chr15.8560_chr15_55913473_55925593_-_2.R 

fitVsDatCorrelation=0.848245834405559
cont.fitVsDatCorrelation=0.233491869243924

fstatistic=8950.22879655609,51,669
cont.fstatistic=2645.87712095592,51,669

residuals=-0.477923289800964,-0.0895105501651203,-0.0050583004598707,0.091647077583713,1.32781880113960
cont.residuals=-0.696577012157769,-0.222566949938725,-0.0378385500461469,0.188461548667836,1.26973709274832

predictedValues:
Include	Exclude	Both
chr15.8560_chr15_55913473_55925593_-_2.R.tl.Lung	57.1368308544917	62.6712622827451	52.7338633471292
chr15.8560_chr15_55913473_55925593_-_2.R.tl.cerebhem	57.438295579481	52.6826867058626	61.896465714839
chr15.8560_chr15_55913473_55925593_-_2.R.tl.cortex	66.4513129852375	62.9105953051457	64.2010739329834
chr15.8560_chr15_55913473_55925593_-_2.R.tl.heart	75.3516394125952	64.276254050497	68.718903185098
chr15.8560_chr15_55913473_55925593_-_2.R.tl.kidney	71.4871312878789	69.9728754774185	68.2409209228158
chr15.8560_chr15_55913473_55925593_-_2.R.tl.liver	63.0411777792565	65.9638749722401	59.7630632658041
chr15.8560_chr15_55913473_55925593_-_2.R.tl.stomach	62.3091302605564	56.5403184442595	57.3138998513974
chr15.8560_chr15_55913473_55925593_-_2.R.tl.testicle	67.8928268066486	57.3258076281185	64.0237728936484


diffExp=-5.53443142825343,4.75560887361844,3.54071768009183,11.0753853620982,1.51425581046041,-2.92269719298362,5.7688118162969,10.5670191785301
diffExpScore=1.53466936435320
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	69.4974613709286	69.2658518385908	62.882924004535
cerebhem	70.7357847805915	64.9745812337764	60.6281772094069
cortex	67.4460748315367	64.5503362396346	65.079640579445
heart	67.8599644998171	69.1727557189592	63.2322065925502
kidney	62.275489692888	53.7754593582405	63.0254040622526
liver	64.4461346207714	67.9446061964578	66.7156897961424
stomach	67.1131025255123	61.9414978593391	76.1528900641611
testicle	69.0233444539441	59.3592113754221	73.7090009398715
cont.diffExp=0.231609532337743,5.76120354681517,2.89573859190214,-1.31279121914204,8.50003033464751,-3.49847157568645,5.17160466617321,9.66413307852203
cont.diffExpScore=1.30347053480167

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=0.534470593498158
cont.tran.correlation=0.486051780650532

tran.covariance=0.00490135004897945
cont.tran.covariance=0.00186088827113363

tran.mean=63.340751239527
cont.tran.mean=65.5863535372756

weightedLogRatios:
wLogRatio
Lung	-0.378292110078957
cerebhem	0.346345602590626
cortex	0.228278417906829
heart	0.67448150617865
kidney	0.0911802303974698
liver	-0.188819721188045
stomach	0.396731302118866
testicle	0.699276373529986

cont.weightedLogRatios:
wLogRatio
Lung	0.0141526972126719
cerebhem	0.358212472713915
cortex	0.183843118782877
heart	-0.0809934043192378
kidney	0.595542629606585
liver	-0.221615530670363
stomach	0.334089950844909
testicle	0.627336922250894

varWeightedLogRatios=0.146245206717362
cont.varWeightedLogRatios=0.0954367038171835

