fitVsDatCorrelation=0.893325021051294
cont.fitVsDatCorrelation=0.293852777199424

fstatistic=10281.9383509900,52,692
cont.fstatistic=2262.54607007501,52,692

residuals=-0.710691475054317,-0.0838911452822703,-0.0060151023168109,0.0843541687178502,0.690416553443976
cont.residuals=-0.643958557753501,-0.237318689595586,-0.0541251530294007,0.166120439540985,1.26062573000059

predictedValues:
Include	Exclude	Both
Lung	71.9369940237227	45.5756919567523	84.1326045049103
cerebhem	74.6081223633527	53.4555122132406	123.133257873528
cortex	84.937971046728	46.7353783725449	115.681206180156
heart	74.1245188764848	47.8609023774879	105.100317967305
kidney	69.0146024826055	46.881848353012	83.0181137443121
liver	64.0731827720327	51.5273451137909	79.0433545925946
stomach	67.8600417209075	47.2978634935954	78.7549370091433
testicle	66.5450786571244	47.8906592867784	92.5219720768198


diffExp=26.3613020669704,21.1526101501122,38.2025926741831,26.2636164989969,22.1327541295934,12.5458376582418,20.5621782273122,18.6544193703460
diffExpScore=0.994648838330495
diffExp1.5=1,0,1,1,0,0,0,0
diffExp1.5Score=0.75
diffExp1.4=1,0,1,1,1,0,1,0
diffExp1.4Score=0.833333333333333
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	64.6017566635037	73.6216941435185	69.4802087560605
cerebhem	67.7404277921212	63.1127433052016	76.9575637442908
cortex	62.2503261047573	60.4675918662039	65.81575579784
heart	69.5788427049053	65.1338568141999	61.3383170248035
kidney	66.0157282408086	79.054185367659	71.4450535629696
liver	64.1816958117297	66.1923086548452	63.0378572568821
stomach	62.8776280690404	63.2214377773398	60.7882423817759
testicle	61.1230851529054	68.962632811545	67.8949560306436
cont.diffExp=-9.01993748001483,4.62768448691952,1.78273423855343,4.44498589070544,-13.0384571268505,-2.01061284311548,-0.343809708299382,-7.8395476586396
cont.diffExpScore=1.92471518664712

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=-0.182320321778895
cont.tran.correlation=0.0826277192198255

tran.covariance=-0.000908736123383637
cont.tran.covariance=0.000349246256530751

tran.mean=60.020357069385
cont.tran.mean=66.1334963300178

weightedLogRatios:
wLogRatio
Lung	1.84738205563881
cerebhem	1.38212464974564
cortex	2.47523646170023
heart	1.78785755438497
kidney	1.56259442986475
liver	0.882781757084884
stomach	1.45726954594611
testicle	1.32682369282591

cont.weightedLogRatios:
wLogRatio
Lung	-0.553322410772199
cerebhem	0.295800155529834
cortex	0.119613888253598
heart	0.277891423450227
kidney	-0.771431678386245
liver	-0.128848993030809
stomach	-0.0225968748347999
testicle	-0.5036049612665

varWeightedLogRatios=0.217148029078262
cont.varWeightedLogRatios=0.163449559465109

