fitVsDatCorrelation=0.876959570714357
cont.fitVsDatCorrelation=0.243461041161138

fstatistic=8135.97504404317,68,1060
cont.fstatistic=1985.56423670409,68,1060

residuals=-0.929134629769005,-0.107999107510856,-0.000286185305448613,0.097746528173256,1.04737008673208
cont.residuals=-0.79309926498988,-0.263276023716715,-0.0921978480879174,0.188420160486003,1.77102527191630

predictedValues:
Include	Exclude	Both
Lung	99.0744362181728	54.5203322670979	137.272200226615
cerebhem	64.175329360951	61.9088269234632	75.543235735614
cortex	60.9008996240704	50.8502099010942	70.3939447131247
heart	75.2797928189507	50.5967361999959	97.1181761818966
kidney	70.0240090814083	52.8332164474197	81.6338735585044
liver	65.5293997230343	49.0257990367525	77.8979886771636
stomach	61.7745352662597	55.3757361375012	78.2054625363094
testicle	63.2236516131406	49.9521790958813	74.8723865540797


diffExp=44.554103951075,2.26650243748781,10.0506897229762,24.6830566189548,17.1907926339886,16.5036006862819,6.3987991287585,13.2714725172592
diffExpScore=0.992642677846371
diffExp1.5=1,0,0,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=1,0,0,1,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,0,1,1,1,0,0
diffExp1.3Score=0.8
diffExp1.2=1,0,0,1,1,1,0,1
diffExp1.2Score=0.833333333333333

cont.predictedValues:
Include	Exclude	Both
Lung	73.2009235018767	69.4614282418947	69.2293021297239
cerebhem	76.374959653421	70.486607968176	69.1890538723303
cortex	70.3820485116682	71.346725926337	63.3191256679299
heart	70.6361835688723	79.6412023016015	66.9079447978064
kidney	74.8616995654969	65.7948061168	59.8092127772486
liver	72.4869645089578	70.4991757040225	74.1324781243269
stomach	70.7651924927804	76.58734083704	67.9080695334422
testicle	77.6347976748619	59.8322609510213	72.7006699112034
cont.diffExp=3.73949525998201,5.88835168524503,-0.9646774146687,-9.00501873272918,9.06689344869687,1.98778880493528,-5.82214834425966,17.8025367238405
cont.diffExpScore=2.29082020662860

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

tran.correlation=0.0486474141719091
cont.tran.correlation=-0.815563617463689

tran.covariance=0.000628842299936892
cont.tran.covariance=-0.00268652032920640

tran.mean=61.5653181071996
cont.tran.mean=71.8745198453018

weightedLogRatios:
wLogRatio
Lung	2.56672136401959
cerebhem	0.148989120873299
cortex	0.724893617708115
heart	1.63799058666639
kidney	1.15721264198703
liver	1.17146719189638
stomach	0.444923878011477
testicle	0.949252012854816

cont.weightedLogRatios:
wLogRatio
Lung	0.223745704003028
cerebhem	0.3446403372249
cortex	-0.0580024460817932
heart	-0.518057158161174
kidney	0.548822328828806
liver	0.118716664526052
stomach	-0.339890562875874
testicle	1.09965031725406

varWeightedLogRatios=0.56372316935036
cont.varWeightedLogRatios=0.261246522409485