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.61895781693329	0.0891287077996746	40.6037280947374	1.14417767256084e-182	***
df.mm.trans1	0.158794901665182	0.0799618165405904	1.98588411988582	0.0474539263738841	*  
df.mm.trans2	0.371714388168505	0.073655417814921	5.04666729476082	5.80063471506664e-07	***
df.mm.exp2	-0.328559074835245	0.100925421319876	-3.25546399052325	0.00118914720187157	** 
df.mm.exp3	-0.0419299189529191	0.100925421319876	-0.415454485149237	0.677942443161455	   
df.mm.exp4	0.037237508117593	0.100925421319876	0.368960640744528	0.712273800047216	   
df.mm.exp5	0.0764866060723437	0.100925421319876	0.757852729986874	0.448806015341758	   
df.mm.exp6	0.0244134519421211	0.100925421319876	0.241895962611286	0.808934907607862	   
df.mm.exp7	-0.0995753427398588	0.100925421319876	-0.986623007738174	0.3241841746756	   
df.mm.exp8	-0.110667283905754	0.100925421319876	-1.09652535960194	0.273243480375924	   
df.mm.trans1:exp2	0.333821393630814	0.0964375792801355	3.4615281316956	0.000571288538262048	***
df.mm.trans2:exp2	0.154942945128646	0.0841776203599097	1.84066672906851	0.0661130971279725	.  
df.mm.trans1:exp3	0.192950530467293	0.0964375792801355	2.00078156158195	0.0458195202645617	*  
df.mm.trans2:exp3	0.0457415095242716	0.0841776203599097	0.543392760791993	0.58704042078593	   
df.mm.trans1:exp4	0.239479241573788	0.0964375792801355	2.48325645833706	0.0132626534136531	*  
df.mm.trans2:exp4	-0.0119502502241461	0.0841776203599097	-0.141964695284229	0.88715063292836	   
df.mm.trans1:exp5	0.147581912996238	0.0964375792801355	1.53033614176002	0.126406236701380	   
df.mm.trans2:exp5	0.0337180619302623	0.0841776203599097	0.400558506953480	0.6888730826201	   
df.mm.trans1:exp6	0.0739257440345869	0.0964375792801355	0.766565736991849	0.443610160375261	   
df.mm.trans2:exp6	0.0267907855566216	0.0841776203599097	0.31826494312948	0.750383299939137	   
df.mm.trans1:exp7	0.186234378489380	0.0964375792801355	1.93113908374244	0.053888003392022	.  
df.mm.trans2:exp7	-0.00337367858850238	0.0841776203599097	-0.0400780940834142	0.968042823311078	   
df.mm.trans1:exp8	0.283148736949164	0.0964375792801355	2.93608299858567	0.00343795189150921	** 
df.mm.trans2:exp8	0.0215151953630531	0.0841776203599096	0.255592819933169	0.798343825555749	   
df.mm.trans1:probe2	-0.0151131911069291	0.0482187896400677	-0.313429499573559	0.754052069633671	   
df.mm.trans1:probe3	0.114036978294070	0.0482187896400677	2.36499047664421	0.0183149526594987	*  
df.mm.trans1:probe4	0.190843118599157	0.0482187896400677	3.95785792268363	8.3713816961346e-05	***
df.mm.trans1:probe5	-0.0181069235493132	0.0482187896400677	-0.375515928219549	0.707395833998107	   
df.mm.trans1:probe6	0.029620887677429	0.0482187896400677	0.614301766977894	0.539224782328311	   
df.mm.trans1:probe7	0.157732401925725	0.0482187896400677	3.2711812781517	0.00112597595053421	** 
df.mm.trans1:probe8	-0.086723890035519	0.0482187896400677	-1.79854970817134	0.0725405905849874	.  
df.mm.trans1:probe9	-0.0136352893728952	0.0482187896400677	-0.282779586021896	0.777433274276727	   
df.mm.trans1:probe10	0.776884182573893	0.0482187896400677	16.1116483506325	1.35781243101364e-49	***
df.mm.trans1:probe11	0.550304558918905	0.0482187896400677	11.4126580743044	1.09061602340296e-27	***
