fitVsDatCorrelation=0.942528969842811
cont.fitVsDatCorrelation=0.228573202123562

fstatistic=9385.71639082202,74,1198
cont.fstatistic=1091.29264703045,74,1198

residuals=-0.792948718578174,-0.0969524813543453,-0.00953276073386471,0.0795848852611438,1.12787800800630
cont.residuals=-0.860189333743112,-0.314391588007992,-0.14362066124601,0.105596970444203,2.0769356594982

predictedValues:
Include	Exclude	Both
Lung	69.312159899917	70.5799280891919	68.5174730992791
cerebhem	63.6771527611958	84.1766062909689	73.8652876686111
cortex	61.462489437765	56.9221815673712	65.3484342021807
heart	63.0629549444948	61.0943225140754	61.3395784907099
kidney	64.7214306618453	63.275093166313	59.8254823425234
liver	61.0395535113417	62.860312277569	56.718115889956
stomach	60.1632104203073	60.6118106024651	65.1390497011099
testicle	61.5678832632353	63.9223477531646	60.984605397051


diffExp=-1.26776818927496,-20.4994535297731,4.54030787039374,1.96863243041947,1.44633749553234,-1.82075876622733,-0.448600182157861,-2.35446448992924
diffExpScore=1.76717092336667
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,-1,0,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,-1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	63.528195156251	75.3936856002249	71.9267554750707
cerebhem	67.877244370806	63.9608594079191	67.2630366434384
cortex	62.0265144061884	73.5594035705764	65.5227434716305
heart	63.47870840243	80.3704145899163	70.0802498981573
kidney	64.4869800624248	76.314509386604	71.0206922476628
liver	65.6718743775394	85.1798496510321	76.665108981584
stomach	62.537677709138	84.5451648434058	74.1824977367593
testicle	70.7752084445748	82.293295295531	69.3996094513526
cont.diffExp=-11.8654904439739,3.91638496288686,-11.5328891643880,-16.8917061874862,-11.8275293241792,-19.5079752734927,-22.0074871342679,-11.5180868509563
cont.diffExpScore=1.06683410444874

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,-1,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,-1,0,-1,-1,0
cont.diffExp1.2Score=0.75

tran.correlation=0.433434689800562
cont.tran.correlation=-0.0856256479338429

tran.covariance=0.00254052829296900
cont.tran.covariance=-0.000466080117278

tran.mean=64.2780898225763
cont.tran.mean=71.3749740796601

weightedLogRatios:
wLogRatio
Lung	-0.0769911292738563
cerebhem	-1.19824226724218
cortex	0.313111506613778
heart	0.130926512058899
kidney	0.093991192412575
liver	-0.121281639891851
stomach	-0.0304635352319424
testicle	-0.155327594074220

cont.weightedLogRatios:
wLogRatio
Lung	-0.725560364828999
cerebhem	0.248890000331378
cortex	-0.718419165490308
heart	-1.00715801188396
kidney	-0.715810574090844
liver	-1.12223289538303
stomach	-1.29245950627286
testicle	-0.653619837192351

varWeightedLogRatios=0.209882386609429
cont.varWeightedLogRatios=0.215537228112587

