fitVsDatCorrelation=0.849048279525695
cont.fitVsDatCorrelation=0.265076809857513

fstatistic=13045.6409524683,62,922
cont.fstatistic=3906.04623788763,62,922

residuals=-0.528530807498909,-0.0791705346424605,-0.00308922355968927,0.0754705808856444,0.9705349633274
cont.residuals=-0.54633195261858,-0.166356989292875,-0.048375280728982,0.123887032879163,1.59336734652624

predictedValues:
Include	Exclude	Both
Lung	70.0167306850888	41.4661010134361	51.3362520169355
cerebhem	68.9923808748697	43.3102324677567	57.5536137414766
cortex	92.095818403958	57.4374327610489	77.360657370756
heart	68.512661465552	46.3643557780192	52.834680917938
kidney	73.1555435569336	43.0607288282923	55.5488972016805
liver	75.5584993465289	43.9864196023068	53.6927516618497
stomach	70.2676589111397	47.8432058965085	53.6766911558599
testicle	74.2134156105548	43.9445881023239	53.4944360964984


diffExp=28.5506296716527,25.682148407113,34.6583856429092,22.1483056875328,30.0948147286413,31.5720797442221,22.4244530146312,30.2688275082309
diffExpScore=0.995583031931749
diffExp1.5=1,1,1,0,1,1,0,1
diffExp1.5Score=0.857142857142857
diffExp1.4=1,1,1,1,1,1,1,1
diffExp1.4Score=0.888888888888889
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	51.2520620895863	61.3142785951563	54.0046456058238
cerebhem	53.021246806299	57.4803690878037	56.8081963089106
cortex	52.3703208604629	63.9805569356392	57.4048769890359
heart	54.8668294136808	53.7171503441769	53.9059080882131
kidney	51.7755619229074	58.8073377394728	52.4353139382431
liver	51.1669390638728	64.2147426813127	54.7170429668319
stomach	51.0258774085098	57.6100012779451	54.8679087992036
testicle	52.2661365788116	56.0851037345422	55.829946535224
cont.diffExp=-10.0622165055700,-4.45912228150477,-11.6102360751763,1.14967906950391,-7.03177581656544,-13.0478036174399,-6.58412386943526,-3.81896715573063
cont.diffExpScore=1.02301192101982

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,-1,0,0,-1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.838101232520611
cont.tran.correlation=-0.61744922482493

tran.covariance=0.00792063279720009
cont.tran.covariance=-0.000958724588637363

tran.mean=60.0141108315199
cont.tran.mean=55.6846571587612

weightedLogRatios:
wLogRatio
Lung	2.08851970967163
cerebhem	1.86298385094678
cortex	2.02392354974283
heart	1.57435804303235
kidney	2.13453313381029
liver	2.19353357915954
stomach	1.56063934044925
testicle	2.11960962472930

cont.weightedLogRatios:
wLogRatio
Lung	-0.721757137467446
cerebhem	-0.323896821437757
cortex	-0.812662728239217
heart	0.0845862983669814
kidney	-0.510742444117353
liver	-0.919610200057614
stomach	-0.484605349143996
testicle	-0.281494564011338

varWeightedLogRatios=0.0638345540462368
cont.varWeightedLogRatios=0.106249469746801

