chr8.23479_chr8_11500426_11503646_-_0.R 

fitVsDatCorrelation=0.816296056453314
cont.fitVsDatCorrelation=0.315720496521536

fstatistic=9976.84884858865,44,508
cont.fstatistic=3690.17506019209,44,508

residuals=-0.431710139681363,-0.0978357843492543,0.00139505081758019,0.0839694736894153,0.839155027649614
cont.residuals=-0.659425361454324,-0.152985121478699,-0.0325063836140320,0.109611064478356,0.805557928541261

predictedValues:
Include	Exclude	Both
chr8.23479_chr8_11500426_11503646_-_0.R.tl.Lung	70.6762094802092	71.3957484144364	79.1845975465206
chr8.23479_chr8_11500426_11503646_-_0.R.tl.cerebhem	75.353997108894	94.1955439235813	71.0970866545107
chr8.23479_chr8_11500426_11503646_-_0.R.tl.cortex	68.0488945992763	68.9718862493139	71.8924063523623
chr8.23479_chr8_11500426_11503646_-_0.R.tl.heart	66.7831525082682	82.4353302443192	92.0073943135352
chr8.23479_chr8_11500426_11503646_-_0.R.tl.kidney	77.0962453327872	71.6927415094419	88.0330020116469
chr8.23479_chr8_11500426_11503646_-_0.R.tl.liver	82.749859325919	67.0686480404042	78.541684454381
chr8.23479_chr8_11500426_11503646_-_0.R.tl.stomach	80.9432572697891	73.8699253512009	76.5464672661706
chr8.23479_chr8_11500426_11503646_-_0.R.tl.testicle	75.5963929874827	77.6171101811824	83.2694682131074


diffExp=-0.719538934227145,-18.8415468146873,-0.922991650037616,-15.652177736051,5.40350382334536,15.6812112855148,7.07333191858827,-2.02071719369968
diffExpScore=6.0292271780943
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,-1,0,-1,0,1,0,0
diffExp1.2Score=1.5

cont.predictedValues:
Include	Exclude	Both
Lung	80.8734433165854	72.9132115249678	73.9360998043975
cerebhem	80.1506848708066	79.795472671451	73.0394472778595
cortex	90.2396694065001	71.0794050888307	75.5964630826105
heart	72.8268923368947	81.0554576078578	82.9717075567875
kidney	74.691839991131	73.1434407856911	78.9239306863906
liver	71.7797069041622	75.6652927431953	71.7656603722475
stomach	85.6957826012302	76.3280725778137	70.514105718375
testicle	72.7574013364141	68.3677075612768	82.6093950394564
cont.diffExp=7.96023179161759,0.355212199355591,19.1602643176694,-8.22856527096312,1.54839920543986,-3.88558583903315,9.36771002341652,4.38969377513729
cont.diffExpScore=1.73350926857667

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

tran.correlation=-0.189527603516665
cont.tran.correlation=-0.132329715001130

tran.covariance=-0.00165262456771571
cont.tran.covariance=-0.000554491127600039

tran.mean=75.2809339079066
cont.tran.mean=76.7102175828005

weightedLogRatios:
wLogRatio
Lung	-0.0431829857451711
cerebhem	-0.989513963517167
cortex	-0.0569477168784265
heart	-0.906839670246675
kidney	0.313093599374045
liver	0.905716906940389
stomach	0.397594904046152
testicle	-0.114449291383150

cont.weightedLogRatios:
wLogRatio
Lung	0.449803602510483
cerebhem	0.0194619423287910
cortex	1.04612897705219
heart	-0.464761887545763
kidney	0.0901387729553984
liver	-0.226684332519215
stomach	0.50853705746626
testicle	0.264852005166282

varWeightedLogRatios=0.409366454289354
cont.varWeightedLogRatios=0.220773120133961