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93822471586676	0.082449280841726	47.7654222773245	1.19802413351901e-279	***
df.mm.trans1	-0.0600188977184335	0.0713469427166293	-0.841225922697379	0.400389257904185	   
df.mm.trans2	0.30944160637999	0.0613378552508202	5.04487163945714	5.23927354528856e-07	***
df.mm.exp2	0.0162225159641092	0.077012989006101	0.210646491890141	0.833198952965151	   
df.mm.exp3	-0.287898682565732	0.077012989006101	-3.73831331936648	0.000194001182292104	***
df.mm.exp4	-0.128150197297189	0.077012989006101	-1.66400757782609	0.0963724348137632	.  
df.mm.exp5	-0.0421249028191648	0.077012989006101	-0.546984390072532	0.584491325948313	   
df.mm.exp6	-0.0539339557359818	0.077012989006101	-0.70032284724995	0.4838616851567	   
df.mm.exp7	-0.243250762991460	0.077012989006101	-3.15856800431664	0.00162509614271043	** 
df.mm.exp8	-0.101089435708713	0.077012989006101	-1.31262838922801	0.189559603511271	   
df.mm.trans1:exp2	-0.101017045426716	0.0723221170075284	-1.39676560375300	0.162742757461504	   
df.mm.trans2:exp2	0.159948732265459	0.0468501567116204	3.41404903402995	0.000661450758892329	***
df.mm.trans1:exp3	0.16770538512083	0.0723221170075284	2.3188672021779	0.0205698272636372	*  
df.mm.trans2:exp3	0.0728379825655579	0.0468501567116204	1.55470093758495	0.120281431271094	   
df.mm.trans1:exp4	0.0336633512347546	0.0723221170075284	0.465464129475778	0.64168372008295	   
df.mm.trans2:exp4	-0.0161766614438904	0.0468501567116204	-0.345285108510173	0.729940655556644	   
df.mm.trans1:exp5	-0.0264030776038997	0.0723221170075284	-0.365076116358033	0.715119028180834	   
df.mm.trans2:exp5	-0.0671291177388144	0.0468501567116204	-1.43284724002138	0.152162389037082	   
df.mm.trans1:exp6	-0.0731643303387532	0.0723221170075284	-1.01164530804784	0.311911948428675	   
df.mm.trans2:exp6	-0.061896844471803	0.0468501567116204	-1.32116622048461	0.186698224439953	   
df.mm.trans1:exp7	0.101691447573956	0.0723221170075284	1.40609058171473	0.159956540485753	   
df.mm.trans2:exp7	0.0909947321161036	0.0468501567116204	1.94225032535556	0.0523409198231369	.  
df.mm.trans1:exp8	-0.0173905636466592	0.0723221170075284	-0.240459825655393	0.810014945167069	   
df.mm.trans2:exp8	0.00201266665606812	0.0468501567116204	0.0429596568578588	0.96574085208255	   
df.mm.trans1:probe2	-0.00285772642386271	0.0529343694475795	-0.0539862182866407	0.956955145462808	   
df.mm.trans1:probe3	0.176110585123842	0.0529343694475795	3.32696104556876	0.000904626331372904	***
df.mm.trans1:probe4	0.198169098862999	0.0529343694475795	3.7436754405707	0.000189952625440122	***
df.mm.trans1:probe5	0.204650021013603	0.0529343694475795	3.86610860107187	0.000116523501646086	***
df.mm.trans1:probe6	0.296662667939083	0.0529343694475795	5.60434876310118	2.59208688913665e-08	***
df.mm.trans1:probe7	0.170939470254886	0.0529343694475795	3.22927187078645	0.00127474520633079	** 
df.mm.trans1:probe8	0.606170573073349	0.0529343694475795	11.4513609852978	6.92324318901447e-29	***
df.mm.trans1:probe9	0.629118330601016	0.0529343694475795	11.8848743673810	7.13668257072322e-31	***
df.mm.trans1:probe10	0.607715210945201	0.0529343694475795	11.4805412303441	5.10967018695916e-29	***
df.mm.trans1:probe11	1.62083611701484	0.0529343694475795	30.6197303175576	1.43891060624982e-152	***
df.mm.trans1:probe12	2.12773703815712	0.0529343694475795	40.1957567524102	2.29696542736667e-224	***
df.mm.trans1:probe13	1.92307319250424	0.0529343694475795	36.3293869856833	1.92286472340798e-195	***
df.mm.trans1:probe14	1.74491674821519	0.0529343694475795	32.9637769642872	3.8969667694262e-170	***
