fitVsDatCorrelation=0.923201277376897
cont.fitVsDatCorrelation=0.228298705954183

fstatistic=6933.38128803398,68,1060
cont.fstatistic=1067.20596143434,68,1060

residuals=-0.815444975049726,-0.112462560658638,-0.00725919499339186,0.101707264656967,1.51400662197488
cont.residuals=-0.879523518561271,-0.351809444697635,-0.102671113555910,0.187874372139036,2.37440385670564

predictedValues:
Include	Exclude	Both
Lung	67.0093957965912	64.2876346701067	76.7362331833132
cerebhem	63.074459694092	57.9803065647221	77.0950117654276
cortex	80.0095499385038	61.7021582418087	84.4686770229466
heart	69.7470459345449	63.3710562966272	70.0649753076528
kidney	66.944458385871	70.3416814578251	81.1874472613413
liver	67.927737314392	71.3366125812345	80.3544274618013
stomach	90.635806882847	76.2071537519533	73.8465294373232
testicle	68.8943852971401	67.629848225778	75.8053332856425


diffExp=2.72176112648449,5.09415312936992,18.3073916966950,6.37598963791768,-3.3972230719541,-3.40887526684241,14.4286531308937,1.2645370713621
diffExpScore=1.29755299838427
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=0,0,1,0,0,0,0,0
diffExp1.2Score=0.5

cont.predictedValues:
Include	Exclude	Both
Lung	76.0732034663989	77.2128122396183	76.887703407722
cerebhem	76.5437691747951	65.0415646530783	70.8271655232795
cortex	83.683374866194	87.2027471905728	81.1631307101476
heart	73.7120199151542	79.3645624272029	69.2044213154045
kidney	76.5561805968	68.2641085521688	81.909411454254
liver	85.3378695755192	68.0419300163876	69.7769738815768
stomach	82.9435991532726	64.5765593120788	74.4598927619817
testicle	74.3182593115727	60.1157729608208	67.3636291921889
cont.diffExp=-1.13960877321936,11.5022045217168,-3.51937232437884,-5.65254251204871,8.29207204463114,17.2959395591316,18.3670398411938,14.2024863507519
cont.diffExpScore=1.32516365253983

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=0,0,0,0,0,1,1,1
cont.diffExp1.2Score=0.75

tran.correlation=0.519666068162978
cont.tran.correlation=0.144587254683735

tran.covariance=0.00521655727917991
cont.tran.covariance=0.00100617721590659

tran.mean=69.1937056896274
cont.tran.mean=74.9367708382272

weightedLogRatios:
wLogRatio
Lung	0.17349596662234
cerebhem	0.345457403604402
cortex	1.10484519297059
heart	0.402351664578375
kidney	-0.209321611901192
liver	-0.207755953088947
stomach	0.766427961836328
testicle	0.0782379399233387

cont.weightedLogRatios:
wLogRatio
Lung	-0.0645199439484277
cerebhem	0.693103274494365
cortex	-0.183222695867364
heart	-0.320451946844413
kidney	0.490743074388243
liver	0.981483363811815
stomach	1.07457963713276
testicle	0.891245426777365

varWeightedLogRatios=0.20855976883533
cont.varWeightedLogRatios=0.312451068599928

