fitVsDatCorrelation=0.750357741989331
cont.fitVsDatCorrelation=0.306738682540592

fstatistic=9794.81452574061,55,761
cont.fstatistic=4717.3328776409,55,761

residuals=-0.458114461328716,-0.0858883867807453,-0.00914550241080902,0.0673737695906776,1.27712618627594
cont.residuals=-0.419579085730776,-0.126874421567861,-0.0371946319604407,0.0704315630279568,1.62665115673486

predictedValues:
Include	Exclude	Both
Lung	47.9419707071887	45.9510640367026	56.7786159472317
cerebhem	59.3085427469711	62.2735831336011	56.9429345286711
cortex	48.7400354854202	46.9388162050415	51.3654789645865
heart	49.5869076222763	48.0167418512701	56.2168385131773
kidney	48.4946135520731	46.7825964844381	56.0758229585915
liver	51.7694867044772	49.915274171512	59.6708457262347
stomach	51.919370141718	47.6828192486165	66.0715879452363
testicle	50.3325764863671	52.0558579690898	54.3899985224421


diffExp=1.99090667048615,-2.96504038663,1.80121928037869,1.57016577100618,1.71201706763506,1.85421253296518,4.23655089310144,-1.72328148272269
diffExpScore=1.88391520644485
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=0,0,0,0,0,0,0,0
diffExp1.2Score=0

cont.predictedValues:
Include	Exclude	Both
Lung	54.5707032624981	48.8077172515668	49.6953308524267
cerebhem	54.7247277480933	50.078691904151	47.6094184073406
cortex	53.2298867559827	49.5084551005191	47.4504667099322
heart	54.6609857308148	48.6868948907522	56.7197190870668
kidney	52.0306778383944	57.1880885671596	50.324626003269
liver	53.8272250901111	49.5098044045652	52.5540218921965
stomach	53.4245765552028	50.2305675507671	49.7094004511275
testicle	52.7956719149164	49.5788692472062	53.015299007312
cont.diffExp=5.76298601093131,4.64603584394231,3.72143165546357,5.97409084006259,-5.15741072876521,4.31742068554591,3.19400900443571,3.2168026677102
cont.diffExpScore=1.34919188980385

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

tran.correlation=0.938857663362691
cont.tran.correlation=-0.718048313480269

tran.covariance=0.00631672078499048
cont.tran.covariance=-0.000690255233760083

tran.mean=50.4818910341727
cont.tran.mean=52.0533464882938

weightedLogRatios:
wLogRatio
Lung	0.163243605789494
cerebhem	-0.200362787641261
cortex	0.145640337342854
heart	0.125092929865432
kidney	0.138859211570045
liver	0.143289910034430
stomach	0.332578129448146
testicle	-0.132487401072138

cont.weightedLogRatios:
wLogRatio
Lung	0.440150536379478
cerebhem	0.351150070182829
cortex	0.285440339529355
heart	0.456396491729847
kidney	-0.377962480471696
liver	0.329750480847177
stomach	0.243348752336814
testicle	0.247371602440305

varWeightedLogRatios=0.0296531737085163
cont.varWeightedLogRatios=0.0701311593382123

