fitVsDatCorrelation=0.935497951334527
cont.fitVsDatCorrelation=0.253260779754479

fstatistic=8306.61625119572,64,968
cont.fstatistic=1094.99524252745,64,968

residuals=-1.00471192884873,-0.112956598913450,0.00838514470841976,0.107300576922533,1.00501418334435
cont.residuals=-1.15542544759709,-0.422594256076384,-0.102539750268121,0.329469338674082,1.94342029056322

predictedValues:
Include	Exclude	Both
Lung	127.342596523898	73.5110645433738	163.382498606319
cerebhem	91.3959741235178	61.5635657980371	139.577349563895
cortex	111.074408915700	78.6731891130725	163.063487481926
heart	106.392699751788	58.7244952770952	120.833146760042
kidney	124.287989703871	64.4409965961226	138.098242052422
liver	129.481820046822	62.7471024658923	139.284189666303
stomach	152.323324337648	63.3187405007102	171.406839334642
testicle	120.064976435197	61.5763964842478	157.366367608637


diffExp=53.8315319805238,29.8324083254807,32.4012198026274,47.6682044746925,59.8469931077484,66.73471758093,89.0045838369377,58.4885799509491
diffExpScore=0.99772110021876
diffExp1.5=1,0,0,1,1,1,1,1
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	117.898035064562	100.254463670604	125.380794227203
cerebhem	88.1903013454918	112.030025049146	107.231892034333
cortex	110.948691830431	118.296248762373	104.443230430828
heart	109.745691293402	116.507184063837	108.217467650928
kidney	96.895008355193	141.292586494484	109.136800431974
liver	108.721018900762	105.252243416827	108.443577898612
stomach	100.432925222953	106.668201717253	106.704387252515
testicle	104.828756895055	95.7016405749554	102.394324235716
cont.diffExp=17.6435713939579,-23.8397237036538,-7.34755693194212,-6.76149277043548,-44.3975781392908,3.46877548393432,-6.23527649430038,9.12711632009922
cont.diffExpScore=2.00230462698347

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

tran.correlation=0.0513240474496174
cont.tran.correlation=-0.357081231264370

tran.covariance=0.00150732921239917
cont.tran.covariance=-0.00383697835687032

tran.mean=92.932458788562
cont.tran.mean=108.353938916083

weightedLogRatios:
wLogRatio
Lung	2.51215022979569
cerebhem	1.70603262473760
cortex	1.56506092924957
heart	2.59700190678694
kidney	2.95200535894553
liver	3.2608872927874
stomach	4.02666088728983
testicle	2.97428235690374

cont.weightedLogRatios:
wLogRatio
Lung	0.760089039086273
cerebhem	-1.10043394089440
cortex	-0.304021176498961
heart	-0.282677170738105
kidney	-1.79633625472302
liver	0.151509816846807
stomach	-0.279457443385354
testicle	0.419644149309289

varWeightedLogRatios=0.647901539948839
cont.varWeightedLogRatios=0.675123862652138

