fitVsDatCorrelation=0.71883725883941 cont.fitVsDatCorrelation=0.253862452060789 fstatistic=7843.42763735719,55,761 cont.fstatistic=4044.93960805384,55,761 residuals=-0.5171188963151,-0.0946881801109324,-0.00846593240792924,0.0719164687623529,1.60907522021417 cont.residuals=-0.496863348479093,-0.155210551327469,-0.0485227793235732,0.111678057647330,1.64486662085794 predictedValues: Include Exclude Both Lung 53.1508227249361 55.0227308654423 60.1359911992637 cerebhem 56.4626480347201 75.2155378120896 58.6858425080229 cortex 55.2653956419664 55.8460851751772 66.5678417612566 heart 52.9874862929543 58.3626625232254 69.62296277509 kidney 51.872703993142 52.8820814125722 59.3533137133338 liver 52.0163391812241 55.2519214422076 59.3076292665019 stomach 57.8972818809731 77.9613905342748 69.5230170005355 testicle 58.3479235147162 54.1461396210448 68.002918061692 diffExp=-1.87190814050621,-18.7528897773694,-0.580689533210787,-5.37517623027107,-1.00937741943027,-3.23558226098346,-20.0641086533017,4.20178389367139 diffExpScore=1.15525029025143 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,-1,0,0,0,0,-1,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,-1,0,0,0,0,-1,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 60.7758793415043 63.4795330472134 56.5337082319081 cerebhem 58.9888386138844 61.7158385531154 56.1029924711851 cortex 58.0411355313633 64.1187237311577 64.5278650963432 heart 56.8810177399966 59.0911586003498 56.4282898743532 kidney 57.9815841998329 63.5066567224922 61.9414383249865 liver 61.5638869376308 60.2238414436664 51.1494825377161 stomach 56.163985955713 61.7822432626739 58.239893114798 testicle 59.0657566548493 63.0115621931304 62.5995718810123 cont.diffExp=-2.70365370570916,-2.72699993923097,-6.07758819979436,-2.21014086035322,-5.52507252265931,1.34004549396436,-5.61825730696087,-3.94580553828114 cont.diffExpScore=1.05901791889901 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=0.567418022730267 cont.tran.correlation=0.0735119081069966 tran.covariance=0.00412788422995058 cont.tran.covariance=7.35339863091211e-05 tran.mean=57.6680719156666 cont.tran.mean=60.3994776580359 weightedLogRatios: wLogRatio Lung -0.138120318487223 cerebhem -1.19786481060048 cortex -0.0419914807304546 heart -0.388256807352578 kidney -0.0762861978926937 liver -0.240278453919941 stomach -1.25189608407111 testicle 0.301119866823929 cont.weightedLogRatios: wLogRatio Lung -0.179710923262371 cerebhem -0.185286126067125 cortex -0.409386038593247 heart -0.154766535294560 kidney -0.373691967547999 liver 0.0904289054577888 stomach -0.388601759431015 testicle -0.265844615835412 varWeightedLogRatios=0.311087519988473 cont.varWeightedLogRatios=0.0274341414632572 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.73733903657340 0.0861059083357763 43.4039789929325 4.72447785070296e-208 *** df.mm.trans1 0.140510685968241 0.0753982998577022 1.86357896973041 0.0627658070937195 . df.mm.trans2 0.189396040650770 0.0676149069334058 2.80109888840499 0.00522213744888347 ** df.mm.exp2 0.397467183602819 0.0891678986847224 4.45751430128654 9.53885341651032e-06 *** df.mm.exp3 -0.0477466758026612 0.0891678986847224 -0.535469339380562 0.592481799187969 df.mm.exp4 -0.0906337696068804 0.0891678986847224 -1.01643944674912 