chr17.10629_chr17_12357094_12358862_+_2.R 

fitVsDatCorrelation=0.916945421773385
cont.fitVsDatCorrelation=0.283431861467546

fstatistic=9747.15298378335,58,830
cont.fstatistic=1675.57773258558,58,830

residuals=-0.939525151260962,-0.0937185214057306,-0.00355835737114859,0.10049584002916,0.580523654368815
cont.residuals=-0.914244003713052,-0.309179925257952,-0.0587183438629021,0.254682033978374,1.64788475956440

predictedValues:
Include	Exclude	Both
chr17.10629_chr17_12357094_12358862_+_2.R.tl.Lung	73.2174716021319	52.0816628164391	86.857428038004
chr17.10629_chr17_12357094_12358862_+_2.R.tl.cerebhem	78.3775829787637	55.8158697353446	89.5124335460671
chr17.10629_chr17_12357094_12358862_+_2.R.tl.cortex	91.21076058579	51.7101644882021	117.529025520337
chr17.10629_chr17_12357094_12358862_+_2.R.tl.heart	86.2842427246343	50.8069209418683	112.258483839857
chr17.10629_chr17_12357094_12358862_+_2.R.tl.kidney	78.4378009640538	53.7164675586377	97.4935223583056
chr17.10629_chr17_12357094_12358862_+_2.R.tl.liver	71.4056227956546	57.9549193556665	91.0557790472904
chr17.10629_chr17_12357094_12358862_+_2.R.tl.stomach	74.2882352779062	50.6758998617095	96.341718728792
chr17.10629_chr17_12357094_12358862_+_2.R.tl.testicle	88.0342553809399	53.3369178490759	119.422534288947


diffExp=21.1358087856928,22.5617132434191,39.5005960975879,35.477321782766,24.7213334054161,13.4507034399881,23.6123354161967,34.697337531864
diffExpScore=0.995373736185112
diffExp1.5=0,0,1,1,0,0,0,1
diffExp1.5Score=0.75
diffExp1.4=1,1,1,1,1,0,1,1
diffExp1.4Score=0.875
diffExp1.3=1,1,1,1,1,0,1,1
diffExp1.3Score=0.875
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	78.2587597386212	70.8057283116529	74.9541837528302
cerebhem	83.3698893577266	71.8660676316672	70.4195373287722
cortex	76.7412502886167	72.6593271164197	74.1984898751317
heart	77.3955023087738	76.9885599872834	68.8029994674914
kidney	68.8017287368953	72.4250046735531	64.9234035477869
liver	85.4851948427718	60.3144046988656	69.3277621199605
stomach	75.5673166362115	92.9918805363968	74.5984937732511
testicle	75.5199788120604	59.3891013700511	67.7687033869655
cont.diffExp=7.4530314269683,11.5038217260594,4.08192317219698,0.406942321490462,-3.62327593665779,25.1707901439062,-17.4245639001853,16.1308774420093
cont.diffExpScore=1.91937576524399

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,1,0,0
cont.diffExp1.4Score=0.5
cont.diffExp1.3=0,0,0,0,0,1,0,0
cont.diffExp1.3Score=0.5
cont.diffExp1.2=0,0,0,0,0,1,-1,1
cont.diffExp1.2Score=1.5

tran.correlation=-0.412691759098359
cont.tran.correlation=-0.309580053675260

tran.covariance=-0.00176818607942406
cont.tran.covariance=-0.00286109916167788

tran.mean=66.7096746823011
cont.tran.mean=74.911230940473

weightedLogRatios:
wLogRatio
Lung	1.40442306862144
cerebhem	1.42303045272516
cortex	2.4002705244643
heart	2.22058837079989
kidney	1.57984569454416
liver	0.86907766510961
stomach	1.57464717845574
testicle	2.11822704141109

cont.weightedLogRatios:
wLogRatio
Lung	0.431346551288444
cerebhem	0.645759312134048
cortex	0.235744320598193
heart	0.0229129164407886
kidney	-0.218475256396519
liver	1.49063759355108
stomach	-0.918917607410379
testicle	1.01022551898992

varWeightedLogRatios=0.260136122425739
cont.varWeightedLogRatios=0.554732825738109