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.56472392544214	0.094672603576119	37.6531730489065	8.1996367162034e-192	***
df.mm.trans1	1.52048952028576	0.0810975783935062	18.7488893060155	3.77661760364616e-67	***
df.mm.trans2	0.700776452627065	0.0709996242537621	9.870143116848	5.85632021240168e-22	***
df.mm.exp2	-0.351570191556587	0.0898570588990006	-3.91254950767699	9.77108083454107e-05	***
df.mm.exp3	-0.0668597947730484	0.0898570588990006	-0.744068363601784	0.457015692372061	   
df.mm.exp4	-0.102639671289273	0.0898570588990006	-1.14225496078878	0.253630511384274	   
df.mm.exp5	0.0121631345187807	0.0898570588990006	0.135360923980965	0.892354659413422	   
df.mm.exp6	0.0179135820125294	0.0898570588990006	0.199356424882149	0.842025811297517	   
df.mm.exp7	-0.0180760975792244	0.0898570588990006	-0.201165025883408	0.840611798818556	   
df.mm.exp8	-0.198487877546151	0.0898570588990006	-2.20892915902413	0.0274131645755812	*  
df.mm.trans1:exp2	0.0198905573955923	0.0822060249574693	0.241959849121558	0.808862523717161	   
df.mm.trans2:exp2	0.174204489900293	0.0569848156984559	3.05703348804572	0.00229675968843441	** 
df.mm.trans1:exp3	-0.0698209427696896	0.0822060249574693	-0.849340943146353	0.395901604285617	   
df.mm.trans2:exp3	0.134726287253643	0.0569848156984559	2.3642488898546	0.0182632576728649	*  
df.mm.trans1:exp4	-0.0771044303747943	0.0822060249574692	-0.937941354233898	0.34850850734111	   
df.mm.trans2:exp4	-0.121939325782334	0.0569848156984559	-2.13985645628118	0.0326158375040670	*  
df.mm.trans1:exp5	-0.0364428289932382	0.0822060249574693	-0.443310925349967	0.657639913417536	   
df.mm.trans2:exp5	-0.143849043734587	0.0569848156984559	-2.52433989601346	0.0117505014445966	*  
df.mm.trans1:exp6	-0.00125416132912686	0.0822060249574693	-0.0152563188619778	0.987830834677788	   
df.mm.trans2:exp6	-0.176237113803296	0.0569848156984559	-3.09270305858112	0.00204026316685441	** 
df.mm.trans1:exp7	0.197200428217451	0.0822060249574693	2.39885614612161	0.0166346856963878	*  
df.mm.trans2:exp7	-0.131178491518435	0.0569848156984559	-2.30199027426159	0.0215472373696431	*  
df.mm.trans1:exp8	0.139639879216997	0.0822060249574693	1.69865748026671	0.089705081646955	.  
df.mm.trans2:exp8	0.0213305678045427	0.0569848156984559	0.374320203427817	0.708248082769863	   
df.mm.trans1:probe2	0.134670358694038	0.0601686493146024	2.23821475516078	0.0254337625671692	*  
df.mm.trans1:probe3	-0.144498169892131	0.0601686493146024	-2.40155249516400	0.0165133348246529	*  
df.mm.trans1:probe4	-0.482812868679643	0.0601686493146024	-8.02432619278474	2.93922191873705e-15	***
df.mm.trans1:probe5	-0.408470990953512	0.0601686493146024	-6.78876783186123	1.9696368407051e-11	***
df.mm.trans1:probe6	-0.873331536064936	0.0601686493146024	-14.5147272876040	2.45061886801509e-43	***
df.mm.trans1:probe7	-0.696271023588225	0.0601686493146024	-11.5719902560493	4.24759302733527e-29	***
df.mm.trans1:probe8	-0.601730805373965	0.0601686493146024	-10.0007364670546	1.78321613210826e-22	***
df.mm.trans1:probe9	-0.48690627103383	0.0601686493146024	-8.09235833910705	1.74492898519026e-15	***
df.mm.trans1:probe10	-0.377036807820977	0.0601686493146024	-6.26633325022096	5.55564747037552e-10	***
df.mm.trans1:probe11	-0.0788256477961093	0.0601686493146024	-1.31007839953255	0.190480108696862	   
df.mm.trans1:probe12	-0.089522487961931	0.0601686493146024	-1.48785935834868	0.137113658491896	   
df.mm.trans1:probe13	0.0065316054195596	0.0601686493146024	0.108554961661312	0.913577982062563	   
df.mm.trans1:probe14	0.101385904791183	0.0601686493146024	1.68502876408392	0.0923053450190402	.  
df.mm.trans1:probe15	-0.0331858816892751	0.0601686493146024	-0.551547725722691	0.581385552333217	   
df.mm.trans1:probe16	-0.208168489732676	0.0601686493146024	-3.45975008752864	0.000564260919969241	***
df.mm.trans1:probe17	-0.799771543332234	0.0601686493146024	-13.2921638169155	3.73672619279362e-37	***
df.mm.trans1:probe18	-0.821931735580443	0.0601686493146024	-13.6604651249994	5.59291956645132e-39	***
df.mm.trans1:probe19	-0.566055571326979	0.0601686493146024	-9.40781582726343	3.56462037090493e-20	***
