chr16.9446_chr16_38410819_38412052_+_2.R 

fitVsDatCorrelation=0.80294658751684
cont.fitVsDatCorrelation=0.241744249520363

fstatistic=11570.4994374756,62,922
cont.fstatistic=4356.61284459017,62,922

residuals=-0.576168548498508,-0.084064672175723,-0.000686930495639511,0.0724245505577004,1.00008844046924
cont.residuals=-0.536342051613021,-0.174982066193713,-0.00249992863936212,0.153651276443781,1.21429117400491

predictedValues:
Include	Exclude	Both
chr16.9446_chr16_38410819_38412052_+_2.R.tl.Lung	66.2139752723648	46.4757078113499	67.1154356388273
chr16.9446_chr16_38410819_38412052_+_2.R.tl.cerebhem	75.4217091970271	51.9848350267495	61.6869940253067
chr16.9446_chr16_38410819_38412052_+_2.R.tl.cortex	63.3134748849501	48.5907530778263	60.6921429622723
chr16.9446_chr16_38410819_38412052_+_2.R.tl.heart	61.5891528580038	50.0251937536843	66.4481266754677
chr16.9446_chr16_38410819_38412052_+_2.R.tl.kidney	65.3126851373913	46.6745406777672	60.3609073762447
chr16.9446_chr16_38410819_38412052_+_2.R.tl.liver	63.8998090216419	53.7373597840066	61.7846481165733
chr16.9446_chr16_38410819_38412052_+_2.R.tl.stomach	64.4391916960133	49.945313237404	69.2395761823131
chr16.9446_chr16_38410819_38412052_+_2.R.tl.testicle	61.4278516371184	49.8766704555237	66.9410187713849


diffExp=19.7382674610149,23.4368741702776,14.7227218071238,11.5639591043195,18.6381444596241,10.1624492376352,14.4938784586092,11.5511811815947
diffExpScore=0.992019630169903
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=1,1,0,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,1,1,0,1,0,0,0
diffExp1.3Score=0.8
diffExp1.2=1,1,1,1,1,0,1,1
diffExp1.2Score=0.875

cont.predictedValues:
Include	Exclude	Both
Lung	61.9671822898452	65.2154538476834	66.4562177015152
cerebhem	63.6227078212022	62.370752587904	60.7959565287277
cortex	67.9816281002677	57.3845403848704	65.78781581654
heart	63.9316139950978	54.8745926709773	63.018721842847
kidney	61.7438637931233	67.0783891652759	60.1195969970329
liver	64.898230008986	59.8145701450449	58.2749568289977
stomach	64.747954183458	67.9286326907563	65.6309099084409
testicle	62.0476220697958	57.800266591521	62.9301816289202
cont.diffExp=-3.24827155783822,1.25195523329824,10.5970877153973,9.05702132412048,-5.33452537215258,5.08365986394114,-3.18067850729830,4.24735547827479
cont.diffExpScore=2.15679412341785

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=0.189739406613747
cont.tran.correlation=-0.366143042651306

tran.covariance=0.000546277517167429
cont.tran.covariance=-0.000928084647669286

tran.mean=57.4330139705514
cont.tran.mean=62.7130000216131

weightedLogRatios:
wLogRatio
Lung	1.42147890026217
cerebhem	1.53956483620412
cortex	1.06283236332088
heart	0.835268671241606
kidney	1.34771027345145
liver	0.705079550752042
stomach	1.02893841473296
testicle	0.836095485177509

cont.weightedLogRatios:
wLogRatio
Lung	-0.212139756919226
cerebhem	0.0823386089900757
cortex	0.700643998804594
heart	0.623494001668606
kidney	-0.345095026935031
liver	0.337054507578734
stomach	-0.201148506620286
testicle	0.29019056224912

varWeightedLogRatios=0.0942830877940556
cont.varWeightedLogRatios=0.155069472607657

