fitVsDatCorrelation=0.87002373694892
cont.fitVsDatCorrelation=0.265847363174474

fstatistic=9335.63026712605,55,761
cont.fstatistic=2431.45321682800,55,761

residuals=-0.646283272130076,-0.0969737174494493,0.00378120162760844,0.093084738199803,0.717035118134944
cont.residuals=-0.75572611613402,-0.217687754442486,-0.0374047814486864,0.167795290767666,1.16742233590644

predictedValues:
Include	Exclude	Both
Lung	79.8578156521531	75.5618852466473	110.704711626971
cerebhem	82.3936316476526	68.6883612365043	102.911084007178
cortex	86.9163078237253	64.78234507521	130.834022949784
heart	80.5292985275445	66.077959383439	125.118279615293
kidney	72.4483895876917	77.7395072879256	92.8454430902207
liver	67.9543451382625	76.8317059587584	92.5309938887187
stomach	73.5375419883394	68.2869235794094	108.306140944188
testicle	77.9070141825149	67.6069813821995	112.397600841819


diffExp=4.29593040550587,13.7052704111483,22.1339627485154,14.4513391441055,-5.29111770023395,-8.87736082049591,5.25061840892992,10.3000328003154
diffExpScore=1.47985944645151
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=0,0,1,0,0,0,0,0
diffExp1.3Score=0.5
diffExp1.2=0,0,1,1,0,0,0,0
diffExp1.2Score=0.666666666666667

cont.predictedValues:
Include	Exclude	Both
Lung	79.0904218794711	70.0852136628468	67.0619315668706
cerebhem	79.9523747229323	69.7652467848171	78.5649433437772
cortex	75.7562372260686	78.7433980350787	83.419204988958
heart	78.2974965016571	83.9423187794797	76.5527673909187
kidney	76.8177192497271	75.3854556977147	65.5701989595879
liver	79.0084302920887	70.3074759952411	78.5139012653211
stomach	78.7887322564125	75.7195808427485	72.1410928717477
testicle	72.5512009352003	68.1455498186026	65.6645224446184
cont.diffExp=9.0052082166243,10.1871279381152,-2.98716080901009,-5.64482227782253,1.43226355201246,8.70095429684764,3.06915141366403,4.40565111659772
cont.diffExpScore=1.55758906828320

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

tran.correlation=-0.699239347013045
cont.tran.correlation=0.0835763986228069

tran.covariance=-0.00401947003854672
cont.tran.covariance=0.000229016196078350

tran.mean=74.1950008561236
cont.tran.mean=75.7723032925055

weightedLogRatios:
wLogRatio
Lung	0.240680302157590
cerebhem	0.786029552606747
cortex	1.26911132981283
heart	0.848447433456244
kidney	-0.304380525293855
liver	-0.525531838812869
stomach	0.315627341094881
testicle	0.607578508018198

cont.weightedLogRatios:
wLogRatio
Lung	0.521010887570435
cerebhem	0.587879642264815
cortex	-0.168108889364971
heart	-0.305977107888396
kidney	0.081532917431305
liver	0.503017297982603
stomach	0.172716400428884
testicle	0.266434383964357

varWeightedLogRatios=0.361950501924593
cont.varWeightedLogRatios=0.107942798214448