df.mm.trans1:probe15	0.883603226701576	0.0529343694475795	16.6924294352198	2.10775761657783e-56	***
df.mm.trans1:probe16	1.98274028132077	0.0529343694475795	37.4565769274774	6.65888998174977e-204	***
df.mm.trans1:probe17	0.253791498292836	0.0529343694475795	4.7944558694359	1.83587536631623e-06	***
df.mm.trans1:probe18	0.287509899026693	0.0529343694475795	5.43144089609704	6.76442821340301e-08	***
df.mm.trans1:probe19	0.179923311085140	0.0529343694475795	3.39898846369214	0.000698596537184063	***
df.mm.trans1:probe20	0.081532052755344	0.0529343694475795	1.54024792599229	0.123764009832202	   
df.mm.trans1:probe21	0.0632470813213885	0.0529343694475795	1.19482071821072	0.232393570286080	   
df.mm.trans1:probe22	0.269662894452571	0.0529343694475795	5.09428745948539	4.06342333638511e-07	***
df.mm.trans1:probe23	-0.00698939881238213	0.0529343694475795	-0.132038954753276	0.894975651787099	   
df.mm.trans1:probe24	0.195579726166315	0.0529343694475795	3.69475877784842	0.000230026428892238	***
df.mm.trans1:probe25	0.328152827808317	0.0529343694475795	6.19923938327601	7.79293269636226e-10	***
df.mm.trans1:probe26	0.191666130915973	0.0529343694475795	3.62082580592139	0.000305931341054612	***
df.mm.trans1:probe27	0.226130211301069	0.0529343694475795	4.27189770391059	2.09165591305656e-05	***
df.mm.trans1:probe28	0.144283318223490	0.0529343694475795	2.72570202930958	0.00650999819194848	** 
df.mm.trans1:probe29	0.261568153376666	0.0529343694475795	4.94136713266596	8.8583725713646e-07	***
df.mm.trans1:probe30	0.129566927303835	0.0529343694475795	2.44769001040325	0.0145202670535662	*  
df.mm.trans1:probe31	0.578561832716539	0.0529343694475795	10.9297954949569	1.42280301506685e-26	***
df.mm.trans1:probe32	0.585712087512554	0.0529343694475795	11.0648732312299	3.65090446062084e-27	***
df.mm.trans2:probe2	0.269037351566595	0.0529343694475795	5.082470129979	4.31892334333872e-07	***
df.mm.trans2:probe3	0.0975256588047066	0.0529343694475795	1.84238822191479	0.0656653721347942	.  
df.mm.trans2:probe4	-0.0850499569117134	0.0529343694475795	-1.60670577168087	0.108382446178522	   
df.mm.trans2:probe5	-0.00396228101659705	0.0529343694475795	-0.0748527102135575	0.940344399419841	   
df.mm.trans2:probe6	-0.0868817524076087	0.0529343694475795	-1.64131080268458	0.100995391510085	   
df.mm.trans3:probe2	0.0856260612217394	0.0529343694475795	1.61758914133348	0.106014416920525	   
df.mm.trans3:probe3	0.204737814640611	0.0529343694475795	3.86776713838749	0.000115743795005024	***
df.mm.trans3:probe4	-0.226951762013827	0.0529343694475795	-4.28741787957965	1.95283127136718e-05	***
df.mm.trans3:probe5	0.0406156932048537	0.0529343694475795	0.76728397124056	0.443063883292031	   
df.mm.trans3:probe6	-0.0962312047239388	0.0529343694475795	-1.81793427839423	0.0693238442794056	.  
df.mm.trans3:probe7	-0.140627282388909	0.0529343694475795	-2.65663469417863	0.00799736189371461	** 
df.mm.trans3:probe8	0.143407259689793	0.0529343694475795	2.70915212906822	0.00684170873913983	** 
df.mm.trans3:probe9	-0.147992884680426	0.0529343694475795	-2.79578062844372	0.00526016239826928	** 
df.mm.trans3:probe10	-0.136501233693086	0.0529343694475795	-2.57868819667839	0.0100360853953875	*  
df.mm.trans3:probe11	-0.0501155274104073	0.0529343694475795	-0.946748359022891	0.343957940823091	   
df.mm.trans3:probe12	0.249793712670462	0.0529343694475795	4.71893243042842	2.64990143365031e-06	***
df.mm.trans3:probe13	0.358158376334739	0.0529343694475795	6.76608373864585	2.06495800305113e-11	***
df.mm.trans3:probe14	0.337877438229366	0.0529343694475795	6.38295008999709	2.47809519986290e-10	***