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.86402465380696	0.0698752218736443	55.2989250008293	4.70815086020378e-295	***
df.mm.trans1	0.344520271342279	0.0598375650968527	5.7575917533516	1.16151505452761e-08	***
df.mm.trans2	-0.0332626989508344	0.0526001561284588	-0.632368825476507	0.527302681406147	   
df.mm.exp2	0.326566826892616	0.0668134914135745	4.88773779042877	1.20265838092366e-06	***
df.mm.exp3	0.100309923517379	0.0668134914135745	1.50134233962512	0.133609413186822	   
df.mm.exp4	0.0111836713181614	0.0668134914135745	0.167386422735113	0.867102712612475	   
df.mm.exp5	0.0966362047083041	0.0668134914135745	1.44635765417687	0.148416765061145	   
df.mm.exp6	0.192362669887806	0.0668134914135745	2.87909920313973	0.00408036354295236	** 
df.mm.exp7	0.0136708229578700	0.0668134914135745	0.204611713422486	0.837920661550926	   
df.mm.exp8	-0.00180245939549569	0.0668134914135745	-0.0269774765150124	0.978483536431888	   
df.mm.trans1:exp2	-0.196363220431207	0.0609920939881541	-3.21948645457794	0.00132909072829041	** 
df.mm.trans2:exp2	-0.214544548374786	0.0431279232577910	-4.97460884198797	7.7974956486319e-07	***
df.mm.trans1:exp3	-0.145103291137989	0.0609920939881541	-2.37905081872039	0.0175599756758504	*  
df.mm.trans2:exp3	-0.0558064400312611	0.043127923257791	-1.29397466457372	0.195998203805852	   
df.mm.trans1:exp4	-0.0835894537481734	0.0609920939881541	-1.37049653950901	0.170865520058338	   
df.mm.trans2:exp4	0.0624133188521194	0.0431279232577910	1.44716726745808	0.148189983841105	   
df.mm.trans1:exp5	-0.110341475410063	0.0609920939881541	-1.80911111908199	0.0707593440782393	.  
df.mm.trans2:exp5	-0.092367119620049	0.0431279232577911	-2.14170107537842	0.0324790344414450	*  
df.mm.trans1:exp6	-0.227937844711839	0.0609920939881541	-3.73717034139062	0.000197553550896939	***
df.mm.trans2:exp6	-0.0471839607743958	0.0431279232577910	-1.09404666884515	0.274220248030378	   
df.mm.trans1:exp7	-0.0408403557372629	0.0609920939881541	-0.669600813265977	0.50327994235937	   
df.mm.trans2:exp7	0.0583280852243358	0.0431279232577910	1.35244363322778	0.176565037874237	   
df.mm.trans1:exp8	-0.0732257460724992	0.0609920939881541	-1.20057766973407	0.230223425981311	   
df.mm.trans2:exp8	0.072426063458099	0.0431279232577910	1.67933111513815	0.093426334794931	.  
df.mm.trans1:probe2	0.0837857215327196	0.0441929706260669	1.89590607614188	0.0582849235835609	.  
df.mm.trans1:probe3	-0.338982355171352	0.0441929706260669	-7.6705039369181	4.34751170279738e-14	***
df.mm.trans1:probe4	-0.357369908951140	0.0441929706260669	-8.0865781115956	1.92150287598765e-15	***
df.mm.trans1:probe5	0.115795946924159	0.0441929706260669	2.62023451430661	0.00893143279131403	** 
df.mm.trans1:probe6	0.157884099855221	0.0441929706260669	3.57260662993527	0.000371745238578336	***
df.mm.trans1:probe7	-0.315246943715691	0.0441929706260669	-7.13341826199267	1.97933606668662e-12	***
df.mm.trans1:probe8	-0.290774528664413	0.0441929706260669	-6.57965564534605	7.89605209121406e-11	***