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.69705705432448	0.0890019344727779	41.539064023999	1.27899713489459e-224	***
df.mm.trans1	0.978124357291894	0.0777916758742295	12.573637812782	6.77173049393396e-34	***
df.mm.trans2	0.248109053119303	0.0678030702237184	3.65925985800731	0.000265399648440998	***
df.mm.exp2	0.290097144905939	0.087096149746208	3.33076887728404	0.000895926901472981	***
df.mm.exp3	0.111545617998293	0.087096149746208	1.28071812959963	0.200572838441707	   
df.mm.exp4	-0.00330906497557776	0.0870961497462079	-0.0379932406337151	0.969700225220712	   
df.mm.exp5	0.141254637215709	0.087096149746208	1.62182412916432	0.105138388982625	   
df.mm.exp6	0.0469660976980392	0.0870961497462079	0.539244247132567	0.589831574367031	   
df.mm.exp7	0.105813920050807	0.0870961497462079	1.21490927393624	0.224671247183938	   
df.mm.exp8	0.0694801112804322	0.087096149746208	0.797740330461133	0.425199862230944	   
df.mm.trans1:exp2	-0.724349734535819	0.0825750692882999	-8.77201485604387	6.88706103152822e-18	***
df.mm.trans2:exp2	-0.163008076866198	0.0591147533178762	-2.75748552970624	0.00592486494718345	** 
df.mm.trans1:exp3	-0.598169119844653	0.0825750692882999	-7.24394329911156	8.371116647203e-13	***
df.mm.trans2:exp3	-0.181235068881061	0.0591147533178763	-3.06581790008512	0.00222571962038136	** 
df.mm.trans1:exp4	-0.271350640495751	0.0825750692882999	-3.28610854140935	0.00104911550288243	** 
df.mm.trans2:exp4	-0.0713775640049887	0.0591147533178762	-1.20744078252635	0.227531786057977	   
df.mm.trans1:exp5	-0.488287915700969	0.0825750692882999	-5.91326074454962	4.51934612098005e-09	***
df.mm.trans2:exp5	-0.172688246137888	0.0591147533178762	-2.92123770202162	0.00356034334900503	** 
df.mm.trans1:exp6	-0.46033865363217	0.0825750692882999	-5.57478979551272	3.14329086618882e-08	***
df.mm.trans2:exp6	-0.153193128408819	0.0591147533178762	-2.59145339886741	0.00968850004033157	** 
df.mm.trans1:exp7	-0.578194139816482	0.0825750692882999	-7.00204244210555	4.47149896037892e-12	***
df.mm.trans2:exp7	-0.0902460993116665	0.0591147533178762	-1.52662566020344	0.127152435172383	   
df.mm.trans1:exp8	-0.518673094356035	0.0825750692883	-6.28123111280908	4.89389435655576e-10	***
df.mm.trans2:exp8	-0.156987682811545	0.0591147533178762	-2.65564303326073	0.008034493037695	** 
df.mm.trans1:probe2	0.511452692410367	0.0553930404526181	9.23315796048154	1.39010528429217e-19	***
df.mm.trans1:probe3	-0.454591840961235	0.0553930404526181	-8.20665984836275	6.54552217650478e-16	***
df.mm.trans1:probe4	-0.215167220583983	0.0553930404526181	-3.88437281697927	0.000108949761431907	***
df.mm.trans1:probe5	-0.0708863097757446	0.0553930404526181	-1.27969703768796	0.200931721039754	   
df.mm.trans1:probe6	0.742837848912438	0.0553930404526181	13.4103100830481	5.43291558537486e-38	***
df.mm.trans1:probe7	-0.179797986067326	0.0553930404526181	-3.24585876850578	0.00120762246735480	** 
df.mm.trans1:probe8	-0.247183015456916	0.0553930404526181	-4.46234785881361	8.97268043793165e-06	***
df.mm.trans1:probe9	0.173752621368222	0.0553930404526181	3.13672295198971	0.00175555756560037	** 
df.mm.trans1:probe10	0.137996840069523	0.0553930404526181	2.49123064814546	0.012882202153715	*  
df.mm.trans1:probe11	-0.501332815266778	0.0553930404526181	-9.05046574750859	6.6560489157309e-19	***