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.30513492833123	0.0765176770133006	56.2632727021089	2.17946907016835e-220	***
df.mm.trans1	-0.0277208843903759	0.0610731790138396	-0.453896208417351	0.650097381766887	   
df.mm.trans2	-0.00339462999270366	0.0610731790138396	-0.0555829915442	0.95569586777273	   
df.mm.exp2	0.448957952520985	0.081592957065009	5.50241060834796	5.94940234127141e-08	***
df.mm.exp3	0.0241892820156400	0.081592957065009	0.29646286745518	0.766997784067426	   
df.mm.exp4	-0.062969529669422	0.081592957065009	-0.771752022901328	0.440620160220945	   
df.mm.exp5	-0.0148332117871614	0.081592957065009	-0.181795247049854	0.85581593065608	   
df.mm.exp6	0.103343976952309	0.081592957065009	1.26657962488074	0.205886067231933	   
df.mm.exp7	0.203590657205057	0.081592957065009	2.49519890598948	0.0129049603992984	*  
df.mm.exp8	0.100548977447774	0.081592957065009	1.23232422337214	0.218398040871097	   
df.mm.trans1:exp2	-0.384869998325058	0.063576719943144	-6.05363093077535	2.75420048653227e-09	***
df.mm.trans2:exp2	-0.171823398034445	0.063576719943144	-2.70261501675622	0.0071099115439994	** 
df.mm.trans1:exp3	-0.0620718137001453	0.063576719943144	-0.976329287759035	0.329366001871849	   
df.mm.trans2:exp3	-0.0587286276653435	0.063576719943144	-0.923744221436148	0.356058053450379	   
df.mm.trans1:exp4	0.00631135374106017	0.063576719943144	0.099271458903579	0.920961899783945	   
df.mm.trans2:exp4	0.206745318236012	0.063576719943144	3.25190287295259	0.00122249679234622	** 
df.mm.trans1:exp5	0.101778775802665	0.063576719943144	1.60088120138448	0.110024884832435	   
df.mm.trans2:exp5	0.0189843985339661	0.063576719943144	0.298606133675088	0.765362689527566	   
df.mm.trans1:exp6	0.054369320613395	0.063576719943144	0.855176559313173	0.392856550743798	   
df.mm.trans2:exp6	-0.165865606041551	0.063576719943144	-2.60890474044403	0.00935091451818026	** 
df.mm.trans1:exp7	-0.0679512922512801	0.063576719943144	-1.06880776976302	0.285663902375400	   
df.mm.trans2:exp7	-0.169523197764062	0.063576719943144	-2.66643510259203	0.00791041939840702	** 
df.mm.trans1:exp8	-0.0332494239074437	0.063576719943144	-0.522981115370191	0.601215417362831	   
df.mm.trans2:exp8	-0.0169994040941549	0.063576719943144	-0.267384100805409	0.789281958213093	   
df.mm.trans1:probe2	0.00233239200337155	0.0442894851281044	0.0526624321015532	0.958021574220905	   
df.mm.trans1:probe3	0.00386428903197474	0.0442894851281044	0.0872507102035063	0.930506613070838	   
df.mm.trans1:probe4	-0.325635925465040	0.0442894851281044	-7.35244323846076	7.84297794689903e-13	***
df.mm.trans1:probe5	-0.112218226880731	0.0442894851281044	-2.5337442184335	0.0115845834149105	*  
df.mm.trans1:probe6	0.103472008235193	0.0442894851281044	2.33626577360082	0.0198653575974477	*  
df.mm.trans2:probe2	-0.0695892711480599	0.0442894851281044	-1.57123685106696	0.116750248696410	   
df.mm.trans2:probe3	-0.241767773508840	0.0442894851281044	-5.45880749820284	7.50805123116146e-08	***
df.mm.trans2:probe4	-0.0294297843731236	0.0442894851281044	-0.664486938333103	0.506680210927678	   
df.mm.trans2:probe5	-0.190454782379564	0.0442894851281044	-4.30022570433334	2.04651379638232e-05	***
df.mm.trans2:probe6	-0.0382919938880708	0.0442894851281044	-0.864584308833433	0.387675055230559	   
df.mm.trans3:probe2	-0.0094391170282739	0.0442894851281044	-0.213123205225165	0.831316364974843	   
df.mm.trans3:probe3	-0.100432647530684	0.0442894851281044	-2.26764089129032	0.0237703011706987	*  
df.mm.trans3:probe4	-0.0765291279042127	0.0442894851281044	-1.72792995183523	0.0846084997317265	.  
df.mm.trans3:probe5	0.588438536545416	0.0442894851281044	13.2861904996953	8.74003592231716e-35	***
df.mm.trans3:probe6	0.0846726602127597	0.0442894851281044	1.91180050903391	0.0564640605806399	.  
df.mm.trans3:probe7	0.152063372146568	0.0442894851281044	3.43339670142324	0.000644765820769643	***
df.mm.trans3:probe8	-0.148329446322453	0.0442894851281044	-3.34908942593078	0.000871005545455846	***
df.mm.trans3:probe9	-0.0881885446928726	0.0442894851281044	-1.99118468949894	0.0469957264662289	*  
df.mm.trans3:probe10	0.185896583539984	0.0442894851281044	4.19730739705576	3.18884721690518e-05	***
