fitVsDatCorrelation=0.96658639548836
cont.fitVsDatCorrelation=0.246173653200695

fstatistic=8709.90655173744,53,715
cont.fstatistic=596.709323152465,53,715

residuals=-0.791583968147958,-0.0936799927808022,-0.00090846746671128,0.0931468034528657,1.14628413642014
cont.residuals=-1.13816688904703,-0.529232912449588,-0.250949655982114,0.491289597687572,1.92774760439055

predictedValues:
Include	Exclude	Both
Lung	71.3716064605732	317.302925881527	56.0595888086648
cerebhem	71.2562215001724	169.76192565441	61.0702871371727
cortex	62.181691823556	193.965727890183	55.0127919096784
heart	65.4164492015326	245.856215124515	54.2244069687952
kidney	70.0154691017247	355.994684287365	58.3840160431668
liver	72.9371495569842	348.710513160191	56.8816073730852
stomach	74.6050135469571	250.790680649588	53.834902444924
testicle	74.0729783169289	384.217547124926	56.6517682207333


diffExp=-245.931319420954,-98.5057041542376,-131.784036066627,-180.439765922983,-285.979215185640,-275.773363603207,-176.185667102631,-310.144568807998
diffExpScore=0.999413745432552
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
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	97.7801025147624	86.203208582448	100.779061345912
cerebhem	84.7741971930763	80.0653108389878	73.6923339148818
cortex	93.583585411468	101.147937036586	100.392619582313
heart	95.1405975533653	113.608616031570	77.3305104131352
kidney	95.5733653598937	81.5186922417281	74.7443819805794
liver	93.3214926342596	122.77331233637	98.1223918801121
stomach	94.5133381029642	110.333027102054	65.7365705746904
testicle	90.196343252737	79.8484679651208	129.403395118153
cont.diffExp=11.5768939323144,4.70888635408855,-7.5643516251185,-18.4680184782043,14.0546731181657,-29.4518197021102,-15.8196889990896,10.3478752876162
cont.diffExpScore=3.54231405427935

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

tran.correlation=0.509248665333585
cont.tran.correlation=0.336839716601665

tran.covariance=0.00944098073434438
cont.tran.covariance=0.00283640157447694

tran.mean=176.778549955071
cont.tran.mean=95.023849634837

weightedLogRatios:
wLogRatio
Lung	-7.48049014375566
cerebhem	-4.08043474405911
cortex	-5.3455337712094
heart	-6.41168452375642
kidney	-8.23152312797849
liver	-7.93574934653189
stomach	-5.96313794160833
testicle	-8.4417126132062

cont.weightedLogRatios:
wLogRatio
Lung	0.569545943301554
cerebhem	0.252105998524669
cortex	-0.355822342552171
heart	-0.82387242296253
kidney	0.712654777673236
liver	-1.28180704502363
stomach	-0.71594950001731
testicle	0.541179541714416