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	3.65542280561176	0.0747104713685558	48.9278509243921	3.32577677906383e-237	***
df.mm.trans1	0.205999306273802	0.0654199303117526	3.14887688342271	0.00170301268183423	** 
df.mm.trans2	0.145006435289048	0.0586666079734845	2.47170307433806	0.0136648836071399	*  
df.mm.exp2	0.513832480988458	0.0773672314761625	6.6414743190956	5.90971446471268e-11	***
df.mm.exp3	0.137970849840403	0.0773672314761625	1.78332411807851	0.074932016989737	.  
df.mm.exp4	0.0876516827836543	0.0773672314761625	1.13293032607300	0.257600324503467	   
df.mm.exp5	0.0418506725900367	0.0773672314761625	0.540935377827643	0.588710423809442	   
df.mm.exp6	0.109875846815695	0.0773672314761625	1.42018584249779	0.155963129102051	   
df.mm.exp7	-0.0348842953370344	0.0773672314761625	-0.450892382620445	0.652195557329808	   
df.mm.exp8	0.216380992990231	0.0773672314761626	2.79680413608828	0.00529132864095756	** 
df.mm.trans1:exp2	-0.301070461492213	0.07257689636194	-4.14829617390607	3.72712140746222e-05	***
df.mm.trans2:exp2	-0.209872176804049	0.0578963346294066	-3.6249648297675	0.000308236879467485	***
df.mm.trans1:exp3	-0.121461409534041	0.07257689636194	-1.67355474844660	0.0946290872162925	.  
df.mm.trans2:exp3	-0.116702883993453	0.0578963346294066	-2.01572145698801	0.0441801360126074	*  
df.mm.trans1:exp4	-0.0539161790702736	0.07257689636194	-0.74288350388248	0.457781470186876	   
df.mm.trans2:exp4	-0.0436789490564864	0.0578963346294066	-0.754433753640444	0.450822197873387	   
df.mm.trans1:exp5	-0.0303892775421341	0.07257689636194	-0.418718339657061	0.675540137516102	   
df.mm.trans2:exp5	-0.023916413754125	0.0578963346294066	-0.41309029159123	0.679656828619647	   
df.mm.trans1:exp6	-0.0330662668190810	0.07257689636194	-0.455603208136375	0.648805303885328	   
df.mm.trans2:exp6	-0.0271258002107045	0.0578963346294066	-0.468523618711552	0.639544356502135	   
df.mm.trans1:exp7	0.114584900390607	0.07257689636194	1.57880683984024	0.114795919834662	   
df.mm.trans2:exp7	0.0718784399462669	0.0578963346294066	1.24150242681786	0.214802723436172	   
df.mm.trans1:exp8	-0.167719817596995	0.07257689636194	-2.31092573538249	0.0211033964307501	*  
df.mm.trans2:exp8	-0.091640664162883	0.0578963346294066	-1.58284051571612	0.113873296893512	   
df.mm.trans1:probe2	0.0308343715552769	0.0444440908004024	0.693778880386	0.488032599129889	   
df.mm.trans1:probe3	0.475721705492019	0.0444440908004024	10.7038235437974	5.29478005374105e-25	***
df.mm.trans1:probe4	0.0125394611894411	0.0444440908004024	0.282140121748820	0.777912781579805	   
df.mm.trans1:probe5	-0.194363023255861	0.0444440908004024	-4.37320282079211	1.39485174534932e-05	***