df.mm.trans1:probe12	0.58489012695819	0.0482187896400677	12.1299213714019	9.5244669245205e-31	***
df.mm.trans1:probe13	0.455490180122202	0.0482187896400677	9.44632131005854	5.75467716788478e-20	***
df.mm.trans1:probe14	0.621063606614376	0.0482187896400677	12.8801160553872	4.53056939485216e-34	***
df.mm.trans1:probe15	0.695063098860028	0.0482187896400677	14.4147769790236	3.21220223603521e-41	***
df.mm.trans1:probe16	0.499260473470219	0.0482187896400677	10.3540648198966	2.10184822767301e-23	***
df.mm.trans1:probe17	0.239447550500578	0.0482187896400677	4.96585568173631	8.69194294017678e-07	***
df.mm.trans1:probe18	0.484119774151654	0.0482187896400677	10.0400648329292	3.44669183714551e-22	***
df.mm.trans1:probe19	0.591309397396283	0.0482187896400677	12.2630493591845	2.50114087930663e-31	***
df.mm.trans1:probe20	0.312313763872354	0.0482187896400677	6.47701375757542	1.81284557487287e-10	***
df.mm.trans1:probe21	0.255908326445574	0.0482187896400677	5.30723247837239	1.51560559950752e-07	***
df.mm.trans2:probe2	0.434486006005804	0.0482187896400677	9.01071987183945	2.12013834856261e-18	***
df.mm.trans2:probe3	0.0715866173844795	0.0482187896400677	1.48462078618817	0.138115312610830	   
df.mm.trans2:probe4	0.196948514046968	0.0482187896400677	4.08447651874100	4.95292179229975e-05	***
df.mm.trans2:probe5	0.317155991474139	0.0482187896400677	6.57743576397439	9.65461351704903e-11	***
df.mm.trans2:probe6	0.304900077121673	0.0482187896400677	6.32326276535805	4.68203840411281e-10	***
df.mm.trans3:probe2	-0.117335830247594	0.0482187896400677	-2.43340471885452	0.0152184141000475	*  
df.mm.trans3:probe3	-0.435292037716985	0.0482187896400677	-9.02743600505635	1.85049241653783e-18	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.29403406246334	0.163641691535593	26.2404648972317	6.80412709215379e-105	***
df.mm.trans1	-0.0128544709771943	0.146811136837875	-0.0875578737012962	0.93025428654443	   
df.mm.trans2	-0.114949649537824	0.135232490850030	-0.850015028306329	0.395620825297801	   
df.mm.exp2	-0.00977962825592106	0.185300640741336	-0.0527770881784085	0.957925272134613	   
df.mm.exp3	-0.134805648888102	0.185300640741335	-0.72749693875198	0.467176103999132	   
df.mm.exp4	-0.0307280542701018	0.185300640741335	-0.165828105867134	0.868342327237348	   
df.mm.exp5	-0.36512034511117	0.185300640741335	-1.9704213846775	0.0492020384392521	*  
df.mm.exp6	-0.153885246184427	0.185300640741335	-0.830462569199847	0.406573563713574	   
df.mm.exp7	-0.33814098341795	0.185300640741335	-1.82482360592572	0.0684734606293745	.  
df.mm.exp8	-0.320040437837103	0.185300640741335	-1.72714156063742	0.0846038109854937	.  
df.mm.trans1:exp2	0.0274409978960592	0.177060893067911	0.154980568665348	0.876883381778089	   
df.mm.trans2:exp2	-0.0541762625620577	0.154551418114320	-0.350538760647178	0.726044750706209	   
df.mm.trans1:exp3	0.104843811231544	0.177060893067911	0.592134205441583	0.553960783930531	   
df.mm.trans2:exp3	0.0642989488581923	0.154551418114320	0.416035968111473	0.677517108385566	   
df.mm.trans1:exp4	0.0068840656208675	0.177060893067910	0.0388796503936482	0.96899793999014	   
df.mm.trans2:exp4	0.0293831096002957	0.154551418114320	0.190118667035208	0.849273814944756	   
df.mm.trans1:exp5	0.255398044756307	0.177060893067911	1.44243056911698	0.149648939569313	   