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.48531965972862	0.0951972189435267	47.1160786996249	3.00604930129726e-262	***
df.mm.trans1	-0.263029637917506	0.0810623135295648	-3.24478325950535	0.00121214700088754	** 
df.mm.trans2	-0.360085533587585	0.0708093658168204	-5.08528115502552	4.33833394033273e-07	***
df.mm.exp2	-0.168445393991489	0.0888894517283139	-1.89499868337960	0.0583644795628091	.  
df.mm.exp3	0.0402579791010181	0.0888894517283139	0.452899397153046	0.650713868854103	   
df.mm.exp4	0.116633116260091	0.0888894517283138	1.31211424969269	0.189765655191972	   
df.mm.exp5	0.0326410298560298	0.0888894517283138	0.367209260732032	0.713536228971636	   
df.mm.exp6	0.0715808037035462	0.0888894517283138	0.805278942684104	0.420839323656427	   
df.mm.exp7	0.510489527791157	0.0888894517283138	5.74297082348356	1.21438104780828e-08	***
df.mm.exp8	0.0906293082848796	0.0888894517283139	1.01957326232457	0.308163532959471	   
df.mm.trans1:exp2	0.107928477066522	0.0804839781044797	1.34099332076274	0.180209895396098	   
df.mm.trans2:exp2	0.0651814989839478	0.054023244894468	1.20654542523828	0.22787645792891	   
df.mm.trans1:exp3	0.137055177350756	0.0804839781044797	1.70288771229522	0.0888822420981162	.  
df.mm.trans2:exp3	-0.0813063751025049	0.0540232448944679	-1.50502575810345	0.132615356758996	   
df.mm.trans1:exp4	-0.0765908939772668	0.0804839781044796	-0.951629079241597	0.341502061781072	   
df.mm.trans2:exp4	-0.130993190255076	0.054023244894468	-2.42475605660055	0.0154849870897726	*  
df.mm.trans1:exp5	-0.0336105789026535	0.0804839781044796	-0.417605835275962	0.676319901624706	   
df.mm.trans2:exp5	0.0573561957125612	0.0540232448944679	1.06169475426002	0.288616110159505	   
df.mm.trans1:exp6	-0.0579691954610701	0.0804839781044796	-0.720257582022323	0.471525207137198	   
df.mm.trans2:exp6	0.0324615864495638	0.0540232448944679	0.600881833606554	0.548047153525197	   
df.mm.trans1:exp7	-0.208473018692255	0.0804839781044796	-2.59024247560959	0.00972242974393539	** 
df.mm.trans2:exp7	-0.340401494154872	0.0540232448944679	-6.301019030231	4.32793491666572e-10	***
df.mm.trans1:exp8	-0.0628874695222186	0.0804839781044797	-0.781366316667172	0.434761553148518	   
df.mm.trans2:exp8	-0.0399471867943843	0.0540232448944679	-0.739444416425177	0.459800897432227	   
df.mm.trans1:probe2	-0.523294787757838	0.0611319697844298	-8.56008385797377	3.91322865609617e-17	***
df.mm.trans1:probe3	-0.269171000222087	0.0611319697844298	-4.40311348008688	1.17516449357142e-05	***
df.mm.trans1:probe4	-0.362619744593323	0.0611319697844298	-5.93175299065337	4.05312914584615e-09	***
df.mm.trans1:probe5	0.0276739869901377	0.0611319697844298	0.452692545123685	0.650862784143553	   
df.mm.trans1:probe6	-0.199552544591875	0.0611319697844298	-3.26429109507773	0.00113246837269585	** 
df.mm.trans1:probe7	-0.487718508452373	0.0611319697844298	-7.97812519655786	3.83342751124791e-15	***
df.mm.trans1:probe8	-0.308888390241725	0.0611319697844298	-5.05281264992704	5.12439011857029e-07	***
df.mm.trans1:probe9	-0.480898754525603	0.0611319697844298	-7.86656730056958	8.94442692972185e-15	***
df.mm.trans1:probe10	0.454399256682225	0.0611319697844298	7.43308711112332	2.18195416258081e-13	***
df.mm.trans1:probe11	0.747176170621875	0.0611319697844298	12.2223473782482	3.11309534798438e-32	***
