fitVsDatCorrelation=0.901521530731239
cont.fitVsDatCorrelation=0.31608845238705

fstatistic=4381.0029894722,43,485
cont.fstatistic=902.513753325028,43,485

residuals=-0.82735081032727,-0.117800222803840,-0.0044904910260712,0.111929026287433,0.752627821047565
cont.residuals=-0.886769533361906,-0.392612490726435,-0.0913702197433787,0.324173717701208,1.57785588388699

predictedValues:
Include	Exclude	Both
Lung	114.655105593267	55.706233085767	71.2679607678131
cerebhem	94.9147396593623	56.4248139980277	87.0017396463881
cortex	190.483002409619	56.5475943836435	177.031371039979
heart	135.579699264318	77.3297043208716	70.3784466331679
kidney	100.897323034313	56.7193942441309	65.2346851220882
liver	112.015843934465	63.4999579091885	64.2510801738678
stomach	199.881826487997	57.9028658920073	62.3501916195598
testicle	129.83576614512	71.6997008948663	61.3232156689169


diffExp=58.9488725075005,38.4899256613347,133.935408025975,58.2499949434465,44.1779287901817,48.515886025277,141.97896059599,58.1360652502536
diffExpScore=0.998286007256437
diffExp1.5=1,1,1,1,1,1,1,1
diffExp1.5Score=0.888888888888889
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	58.4760764564507	79.5327241572947	90.4837022234114
cerebhem	66.3423990444272	84.967150943901	70.9417634389614
cortex	76.4454200215653	83.537866495113	86.6602142232325
heart	88.8481700237542	88.158638718729	89.167461381301
kidney	58.3666603028361	90.422499393844	79.0618923973574
liver	84.537963478979	71.900182129357	76.9554974330804
stomach	64.6275509592063	78.0856592819483	78.8118170523304
testicle	87.3146290465138	77.2823889249008	73.380702811895
cont.diffExp=-21.0566477008440,-18.6247518994738,-7.09244647354767,0.689531305025184,-32.0558390910078,12.6377813496221,-13.458108322742,10.0322401216130
cont.diffExpScore=1.65380031139689

cont.diffExp1.5=0,0,0,0,-1,0,0,0
cont.diffExp1.5Score=0.5
cont.diffExp1.4=0,0,0,0,-1,0,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=-1,0,0,0,-1,0,0,0
cont.diffExp1.3Score=0.666666666666667
cont.diffExp1.2=-1,-1,0,0,-1,0,-1,0
cont.diffExp1.2Score=0.8

tran.correlation=-0.0490618239892546
cont.tran.correlation=-0.282420579644086

tran.covariance=0.00095546231736202
cont.tran.covariance=-0.00398449446651511

tran.mean=98.3808482035603
cont.tran.mean=77.4278737111763

weightedLogRatios:
wLogRatio
Lung	3.16237311449413
cerebhem	2.23263133191525
cortex	5.63800961386899
heart	2.59899617635180
kidney	2.49178384300423
liver	2.51721985963914
stomach	5.79615780826219
testicle	2.71322261558415

cont.weightedLogRatios:
wLogRatio
Lung	-1.29860055934969
cerebhem	-1.06856194087699
cortex	-0.388689997880538
heart	0.0349275550544641
kidney	-1.87602234666208
liver	0.705370650522084
stomach	-0.8064553154218
testicle	0.53806491562953

varWeightedLogRatios=2.12665649902765
cont.varWeightedLogRatios=0.824887684443615

