fitVsDatCorrelation=0.898584142793342
cont.fitVsDatCorrelation=0.278019623546085

fstatistic=11283.2502941026,61,899
cont.fstatistic=2342.88284503991,61,899

residuals=-0.663314627346451,-0.0956629132276208,-0.00406626342911356,0.0933859981193395,0.762982521044211
cont.residuals=-0.83340545411522,-0.251734875547535,-0.0433783655421006,0.240363744763831,1.09034751091296

predictedValues:
Include	Exclude	Both
Lung	72.6845536605231	52.9224632547495	77.439726039028
cerebhem	53.6515441781044	44.4069429273676	74.1501193612514
cortex	68.5729682264977	47.727021824931	77.4705753108787
heart	67.674109676427	51.9148081077394	74.2538309647621
kidney	76.5161267386393	55.0108129392723	79.6245366257661
liver	73.3165045893336	54.4652887222814	80.1608906180627
stomach	67.4612566277005	47.1587787748405	74.932065453705
testicle	67.8528215901137	55.0726511468116	75.6626223375178


diffExp=19.7620904057736,9.24460125073685,20.8459464015666,15.7593015686877,21.5053137993670,18.8512158670522,20.3024778528601,12.7801704433021
diffExpScore=0.9928597499455
diffExp1.5=0,0,0,0,0,0,0,0
diffExp1.5Score=0
diffExp1.4=0,0,1,0,0,0,1,0
diffExp1.4Score=0.666666666666667
diffExp1.3=1,0,1,1,1,1,1,0
diffExp1.3Score=0.857142857142857
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	76.611957044018	63.9797913767351	69.6926663293111
cerebhem	74.1341395724104	63.1754995319118	74.470946760645
cortex	80.3311295923417	67.8583280395722	69.113856236658
heart	79.0113298634069	61.6814186594464	65.9730973597354
kidney	80.1916581922308	63.2452954557241	72.4871719545174
liver	80.7947465204256	67.9644659404995	82.4103446340328
stomach	78.0913342652664	63.912343077957	80.3372756847109
testicle	78.5222539239102	63.6861074844271	68.347711263496
cont.diffExp=12.6321656672830,10.9586400404986,12.4728015527695,17.3299112039605,16.9463627365067,12.8302805799261,14.1789911873094,14.8361464394831
cont.diffExpScore=0.991164930382013

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=0,0,0,1,1,0,1,1
cont.diffExp1.2Score=0.8

tran.correlation=0.788861038346976
cont.tran.correlation=0.497870802356355

tran.covariance=0.00699840255371889
cont.tran.covariance=0.000483910330700755

tran.mean=59.7755408115833
cont.tran.mean=71.4494874087677

weightedLogRatios:
wLogRatio
Lung	1.30965306271236
cerebhem	0.735267990633634
cortex	1.46652611269582
heart	1.08217740038553
kidney	1.37681233463752
liver	1.23233496629691
stomach	1.44378237271472
testicle	0.858332019982753

cont.weightedLogRatios:
wLogRatio
Lung	0.765548614088464
cerebhem	0.675972643302452
cortex	0.725862872155968
heart	1.05129307141806
kidney	1.01267663392183
liver	0.74452801412033
stomach	0.853099982801438
testicle	0.891832945301576

varWeightedLogRatios=0.0743231637172493
cont.varWeightedLogRatios=0.0188443327666846