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.54205104358464	0.0796740814827754	57.0078871203125	3.08617492213693e-289	***
df.mm.trans1	-0.0653034806294472	0.0684463998822398	-0.9540820370655	0.340319892981310	   
df.mm.trans2	-0.566048003607873	0.060653666865535	-9.33246138049266	9.20739600140225e-20	***
df.mm.exp2	0.107239665105689	0.0778613195741157	1.37731630663680	0.168785777019231	   
df.mm.exp3	-0.0898370672543167	0.0778613195741156	-1.15380869147487	0.248910738789767	   
df.mm.exp4	-0.117103509943427	0.0778613195741156	-1.50400109558838	0.132961486228665	   
df.mm.exp5	-0.0157393440311398	0.0778613195741156	-0.202145868028318	0.839852226313892	   
df.mm.exp6	0.0345908125774113	0.0778613195741156	0.444261833303307	0.656969098624283	   
df.mm.exp7	-0.116477378065879	0.0778613195741156	-1.49595946617633	0.135044343381370	   
df.mm.exp8	-0.110292158983750	0.0778613195741156	-1.41652054687776	0.156998345682897	   
df.mm.trans1:exp2	-0.0391357856050439	0.071252187403631	-0.549257321509958	0.582976608405948	   
df.mm.trans2:exp2	-0.0379943578039657	0.0527005581794081	-0.720947920031925	0.471144567046034	   
df.mm.trans1:exp3	0.309575870677516	0.071252187403631	4.34479111390397	1.56675224754084e-05	***
df.mm.trans2:exp3	0.0826785091224422	0.0527005581794081	1.56883554897048	0.117067396369014	   
df.mm.trans1:exp4	0.281316428585450	0.071252187403631	3.94817954137804	8.54252887449455e-05	***
df.mm.trans2:exp4	0.0923231687516742	0.0527005581794081	1.75184422975899	0.0801698949789506	.  
df.mm.trans1:exp5	0.08461123476558	0.071252187403631	1.18748964556376	0.235374261951954	   
df.mm.trans2:exp5	0.0466460314738446	0.0527005581794081	0.885114562070632	0.376351187399018	   
df.mm.trans1:exp6	-0.0596482712693437	0.071252187403631	-0.83714301894266	0.402753152583212	   
df.mm.trans2:exp6	0.0722617177535217	0.0527005581794081	1.37117556720219	0.170690838586610	   
df.mm.trans1:exp7	0.130995900867341	0.071252187403631	1.83848251738957	0.0663484889909959	.  
df.mm.trans2:exp7	0.0891149022444619	0.0527005581794081	1.69096695221119	0.0912185404607946	.  
df.mm.trans1:exp8	0.294584087833999	0.071252187403631	4.13438658613009	3.92079662765776e-05	***
df.mm.trans2:exp8	0.134107967451519	0.0527005581794081	2.54471626268125	0.0111166855704005	*  
df.mm.trans1:probe2	-0.646687231622189	0.0496364502160564	-13.0284746150723	1.94478544811646e-35	***
df.mm.trans1:probe3	-0.23715397618802	0.0496364502160564	-4.77781902524741	2.09515601291003e-06	***
df.mm.trans1:probe4	-0.0835402264932184	0.0496364502160564	-1.68304192039492	0.0927430445691484	.  
df.mm.trans1:probe5	-0.0110112795852156	0.0496364502160564	-0.221838579054022	0.824494115154817	   
df.mm.trans1:probe6	-0.212467489831178	0.0496364502160564	-4.28047309802282	2.08257619756864e-05	***
df.mm.trans1:probe7	0.0340696628578997	0.0496364502160564	0.686383951906354	0.492662719461458	   
df.mm.trans1:probe8	0.0159137679787897	0.0496364502160564	0.320606487964401	0.748589320618383	   
df.mm.trans1:probe9	0.00358620486181248	0.0496364502160564	0.0722494224748654	0.942420820431732	   
df.mm.trans1:probe10	-0.402654624960194	0.0496364502160564	-8.11207536412311	1.78464732205406e-15	***
df.mm.trans1:probe11	-0.057402738466418	0.0496364502160564	-1.15646340978367	0.247824404512034	   
df.mm.trans1:probe12	-0.207318375794888	0.0496364502160564	-4.17673654929951	3.27017777879105e-05	***
df.mm.trans1:probe13	-0.446703114841492	0.0496364502160564	-8.99949760502803	1.51944731903113e-18	***
df.mm.trans1:probe14	-0.561780706699877	0.0496364502160564	-11.3179065838627	1.02032346871546e-27	***
df.mm.trans1:probe15	-0.129066058055137	0.0496364502160564	-2.60022740331634	0.0094818401293962	** 
df.mm.trans1:probe16	-0.651724037712597	0.0496364502160564	-13.1299485534479	6.43011072490505e-36	***