df.mm.trans1:probe20	-0.766070397610606	0.0601686493146024	-12.7320524282517	1.91453546497284e-34	***
df.mm.trans1:probe21	-0.830650611458703	0.0601686493146024	-13.8053724143864	1.04840763498489e-39	***
df.mm.trans1:probe22	-0.7956451210332	0.0601686493146024	-13.2235828807296	8.10141398150012e-37	***
df.mm.trans2:probe2	0.0589854128517883	0.0601686493146024	0.980334668032394	0.327165891771470	   
df.mm.trans2:probe3	0.2137594398697	0.0601686493146024	3.55267140453862	0.000399735344637692	***
df.mm.trans2:probe4	0.0325219598621598	0.0601686493146024	0.540513377525113	0.588967378483699	   
df.mm.trans2:probe5	0.223116544440469	0.0601686493146024	3.7081860234865	0.000220611017690114	***
df.mm.trans2:probe6	0.142263294300193	0.0601686493146024	2.36440897245914	0.018255413467663	*  
df.mm.trans3:probe2	0.0953278546208706	0.0601686493146024	1.58434426743456	0.113442017361107	   
df.mm.trans3:probe3	-1.00048164660907	0.0601686493146024	-16.6279558874236	8.35614675889276e-55	***
df.mm.trans3:probe4	-1.47923565122305	0.0601686493146024	-24.584823958547	4.37665586300179e-104	***
df.mm.trans3:probe5	-0.518172308963683	0.0601686493146024	-8.61199835572721	2.87875412251432e-17	***
df.mm.trans3:probe6	-1.74411778739586	0.0601686493146024	-28.9871520677892	1.67800166466654e-133	***
df.mm.trans3:probe7	-0.0828620729914723	0.0601686493146024	-1.37716358827025	0.168780043256840	   
df.mm.trans3:probe8	-0.337624752625814	0.0601686493146024	-5.61130682625903	2.62108273749091e-08	***
df.mm.trans3:probe9	-0.00519453903336653	0.0601686493146024	-0.086332983913366	0.931219572813558	   
df.mm.trans3:probe10	-1.83565969427713	0.0601686493146024	-30.5085740695134	8.8297461023858e-144	***
df.mm.trans3:probe11	-1.05647791174299	0.0601686493146024	-17.5586110670195	3.96023597484105e-60	***
df.mm.trans3:probe12	-1.04227058687951	0.0601686493146024	-17.3224860247371	9.18362880698265e-59	***
df.mm.trans3:probe13	-1.14037805052897	0.0601686493146024	-18.9530272578715	2.24502929054106e-68	***
df.mm.trans3:probe14	-0.167132647575880	0.0601686493146024	-2.77773640392020	0.00557970410241953	** 
df.mm.trans3:probe15	-0.0341576448434191	0.0601686493146024	-0.567698381674149	0.570371431163296	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.58014845741924	0.259206813892309	17.6698613305827	8.93694422498911e-61	***
df.mm.trans1	0.0808485268899579	0.222039366360734	0.364117985990863	0.71584942429067	   
df.mm.trans2	0.0528183104487435	0.194391890527990	0.271710462330929	0.785902592887225	   
df.mm.exp2	-0.0229060651385719	0.246022197163053	-0.0931056847825434	0.92583886720964	   
df.mm.exp3	0.287439973823514	0.246022197163052	1.16834975517681	0.242953374227418	   
df.mm.exp4	0.225799665369522	0.246022197163052	0.917802003125238	0.3589511493861	   
df.mm.exp5	0.285682409464879	0.246022197163053	1.16120582922663	0.245844467182365	   
df.mm.exp6	0.112738639203301	0.246022197163053	0.458245802628058	0.646878710335471	   
df.mm.exp7	0.0629746454933923	0.246022197163053	0.255971396969744	0.798027307655106	   
df.mm.exp8	0.038556018135547	0.246022197163053	0.156717640034707	0.875500063212842	   
df.mm.trans1:exp2	-0.267417081249583	0.225074213733277	-1.18812847022309	0.235074223611444	   
df.mm.trans2:exp2	0.133961390714363	0.156020347592544	0.858614871594898	0.390765586173679	   
df.mm.trans1:exp3	-0.348192256422319	0.225074213733277	-1.54701087542149	0.122187474401453	   
df.mm.trans2:exp3	-0.121959503461867	0.156020347592544	-0.781689730498301	0.434588085220385	   
df.mm.trans1:exp4	-0.297454014755916	0.225074213733277	-1.32158193434105	0.186619657576419	   
df.mm.trans2:exp4	-0.0755583190690231	0.156020347592544	-0.48428503227251	0.628293146784228	   
df.mm.trans1:exp5	-0.481874546542693	0.225074213733277	-2.14095848009375	0.0325266068284145	*  
df.mm.trans2:exp5	0.0574388218385618	0.156020347592544	0.368149556931937	0.712842209470771	   
df.mm.trans1:exp6	-0.193773638901974	0.225074213733277	-0.860932204040063	0.389488576506675	   
df.mm.trans2:exp6	-0.0640904423317322	0.156020347592544	-0.410782589070421	0.681322874699621	   
df.mm.trans1:exp7	-0.223304692814204	0.225074213733277	-0.992138055756266	0.321378117792754	   