0.309743209236553 df.mm.exp5 -0.0509221346507381 0.0891678986847224 -0.571081469922122 0.568113022512433 df.mm.exp6 -0.003548406630234 0.0891678986847224 -0.0397946647008064 0.968267267209752 df.mm.exp7 0.288954817041404 0.0891678986847224 3.2405699955214 0.00124478760260498 ** df.mm.exp8 -0.0457116198730731 0.0891678986847225 -0.512646597568696 0.608347313871082 df.mm.trans1:exp2 -0.337021444772652 0.0836469034522321 -4.02909648610140 6.16146889291e-05 *** df.mm.trans2:exp2 -0.084855742463253 0.0667271453553604 -1.27168249160589 0.20387445282679 df.mm.trans1:exp3 0.0867600475660958 0.0836469034522322 1.03721768511899 0.299963873977711 df.mm.trans2:exp3 0.0625997152046724 0.0667271453553604 0.938144661685929 0.348467717622108 df.mm.trans1:exp4 0.087555963630279 0.0836469034522322 1.04673287374325 0.295555139914688 df.mm.trans2:exp4 0.149563726305229 0.0667271453553604 2.24142252015602 0.0252862273029048 * df.mm.trans1:exp5 0.0265812678781051 0.0836469034522322 0.317779460817516 0.750739407551666 df.mm.trans2:exp5 0.0112403021593314 0.0667271453553604 0.168451716306315 0.866272685396915 df.mm.trans1:exp6 -0.0180272930908092 0.0836469034522322 -0.215516562440401 0.82942234804684 df.mm.trans2:exp6 0.00770513534300264 0.0667271453553604 0.115472275967575 0.908101256016811 df.mm.trans1:exp7 -0.203417962596131 0.0836469034522322 -2.43186482942905 0.0152509985197034 * df.mm.trans2:exp7 0.0595125056334087 0.0667271453553604 0.891878489878001 0.372739977127019 df.mm.trans1:exp8 0.139001807278664 0.0836469034522322 1.66176871518074 0.0969710013382034 . df.mm.trans2:exp8 0.0296519119937944 0.0667271453553604 0.444375551147602 0.656897428130952 df.mm.trans1:probe2 0.143700875730153 0.0512230580054544 2.80539431509246 0.00515375564530269 ** df.mm.trans1:probe3 0.0125852401116192 0.0512230580054544 0.245694821856967 0.805984727603163 df.mm.trans1:probe4 0.256511680633835 0.0512230580054544 5.00773851897949 6.84972851682382e-07 *** df.mm.trans1:probe5 0.0522788933795556 0.0512230580054544 1.02061250177584 0.30776240477353 df.mm.trans1:probe6 0.342518720718310 0.0512230580054544 6.6868073491793 4.41496023827178e-11 *** df.mm.trans1:probe7 0.028223120013905 0.0512230580054544 0.550984675903179 0.581805897595087 df.mm.trans1:probe8 0.00191274912598282 0.0512230580054544 0.0373415645309412 0.97022245795253 df.mm.trans1:probe9 0.0100295598200209 0.0512230580054544 0.195801660630120 0.844817680197316 df.mm.trans1:probe10 0.0598920834418621 0.0512230580054544 1.16924068522978 0.242672773537398 df.mm.trans1:probe11 0.0452058632751964 0.0512230580054544 0.882529568429568 0.377769179271245 df.mm.trans1:probe12 0.115195801887684 0.0512230580054544 2.24890520740518 0.0248037827521087 * df.mm.trans1:probe13 0.034382883739073 0.0512230580054544 0.671238404692899 0.502272354536358 df.mm.trans1:probe14 -0.0312690055715715 0.0512230580054544 -0.610447848862165 0.541747437206962 df.mm.trans1:probe15 0.335380043723369 0.0512230580054544 6.54744282716695 1.07624430824861e-10 *** df.mm.trans1:probe16 0.220416784086096 0.0512230580054544 4.30307741608527 1.90418470235932e-05 *** df.mm.trans1:probe17 0.129330967092942 0.0512230580054544 2.52485837684995 0.0117766381885371 * df.mm.trans1:probe18 0.0378365279440709 