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.21217317854150	0.0842343459166085	50.0054120763462	1.06680449511910e-242	***
df.mm.trans1	0.370773049133413	0.0737594735888643	5.02678545674152	6.22320422596184e-07	***
df.mm.trans2	0.187538203649493	0.0661452572747713	2.83524792821423	0.00470046573132805	** 
df.mm.exp2	0.00888922455806383	0.0872297821094496	0.101905843888390	0.918858255799807	   
df.mm.exp3	-0.236284069946184	0.0872297821094496	-2.70875455873216	0.00690539930953353	** 
df.mm.exp4	-0.248136544781183	0.0872297821094496	-2.84463102830911	0.0045656523680664	** 
df.mm.exp5	0.106968478893087	0.0872297821094496	1.22628391710151	0.220471155825980	   
df.mm.exp6	0.0345764794022602	0.0872297821094496	0.396383879061810	0.691932976416765	   
df.mm.exp7	-0.161780852478249	0.0872297821094496	-1.85465157158433	0.0640326213866414	.  
df.mm.exp8	-0.151148690893301	0.0872297821094496	-1.73276474201954	0.0835426128812816	.  
df.mm.trans1:exp2	0.0223711744946633	0.0818287889464252	0.273390023031016	0.784627599512868	   
df.mm.trans2:exp2	-0.104261446646039	0.0652767917153007	-1.59722075650971	0.110631633857205	   
df.mm.trans1:exp3	0.320981997517804	0.0818287889464253	3.9226047660067	9.55150865036532e-05	***
df.mm.trans2:exp3	0.0823651909911703	0.0652767917153007	1.2617836879974	0.207413231468586	   
df.mm.trans1:exp4	0.25650987080684	0.0818287889464253	3.13471425044285	0.00178628915135768	** 
df.mm.trans2:exp4	0.114019799867604	0.0652767917153007	1.74671268105350	0.0810906234135833	.  
df.mm.trans1:exp5	-0.204341787333133	0.0818287889464253	-2.49718699206118	0.0127285259130720	*  
df.mm.trans2:exp5	-0.0785568840853032	0.0652767917153007	-1.20344278603523	0.22917924913049	   
df.mm.trans1:exp6	-0.195988143784814	0.0818287889464253	-2.39510013906146	0.0168566268577680	*  
df.mm.trans2:exp6	-0.0179110790863794	0.0652767917153007	-0.274386632916904	0.783861980514078	   
df.mm.trans1:exp7	0.0793291546007329	0.0818287889464253	0.969452873763915	0.332627292354655	   
df.mm.trans2:exp7	0.0605471524312302	0.0652767917153007	0.927544856911804	0.353937794133737	   
df.mm.trans1:exp8	0.126416931523883	0.0818287889464253	1.54489554533980	0.122787257066658	   
df.mm.trans2:exp8	0.0399079508460948	0.0652767917153007	0.611365077808205	0.541140494463067	   
df.mm.trans1:probe2	-0.285469700494477	0.0501096947971595	-5.69689561371382	1.74373875659578e-08	***
df.mm.trans1:probe3	-0.117460009408175	0.0501096947971595	-2.34405756977058	0.0193315634317090	*  
df.mm.trans1:probe4	-0.189888989172729	0.0501096947971595	-3.78946608917469	0.000162899396435269	***
df.mm.trans1:probe5	-0.527663691942055	0.0501096947971596	-10.5301717377845	2.67486712445207e-24	***
df.mm.trans1:probe6	-0.0923929254492344	0.0501096947971595	-1.84381337430280	0.0655989378532715	.  
df.mm.trans1:probe7	0.284801815485615	0.0501096947971595	5.68356715478855	1.87929930689177e-08	***
df.mm.trans1:probe8	-0.728877014621798	0.0501096947971595	-14.5456286966472	1.78804593512095e-42	***
df.mm.trans1:probe9	-0.364975930482824	0.0501096947971595	-7.28353928237282	8.13600808564508e-13	***
df.mm.trans1:probe10	-0.523249685809429	0.0501096947971596	-10.442084868557	6.03856311574666e-24	***
df.mm.trans1:probe11	-0.285914741415145	0.0501096947971595	-5.70577694740523	1.65874268154440e-08	***
df.mm.trans1:probe12	-0.149941376359752	0.0501096947971595	-2.99226281394656	0.00285869162480562	** 
df.mm.trans1:probe13	-0.440601816773612	0.0501096947971595	-8.79274596576843	9.64217031107079e-18	***
df.mm.trans1:probe14	-0.367120737758414	0.0501096947971596	-7.32634152421987	6.04266743429673e-13	***
