fitVsDatCorrelation=0.9322363741117
cont.fitVsDatCorrelation=0.249915298471494

fstatistic=8346.53937931634,56,784
cont.fstatistic=1153.61669162644,56,784

residuals=-0.855833014530164,-0.10329303759236,-0.0071562062665328,0.0926404525853786,0.800095378106117
cont.residuals=-1.07489262228944,-0.383737819686158,-0.0806038948488053,0.385143127671867,1.60218507451858

predictedValues:
Include	Exclude	Both
Lung	96.3531796951432	82.8927368270173	69.4793811167803
cerebhem	79.0726860505643	71.3255272516178	61.6161605564949
cortex	84.0027996049099	84.4432833868415	56.4984669322968
heart	87.3336700389646	79.0983968291797	59.1254874730451
kidney	99.1229036873876	84.1391425890338	66.0699275374374
liver	98.6068032911004	82.33937084234	67.5258839919479
stomach	106.061241068388	103.778974810885	62.8638116458144
testicle	92.8010091459932	80.036381264375	64.2816849234755


diffExp=13.4604428681259,7.74715879894651,-0.440483781931633,8.23527320978496,14.9837610983538,16.2674324487604,2.28226625750246,12.7646278816181
diffExpScore=0.998439951648559
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	90.043698889196	75.1792793708428	93.3144572420255
cerebhem	83.3859472953023	92.7793876444879	103.087426524899
cortex	97.9846167541773	81.5618653870269	99.897505641858
heart	86.8435619028314	73.8482634655656	81.700258420558
kidney	84.1143020845217	97.0447627417001	72.2541104229395
liver	94.499551478061	75.9631942638733	81.6580364383235
stomach	84.400265570599	93.1363669918589	109.378684791609
testicle	92.477486144892	84.9457121790817	100.413152177390
cont.diffExp=14.8644195183532,-9.39344034918557,16.4227513671504,12.9952984372658,-12.9304606571784,18.5363572141877,-8.7361014212598,7.53177396581026
cont.diffExpScore=2.51697933947906

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=0,0,1,0,0,1,0,0
cont.diffExp1.2Score=0.666666666666667

tran.correlation=0.776580832312522
cont.tran.correlation=-0.59548264502872

tran.covariance=0.00795774243807849
cont.tran.covariance=-0.00380914667287126

tran.mean=88.2130066489838
cont.tran.mean=86.7630163852511

weightedLogRatios:
wLogRatio
Lung	0.676042455518635
cerebhem	0.445326484116029
cortex	-0.0231869319645980
heart	0.437793337834488
kidney	0.739861549874407
liver	0.811488328912852
stomach	0.101221047822389
testicle	0.659451484030975

cont.weightedLogRatios:
wLogRatio
Lung	0.795665146793768
cerebhem	-0.477880247184693
cortex	0.824250713869235
heart	0.710475978643843
kidney	-0.644006222606087
liver	0.969330710200543
stomach	-0.441728448145643
testicle	0.380970102595094

varWeightedLogRatios=0.0925277513471377
cont.varWeightedLogRatios=0.45428198955979

