chr16.9535_chr16_4221023_4222307_+_0.R 

fitVsDatCorrelation=0.937696462611132
cont.fitVsDatCorrelation=0.300323695848039

fstatistic=7403.32410083958,37,347
cont.fstatistic=974.239419075126,37,347

residuals=-0.486151848987781,-0.0944274896864573,-0.0070123934705654,0.089013261493765,0.742009233028098
cont.residuals=-0.804833659868312,-0.338753989902895,-0.0129369380945232,0.322019580662822,1.23896980096369

predictedValues:
Include	Exclude	Both
chr16.9535_chr16_4221023_4222307_+_0.R.tl.Lung	45.7151399812984	91.9465611616189	108.423692014505
chr16.9535_chr16_4221023_4222307_+_0.R.tl.cerebhem	54.2637208111439	83.6098776182963	119.783854781961
chr16.9535_chr16_4221023_4222307_+_0.R.tl.cortex	44.9414302547771	94.6514177717359	113.169549103478
chr16.9535_chr16_4221023_4222307_+_0.R.tl.heart	45.9643985339022	108.456418341722	102.061726544333
chr16.9535_chr16_4221023_4222307_+_0.R.tl.kidney	45.0137964391395	88.8950562402305	94.6910070545934
chr16.9535_chr16_4221023_4222307_+_0.R.tl.liver	49.6159186419106	85.3497046993025	86.1771731443704
chr16.9535_chr16_4221023_4222307_+_0.R.tl.stomach	45.2956597526432	126.204048312362	92.5556113669046
chr16.9535_chr16_4221023_4222307_+_0.R.tl.testicle	48.3359024610788	92.381571993004	97.1186754477564


diffExp=-46.2314211803205,-29.3461568071524,-49.7099875169588,-62.4920198078194,-43.881259801091,-35.7337860573919,-80.9083885597183,-44.0456695319251
diffExpScore=0.997457726370272
diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.5Score=0.888888888888889
diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.4Score=0.888888888888889
diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.3Score=0.888888888888889
diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	74.8134308931388	77.2261899167397	75.4358360758758
cerebhem	81.7824655534684	64.8621759595866	82.3879946570936
cortex	70.0622991505459	60.1530419056776	68.1674397115024
heart	78.8011267943685	73.0746897736961	75.7993126432792
kidney	74.8768499748974	86.2572542356223	70.1019581244067
liver	86.4884341641965	63.2753057510611	59.5631071805485
stomach	66.9059813424692	80.330026640132	85.8809581998231
testicle	68.6538084521929	78.5942804123072	75.0664966321441
cont.diffExp=-2.41275902360097,16.9202895938819,9.90925724486835,5.72643702067241,-11.3804042607249,23.2131284131354,-13.4240452976628,-9.94047196011434
cont.diffExpScore=4.73839921999947

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=0,0,0,0,0,1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,1,0,0,0,1,-1,0
cont.diffExp1.2Score=1.5

tran.correlation=-0.51425877843342
cont.tran.correlation=-0.4542359394466

tran.covariance=-0.00496687049331089
cont.tran.covariance=-0.00503097423194246

tran.mean=71.9150389383853
cont.tran.mean=74.1348350575063

weightedLogRatios:
wLogRatio
Lung	-2.91517509130217
cerebhem	-1.82001115913800
cortex	-3.1117812025264
heart	-3.65464728114051
kidney	-2.82212645823369
liver	-2.26499884756724
stomach	-4.43234844954721
testicle	-2.72189082466595

cont.weightedLogRatios:
wLogRatio
Lung	-0.137467038905343
cerebhem	0.993988700909872
cortex	0.636373650516831
heart	0.326617845068929
kidney	-0.620655892593247
liver	1.34498974181446
stomach	-0.785310759395855
testicle	-0.581008094545242

varWeightedLogRatios=0.647600294397057
cont.varWeightedLogRatios=0.641962047515432

