fitVsDatCorrelation=0.83568247941335
cont.fitVsDatCorrelation=0.268873738925648

fstatistic=10468.7911159903,65,991
cont.fstatistic=3393.53562338689,65,991

residuals=-0.485977111591721,-0.0923868218966118,-0.00316298424135673,0.081464328687005,1.02979041056684
cont.residuals=-0.606095051983207,-0.190557540855391,-0.0500214425181144,0.143653766204142,1.31001607975523

predictedValues:
Include	Exclude	Both
Lung	48.6359330613166	49.619397103052	62.7487925112563
cerebhem	56.8420708747791	61.0326091114228	53.6004816510369
cortex	51.2402743693649	48.0226137127195	70.1689409038637
heart	55.7565974922809	49.8107962644226	91.2884414159256
kidney	51.4449262353686	49.7728998434475	72.3822459583632
liver	49.8837885452436	55.2953859701796	59.9710619996719
stomach	55.2388549466644	50.508348710039	58.7145001876216
testicle	53.2407815239015	53.341641333658	58.318834894102


diffExp=-0.983464041735381,-4.19053823664375,3.21766065664538,5.94580122785825,1.67202639192116,-5.41159742493601,4.7305062366254,-0.100859809756564
diffExpScore=4.46505616961510
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	54.3808045563035	53.1419357095028	55.2330384250519
cerebhem	52.9625681316226	63.4046022736916	59.2383743263101
cortex	57.1562179871241	60.4493303779898	59.0305914001596
heart	54.9356943193116	60.6740941585726	62.7750450707953
kidney	59.9472385814891	55.6976022295191	58.8543237860071
liver	55.7987832978301	65.9622254903139	60.4067164679608
stomach	59.940929784154	66.0277103179422	60.6085485345065
testicle	55.595943957862	65.5628554162857	53.3996517017417
cont.diffExp=1.23886884680061,-10.4420341420690,-3.29311239086578,-5.73839983926096,4.24963635197002,-10.1634421924838,-6.08678053378826,-9.96691145842369
cont.diffExpScore=1.24214766115680

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=0.413218443879467
cont.tran.correlation=0.00208808804692384

tran.covariance=0.00175131154586421
cont.tran.covariance=9.56357658313483e-06

tran.mean=52.4804324436163
cont.tran.mean=58.8524085368447

weightedLogRatios:
wLogRatio
Lung	-0.0779622428999204
cerebhem	-0.289921040442312
cortex	0.253195700176044
heart	0.447065669111563
kidney	0.129653324375596
liver	-0.407976701855461
stomach	0.355148683439928
testicle	-0.00752461078912484

cont.weightedLogRatios:
wLogRatio
Lung	0.0918220180673905
cerebhem	-0.730522241093757
cortex	-0.228203157781644
heart	-0.40296196710643
kidney	0.298279973490834
liver	-0.686960160722415
stomach	-0.400565888276001
testicle	-0.676178448609115

varWeightedLogRatios=0.0919117209414578
cont.varWeightedLogRatios=0.142839750086447