df.mm.trans1:probe12	-0.424893497545494	0.0553930404526181	-7.67052131592123	3.86704617891892e-14	***
df.mm.trans1:probe13	-0.358103417528339	0.0553930404526181	-6.46477273322182	1.54493127948998e-10	***
df.mm.trans1:probe14	-0.315088553877316	0.0553930404526181	-5.68823359943267	1.65949889656365e-08	***
df.mm.trans1:probe15	-0.0962351184316075	0.0553930404526181	-1.73731424823891	0.0826221778033334	.  
df.mm.trans1:probe16	-0.259058648796198	0.0553930404526181	-4.6767364036966	3.29078511934706e-06	***
df.mm.trans1:probe17	-0.237605045427648	0.0553930404526181	-4.28943859167452	1.95479964818069e-05	***
df.mm.trans1:probe18	-0.276588560077434	0.0553930404526181	-4.99320055041971	6.9398502861046e-07	***
df.mm.trans1:probe19	-0.373785823542219	0.0553930404526181	-6.7478842195338	2.46411477039136e-11	***
df.mm.trans1:probe20	-0.282756440871019	0.0553930404526181	-5.10454812663482	3.92838668541707e-07	***
df.mm.trans1:probe21	0.0199234472671673	0.0553930404526181	0.359674195609634	0.719162433626877	   
df.mm.trans1:probe22	-0.338103483427596	0.0553930404526181	-6.10371773538593	1.45150801900251e-09	***
df.mm.trans1:probe23	-0.275442926426610	0.0553930404526181	-4.97251864450766	7.70408593327895e-07	***
df.mm.trans1:probe24	-0.109588651081381	0.0553930404526181	-1.97838302764985	0.0481440833480007	*  
df.mm.trans1:probe25	-0.407617312078109	0.0553930404526181	-7.35863763294914	3.71750959827057e-13	***
df.mm.trans1:probe26	0.7399245792557	0.0553930404526181	13.3577173812767	9.9536397356924e-38	***
df.mm.trans1:probe27	0.186374812701410	0.0553930404526181	3.36458896602418	0.000794032233743296	***
df.mm.trans1:probe28	-0.175321676516786	0.0553930404526181	-3.16504880548581	0.0015947517672046	** 
df.mm.trans1:probe29	-0.273860950863097	0.0553930404526181	-4.94395954122344	8.89402406411744e-07	***
df.mm.trans1:probe30	0.0811056569598575	0.0553930404526181	1.46418496434102	0.143439909351599	   
df.mm.trans1:probe31	0.0957426636613832	0.0553930404526181	1.72842405614617	0.0842034322852636	.  
df.mm.trans1:probe32	-0.0678103514560602	0.0553930404526181	-1.22416734849685	0.221161114110561	   
df.mm.trans2:probe2	0.0605420241103426	0.0553930404526181	1.09295362044856	0.274662488759947	   
df.mm.trans2:probe3	0.111675986161313	0.0553930404526181	2.01606528994988	0.0440450487556903	*  
df.mm.trans2:probe4	0.230056382798762	0.0553930404526181	4.15316402419807	3.54272584030367e-05	***
df.mm.trans2:probe5	0.193734704218838	0.0553930404526181	3.49745568460995	0.000489190280576229	***
df.mm.trans2:probe6	0.205104809313172	0.0553930404526181	3.70271802445316	0.000224290259735623	***
df.mm.trans3:probe2	0.161977984087181	0.0553930404526181	2.92415766969378	0.00352738228885037	** 
df.mm.trans3:probe3	-0.0409705762232752	0.0553930404526181	-0.739634002548036	0.459685870110413	   
df.mm.trans3:probe4	0.276604190118335	0.0553930404526181	4.99348271656862	6.92994749418994e-07	***
df.mm.trans3:probe5	0.215609733125631	0.0553930404526181	3.89236141153975	0.000105471941426341	***
df.mm.trans3:probe6	0.335385117511106	0.0553930404526181	6.05464359368368	1.95091128036461e-09	***
df.mm.trans3:probe7	-0.338704405407313	0.0553930404526181	-6.1145660653351	1.35927583565650e-09	***
df.mm.trans3:probe8	-0.438414616547292	0.0553930404526181	-7.91461549979914	6.21748421073782e-15	***