df.mm.trans1:probe9	-0.309514729126899	0.0441929706260669	-7.00370952081539	4.8028959571547e-12	***
df.mm.trans1:probe10	0.0971550926010047	0.0441929706260669	2.19842864656169	0.0281664101841818	*  
df.mm.trans1:probe11	0.091261448143863	0.0441929706260669	2.06506706498778	0.0391957274383555	*  
df.mm.trans1:probe12	0.126475644557734	0.0441929706260669	2.86189506534628	0.00430644731835444	** 
df.mm.trans1:probe13	-0.0792612095247398	0.0441929706260669	-1.79352526888039	0.0732165909193586	.  
df.mm.trans1:probe14	0.0959147922803033	0.0441929706260669	2.17036308991025	0.0302339972709030	*  
df.mm.trans1:probe15	0.0565807103923395	0.0441929706260669	1.28031018487284	0.200758073383433	   
df.mm.trans1:probe16	0.114338308711818	0.0441929706260669	2.58725102865967	0.00982655408147943	** 
df.mm.trans1:probe17	0.0132554854458438	0.0441929706260669	0.299945562791046	0.764286288528119	   
df.mm.trans1:probe18	0.0151137198200631	0.0441929706260669	0.341993751629550	0.73243353136406	   
df.mm.trans1:probe19	0.0569690877319616	0.0441929706260669	1.28909840015052	0.197687199469657	   
df.mm.trans1:probe20	0.171152158192787	0.0441929706260669	3.87283669253577	0.000115173136954039	***
df.mm.trans1:probe21	-0.0524007588865604	0.0441929706260669	-1.18572610404362	0.236035971079658	   
df.mm.trans2:probe2	0.0547588919100237	0.0441929706260669	1.23908601603995	0.215628975577721	   
df.mm.trans2:probe3	0.00133624642427739	0.0441929706260669	0.0302366282543862	0.97588488043192	   
df.mm.trans2:probe4	0.0183557543324800	0.0441929706260669	0.41535461573278	0.677978965382463	   
df.mm.trans2:probe5	0.00308728349381801	0.0441929706260669	0.0698591529395175	0.944320913332732	   
df.mm.trans2:probe6	0.0858179952995266	0.0441929706260669	1.94189243410823	0.0524544150818406	.  
df.mm.trans3:probe2	0.368349464842913	0.0441929706260669	8.33502386521274	2.7938382408518e-16	***
df.mm.trans3:probe3	-0.253397143175862	0.0441929706260669	-5.7338789311076	1.32980039385480e-08	***
df.mm.trans3:probe4	0.179710874023535	0.0441929706260669	4.06650359723802	5.18009638599862e-05	***
df.mm.trans3:probe5	-0.236658084244648	0.0441929706260669	-5.35510695234995	1.08002810071872e-07	***
df.mm.trans3:probe6	0.151504316140651	0.0441929706260669	3.42824467317631	0.000634389169437671	***
df.mm.trans3:probe7	-0.0953080616354584	0.0441929706260669	-2.15663396882494	0.0312921357280906	*  
df.mm.trans3:probe8	-0.19573472274332	0.0441929706260669	-4.42909177569221	1.05975194665363e-05	***
df.mm.trans3:probe9	-0.114017737551369	0.0441929706260669	-2.57999713384546	0.0100338409591508	*  
df.mm.trans3:probe10	0.162556290366057	0.0441929706260669	3.67832911124047	0.000248356938271729	***
df.mm.trans3:probe11	0.00169335691134603	0.0441929706260669	0.0383173361590501	0.969442963439242	   
df.mm.trans3:probe12	0.153342092595698	0.0441929706260669	3.46982993954362	0.000544918462603418	***
df.mm.trans3:probe13	0.371868809436698	0.0441929706260669	8.41465971100287	1.49039972157295e-16	***
