fitVsDatCorrelation=0.869210358345918
cont.fitVsDatCorrelation=0.259443449917273

fstatistic=15284.0011268510,69,1083
cont.fstatistic=3994.60980272555,69,1083

residuals=-0.383280788436878,-0.0875382682880013,-0.00187441722738051,0.0801193320009962,0.868858076304253
cont.residuals=-0.573235232858646,-0.166952903965439,-0.0386945052297062,0.109830758010961,1.55909826515989

predictedValues:
Include	Exclude	Both
Lung	50.6754836488762	54.0981778697456	69.2519888110048
cerebhem	57.6455725074535	47.4226130721755	68.6528402560681
cortex	49.6410784030034	57.8765583804613	72.4524954391221
heart	51.6859654208782	60.1369339335169	70.2431058290538
kidney	51.4163234329188	54.6747519287481	67.8273739414054
liver	56.6284484731569	57.7673738956081	63.6397417846413
stomach	52.594868730147	50.7261790570893	67.230818271119
testicle	54.6682070560152	51.3511600510026	71.4155239618635


diffExp=-3.42269422086941,10.222959435278,-8.23547997745788,-8.45096851263872,-3.25842849582929,-1.13892542245118,1.86868967305769,3.3170470050126
diffExpScore=3.95286009856815
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=0,1,0,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	62.0274195541982	56.6036772732333	61.6985667046998
cerebhem	62.1373619955032	59.3534299983051	62.1994328426731
cortex	62.6962578940344	66.0066847906592	61.7583648275042
heart	57.3646094311586	62.0744158351672	60.5351394360195
kidney	61.883928295897	55.6997914762398	58.9088308626038
liver	60.1659054585488	53.6566655816966	59.8527802595199
stomach	61.9909936366175	50.9084465731392	59.822180126135
testicle	60.4786838379767	51.067886800861	60.0831830852785
cont.diffExp=5.42374228096494,2.78393199719807,-3.31042689662483,-4.70980640400863,6.18413681965723,6.50923987685221,11.0825470634783,9.41079703711564
cont.diffExpScore=1.43755151616137

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

tran.correlation=-0.492716472128359
cont.tran.correlation=-0.033233296571556

tran.covariance=-0.00217732742396768
cont.tran.covariance=-0.000130838525116258

tran.mean=53.6881059912998
cont.tran.mean=59.0072599020772

weightedLogRatios:
wLogRatio
Lung	-0.258695957623657
cerebhem	0.772405496210286
cortex	-0.611145445360517
heart	-0.608917490956947
kidney	-0.243983646955979
liver	-0.0805759730217507
stomach	0.142699036917271
testicle	0.248500370235343

cont.weightedLogRatios:
wLogRatio
Lung	0.373497563774864
cerebhem	0.188228582171667
cortex	-0.214257018852456
heart	-0.322638728035329
kidney	0.428781980613382
liver	0.462564027194566
stomach	0.793456161746985
testicle	0.67953808530704

varWeightedLogRatios=0.214970697694019
cont.varWeightedLogRatios=0.157225621814803