df.mm.trans1:probe6	-0.0892722253739773	0.0444440908004024	-2.00864105365317	0.0449280209807244	*  
df.mm.trans1:probe7	0.00926811989018855	0.0444440908004025	0.208534356835231	0.834867560516858	   
df.mm.trans1:probe8	-0.0422663909783538	0.0444440908004024	-0.951001364122203	0.341905615319043	   
df.mm.trans1:probe9	-0.00339319814175563	0.0444440908004025	-0.0763475656863904	0.939162651059264	   
df.mm.trans1:probe10	-0.115295804124413	0.0444440908004025	-2.59417623463610	0.00966418952798265	** 
df.mm.trans1:probe11	-0.0307478277756030	0.0444440908004024	-0.691831629849083	0.489254086505176	   
df.mm.trans1:probe12	-0.0925327218054866	0.0444440908004025	-2.08200280710093	0.037675797005636	*  
df.mm.trans1:probe13	-0.0412538591805041	0.0444440908004024	-0.92821921739325	0.353588176399129	   
df.mm.trans1:probe14	-0.105370822369152	0.0444440908004024	-2.37086236823632	0.0179948059845607	*  
df.mm.trans1:probe15	-0.0540236982950387	0.0444440908004025	-1.21554288370209	0.224536057735741	   
df.mm.trans1:probe16	-0.0763482831031006	0.0444440908004025	-1.71785003873697	0.0862307662261458	.  
df.mm.trans1:probe17	0.0635699590619155	0.0444440908004024	1.43033546005940	0.153031210763324	   
df.mm.trans1:probe18	0.160660724284132	0.0444440908004024	3.61489506008024	0.000320256144491089	***
df.mm.trans1:probe19	0.097014472757659	0.0444440908004025	2.18284300590936	0.0293523091937241	*  
df.mm.trans1:probe20	0.0574414835061663	0.0444440908004025	1.29244366285126	0.196595836125960	   
df.mm.trans1:probe21	0.0637211100217831	0.0444440908004024	1.43373638371754	0.152058240961104	   
df.mm.trans1:probe22	0.114034720332171	0.0444440908004025	2.56580162353414	0.0104841554549653	*  
df.mm.trans2:probe2	0.071906330708597	0.0444440908004024	1.61790531460137	0.106097381273733	   
df.mm.trans2:probe3	0.00865150792533726	0.0444440908004025	0.194660477231743	0.84571070914954	   
df.mm.trans2:probe4	0.0660516170550069	0.0444440908004024	1.48617320920442	0.137647466284833	   
df.mm.trans2:probe5	0.0615185224799	0.0444440908004025	1.38417776968773	0.166709802367724	   
df.mm.trans2:probe6	0.117645190256546	0.0444440908004025	2.64703784322843	0.00828795656881293	** 
df.mm.trans3:probe2	0.000662090364090652	0.0444440908004024	0.0148971517285411	0.988118136852754	   
df.mm.trans3:probe3	-0.202446752165104	0.0444440908004024	-4.55508816851015	6.09672744968036e-06	***
df.mm.trans3:probe4	0.0238637175359692	0.0444440908004024	0.53693791696945	0.591467441605578	   
df.mm.trans3:probe5	-0.163783117832588	0.0444440908004025	-3.68514947393423	0.000244777991352667	***
df.mm.trans3:probe6	0.53809142928116	0.0444440908004025	12.1071534953368	5.56127433882997e-31	***

