chr11.3540_chr11_69526795_69527566_-_2.R 

fitVsDatCorrelation=0.875979299585055
cont.fitVsDatCorrelation=0.259770898645768

fstatistic=7432.89044026927,63,945
cont.fstatistic=1843.22275697702,63,945

residuals=-1.00858547273234,-0.106831820700559,-0.00031382281029307,0.109118288265411,0.907861791000451
cont.residuals=-0.740140820647529,-0.283714929385056,-0.0994857201598698,0.207613099764819,1.36253868858495

predictedValues:
Include	Exclude	Both
chr11.3540_chr11_69526795_69527566_-_2.R.tl.Lung	81.2063264148323	55.200667509406	109.341529362471
chr11.3540_chr11_69526795_69527566_-_2.R.tl.cerebhem	59.50237687669	68.8385825582735	79.6698106598359
chr11.3540_chr11_69526795_69527566_-_2.R.tl.cortex	59.7339587545684	55.8280958881591	73.656336377219
chr11.3540_chr11_69526795_69527566_-_2.R.tl.heart	72.5982580466996	54.2794440172278	91.6360990718172
chr11.3540_chr11_69526795_69527566_-_2.R.tl.kidney	62.8906253942894	54.2839805712307	69.9559729377926
chr11.3540_chr11_69526795_69527566_-_2.R.tl.liver	62.8871766802883	53.064997998432	70.1862254188757
chr11.3540_chr11_69526795_69527566_-_2.R.tl.stomach	63.7804406727114	52.8127892697202	70.2610198777895
chr11.3540_chr11_69526795_69527566_-_2.R.tl.testicle	63.6426847999915	57.3129218579295	69.2923979880114


diffExp=26.0056589054263,-9.33620568158347,3.90586286640928,18.3188140294718,8.6066448230587,9.82217868185625,10.9676514029912,6.32976294206205
diffExpScore=1.23369909242243
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,0,0,0,0,0,0,0
diffExp1.4Score=0.5
diffExp1.3=1,0,0,1,0,0,0,0
diffExp1.3Score=0.666666666666667
diffExp1.2=1,0,0,1,0,0,1,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	67.5921847410383	75.1593676230948	70.4386208499198
cerebhem	77.3385055189311	81.531086853506	81.8567970793896
cortex	71.135720744326	88.8126971080138	67.0210822807017
heart	75.253249930192	77.7083938837538	66.423807826573
kidney	79.4809838830292	78.2714175354157	78.5237086413819
liver	77.3685400427123	81.9472047031953	69.9112006225964
stomach	85.1385398727856	74.9965427202053	75.741617861259
testicle	71.0607822979393	73.6287241243908	68.0864537846646
cont.diffExp=-7.56718288205644,-4.19258133457487,-17.6769763636878,-2.45514395356184,1.20956634761346,-4.57866466048294,10.1419971525802,-2.56794182645153
cont.diffExpScore=1.75655111495597

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

tran.correlation=-0.319693995214980
cont.tran.correlation=-0.101219681010686

tran.covariance=-0.00306545928028392
cont.tran.covariance=-0.000318466372395044

tran.mean=61.1164579569031
cont.tran.mean=77.276496348908

weightedLogRatios:
wLogRatio
Lung	1.62281399649486
cerebhem	-0.606150451626922
cortex	0.274286643632811
heart	1.20375987168254
kidney	0.598651706300283
liver	0.688882134290948
stomach	0.766301903082357
testicle	0.429603777310578

cont.weightedLogRatios:
wLogRatio
Lung	-0.452759602988443
cerebhem	-0.230945090991166
cortex	-0.97111175914084
heart	-0.139233019833159
kidney	0.0669822841387878
liver	-0.251674265571460
stomach	0.55565873893933
testicle	-0.151984020270701

varWeightedLogRatios=0.433169777823512
cont.varWeightedLogRatios=0.187192673125113