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.29553227772916	0.0733671524048401	44.918361551562	5.67033772022855e-232	***
df.mm.trans1	0.831524524330264	0.0627234236831391	13.2570015395028	9.0853932329549e-37	***
df.mm.trans2	0.644410144666173	0.0552988972119785	11.6532187286835	2.51349289260110e-29	***
df.mm.exp2	-0.435642472558929	0.070326996778421	-6.1945268888917	8.88712183764536e-10	***
df.mm.exp3	-0.161958924988338	0.070326996778421	-2.30294100995982	0.0215096943622293	*  
df.mm.exp4	-0.0486384976301101	0.070326996778421	-0.691604929233011	0.489364027705635	   
df.mm.exp5	0.0622521212364928	0.070326996778421	0.885180998594754	0.376295709792041	   
df.mm.exp6	0.00285672047106333	0.070326996778421	0.0406205383696958	0.967607429517453	   
df.mm.exp7	-0.156965146733751	0.070326996778421	-2.23193302606537	0.0258651187501855	*  
df.mm.exp8	-0.00574693827209619	0.070326996778421	-0.0817173850065437	0.934889642597788	   
df.mm.trans1:exp2	0.132023828542805	0.0639336334349282	2.06501369388273	0.0392079338547149	*  
df.mm.trans2:exp2	0.260210417082791	0.0456577467352234	5.69915152825638	1.63184137145161e-08	***
df.mm.trans1:exp3	0.103728437763658	0.0639336334349282	1.62243927320716	0.105059930462455	   
df.mm.trans2:exp3	0.058628772838866	0.0456577467352234	1.28409255890054	0.199440398103338	   
df.mm.trans1:exp4	-0.0227867179494310	0.0639336334349282	-0.356412059274301	0.721615628601975	   
df.mm.trans2:exp4	0.0294146821734603	0.0456577467352234	0.644242965909833	0.519582227720928	   
df.mm.trans1:exp5	-0.0108794907019706	0.0639336334349282	-0.170168503140741	0.864915907905674	   
df.mm.trans2:exp5	-0.0235502413586549	0.0456577467352234	-0.515799465427554	0.606121300732697	   
df.mm.trans1:exp6	0.00580013288291861	0.0639336334349282	0.0907211520962906	0.927734371801923	   
df.mm.trans2:exp6	0.0258789891827238	0.0456577467352234	0.566803905869474	0.570988898815886	   
df.mm.trans1:exp7	0.0823897088167767	0.0639336334349282	1.28867552789148	0.197842438967706	   
df.mm.trans2:exp7	0.041657441780552	0.0456577467352234	0.912384967706142	0.361810698195	   
df.mm.trans1:exp8	-0.0630409857285763	0.0639336334349282	-0.986037901204841	0.324379813062981	   
df.mm.trans2:exp8	0.0455722968586952	0.0456577467352234	0.998128469260129	0.318485643284337	   
df.mm.trans1:probe2	0.0961998268205065	0.0463243184429159	2.07665930237162	0.0381165846656392	*  
df.mm.trans1:probe3	0.146337205450929	0.0463243184429159	3.15897158058042	0.00163615912448658	** 
df.mm.trans1:probe4	0.171775198747475	0.0463243184429159	3.70809986031739	0.000221613849252266	***
df.mm.trans1:probe5	0.165724756227101	0.0463243184429159	3.57748935758911	0.000365435302722679	***
df.mm.trans1:probe6	-0.195221267038259	0.0463243184429159	-4.21422858662939	2.75932054903823e-05	***
df.mm.trans1:probe7	0.134959179016161	0.0463243184429159	2.91335487606726	0.00366425663270891	** 
df.mm.trans1:probe8	-0.166280306180428	0.0463243184429159	-3.58948197770745	0.000349256612979604	***
df.mm.trans1:probe9	0.371080081831602	0.0463243184429159	8.01048119658518	3.52836332837789e-15	***
df.mm.trans1:probe10	0.196250105835272	0.0463243184429159	4.23643806173003	2.50498903958760e-05	***
df.mm.trans1:probe11	0.230001010198235	0.0463243184429159	4.96501660314029	8.21839179296367e-07	***
df.mm.trans1:probe12	0.37575282010764	0.0463243184429159	8.11135128886287	1.63431995849027e-15	***
df.mm.trans1:probe13	0.25123233782448	0.0463243184429159	5.4233358691303	7.52114634777993e-08	***
df.mm.trans1:probe14	0.113891670793849	0.0463243184429159	2.45857196872081	0.0141366494472191	*  
df.mm.trans1:probe15	0.872705898409518	0.0463243184429159	18.8390445395312	5.52221774210854e-67	***
df.mm.trans1:probe16	0.623840975834676	0.0463243184429159	13.4668139068990	8.64833211771948e-38	***
df.mm.trans1:probe17	0.7170562724778	0.0463243184429159	15.4790463536211	4.34087139426291e-48	***
df.mm.trans1:probe18	0.394138955804075	0.0463243184429159	8.50825158474293	7.31946220670596e-17	***