df.mm.trans3:probe14	-0.0580638258518656	0.0441929706260669	-1.31387017051117	0.189216707330709	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.14192865294149	0.113753226646193	36.4115267325484	1.30168187881746e-180	***
df.mm.trans1	0.0294221926042223	0.0974124435229303	0.30203731207396	0.762691735920412	   
df.mm.trans2	0.0391589966382362	0.0856303181766719	0.457302944471649	0.647561045262963	   
df.mm.exp2	0.0707855828607287	0.108768888713303	0.650788876287113	0.515344926381571	   
df.mm.exp3	-0.0251802614186006	0.108768888713303	-0.231502424236140	0.816975868674452	   
df.mm.exp4	-0.088325464737723	0.108768888713303	-0.812047137583011	0.416974186033682	   
df.mm.exp5	0.124762620630343	0.108768888713303	1.14704325939374	0.251661309241705	   
df.mm.exp6	0.0911391461863134	0.108768888713303	0.837915577371955	0.402295275759438	   
df.mm.exp7	0.0971549269987723	0.108768888713303	0.893223495689623	0.371970577201739	   
df.mm.exp8	-0.0648883473000731	0.108768888713303	-0.596570839949538	0.55094039005852	   
df.mm.trans1:exp2	-0.0444200620260186	0.0992919565050755	-0.447368181568142	0.654714324672273	   
df.mm.trans2:exp2	-0.115385588941718	0.0702100157620186	-1.64343488161041	0.100633924245738	   
df.mm.trans1:exp3	0.117812828093613	0.0992919565050755	1.18652942534767	0.235718934704625	   
df.mm.trans2:exp3	-0.10274126585711	0.0702100157620186	-1.46334201384256	0.143714632329881	   
df.mm.trans1:exp4	0.119534518483014	0.0992919565050755	1.20386910169208	0.228949157173059	   
df.mm.trans2:exp4	-0.0843205497978563	0.0702100157620186	-1.20097608414825	0.230068912791068	   
df.mm.trans1:exp5	-0.128372948832432	0.0992919565050755	-1.29288366702564	0.196375170680689	   
df.mm.trans2:exp5	-0.0965971606509985	0.0702100157620186	-1.37583163317355	0.169207877547947	   
df.mm.trans1:exp6	-0.0449237224381789	0.0992919565050755	-0.452440701335989	0.651057962614077	   
df.mm.trans2:exp6	-0.177586330047626	0.0702100157620186	-2.52935892579153	0.0115927813942842	*  
df.mm.trans1:exp7	-0.0532577495417392	0.0992919565050755	-0.536375265593813	0.591828607266752	   
df.mm.trans2:exp7	-0.0563937552582453	0.0702100157620186	-0.803215248510919	0.422057345131539	   
df.mm.trans1:exp8	0.0661856084076784	0.0992919565050755	0.66657573017302	0.505209936344423	   
df.mm.trans2:exp8	-0.0558147277731956	0.0702100157620186	-0.794968170387302	0.426836606516787	   
df.mm.trans1:probe2	-0.0696967667606649	0.0719438574790652	-0.968766051791785	0.332915982220033	   
df.mm.trans1:probe3	-0.0110012635968076	0.0719438574790652	-0.152914563971063	0.878499108497391	   
df.mm.trans1:probe4	-0.0696678856672448	0.0719438574790652	-0.968364612469068	0.333116251823687	   
df.mm.trans1:probe5	-0.0322685646061415	0.0719438574790652	-0.448524248446523	0.653880277001307	   
df.mm.trans1:probe6	-0.127759732377302	0.0719438574790652	-1.77582544019798	0.0760914623698503	.  
df.mm.trans1:probe7	0.0445451350287225	0.0719438574790652	0.619165229521986	0.535960470294489	   
df.mm.trans1:probe8	-0.133351106078554	0.0719438574790652	-1.85354400988796	0.0641236906570343	.  
df.mm.trans1:probe9	-0.0785427017180325	0.0719438574790652	-1.09172213542883	0.2752404472321	   