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.74142176682318	0.0608720062610336	61.4637498685204	0	***
df.mm.trans1	0.166716756197955	0.0519148715610274	3.21134871733188	0.00136002055297475	** 
df.mm.trans2	0.230808644855729	0.0452203471095592	5.10408830557025	3.92379204862767e-07	***
df.mm.exp2	0.0058592468815781	0.0566940205193229	0.103348586463031	0.917705449373985	   
df.mm.exp3	0.00170913452225427	0.0566940205193229	0.0301466452122892	0.975955654525146	   
df.mm.exp4	0.111357434180191	0.0566940205193229	1.96418305070174	0.0497643094887291	*  
df.mm.exp5	0.045900992023284	0.0566940205193229	0.809626687308931	0.418332609480868	   
df.mm.exp6	0.261206609966681	0.0566940205193229	4.60730439601923	4.5636479879594e-06	***
df.mm.exp7	0.00243806726257569	0.0566940205193229	0.0430039577409179	0.965706305985804	   
df.mm.exp8	-0.00703630133484893	0.0566940205193229	-0.124110113736788	0.901251120891014	   
df.mm.trans1:exp2	0.123011960936200	0.0515444872479577	2.38652021785460	0.0171795856997259	*  
df.mm.trans2:exp2	-0.137560567412745	0.0342124739485486	-4.02077229549722	6.2026976702437e-05	***
df.mm.trans1:exp3	-0.0223326863697883	0.0515444872479577	-0.433270123773968	0.664904818771489	   
df.mm.trans2:exp3	0.0658027996776828	0.0342124739485486	1.92335695385964	0.0546971597215692	.  
df.mm.trans1:exp4	-0.0916133878210832	0.0515444872479577	-1.77736539274068	0.0757887291582854	.  
df.mm.trans2:exp4	-0.00553374457332763	0.0342124739485486	-0.161746402252281	0.871535731546898	   
df.mm.trans1:exp5	-0.0313875298872462	0.0515444872479577	-0.608940578577388	0.54269153295869	   
df.mm.trans2:exp5	-0.0352994671720368	0.0342124739485486	-1.03177183927485	0.302409367604223	   
df.mm.trans1:exp6	-0.150137364253888	0.0515444872479577	-2.91277248586535	0.00365588819090842	** 
df.mm.trans2:exp6	-0.195582963637052	0.0342124739485486	-5.71671501836391	1.40340927766082e-08	***
df.mm.trans1:exp7	0.0347382586394510	0.0515444872479577	0.673947118192108	0.500488748097442	   
df.mm.trans2:exp7	-0.0667964422227184	0.0342124739485486	-1.95240023633404	0.0511480669716187	.  
df.mm.trans1:exp8	0.082876381523621	0.0515444872479577	1.60786120783275	0.108157048238015	   
df.mm.trans2:exp8	-0.0450766759885473	0.0342124739485486	-1.31755090427945	0.187932684768712	   
df.mm.trans1:probe2	-0.0424546329754995	0.0391508485441116	-1.08438602365580	0.278435016180669	   
df.mm.trans1:probe3	-0.0650742204383515	0.0391508485441116	-1.66214074172701	0.0967739419043428	.  
df.mm.trans1:probe4	0.483777104294536	0.0391508485441116	12.3567463358926	6.58014663382059e-33	***
df.mm.trans1:probe5	0.0392245295562845	0.0391508485441116	1.00188197740056	0.316624392693233	   
df.mm.trans1:probe6	0.0413810614625426	0.0391508485441116	1.05696461255286	0.290763445706016	   
df.mm.trans1:probe7	-0.186634914523872	0.0391508485441116	-4.76707201667899	2.12401227885901e-06	***
df.mm.trans1:probe8	-0.0313302962478254	0.0391508485441116	-0.800245650168355	0.423743986293379	   
df.mm.trans1:probe9	0.00936768455303906	0.0391508485441116	0.23927155863517	0.810940290586523	   
df.mm.trans1:probe10	0.0292567768808928	0.0391508485441116	0.747283340434601	0.455054758455693	   
df.mm.trans1:probe11	0.236640693227316	0.0391508485441116	6.0443311454844	2.06147674889071e-09	***
df.mm.trans1:probe12	0.161021788502969	0.0391508485441116	4.11285564657799	4.20375635686672e-05	***