df.mm.trans3:probe9	-0.183409250935297	0.0553930404526181	-3.31105224477037	0.000960800428213522	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54766543485931	0.179630388217175	25.3167934445542	5.58964593506604e-111	***
df.mm.trans1	-0.066425888963757	0.157005002420779	-0.423081353712115	0.672321725097379	   
df.mm.trans2	-0.29912421483306	0.136845248350508	-2.18585751744116	0.0290442346990371	*  
df.mm.exp2	0.0576795219851402	0.175783990357173	0.328127276368809	0.742880233442318	   
df.mm.exp3	0.076746834474229	0.175783990357173	0.436597407524361	0.662492258189893	   
df.mm.exp4	0.135200955695290	0.175783990357173	0.769131224183594	0.441986753684759	   
df.mm.exp5	0.114468165344354	0.175783990357173	0.651186522229741	0.515067229215048	   
df.mm.exp6	-0.0634014061592313	0.175783990357173	-0.360677932219008	0.718412085649723	   
df.mm.exp7	0.0830887442958847	0.175783990357173	0.472675265404192	0.636542181052246	   
df.mm.exp8	-0.139345284670435	0.175783990357173	-0.79270748369804	0.428125649912608	   
df.mm.trans1:exp2	-0.0152326697965063	0.166659206242919	-0.0914001100803487	0.927191943330604	   
df.mm.trans2:exp2	-0.0430283972913264	0.119309834676689	-0.360644178310419	0.718437314128273	   
df.mm.trans1:exp3	-0.116016633591932	0.166659206242919	-0.696130962143361	0.486499422503375	   
df.mm.trans2:exp3	-0.0499669882936989	0.119309834676689	-0.418800247516071	0.6754469675174	   
df.mm.trans1:exp4	-0.170866464380825	0.166659206242919	-1.02524467884345	0.305481657273350	   
df.mm.trans2:exp4	0.00155901091003439	0.119309834676689	0.0130669103201682	0.989576869539183	   
df.mm.trans1:exp5	-0.0920337969066285	0.166659206242919	-0.552227500546727	0.580908907964349	   
df.mm.trans2:exp5	-0.168698873705612	0.119309834676689	-1.41395614337041	0.157668404630637	   
df.mm.trans1:exp6	0.0536001149439408	0.166659206242919	0.321615085972595	0.747807721254492	   
df.mm.trans2:exp6	0.0782308145856658	0.119309834676689	0.655694602189829	0.512162927513806	   
df.mm.trans1:exp7	-0.116929532978023	0.166659206242919	-0.701608603653068	0.48307729233974	   
df.mm.trans2:exp7	0.0145714463872596	0.119309834676689	0.122131142220974	0.902818307973692	   
df.mm.trans1:exp8	0.198152997959360	0.166659206242919	1.18897120913043	0.234717277053923	   
df.mm.trans2:exp8	-0.0098813282360228	0.119309834676689	-0.0828207352964627	0.934009717940743	   
df.mm.trans1:probe2	-0.275528285004592	0.111798394270597	-2.46451021772014	0.0138778127297512	*  
df.mm.trans1:probe3	-0.331133046833707	0.111798394270597	-2.96187659039388	0.0031258887015975	** 
df.mm.trans1:probe4	-0.242385267411472	0.111798394270597	-2.16805678644008	0.0303763139116471	*  
df.mm.trans1:probe5	-0.1460577647703	0.111798394270597	-1.30643884219644	0.191686755214118	   
df.mm.trans1:probe6	-0.334582039224638	0.111798394270597	-2.99272669708309	0.00282902327403244	** 
df.mm.trans1:probe7	-0.285896945600022	0.111798394270597	-2.55725448889754	0.0106884885721909	*  
df.mm.trans1:probe8	-0.210423956826579	0.111798394270597	-1.88217333709882	0.0600862434114636	.  
df.mm.trans1:probe9	-0.129233891630674	0.111798394270597	-1.15595481020842	0.247960114255095	   
df.mm.trans1:probe10	-0.242086182618673	0.111798394270597	-2.16538157097969	0.0305809833784684	*  
df.mm.trans1:probe11	-0.123671167370697	0.111798394270597	-1.1061980646284	0.268891788102378	   