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.43361616518992	0.0637699868352645	69.5251227923753	0	***
df.mm.trans1	0.0264513156861623	0.0535700387866274	0.493770702528692	0.621585727818638	   
df.mm.trans2	-0.72688740673313	0.0479428762775612	-15.1615310379977	1.58911425499907e-46	***
df.mm.exp2	-0.0855452311967762	0.0608226285810166	-1.40647047312052	0.159921414853495	   
df.mm.exp3	0.189834116192602	0.0608226285810167	3.12111003127299	0.00185780631556471	** 
df.mm.exp4	0.0611684219590685	0.0608226285810166	1.00568527513722	0.314830845659649	   
df.mm.exp5	0.00272235808355555	0.0608226285810166	0.0447589679543252	0.96430912291982	   
df.mm.exp6	0.0902968047843443	0.0608226285810166	1.48459227907369	0.137993650639325	   
df.mm.exp7	0.102048614647682	0.0608226285810166	1.67780671484383	0.0937237472228683	.  
df.mm.exp8	0.0750834617902808	0.0608226285810166	1.23446591411728	0.217343871230516	   
df.mm.trans1:exp2	0.070807084315676	0.0531878457575302	1.33126437642290	0.183431102444351	   
df.mm.trans2:exp2	0.129057903550804	0.0390292085438662	3.30670050369459	0.000980488973794096	***
df.mm.trans1:exp3	0.084261199979825	0.0531878457575302	1.58421907824487	0.113486788464809	   
df.mm.trans2:exp3	0.135985862888263	0.0390292085438662	3.48420754511136	0.000516828765920068	***
df.mm.trans1:exp4	-0.082884078136381	0.0531878457575302	-1.55832741401538	0.119498818759575	   
df.mm.trans2:exp4	0.0504862972325036	0.0390292085438662	1.29355165313589	0.196144301718703	   
df.mm.trans1:exp5	0.0411313266498354	0.0531878457575302	0.773321913381162	0.439530105455516	   
df.mm.trans2:exp5	0.0350128091982358	0.0390292085438662	0.897092472651189	0.369903752992856	   
df.mm.trans1:exp6	-0.0141238460481077	0.0531878457575302	-0.265546495575225	0.790647858680307	   
df.mm.trans2:exp6	-0.0312921142978467	0.0390292085438662	-0.801761436250404	0.422897554840238	   
df.mm.trans1:exp7	-0.0984711889000625	0.0531878457575302	-1.85138517075814	0.064433754745619	.  
df.mm.trans2:exp7	0.0410042552474639	0.0390292085438662	1.05060432371766	0.29371564071374	   
df.mm.trans1:exp8	-0.0168727478612109	0.0531878457575302	-0.317229389927343	0.751141371446508	   
df.mm.trans2:exp8	-0.0170302332335538	0.0390292085438662	-0.43634585145156	0.66268797427621	   
df.mm.trans1:probe2	-0.306891259886427	0.0403990689367686	-7.59649338371546	7.46176466186194e-14	***
df.mm.trans1:probe3	-0.206384210952278	0.0403990689367686	-5.10863780735403	3.94364130451193e-07	***
df.mm.trans1:probe4	-0.434380448495547	0.0403990689367686	-10.7522390967828	1.74337860165094e-25	***
df.mm.trans1:probe5	-0.672012033310821	0.0403990689367686	-16.6343445776593	1.55392090741478e-54	***
df.mm.trans1:probe6	-0.567523576955682	0.0403990689367686	-14.0479370414193	9.17926244888156e-41	***
df.mm.trans1:probe7	-0.550043621209492	0.0403990689367686	-13.6152548978394	1.33761906496159e-38	***
df.mm.trans1:probe8	-0.383790668439297	0.0403990689367686	-9.49998795863327	1.74997717187861e-20	***
df.mm.trans1:probe9	-0.558546657212024	0.0403990689367686	-13.8257309366769	1.20049489432709e-39	***
df.mm.trans1:probe10	-0.620554911900826	0.0403990689367686	-15.3606240993350	1.3907232982898e-47	***
df.mm.trans1:probe11	-0.685722810654272	0.0403990689367686	-16.9737280759501	1.94411424990600e-56	***
df.mm.trans1:probe12	-0.613450347915756	0.0403990689367686	-15.1847645022688	1.19707041916896e-46	***
df.mm.trans1:probe13	-0.674380160830169	0.0403990689367686	-16.6929629463909	7.31539630905228e-55	***
df.mm.trans1:probe14	-0.546364709402712	0.0403990689367686	-13.5241906257262	3.76753594258194e-38	***
df.mm.trans1:probe15	-0.576618598765809	0.0403990689367686	-14.2730665320113	6.60731997284052e-42	***
df.mm.trans2:probe2	-0.00136598922811559	0.0403990689367686	-0.0338123938017878	0.973034071137419	   
df.mm.trans2:probe3	0.119479957271126	0.0403990689367686	2.95749284366263	0.00318067046432112	** 
df.mm.trans2:probe4	0.0992892283244159	0.0403990689367686	2.45771080714311	0.0141656620962864	*  