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.11129280614473	0.0909595404782267	34.205238832418	1.67818443601071e-167	***
df.mm.trans1	1.10219167932017	0.0777979892442625	14.1673543240253	1.85461577154415e-41	***
df.mm.trans2	0.881981564429478	0.068295921067627	12.9141177195068	2.96577598647929e-35	***
df.mm.exp2	0.226397992349316	0.086535060290209	2.61625740584284	0.00903180213588403	** 
df.mm.exp3	0.099275841575853	0.086535060290209	1.14723259269614	0.251575870286633	   
df.mm.exp4	0.0477693101987116	0.086535060290209	0.552022614169443	0.58106338044076	   
df.mm.exp5	0.174268272101565	0.086535060290209	2.01384585065439	0.0443087426019027	*  
df.mm.exp6	0.148215820555684	0.086535060290209	1.71278346670839	0.087080405677994	.  
df.mm.exp7	0.156490869889782	0.086535060290209	1.80841001745379	0.0708604772445476	.  
df.mm.exp8	0.249983376669074	0.086535060290209	2.88881033688214	0.00395534807874358	** 
df.mm.trans1:exp2	-0.537374888733618	0.0788613368258957	-6.81417422481684	1.68671477084728e-11	***
df.mm.trans2:exp2	-0.00560865736912652	0.0551914387592003	-0.101621872798008	0.919078374775411	   
df.mm.trans1:exp3	-0.406368315181777	0.0788613368258957	-5.15294733183293	3.12181727195342e-07	***
df.mm.trans2:exp3	-0.0879736341386382	0.0551914387592003	-1.59397247320307	0.111276583506630	   
df.mm.trans1:exp4	-0.159821538148333	0.0788613368258957	-2.02661461980001	0.0429822459669357	*  
df.mm.trans2:exp4	-0.064598763922246	0.0551914387592003	-1.17044899307825	0.242115406490878	   
df.mm.trans1:exp5	-0.429864314984528	0.0788613368258957	-5.45088800527882	6.3971340944511e-08	***
df.mm.trans2:exp5	-0.191014151563827	0.0551914387592003	-3.46093807043553	0.000562387434847891	***
df.mm.trans1:exp6	-0.403866701636565	0.0788613368258957	-5.12122565875585	3.67868460740949e-07	***
df.mm.trans2:exp6	-0.187673326832711	0.0551914387592003	-3.40040649513647	0.000701026312598374	***
df.mm.trans1:exp7	-0.398037454715438	0.0788613368258957	-5.04730798051517	5.37395998337641e-07	***
df.mm.trans2:exp7	-0.200712533262551	0.0551914387592002	-3.63666064474707	0.000291121468348178	***
df.mm.trans1:exp8	-0.4936921424313	0.0788613368258957	-6.2602558148518	5.82351510832291e-10	***
df.mm.trans2:exp8	-0.212432311789319	0.0551914387592003	-3.84900840719444	0.000126570910395404	***
df.mm.trans1:probe2	0.313472346391045	0.0577205882709079	5.43085848189528	7.13400730016237e-08	***
df.mm.trans1:probe3	0.0451132169718764	0.0577205882709079	0.781579299922246	0.434657622359573	   
df.mm.trans1:probe4	0.0298850613076240	0.0577205882709079	0.517753928067405	0.604751147076788	   
df.mm.trans1:probe5	-0.0525612433599167	0.0577205882709079	-0.910615171023965	0.362730383040702	   
df.mm.trans1:probe6	-0.0251964969562402	0.0577205882709079	-0.436525297316481	0.662555364271296	   
df.mm.trans1:probe7	-0.0662995002748093	0.0577205882709079	-1.14862828430710	0.250999944076367	   
df.mm.trans1:probe8	-0.0615151473088594	0.0577205882709079	-1.06574013106280	0.286813341642427	   
df.mm.trans1:probe9	0.00533971651635328	0.0577205882709079	0.0925097383154112	0.92631266176188	   