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.71043258907276	0.0912492512756053	51.621602623846	2.0750109756404e-254	***
df.mm.trans1	-0.455263339607343	0.0795319024743152	-5.72428579530547	1.47892370349245e-08	***
df.mm.trans2	-0.201096449530172	0.0709736404064416	-2.83339629161703	0.00472389497837502	** 
df.mm.exp2	-0.227840193952781	0.0928555613265628	-2.45370541837006	0.0143558452388249	*  
df.mm.exp3	0.08817892307557	0.0928555613265628	0.949635345646713	0.342590208928481	   
df.mm.exp4	0.0162287793672430	0.0928555613265627	0.174774446844042	0.861301991515716	   
df.mm.exp5	0.0935809236099058	0.0928555613265627	1.00781172686892	0.313855792971296	   
df.mm.exp6	0.0449408360598139	0.0928555613265627	0.483986477684002	0.628530625855993	   
df.mm.exp7	0.420771595538177	0.0928555613265628	4.53146359277685	6.7706642435785e-06	***
df.mm.exp8	0.00512625199561068	0.0928555613265627	0.0552067309957044	0.955987831770871	   
df.mm.trans1:exp2	0.0301873041759537	0.08670697089545	0.348153139986323	0.727818637460167	   
df.mm.trans2:exp2	0.0775470386303339	0.0676309708795704	1.14662021884058	0.251888437149597	   
df.mm.trans1:exp3	-0.225349191980018	0.08670697089545	-2.59897433450582	0.00952601056772079	** 
df.mm.trans2:exp3	-0.0696462612283746	0.0676309708795704	-1.02979833532765	0.303422269640485	   
df.mm.trans1:exp4	-0.114513104695950	0.08670697089545	-1.32069086848885	0.186989838513738	   
df.mm.trans2:exp4	-0.0630836170813881	0.0676309708795704	-0.932762257601185	0.351229894490342	   
df.mm.trans1:exp5	-0.0652407878734087	0.08670697089545	-0.752428405693876	0.452019316129091	   
df.mm.trans2:exp5	-0.0786564804251318	0.0676309708795704	-1.16302456407427	0.24517340170044	   
df.mm.trans1:exp6	-0.021820973758251	0.08670697089545	-0.251663430666520	0.801367177528675	   
df.mm.trans2:exp6	-0.0516389053556459	0.0676309708795704	-0.76353931762415	0.44537152637466	   
df.mm.trans1:exp7	-0.324775318151102	0.08670697089545	-3.74566559985946	0.000193096226432150	***
df.mm.trans2:exp7	-0.196055644767899	0.0676309708795705	-2.89890330152162	0.0038492475325447	** 
df.mm.trans1:exp8	-0.042689133666806	0.0867069708954499	-0.492337965747644	0.622618282482496	   
df.mm.trans2:exp8	-0.0401923995277575	0.0676309708795704	-0.594289849828233	0.552489714074558	   
df.mm.trans1:probe2	-0.138204997358107	0.0551013183632508	-2.50819765231391	0.0123357876768695	*  
df.mm.trans1:probe3	0.266768542524562	0.0551013183632508	4.84141850773721	1.55310588403720e-06	***
df.mm.trans1:probe4	-0.396700243800595	0.0551013183632508	-7.19946918847535	1.41772106887589e-12	***
df.mm.trans1:probe5	0.352710988822820	0.0551013183632508	6.40113520510711	2.65347314485858e-10	***
df.mm.trans1:probe6	-0.377393779990446	0.0551013183632508	-6.84908802911955	1.50221088220097e-11	***