df.mm.trans1:probe12	0.282195295693838	0.0611319697844298	4.61616559533327	4.38736532892646e-06	***
df.mm.trans1:probe13	0.605769890404926	0.0611319697844298	9.90921595592384	3.38086517048453e-22	***
df.mm.trans1:probe14	0.653921271734057	0.0611319697844298	10.6968788023678	1.97781886817139e-25	***
df.mm.trans1:probe15	0.349284874724078	0.0611319697844298	5.71362048296112	1.43621449263188e-08	***
df.mm.trans1:probe16	-0.361641320214749	0.0611319697844298	-5.91574787283982	4.45372669635467e-09	***
df.mm.trans1:probe17	-0.331102614644102	0.0611319697844298	-5.41619410942707	7.53148573567198e-08	***
df.mm.trans1:probe18	-0.267437800539579	0.0611319697844298	-4.37476170786983	1.33565825862318e-05	***
df.mm.trans1:probe19	0.00377988925171459	0.0611319697844298	0.0618316286068263	0.950708571976375	   
df.mm.trans1:probe20	-0.398275750658059	0.0611319697844298	-6.5150158266207	1.12103495152786e-10	***
df.mm.trans1:probe21	0.150656344566239	0.0611319697844298	2.46444446494199	0.0138803443534636	*  
df.mm.trans2:probe2	0.187839272115935	0.0611319697844298	3.07268476344398	0.00217558646386474	** 
df.mm.trans2:probe3	0.103450157862026	0.0611319697844298	1.69224316224102	0.0908935054568266	.  
df.mm.trans2:probe4	0.151199163768511	0.0611319697844298	2.47332393020684	0.0135421388496285	*  
df.mm.trans2:probe5	0.331347393738268	0.0611319697844298	5.42019821881581	7.36917826660443e-08	***
df.mm.trans2:probe6	0.217626686481679	0.0611319697844298	3.55994886552973	0.000387372490007743	***
df.mm.trans3:probe2	0.95435472247173	0.0611319697844298	15.6113851040148	1.30861079067143e-49	***
df.mm.trans3:probe3	0.106454909008348	0.0611319697844298	1.74139504065942	0.0819044625316846	.  
df.mm.trans3:probe4	-0.0153440357828559	0.0611319697844298	-0.250998550136102	0.801863875870135	   
df.mm.trans3:probe5	0.03200211699063	0.0611319697844298	0.523492324940278	0.600741110566456	   
df.mm.trans3:probe6	0.253664354257711	0.0611319697844298	4.14945494398773	3.5996794444137e-05	***
df.mm.trans3:probe7	0.130176090076656	0.0611319697844298	2.12942737712685	0.0334488524729822	*  
df.mm.trans3:probe8	2.00557974950484	0.0611319697844298	32.8073797814324	1.68330210843705e-163	***
df.mm.trans3:probe9	0.0712177555820299	0.0611319697844298	1.16498381833868	0.244287644008145	   
df.mm.trans3:probe10	0.233406781943865	0.0611319697844298	3.81808050299915	0.000142289650122745	***
df.mm.trans3:probe11	0.470956785460523	0.0611319697844298	7.70393604395969	3.01984854924774e-14	***
df.mm.trans3:probe12	0.815464443486678	0.0611319697844298	13.3394105631187	1.22840352076808e-37	***
df.mm.trans3:probe13	0.118974848681356	0.0611319697844298	1.94619687703338	0.0518950596699195	.  
df.mm.trans3:probe14	0.345576412434542	0.0611319697844298	5.65295726038522	2.02661031209596e-08	***
df.mm.trans3:probe15	0.198324382602491	0.0611319697844298	3.24420075619752	0.00121460406162737	** 
df.mm.trans3:probe16	0.148504900736070	0.0611319697844298	2.42925103280239	0.0152953899450168	*  
df.mm.trans3:probe17	0.611412977853225	0.0611319697844298	10.0015258793272	1.4469082708109e-22	***
df.mm.trans3:probe18	0.671604308924145	0.0611319697844298	10.9861388614244	1.14803444171784e-26	***
df.mm.trans3:probe19	2.2708421163597	0.0611319697844298	37.1465556298511	4.17562279451675e-194	***
df.mm.trans3:probe20	0.140216578413689	0.0611319697844298	2.29367021720608	0.0220044016270906	*  

