chr19.12171_chr19_8473437_8476007_-_1.R 

fitVsDatCorrelation=0.862081661682644
cont.fitVsDatCorrelation=0.304298501712454

fstatistic=6186.02335377797,39,393
cont.fstatistic=1743.55823010475,39,393

residuals=-0.413206175892742,-0.0897424366413398,-0.00159173187124965,0.0869889331664883,0.793013600143751
cont.residuals=-0.655812758187326,-0.180388323845651,-0.0601153405336111,0.100150293340876,1.62003464144768

predictedValues:
Include	Exclude	Both
chr19.12171_chr19_8473437_8476007_-_1.R.tl.Lung	52.3400115553297	50.1998807617763	58.0956893967144
chr19.12171_chr19_8473437_8476007_-_1.R.tl.cerebhem	73.1163314767729	51.3109965692318	101.290641529007
chr19.12171_chr19_8473437_8476007_-_1.R.tl.cortex	93.4483860839793	49.778548263814	115.078514682533
chr19.12171_chr19_8473437_8476007_-_1.R.tl.heart	69.0831585731726	49.9950287569491	73.8808447981578
chr19.12171_chr19_8473437_8476007_-_1.R.tl.kidney	53.4232035824958	50.7285141189757	59.3011796468003
chr19.12171_chr19_8473437_8476007_-_1.R.tl.liver	57.0144459358699	51.1698506143154	57.1098499790299
chr19.12171_chr19_8473437_8476007_-_1.R.tl.stomach	53.3261575432602	49.1563762987162	56.7185955936254
chr19.12171_chr19_8473437_8476007_-_1.R.tl.testicle	52.5796766565839	49.2097128781057	59.0304937910096


diffExp=2.14013079355344,21.8053349075410,43.6698378201653,19.0881298162236,2.69468946352019,5.84459532155454,4.16978124454402,3.36996377847826
diffExpScore=0.990364460722066
diffExp1.5=0,0,1,0,0,0,0,0
diffExp1.5Score=0.5
diffExp1.4=0,1,1,0,0,0,0,0
diffExp1.4Score=0.666666666666667
diffExp1.3=0,1,1,1,0,0,0,0
diffExp1.3Score=0.75
diffExp1.2=0,1,1,1,0,0,0,0
diffExp1.2Score=0.75

cont.predictedValues:
Include	Exclude	Both
Lung	69.1234497637291	53.1602163982369	59.6548867943171
cerebhem	52.0451630692026	53.0849365297765	55.9919924818316
cortex	57.9337754847711	54.2258976083701	55.229889769625
heart	62.8904191922061	54.9358835207088	55.2315570855273
kidney	61.6843421910242	53.174095621177	53.4566699967013
liver	58.6233178678827	61.3074809774856	52.132060574635
stomach	62.9339764925558	54.0742903754638	61.591676426399
testicle	52.5617526995992	55.1723433595846	61.9791902284891
cont.diffExp=15.9632333654922,-1.03977346057390,3.70787787640102,7.95453567149724,8.51024656984718,-2.68416310960288,8.85968611709202,-2.61059065998545
cont.diffExpScore=1.29421948644766

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

tran.correlation=0.0793095149290212
cont.tran.correlation=-0.149038285598053

tran.covariance=0.000426675930519013
cont.tran.covariance=-0.000602533413592161

tran.mean=56.6175174793343
cont.tran.mean=57.3082088219859

weightedLogRatios:
wLogRatio
Lung	0.164358990001293
cerebhem	1.45730589394579
cortex	2.65943453163174
heart	1.31735649944045
kidney	0.204562698734618
liver	0.43145159788892
stomach	0.320447641073233
testicle	0.260265586835292

cont.weightedLogRatios:
wLogRatio
Lung	1.07780174145893
cerebhem	-0.0783737375747648
cortex	0.266302660580826
heart	0.550885425628741
kidney	0.600931527546168
liver	-0.183264067629533
stomach	0.616957109631812
testicle	-0.193224621428596

varWeightedLogRatios=0.793050728832784
cont.varWeightedLogRatios=0.21024799816363