df.mm.trans1:probe7	-0.327556178743312	0.0551013183632508	-5.94461599963773	4.16890662765800e-09	***
df.mm.trans1:probe8	0.194932959378755	0.0551013183632508	3.53771860944735	0.000427337801750885	***
df.mm.trans1:probe9	0.232000141065025	0.0551013183632508	4.21042813414343	2.84437024591258e-05	***
df.mm.trans1:probe10	-0.308882822188193	0.0551013183632508	-5.60572471518574	2.87346838119845e-08	***
df.mm.trans1:probe11	0.248009861216199	0.0551013183632508	4.5009787167199	7.78986853937524e-06	***
df.mm.trans1:probe12	0.773109769440943	0.0551013183632508	14.0306945896336	4.60330666930685e-40	***
df.mm.trans1:probe13	0.758594861749849	0.0551013183632508	13.7672724407223	8.81676098968937e-39	***
df.mm.trans1:probe14	0.568431042784819	0.0551013183632508	10.3161060328445	1.74682248692578e-23	***
df.mm.trans1:probe15	0.742358334496279	0.0551013183632508	13.4726056752825	2.30282817346205e-37	***
df.mm.trans1:probe16	0.745989191270248	0.0551013183632508	13.5384998658721	1.11434103683145e-37	***
df.mm.trans1:probe17	0.88614277775654	0.0551013183632508	16.0820612660249	1.66004536677340e-50	***
df.mm.trans1:probe18	0.966827347012263	0.0551013183632508	17.5463559807868	2.16929818027034e-58	***
df.mm.trans1:probe19	0.645553368911222	0.0551013183632508	11.7157517839313	2.50752979602674e-29	***
df.mm.trans1:probe20	0.943719690796204	0.0551013183632508	17.1269893140272	4.2292532424738e-56	***
df.mm.trans1:probe21	1.13195296023185	0.0551013183632508	20.5431193636701	2.30460965668486e-75	***
df.mm.trans1:probe22	1.16431942897119	0.0551013183632508	21.1305185348836	8.58767480123572e-79	***
df.mm.trans2:probe2	-0.287355861093381	0.0551013183632508	-5.21504511378498	2.35343973030667e-07	***
df.mm.trans2:probe3	-0.220303161841421	0.0551013183632508	-3.99814683904822	6.98715017877986e-05	***
df.mm.trans2:probe4	-0.299954615679132	0.0551013183632507	-5.44369217632338	6.98468120851422e-08	***
df.mm.trans2:probe5	-0.252253449518863	0.0551013183632508	-4.57799299566488	5.45747804462305e-06	***
df.mm.trans2:probe6	-0.133385945651760	0.0551013183632508	-2.42073964133534	0.0157151522209371	*  
df.mm.trans3:probe2	0.155633914612950	0.0551013183632508	2.82450437187268	0.00485559648057327	** 
df.mm.trans3:probe3	0.19385210111116	0.0551013183632508	3.51810277629306	0.000459670792694933	***
df.mm.trans3:probe4	-0.0726366972059941	0.0551013183632508	-1.31823882556027	0.187808711532746	   
df.mm.trans3:probe5	-0.0913531307903397	0.0551013183632507	-1.65791188857047	0.0977352806966734	.  
df.mm.trans3:probe6	0.942564087795212	0.0551013183632508	17.1060169845926	5.49714546660857e-56	***
df.mm.trans3:probe7	0.180640362758315	0.0551013183632508	3.27833104767945	0.00109025107675442	** 