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.28287849915978	0.0879859896701499	37.3113777712446	1.69295858988083e-123	***
df.mm.trans1	0.5414668353675	0.073304403873478	7.38655260469836	1.13124364962421e-12	***
df.mm.trans2	1.25618647243048	0.073304403873478	17.1365757860692	3.94826323671678e-48	***
df.mm.exp2	-0.0232618223003686	0.100998069309584	-0.230319475009620	0.817979249412262	   
df.mm.exp3	-0.0309166388202862	0.100998069309584	-0.306111186398217	0.759703565884117	   
df.mm.exp4	0.231047318636936	0.100998069309584	2.28764094419190	0.0227596027589981	*  
df.mm.exp5	0.0862160688695711	0.100998069309584	0.853640762233757	0.393892953342598	   
df.mm.exp6	0.237072923208421	0.100998069309584	2.34730153585148	0.0194713783060804	*  
df.mm.exp7	0.465711118143987	0.100998069309584	4.61108931415775	5.64009871792653e-06	***
df.mm.exp8	0.170577980388502	0.100998069309584	1.68892317996335	0.0921324716579758	.  
df.mm.trans1:exp2	0.194688167265535	0.0853589479963606	2.28081732302788	0.023164951498824	*  
df.mm.trans2:exp2	-0.071784062606996	0.0853589479963606	-0.840967049055673	0.400945521415397	   
df.mm.trans1:exp3	0.0138471972902670	0.0853589479963606	0.162223148425603	0.87122455277971	   
df.mm.trans2:exp3	0.0599099441152272	0.0853589479963606	0.701858979304449	0.483237591149615	   
df.mm.trans1:exp4	-0.225609700367754	0.0853589479963606	-2.64307030092935	0.0085883489121231	** 
df.mm.trans2:exp4	-0.0659064519663572	0.0853589479963606	-0.772109468466824	0.440575121365174	   
df.mm.trans1:exp5	-0.101676572205933	0.0853589479963606	-1.19116477642470	0.234403035235790	   
df.mm.trans2:exp5	-0.119967089589508	0.0853589479963606	-1.40544245688949	0.160784425852318	   
df.mm.trans1:exp6	-0.155190734261540	0.0853589479963606	-1.81809567601696	0.0699116157870908	.  
df.mm.trans2:exp6	-0.311523485200617	0.0853589479963606	-3.64957034397729	0.000303068364127311	***
df.mm.trans1:exp7	-0.474929435099874	0.0853589479963606	-5.56390918876042	5.28099539969652e-08	***
df.mm.trans2:exp7	-0.149018641353887	0.0853589479963606	-1.74578816693290	0.0817328051727394	.  
df.mm.trans1:exp8	-0.114832907336292	0.0853589479963606	-1.34529431338807	0.179408684940883	   
df.mm.trans2:exp8	-0.165858010283649	0.0853589479963606	-1.94306530453867	0.0528167056457472	.  
df.mm.trans1:probe2	-0.0752637779716247	0.0467530213025171	-1.60981634715386	0.108347255946884	   
df.mm.trans1:probe3	-0.044309589300391	0.0467530213025171	-0.947737452381616	0.343922663975941	   
df.mm.trans1:probe4	0.0527493875610525	0.0467530213025171	1.12825623011047	0.259991188385178	   
df.mm.trans1:probe5	-0.0416950129167477	0.0467530213025171	-0.891814298951048	0.373110461164063	   
df.mm.trans1:probe6	0.0893609839024625	0.0467530213025171	1.91134137244840	0.0567843026675077	.  
df.mm.trans2:probe2	0.244935804465624	0.0467530213025171	5.2389299694828	2.80838287131621e-07	***
df.mm.trans2:probe3	-0.117280917914930	0.0467530213025171	-2.50852061850848	0.0125793066402505	*  
df.mm.trans2:probe4	-0.310678336407263	0.0467530213025171	-6.64509646118927	1.17500702790377e-10	***
df.mm.trans2:probe5	-0.166107390752195	0.0467530213025171	-3.55286965685898	0.00043374543058075	***
df.mm.trans2:probe6	0.17055663817985	0.0467530213025171	3.64803457462689	0.000304817465185964	***
df.mm.trans3:probe2	-0.770719870373244	0.0467530213025171	-16.4849211644799	1.6885508490549e-45	***
df.mm.trans3:probe3	-0.626941484751342	0.0467530213025171	-13.409646420382	2.52038585167648e-33	***