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.04651276412853	0.0899393447677127	44.9915748728157	4.15451362398717e-157	***
df.mm.trans1	-0.0604069107299459	0.0734351674937174	-0.822588315538518	0.41124096310072	   
df.mm.trans2	-0.0970750781890451	0.0734351674937174	-1.32191539152342	0.186965243655223	   
df.mm.exp2	-0.199719482397652	0.0997787443820376	-2.00162352848374	0.0460121913703639	*  
df.mm.exp3	-0.112303535948483	0.0997787443820375	-1.12552564821311	0.261053233148917	   
df.mm.exp4	0.0330986602377228	0.0997787443820376	0.331720552736093	0.740277108322688	   
df.mm.exp5	0.0104218370684638	0.0997787443820376	0.104449471007173	0.916865949239681	   
df.mm.exp6	0.121796269783247	0.0997787443820375	1.22066348436805	0.222945331149775	   
df.mm.exp7	0.0216491184275250	0.0997787443820376	0.216971245344939	0.828343344651366	   
df.mm.exp8	-0.031315765366022	0.0997787443820375	-0.313852068994963	0.753799918737908	   
df.mm.trans1:exp2	0.534010119139078	0.0814690036371952	6.55476433119494	1.75390467925009e-10	***
df.mm.trans2:exp2	0.221611918246219	0.0814690036371952	2.72019919665546	0.006813906110345	** 
df.mm.trans1:exp3	0.691951681455188	0.0814690036371952	8.4934349330777	4.18414033933428e-16	***
df.mm.trans2:exp3	0.103875017990137	0.0814690036371953	1.27502501997843	0.203053641129123	   
df.mm.trans1:exp4	0.244451197529284	0.0814690036371952	3.00054237336564	0.00286677325058079	** 
df.mm.trans2:exp4	-0.0371877360450165	0.0814690036371953	-0.456464844109596	0.648307908813423	   
df.mm.trans1:exp5	0.0100622206599099	0.0814690036371952	0.123509803860126	0.901766552365505	   
df.mm.trans2:exp5	5.36726630519462e-05	0.0814690036371952	0.000658810844072255	0.999474679311056	   
df.mm.trans1:exp6	-0.0362527144634752	0.0814690036371952	-0.44498782168638	0.656573538043632	   
df.mm.trans2:exp6	-0.102658417801688	0.0814690036371952	-1.26009173082386	0.208383990044010	   
df.mm.trans1:exp7	-0.00298326485414139	0.0814690036371952	-0.0366184035762451	0.970807861990223	   
df.mm.trans2:exp7	-0.0426552002719182	0.0814690036371952	-0.52357581862513	0.600868710942359	   
df.mm.trans1:exp8	0.0358843172210534	0.0814690036371952	0.440465890326296	0.659841879352592	   
df.mm.trans2:exp8	0.0113941341704852	0.0814690036371952	0.139858518722367	0.888843423910927	   
df.mm.trans1:probe2	-0.105747226076771	0.0498893721910188	-2.11963433157430	0.0346632365674363	*  
df.mm.trans1:probe3	-0.0517236714286422	0.0498893721910188	-1.03676733454573	0.300481790049253	   
df.mm.trans1:probe4	-0.0701097711634806	0.0498893721910188	-1.40530473895404	0.160720672537668	   
df.mm.trans1:probe5	-0.0416740326071803	0.0498893721910188	-0.835328864184076	0.404040102823596	   
df.mm.trans1:probe6	-0.0708821249909473	0.0498893721910188	-1.42078606881543	0.156171877871343	   
df.mm.trans2:probe2	-0.0507580531445377	0.0498893721910188	-1.01741214441811	0.309583330816736	   
df.mm.trans2:probe3	-0.128206170236456	0.0498893721910188	-2.56980925207025	0.0105431928137535	*  
df.mm.trans2:probe4	-0.0495047392057017	0.0498893721910188	-0.992290282109698	0.321666492254236	   
df.mm.trans2:probe5	-0.0587989195496684	0.0498893721910188	-1.17858607890547	0.239276133557949	   
df.mm.trans2:probe6	-0.113832531960125	0.0498893721910188	-2.28169902648358	0.0230411533609311	*  
df.mm.trans3:probe2	0.490040233012086	0.0498893721910188	9.8225375764561	1.65032720643763e-20	***
df.mm.trans3:probe3	0.134266067364467	0.0498893721910188	2.69127594651588	0.00742187795302654	** 
df.mm.trans3:probe4	-0.155280830899450	0.0498893721910188	-3.11250320619197	0.00199073805027172	** 
df.mm.trans3:probe5	0.0952741012291447	0.0498893721910188	1.90970735940221	0.0568986802164261	.  
