fitVsDatCorrelation=0.77668325803611 cont.fitVsDatCorrelation=0.281827244855873 fstatistic=11834.6780439529,51,669 cont.fstatistic=5093.23055234981,51,669 residuals=-0.416031801299256,-0.0800690452617387,-0.00924403020327275,0.065166088154981,0.988010100535349 cont.residuals=-0.418654161365359,-0.118743272042791,-0.0333248748065666,0.0647190680615643,1.19582429090998 predictedValues: Include Exclude Both Lung 47.0816808088435 44.8151866208758 67.053353008662 cerebhem 57.7804434411326 64.0355053823831 73.2659235559071 cortex 47.4345676929125 53.4557216300277 84.5711613428477 heart 47.345271054781 46.9208035815458 56.7398877337675 kidney 46.4787515822159 46.4121108967841 70.2025343379055 liver 51.480955017598 47.4975163527268 65.8283971135185 stomach 48.7637598581289 47.8521658137177 61.054971394886 testicle 51.3389461680167 46.6758258772969 58.3092487985058 diffExp=2.2664941879677,-6.25506194125047,-6.02115393711516,0.424467473235161,0.0666406854317714,3.9834386648712,0.911594044411196,4.66312029071975 diffExpScore=23.6566017699177 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 50.6837037112694 47.7104467982205 49.9604442018013 cerebhem 52.7347407881126 53.4844166327326 49.6853241992159 cortex 49.6904156456844 51.2185683499631 51.2570873804035 heart 51.3298165197414 56.4868851519867 55.4387797549734 kidney 49.6263586980874 50.1110984273557 49.2940413946611 liver 52.503362316708 53.0915440772479 50.1515650027156 stomach 53.7908356640826 53.981334224525 57.1252949680791 testicle 51.9768083147155 48.5150843467255 55.6382291967886 cont.diffExp=2.97325691304894,-0.749675844619965,-1.52815270427871,-5.15706863224523,-0.484739729268362,-0.588181760539953,-0.190498560442357,3.46172396799002 cont.diffExpScore=4.63737000655308 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=0.778582342585811 cont.tran.correlation=0.437895040355352 tran.covariance=0.00639167301628249 cont.tran.covariance=0.000726816324790189 tran.mean=49.7105757361867 cont.tran.mean=51.6834637291974 weightedLogRatios: wLogRatio Lung 0.188823002599174 cerebhem -0.422254883977382 cortex -0.468342302540534 heart 0.0346989937384841 kidney 0.0055072265125956 liver 0.314160673759289 stomach 0.0731734046513465 testicle 0.370498300339966 cont.weightedLogRatios: wLogRatio Lung 0.235491335789602 cerebhem -0.056072956404601 cortex -0.118766022008273 heart -0.381619855330128 kidney -0.0380007251040657 liver -0.0441880436754248 stomach -0.0140944350334969 testicle 0.269925083927160 varWeightedLogRatios=0.0963848696215958 cont.varWeightedLogRatios=0.0417258807533015 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.55272710220600 0.0727218004401906 48.8536735985781 7.5670378443624e-223 *** df.mm.trans1 0.226709313756690 0.0652423602771135 3.47487909379358 0.000544079274750217 *** df.mm.trans2 0.221878088103582 0.0600968501385039 3.69200860930689 0.000240624870022272 *** df.mm.exp2 0.473050024221249 0.082346962384582 5.74459592100046 1.39930432077629e-08 *** df.mm.exp3 -0.0483308446196364 0.082346962384582 -0.58691715176959 0.557457352948279 df.mm.exp4 0.218508206152462 0.082346962384582 2.6535065754092 0.0081551908586992 ** df.mm.exp5 -0.023771159106479 0.082346962384582 -0.28867074653539 0.772922771269973 df.mm.exp6 0.165895607981573 0.082346962384582 2.01459292702014 0.0443469778864614 * df.mm.exp7 0.194386725921688 0.082346962384582 2.36058162065348 0.0185322480289495 * df.mm.exp8 0.266972874063327 0.082346962384582 3.24204884226928 0.00124564511957759 ** df.mm.trans1:exp2 -0.268283637442334 0.0786852470823175 -3.40957990716691 0.000689705384202758 *** df.mm.trans2:exp2 -0.116159392141637 0.0686821144440044 -1.69126115411517 0.091252673653208 . df.mm.trans1:exp3 0.055798100872538 0.0786852470823175 0.709130401715129 0.478490605636065 df.mm.trans2:exp3 0.224637453709732 0.0686821144440044 3.27068343087887 0.00112792827658197 ** df.mm.trans1:exp4 -0.212925246512271 0.0786852470823175 -2.70603771872911 0.00698268117779166 ** df.mm.trans2:exp4 -0.172594124933358 0.0686821144440044 -2.51294134332559 0.0122069213836107 * df.mm.trans1:exp5 0.0108824291655484 0.0786852470823175 0.138303297874424 0.890042337040602 df.mm.trans2:exp5 0.0587845259369523 0.0686821144440044 0.85589278100747 0.392363527084281 df.mm.trans1:exp6 -0.0765676570675516 0.0786852470823175 -0.973087839292789 0.3308613484038 df.mm.trans2:exp6 -0.107765254669944 0.0686821144440044 -1.56904392857334 0.117110535983782 df.mm.trans1:exp7 -0.159283297681003 0.0786852470823175 -2.02430955722064 0.0433351632136861 * df.mm.trans2:exp7 -0.128817415533334 0.0686821144440044 -1.87555983935755 0.0611510360211971 . df.mm.trans1:exp8 -0.180407208236927 0.0786852470823175 -2.29277043571067 0.022170670569002 * df.mm.trans2:exp8 -0.226293559632036 0.0686821144440044 -3.29479605373143 0.00103688702809210 ** df.mm.trans1:probe2 0.143406287964691 0.0393426235411587 3.64506164197883 0.000288065027103811 *** df.mm.trans1:probe3 0.052215061539787 0.0393426235411587 1.32718809372643 0.184899224695019 df.mm.trans1:probe4 0.0122385385119954 0.0393426235411587 0.311075810671139 0.755839892164186 df.mm.trans1:probe5 0.155649423423680 0.0393426235411587 3.95625429658612 8.42644769686067e-05 *** df.mm.trans1:probe6 0.176205049382837 0.0393426235411587 4.47873155176084 8.82864185649047e-06 *** df.mm.trans1:probe7 0.12755303717701 0.0393426235411587 3.24210806743909 0.00124539032316694 ** df.mm.trans1:probe8 0.154156039994618 0.0393426235411587 3.91829588673327 9.83456107674046e-05 *** df.mm.trans1:probe9 -0.0128661140078935 0.0393426235411587 -0.327027352266771 0.743749492235521 df.mm.trans1:probe10 0.0768072848444346 0.0393426235411587 1.95226647160634 0.0513233236133209 . df.mm.trans1:probe11 0.0838204503569078 0.0393426235411587 2.13052518648681 0.033492410942226 * df.mm.trans1:probe12 0.0776178120536468 0.0393426235411587 1.97286822960462 0.0489218598728973 * df.mm.trans1:probe13 0.277596759206035 0.0393426235411587 7.05587818554153 4.29632549196905e-12 *** df.mm.trans1:probe14 0.177550878336162 0.0393426235411588 4.51293946247421 7.55300444475962e-06 *** df.mm.trans1:probe15 0.0436874023594291 0.0393426235411587 1.11043439474048 0.267210834959374 df.mm.trans1:probe16 -0.0229908909074538 0.0393426235411587 -0.584376150802491 0.559164271169817 df.mm.trans1:probe17 0.0669839061300742 0.0393426235411587 1.70257852936518 0.089111491993828 . df.mm.trans1:probe18 0.0241397672578079 0.0393426235411587 0.613577974345149 0.539702801211462 df.mm.trans1:probe19 0.0426912971244164 0.0393426235411587 1.08511566545008 0.27826129553357 df.mm.trans1:probe20 0.0648372535867757 0.0393426235411587 1.64801550458234 0.0998190203977191 . df.mm.trans1:probe21 0.0174423616137815 0.0393426235411587 0.443345157079674 0.657659432577441 df.mm.trans2:probe2 0.0865027071909951 0.0393426235411587 2.19870205403306 0.0282407314661055 * df.mm.trans2:probe3 0.00740856104263461 0.0393426235411587 0.188308769873571 0.850691716012649 df.mm.trans2:probe4 0.0363188511901145 0.0393426235411587 0.923142584838532 0.356265721016514 df.mm.trans2:probe5 0.0817648743435383 0.0393426235411587 2.07827711992819 0.0380644999681361 * df.mm.trans2:probe6 0.0394819149047499 0.0393426235411587 1.00354046962439 0.315962993397692 df.mm.trans3:probe2 0.596432041119649 0.0393426235411587 15.1599458154001 7.65966441832255e-45 *** df.mm.trans3:probe3 0.0160902948724962 0.0393426235411587 0.408978695985109 0.682686186373457 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93085476363852 0.110771538556510 35.4861439577567 6.27926583865138e-156 *** df.mm.trans1 0.0228507062973211 0.0993786812648815 0.229935696534505 0.818212023760781 df.mm.trans2 -0.0771486197214444 0.0915409204935336 -0.842777408240003 0.399654145912909 df.mm.exp2 0.159431947065711 0.125432809193129 1.27105458365549 0.204150999306664 df.mm.exp3 0.0255370598235823 0.125432809193129 0.203591548238889 0.838734609303231 df.mm.exp4 0.077477706490891 0.125432809193129 0.617682941084406 0.536994552460521 df.mm.exp5 0.0414381763459156 0.125432809193129 0.330361542665549 0.741230225116234 df.mm.exp6 0.138321920527375 0.125432809193129 1.10275709694423 