df.mm.trans1:probe10	-0.0350661503622819	0.0719438574790652	-0.487409927560331	0.626083724926371	   
df.mm.trans1:probe11	-0.125117160931365	0.0719438574790652	-1.73909441772388	0.082351914441989	.  
df.mm.trans1:probe12	-0.115807736923027	0.0719438574790652	-1.60969596267097	0.107806548646325	   
df.mm.trans1:probe13	-0.0699817307099057	0.0719438574790652	-0.972726972977083	0.330944137451902	   
df.mm.trans1:probe14	-0.0984770755686859	0.0719438574790652	-1.36880449588544	0.171393783600546	   
df.mm.trans1:probe15	-0.0918812430695678	0.0719438574790652	-1.27712422281922	0.201879914712435	   
df.mm.trans1:probe16	-0.071532011254432	0.0719438574790652	-0.994275449787314	0.320349648455242	   
df.mm.trans1:probe17	-0.0706727950674449	0.0719438574790652	-0.982332579094885	0.326193724814684	   
df.mm.trans1:probe18	-0.130350551550357	0.0719438574790652	-1.81183712019177	0.0703366059463816	.  
df.mm.trans1:probe19	-0.145879804574408	0.0719438574790652	-2.02768950242705	0.0428791246730287	*  
df.mm.trans1:probe20	-0.0973585873383024	0.0719438574790652	-1.35325781449282	0.176304972373033	   
df.mm.trans1:probe21	-0.0362394159323532	0.0719438574790652	-0.503717999036936	0.614579887436715	   
df.mm.trans2:probe2	-0.00340552464168294	0.0719438574790652	-0.0473358638390207	0.96225579588856	   
df.mm.trans2:probe3	0.0283476120644451	0.0719438574790652	0.394024077353565	0.693654373844311	   
df.mm.trans2:probe4	-0.0279110737572056	0.0719438574790652	-0.387956313926138	0.698137914435779	   
df.mm.trans2:probe5	-0.0194167682852414	0.0719438574790652	-0.269887784247480	0.787306978517023	   
df.mm.trans2:probe6	-0.0454379759544673	0.0719438574790652	-0.631575474913744	0.527820867781661	   
df.mm.trans3:probe2	-0.0654815318322269	0.0719438574790652	-0.910175435773391	0.362967872810807	   
df.mm.trans3:probe3	-0.0232507846109829	0.0719438574790652	-0.323179565646012	0.746632536823113	   
df.mm.trans3:probe4	-0.0669993897731537	0.0719438574790652	-0.931273247235175	0.351956015881007	   
df.mm.trans3:probe5	-0.0104075011204168	0.0719438574790652	-0.144661427467178	0.885009810103125	   
df.mm.trans3:probe6	0.028324424506769	0.0719438574790652	0.393701776625073	0.693892257317718	   
df.mm.trans3:probe7	0.00860824106576277	0.0719438574790652	0.119652203362430	0.904784730473378	   
df.mm.trans3:probe8	0.00656490110770583	0.0719438574790652	0.0912503351605262	0.927313500703408	   
df.mm.trans3:probe9	0.000527670156061356	0.0719438574790652	0.00733447127456158	0.994149577688848	   
df.mm.trans3:probe10	-0.0331975447318010	0.0719438574790652	-0.461436818862001	0.644594080938347	   
df.mm.trans3:probe11	-0.0576245358403452	0.0719438574790652	-0.800965334074744	0.423358065970561	   
df.mm.trans3:probe12	-0.00760512021968297	0.0719438574790652	-0.105709097151150	0.9158361556334	   
df.mm.trans3:probe13	0.0140951681784519	0.0719438574790652	0.195918993953771	0.844716740947265	   
df.mm.trans3:probe14	0.0109151182211591	0.0719438574790652	0.151717166741237	0.879443206944249