df.mm.trans1:probe17	-0.613797255188948	0.0496364502160564	-12.3658571980314	2.32757975435575e-32	***
df.mm.trans1:probe18	-0.427308428478753	0.0496364502160564	-8.60876284703631	3.67451223110903e-17	***
df.mm.trans1:probe19	-0.583130612201605	0.0496364502160564	-11.7480321349204	1.37489563043652e-29	***
df.mm.trans1:probe20	-0.464541588326801	0.0496364502160564	-9.35888014361936	7.34571665094152e-20	***
df.mm.trans2:probe2	-0.0461001796515271	0.0496364502160564	-0.928756578096607	0.353285267263426	   
df.mm.trans2:probe3	-0.158822544428795	0.0496364502160564	-3.1997160098572	0.00142788373460546	** 
df.mm.trans2:probe4	0.0413846813404037	0.0496364502160564	0.833755862078482	0.404658447229915	   
df.mm.trans2:probe5	-0.167292999462849	0.0496364502160564	-3.37036590518984	0.000785346062321346	***
df.mm.trans2:probe6	-0.0634009037381856	0.0496364502160564	-1.27730535649136	0.20185164150273	   
df.mm.trans3:probe2	-0.0599597725904756	0.0496364502160564	-1.20797865942234	0.227399545608240	   
df.mm.trans3:probe3	0.68926453640255	0.0496364502160564	13.8862576474010	1.41796967724718e-39	***
df.mm.trans3:probe4	0.145283034551903	0.0496364502160564	2.92694247714167	0.00351659849963791	** 
df.mm.trans3:probe5	0.448791930698108	0.0496364502160564	9.04157990236243	1.07089367669189e-18	***
df.mm.trans3:probe6	0.965545118992136	0.0496364502160564	19.4523402618305	9.86709265752745e-70	***
df.mm.trans3:probe7	0.7473020130871	0.0496364502160564	15.0555088011786	1.80926496288895e-45	***
df.mm.trans3:probe8	0.362587250618294	0.0496364502160564	7.30485860773751	6.5164433159245e-13	***
df.mm.trans3:probe9	0.811316540933935	0.0496364502160564	16.3451765265738	2.83135558537727e-52	***
df.mm.trans3:probe10	0.532826037470863	0.0496364502160564	10.7345717744035	2.91556678831462e-25	***
df.mm.trans3:probe11	1.44629633563028	0.0496364502160564	29.1377874391677	4.07433775280912e-129	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.12131516111999	0.191489761407544	21.5223786944341	5.47831908347257e-82	***
df.mm.trans1	0.23402195693404	0.164505000104570	1.42258263752032	0.155232889628226	   
df.mm.trans2	0.101879223330897	0.145775840529582	0.69887591085591	0.484825363795655	   
df.mm.exp2	0.140537045822241	0.187132945001032	0.751001090809884	0.452864864025659	   
df.mm.exp3	0.0163937144657741	0.187132945001032	0.0876046409983219	0.930212044234038	   
df.mm.exp4	0.158254536619125	0.187132945001032	0.845679720469594	0.397975165775167	   
df.mm.exp5	0.037488680822983	0.187132945001032	0.200331805940297	0.841270163958698	   
df.mm.exp6	0.00598509564515925	0.187132945001032	0.031983121118124	0.974493200572146	   
df.mm.exp7	0.24233210616013	0.187132945001032	1.29497297313839	0.195689650235918	   
df.mm.exp8	-0.110676145125245	0.187132945001032	-0.59143057426171	0.554392997399719	   
df.mm.trans1:exp2	-0.0772706092202595	0.171248467654272	-0.451219273834662	0.651949384188174	   
df.mm.trans2:exp2	-0.125672736640109	0.126661231908911	-0.99219575513435	0.321391203154974	   
df.mm.trans1:exp3	-0.0359751059477694	0.171248467654272	-0.210075491130223	0.833660310225324	   
df.mm.trans2:exp3	0.00944814578556066	0.126661231908911	0.0745938251441875	0.940555861089524	   
df.mm.trans1:exp4	-0.169346636113956	0.171248467654272	-0.988894314989398	0.323003041739463	   
df.mm.trans2:exp4	-0.074537603180465	0.126661231908911	-0.588480011263977	0.556370246301886	   
df.mm.trans1:exp5	-0.166280577905918	0.171248467654272	-0.970990165247003	0.331836048315711	   
df.mm.trans2:exp5	-0.0148769784214642	0.126661231908911	-0.117454869159674	0.906528021532298	   
df.mm.trans1:exp6	0.0823373368737015	0.171248467654272	0.480806269402244	0.630780904687704	   