df.mm.trans1:probe13	0.0604597462605918	0.0391508485441116	1.54427677838122	0.122813433908074	   
df.mm.trans1:probe14	0.0468126358177087	0.0391508485441116	1.19569913701780	0.23207572397611	   
df.mm.trans1:probe15	-0.0154823687752918	0.0391508485441116	-0.39545423282072	0.692585573231413	   
df.mm.trans1:probe16	0.123091506710924	0.0391508485441116	3.14403164396899	0.00171170402555579	** 
df.mm.trans1:probe17	-0.132182311746498	0.0391508485441116	-3.37623108213266	0.000760952393216242	***
df.mm.trans1:probe18	-0.0730966893580151	0.0391508485441116	-1.86705249250617	0.0621642715764826	.  
df.mm.trans1:probe19	0.124031443150693	0.0391508485441116	3.16803971722211	0.0015776504139667	** 
df.mm.trans1:probe20	-0.0420174612770102	0.0391508485441116	-1.07321968334016	0.283411632535804	   
df.mm.trans1:probe21	0.0845756991499083	0.0391508485441116	2.16025200717211	0.0309719913502637	*  
df.mm.trans1:probe22	-0.124611811293376	0.0391508485441116	-3.18286361413023	0.00149978347350890	** 
df.mm.trans2:probe2	0.126648807323631	0.0391508485441116	3.23489303637787	0.00125368943669222	** 
df.mm.trans2:probe3	0.116065134137998	0.0391508485441116	2.96456241573478	0.00309750752510447	** 
df.mm.trans2:probe4	0.0463352262045178	0.0391508485441116	1.18350503060774	0.236868767283809	   
df.mm.trans2:probe5	0.130798246671996	0.0391508485441116	3.34087897289441	0.000863636674172362	***
df.mm.trans2:probe6	0.0629749991191034	0.0391508485441116	1.60852194680146	0.108012365170524	   
df.mm.trans3:probe2	0.0820213703692871	0.0391508485441116	2.09500875254014	0.0364020435838785	*  
df.mm.trans3:probe3	0.115264385192579	0.0391508485441116	2.94410950155294	0.00330799347948342	** 
df.mm.trans3:probe4	0.653326099578889	0.0391508485441116	16.6874058640844	8.1962955774498e-56	***
df.mm.trans3:probe5	-0.0684868395942028	0.0391508485441116	-1.74930664700761	0.0805212798552235	.  
df.mm.trans3:probe6	-0.150967913105407	0.0391508485441116	-3.8560572431862	0.000122019308866721	***
df.mm.trans3:probe7	0.0666971489207841	0.0391508485441116	1.70359395520217	0.0887438549816148	.  
df.mm.trans3:probe8	-0.207434770886944	0.0391508485441116	-5.29834674344862	1.41512901915278e-07	***
df.mm.trans3:probe9	-0.0502514338841472	0.0391508485441116	-1.28353371006834	0.199579719459560	   
df.mm.trans3:probe10	0.148663582076521	0.0391508485441116	3.79719948876766	0.000154465267170263	***
df.mm.trans3:probe11	0.0785884757029134	0.0391508485441116	2.00732496549512	0.0449631660113313	*  
df.mm.trans3:probe12	0.0743155314318934	0.0391508485441116	1.89818443776925	0.0579374582412239	.  
df.mm.trans3:probe13	0.127624521189333	0.0391508485441116	3.25981494489288	0.00114955016202493	** 
df.mm.trans3:probe14	0.606942601881405	0.0391508485441116	15.5026678718740	4.17530402602139e-49	***
df.mm.trans3:probe15	-0.0541315992842436	0.0391508485441116	-1.38264178931532	0.167059757764454	   
df.mm.trans3:probe16	0.0109840218440167	0.0391508485441116	0.280556418378543	0.779104230588351	   
df.mm.trans3:probe17	-0.231717282973277	0.0391508485441116	-5.91857626565103	4.35305602519446e-09	***
df.mm.trans3:probe18	-0.0672996123835955	0.0391508485441116	-1.71898221587122	0.0859033716605963	.  
df.mm.trans3:probe19	0.745874810877773	0.0391508485441116	19.0513063857962	5.14555286610125e-70	***