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.31866915474966	0.241163459376079	17.9076430812637	7.36273203039474e-63	***
df.mm.trans1	0.0683816968984085	0.205355452320675	0.332991873970926	0.739206265067767	   
df.mm.trans2	0.0248495266604322	0.179381622762961	0.138528831870748	0.889848796541712	   
df.mm.exp2	-0.0832689009802488	0.225183969854835	-0.369781654679631	0.711619048941082	   
df.mm.exp3	0.162899551192948	0.225183969854834	0.723406516449468	0.469589767970715	   
df.mm.exp4	0.101237552463718	0.225183969854834	0.449577083701743	0.653107327760884	   
df.mm.exp5	-0.180120433692332	0.225183969854834	-0.799881242916389	0.423958821154832	   
df.mm.exp6	0.0855228531924316	0.225183969854834	0.379791035958573	0.704176614626959	   
df.mm.exp7	-0.0601638187693403	0.225183969854834	-0.267176295045003	0.789385397884306	   
df.mm.exp8	-0.141391846029491	0.225183969854834	-0.627894810277301	0.530208095459882	   
df.mm.trans1:exp2	0.0894355440665868	0.203890353094656	0.438645294929998	0.661007957889861	   
df.mm.trans2:exp2	-0.0882699822016142	0.136857281862402	-0.644978338020494	0.51908078838458	   
df.mm.trans1:exp3	-0.0675553012793825	0.203890353094656	-0.331331523311551	0.740459573311359	   
df.mm.trans2:exp3	-0.0412291212089287	0.136857281862402	-0.301256320802725	0.763278130306193	   
df.mm.trans1:exp4	-0.132767754303738	0.203890353094656	-0.651172320262257	0.51507639225878	   
df.mm.trans2:exp4	-0.0737510057911745	0.136857281862402	-0.538889891626847	0.590075987427215	   
df.mm.trans1:exp5	0.186449211672255	0.203890353094656	0.91445823131071	0.360684077962899	   
df.mm.trans2:exp5	0.056939160193423	0.136857281862402	0.416047720797717	0.677459301873375	   
df.mm.trans1:exp6	0.0293993800854993	0.203890353094656	0.144192109333641	0.885376180210953	   
df.mm.trans2:exp6	-0.211964125072673	0.136857281862402	-1.54879683556615	0.121729059576752	   
df.mm.trans1:exp7	0.146628587099388	0.203890353094656	0.719154118249604	0.472204473204844	   
df.mm.trans2:exp7	-0.118550100151195	0.136857281862402	-0.86623158474232	0.386559197924579	   
df.mm.trans1:exp8	0.118052438517768	0.203890353094656	0.578999627623195	0.562712436298561	   
df.mm.trans2:exp8	-0.108901306504228	0.136857281862402	-0.795728988784969	0.426367727281124	   
df.mm.trans1:probe2	-0.138780127312884	0.154865840360660	-0.896131303001907	0.370386100344490	   
df.mm.trans1:probe3	-0.152306270639322	0.154865840360660	-0.983472341509412	0.325599439998551	   
df.mm.trans1:probe4	0.0203671649545069	0.154865840360661	0.131514896423089	0.895392960078968	   
df.mm.trans1:probe5	-0.209876677589871	0.154865840360660	-1.35521608316655	0.175637433379377	   
df.mm.trans1:probe6	-0.109415551440737	0.154865840360660	-0.706518307626291	0.480021137814954	   
df.mm.trans1:probe7	-0.0314649960857831	0.154865840360660	-0.203175832788597	0.839036595461798	   
df.mm.trans1:probe8	-0.136193242256840	0.154865840360660	-0.879427263879918	0.379368972595704	   
df.mm.trans1:probe9	-0.278645328245467	0.154865840360661	-1.79926914545223	0.072260443068169	.  
df.mm.trans1:probe10	0.00779721897404597	0.154865840360661	0.0503482172433078	0.959854386069227	   
df.mm.trans1:probe11	-0.0423922900147774	0.154865840360660	-0.273735576005992	0.784341164551597	   