df.mm.trans3:probe6	0.474064587703373	0.0498893721910188	9.50231616241335	2.06383972106679e-19	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.9965097888317	0.169059397461576	23.6396784138547	1.69886542774892e-77	***
df.mm.trans1	0.198917998507481	0.138036420001078	1.44105445871407	0.150365563248013	   
df.mm.trans2	-0.0763829691132006	0.138036420001078	-0.553353738908934	0.580335860995921	   
df.mm.exp2	-0.221831887993833	0.187554561891310	-1.18275922353937	0.237619625932447	   
df.mm.exp3	-0.0796733897879032	0.187554561891310	-0.424801129785767	0.671214129223816	   
df.mm.exp4	0.0153978408877761	0.187554561891310	0.0820979278376564	0.93461063686491	   
df.mm.exp5	-0.00389820824191876	0.187554561891310	-0.0207843957652057	0.98342819366769	   
df.mm.exp6	0.112626014028009	0.187554561891310	0.600497332041843	0.548520966253499	   
df.mm.exp7	-0.108709972902154	0.187554561891310	-0.579617855230589	0.562504461507582	   
df.mm.exp8	-0.274976433238684	0.187554561891310	-1.46611434275875	0.143416790822821	   
df.mm.trans1:exp2	-0.0619502828354145	0.153137658521653	-0.404539833202786	0.686035870624885	   
df.mm.trans2:exp2	0.220414790291327	0.153137658521653	1.43932454250084	0.150854592928611	   
df.mm.trans1:exp3	-0.0969200870314126	0.153137658521653	-0.632895187030098	0.527170173193176	   
df.mm.trans2:exp3	0.099521695235073	0.153137658521653	0.649883877002085	0.516146914257959	   
df.mm.trans1:exp4	-0.109898039898416	0.153137658521653	-0.717642159083144	0.473404303785845	   
df.mm.trans2:exp4	0.0174586056223657	0.153137658521653	0.114005959023444	0.909291288156406	   
df.mm.trans1:exp5	-0.109965699320238	0.153137658521653	-0.718083980007373	0.473132160952349	   
df.mm.trans2:exp5	0.0041592570558964	0.153137658521653	0.0271602497781975	0.978345706222526	   
df.mm.trans1:exp6	-0.277387513784898	0.153137658521653	-1.81136055273875	0.0708483530484514	.  
df.mm.trans2:exp6	0.0299655557189946	0.153137658521653	0.195677248877079	0.844963925112929	   
df.mm.trans1:exp7	0.0149021245577364	0.153137658521653	0.0973119525373265	0.922528272236954	   
df.mm.trans2:exp7	0.125758517152934	0.153137658521653	0.821212224132006	0.412023269249827	   
df.mm.trans1:exp8	0.00107112047635127	0.153137658521653	0.00699449427849142	0.994422795588652	   
df.mm.trans2:exp8	0.312127930338273	0.153137658521653	2.03821798864804	0.0421971475052385	*  
df.mm.trans1:probe2	0.0326352703566157	0.0937772809456552	0.348008281190495	0.728020290554483	   
df.mm.trans1:probe3	0.0357190808860991	0.0937772809456552	0.380892690915176	0.70348868617759	   
df.mm.trans1:probe4	0.0686160203340977	0.0937772809456552	0.731691297104906	0.46479308087755	   
df.mm.trans1:probe5	0.161582787375138	0.0937772809456552	1.72304833053089	0.0856663845250165	.  
df.mm.trans1:probe6	0.187041789296109	0.0937772809456552	1.99453201681655	0.0467842746160514	*  
df.mm.trans2:probe2	0.124291508340348	0.0937772809456552	1.32539040465863	0.185811669037018	   
df.mm.trans2:probe3	0.16831433814117	0.0937772809456552	1.79483065027989	0.0734489990186972	.  
df.mm.trans2:probe4	0.0987337985748754	0.0937772809456552	1.05285414099490	0.293054745436692	   
df.mm.trans2:probe5	0.0825943132937988	0.0937772809456552	0.880749713159874	0.378991594482057	   
df.mm.trans2:probe6	0.164267859429385	0.0937772809456552	1.75168076716342	0.0806087229997493	.  
df.mm.trans3:probe2	0.00191526223809615	0.0937772809456552	0.0204235206948052	0.983715885978133	   
df.mm.trans3:probe3	-0.0725765341427398	0.0937772809456552	-0.773924488008973	0.439440748892166	   
df.mm.trans3:probe4	-0.0350064896884906	0.0937772809456552	-0.373293929355631	0.709130964977327	   
df.mm.trans3:probe5	0.0537309691430122	0.0937772809456552	0.57296360697587	0.566997005077408	   
df.mm.trans3:probe6	-0.130875698988004	0.0937772809456552	-1.39560134041259	0.163622578942133