df.mm.trans3:probe20	0.0960808244601637	0.0391508485441116	2.45411857042916	0.0142796119585886	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17164324202465	0.118896836956963	35.0862423996584	9.23130272546429e-181	***
df.mm.trans1	-0.0253318055210698	0.101401520974419	-0.249816820079657	0.80277638776778	   
df.mm.trans2	-0.148056818988590	0.0883255960772275	-1.67626175835979	0.093975550595471	.  
df.mm.exp2	0.0411216909845077	0.110736283033202	0.371347943583932	0.71045102620952	   
df.mm.exp3	0.163438565450227	0.110736283033202	1.47592605579170	0.140254420363463	   
df.mm.exp4	0.0331476697356353	0.110736283033202	0.299338833015524	0.764738962095817	   
df.mm.exp5	0.0278561100540675	0.110736283033202	0.251553594639939	0.801433855283896	   
df.mm.exp6	-0.0535661341962356	0.110736283033202	-0.483727038049265	0.628677391307913	   
df.mm.exp7	-0.075748322661983	0.110736283033202	-0.684042488939887	0.494094673139129	   
df.mm.exp8	-0.101672945990048	0.110736283033202	-0.91815386253812	0.358742748591984	   
df.mm.trans1:exp2	-0.0393507786748242	0.100678076389832	-0.390857474495793	0.695979463381146	   
df.mm.trans2:exp2	0.00631426853705529	0.066824722673201	0.0944900073575245	0.924737398581599	   
df.mm.trans1:exp3	-0.152713340458373	0.100678076389832	-1.51684801631546	0.129596885492805	   
df.mm.trans2:exp3	-0.00975649643625885	0.066824722673201	-0.146001300805571	0.883947531492032	   
df.mm.trans1:exp4	-0.111296655149601	0.100678076389832	-1.10547061625069	0.269201236821478	   
df.mm.trans2:exp4	0.0591122983942435	0.066824722673201	0.884587260965163	0.376575621509953	   
df.mm.trans1:exp5	-0.0301721420387312	0.100678076389832	-0.299689298014621	0.764471669279134	   
df.mm.trans2:exp5	-0.043953659412381	0.0668247226732009	-0.657745481823121	0.510841465581719	   
df.mm.trans1:exp6	0.0230954334073983	0.100678076389832	0.229398834737081	0.818602256605647	   
df.mm.trans2:exp6	9.78849351979758e-05	0.066824722673201	0.00146480121850519	0.998831527908175	   
df.mm.trans1:exp7	0.0751608950745634	0.100678076389832	0.746546793201884	0.455499202617599	   
df.mm.trans2:exp7	-0.0302967756768049	0.0668247226732009	-0.453376751370418	0.650368298604666	   
df.mm.trans1:exp8	0.0763873775311532	0.100678076389832	0.758729013011496	0.448179766351926	   
df.mm.trans2:exp8	-0.00124514528021544	0.0668247226732009	-0.0186330033317862	0.98513730664831	   
df.mm.trans1:probe2	0.113976404038497	0.0764704885216796	1.49046261168038	0.136393817917184	   
df.mm.trans1:probe3	0.000481371561991207	0.0764704885216796	0.00629486709575207	0.994978615203147	   
df.mm.trans1:probe4	-0.0285206280825427	0.0764704885216796	-0.372962545864435	0.709249302320611	   
df.mm.trans1:probe5	-0.0696903040279592	0.0764704885216796	-0.911335933315004	0.362321197891013	   
df.mm.trans1:probe6	-0.0703800147695486	0.0764704885216796	-0.920355239388796	0.3575921053955	   
df.mm.trans1:probe7	-0.0763730948367541	0.0764704885216796	-0.99872638861333	0.318150343825091	   
df.mm.trans1:probe8	-0.00660579151890364	0.0764704885216796	-0.0863835401944749	0.931177498670738	   
df.mm.trans1:probe9	-0.141545691688243	0.0764704885216796	-1.85098453566325	0.0644439045862146	.  
df.mm.trans1:probe10	-0.131465604386309	0.0764704885216796	-1.71916783752517	0.0858695626060496	.  
df.mm.trans1:probe11	-0.0840501948091731	0.0764704885216796	-1.09911936531365	0.271960276662335	   
df.mm.trans1:probe12	0.0541974963880698	0.0764704885216796	0.70873741538482	0.478639967902277	   