df.mm.trans1:probe10	0.162548311342298	0.0577205882709079	2.81612360877869	0.00496186469662692	** 
df.mm.trans1:probe11	0.350281005703246	0.0577205882709079	6.06856264283421	1.86590792449692e-09	***
df.mm.trans1:probe12	0.233250294788886	0.0577205882709079	4.04102421295744	5.75432162159023e-05	***
df.mm.trans1:probe13	0.257750571210584	0.0577205882709079	4.46548760038357	8.95320662857745e-06	***
df.mm.trans1:probe14	0.198804654703656	0.0577205882709079	3.44425898382358	0.000597789547508804	***
df.mm.trans1:probe15	0.205375158408738	0.0577205882709079	3.55809191418533	0.00039213103135428	***
df.mm.trans1:probe16	1.0739139538991	0.0577205882709079	18.6053882344156	4.48840781818274e-66	***
df.mm.trans1:probe17	0.519121496924438	0.0577205882709079	8.993697266008	1.28116077538587e-18	***
df.mm.trans1:probe18	1.07592947652778	0.0577205882709079	18.6403068429930	2.78551823028131e-66	***
df.mm.trans1:probe19	1.02587943157977	0.0577205882709079	17.7731977845560	3.43109439977989e-61	***
df.mm.trans1:probe20	0.315620805705839	0.0577205882709079	5.46808019738977	5.82401016923806e-08	***
df.mm.trans1:probe21	0.999599012416331	0.0577205882709079	17.3178937076105	1.44772904239321e-58	***
df.mm.trans2:probe2	0.163177768187517	0.0577205882709079	2.82702884838339	0.00479738122526504	** 
df.mm.trans2:probe3	-0.0610635034710111	0.0577205882709078	-1.05791547349472	0.290364326886571	   
df.mm.trans2:probe4	0.105838143915898	0.0577205882709078	1.83362898900395	0.0670234546243275	.  
df.mm.trans2:probe5	0.0467900062548813	0.0577205882709078	0.81062940722772	0.41778262008418	   
df.mm.trans2:probe6	0.116971764258679	0.0577205882709079	2.02651718845414	0.0429922390028846	*  
df.mm.trans3:probe2	-0.528715085796307	0.0577205882709079	-9.15990466546904	3.14647972262802e-19	***
df.mm.trans3:probe3	-0.602066689275712	0.0577205882709079	-10.4307095147740	3.48032185817051e-24	***
df.mm.trans3:probe4	-0.196533239821347	0.0577205882709078	-3.40490708270212	0.000689713147165214	***
df.mm.trans3:probe5	-0.0728480509480735	0.0577205882709079	-1.26208088188855	0.207231075740832	   
df.mm.trans3:probe6	-0.517604116372149	0.0577205882709079	-8.96740888957694	1.5966621645685e-18	***
df.mm.trans3:probe7	-0.51148705246541	0.0577205882709079	-8.86143173151283	3.85758574717785e-18	***
df.mm.trans3:probe8	-0.226288978649164	0.0577205882709079	-3.920420519401	9.4777326650071e-05	***
df.mm.trans3:probe9	-0.24332868306651	0.0577205882709079	-4.21563068492065	2.72989316163759e-05	***
df.mm.trans3:probe10	-0.780006323595614	0.0577205882709079	-13.5134853431276	3.57059790914191e-38	***
df.mm.trans3:probe11	-0.72230323442684	0.0577205882709078	-12.5137885122854	2.3506065307049e-33	***
df.mm.trans3:probe12	-0.637145266516946	0.0577205882709079	-11.0384402793427	9.88558833642107e-27	***
df.mm.trans3:probe13	-0.165384541938942	0.0577205882709079	-2.86526085220616	0.00425902429759589	** 
df.mm.trans3:probe14	-0.769837350284597	0.0577205882709079	-13.3373094998862	2.63105338617574e-37	***