df.mm.trans2:exp5	0.111985536166557	0.154551418114320	0.724584332728168	0.468960402737371	   
df.mm.trans1:exp6	0.0784247740521741	0.177060893067911	0.442925440470329	0.657962846487272	   
df.mm.trans2:exp6	0.134625978335040	0.154551418114320	0.871075658687638	0.384025313384041	   
df.mm.trans1:exp7	0.303230052103074	0.177060893067911	1.71257496135396	0.0872541333496403	.  
df.mm.trans2:exp7	0.226379313805943	0.154551418114320	1.46475080311779	0.143458680710611	   
df.mm.trans1:exp8	0.313194985768085	0.177060893067911	1.76885465978058	0.0773737332387085	.  
df.mm.trans2:exp8	0.16569572486488	0.154551418114320	1.07210743768340	0.284058368954963	   
df.mm.trans1:probe2	-0.107453705315282	0.0885304465339552	-1.2137485974847	0.225272058713012	   
df.mm.trans1:probe3	-0.087197318330894	0.0885304465339552	-0.984941585011096	0.325008836960836	   
df.mm.trans1:probe4	-0.0750996512703854	0.0885304465339552	-0.848291793508366	0.396578892762519	   
df.mm.trans1:probe5	0.0320647733112962	0.0885304465339552	0.362189219264787	0.717325014076514	   
df.mm.trans1:probe6	-0.0448979007674374	0.0885304465339553	-0.507146439730394	0.612219196879559	   
df.mm.trans1:probe7	-0.0283324685327259	0.0885304465339552	-0.320030787621287	0.749044914545347	   
df.mm.trans1:probe8	-0.100292915327806	0.0885304465339552	-1.13286354304492	0.257677401358005	   
df.mm.trans1:probe9	0.066023079838409	0.0885304465339552	0.745766935820054	0.456070163917739	   
df.mm.trans1:probe10	0.0139859875534317	0.0885304465339553	0.157979408226157	0.874520696219178	   
df.mm.trans1:probe11	-0.0154698174271499	0.0885304465339552	-0.174740081325768	0.861336740234182	   
df.mm.trans1:probe12	-0.0705640444884833	0.0885304465339553	-0.797059624695544	0.425699186218802	   
df.mm.trans1:probe13	-0.0519022763142069	0.0885304465339553	-0.586264707185229	0.557895390140455	   
df.mm.trans1:probe14	-0.0831674057799762	0.0885304465339552	-0.939421510181561	0.347853421958853	   
df.mm.trans1:probe15	-0.0665381863112779	0.0885304465339552	-0.751585346243088	0.452564772977519	   
df.mm.trans1:probe16	-0.0437606671251445	0.0885304465339553	-0.494300761358527	0.621256052800933	   
df.mm.trans1:probe17	-0.0532898886698686	0.0885304465339552	-0.601938550591515	0.547418979981395	   
df.mm.trans1:probe18	-0.0437751025261798	0.0885304465339553	-0.494463817138775	0.621140980565715	   
df.mm.trans1:probe19	-0.0852411696864614	0.0885304465339552	-0.962845812076275	0.335972794471335	   
df.mm.trans1:probe20	-0.0601151469474538	0.0885304465339552	-0.679033590149091	0.497351421619073	   
df.mm.trans1:probe21	-0.052320974751109	0.0885304465339552	-0.590994135910539	0.554723960667198	   
df.mm.trans2:probe2	0.137182496624159	0.0885304465339552	1.54955161749403	0.121722080540867	   
df.mm.trans2:probe3	0.0666391468041855	0.0885304465339552	0.75272575044142	0.451879511230731	   
df.mm.trans2:probe4	0.0534149426005903	0.0885304465339552	0.603351103398121	0.54647964114086	   
df.mm.trans2:probe5	0.100423927143266	0.0885304465339552	1.13434339343075	0.257056818179511	   
df.mm.trans2:probe6	0.172148007095676	0.0885304465339552	1.94450625559253	0.0522531877909325	.  
df.mm.trans3:probe2	-0.061909466709918	0.0885304465339552	-0.699301417012203	0.484606737851142	   
df.mm.trans3:probe3	-0.0129564139825091	0.0885304465339552	-0.146349809469669	0.883689334425337