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.40364724272845	0.117360468951955	37.5224066677105	1.62181698830181e-145	***
df.mm.trans1	0.341740249943285	0.0939531928255918	3.63734578533875	0.000304968876034264	***
df.mm.trans2	-0.322352380920485	0.0939531928255918	-3.43098910453078	0.000652766634929486	***
df.mm.exp2	-0.375613794019839	0.125809725926097	-2.98557040208865	0.00297355675507455	** 
df.mm.exp3	-0.387255045209686	0.125809725926097	-3.07810101611037	0.00220121435214967	** 
df.mm.exp4	0.508177031121828	0.125809725926097	4.03925076047254	6.23259430465428e-05	***
df.mm.exp5	-0.0213454265822923	0.125809725926097	-0.169664359612635	0.865344848387725	   
df.mm.exp6	0.211307315124446	0.125809725926097	1.67957853471974	0.0936833541609828	.  
df.mm.exp7	0.728152742333562	0.125809725926097	5.78773013750379	1.28197074380023e-08	***
df.mm.exp8	0.52702467975227	0.125809725926097	4.18906150436934	3.32822331494044e-05	***
df.mm.trans1:exp2	0.186664265068911	0.098693268845681	1.89135760981616	0.0591722104544053	.  
df.mm.trans2:exp2	0.388430775007633	0.0986932688456811	3.93573725493874	9.5071579099685e-05	***
df.mm.trans1:exp3	0.894889469291572	0.0986932688456811	9.06738098512919	3.02855101150203e-18	***
df.mm.trans2:exp3	0.402245661967776	0.0986932688456811	4.07571526075133	5.35943729569533e-05	***
df.mm.trans1:exp4	-0.34054591757114	0.098693268845681	-3.45054856885554	0.000608303412558892	***
df.mm.trans2:exp4	-0.180190921374262	0.098693268845681	-1.82576708099529	0.0685000873721204	.  
df.mm.trans1:exp5	-0.106479717596806	0.0986932688456811	-1.07889543878924	0.281170609526537	   
df.mm.trans2:exp5	0.039369583726965	0.0986932688456811	0.398908498902028	0.69013620100975	   
df.mm.trans1:exp6	-0.234595530441561	0.0986932688456811	-2.37701651982345	0.0178402742108593	*  
df.mm.trans2:exp6	-0.0803601173376985	0.098693268845681	-0.814241115707205	0.415906433489447	   
df.mm.trans1:exp7	-0.172354958284317	0.0986932688456811	-1.74636994295745	0.0813797807905565	.  
df.mm.trans2:exp7	-0.689477906972531	0.0986932688456811	-6.98606819934816	9.3735701376786e-12	***
df.mm.trans1:exp8	-0.402682905633033	0.0986932688456811	-4.08014558989506	5.26164363249978e-05	***
df.mm.trans2:exp8	-0.274630149039366	0.0986932688456811	-2.78266342022558	0.00560175057225264	** 
df.mm.trans1:probe2	0.160328152041433	0.0675706620258767	2.37274798314146	0.0180453091202778	*  
df.mm.trans1:probe3	0.0272490588180769	0.0675706620258767	0.403267601664783	0.68692909020069	   
df.mm.trans1:probe4	-0.0296426779744683	0.0675706620258767	-0.43869154283432	0.661080407091167	   
df.mm.trans1:probe5	-0.302467027822643	0.0675706620258767	-4.47630700594306	9.4756930853091e-06	***
df.mm.trans1:probe6	0.0891892571138304	0.0675706620258767	1.31994055466964	0.187477514089208	   
df.mm.trans2:probe2	-0.140308224603755	0.0675706620258767	-2.07646662615237	0.0383768554174271	*  
df.mm.trans2:probe3	-0.237545126137396	0.0675706620258767	-3.51550686370997	0.00048010304118015	***
df.mm.trans2:probe4	-0.320454414527738	0.0675706620258767	-4.74250813770357	2.77973631894153e-06	***
df.mm.trans2:probe5	-0.17726166163544	0.0675706620258767	-2.62335244795376	0.00898112901963289	** 
df.mm.trans2:probe6	-0.103675637795061	0.0675706620258767	-1.53432917018569	0.125600741427489	   
df.mm.trans3:probe2	-0.358839283919359	0.0675706620258767	-5.31057818823706	1.66751808977675e-07	***
df.mm.trans3:probe3	0.496621241274198	0.0675706620258767	7.34965777135664	8.49949915007454e-13	***
df.mm.trans3:probe4	-0.115971240975661	0.0675706620258767	-1.71629576355562	0.0867464646334388	.  
df.mm.trans3:probe5	-0.510998538224967	0.0675706620258767	-7.56243202159652	1.99782946198196e-13	***
df.mm.trans3:probe6	-0.129769427632667	0.0675706620258767	-1.92049957395668	0.0553808684287245	.  
df.mm.trans3:probe7	0.0095898961416615	0.0675706620258767	0.141923963065346	0.887199000290477	   
df.mm.trans3:probe8	-0.0611995564217839	0.0675706620258767	-0.905711955261694	0.365538048102811	   
df.mm.trans3:probe9	-0.531933028581961	0.0675706620258767	-7.87224828991985	2.29503341838272e-14	***