df.mm.trans3:probe15	-0.0412979264451276	0.0577205882709079	-0.715479999117445	0.474489230698706	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.38844417374132	0.182102542821073	24.0987528551604	2.08334563835987e-100	***
df.mm.trans1	-0.141331542289640	0.155752894014872	-0.90740877197534	0.364421999699202	   
df.mm.trans2	-0.0532874846495042	0.136729592358695	-0.389728980612406	0.696824780366657	   
df.mm.exp2	0.0658435778551884	0.173244658440134	0.380061229292914	0.703985366403653	   
df.mm.exp3	0.267750584108658	0.173244658440134	1.54550556721021	0.122558821555499	   
df.mm.exp4	0.199405405812961	0.173244658440134	1.15100464053769	0.250021472465565	   
df.mm.exp5	0.093938272569999	0.173244658440134	0.542228969226546	0.587788616482567	   
df.mm.exp6	0.22906813675559	0.173244658440134	1.32222337368483	0.186413714420542	   
df.mm.exp7	0.156032620540210	0.173244658440134	0.900648954750478	0.368004460289955	   
df.mm.exp8	0.0634311232605732	0.173244658440134	0.366136098115211	0.714345472917631	   
df.mm.trans1:exp2	0.0688560185909878	0.157881733908961	0.436124033390031	0.66284635247638	   
df.mm.trans2:exp2	0.0155300426824882	0.110494196509378	0.140550754456775	0.88825480639957	   
df.mm.trans1:exp3	-0.216653338019371	0.157881733908961	-1.37225081493151	0.170310997353670	   
df.mm.trans2:exp3	-0.100831719648490	0.110494196509378	-0.912552177705842	0.361710856238299	   
df.mm.trans1:exp4	-0.0920386807013396	0.157881733908961	-0.582959652282585	0.56005954290153	   
df.mm.trans2:exp4	-0.166052885649291	0.110494196509378	-1.50281997512147	0.133219559081864	   
df.mm.trans1:exp5	0.0680871580125137	0.157881733908961	0.431254182018134	0.666381918626503	   
df.mm.trans2:exp5	-0.0533665348447657	0.110494196509378	-0.482980432734639	0.629221499369724	   
df.mm.trans1:exp6	-0.093980264209259	0.157881733908961	-0.595257360572507	0.551813967328735	   
df.mm.trans2:exp6	-0.142603702698692	0.110494196509378	-1.29059902876066	0.197158420792816	   
df.mm.trans1:exp7	0.0747548239662506	0.157881733908961	0.473486210946709	0.635975694869107	   
df.mm.trans2:exp7	-0.158201365897448	0.110494196509378	-1.43176176573238	0.152542660777049	   
df.mm.trans1:exp8	-0.0133878897766740	0.157881733908961	-0.0847969517765355	0.932440777547035	   
df.mm.trans2:exp8	-0.084006661000502	0.110494196509378	-0.760281206202283	0.447276152060059	   
df.mm.trans1:probe2	0.148198204403624	0.115557596729247	1.28246180777586	0.199995182891430	   
df.mm.trans1:probe3	-0.0777649366907984	0.115557596729247	-0.672953911225782	0.501141139184121	   
df.mm.trans1:probe4	-0.140387438644893	0.115557596729247	-1.21486983650086	0.224719198430993	   
df.mm.trans1:probe5	-0.0472390070383379	0.115557596729247	-0.408791878469223	0.682785021397739	   
df.mm.trans1:probe6	-0.0371999350169423	0.115557596729247	-0.321916828229841	0.747586899286562	   
df.mm.trans1:probe7	0.00303827299123420	0.115557596729247	0.0262922826125652	0.979029761339252	   
df.mm.trans1:probe8	0.0894556956594771	0.115557596729247	0.774122153726276	0.439052180014174	   
df.mm.trans1:probe9	-0.0688060752204637	0.115557596729247	-0.595426671789283	0.551700862813031	   