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12118329860203	0.083470390918628	49.3729962594714	5.87398339852016e-229	***
df.mm.trans1	0.438841488402250	0.0749686176731365	5.8536692021667	7.42550314824738e-09	***
df.mm.trans2	-0.257462123115872	0.0689438256223996	-3.73437535256562	0.000203667250949793	***
df.mm.exp2	-0.184939002544181	0.0944359671149661	-1.95835345572346	0.0505898992908034	.  
df.mm.exp3	-0.127186479778932	0.0944359671149662	-1.34680126295626	0.178485152379816	   
df.mm.exp4	-0.143640976823585	0.0944359671149662	-1.52104098906211	0.128706332928812	   
df.mm.exp5	0.000118885352753584	0.0944359671149662	0.00125889908670977	0.998995906947801	   
df.mm.exp6	0.0693710795021257	0.0944359671149662	0.734583248537853	0.462842187273335	   
df.mm.exp7	0.0448005544692021	0.0944359671149662	0.474401394276631	0.635363360675364	   
df.mm.exp8	-0.123417055759391	0.0944359671149662	-1.30688613173351	0.191685508276109	   
df.mm.trans1:exp2	0.221397729788785	0.0904155548630547	2.44866859606313	0.014585640106965	*  
df.mm.trans2:exp2	0.344414259839179	0.0786966392624718	4.376479898849	1.39227771313692e-05	***
df.mm.trans1:exp3	0.293317064575371	0.0904155548630547	3.24409959126651	0.00123487513360212	** 
df.mm.trans2:exp3	0.152313421245204	0.0786966392624718	1.93545013704083	0.0533421441829292	.  
df.mm.trans1:exp4	0.173596690511759	0.0904155548630547	1.91998700638062	0.0552703243957086	.  
df.mm.trans2:exp4	0.192565410385864	0.0786966392624718	2.44693308622257	0.0146554394735975	*  
df.mm.trans1:exp5	-0.0415914258668695	0.0904155548630548	-0.460002993178162	0.645658566806883	   
df.mm.trans2:exp5	0.0281371832005469	0.0786966392624718	0.357539832250051	0.720796730196934	   
df.mm.trans1:exp6	-0.185135821509225	0.0904155548630547	-2.04761030101110	0.0409752079025887	*  
df.mm.trans2:exp6	0.0533670570709919	0.0786966392624718	0.678136418163934	0.497911890202796	   
df.mm.trans1:exp7	-0.103143833208321	0.0904155548630547	-1.14077531642144	0.254358077620143	   
df.mm.trans2:exp7	-0.00770993252363569	0.0786966392624718	-0.0979702894036078	0.921984255092862	   
df.mm.trans1:exp8	0.0455059954077	0.0904155548630547	0.503298303888355	0.614914719022213	   
df.mm.trans2:exp8	0.172963033445468	0.0786966392624718	2.19784523286433	0.0282906606547244	*  
df.mm.trans1:probe2	-0.392914500463934	0.0452077774315274	-8.69130319576207	2.58012648715547e-17	***
df.mm.trans1:probe3	0.262899441589185	0.0452077774315274	5.81535869546736	9.24315769484022e-09	***
df.mm.trans1:probe4	-0.494538091422879	0.0452077774315274	-10.9392259367742	8.43154318322389e-26	***
df.mm.trans1:probe5	-0.34624877200604	0.0452077774315274	-7.65905319124515	6.32849628631704e-14	***
df.mm.trans1:probe6	-0.519781712515167	0.0452077774315274	-11.4976170483594	4.03851791222883e-28	***
df.mm.trans1:probe7	-0.245575833117955	0.0452077774315274	-5.43215895738093	7.7173669127003e-08	***
df.mm.trans1:probe8	-0.127714444865396	0.0452077774315274	-2.8250547167207	0.00486342581163339	** 
df.mm.trans1:probe9	0.277300920188626	0.0452077774315274	6.13392066461642	1.44185787588029e-09	***
df.mm.trans1:probe10	-0.126297722428493	0.0452077774315274	-2.7937166922171	0.00535472470345522	** 
df.mm.trans1:probe11	-0.295917224281785	0.0452077774315274	-6.54571494318622	1.15342967545424e-10	***
df.mm.trans1:probe12	-0.411134185268757	0.0452077774315274	-9.09432422090356	9.94565733369583e-19	***