df.mm.trans3:probe10	-0.363382495837381	0.0675706620258767	-5.37781464532967	1.17453036590342e-07	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.81629715094588	0.257301948777424	14.8319791944021	2.61911562640007e-41	***
df.mm.trans1	0.200588839755515	0.205983665741677	0.973809447624224	0.330636592260708	   
df.mm.trans2	0.533914566659318	0.205983665741677	2.59202381284396	0.00982910112457604	** 
df.mm.exp2	0.435618076670021	0.275826161441043	1.57932109990638	0.114914366697026	   
df.mm.exp3	0.360265619980952	0.275826161441042	1.30613288492563	0.192126479834178	   
df.mm.exp4	0.535934137924193	0.275826161441042	1.94301416197879	0.0525927117441409	.  
df.mm.exp5	0.261390437163418	0.275826161441042	0.94766368714916	0.343772620727005	   
df.mm.exp6	0.429635669709475	0.275826161441042	1.55763205152427	0.119972761570754	   
df.mm.exp7	0.219767742968857	0.275826161441043	0.796761778580718	0.425979249015889	   
df.mm.exp8	0.581706592203111	0.275826161441043	2.10896090916108	0.0354599839405242	*  
df.mm.trans1:exp2	-0.309406602073326	0.216375843007272	-1.42994983993165	0.153375276525761	   
df.mm.trans2:exp2	-0.369521915944106	0.216375843007272	-1.70777805326303	0.0883174600188532	.  
df.mm.trans1:exp3	-0.0923063186880417	0.216375843007272	-0.426601774972353	0.669858693780753	   
df.mm.trans2:exp3	-0.311134161546826	0.216375843007272	-1.43793390806740	0.151097710562310	   
df.mm.trans1:exp4	-0.117622900912053	0.216375843007272	-0.543604587634581	0.586963376589577	   
df.mm.trans2:exp4	-0.432964795255726	0.216375843007272	-2.00098490311219	0.0459507841215235	*  
df.mm.trans1:exp5	-0.263263316704455	0.216375843007272	-1.21669458589056	0.224312189672076	   
df.mm.trans2:exp5	-0.133065875218672	0.216375843007272	-0.614975652407742	0.538859221250081	   
df.mm.trans1:exp6	-0.0610526854309802	0.216375843007272	-0.282160358487561	0.77794091691937	   
df.mm.trans2:exp6	-0.530525433479269	0.216375843007272	-2.45186997820934	0.0145631390745440	*  
df.mm.trans1:exp7	-0.119744658795529	0.216375843007272	-0.553410478412343	0.580237380694341	   
df.mm.trans2:exp7	-0.238129884519041	0.216375843007272	-1.10053821724932	0.271643604962326	   
df.mm.trans1:exp8	-0.180806292403973	0.216375843007272	-0.835612191689513	0.403784566511317	   
df.mm.trans2:exp8	-0.610409051773509	0.216375843007272	-2.82105915008725	0.00498282062295722	** 
df.mm.trans1:probe2	0.262592661656342	0.148142412642849	1.77256909059134	0.0769277918231172	.  
df.mm.trans1:probe3	0.123382400004074	0.148142412642849	0.832863444053204	0.405331685659301	   
df.mm.trans1:probe4	0.163556966504780	0.148142412642849	1.10405226691625	0.270117919651292	   
df.mm.trans1:probe5	0.229850514671364	0.148142412642849	1.55155104180395	0.12142195055116	   
df.mm.trans1:probe6	0.0483251440658126	0.148142412642849	0.326207351451186	0.744408188031508	   
df.mm.trans2:probe2	0.225844910096453	0.148142412642849	1.52451216412233	0.128032601980442	   
df.mm.trans2:probe3	0.0519964876605714	0.148142412642849	0.350989880163	0.725748350228436	   
df.mm.trans2:probe4	0.162681971051063	0.148142412642849	1.09814581893753	0.272685686713158	   
df.mm.trans2:probe5	0.176956112486246	0.148142412642849	1.1945000039446	0.232866274801447	   
df.mm.trans2:probe6	-0.202169977598734	0.148142412642849	-1.36470018269607	0.172980101389396	   
df.mm.trans3:probe2	-0.0998723171650941	0.148142412642849	-0.674164240904273	0.500528088787138	   
df.mm.trans3:probe3	-0.00325466948852825	0.148142412642849	-0.0219698696036145	0.982481025935239	   
df.mm.trans3:probe4	0.0959091789360346	0.148142412642849	0.647412022155047	0.517671632526004	   
df.mm.trans3:probe5	-0.176938371234206	0.148142412642849	-1.19438024585694	0.232913052279015	   
df.mm.trans3:probe6	-0.238984194173504	0.148142412642849	-1.61320576538511	0.107350244973171	   
df.mm.trans3:probe7	-0.103793152535236	0.148142412642849	-0.700630904300629	0.483869362022473	   
df.mm.trans3:probe8	-0.121786512531224	0.148142412642849	-0.82209078655168	0.411429191092111	   
df.mm.trans3:probe9	0.0906449483133493	0.148142412642849	0.611877089729068	0.540905802055083	   
df.mm.trans3:probe10	0.101767001864365	0.148142412642849	0.686953857770027	0.492440132027744