df.mm.trans2:exp7	-0.00096313796004293	0.156020347592544	-0.00617315609729457	0.995075837273758	   
df.mm.trans1:exp8	-0.156048027320611	0.225074213733277	-0.693318104869777	0.48827626049941	   
df.mm.trans2:exp8	-0.0850321675175898	0.156020347592544	-0.545006909865733	0.585874306161014	   
df.mm.trans1:probe2	0.230398905597564	0.164737456200854	1.39858239231674	0.162258541867913	   
df.mm.trans1:probe3	0.0824918710922217	0.164737456200854	0.500747510582199	0.616662665415651	   
df.mm.trans1:probe4	0.0943491160448815	0.164737456200854	0.572724128566412	0.566964497411182	   
df.mm.trans1:probe5	0.333353808217851	0.164737456200854	2.02354592516843	0.0432912510533693	*  
df.mm.trans1:probe6	0.152084197215936	0.164737456200854	0.92319136596664	0.356137540012456	   
df.mm.trans1:probe7	0.320398350839676	0.164737456200854	1.94490286683215	0.0520759619263498	.  
df.mm.trans1:probe8	0.399148232337840	0.164737456200854	2.42293550928201	0.015578241566495	*  
df.mm.trans1:probe9	-0.0228055558746835	0.164737456200854	-0.138435765615308	0.889924824721733	   
df.mm.trans1:probe10	0.457146633463492	0.164737456200854	2.77500116856316	0.00562646801881415	** 
df.mm.trans1:probe11	0.302173961744316	0.164737456200854	1.83427599717149	0.066919844646264	.  
df.mm.trans1:probe12	0.176876882290199	0.164737456200854	1.07368953223694	0.283229427716768	   
df.mm.trans1:probe13	0.329140207405181	0.164737456200854	1.99796825200385	0.045999512321252	*  
df.mm.trans1:probe14	0.269635378437006	0.164737456200854	1.63675817664840	0.102006048760656	   
df.mm.trans1:probe15	0.174958990525557	0.164737456200854	1.06204742115382	0.288479045873317	   
df.mm.trans1:probe16	0.0834501326062446	0.164737456200854	0.506564411826895	0.612575842149881	   
df.mm.trans1:probe17	0.00110674030386455	0.164737456200854	0.00671820683278711	0.994641071058304	   
df.mm.trans1:probe18	0.226517326567644	0.164737456200854	1.37502017932987	0.169443349407444	   
df.mm.trans1:probe19	0.181546701865908	0.164737456200854	1.10203657415081	0.270719697303607	   
df.mm.trans1:probe20	0.103590193499645	0.164737456200854	0.628819916785312	0.529615310971139	   
df.mm.trans1:probe21	-0.0155766636688388	0.164737456200854	-0.0945544749085306	0.924688279873633	   
df.mm.trans1:probe22	0.146471397606966	0.164737456200854	0.889120185444545	0.374159385037818	   
df.mm.trans2:probe2	-0.0174352931861755	0.164737456200854	-0.105836848451258	0.915733727690508	   
df.mm.trans2:probe3	-0.126051256074185	0.164737456200854	-0.765164516808482	0.444360092274117	   
df.mm.trans2:probe4	-0.159870793636158	0.164737456200854	-0.970458068996996	0.332060634981128	   
df.mm.trans2:probe5	-0.194558544680773	0.164737456200854	-1.18102190702496	0.237884111422756	   
df.mm.trans2:probe6	-0.0324428353021736	0.164737456200854	-0.196936604767152	0.843918488717532	   
df.mm.trans3:probe2	0.0616070730137712	0.164737456200854	0.373971253621019	0.708507598863012	   
df.mm.trans3:probe3	0.169033527375226	0.164737456200854	1.02607829010747	0.305111015845593	   
df.mm.trans3:probe4	-0.0176626533271372	0.164737456200854	-0.107216984737231	0.914639057922146	   
df.mm.trans3:probe5	0.162503727599669	0.164737456200854	0.986440675650218	0.32416340783542	   
df.mm.trans3:probe6	0.0949969213977859	0.164737456200854	0.576656478669685	0.564305603224436	   
df.mm.trans3:probe7	-0.0723781631700031	0.164737456200854	-0.439354624255925	0.660502659320478	   
df.mm.trans3:probe8	0.171137006215837	0.164737456200854	1.03884696390589	0.299135328866977	   
df.mm.trans3:probe9	0.197352246365006	0.164737456200854	1.19798041633219	0.231217879093664	   
df.mm.trans3:probe10	0.0554853900083503	0.164737456200854	0.33681101607336	0.736332375015352	   
df.mm.trans3:probe11	0.247793094602603	0.164737456200854	1.50416972749952	0.132863856693228	   
df.mm.trans3:probe12	0.0276692812822146	0.164737456200854	0.167959867296234	0.86664993574414	   
df.mm.trans3:probe13	0.178508401789545	0.164737456200854	1.08359328780639	0.278814991328228	   
df.mm.trans3:probe14	0.307170283856954	0.164737456200854	1.86460499597882	0.0625392570023792	.  
df.mm.trans3:probe15	0.179886347415884	0.164737456200854	1.09195778279203	0.275123355905671