df.mm.trans2:exp6	-0.166354042328731	0.126661231908911	-1.31337773856775	0.189418643411564	   
df.mm.trans1:exp7	-0.277329004774860	0.171248467654272	-1.61945393482148	0.105729501383725	   
df.mm.trans2:exp7	0.030240171338032	0.126661231908911	0.23874843851021	0.811359588961129	   
df.mm.trans1:exp8	0.075052617705497	0.171248467654272	0.4382673826724	0.661306508165059	   
df.mm.trans2:exp8	-0.0651530297425346	0.126661231908911	-0.514388094609643	0.60711761360914	   
df.mm.trans1:probe2	-0.222885507697377	0.119296913526953	-1.86832585276416	0.0620687807621029	.  
df.mm.trans1:probe3	-0.0163921618451357	0.119296913526953	-0.137406420338211	0.890742881172845	   
df.mm.trans1:probe4	0.0436665979840768	0.119296913526953	0.366032923175427	0.714433782769647	   
df.mm.trans1:probe5	0.113175770959872	0.119296913526953	0.948689849669094	0.34305451294973	   
df.mm.trans1:probe6	-0.0639228843739083	0.119296913526953	-0.535830160932587	0.592219489600194	   
df.mm.trans1:probe7	-0.0735205964579605	0.119296913526953	-0.61628246938132	0.537877086819195	   
df.mm.trans1:probe8	0.0686005003955634	0.119296913526953	0.575040027167712	0.56542006668892	   
df.mm.trans1:probe9	0.116554459445821	0.119296913526953	0.977011525277122	0.328848116623598	   
df.mm.trans1:probe10	-0.0430692752358473	0.119296913526953	-0.361025897171401	0.718171957685861	   
df.mm.trans1:probe11	0.160463407587184	0.119296913526953	1.3450759356899	0.178968108596584	   
df.mm.trans1:probe12	-0.0237842241694583	0.119296913526953	-0.199369987590540	0.842022166153779	   
df.mm.trans1:probe13	0.0498307599667086	0.119296913526953	0.417703681457359	0.67627181218523	   
df.mm.trans1:probe14	-0.0995775443209263	0.119296913526953	-0.834703441832368	0.404124884994871	   
df.mm.trans1:probe15	0.0498869246759642	0.119296913526953	0.418174479130116	0.675927722166755	   
df.mm.trans1:probe16	0.103317541365943	0.119296913526953	0.866053767121143	0.386710946996628	   
df.mm.trans1:probe17	-0.0171360872094235	0.119296913526953	-0.143642334933936	0.885817775259266	   
df.mm.trans1:probe18	0.114951324322417	0.119296913526953	0.963573330809148	0.335540528002265	   
df.mm.trans1:probe19	-0.0641935670356502	0.119296913526953	-0.538099143873883	0.590652862710385	   
df.mm.trans1:probe20	-0.0507722684470485	0.119296913526953	-0.425595825960554	0.670512711230863	   
df.mm.trans2:probe2	0.101558472547164	0.119296913526953	0.85130846678794	0.394843562137555	   
df.mm.trans2:probe3	0.217505965791375	0.119296913526953	1.82323213032861	0.0686278059948858	.  
df.mm.trans2:probe4	0.0224042657305566	0.119296913526953	0.187802559749333	0.851077331032252	   
df.mm.trans2:probe5	0.139074782400872	0.119296913526953	1.16578692850633	0.244035494545315	   
df.mm.trans2:probe6	0.144130376183588	0.119296913526953	1.20816517311677	0.227327848126400	   
df.mm.trans3:probe2	-0.138684989369342	0.119296913526953	-1.16251950925796	0.245358648926794	   
df.mm.trans3:probe3	-0.112209679078434	0.119296913526953	-0.940591636120424	0.347187828901695	   
df.mm.trans3:probe4	-0.177223978187723	0.119296913526953	-1.48557052272507	0.137772463896263	   
df.mm.trans3:probe5	-0.0790486581238229	0.119296913526953	-0.662621150763998	0.507757175190186	   
df.mm.trans3:probe6	-0.08735187753724	0.119296913526953	-0.732222443605	0.464239513436780	   
df.mm.trans3:probe7	-0.203991664489503	0.119296913526953	-1.70994922214324	0.0876490030610781	.  
df.mm.trans3:probe8	-0.148395564232091	0.119296913526953	-1.24391788391544	0.213880974977137	   
df.mm.trans3:probe9	-0.193165415203683	0.119296913526953	-1.61919876627857	0.105784384863740	   
df.mm.trans3:probe10	-0.191767165688679	0.119296913526953	-1.60747801446978	0.108329861693688	   
df.mm.trans3:probe11	-0.211893283993308	0.119296913526953	-1.77618412521154	0.076068851477654	.