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.76380818881173	0.0728491152561706	51.6658050763756	1.99945665489552e-283	***
df.mm.trans1	-0.000101705282213162	0.0623362304030904	-0.00163155971343624	0.99869853264548	   
df.mm.trans2	0.159588453644547	0.0545070450515633	2.92785003284579	0.00349120946861902	** 
df.mm.exp2	0.52052186479943	0.0688279975189276	7.56264723023922	8.99064715860616e-14	***
df.mm.exp3	-0.0923130393159355	0.0688279975189276	-1.34121349804706	0.180158440680720	   
df.mm.exp4	-0.234401796585848	0.0688279975189276	-3.40561697325841	0.000686638301579892	***
df.mm.exp5	-0.083583663761365	0.0688279975189276	-1.21438465122249	0.224890167537434	   
df.mm.exp6	0.178918279906774	0.0688279975189276	2.59949855227986	0.00947480993613347	** 
df.mm.exp7	0.211513410926528	0.0688279975189276	3.07307227510669	0.00217658435511737	** 
df.mm.exp8	0.236011738386592	0.0688279975189276	3.42900777146232	0.000630738593251631	***
df.mm.trans1:exp2	-0.364607749942082	0.062874394894645	-5.79898622568112	8.96515173126068e-09	***
df.mm.trans2:exp2	-0.3134953957724	0.0431537519702184	-7.26461504410443	7.56631970638553e-13	***
df.mm.trans1:exp3	0.144476249809959	0.062874394894645	2.29785511338994	0.0217776044919076	*  
df.mm.trans2:exp3	0.0596032303883523	0.0431537519702184	1.38118304126803	0.167534038371252	   
df.mm.trans1:exp4	0.37103491947953	0.062874394894645	5.90120859375668	4.94958430256485e-09	***
df.mm.trans2:exp4	0.238251721669908	0.0431537519702184	5.52099668724824	4.30507618019008e-08	***
df.mm.trans1:exp5	0.139732884620314	0.062874394894645	2.22241319148210	0.0264804744359839	*  
df.mm.trans2:exp5	0.0866724919131832	0.0431537519702184	2.00845785026985	0.0448655796883669	*  
df.mm.trans1:exp6	-0.153584829900259	0.062874394894645	-2.44272458061206	0.0147506178821586	*  
df.mm.trans2:exp6	-0.0706106391121836	0.0431537519702184	-1.63625724041131	0.102103280494857	   
df.mm.trans1:exp7	-0.0842094324128865	0.062874394894645	-1.33932791804981	0.180771028306174	   
df.mm.trans2:exp7	-0.193756595227096	0.0431537519702184	-4.4899130754798	7.96202728894385e-06	***
df.mm.trans1:exp8	-0.145549686819116	0.062874394894645	-2.31492783450251	0.0208204535779402	*  
df.mm.trans2:exp8	-0.163676276855197	0.0431537519702184	-3.7928631783432	0.000157932063245073	***
df.mm.trans1:probe2	0.366609346387975	0.0464358180720312	7.89496904780036	7.66883727137307e-15	***
df.mm.trans1:probe3	0.100070562965871	0.0464358180720312	2.15502961120749	0.0313998377224118	*  
df.mm.trans1:probe4	0.597177144034215	0.0464358180720312	12.8602696975829	4.07810906500299e-35	***
df.mm.trans1:probe5	0.367265377972111	0.0464358180720312	7.90909675376903	6.89306506297513e-15	***
df.mm.trans1:probe6	0.301996675105216	0.0464358180720312	6.50352869064909	1.24225112542164e-10	***
df.mm.trans1:probe7	0.502792287901587	0.0464358180720312	10.8276823533432	6.70875802702558e-26	***
df.mm.trans1:probe8	0.269105287390778	0.0464358180720312	5.79520935699553	9.1624898804541e-09	***
df.mm.trans1:probe9	0.250740816929313	0.0464358180720312	5.39972864353039	8.3552914539514e-08	***
df.mm.trans1:probe10	0.371253126932321	0.0464358180720312	7.99497332762466	3.59214537740622e-15	***
df.mm.trans1:probe11	0.0517393709885673	0.0464358180720312	1.11421254403894	0.265458168020198	   
df.mm.trans1:probe12	0.52967248137664	0.0464358180720312	11.4065500160891	2.09947939736301e-28	***
df.mm.trans1:probe13	0.331528245084138	0.0464358180720312	7.1394940123564	1.80999587246552e-12	***
df.mm.trans1:probe14	0.124174035829534	0.0464358180720312	2.67410031706377	0.00761630507901905	** 
df.mm.trans1:probe15	0.210043648088061	0.0464358180720312	4.52331103033958	6.82455687233436e-06	***
df.mm.trans1:probe16	0.0485777216984654	0.0464358180720312	1.04612610944232	0.295757900994342	   
df.mm.trans1:probe17	0.0433276967670792	0.0464358180720312	0.933066295932793	0.351013012495954	   
df.mm.trans1:probe18	0.0336428028301327	0.0464358180720312	0.724501133541915	0.468929141346762	   
df.mm.trans1:probe19	-0.0440739716601933	0.0464358180720312	-0.9491374006123	0.342782133193553	   
df.mm.trans1:probe20	0.0155730974209949	0.0464358180720312	0.335368215045504	0.737418321481177	   