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.08326620052928	0.107572651467851	37.9582184208745	9.79031668038093e-178	***
df.mm.trans1	5.87001451605399e-05	0.0941955690221913	0.000623173104317793	0.999502943136997	   
df.mm.trans2	-0.225084780232325	0.0844717274128834	-2.66461675552281	0.00787085341513617	** 
df.mm.exp2	0.0714060402875849	0.111398015220134	0.640999214810778	0.52171617645527	   
df.mm.exp3	0.0356024968408726	0.111398015220134	0.319597227746997	0.749361392180341	   
df.mm.exp4	-0.133036447426286	0.111398015220134	-1.19424432440194	0.232754514233101	   
df.mm.exp5	0.0982099708926513	0.111398015220134	0.881613291750111	0.378264329537879	   
df.mm.exp6	-0.0553661556561722	0.111398015220134	-0.497012047717035	0.619324107084047	   
df.mm.exp7	0.00722594568078253	0.111398015220134	0.0648660181826698	0.948297712492713	   
df.mm.exp8	-0.0820612238143514	0.111398015220134	-0.736648885998462	0.461562924169476	   
df.mm.trans1:exp2	-0.0685875402618379	0.104500601240313	-0.65633632197112	0.511806155292153	   
df.mm.trans2:exp2	-0.0456988747188439	0.083362641304074	-0.54819369928734	0.583719674817535	   
df.mm.trans1:exp3	-0.0604796455673149	0.104500601240313	-0.578749259329464	0.562929619585479	   
df.mm.trans2:exp3	-0.0213474724983027	0.083362641304074	-0.256079607895766	0.797958573105053	   
df.mm.trans1:exp4	0.134689493162506	0.104500601240313	1.28888725580410	0.197828946315412	   
df.mm.trans2:exp4	0.130557901791558	0.083362641304074	1.56614401546292	0.117730613929577	   
df.mm.trans1:exp5	-0.145873636299213	0.104500601240313	-1.39591193321230	0.163148115731926	   
df.mm.trans2:exp5	0.0602472232177018	0.083362641304074	0.722712503769449	0.470078574042912	   
df.mm.trans1:exp6	0.0416483689330791	0.104500601240313	0.398546691968815	0.690339043173397	   
df.mm.trans2:exp6	0.0696484336396962	0.083362641304074	0.83548736640489	0.403705586578634	   
df.mm.trans1:exp7	-0.0284522389357641	0.104500601240313	-0.272268662553762	0.785489304537608	   
df.mm.trans2:exp7	0.0215093702999118	0.083362641304074	0.258021698490264	0.796459921898469	   
df.mm.trans1:exp8	0.0489932713967417	0.104500601240313	0.468832435557715	0.639323686032969	   
df.mm.trans2:exp8	0.0977375027725538	0.083362641304074	1.17243769203576	0.241388260321286	   
df.mm.trans1:probe2	-0.0870540363787808	0.0639932877132056	-1.36036199247841	0.174118200990693	   
df.mm.trans1:probe3	-0.0992929894331914	0.0639932877132056	-1.55161569254241	0.121169959941121	   
df.mm.trans1:probe4	-0.0826796510549623	0.0639932877132056	-1.29200505255336	0.196747609478736	   
df.mm.trans1:probe5	-0.107814226038550	0.0639932877132056	-1.68477398007325	0.0924422009144722	.  
df.mm.trans1:probe6	-0.213999669675213	0.0639932877132056	-3.34409556568309	0.000865924814748641	***
df.mm.trans1:probe7	-0.120568630477842	0.0639932877132056	-1.88408245280640	0.0599347956590717	.  
df.mm.trans1:probe8	-0.175141789181124	0.0639932877132056	-2.73687749824709	0.0063472324033285	** 
df.mm.trans1:probe9	-0.0177621614241851	0.0639932877132056	-0.277562882904041	0.78142331290345	   
df.mm.trans1:probe10	-0.00360238086315939	0.0639932877132056	-0.0562931049785087	0.955123087831256	   
df.mm.trans1:probe11	-0.128097288025327	0.0639932877132056	-2.00173006580647	0.0456682998252532	*  
df.mm.trans1:probe12	-0.175706018962499	0.0639932877132056	-2.74569451330504	0.00618080953246357	** 
df.mm.trans1:probe13	-0.0129763889565079	0.0639932877132056	-0.202777344628133	0.839363268093627	   
df.mm.trans1:probe14	-0.0548501344611461	0.0639932877132056	-0.857123245596698	0.391646609528496	   
df.mm.trans1:probe15	-0.206028609137071	0.0639932877132056	-3.21953468089366	0.00133847997268416	** 
df.mm.trans1:probe16	-0.138140824207779	0.0639932877132056	-2.15867677914714	0.0311872362207693	*  
df.mm.trans1:probe17	-0.114574823359873	0.0639932877132056	-1.79041939325504	0.0737839211327762	.  
df.mm.trans1:probe18	-0.169886066058509	0.0639932877132056	-2.65474821077917	0.00810262866498505	** 
df.mm.trans1:probe19	-0.0835790929660258	0.0639932877132056	-1.30606030652147	0.191926684892691	   
df.mm.trans1:probe20	-0.132150231649806	0.0639932877132056	-2.06506395236411	0.0392551136560766	*  
df.mm.trans1:probe21	-0.106597138438830	0.0639932877132056	-1.66575499162598	0.0961737754206078	.  
df.mm.trans1:probe22	-0.116674351209482	0.0639932877132056	-1.82322795685030	0.0686610457450765	.  
df.mm.trans2:probe2	0.183742252007594	0.0639932877132056	2.87127382532774	0.00420176667421414	** 
df.mm.trans2:probe3	0.0884823402336484	0.0639932877132056	1.38268158107759	0.167168120258376	   
df.mm.trans2:probe4	0.0594699465954612	0.0639932877132056	0.929315381669147	0.353020344204678	   
df.mm.trans2:probe5	0.0030748001367529	0.0639932877132056	0.0480487914690838	0.961689964362281	   
df.mm.trans2:probe6	0.0217149065492541	0.0639932877132056	0.339331003691705	0.734454009153824	   
df.mm.trans3:probe2	0.133008034660141	0.0639932877132056	2.07846853026576	0.0380007722722346	*  
df.mm.trans3:probe3	0.0488315196360299	0.0639932877132056	0.763072524963475	0.445656621267660	   
df.mm.trans3:probe4	-0.00649824615925664	0.0639932877132056	-0.101545746303572	0.919143993528607	   
df.mm.trans3:probe5	0.0675267052598548	0.0639932877132056	1.05521544013311	0.291661691007577	   
df.mm.trans3:probe6	0.0431571562989227	0.0639932877132056	0.6744012980289	0.50026102934542