df.mm.trans1:probe15	-0.320207887919450	0.0501096947971595	-6.39013845954617	2.88640640265601e-10	***
df.mm.trans1:probe16	-0.365599704937556	0.0501096947971595	-7.29598746145786	7.46287768614908e-13	***
df.mm.trans1:probe17	-0.105011750552243	0.0501096947971595	-2.09563740065316	0.0364442105057468	*  
df.mm.trans1:probe18	-0.139560325073952	0.0501096947971595	-2.78509629002696	0.00548418332931447	** 
df.mm.trans1:probe19	-0.357500457197094	0.0501096947971595	-7.13435710682794	2.26834699424266e-12	***
df.mm.trans1:probe20	-0.275150071803661	0.0501096947971595	-5.49095485249808	5.45131766346815e-08	***
df.mm.trans1:probe21	-0.412202332108689	0.0501096947971595	-8.2259996549022	8.35922221830756e-16	***
df.mm.trans1:probe22	0.0884299322002295	0.0501096947971595	1.76472701656211	0.0780107394460149	.  
df.mm.trans2:probe2	-0.0377916945620009	0.0501096947971595	-0.75417930033258	0.450974862100971	   
df.mm.trans2:probe3	-0.350538581961967	0.0501096947971595	-6.99542440601409	5.80032167998907e-12	***
df.mm.trans2:probe4	-0.12329680472012	0.0501096947971595	-2.46053793021922	0.0140939835173364	*  
df.mm.trans2:probe5	-0.0706379462552894	0.0501096947971595	-1.40966626400793	0.159046784705948	   
df.mm.trans2:probe6	-0.314847647013497	0.0501096947971595	-6.28316832277622	5.57981425059177e-10	***
df.mm.trans3:probe2	-0.326957404835904	0.0501096947971595	-6.52483329143001	1.24176202131322e-10	***
df.mm.trans3:probe3	-0.348940428019674	0.0501096947971595	-6.96353129732998	7.17959595859308e-12	***
df.mm.trans3:probe4	-0.419947811581239	0.0501096947971595	-8.38057013280878	2.53446150518318e-16	***
df.mm.trans3:probe5	0.255501080462103	0.0501096947971595	5.09883529517299	4.31702923275763e-07	***
df.mm.trans3:probe6	0.458636381616932	0.0501096947971595	9.1526476757334	5.01861460053548e-19	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.2972989933039	0.164709055651394	26.0902412214609	1.09223171370805e-107	***
df.mm.trans1	0.014130125609954	0.144226836547091	0.0979715422472048	0.9219806887352	   
df.mm.trans2	-0.0603929503925308	0.129338249653316	-0.466938052389070	0.64067785359798	   
df.mm.exp2	-0.152045530589451	0.170566232569172	-0.891416362425603	0.372987595490126	   
df.mm.exp3	-0.144850412404454	0.170566232569172	-0.849232642491006	0.396018920186182	   
df.mm.exp4	0.0379781639970446	0.170566232569172	0.222659335467486	0.823860398734594	   
df.mm.exp5	0.0662413112756154	0.170566232569172	0.388361226474013	0.697857353264363	   
df.mm.exp6	-0.155530072863616	0.170566232569172	-0.911845624546711	0.362138745350036	   
df.mm.exp7	0.000495886759947125	0.170566232569172	0.00290729737344712	0.99768107751314	   
df.mm.exp8	-0.093307472034673	0.170566232569172	-0.547045394796022	0.584507921293809	   
df.mm.trans1:exp2	0.162884893342412	0.160005309067231	1.01799680455585	0.309003000847296	   
df.mm.trans2:exp2	0.147469679565362	0.127640080805329	1.15535557980628	0.248307469746096	   
df.mm.trans1:exp3	0.101779414335612	0.160005309067231	0.636100232729446	0.524902350633774	   
df.mm.trans2:exp3	0.261333012385843	0.127640080805329	2.04742123897913	0.0409595913106377	*  
df.mm.trans1:exp4	-0.0480543133570186	0.160005309067231	-0.300329493047179	0.764007861831031	   
df.mm.trans2:exp4	0.142439878041223	0.127640080805329	1.11594945053714	0.264796020764976	   
df.mm.trans1:exp5	-0.0953977570227946	0.160005309067231	-0.596216197943223	0.551208103710213	   
df.mm.trans2:exp5	0.00666121014098233	0.127640080805329	0.0521874484797743	0.958393029524513	   