df.mm.trans1:probe13	0.00306891038237894	0.0764704885216796	0.040131957330296	0.967995320800286	   
df.mm.trans1:probe14	-0.0899747328662487	0.0764704885216796	-1.17659419477542	0.239616058442411	   
df.mm.trans1:probe15	-0.0784629118503484	0.0764704885216796	-1.02605480057976	0.305094869307752	   
df.mm.trans1:probe16	-0.023255072294608	0.0764704885216796	-0.304105188082002	0.76110617328282	   
df.mm.trans1:probe17	-0.0322067913897669	0.0764704885216796	-0.421166282737114	0.673717232645792	   
df.mm.trans1:probe18	-0.010965602582918	0.0764704885216796	-0.143396528450439	0.886003700117678	   
df.mm.trans1:probe19	-0.0713046569916542	0.0764704885216796	-0.932446730367613	0.35131352810463	   
df.mm.trans1:probe20	-0.0306374999549604	0.0764704885216796	-0.400644752599882	0.68876071403672	   
df.mm.trans1:probe21	-0.0111152490446847	0.0764704885216796	-0.145353446271413	0.884458865664523	   
df.mm.trans1:probe22	-0.00203607190999133	0.0764704885216796	-0.0266255904644064	0.978763267428404	   
df.mm.trans2:probe2	0.0568821615419755	0.0764704885216796	0.743844620867687	0.457131829361784	   
df.mm.trans2:probe3	0.0910767446731878	0.0764704885216796	1.19100513719573	0.233912531998717	   
df.mm.trans2:probe4	0.108130105429758	0.0764704885216796	1.41401091480019	0.157646091881381	   
df.mm.trans2:probe5	0.0859440669076019	0.0764704885216796	1.12388541735596	0.261310691393151	   
df.mm.trans2:probe6	-0.0173573101380308	0.0764704885216796	-0.226980505467935	0.820481737872264	   
df.mm.trans3:probe2	0.132105284893791	0.0764704885216796	1.72753290122292	0.0843570951809007	.  
df.mm.trans3:probe3	0.135182161392224	0.0764704885216796	1.76776903097590	0.0773810598411317	.  
df.mm.trans3:probe4	0.171174844141337	0.0764704885216796	2.23844318835243	0.0253947287319650	*  
df.mm.trans3:probe5	0.0562043513593285	0.0764704885216796	0.734980937690681	0.462510222795616	   
df.mm.trans3:probe6	0.143768196017314	0.0764704885216796	1.88004809170999	0.0603697633167178	.  
df.mm.trans3:probe7	0.0796517211859306	0.0764704885216796	1.04160078908544	0.297829264681349	   
df.mm.trans3:probe8	0.120907707455547	0.0764704885216796	1.58110285147805	0.114146509565877	   
df.mm.trans3:probe9	0.0317104614222081	0.0764704885216796	0.414675805467335	0.678461371056795	   
df.mm.trans3:probe10	0.176615575513395	0.0764704885216796	2.30959130676043	0.0210981896039897	*  
df.mm.trans3:probe11	0.0814396030028068	0.0764704885216796	1.06498081256168	0.287121988372208	   
df.mm.trans3:probe12	0.0953397517963977	0.0764704885216796	1.24675222611359	0.212757937839785	   
df.mm.trans3:probe13	0.156100170724081	0.0764704885216796	2.04131258661734	0.0414617491765442	*  
df.mm.trans3:probe14	0.172966306279392	0.0764704885216796	2.26187003147437	0.0239030269721732	*  
df.mm.trans3:probe15	0.0629038423749097	0.0764704885216796	0.822589780593285	0.410922387068687	   
df.mm.trans3:probe16	0.119456798912866	0.0764704885216796	1.56212940733339	0.118549652844892	   
df.mm.trans3:probe17	0.146181634453824	0.0764704885216796	1.91160848164820	0.0561901265748464	.  
df.mm.trans3:probe18	0.284971503079695	0.0764704885216796	3.72655528411989	0.000204142133911704	***
df.mm.trans3:probe19	0.160746116319456	0.0764704885216796	2.10206733900862	0.0357778317862507	*  
df.mm.trans3:probe20	0.152695630812180	0.0764704885216796	1.99679162202410	0.0460977558210766	*