df.mm.trans3:probe15	-0.0301759117807241	0.0529343694475795	-0.570062741761893	0.568742031556197	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08668297473203	0.240229594684156	17.0115717012512	2.68611003013963e-58	***
df.mm.trans1	-0.0771639145109329	0.207881099213851	-0.371192546136928	0.710559755980177	   
df.mm.trans2	0.245322820555168	0.178717970069216	1.37268132835303	0.170108343179947	   
df.mm.exp2	-0.0311977202131494	0.224390060719463	-0.139033431842390	0.889447098399802	   
df.mm.exp3	0.0446988904806728	0.224390060719463	0.199201739762244	0.842138741407433	   
df.mm.exp4	0.0891506315802258	0.224390060719464	0.397302052035555	0.691215507395176	   
df.mm.exp5	0.0397961162414793	0.224390060719463	0.177352401946328	0.859261572447773	   
df.mm.exp6	0.0914299036194387	0.224390060719463	0.407459685719975	0.683743194394378	   
df.mm.exp7	0.0679678268698991	0.224390060719463	0.302900345282556	0.762018394681676	   
df.mm.exp8	0.231358138345254	0.224390060719464	1.03105341477002	0.302723877290426	   
df.mm.trans1:exp2	0.0974147385967178	0.210722430542123	0.462289364953223	0.643957769536857	   
df.mm.trans2:exp2	-0.133254481416692	0.136505668003667	-0.976182772228276	0.329170971336522	   
df.mm.trans1:exp3	-0.0686207707002038	0.210722430542123	-0.325645307543501	0.744749596100313	   
df.mm.trans2:exp3	-0.0693291249965922	0.136505668003667	-0.507884588314163	0.611627777028794	   
df.mm.trans1:exp4	-0.0899299082169847	0.210722430542123	-0.426769508996375	0.669623896756568	   
df.mm.trans2:exp4	-0.0252280270050497	0.136505668003667	-0.184813036513414	0.853406963192322	   
df.mm.trans1:exp5	-0.0248165978738209	0.210722430542123	-0.117769132645136	0.906270329346968	   
df.mm.trans2:exp5	-0.0276565598407861	0.136505668003667	-0.202603747120912	0.839479162781962	   
df.mm.trans1:exp6	-0.0582429870127449	0.210722430542123	-0.276396712314412	0.782291056613095	   
df.mm.trans2:exp6	0.0306114696485224	0.136505668003667	0.224250539162228	0.822600598385992	   
df.mm.trans1:exp7	-0.0836824339787982	0.210722430542123	-0.397121624705587	0.691348511339452	   
df.mm.trans2:exp7	0.0465945337378248	0.136505668003667	0.341337721863485	0.732909211115174	   
df.mm.trans1:exp8	-0.123333187739661	0.210722430542123	-0.585287420149639	0.558464696902968	   
df.mm.trans2:exp8	-0.143792026772744	0.136505668003667	-1.05337770127524	0.2923803261139	   
df.mm.trans1:probe2	0.260917517551831	0.154233026503463	1.69170976843907	0.0909613896061863	.  
df.mm.trans1:probe3	0.328789780307863	0.154233026503463	2.13177286189402	0.0332285298693727	*  
df.mm.trans1:probe4	0.354102485008554	0.154233026503463	2.29589273475486	0.0218540546796156	*  
df.mm.trans1:probe5	0.247941918085001	0.154233026503463	1.60757993087449	0.108190709723358	   
df.mm.trans1:probe6	0.179180516988524	0.154233026503463	1.16175193504681	0.245567646204768	   
df.mm.trans1:probe7	0.227644750221016	0.154233026503463	1.47597927228579	0.140212236983896	   
df.mm.trans1:probe8	0.227310479131659	0.154233026503463	1.47381196028436	0.140794931953287	   
df.mm.trans1:probe9	0.037094754627416	0.154233026503463	0.240511098487607	0.809975210623519	   
df.mm.trans1:probe10	0.227551995109139	0.154233026503463	1.47537787637221	0.140373739412260	   
df.mm.trans1:probe11	0.0748983602156636	0.154233026503463	0.485618170852544	0.627326710336725	   
df.mm.trans1:probe12	0.357277577354111	0.154233026503463	2.31647906712179	0.0207001783060237	*  
df.mm.trans1:probe13	0.0328972519020417	0.154233026503463	0.213295768408611	0.831132565149256	   
df.mm.trans1:probe14	0.426424038170053	0.154233026503463	2.76480367297	0.00578309525261086	** 