varWeightedLogRatios=2.40394683189396
cont.varWeightedLogRatios=0.57112708133035

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	5.73611131068102	0.0925628354518514	61.9699178690868	1.0166813674979e-289	***
df.mm.trans1	-1.86578834026315	0.0814348498894465	-22.9114235833440	1.53096942071772e-87	***
df.mm.trans2	0.067712652535164	0.0736307167222725	0.919625063417069	0.358078867436745	   
df.mm.exp2	-0.712688126662579	0.0981116578313201	-7.26405141260459	9.84499194235674e-13	***
df.mm.exp3	-0.611165473022939	0.09811165783132	-6.22928494464638	7.99509740767192e-10	***
df.mm.exp4	-0.308952333293495	0.09811165783132	-3.14898698200235	0.00170654487849004	** 
df.mm.exp5	0.0552480187861383	0.09811165783132	0.563113701341427	0.573533997391571	   
df.mm.exp6	0.101526333528964	0.0981116578313201	1.03480397511491	0.301110325600009	   
df.mm.exp7	-0.150437471403879	0.09811165783132	-1.53332921621323	0.125637095875314	   
df.mm.exp8	0.217994691287272	0.09811165783132	2.22190406426586	0.0266020531864888	*  
df.mm.trans1:exp2	0.711070139664915	0.0923009881508712	7.70381936217876	4.41510861817391e-14	***
df.mm.trans2:exp2	0.087228225126857	0.0758512355975192	1.14999082664528	0.250532139450685	   
df.mm.trans1:exp3	0.473325964132756	0.0923009881508712	5.1280703881423	3.77307012399866e-07	***
df.mm.trans2:exp3	0.118990036389960	0.0758512355975192	1.56872904511857	0.117153510229043	   
df.mm.trans1:exp4	0.221825955339613	0.0923009881508712	2.40328906313577	0.0165020284648022	*  
df.mm.trans2:exp4	0.0538422872502896	0.0758512355975192	0.709840608740863	0.478034411099409	   
df.mm.trans1:exp5	-0.0744319356183322	0.0923009881508712	-0.806404537042106	0.420277764449118	   
df.mm.trans2:exp5	0.0598108604472042	0.0758512355975192	0.788528492331645	0.430648999249135	   
df.mm.trans1:exp6	-0.0798283498315875	0.0923009881508713	-0.864869937265498	0.387400250353299	   
df.mm.trans2:exp6	-0.00714115044727192	0.0758512355975192	-0.0941467912950581	0.92501892745221	   
df.mm.trans1:exp7	0.194745060443694	0.0923009881508712	2.10989139277006	0.0352145802959196	*  
df.mm.trans2:exp7	-0.0848007987523572	0.0758512355975192	-1.11798836346353	0.263947455571599	   
df.mm.trans1:exp8	-0.180844012215374	0.0923009881508712	-1.95928576538936	0.0504675295876964	.  
df.mm.trans2:exp8	-0.0266426898754791	0.0758512355975192	-0.351249253431416	0.725504838318862	   
df.mm.trans1:probe2	-0.00331976820429891	0.0538921663653889	-0.061600199587281	0.95089843274979	   
df.mm.trans1:probe3	0.399934850216249	0.0538921663653889	7.42102010716532	3.30713016178125e-13	***
df.mm.trans1:probe4	0.231792008427204	0.0538921663653889	4.30103341653876	1.93635390644397e-05	***
df.mm.trans1:probe5	0.0933598727305158	0.0538921663653889	1.73234588673864	0.0836432330963378	.  
df.mm.trans1:probe6	0.0380979044609491	0.0538921663653889	0.706928428199477	0.479841277092488	   
df.mm.trans1:probe7	0.543601923445894	0.0538921663653889	10.0868448998742	1.84656173446219e-22	***
df.mm.trans1:probe8	0.488619219790732	0.0538921663653889	9.0666093561334	1.16492376320005e-18	***
df.mm.trans1:probe9	0.0319743795436508	0.0538921663653889	0.593302917660881	0.553166070889991	   
df.mm.trans1:probe10	1.02931555587879	0.0538921663653889	19.0995394191435	5.0625233664838e-66	***
df.mm.trans1:probe11	0.859406904971095	0.0538921663653889	15.9467871294005	3.30445710589425e-49	***
df.mm.trans1:probe12	1.26717958721404	0.0538921663653889	23.5132426969545	5.23552085361807e-91	***