df.mm.trans1:probe19	0.470634822686511	0.0463243184429159	10.1595628064439	4.97928423402022e-23	***
df.mm.trans1:probe20	0.438371619291402	0.0463243184429159	9.46309916748358	2.53944069989140e-20	***
df.mm.trans2:probe2	0.0254833782320457	0.0463243184429159	0.550108001339471	0.582381944196381	   
df.mm.trans2:probe3	0.204074357914567	0.0463243184429159	4.40533967415067	1.18340674440062e-05	***
df.mm.trans2:probe4	0.123538741741786	0.0463243184429159	2.66682265156301	0.00779480007827596	** 
df.mm.trans2:probe5	0.130353929235029	0.0463243184429159	2.81394165346782	0.00500060623839849	** 
df.mm.trans2:probe6	0.0942588435191495	0.0463243184429159	2.03475942415217	0.0421680699299914	*  
df.mm.trans3:probe2	-0.181504140605945	0.0463243184429159	-3.91811788509327	9.60097860118637e-05	***
df.mm.trans3:probe3	-0.700657063553761	0.0463243184429159	-15.1250377146328	3.25003599687683e-46	***
df.mm.trans3:probe4	-0.483463305797512	0.0463243184429159	-10.4364904233458	3.79409724963472e-24	***
df.mm.trans3:probe5	-0.82574836302061	0.0463243184429159	-17.8253753271763	4.29616807978082e-61	***
df.mm.trans3:probe6	-0.745797899553999	0.0463243184429159	-16.0994899573757	1.96659704199401e-51	***
df.mm.trans3:probe7	-0.0126385362466952	0.0463243184429159	-0.272827246498387	0.785048669886518	   
df.mm.trans3:probe8	-0.106390273795566	0.0463243184429159	-2.29663980759193	0.0218684821799211	*  
df.mm.trans3:probe9	0.243827053374323	0.0463243184429159	5.26347848322439	1.76865100569281e-07	***
df.mm.trans3:probe10	-0.88110983571222	0.0463243184429159	-19.0204597785499	4.70745235693983e-68	***
df.mm.trans3:probe11	-0.904844603623933	0.0463243184429159	-19.5328206444947	4.26443533260251e-71	***
df.mm.trans3:probe12	-0.51068738657738	0.0463243184429159	-11.0241748555175	1.35157912296986e-26	***
df.mm.trans3:probe13	-0.241602204289032	0.0463243184429159	-5.21545081309185	2.27669509238584e-07	***
df.mm.trans3:probe14	-0.556922021355442	0.0463243184429159	-12.0222388601728	5.59087594425078e-31	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.20921931674628	0.160607293481318	26.2081455051474	6.32547637204801e-113	***
df.mm.trans1	0.233733920074467	0.137307214269998	1.70226977014374	0.0890505174484258	.  
df.mm.trans2	-0.0952440321482769	0.121054258242297	-0.786787953858185	0.431613360270789	   
df.mm.exp2	-0.111841704354544	0.153952119457023	-0.726470702378124	0.467739364409592	   
df.mm.exp3	0.114598756648858	0.153952119457023	0.744379207334325	0.456841619882396	   
df.mm.exp4	0.0491016033854921	0.153952119457023	0.318940743126302	0.749845535998371	   
df.mm.exp5	-0.00519470698488004	0.153952119457023	-0.0337423544619026	0.973090094216885	   
df.mm.exp6	-0.0540393576433396	0.153952119457023	-0.351014054460128	0.725660044802672	   
df.mm.exp7	-0.124067460543611	0.153952119457023	-0.805883419995691	0.420523181737946	   
df.mm.exp8	0.039515102014251	0.153952119457023	0.256671373889608	0.797491198578439	   
df.mm.trans1:exp2	0.078964691785802	0.139956472233657	0.564208932431296	0.57275267751855	   
df.mm.trans2:exp2	0.0991909909527468	0.0999489129568006	0.992416905980944	0.321261228173529	   
df.mm.trans1:exp3	-0.0671947064081078	0.139956472233657	-0.480111461340111	0.631264932502818	   
df.mm.trans2:exp3	-0.0557439097342844	0.0999489129568006	-0.557724022054924	0.577171698645681	   
df.mm.trans1:exp4	-0.0182635069541099	0.139956472233657	-0.130494193391921	0.896204641343626	   
df.mm.trans2:exp4	-0.0856861478381976	0.0999489129568006	-0.857299447320977	0.39150788978654	   
df.mm.trans1:exp5	0.050861042136925	0.139956472233657	0.363406145676583	0.716386947201463	   
df.mm.trans2:exp5	-0.00635182207903982	0.0999489129568007	-0.0635506869572976	0.949342125538502	   
df.mm.trans1:exp6	0.107198141000002	0.139956472233657	0.765939147287417	0.443913563498594	   
df.mm.trans2:exp6	0.114457092780452	0.0999489129568006	1.14515595412150	0.252449324968135	   
df.mm.trans1:exp7	0.143193392577235	0.139956472233657	1.02312805039965	0.306522534472833	   