df.mm.trans2:probe5	0.159064891198411	0.0403990689367686	3.93734052255944	8.85940719184783e-05	***
df.mm.trans2:probe6	0.0953667026677295	0.0403990689367686	2.36061635026774	0.0184519215776011	*  
df.mm.trans3:probe2	-0.0144414324612627	0.0403990689367686	-0.35746943781962	0.720822210825573	   
df.mm.trans3:probe3	-0.0742647837886277	0.0403990689367686	-1.838279587702	0.066342756597876	.  
df.mm.trans3:probe4	0.152746910871835	0.0403990689367686	3.78095126674602	0.000166284411462027	***
df.mm.trans3:probe5	0.0232838999461302	0.0403990689367686	0.57634743965445	0.564521028245645	   
df.mm.trans3:probe6	0.62960023084188	0.0403990689367686	15.5845232925371	8.78853117860717e-49	***
df.mm.trans3:probe7	0.543593218555447	0.0403990689367686	13.4555877861013	8.1948831222869e-38	***
df.mm.trans3:probe8	0.324414654663693	0.0403990689367686	8.03025077561704	2.95494567173263e-15	***
df.mm.trans3:probe9	0.165543604159914	0.0403990689367686	4.09770840063214	4.53995059148544e-05	***
df.mm.trans3:probe10	0.1644420472615	0.0403990689367686	4.07044151237445	5.09482778584367e-05	***
df.mm.trans3:probe11	0.110427451655988	0.0403990689367686	2.73341575838853	0.00638814343742449	** 
df.mm.trans3:probe12	0.0467778116130702	0.0403990689367686	1.15789330903357	0.247207430631156	   
df.mm.trans3:probe13	0.640833923944985	0.0403990689367686	15.8625914114011	2.75809597096447e-50	***
df.mm.trans3:probe14	0.26095890452582	0.0403990689367686	6.45952769194421	1.69771254349651e-10	***
df.mm.trans3:probe15	0.325058280271247	0.0403990689367686	8.04618246970044	2.61694970008336e-15	***
df.mm.trans3:probe16	0.0843987920831685	0.0403990689367686	2.08912715823394	0.0369699583207028	*  
df.mm.trans3:probe17	0.152930421392113	0.0403990689367686	3.78549371104256	0.000163321835821085	***
df.mm.trans3:probe18	0.145359027298462	0.0403990689367686	3.59807864696025	0.000337630975601135	***
df.mm.trans3:probe19	0.104514123381594	0.0403990689367686	2.58704287331908	0.00983244864211848	** 
df.mm.trans3:probe20	0.318164822963289	0.0403990689367686	7.87554840586228	9.51108618290426e-15	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.93228544947363	0.116386495494558	33.786440881859	1.79326396193527e-163	***
df.mm.trans1	-0.0264003970567176	0.0977705875020704	-0.270023917532035	0.787202278866981	   
df.mm.trans2	0.198283734469479	0.0875004626908417	2.26608783967302	0.0236770960516188	*  
df.mm.exp2	-0.0812429439247008	0.111007276912233	-0.731870433944027	0.464433634125852	   
df.mm.exp3	0.00309148361830011	0.111007276912233	0.0278493780254097	0.977788309870186	   
df.mm.exp4	-0.0622973204425375	0.111007276912233	-0.561200330062977	0.574797344572369	   
df.mm.exp5	-0.00209392155190581	0.111007276912233	-0.0188629215142476	0.984954539710838	   
df.mm.exp6	0.0314526645831421	0.111007276912233	0.283338763529979	0.776980770959844	   
df.mm.exp7	-0.0825980743894401	0.111007276912233	-0.744078016207402	0.457018863118455	   
df.mm.exp8	-0.102790003880660	0.111007276912233	-0.925975366118838	0.354701086552616	   
df.mm.trans1:exp2	0.115179807178920	0.0970730476488164	1.18652715628759	0.23571982977985	   
df.mm.trans2:exp2	0.0166736806742766	0.071232142733247	0.234075236746939	0.814978535278186	   
df.mm.trans1:exp3	0.0184926983498710	0.0970730476488164	0.19050291298953	0.848956997191132	   
df.mm.trans2:exp3	0.0394750101124075	0.071232142733247	0.554174121368707	0.57959406962657	   
df.mm.trans1:exp4	0.130450433137031	0.0970730476488164	1.34383782416068	0.179331434483028	   
df.mm.trans2:exp4	-0.0699831013616909	0.071232142733247	-0.982465200067987	0.326128449873076	   
df.mm.trans1:exp5	0.0122563288418069	0.0970730476488164	0.126258824036791	0.899554575978009	   
df.mm.trans2:exp5	-0.0396521854340921	0.071232142733247	-0.556661415936668	0.577893867733606	   
df.mm.trans1:exp6	-0.0331149156177154	0.0970730476488164	-0.341133985383008	0.733080440279581	   
df.mm.trans2:exp6	0.0147674109384524	0.071232142733247	0.207313866631163	0.835810523664553	   
df.mm.trans1:exp7	0.0781751254932452	0.0970730476488164	0.805322665628685	0.420841135728943	   