df.mm.trans3:probe4	-0.180279368130510	0.0467530213025171	-3.85599396804767	0.000137356314600038	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.36607945577648	0.241539753604619	18.0760284409472	6.17625757632219e-52	***
df.mm.trans1	-0.0450989726009739	0.201235761694686	-0.224110129438116	0.822803504901657	   
df.mm.trans2	-0.0475145768437994	0.201235761694686	-0.236113981151563	0.813483529302801	   
df.mm.exp2	-0.173565850178699	0.277260605546788	-0.626002564758192	0.531724799965322	   
df.mm.exp3	-0.214143811768616	0.277260605546788	-0.772355709698825	0.440429497701448	   
df.mm.exp4	-0.00813349447653982	0.277260605546788	-0.0293351969728974	0.976614120182455	   
df.mm.exp5	0.184774564387383	0.277260605546788	0.666429203034408	0.505579634488208	   
df.mm.exp6	0.182015827305443	0.277260605546788	0.656479224470019	0.511950932357536	   
df.mm.exp7	-0.201983909963518	0.277260605546788	-0.72849840879913	0.466799885619669	   
df.mm.exp8	-0.0634524387057825	0.277260605546788	-0.228854865914497	0.819116538459256	   
df.mm.trans1:exp2	0.262631287015644	0.234327980446473	1.12078500619194	0.263154453687724	   
df.mm.trans2:exp2	-0.000908148070528917	0.234327980446473	-0.00387554259972963	0.996909999182952	   
df.mm.trans1:exp3	0.148531219621705	0.234327980446473	0.63386036673343	0.526589549376758	   
df.mm.trans2:exp3	-0.0357028222168803	0.234327980446473	-0.152362607951703	0.878989498571399	   
df.mm.trans1:exp4	0.060063364402223	0.234327980446473	0.256321777227723	0.797854276724198	   
df.mm.trans2:exp4	-0.0471230869743616	0.234327980446473	-0.201098848223658	0.840739106727213	   
df.mm.trans1:exp5	-0.183927227002679	0.234327980446473	-0.784913635376521	0.433039752004164	   
df.mm.trans2:exp5	-0.0741790520584654	0.234327980446473	-0.316560796184603	0.751767237270904	   
df.mm.trans1:exp6	-0.0370025576487353	0.234327980446473	-0.157909258545365	0.874620152565016	   
df.mm.trans2:exp6	-0.381259335912408	0.234327980446473	-1.62703290996654	0.104637786046261	   
df.mm.trans1:exp7	0.0902748540560761	0.234327980446473	0.385249998246357	0.700288244457723	   
df.mm.trans2:exp7	0.241388744657417	0.234327980446473	1.03013197227873	0.303665535391281	   
df.mm.trans1:exp8	-0.0224683805146798	0.234327980446473	-0.0958843261989883	0.92366779319416	   
df.mm.trans2:exp8	0.0810127200765175	0.234327980446473	0.345723630281631	0.72975992484311	   
df.mm.trans1:probe2	-0.179446481540833	0.128346720745163	-1.39813842144928	0.162964496141787	   
df.mm.trans1:probe3	0.0127009676947346	0.128346720745163	0.0989582563620998	0.921228555285778	   
df.mm.trans1:probe4	-0.0312450272960829	0.128346720745163	-0.243442349868222	0.807806592591698	   
df.mm.trans1:probe5	0.096830180496077	0.128346720745163	0.754442185463679	0.451095432877079	   
df.mm.trans1:probe6	0.0413297914593750	0.128346720745163	0.322016731081403	0.747633925303126	   
df.mm.trans2:probe2	-0.00196716041689338	0.128346720745163	-0.0153269238627393	0.987780171318563	   
df.mm.trans2:probe3	0.187062449343996	0.128346720745163	1.45747743501305	0.145889011145348	   
df.mm.trans2:probe4	-0.0219365608867015	0.128346720745163	-0.170916411103774	0.864389109857789	   
df.mm.trans2:probe5	0.127461231502952	0.128346720745163	0.993100803533822	0.321352830174585	   
df.mm.trans2:probe6	-0.00888227761591877	0.128346720745163	-0.0692053335242968	0.944866040536893	   
df.mm.trans3:probe2	0.0729046060326067	0.128346720745163	0.568028583896285	0.570382923172447	   
df.mm.trans3:probe3	0.167044136129348	0.128346720745163	1.30150684925578	0.193948443047845	   
df.mm.trans3:probe4	-0.0406826009877019	0.128346720745163	-0.31697421446769	0.751453787252645