df.mm.trans1:probe13	1.01212143295294	0.0538921663653889	18.7804926246749	2.86244216181494e-64	***
df.mm.trans1:probe14	1.60558352360974	0.0538921663653889	29.7925214719313	1.96076202712598e-127	***
df.mm.trans1:probe15	1.61676888234047	0.0538921663653889	30.0000722067615	1.24003805737239e-128	***
df.mm.trans1:probe16	0.199824139840615	0.0538921663653889	3.70785131341367	0.000225205578371028	***
df.mm.trans1:probe17	0.239439334762869	0.0538921663653889	4.44293393476651	1.02808814697150e-05	***
df.mm.trans1:probe18	0.304672179542516	0.0538921663653889	5.65336671524464	2.27425970583123e-08	***
df.mm.trans1:probe19	0.129402134465543	0.0538921663653889	2.40113068730985	0.0165988846192668	*  
df.mm.trans1:probe20	0.145218518010566	0.0538921663653889	2.69461273881596	0.0072124969544375	** 
df.mm.trans1:probe21	0.104013347644618	0.0538921663653889	1.93002721284960	0.0539987038036426	.  
df.mm.trans2:probe2	0.100282482836907	0.0538921663653889	1.86079888043452	0.0631828679937484	.  
df.mm.trans2:probe3	-0.374840751197405	0.0538921663653889	-6.95538473358047	7.94814988066229e-12	***
df.mm.trans2:probe4	-0.050936179343873	0.0538921663653889	-0.945149968522804	0.344901597121281	   
df.mm.trans2:probe5	-0.235267858254066	0.0538921663653889	-4.36552979998893	1.45535303040477e-05	***
df.mm.trans2:probe6	0.0771248276119522	0.0538921663653889	1.43109533005309	0.152839811180427	   
df.mm.trans3:probe2	0.0643095217066015	0.0538921663653889	1.19329999225830	0.233147706154338	   
df.mm.trans3:probe3	0.232315990902243	0.0538921663653889	4.31075621134138	1.85520261573864e-05	***
df.mm.trans3:probe4	0.114579460949677	0.0538921663653889	2.12608749429051	0.0338379931976790	*  
df.mm.trans3:probe5	0.0306929660067073	0.0538921663653888	0.569525555877806	0.56917841143949	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.65546868853989	0.349980147035112	13.3020936415368	3.10382555571896e-36	***
df.mm.trans1	-0.0966978127259143	0.307905225666039	-0.314050573570956	0.75357423452742	   
df.mm.trans2	-0.154925944322770	0.278397792580221	-0.55649128136721	0.578049174517635	   
df.mm.exp2	0.0964372330970709	0.370960248419845	0.259966488344393	0.794964464127494	   
df.mm.exp3	0.119852576697688	0.370960248419845	0.323087385260864	0.746723527246928	   
df.mm.exp4	0.513528645702599	0.370960248419845	1.38432257334861	0.166691593716197	   
df.mm.exp5	0.220154575063503	0.370960248419845	0.593472146951811	0.553052904826579	   
df.mm.exp6	0.333676615712729	0.370960248419845	0.899494264234696	0.368692280661135	   
df.mm.exp7	0.64009098353301	0.370960248419845	1.72549750616020	0.0848699231054329	.  
df.mm.exp8	-0.407312941000874	0.370960248419845	-1.09799619429812	0.272575765515213	   
df.mm.trans1:exp2	-0.239167120557364	0.348990102202859	-0.685312044805048	0.493369148791751	   
df.mm.trans2:exp2	-0.170301945508321	0.286793576035421	-0.59381366857146	0.552824559047059	   
df.mm.trans1:exp3	-0.163718683701332	0.348990102202859	-0.46912128071233	0.639125958389354	   
df.mm.trans2:exp3	0.040024192106384	0.286793576035421	0.139557491697237	0.889048929050447	   
df.mm.trans1:exp4	-0.540893979511205	0.348990102202859	-1.54988343823229	0.121611986277787	   
df.mm.trans2:exp4	-0.237476696454443	0.286793576035421	-0.82804050124579	0.407923859302132	   
df.mm.trans1:exp5	-0.242981504418325	0.348990102202859	-0.696241821428751	0.486503662435906	   
df.mm.trans2:exp5	-0.276029627965851	0.286793576035421	-0.962467959644116	0.336139972039977	   