0.0512230580054544 0.738662028730146 0.460339994738288 df.mm.trans1:probe19 0.294022031339966 0.0512230580054544 5.74003276627213 1.36709278477990e-08 *** df.mm.trans1:probe20 -0.0878982186637468 0.0512230580054544 -1.71598928463793 0.0865709973690734 . df.mm.trans1:probe21 0.085759869611327 0.0512230580054544 1.6742434550119 0.0944936563095792 . df.mm.trans1:probe22 0.581931648576894 0.0512230580054544 11.3607361847652 9.74463916574036e-28 *** df.mm.trans2:probe2 0.181774451918 0.0512230580054544 3.54868410821244 0.000410912943246375 *** df.mm.trans2:probe3 0.0670676485953031 0.0512230580054544 1.30932535476819 0.190819343877254 df.mm.trans2:probe4 0.182092284986649 0.0512230580054544 3.55488899095519 0.000401492465898143 *** df.mm.trans2:probe5 0.240759444122249 0.0512230580054544 4.70021614282795 3.08465477777083e-06 *** df.mm.trans2:probe6 0.300441903466568 0.0512230580054544 5.86536445041169 6.68099323275295e-09 *** df.mm.trans3:probe2 0.159030374681842 0.0512230580054544 3.10466381497387 0.00197554595148304 ** df.mm.trans3:probe3 0.0258259942652248 0.0512230580054544 0.504186889085668 0.614275990269859 df.mm.trans3:probe4 0.253870316400601 0.0512230580054544 4.95617259659836 8.86681873633843e-07 *** df.mm.trans3:probe5 -0.0406952713425194 0.0512230580054544 -0.794471726740447 0.427168545943292 df.mm.trans3:probe6 -0.221854887992005 0.0512230580054544 -4.33115273922891 1.68195898698243e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.28665927300728 0.119803935577422 35.780621499175 3.25982156764164e-165 *** df.mm.trans1 -0.205635758231843 0.104905844829769 -1.96019352940275 0.0503376863776574 . df.mm.trans2 -0.0833201868174454 0.0940763776944842 -0.885665337668826 0.376077659018097 df.mm.exp2 -0.0503737647513741 0.124064252919100 -0.406029646462483 0.684834959661431 df.mm.exp3 -0.168282194384152 0.124064252919100 -1.35641162079045 0.175370509721478 df.mm.exp4 -0.136001098122894 0.124064252919100 -1.09621502506107 0.273331465337521 df.mm.exp5 -0.137992681025024 0.124064252919100 -1.11226785942125 0.266374239720002 df.mm.exp6 0.0603179205164718 0.124064252919100 0.486182918102962 0.626977449725556 df.mm.exp7 -0.135752301607134 0.124064252919100 -1.09420964067431 0.274209258385669 df.mm.exp8 -0.137862322329348 0.124064252919100 -1.11121712407558 0.266825854644613 df.mm.trans1:exp2 0.0205290258246457 0.116382585424493 0.176392591295066 0.860032454980162 df.mm.trans2:exp2 0.0221968256367714 0.092841185673868 0.239083823366327 0.81110495759314 df.mm.trans1:exp3 0.122241197942311 0.116382585424493 1.05033925390512 0.293895591269384 df.mm.trans2:exp3 0.178301077820319 0.092841185673868 1.92049548404793 0.0551687567429281 . df.mm.trans1:exp4 0.0697697873138805 0.116382585424493 0.599486487255827 0.549026953495072 df.mm.trans2:exp4 0.0643648709456071 0.092841185673868 0.693279286325658 0.488345831330127 df.mm.trans1:exp5 0.0909251384567352 0.116382585424493 0.781260685394602 0.434892161495983 df.mm.trans2:exp5 0.138419871977687 0.092841185673868 1.49093175591194 0.136393783548045 df.mm.trans1:exp6 -0.0474354618023488 0.116382585424493 -0.40758212776708 0.683695118066146 df.mm.trans2:exp6 -0.112967149060693 0.092841185673868 -1.21677839679389 0.224065769713154 df.mm.trans1:exp7 0.0568350449004271 