0.270529197275733 df.mm.exp7 0.0489711822775232 0.125432809193129 0.390417647444395 0.69635202166953 df.mm.exp8 -0.0657213893807123 0.125432809193129 -0.523956928043611 0.60048203164859 df.mm.trans1:exp2 -0.119761923831940 0.119855199242158 -0.99922176584072 0.318048547741821 df.mm.trans2:exp2 -0.0451919978461275 0.104618194849753 -0.431970728524134 0.66590187455602 df.mm.trans1:exp3 -0.0453294225846059 0.119855199242158 -0.378201553801779 0.7054008449977 df.mm.trans2:exp3 0.0454146852008521 0.104618194849753 0.434099300471341 0.664356294228757 df.mm.trans1:exp4 -0.0648103375634934 0.119855199242158 -0.540738641070959 0.588867727014254 df.mm.trans2:exp4 0.0913803991535502 0.104618194849753 0.87346564605503 0.382722738777547 df.mm.trans1:exp5 -0.0625204915223833 0.119855199242158 -0.521633537115613 0.602098152542689 df.mm.trans2:exp5 0.0076539483479935 0.104618194849753 0.073160776277833 0.941700046586205 df.mm.trans1:exp6 -0.103049141968326 0.119855199242158 -0.859780323422796 0.390218134125168 df.mm.trans2:exp6 -0.0314546345698044 0.104618194849753 -0.300661224512409 0.763766251042984 df.mm.trans1:exp7 0.0105274964486986 0.119855199242158 0.0878351253451145 0.930034004346293 df.mm.trans2:exp7 0.074516757663016 0.104618194849753 0.71227340301592 0.47654381486701 df.mm.trans1:exp8 0.0909145814139166 0.119855199242158 0.758536817666383 0.448396822987215 df.mm.trans2:exp8 0.0824457720666207 0.104618194849753 0.788063416550297 0.43093874925018 df.mm.trans1:probe2 0.0606098784033969 0.0599275996210789 1.01138505107216 0.312197819664216 df.mm.trans1:probe3 0.0427207038028033 0.0599275996210789 0.712871933348333 0.476173575698702 df.mm.trans1:probe4 -0.0410967366073841 0.0599275996210789 -0.68577311401154 0.493093764060866 df.mm.trans1:probe5 -0.0317537682711087 0.0599275996210789 -0.529868849609983 0.596378665851768 df.mm.trans1:probe6 -0.0914679761337535 0.0599275996210789 -1.52630802355015 0.127405779177104 df.mm.trans1:probe7 -0.0588242579735807 0.0599275996210789 -0.981588756191228 0.326657329177006 df.mm.trans1:probe8 0.00237309667194517 0.0599275996210789 0.0395993947187977 0.968424324118492 df.mm.trans1:probe9 -0.0723448578412596 0.0599275996210789 -1.20720433153830 0.227780108864506 df.mm.trans1:probe10 -0.0329049336836299 0.0599275996210789 -0.549078119125197 0.583135053247984 df.mm.trans1:probe11 0.0136417914461177 0.0599275996210789 0.227637875242368 0.819997323575203 df.mm.trans1:probe12 -0.0165381132980992 0.0599275996210789 -0.275968225036033 0.78265769632842 df.mm.trans1:probe13 -0.0158570149379005 0.0599275996210789 -0.264602871434266 0.791396906272223 df.mm.trans1:probe14 -0.0247890849153866 0.0599275996210789 -0.413650556206615 0.679262609297409 df.mm.trans1:probe15 -0.0798506560319119 0.0599275996210789 -1.33245210114882 0.183165340043552 df.mm.trans1:probe16 -0.0316403719513791 0.0599275996210789 -0.527976627654714 0.597690632392349 df.mm.trans1:probe17 -0.0949128786785383 0.0599275996210789 -1.58379243084440 0.113713541619919 df.mm.trans1:probe18 -0.0365667569778306 0.0599275996210789 -0.61018224005369 0.54194830223204 df.mm.trans1:probe19 -0.0968802761119611 0.0599275996210789 -1.61662200262539 0.106431312713422 df.mm.trans1:probe20 -0.0486489788078681 0.0599275996210789 -0.811795885626568 0.417197504954093 df.mm.trans1:probe21 -0.0196936917893171 0.0599275996210789 -0.328624739082492 0.742542184393351 df.mm.trans2:probe2 0.0367264205053514 0.0599275996210789 0.612846513752794 0.540186100216538 df.mm.trans2:probe3 -0.0684115527361681 0.0599275996210789 -1.14157004733600 0.254041220873923 df.mm.trans2:probe4 0.042657396037254 0.0599275996210789 0.711815529188153 0.476827153333298 df.mm.trans2:probe5 -0.0240301620306517 0.0599275996210789 -0.400986560159159 0.688558054234734 df.mm.trans2:probe6 0.116056062209490 0.0599275996210789 1.93660455188110 0.0532144729198551 . df.mm.trans3:probe2 0.0190250194485635 0.0599275996210789 0.31746673600909 0.750988532029035 df.mm.trans3:probe3 0.0849991590086362 0.0599275996210789 1.41836415184463 0.156550128250479