df.mm.trans1:exp6	-0.380347279465075	0.348990102202859	-1.08985119367078	0.276145864631533	   
df.mm.trans2:exp6	0.0199556506193764	0.286793576035421	0.0695819303041561	0.94454587501784	   
df.mm.trans1:exp7	-0.674071120619027	0.348990102202859	-1.93149065364383	0.0538172988351002	.  
df.mm.trans2:exp7	-0.393295071849100	0.286793576035421	-1.37135244549734	0.170695330727507	   
df.mm.trans1:exp8	0.326580721239821	0.348990102202859	0.935787918277373	0.349698340141795	   
df.mm.trans2:exp8	0.330736229556219	0.286793576035421	1.15322049443454	0.249205262166247	   
df.mm.trans1:probe2	-0.062649493702325	0.203766319565811	-0.307457551551305	0.758584660172292	   
df.mm.trans1:probe3	-0.168721991186679	0.203766319565811	-0.828017071448291	0.407937119100785	   
df.mm.trans1:probe4	0.0380893062487720	0.203766319565811	0.186926408299141	0.851771360054259	   
df.mm.trans1:probe5	-0.185807387712678	0.203766319565811	-0.911865062433278	0.362147089888555	   
df.mm.trans1:probe6	0.0305300176000316	0.203766319565811	0.149828576504132	0.880942116118221	   
df.mm.trans1:probe7	-0.0616831836326272	0.203766319565811	-0.302715305277452	0.762194878131056	   
df.mm.trans1:probe8	-0.0678944107477563	0.203766319565811	-0.333197414039901	0.739082950301776	   
df.mm.trans1:probe9	0.218460730124233	0.203766319565811	1.07211403037427	0.284030547720302	   
df.mm.trans1:probe10	0.213991675076161	0.203766319565811	1.05018177455498	0.293989368071747	   
df.mm.trans1:probe11	-0.0130074009564900	0.203766319565811	-0.0638348917731173	0.949119537023985	   
df.mm.trans1:probe12	-0.00340793314613414	0.203766319565811	-0.0167247126679023	0.986660897764447	   
df.mm.trans1:probe13	0.207868657684601	0.203766319565811	1.02013256227786	0.308010636658047	   
df.mm.trans1:probe14	0.160860142270237	0.203766319565811	0.789434400213936	0.43011986031186	   
df.mm.trans1:probe15	0.0763502460439137	0.203766319565811	0.374695122366651	0.70799832584302	   
df.mm.trans1:probe16	0.110692071347884	0.203766319565811	0.543230459203214	0.587140447619921	   
df.mm.trans1:probe17	-0.0174665131926598	0.203766319565811	-0.0857183524238834	0.931714309606467	   
df.mm.trans1:probe18	-0.0254958116255527	0.203766319565811	-0.125122795955091	0.900461507211291	   
df.mm.trans1:probe19	0.226218766218405	0.203766319565811	1.11018723163100	0.267291575156895	   
df.mm.trans1:probe20	0.0120280335132428	0.203766319565811	0.0590285653628745	0.952945849450072	   
df.mm.trans1:probe21	-0.0662495465014799	0.203766319565811	-0.325125107243659	0.745181497869003	   
df.mm.trans2:probe2	-0.181371333933983	0.203766319565811	-0.890094763062182	0.373714393213666	   
df.mm.trans2:probe3	-0.169937610758838	0.203766319565811	-0.833982824644152	0.404569192319406	   
df.mm.trans2:probe4	-0.263063762865925	0.203766319565811	-1.29100708805296	0.197118470512853	   
df.mm.trans2:probe5	0.0953298479796278	0.203766319565811	0.46783908244875	0.640042233597302	   
df.mm.trans2:probe6	0.0368540678129669	0.203766319565811	0.180864373913688	0.856525286806499	   
df.mm.trans3:probe2	0.299360073939740	0.203766319565811	1.46913422481999	0.142236216909302	   
df.mm.trans3:probe3	0.457634890856581	0.203766319565811	2.24588092787718	0.0250162442792391	*  
df.mm.trans3:probe4	0.159894288604187	0.203766319565811	0.784694393778585	0.432892675295048	   
df.mm.trans3:probe5	0.128539135726287	0.203766319565811	0.630816397921797	0.528362159539288