df.mm.trans1:probe13	-0.523172588285515	0.0452077774315274	-11.5726235176663	1.94418781842843e-28	***
df.mm.trans1:probe14	-0.284429903654419	0.0452077774315274	-6.29161440385389	5.57167795983325e-10	***
df.mm.trans1:probe15	-0.508852891064293	0.0452077774315274	-11.2558705597729	4.16943153397376e-27	***
df.mm.trans1:probe16	-0.522516769553962	0.0452077774315274	-11.5581167498309	2.24000223562694e-28	***
df.mm.trans1:probe17	-0.449704405722484	0.0452077774315274	-9.9475008786622	7.00546799762675e-22	***
df.mm.trans1:probe18	-0.555809222305678	0.0452077774315274	-12.2945487233368	1.46126245145094e-31	***
df.mm.trans1:probe19	-0.355861311906219	0.0452077774315274	-7.87168341653632	1.35246654012876e-14	***
df.mm.trans1:probe20	-0.552661934563747	0.0452077774315274	-12.2249304425731	2.95971932860052e-31	***
df.mm.trans1:probe21	-0.437607924005369	0.0452077774315274	-9.67992564262154	7.18902803774171e-21	***
df.mm.trans1:probe22	-0.495314276863946	0.0452077774315274	-10.9563952267762	7.17391517418491e-26	***
df.mm.trans2:probe2	-0.114259857123614	0.0452077774315274	-2.52743805635379	0.0117113627842185	*  
df.mm.trans2:probe3	-0.0800327258547594	0.0452077774315274	-1.77033091210862	0.0771122637287269	.  
df.mm.trans2:probe4	-0.0305257062970586	0.0452077774315274	-0.675231299377491	0.499754376512491	   
df.mm.trans2:probe5	-0.0927177205717575	0.0452077774315274	-2.05092410729967	0.0406503869771613	*  
df.mm.trans2:probe6	-0.0815840401562009	0.0452077774315274	-1.80464612045504	0.071564697802907	.  
df.mm.trans3:probe2	-0.108969301554692	0.0452077774315274	-2.41041050336391	0.0161943553884738	*  
df.mm.trans3:probe3	0.498463847438525	0.0452077774315274	11.0260640039982	3.71813897642709e-26	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.17253014590637	0.177532813719685	23.5028672079438	3.01782079852690e-90	***
df.mm.trans1	-0.117380978560831	0.159450428945062	-0.736159691368873	0.461882984054956	   
df.mm.trans2	0.172330009162907	0.146636324769055	1.17522046078500	0.240310728669116	   
df.mm.exp2	-0.208787680991786	0.200855450342908	-1.03949223501446	0.29893892396867	   
df.mm.exp3	-0.179727332762872	0.200855450342908	-0.894809339034788	0.37120013940742	   
df.mm.exp4	0.076360906375539	0.200855450342908	0.380178413108397	0.70392964084719	   
df.mm.exp5	0.064958458644197	0.200855450342908	0.323408991557349	0.74648322906232	   
df.mm.exp6	-0.0155925210711427	0.200855450342908	-0.0776305599102367	0.938144372730569	   
df.mm.exp7	-0.0457019106804343	0.200855450342908	-0.227536323273331	0.820073936213053	   
df.mm.exp8	-0.0976467186536844	0.200855450342908	-0.48615418942816	0.62701175976484	   
df.mm.trans1:exp2	0.256229240216127	0.192304452898907	1.33241449354699	0.183162611687477	   
df.mm.trans2:exp2	0.0547706445854341	0.167379541952423	0.327224247040911	0.74359724309892	   
df.mm.trans1:exp3	0.142649503372279	0.192304452898907	0.741789912931805	0.458466312121648	   
df.mm.trans2:exp3	-0.0171048570438012	0.167379541952423	-0.10219204117946	0.91863384929022	   
df.mm.trans1:exp4	-0.00214197272495675	0.192304452898907	-0.0111384457960668	0.991116200138078	   
df.mm.trans2:exp4	-0.198856158088928	0.167379541952423	-1.18805533680724	0.235219201911375	   
df.mm.trans1:exp5	-0.0433070416817081	0.192304452898907	-0.225200410229056	0.821889856902652	   