df.mm.trans1:probe12	-0.111490741375572	0.154865840360660	-0.719918228034833	0.471734047916018	   
df.mm.trans1:probe13	-0.164931205910675	0.154865840360660	-1.06499409764331	0.287121128560753	   
df.mm.trans1:probe14	-0.225703257035077	0.154865840360660	-1.45741150217147	0.145299002029745	   
df.mm.trans1:probe15	-0.273591132433453	0.154865840360660	-1.76663318260695	0.0775774367684928	.  
df.mm.trans1:probe16	0.0160558687248172	0.154865840360660	0.103675986178911	0.91744609564923	   
df.mm.trans1:probe17	-0.0908431485918377	0.154865840360660	-0.586592552497549	0.557602282695051	   
df.mm.trans1:probe18	-0.152810178055566	0.154865840360660	-0.986726173439495	0.324002067164458	   
df.mm.trans1:probe19	-0.093367740068823	0.154865840360661	-0.602894349401926	0.546707938385997	   
df.mm.trans1:probe20	-0.134538699326808	0.154865840360660	-0.868743546113763	0.38518406840417	   
df.mm.trans1:probe21	0.0325847047178208	0.154865840360661	0.210406017504801	0.833391233516163	   
df.mm.trans2:probe2	-0.0109744419352929	0.154865840360660	-0.0708641874136672	0.94351921301336	   
df.mm.trans2:probe3	0.0746172661792808	0.154865840360660	0.481818753609626	0.630034165901089	   
df.mm.trans2:probe4	0.0866512802473886	0.154865840360660	0.559524812221922	0.57592180629254	   
df.mm.trans2:probe5	-0.160925118829746	0.154865840360660	-1.03912598449712	0.298983112242081	   
df.mm.trans2:probe6	0.089845825432337	0.154865840360661	0.580152635488233	0.56193498885813	   
df.mm.trans3:probe2	-0.114296887452214	0.154865840360661	-0.738038079837572	0.460654665645507	   
df.mm.trans3:probe3	-0.0132342882623087	0.154865840360660	-0.0854564714302905	0.931914637306485	   
df.mm.trans3:probe4	-0.138688135550408	0.154865840360661	-0.895537293617643	0.370703252544024	   
df.mm.trans3:probe5	-0.133840016812963	0.154865840360660	-0.864232012051647	0.387655971047234	   
df.mm.trans3:probe6	-0.0160592506205311	0.154865840360660	-0.103697823762370	0.917428769363133	   
df.mm.trans3:probe7	-0.231040340423680	0.154865840360660	-1.49187412721630	0.136029685136030	   
df.mm.trans3:probe8	-0.282105765321913	0.154865840360661	-1.82161388634788	0.0687953250365845	.  
df.mm.trans3:probe9	-0.0698997649718897	0.154865840360661	-0.451356895808030	0.651824670076514	   
df.mm.trans3:probe10	-0.0140493978120683	0.154865840360660	-0.0907197983709593	0.927732378138755	   
df.mm.trans3:probe11	0.106492670240144	0.154865840360660	0.68764467355834	0.491827001378891	   
df.mm.trans3:probe12	-0.0962179511962194	0.154865840360661	-0.621298738134513	0.534536666056335	   
df.mm.trans3:probe13	0.0497328466666193	0.154865840360661	0.321135032430642	0.748171368127903	   
df.mm.trans3:probe14	-0.147771319822597	0.154865840360660	-0.954189248438895	0.340205398311383	   
df.mm.trans3:probe15	0.177274166287585	0.154865840360660	1.14469508495055	0.252593889098764	   
df.mm.trans3:probe16	-0.0296107294187842	0.154865840360660	-0.191202458526845	0.84840359347145	   
df.mm.trans3:probe17	-0.0416950158423856	0.154865840360660	-0.269233135888998	0.787802678850907	   
df.mm.trans3:probe18	-0.130493923804778	0.154865840360661	-0.842625613891844	0.399627943303011	   
df.mm.trans3:probe19	-0.09312323997509	0.154865840360660	-0.601315562929947	0.547758394068932	   
df.mm.trans3:probe20	-0.172462020977979	0.154865840360661	-1.11362209107147	0.265693800949836