0.116382585424493 0.488346643040515 0.625445011179319 df.mm.trans2:exp7 0.108650759210955 0.092841185673868 1.17028620888818 0.242252167690661 df.mm.trans1:exp8 0.109320676292380 0.116382585424493 0.93932159947852 0.347863691351253 df.mm.trans2:exp8 0.130463019029827 0.092841185673868 1.4052278423944 0.160361622344432 df.mm.trans1:probe2 0.00543892120897735 0.0712694873089707 0.0763148636863107 0.939188658933136 df.mm.trans1:probe3 0.0104520789420418 0.0712694873089707 0.146655733564204 0.883442601464553 df.mm.trans1:probe4 0.0419859830461304 0.0712694873089707 0.589115828266182 0.555958409097342 df.mm.trans1:probe5 0.0262908208791288 0.0712694873089707 0.36889308274594 0.71231002626058 df.mm.trans1:probe6 0.107612449000687 0.0712694873089707 1.50993718439647 0.131474531072836 df.mm.trans1:probe7 0.0269447034350685 0.0712694873089707 0.378067872415816 0.705485605273936 df.mm.trans1:probe8 0.0165843338088793 0.0712694873089707 0.232698935197642 0.816057765095723 df.mm.trans1:probe9 0.0409202425931499 0.0712694873089707 0.574162157442647 0.566027739239372 df.mm.trans1:probe10 0.0419806696757785 0.0712694873089707 0.58904127503797 0.556008392864986 df.mm.trans1:probe11 0.159003177142634 0.0712694873089707 2.23101334310596 0.025970879009765 * df.mm.trans1:probe12 0.0268438851800532 0.0712694873089707 0.376653266266367 0.706536291838716 df.mm.trans1:probe13 -0.0260954963471432 0.0712694873089707 -0.366152435389535 0.714353098715178 df.mm.trans1:probe14 0.0593594116055889 0.0712694873089707 0.83288674925149 0.405169915100178 df.mm.trans1:probe15 0.0259774219214368 0.0712694873089707 0.364495703593578 0.715589140579927 df.mm.trans1:probe16 0.0919732411056873 0.0712694873089707 1.29049954726012 0.197269217014749 df.mm.trans1:probe17 0.0782610886145565 0.0712694873089707 1.09810090642683 0.272507739158386 df.mm.trans1:probe18 0.0657117626336293 0.0712694873089707 0.92201817516594 0.356811307989634 df.mm.trans1:probe19 -0.0776134782269283 0.0712694873089707 -1.08901412311912 0.276492399139705 df.mm.trans1:probe20 -0.00512446298675696 0.0712694873089707 -0.0719026217284426 0.942698295935062 df.mm.trans1:probe21 0.0276496393644261 0.0712694873089707 0.3879590047359 0.698154866283486 df.mm.trans1:probe22 -0.0114111170939493 0.0712694873089707 -0.160112237716533 0.87283514995971 df.mm.trans2:probe2 -0.0873515732122637 0.0712694873089707 -1.22565176922872 0.220708915289874 df.mm.trans2:probe3 -0.0938079216252524 0.0712694873089707 -1.31624240845977 0.188489004434719 df.mm.trans2:probe4 -0.147238194038508 0.0712694873089707 -2.06593592290336 0.039172460521556 * df.mm.trans2:probe5 -0.182803837100436 0.0712694873089707 -2.56496635520804 0.0105092055478902 * df.mm.trans2:probe6 -0.120257031575838 0.0712694873089707 -1.68735648475335 0.0919446132894207 . df.mm.trans3:probe2 -0.0238979760804770 0.0712694873089707 -0.335318478956827 0.737477269084073 df.mm.trans3:probe3 0.0908408987158564 0.0712694873089707 1.27461136800436 0.202835881843636 df.mm.trans3:probe4 0.0486309519408941 0.0712694873089707 0.682353048648533 0.495223376337588 df.mm.trans3:probe5 0.0364844294919911 0.0712694873089707 0.511922154481373 0.6088540122442 df.mm.trans3:probe6 0.0707441249538405 0.0712694873089707 0.992628509408905 0.321206534541611