551313437161	0.429414356945148	   
df.mm.trans1:probe12	0.00635004045214999	0.0824991952039362	0.0769709381582794	0.938663790721908	   
df.mm.trans1:probe13	0.00628864947546706	0.0824991952039361	0.0762267978484112	0.93925562294984	   
df.mm.trans1:probe14	0.0273971015351455	0.0824991952039362	0.332089318779662	0.739899224990071	   
df.mm.trans1:probe15	0.00823137581727387	0.0824991952039362	0.0997752256482757	0.920545021448341	   
df.mm.trans1:probe16	0.0147162309355785	0.0824991952039362	0.178380296913204	0.858464510230296	   
df.mm.trans1:probe17	0.0396529731466593	0.0824991952039362	0.480646787506691	0.630884499077425	   
df.mm.trans1:probe18	-0.104130539113165	0.0824991952039362	-1.26220066578536	0.20720392942828	   
df.mm.trans1:probe19	0.0254828478103264	0.0824991952039362	0.308886016976691	0.757479800795897	   
df.mm.trans1:probe20	0.0896078177101155	0.0824991952039361	1.08616596184492	0.277696723694716	   
df.mm.trans1:probe21	-0.0153685934918753	0.0824991952039362	-0.186287798976517	0.852261101467493	   
df.mm.trans1:probe22	0.0111817861947479	0.0824991952039362	0.135538124549055	0.892216764837944	   
df.mm.trans2:probe2	0.079582751235596	0.0824991952039362	0.96464881916568	0.334980148229883	   
df.mm.trans2:probe3	0.14302299195415	0.0824991952039361	1.73362893541689	0.0833267906270985	.  
df.mm.trans2:probe4	0.172885191684918	0.0824991952039361	2.09559852381044	0.0363969984628497	*  
df.mm.trans2:probe5	0.139068130304821	0.0824991952039362	1.68569075081336	0.092202390697021	.  
df.mm.trans2:probe6	0.0807130482294428	0.0824991952039362	0.978349522439848	0.328164679259036	   
df.mm.trans3:probe2	-0.0419829135760085	0.0824991952039361	-0.508888765184044	0.610955062522277	   
df.mm.trans3:probe3	-0.0836845327859126	0.0824991952039361	-1.01436786842643	0.310680168124085	   
df.mm.trans3:probe4	0.0117406934646851	0.0824991952039361	0.142312824211950	0.886864804326445	   
df.mm.trans3:probe5	-0.0944874733259561	0.0824991952039361	-1.14531387963707	0.252383957707624	   
df.mm.trans3:probe6	-0.152103534917305	0.0824991952039361	-1.84369719657639	0.0655562330498288	.  
df.mm.trans3:probe7	-0.110408397398835	0.0824991952039361	-1.33829666005721	0.181138123132893	   
df.mm.trans3:probe8	-0.0590791638014595	0.0824991952039362	-0.71611806218736	0.474104421344032	   
df.mm.trans3:probe9	-0.0251538010490257	0.0824991952039362	-0.304897532477088	0.760514766428285	   
df.mm.trans3:probe10	0.0265181363359761	0.0824991952039361	0.32143509122027	0.747955409998336	   
df.mm.trans3:probe11	-0.00223361188014145	0.0824991952039362	-0.0270743475087243	0.978406443631795	   
df.mm.trans3:probe12	-0.0282420409923278	0.0824991952039362	-0.342331109079478	0.732181743218448