df.mm.trans1:probe12	-0.343023909919353	0.111798394270597	-3.06823646401483	0.0022079426265245	** 
df.mm.trans1:probe13	-0.13053190374948	0.111798394270597	-1.16756510324773	0.243244794445550	   
df.mm.trans1:probe14	-0.265198814639059	0.111798394270597	-2.37211649030639	0.0178642893488570	*  
df.mm.trans1:probe15	-0.229943693492949	0.111798394270597	-2.05677098488904	0.0399525699437602	*  
df.mm.trans1:probe16	-0.293178176251488	0.111798394270597	-2.62238271098848	0.00885694169071203	** 
df.mm.trans1:probe17	-0.191696781941268	0.111798394270597	-1.71466489471472	0.0866989773852354	.  
df.mm.trans1:probe18	-0.294184754420467	0.111798394270597	-2.63138622285058	0.008627180820273	** 
df.mm.trans1:probe19	-0.274772063029423	0.111798394270597	-2.45774605996902	0.0141403966646392	*  
df.mm.trans1:probe20	-0.27175943476856	0.111798394270597	-2.43079908742511	0.0152305687559580	*  
df.mm.trans1:probe21	-0.204445064993020	0.111798394270597	-1.82869410895277	0.0677263157745621	.  
df.mm.trans1:probe22	-0.374412516961202	0.111798394270597	-3.34899726784065	0.000839590257015535	***
df.mm.trans1:probe23	-0.130332129339713	0.111798394270597	-1.16577818661918	0.243966381687861	   
df.mm.trans1:probe24	-0.315570553020625	0.111798394270597	-2.82267518312309	0.00485155152063499	** 
df.mm.trans1:probe25	-0.331643757129229	0.111798394270597	-2.96644472662566	0.00308020710136826	** 
df.mm.trans1:probe26	-0.167852852122626	0.111798394270597	-1.50138875623164	0.133552850625401	   
df.mm.trans1:probe27	-0.234113800166002	0.111798394270597	-2.09407122252000	0.0364906848681234	*  
df.mm.trans1:probe28	-0.273859603337800	0.111798394270597	-2.44958440704389	0.0144630687645597	*  
df.mm.trans1:probe29	-0.304705695162331	0.111798394270597	-2.72549259003506	0.00652632734894912	** 
df.mm.trans1:probe30	-0.219669546889378	0.111798394270597	-1.96487211039623	0.0496899119456138	*  
df.mm.trans1:probe31	-0.270666058267782	0.111798394270597	-2.42101919292920	0.0156441807953968	*  
df.mm.trans1:probe32	-0.266732206449301	0.111798394270597	-2.38583217755079	0.0172153040024143	*  
df.mm.trans2:probe2	-0.188295169075346	0.111798394270597	-1.68423858235026	0.092429968164446	.  
df.mm.trans2:probe3	-0.0887759990292156	0.111798394270597	-0.79407221909057	0.427331119343154	   
df.mm.trans2:probe4	0.0869439376980958	0.111798394270597	0.77768503085703	0.436928252643616	   
df.mm.trans2:probe5	-0.0326591828753893	0.111798394270597	-0.292125688284403	0.77024769055922	   
df.mm.trans2:probe6	0.106242250616765	0.111798394270597	0.950302115785455	0.342175381232259	   
df.mm.trans3:probe2	0.0424699555834843	0.111798394270597	0.379879835131530	0.70411071389725	   
df.mm.trans3:probe3	0.111068409205771	0.111798394270597	0.993470522813958	0.320707434311656	   
df.mm.trans3:probe4	0.0132211708591167	0.111798394270597	0.118259040707831	0.905884815722503	   
df.mm.trans3:probe5	-0.037368109909209	0.111798394270597	-0.334245497468981	0.738260432195934	   
df.mm.trans3:probe6	0.147347823818441	0.111798394270597	1.3179779976249	0.187795762287210	   
df.mm.trans3:probe7	0.035830268727445	0.111798394270597	0.320490012054389	0.748660067884348	   
df.mm.trans3:probe8	0.087554741919824	0.111798394270597	0.78314847445757	0.433714861873706	   
df.mm.trans3:probe9	0.0976556878408842	0.111798394270597	0.873498125603827	0.382589466740027