df.mm.trans1:probe21	0.0957717796547261	0.0464358180720312	2.06245488140566	0.0394246152628294	*  
df.mm.trans1:probe22	0.0179456944958878	0.0464358180720312	0.386462331040457	0.699237256114585	   
df.mm.trans2:probe2	-0.097839302936944	0.0464358180720312	-2.10697920267445	0.0353701963396474	*  
df.mm.trans2:probe3	-0.0850216550886587	0.0464358180720312	-1.83094987056701	0.0674081566006095	.  
df.mm.trans2:probe4	-0.0693385746604634	0.0464358180720312	-1.49321316042942	0.135699693099728	   
df.mm.trans2:probe5	-0.00887584395224647	0.0464358180720312	-0.191142189817315	0.848453346551268	   
df.mm.trans2:probe6	-0.157250543407454	0.0464358180720312	-3.38640622554612	0.000735961944306128	***
df.mm.trans3:probe2	0.269035893085800	0.0464358180720312	5.79371494367712	9.2417338669923e-09	***
df.mm.trans3:probe3	0.151190812657018	0.0464358180720312	3.25590931600453	0.00116860640014874	** 
df.mm.trans3:probe4	0.485015007261093	0.0464358180720312	10.4448468315716	2.67664655545604e-24	***
df.mm.trans3:probe5	0.0627433361600833	0.0464358180720312	1.35118403777782	0.17694486930742	   
df.mm.trans3:probe6	0.0508215786985774	0.0464358180720312	1.09444779501339	0.274024566371847	   
df.mm.trans3:probe7	-0.0987156002271514	0.0464358180720312	-2.12585035271746	0.0337624593348053	*  
df.mm.trans3:probe8	-0.0143309793354608	0.0464358180720312	-0.308619077480892	0.75767617044651	   
df.mm.trans3:probe9	0.262679739615573	0.0464358180720312	5.65683454113167	2.01697227334946e-08	***
df.mm.trans3:probe10	0.441646385915656	0.0464358180720312	9.51089922073893	1.38286365326885e-20	***
df.mm.trans3:probe11	0.122310897994249	0.0464358180720312	2.6339774568959	0.00857063951469796	** 
df.mm.trans3:probe12	0.489093569133873	0.0464358180720312	10.5326790706086	1.15948368742537e-24	***
df.mm.trans3:probe13	0.78056421771209	0.0464358180720312	16.8095287241689	5.57907278525275e-56	***
df.mm.trans3:probe14	-0.0215707881796648	0.0464358180720312	-0.464529087141401	0.642370760890657	   
df.mm.trans3:probe15	0.198521582644006	0.0464358180720312	4.2751821952627	2.09429676770380e-05	***
df.mm.trans3:probe16	0.274504711731084	0.0464358180720312	5.91148650176191	4.66025287848402e-09	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.79859682508426	0.127758251670441	29.7326926082468	2.21386941395275e-139	***
df.mm.trans1	0.143099055994966	0.109321407460059	1.30897560980678	0.190846055015355	   
df.mm.trans2	0.174285419915615	0.0955910686769781	1.82323958009677	0.0685682540445359	.  
df.mm.exp2	0.0801360013728266	0.120706265245285	0.663892642274896	0.506913446474978	   
df.mm.exp3	0.112121573362519	0.120706265245285	0.928879483883287	0.35317773657936	   
df.mm.exp4	0.0147062020732318	0.120706265245285	0.121834620956481	0.903054694662108	   
df.mm.exp5	0.0809203101340093	0.120706265245285	0.670390306332258	0.502765204125653	   
df.mm.exp6	0.152317699005896	0.120706265245285	1.26188726572207	0.207286267560992	   
df.mm.exp7	0.221581828838174	0.120706265245285	1.83571108250182	0.0666998930060005	.  
df.mm.exp8	0.265899027856732	0.120706265245285	2.20286020213121	0.0278341564771541	*  
df.mm.trans1:exp2	-0.106561833180643	0.110265206905122	-0.966413941183945	0.334072868761061	   
df.mm.trans2:exp2	0.0964340822356026	0.075680368736777	1.27422849340241	0.202881174945775	   
df.mm.trans1:exp3	-0.0623446220377697	0.110265206905122	-0.565406113021798	0.57192554878139	   
df.mm.trans2:exp3	0.0167175600819086	0.075680368736777	0.220896916346348	0.82521820090898	   
df.mm.trans1:exp4	-0.00455412924804027	0.110265206905122	-0.0413015979914576	0.967063778472447	   
df.mm.trans2:exp4	0.117844253758792	0.075680368736777	1.55713107277086	0.119758646955451	   
df.mm.trans1:exp5	0.0165332740795791	0.110265206905122	0.149940988128786	0.880841683581325	   
df.mm.trans2:exp5	-0.0339495783392006	0.075680368736777	-0.448591608443138	0.65382435538577	   
df.mm.trans1:exp6	-0.126576868726919	0.110265206905122	-1.14793117683832	0.251274005995484	   
df.mm.trans2:exp6	0.0637981718471784	0.075680368736777	0.842994992123704	0.399434658323461	   
df.mm.trans1:exp7	-0.124233489327660	0.110265206905122	-1.12667896623598	0.260150981998323	   
df.mm.trans2:exp7	-0.00447368789116589	0.075680368736777	-0.0591129240758031	0.952874082353282	   