df.mm.trans1:probe15	0.278110831579971	0.154233026503463	1.80318598347499	0.0716101931884144	.  
df.mm.trans1:probe16	0.277496143232475	0.154233026503463	1.79920053132229	0.0722385443676719	.  
df.mm.trans1:probe17	0.179659741211555	0.154233026503463	1.16485907904764	0.244307936087162	   
df.mm.trans1:probe18	0.27529264507032	0.154233026503463	1.78491371991678	0.074528267813638	.  
df.mm.trans1:probe19	0.0239447096668764	0.154233026503463	0.155250209437722	0.876650219540264	   
df.mm.trans1:probe20	0.201898792993630	0.154233026503463	1.30905032191076	0.190768335848652	   
df.mm.trans1:probe21	0.200479854139262	0.154233026503463	1.29985035426093	0.193902307320861	   
df.mm.trans1:probe22	0.053314102966582	0.154233026503463	0.345672416441786	0.729649605461877	   
df.mm.trans1:probe23	0.231486015850041	0.154233026503463	1.50088486945981	0.133648841172455	   
df.mm.trans1:probe24	0.21019018912696	0.154233026503463	1.36280921079015	0.173198752983290	   
df.mm.trans1:probe25	0.134166532340606	0.154233026503463	0.86989495947934	0.384532067703306	   
df.mm.trans1:probe26	0.411085112877971	0.154233026503463	2.66535075007908	0.00779420563730945	** 
df.mm.trans1:probe27	0.107808071735597	0.154233026503463	0.698994723631233	0.484691005796572	   
df.mm.trans1:probe28	0.294842576256469	0.154233026503463	1.91166952332255	0.0561569644951664	.  
df.mm.trans1:probe29	0.101764522793799	0.154233026503463	0.659810191765343	0.50950246547661	   
df.mm.trans1:probe30	0.160063835584965	0.154233026503463	1.03780519136328	0.299570177878881	   
df.mm.trans1:probe31	0.350658119854189	0.154233026503463	2.27356051945409	0.0231687768396814	*  
df.mm.trans1:probe32	0.198050748993123	0.154233026503463	1.28410077583919	0.199354973975062	   
df.mm.trans2:probe2	-0.233864032733091	0.154233026503463	-1.5163032071334	0.129706508004827	   
df.mm.trans2:probe3	-0.0836926043511617	0.154233026503463	-0.542637373126323	0.58748043190408	   
df.mm.trans2:probe4	0.0125813229629994	0.154233026503463	0.081573468719535	0.934999525657504	   
df.mm.trans2:probe5	0.136735183529747	0.154233026503463	0.886549311970332	0.375499522770157	   
df.mm.trans2:probe6	-0.0266875212484313	0.154233026503463	-0.173033764903991	0.862654116656691	   
df.mm.trans3:probe2	0.064094983095062	0.154233026503463	0.415572361822407	0.677797376670591	   
df.mm.trans3:probe3	0.0382198308214189	0.154233026503463	0.247805750090502	0.804327171109885	   
df.mm.trans3:probe4	0.00845232905243908	0.154233026503463	0.0548023289437902	0.956305082455676	   
df.mm.trans3:probe5	-0.0303959240827967	0.154233026503463	-0.197077920156836	0.84379999117994	   
df.mm.trans3:probe6	0.133230837638725	0.154233026503463	0.863828199829388	0.387855187302269	   
df.mm.trans3:probe7	0.202853496022279	0.154233026503463	1.31524032576592	0.188680821404971	   
df.mm.trans3:probe8	-0.0567554974936593	0.154233026503463	-0.367985371099393	0.712949148081236	   
df.mm.trans3:probe9	-0.117966541428120	0.154233026503463	-0.764859149187944	0.444506057962162	   
df.mm.trans3:probe10	-0.0793114022521566	0.154233026503463	-0.514230992221215	0.607185343710266	   
df.mm.trans3:probe11	-0.00932243744182629	0.154233026503463	-0.0604438469060126	0.951812219080815	   
df.mm.trans3:probe12	-0.087253356020374	0.154233026503463	-0.565724203164845	0.571687219455633	   
df.mm.trans3:probe13	0.169616381428806	0.154233026503463	1.09974099110995	0.271665929057789	   
df.mm.trans3:probe14	0.111554103772391	0.154233026503463	0.723282855179444	0.469647313325541	   
df.mm.trans3:probe15	-0.0349202723269406	0.154233026503463	-0.226412417097687	0.820919295114617