df.mm.trans2:exp5	0.00623530974997296	0.167379541952423	0.0372525200943931	0.970294406317951	   
df.mm.trans1:exp6	0.00906897579575853	0.192304452898907	0.0471594685356872	0.962399737468353	   
df.mm.trans2:exp6	-0.0907829458844383	0.167379541952423	-0.542377788978793	0.587732895104454	   
df.mm.trans1:exp7	0.0186507331892429	0.192304452898907	0.0969854462966983	0.92276603392845	   
df.mm.trans2:exp7	-0.106594380033392	0.167379541952423	-0.636842345187507	0.524438150278291	   
df.mm.trans1:exp8	0.0422947358638659	0.192304452898907	0.219936331303259	0.8259856043197	   
df.mm.trans2:exp8	0.0322717834148684	0.167379541952423	0.192806020606996	0.847167459124266	   
df.mm.trans1:probe2	0.109456188726831	0.0961522264494535	1.13836353840825	0.255362661606057	   
df.mm.trans1:probe3	0.0727877767750715	0.0961522264494535	0.757005630164326	0.449304153192625	   
df.mm.trans1:probe4	0.0470481354542781	0.0961522264494534	0.489308851095726	0.624778112300307	   
df.mm.trans1:probe5	-0.0118753243479427	0.0961522264494535	-0.123505453658793	0.901742718658009	   
df.mm.trans1:probe6	0.268752322430405	0.0961522264494535	2.79507123604346	0.00533259318519717	** 
df.mm.trans1:probe7	0.116177389614976	0.0961522264494535	1.20826520513333	0.227358002759023	   
df.mm.trans1:probe8	0.16996456083213	0.0961522264494535	1.76766120877585	0.0775581965667898	.  
df.mm.trans1:probe9	0.131315425541472	0.0961522264494535	1.36570343080411	0.172475786715219	   
df.mm.trans1:probe10	0.228558698612933	0.0961522264494535	2.37705050681364	0.0177226033920454	*  
df.mm.trans1:probe11	0.127853270612753	0.0961522264494535	1.32969641300989	0.184056452870291	   
df.mm.trans1:probe12	0.26463087862713	0.0961522264494535	2.75220749845293	0.00607454987249688	** 
df.mm.trans1:probe13	0.0400820732266442	0.0961522264494535	0.41686058354265	0.676909663660482	   
df.mm.trans1:probe14	0.0849212006562693	0.0961522264494535	0.883195364185475	0.377437439974114	   
df.mm.trans1:probe15	0.216326601099237	0.0961522264494535	2.24983454972785	0.0247728907603096	*  
df.mm.trans1:probe16	0.0822424712770217	0.0961522264494534	0.855336109354222	0.392661145100758	   
df.mm.trans1:probe17	0.121302877122934	0.0961522264494535	1.26157117315117	0.207528220219067	   
df.mm.trans1:probe18	0.180429571678487	0.0961522264494535	1.87649915494508	0.061007387422386	.  
df.mm.trans1:probe19	0.0478961182322029	0.0961522264494535	0.498128020544397	0.618552081346756	   
df.mm.trans1:probe20	0.134732160063866	0.0961522264494535	1.40123806841533	0.161591198926834	   
df.mm.trans1:probe21	0.198049442559213	0.0961522264494535	2.05974889893294	0.039796027595497	*  
df.mm.trans1:probe22	0.196659061373765	0.0961522264494535	2.04528869102315	0.0412040839377861	*  
df.mm.trans2:probe2	0.0957930938648503	0.0961522264494535	0.996264958203626	0.31946965967055	   
df.mm.trans2:probe3	-0.24040891710176	0.0961522264494535	-2.50029485513933	0.0126392622025082	*  
df.mm.trans2:probe4	-0.110779844386818	0.0961522264494535	-1.15212978916359	0.24966560979159	   
df.mm.trans2:probe5	-0.0176233141278329	0.0961522264494535	-0.183285554361004	0.854627656296919	   
df.mm.trans2:probe6	-0.140264756687432	0.0961522264494535	-1.45877804255701	0.145079941069555	   
df.mm.trans3:probe2	0.0183960404048802	0.0961522264494535	0.19132204301635	0.84832941289546	   
df.mm.trans3:probe3	0.0222922678217704	0.0961522264494535	0.231843490732784	0.816728117939478