df.mm.trans2:exp7	0.0202815147240252	0.071232142733247	0.284724198175201	0.775919388342894	   
df.mm.trans1:exp8	0.122382827986962	0.0970730476488164	1.26072922351948	0.207725361050768	   
df.mm.trans2:exp8	0.0136475049788525	0.071232142733247	0.191591947892964	0.848104034650992	   
df.mm.trans1:probe2	0.060524354923148	0.0737322726275588	0.820866531930632	0.411934479752973	   
df.mm.trans1:probe3	0.0637930893228837	0.0737322726275587	0.865199010548875	0.387154625432052	   
df.mm.trans1:probe4	0.065416792568678	0.0737322726275587	0.887220619105498	0.375191503314491	   
df.mm.trans1:probe5	0.0623003899056613	0.0737322726275588	0.844954152171019	0.398355639754887	   
df.mm.trans1:probe6	0.109016900645626	0.0737322726275587	1.47855066391753	0.139601985069517	   
df.mm.trans1:probe7	0.0295704605549116	0.0737322726275587	0.401051798637482	0.688474912073434	   
df.mm.trans1:probe8	0.0876846326555805	0.0737322726275587	1.18923002819266	0.234655335932825	   
df.mm.trans1:probe9	0.0996246317598712	0.0737322726275587	1.35116724616779	0.176973317211470	   
df.mm.trans1:probe10	0.110450881550567	0.0737322726275587	1.49799914765253	0.134475741829118	   
df.mm.trans1:probe11	0.130569737265008	0.0737322726275587	1.7708627797837	0.0769138692179709	.  
df.mm.trans1:probe12	0.0600434651993084	0.0737322726275588	0.814344425576082	0.415657940938731	   
df.mm.trans1:probe13	0.0718715428595979	0.0737322726275587	0.974763699779608	0.329933156412636	   
df.mm.trans1:probe14	0.115661037423684	0.0737322726275587	1.56866231436970	0.117069754964086	   
df.mm.trans1:probe15	0.0139501073941665	0.0737322726275587	0.189199476661084	0.849978117742693	   
df.mm.trans2:probe2	-0.124207512808999	0.0737322726275587	-1.68457458834077	0.0924091133948898	.  
df.mm.trans2:probe3	-0.0643280591297734	0.0737322726275587	-0.872454582469084	0.383187578765903	   
df.mm.trans2:probe4	-0.144973915181525	0.0737322726275587	-1.96622062517762	0.0495728025634879	*  
df.mm.trans2:probe5	-0.109113589113568	0.0737322726275587	-1.47986200920090	0.139251670459171	   
df.mm.trans2:probe6	0.0641556791155768	0.0737322726275588	0.87011666437632	0.38446312386815	   
df.mm.trans3:probe2	-0.151632322140472	0.0737322726275587	-2.05652581612949	0.0400128152266824	*  
df.mm.trans3:probe3	-0.209641786077115	0.0737322726275588	-2.84328393261484	0.00456378177922187	** 
df.mm.trans3:probe4	-0.0741326537584933	0.0737322726275587	-1.00543020195454	0.314953533566944	   
df.mm.trans3:probe5	0.00559651224762956	0.0737322726275587	0.0759031567614771	0.939512611585377	   
df.mm.trans3:probe6	-0.113519885255646	0.0737322726275587	-1.53962276232912	0.12399544324039	   
df.mm.trans3:probe7	-0.0789555286268158	0.0737322726275587	-1.0708408382533	0.284521234431628	   
df.mm.trans3:probe8	-0.075486750196086	0.0737322726275587	-1.02379524604361	0.306200531671026	   
df.mm.trans3:probe9	-0.115359356434227	0.0737322726275588	-1.56457074118599	0.118026733392189	   
df.mm.trans3:probe10	-0.165487715848154	0.0737322726275588	-2.24444073064283	0.0250405461033892	*  
df.mm.trans3:probe11	-0.127495880465969	0.0737322726275587	-1.72917334462189	0.0841127972397888	.  
df.mm.trans3:probe12	-0.203301294277962	0.0737322726275587	-2.75729049211451	0.00594342841443606	** 
df.mm.trans3:probe13	-0.163093358587368	0.0737322726275587	-2.21196706374692	0.0272134586332399	*  
df.mm.trans3:probe14	0.0083906842127703	0.0737322726275587	0.113799343404941	0.909421633630161	   
df.mm.trans3:probe15	-0.12591904600243	0.0737322726275587	-1.70778739777194	0.0880124286730006	.  
df.mm.trans3:probe16	-0.211515718793143	0.0737322726275587	-2.86869929890219	0.00421570503024051	** 
df.mm.trans3:probe17	-0.0613890458958186	0.0737322726275587	-0.832593974227689	0.405289365286119	   
df.mm.trans3:probe18	-0.152393563074144	0.0737322726275587	-2.06685020878068	0.0390269478245943	*  
df.mm.trans3:probe19	-0.111607649371688	0.0737322726275587	-1.51368790618252	0.130447716300140	   
df.mm.trans3:probe20	-0.175029927354525	0.0737322726275588	-2.37385775749308	0.0178073279609117	*