df.mm.trans1:exp6	0.154492853508675	0.160005309067231	0.96554829592411	0.334576989484558	   
df.mm.trans2:exp6	0.15869637046252	0.127640080805329	1.24331142272275	0.214135991234926	   
df.mm.trans1:exp7	-0.0043176704769558	0.160005309067231	-0.0269845450886983	0.978479135296772	   
df.mm.trans2:exp7	0.0768290642096093	0.127640080805329	0.601919582977902	0.547406950423046	   
df.mm.trans1:exp8	0.0070082257486473	0.160005309067231	0.043799957573299	0.96507535072764	   
df.mm.trans2:exp8	0.0652414885098314	0.127640080805329	0.511136377368288	0.60940382226197	   
df.mm.trans1:probe2	0.0666965806474054	0.0979828408377584	0.68069653907915	0.496270576961283	   
df.mm.trans1:probe3	0.122981259895188	0.0979828408377584	1.25513058045359	0.209816648377065	   
df.mm.trans1:probe4	0.162221707490562	0.0979828408377584	1.65561343295987	0.0982124037937793	.  
df.mm.trans1:probe5	-0.030348081754331	0.0979828408377585	-0.30972853506648	0.756852192697431	   
df.mm.trans1:probe6	0.0346503084538922	0.0979828408377584	0.353636495509114	0.723709267355657	   
df.mm.trans1:probe7	0.0348496080178502	0.0979828408377584	0.355670520673663	0.722185890237765	   
df.mm.trans1:probe8	0.170165795126013	0.0979828408377584	1.73668974762404	0.0828466063700088	.  
df.mm.trans1:probe9	-0.0274533558271319	0.0979828408377584	-0.280185342580439	0.779411456138602	   
df.mm.trans1:probe10	0.13822932488284	0.0979828408377585	1.41075032833272	0.158726887076522	   
df.mm.trans1:probe11	0.159512945381129	0.0979828408377584	1.62796816276487	0.103945544702566	   
df.mm.trans1:probe12	-0.000214833731750594	0.0979828408377584	-0.00219256484006540	0.998251162387184	   
df.mm.trans1:probe13	0.123466619717953	0.0979828408377584	1.26008409903517	0.208025291601292	   
df.mm.trans1:probe14	0.0987323627426447	0.0979828408377585	1.00764952208446	0.313943027115221	   
df.mm.trans1:probe15	0.122758167955503	0.0979828408377584	1.25285373342837	0.210643775234493	   
df.mm.trans1:probe16	0.0917891934703817	0.0979828408377584	0.936788448728157	0.349164578857084	   
df.mm.trans1:probe17	0.114693910832548	0.0979828408377584	1.17055098476335	0.242145732048745	   
df.mm.trans1:probe18	0.104018986766020	0.0979828408377584	1.06160411227775	0.288752230911949	   
df.mm.trans1:probe19	0.019547035025027	0.0979828408377584	0.199494471255363	0.841929259453888	   
df.mm.trans1:probe20	-0.0228511760829817	0.0979828408377584	-0.233216100774411	0.81565631873816	   
df.mm.trans1:probe21	0.0867008112747195	0.0979828408377584	0.884857088582276	0.376513202087666	   
df.mm.trans1:probe22	0.086407303355159	0.0979828408377584	0.881861585318122	0.378130113685285	   
df.mm.trans2:probe2	0.0737907543331045	0.0979828408377584	0.753098743639087	0.451623489707431	   
df.mm.trans2:probe3	0.103203748415904	0.0979828408377584	1.05328389678750	0.292545206913173	   
df.mm.trans2:probe4	0.0423648196776193	0.0979828408377584	0.432369783478392	0.665595166544821	   
df.mm.trans2:probe5	-0.0087458541052521	0.0979828408377584	-0.0892590379139305	0.928899532174473	   
df.mm.trans2:probe6	-0.0569439064395513	0.0979828408377584	-0.581162027480301	0.561303337292872	   
df.mm.trans3:probe2	0.133616074362349	0.0979828408377584	1.36366809963791	0.173075284843571	   
df.mm.trans3:probe3	-0.141295772081969	0.0979828408377584	-1.44204608555827	0.149700776864665	   
df.mm.trans3:probe4	-0.0958942887123706	0.0979828408377584	-0.978684511415156	0.328046903034539	   
df.mm.trans3:probe5	-0.0923096847471124	0.0979828408377584	-0.942100514313116	0.346440149643409	   
df.mm.trans3:probe6	-0.0766340748369824	0.0979828408377584	-0.782117299128674	0.434388920254494