df.mm.trans1:exp8	-0.243800013831546	0.110265206905122	-2.21103302369281	0.0272612379447804	*  
df.mm.trans2:exp8	-0.0558560869526498	0.075680368736777	-0.738052521214877	0.460657265916751	   
df.mm.trans1:probe2	0.152823715698308	0.0814362523265768	1.87660545926711	0.0608658553619823	.  
df.mm.trans1:probe3	0.181408331500606	0.0814362523265768	2.22761149141686	0.0261303184988788	*  
df.mm.trans1:probe4	0.0591307379607028	0.0814362523265768	0.72609846685449	0.467949857334569	   
df.mm.trans1:probe5	0.0744383557860494	0.0814362523265768	0.914069025273114	0.360902913772132	   
df.mm.trans1:probe6	0.000351361803283109	0.0814362523265768	0.00431456253504978	0.99655835619718	   
df.mm.trans1:probe7	0.077323267100351	0.0814362523265768	0.949494419147238	0.342600698821547	   
df.mm.trans1:probe8	0.159680836993730	0.0814362523265768	1.96080777825305	0.0501812208666612	.  
df.mm.trans1:probe9	0.154335047437215	0.0814362523265768	1.89516392304374	0.0583615020833137	.  
df.mm.trans1:probe10	0.0933917489683814	0.0814362523265768	1.14680804064829	0.25173777247663	   
df.mm.trans1:probe11	0.136434985989088	0.0814362523265768	1.67535934048087	0.0941792163861506	.  
df.mm.trans1:probe12	0.138431793020131	0.0814362523265768	1.69987921920805	0.0894674408591343	.  
df.mm.trans1:probe13	0.00495910747031084	0.0814362523265768	0.0608955757249702	0.951454651302226	   
df.mm.trans1:probe14	0.0461151163224924	0.0814362523265768	0.566272575235423	0.571336710186074	   
df.mm.trans1:probe15	0.113822233311586	0.0814362523265768	1.39768506113388	0.162520448795611	   
df.mm.trans1:probe16	0.0286734237622342	0.0814362523265768	0.352096553353752	0.724840711946949	   
df.mm.trans1:probe17	0.129099505859921	0.0814362523265768	1.58528299340452	0.113221076892171	   
df.mm.trans1:probe18	0.0996113957786555	0.0814362523265768	1.22318246398658	0.221551567423883	   
df.mm.trans1:probe19	0.17164722208697	0.0814362523265768	2.107749524114	0.0353033098525242	*  
df.mm.trans1:probe20	0.0690679462368563	0.0814362523265768	0.848122847793622	0.396574356965461	   
df.mm.trans1:probe21	0.0317750799668981	0.0814362523265768	0.390183475529709	0.696484728871588	   
df.mm.trans1:probe22	0.141462204478522	0.0814362523265768	1.73709128842556	0.0826817629816043	.  
df.mm.trans2:probe2	0.0184173231606135	0.0814362523265768	0.226156320243669	0.821126477221254	   
df.mm.trans2:probe3	0.0705291984474141	0.0814362523265768	0.866066357825221	0.386663395950789	   
df.mm.trans2:probe4	-0.0353570124830565	0.0814362523265768	-0.434167971547454	0.664260977095968	   
df.mm.trans2:probe5	-0.0086722420136343	0.0814362523265768	-0.106491172737870	0.91521421131425	   
df.mm.trans2:probe6	-0.0430665992160825	0.0814362523265768	-0.528838176926122	0.59703613256403	   
df.mm.trans3:probe2	-0.207254347634374	0.0814362523265768	-2.54498876990605	0.0110787669742794	*  
df.mm.trans3:probe3	-0.134215843036132	0.0814362523265768	-1.64810927813694	0.0996472271600446	.  
df.mm.trans3:probe4	-0.110731969325852	0.0814362523265768	-1.35973803020544	0.174222114575669	   
df.mm.trans3:probe5	-0.169372024707473	0.0814362523265768	-2.07981113900299	0.0377994271546437	*  
df.mm.trans3:probe6	-0.0293381273803504	0.0814362523265768	-0.360258810323175	0.718730365591921	   
df.mm.trans3:probe7	-0.110996902050822	0.0814362523265768	-1.36299128311677	0.173194863378097	   
df.mm.trans3:probe8	-0.0703167515643712	0.0814362523265768	-0.863457607091077	0.388094841730724	   
df.mm.trans3:probe9	-0.122724412148975	0.0814362523265768	-1.50699975309305	0.132129410961887	   
df.mm.trans3:probe10	-0.109301237561244	0.0814362523265768	-1.34216929731642	0.179848510980388	   
df.mm.trans3:probe11	-0.110574452834094	0.0814362523265768	-1.35780379959857	0.174835025988089	   
df.mm.trans3:probe12	-0.204457569463564	0.0814362523265768	-2.51064561079807	0.0122093509864013	*  
df.mm.trans3:probe13	-0.135881189168502	0.0814362523265768	-1.6685589683522	0.095520695340266	.  
df.mm.trans3:probe14	-0.0245868946723444	0.0814362523265768	-0.301915841777021	0.762779565900257	   
df.mm.trans3:probe15	-0.0875174620958056	0.0814362523265768	-1.07467447967573	0.282782078101060	   
df.mm.trans3:probe16	-0.0434508893447	0.0814362523265768	-0.533557084263316	0.593767651855575