df.mm.trans1:probe10	-0.165953417975422	0.115557596729247	-1.43610998041309	0.151301987315969	   
df.mm.trans1:probe11	-0.0705224223804475	0.115557596729247	-0.610279413699495	0.541823460801208	   
df.mm.trans1:probe12	-0.0143875404998770	0.115557596729247	-0.124505362755052	0.900941633011985	   
df.mm.trans1:probe13	-0.11744519670698	0.115557596729247	-1.01633471127091	0.309730156221894	   
df.mm.trans1:probe14	-0.0571946133666134	0.115557596729247	-0.494944642199691	0.620754250372163	   
df.mm.trans1:probe15	-0.0939329942126176	0.115557596729247	-0.812867322195216	0.416498867842482	   
df.mm.trans1:probe16	-0.0436244934476602	0.115557596729247	-0.377512986444958	0.705877183147177	   
df.mm.trans1:probe17	-0.0642349747801989	0.115557596729247	-0.555869770558679	0.578431463839617	   
df.mm.trans1:probe18	-0.211268123237153	0.115557596729247	-1.82824954150056	0.0678272253373776	.  
df.mm.trans1:probe19	-0.0495194371717808	0.115557596729247	-0.428526021424671	0.668365849099141	   
df.mm.trans1:probe20	-0.0914733019228383	0.115557596729247	-0.791581899519436	0.428803143494975	   
df.mm.trans1:probe21	-0.100067820070745	0.115557596729247	-0.865956223589565	0.386733962331489	   
df.mm.trans2:probe2	-0.101824924407570	0.115557596729247	-0.88116166560773	0.378454376189067	   
df.mm.trans2:probe3	-0.178391988935778	0.115557596729247	-1.54374955853187	0.122983804252850	   
df.mm.trans2:probe4	0.139537807468342	0.115557596729247	1.20751738888513	0.227535083412782	   
df.mm.trans2:probe5	-0.139325516540782	0.115557596729247	-1.20568028830872	0.228242583720862	   
df.mm.trans2:probe6	-0.0464598724027922	0.115557596729247	-0.402049486297714	0.687738522006409	   
df.mm.trans3:probe2	0.0759024321707483	0.115557596729247	0.656836368348753	0.511446058937795	   
df.mm.trans3:probe3	0.0936480285521893	0.115557596729247	0.810401316770267	0.417913592333976	   
df.mm.trans3:probe4	0.0137708664384327	0.115557596729247	0.119168854564343	0.905166908795584	   
df.mm.trans3:probe5	-0.00852793463920176	0.115557596729247	-0.0737981308072962	0.941186631891711	   
df.mm.trans3:probe6	0.184629340247556	0.115557596729247	1.59772568375704	0.110438337303145	   
df.mm.trans3:probe7	0.133174793663738	0.115557596729247	1.15245381898837	0.249426080585560	   
df.mm.trans3:probe8	0.101084870710511	0.115557596729247	0.874757467891569	0.38192814564563	   
df.mm.trans3:probe9	0.122923232133776	0.115557596729247	1.06373994971345	0.287718257385366	   
df.mm.trans3:probe10	-0.0235485396078552	0.115557596729247	-0.203781839311091	0.838567859215037	   
df.mm.trans3:probe11	0.0112292664490166	0.115557596729247	0.09717462777741	0.922608321859674	   
df.mm.trans3:probe12	0.0455876419417201	0.115557596729247	0.394501471405056	0.693299854609036	   
df.mm.trans3:probe13	-0.0144819254624536	0.115557596729247	-0.125322141272849	0.900295178961325	   
df.mm.trans3:probe14	0.067764960051727	0.115557596729247	0.586417180434284	0.557735246225162	   
df.mm.trans3:probe15	0.110591330729610	0.115557596729247	0.957023457217846	0.338800179096991