df.mm.trans2:exp7	0.123012691924430	0.0999489129568006	1.23075567592815	0.218736166712540	   
df.mm.trans1:exp8	-0.0148861896699151	0.139956472233657	-0.106362995811031	0.915318080530648	   
df.mm.trans2:exp8	-0.0441159298465316	0.0999489129568007	-0.441384788903098	0.659040588849416	   
df.mm.trans1:probe2	-0.0413184071242152	0.101408098360276	-0.407446819261138	0.683776846874905	   
df.mm.trans1:probe3	-0.311407280456312	0.101408098360276	-3.07083246300472	0.00219882409468083	** 
df.mm.trans1:probe4	-0.185458331092957	0.101408098360276	-1.82883156366933	0.0677560032723136	.  
df.mm.trans1:probe5	-0.302694309141476	0.101408098360276	-2.98491258623234	0.00291341247779447	** 
df.mm.trans1:probe6	-0.293692130021169	0.101408098360276	-2.89614078924701	0.00386940391219948	** 
df.mm.trans1:probe7	-0.111156024538931	0.101408098360276	-1.09612571713970	0.273317303477553	   
df.mm.trans1:probe8	0.00314918991528173	0.101408098360276	0.0310546195639473	0.975232873333253	   
df.mm.trans1:probe9	-0.106418690753409	0.101408098360276	-1.04941017999698	0.294271398457610	   
df.mm.trans1:probe10	-0.162581168781864	0.101408098360276	-1.60323654038217	0.109233693661292	   
df.mm.trans1:probe11	-0.202907166983931	0.101408098360276	-2.00089707099185	0.0457037367079671	*  
df.mm.trans1:probe12	-0.127142569690084	0.101408098360276	-1.25377136289825	0.210251053432587	   
df.mm.trans1:probe13	-0.291510952158072	0.101408098360276	-2.87463187725314	0.00414040233253124	** 
df.mm.trans1:probe14	-0.242749186780132	0.101408098360276	-2.39378502018358	0.0168796172277853	*  
df.mm.trans1:probe15	-0.174546030325615	0.101408098360276	-1.72122377944115	0.0855542649506317	.  
df.mm.trans1:probe16	-0.317849162925892	0.101408098360276	-3.13435680251747	0.00177819701011396	** 
df.mm.trans1:probe17	-0.111545449299614	0.101408098360276	-1.09996589131691	0.271641437744827	   
df.mm.trans1:probe18	-0.178862703844753	0.101408098360276	-1.76379112454412	0.0781067727769744	.  
df.mm.trans1:probe19	-0.181748630520897	0.101408098360276	-1.79224966703538	0.0734291455259751	.  
df.mm.trans1:probe20	-0.202363548049839	0.101408098360276	-1.99553636565489	0.0462857856769909	*  
df.mm.trans2:probe2	0.179132438053031	0.101408098360276	1.76645101278421	0.0776595272256852	.  
df.mm.trans2:probe3	0.211278338202209	0.101408098360276	2.08344640732333	0.0374925566944141	*  
df.mm.trans2:probe4	0.0887704397449636	0.101408098360276	0.875378211211353	0.381601965475154	   
df.mm.trans2:probe5	0.199152558003298	0.101408098360276	1.96387232601248	0.049852707676975	*  
df.mm.trans2:probe6	0.213506009207614	0.101408098360276	2.10541379495238	0.035532176407821	*  
df.mm.trans3:probe2	-0.0150301904720359	0.101408098360276	-0.148214893238975	0.882206415216197	   
df.mm.trans3:probe3	-0.164566345220643	0.101408098360276	-1.62281265383739	0.104980049847371	   
df.mm.trans3:probe4	-0.00861740649524096	0.101408098360276	-0.0849774981937398	0.932298199795662	   
df.mm.trans3:probe5	-0.121136611393371	0.101408098360276	-1.19454573502605	0.232579623717310	   
df.mm.trans3:probe6	-0.164594017362519	0.101408098360276	-1.62308553285123	0.104921700979770	   
df.mm.trans3:probe7	-0.135177229530335	0.101408098360276	-1.33300231161111	0.182868750983043	   
df.mm.trans3:probe8	-0.148115506232078	0.101408098360276	-1.46058853905200	0.144477777508045	   
df.mm.trans3:probe9	-0.123903829127212	0.101408098360276	-1.22183367138011	0.222090812092009	   
df.mm.trans3:probe10	-0.231707873316022	0.101408098360276	-2.28490502299752	0.0225505754821094	*  
df.mm.trans3:probe11	-0.0960651689377035	0.101408098360276	-0.947312596242652	0.343734073051473	   
df.mm.trans3:probe12	-0.116438108099539	0.101408098360276	-1.14821311100683	0.251186030596543	   
df.mm.trans3:probe13	-0.122761722388843	0.101408098360276	-1.21057119080079	0.226377959592415	   
df.mm.trans3:probe14	-0.0024835328061826	0.101408098360276	-0.0244904780420915	0.98046681370631