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.45094273821689	0.244172253083077	18.2286999526621	3.6346832881954e-62	***
df.mm.trans1	0.113694192330683	0.212818007245707	0.534232012610751	0.593332473748034	   
df.mm.trans2	-0.155414340043696	0.189917105568426	-0.818327235867132	0.413419006499262	   
df.mm.exp2	0.0339311691155870	0.248470549658768	0.136560124176430	0.891413555870934	   
df.mm.exp3	0.0978318962513278	0.248470549658768	0.393736386005035	0.693882732814201	   
df.mm.exp4	0.0788679922657539	0.248470549658768	0.317413843910539	0.751014117540083	   
df.mm.exp5	0.442964041358915	0.248470549658768	1.78276275384447	0.0750117798360457	.  
df.mm.exp6	0.192108086977608	0.248470549658768	0.773162401908138	0.439659315081977	   
df.mm.exp7	-0.00937645275737328	0.248470549658768	-0.0377366765206189	0.969907239717894	   
df.mm.exp8	0.0754886994052815	0.248470549658768	0.303813468070774	0.761350616788816	   
df.mm.trans1:exp2	-0.110746467312047	0.232017645576132	-0.47731915836422	0.633267902189099	   
df.mm.trans2:exp2	0.176417677399012	0.180972515467373	0.974831326974722	0.329944614413886	   
df.mm.trans1:exp3	-0.0133164975038742	0.232017645576132	-0.0573943308096569	0.95424570326444	   
df.mm.trans2:exp3	-0.0163457321662913	0.180972515467373	-0.0903216277017393	0.92805470373503	   
df.mm.trans1:exp4	-0.115054726957288	0.232017645576132	-0.495887830736283	0.620112531487521	   
df.mm.trans2:exp4	-0.0967311506168424	0.180972515467373	-0.534507410514951	0.593142064600335	   
df.mm.trans1:exp5	-0.511082524086197	0.232017645576132	-2.20277437441066	0.0279009242962049	*  
df.mm.trans2:exp5	-0.187667350443119	0.180972515467373	-1.03699365596183	0.300058541550752	   
df.mm.trans1:exp6	-0.143808094458670	0.232017645576132	-0.619815333879345	0.535559465576181	   
df.mm.trans2:exp6	-0.181734802703534	0.180972515467373	-1.00421217130233	0.315586190980717	   
df.mm.trans1:exp7	-0.0553480947801771	0.232017645576132	-0.238551230199497	0.81151592398541	   
df.mm.trans2:exp7	0.223565530959474	0.180972515467373	1.23535626601698	0.217067999372754	   
df.mm.trans1:exp8	-0.0488185732455776	0.232017645576132	-0.210408881291569	0.833403232477756	   
df.mm.trans2:exp8	0.0466480200895745	0.180972515467373	0.257763008759110	0.796657457438299	   
df.mm.trans1:probe2	-0.021269714218077	0.147444640526050	-0.144255594114451	0.88533571476386	   
df.mm.trans1:probe3	-0.0736215049446428	0.147444640526050	-0.499316249691934	0.617696693129004	   
df.mm.trans1:probe4	-0.135609841060575	0.147444640526050	-0.919733946088161	0.357994699872064	   
df.mm.trans1:probe5	-0.0421789484906586	0.147444640526050	-0.286066338797893	0.77490288209711	   
df.mm.trans1:probe6	0.0626745370115328	0.147444640526050	0.425071652573629	0.670901081180105	   
df.mm.trans1:probe7	-0.0091884824617196	0.147444640526050	-0.062318185516524	0.950325330288649	   
df.mm.trans1:probe8	-0.167762913332317	0.147444640526050	-1.13780272198281	0.255550461436172	   
df.mm.trans1:probe9	-0.143241769533487	0.147444640526050	-0.971495261017505	0.331601336088247	   
df.mm.trans1:probe10	-0.0382274829027368	0.147444640526050	-0.259266683185970	0.79549754414578	   
df.mm.trans1:probe11	0.156194138391842	0.147444640526050	1.05934090133473	0.289770810998068	   
df.mm.trans1:probe12	-0.0704888633449013	0.147444640526050	-0.478070027458525	0.632733634977809	   
df.mm.trans1:probe13	-0.196015545621738	0.147444640526050	-1.32941790845973	0.184096830185393	   
df.mm.trans1:probe14	-0.166092174223195	0.147444640526050	-1.12647142432993	0.260310706955666	   
df.mm.trans1:probe15	-0.143143885083548	0.147444640526050	-0.97083138846446	0.331931663112731	   
df.mm.trans1:probe16	-0.224566519769500	0.147444640526050	-1.52305651102879	0.128147803676527	   
df.mm.trans1:probe17	-0.223131945539522	0.147444640526050	-1.51332693235533	0.130599635436265	   
df.mm.trans1:probe18	-0.215642365804686	0.147444640526050	-1.46253105596325	0.143996480071553	   
df.mm.trans1:probe19	-0.0226833148832661	0.147444640526050	-0.153842925740379	0.877773177324958	   
df.mm.trans1:probe20	-0.194049607976961	0.147444640526050	-1.31608451337827	0.188530341441811	   
df.mm.trans1:probe21	-0.0546028552761782	0.147444640526050	-0.370327840207465	0.711238257394416	   
df.mm.trans1:probe22	0.0567358486467324	0.147444640526050	0.384794241718867	0.700494224540045	   
df.mm.trans2:probe2	-0.00441256886956312	0.147444640526050	-0.0299269532878241	0.976132926286995	   
df.mm.trans2:probe3	0.0791922617706801	0.147444640526050	0.53709827287136	0.59135212616973	   
df.mm.trans2:probe4	0.188624166677105	0.147444640526050	1.27928805010569	0.201174123215072	   
df.mm.trans2:probe5	0.0106028903817421	0.147444640526050	0.0719109921114348	0.942691082575096	   
df.mm.trans2:probe6	0.0425075589697375	0.147444640526050	0.288295042926483	0.773197101067569	   
df.mm.trans3:probe2	0.125293201819897	0.147444640526050	0.849764368327515	0.395715519238858	   
df.mm.trans3:probe3	0.178688726739042	0.147444640526050	1.21190384473467	0.225914302851141	   
df.mm.trans3:probe4	0.154365538943190	0.147444640526050	1.04693896226033	0.295450386611633	   
df.mm.trans3:probe5	0.0692596987378493	0.147444640526050	0.469733579265722	0.638675993941155	   
df.mm.trans3:probe6	0.248922917276092	0.147444640526050	1.68824662861932	0.0917615853827023	.  
df.mm.trans3:probe7	0.110737134438358	0.147444640526050	0.751042113455408	0.452852676616333