df.mm.trans3:probe11	0.487234026937673	0.0442894851281044	11.0011219486607	2.14277927760009e-25	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.42641834793838	0.125692048155126	35.216375362707	1.97093216291147e-138	***
df.mm.trans1	-0.0102027084591831	0.100322085787574	-0.101699524876175	0.919035307536426	   
df.mm.trans2	-0.133539609689272	0.100322085787574	-1.33110878468013	0.183750290919883	   
df.mm.exp2	0.0934213731577202	0.134028975902544	0.697023703483713	0.486106801901283	   
df.mm.exp3	0.0619031091708767	0.134028975902544	0.4618636287715	0.64437684004483	   
df.mm.exp4	-0.114234958691634	0.134028975902544	-0.852315388686533	0.394440704725521	   
df.mm.exp5	-0.141645332587080	0.134028975902544	-1.05682619473326	0.291093222128314	   
df.mm.exp6	-0.0524388715854126	0.134028975902544	-0.391250259373335	0.695776308304948	   
df.mm.exp7	0.151077485343252	0.134028975902544	1.12720017687149	0.260189993014851	   
df.mm.exp8	-0.281046327787663	0.134028975902544	-2.09690722394253	0.0364956100624686	*  
df.mm.trans1:exp2	-0.102398453893529	0.104434536652878	-0.980503741151107	0.327304242724311	   
df.mm.trans2:exp2	-0.00322445439830083	0.104434536652878	-0.0308753646221302	0.97538106303034	   
df.mm.trans1:exp3	0.0476805104620353	0.104434536652878	0.456558835708888	0.64818331384432	   
df.mm.trans2:exp3	-0.0873753261659472	0.104434536652878	-0.836651638110553	0.403181797432603	   
df.mm.trans1:exp4	0.00943474129654812	0.104434536652878	0.090341199366906	0.928051690014705	   
df.mm.trans2:exp4	0.220098690269019	0.104434536652878	2.10752780950796	0.0355612632142353	*  
df.mm.trans1:exp5	0.0621306770742007	0.104434536652878	0.594924620393654	0.552158754772028	   
df.mm.trans2:exp5	0.144797937384832	0.104434536652878	1.38649475571586	0.166204047949826	   
df.mm.trans1:exp6	-0.0668448305739207	0.104434536652878	-0.640064414668693	0.522419290360228	   
df.mm.trans2:exp6	0.08948859200846	0.104434536652878	0.85688695403422	0.391911400373081	   
df.mm.trans1:exp7	-0.093159376756268	0.104434536652878	-0.892036099761836	0.372795914017384	   
df.mm.trans2:exp7	-0.105306541673148	0.104434536652878	-1.00834977631172	0.313766617690338	   
df.mm.trans1:exp8	0.175291460629133	0.104434536652878	1.67848171923978	0.0938680855284892	.  
df.mm.trans2:exp8	0.216677078612643	0.104434536652878	2.07476458992526	0.0385112403731910	*  
df.mm.trans1:probe2	-0.096226089183043	0.0727522882917605	-1.32265378096624	0.186545654875370	   
df.mm.trans1:probe3	-0.0670652603638031	0.0727522882917605	-0.921830253570162	0.357054704954408	   
df.mm.trans1:probe4	-0.126615720255015	0.0727522882917604	-1.74036752970909	0.0824000345136257	.  
df.mm.trans1:probe5	-0.0484229269706428	0.0727522882917605	-0.665586308109664	0.505977649323337	   
df.mm.trans1:probe6	-0.0582822958622211	0.0727522882917605	-0.801106016466315	0.423444797660319	   
df.mm.trans2:probe2	0.000714890151744112	0.0727522882917605	0.00982635967238816	0.992163683149437	   
df.mm.trans2:probe3	-0.0603342521633164	0.0727522882917605	-0.829310714205391	0.40731811525182	   
df.mm.trans2:probe4	0.0170491819652816	0.0727522882917604	0.234345645554251	0.814811070361665	   
df.mm.trans2:probe5	-0.0473936474704016	0.0727522882917605	-0.651438581290221	0.515057995472997	   
df.mm.trans2:probe6	0.0286127382720223	0.0727522882917605	0.393289873677593	0.694270395218344	   
df.mm.trans3:probe2	0.112472210822412	0.0727522882917605	1.54596114381120	0.122736662128155	   
df.mm.trans3:probe3	0.0907448862973332	0.0727522882917604	1.24731315575143	0.212857410425172	   
df.mm.trans3:probe4	-0.0665591538341102	0.0727522882917604	-0.914873681597288	0.360691992098833	   
df.mm.trans3:probe5	-0.00846524890235443	0.0727522882917605	-0.116357149735360	0.907415477686714	   
df.mm.trans3:probe6	-0.0330697971507347	0.0727522882917605	-0.454553360825078	0.649624762415758	   
df.mm.trans3:probe7	0.0204466728159321	0.0727522882917605	0.281045081825252	0.778790253196962	   
df.mm.trans3:probe8	0.0516694631347886	0.0727522882917605	0.710210831136709	0.477899297367899	   
df.mm.trans3:probe9	0.0693059082864682	0.0727522882917604	0.952628568994681	0.341231337047525	   
df.mm.trans3:probe10	-0.0701624810062587	0.0727522882917605	-0.964402394119677	0.335303079061824	   
df.mm.trans3:probe11	0.0593944558752456	0.0727522882917605	0.816392958487497	0.414658079229431