fitVsDatCorrelation=0.78323950482556 cont.fitVsDatCorrelation=0.233384141763065 fstatistic=8642.7505869199,52,692 cont.fstatistic=3525.31146120405,52,692 residuals=-0.553558077558089,-0.0988544086234024,-0.00706391903319109,0.0769301381433287,1.27182051997699 cont.residuals=-0.561335601981593,-0.156320268030283,-0.0503753292143011,0.0953653484411681,1.40065517066802 predictedValues: Include Exclude Both Lung 53.8041755097266 51.3662155665346 62.1567858368479 cerebhem 58.9212295119322 61.1996205007272 64.365542853991 cortex 49.6635506721263 55.8255652138938 68.0624970711229 heart 51.4748049847376 48.8071727877098 65.130004856998 kidney 53.7207747046909 50.190580332723 60.6843337969267 liver 54.4778484365224 51.1608663656463 63.048055221419 stomach 49.9427165231394 53.4912175556196 68.6976852678585 testicle 51.6107529853142 54.1649513917381 63.6485925801991 diffExp=2.43795994319201,-2.27839098879508,-6.16201454176749,2.66763219702782,3.53019437196799,3.31698207087608,-3.54850103248020,-2.55419840642393 diffExpScore=7.379774678739 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 60.5841063483385 62.0923456642143 52.909240602609 cerebhem 57.9123341590474 58.4257840313481 56.2017126067395 cortex 58.1454057548689 58.8147315171956 52.0040189866159 heart 56.5586802957067 61.9070515752357 54.9899714092323 kidney 55.3966772178236 60.411520379912 60.1619646779173 liver 57.8411581038966 53.9732440733536 57.353875016691 stomach 59.3719248395069 55.1070224786461 59.3080128550142 testicle 56.3159699893853 60.0946276326436 54.9396406496934 cont.diffExp=-1.50823931587584,-0.513449872300711,-0.669325762326771,-5.34837127952901,-5.01484316208841,3.86791403054303,4.26490236086086,-3.77865764325826 cont.diffExpScore=2.57376511399837 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=0.441972186561142 cont.tran.correlation=-0.185794189155746 tran.covariance=0.00157329359984225 cont.tran.covariance=-0.000297689907818840 tran.mean=53.1138776901739 cont.tran.mean=58.3095365038202 weightedLogRatios: wLogRatio Lung 0.183727233089504 cerebhem -0.155368870160043 cortex -0.463602944745865 heart 0.208310013116740 kidney 0.268478412805347 liver 0.249165331100604 stomach -0.270801497507718 testicle -0.191664590206545 cont.weightedLogRatios: wLogRatio Lung -0.101221056534754 cerebhem -0.0358667813469797 cortex -0.0465679193527607 heart -0.368691283730983 kidney -0.351654098879372 liver 0.278447047838015 stomach 0.301647230874216 testicle -0.263889716561618 varWeightedLogRatios=0.0795373094096287 cont.varWeightedLogRatios=0.0668954368740058 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68523180197024 0.0815700053933022 45.1787612885562 1.37304816907912e-208 *** df.mm.trans1 0.153683871193545 0.0716624407819333 2.14455256500683 0.0323362494382239 * df.mm.trans2 0.202141516239568 0.0649392956074971 3.11277654536541 0.00192970576247396 ** df.mm.exp2 0.231092255620861 0.0865857274766628 2.66894166458493 0.00778741133912291 ** df.mm.exp3 -0.0875949087738914 0.0865857274766628 -1.01165528461374 0.312056493720537 df.mm.exp4 -0.142087348052983 0.0865857274766628 -1.64100195486929 0.101251405404221 df.mm.exp5 -0.000730164116580255 0.0865857274766628 -0.0084328461267136 0.993274072457161 df.mm.exp6 -0.00579988827012417 0.0865857274766628 -0.0669843453320571 0.94661351392693 df.mm.exp7 -0.133993094324716 0.0865857274766628 -1.54751941491547 0.122195230698931 df.mm.exp8 -0.0122848756458057 0.0865857274766628 -0.141881069823162 0.88721524266892 df.mm.trans1:exp2 -0.140241872625037 0.0812246121464631 -1.72659331844091 0.0846870218424513 . df.mm.trans2:exp2 -0.0559319387473771 0.0670690161072738 -0.833946015518053 0.404599175560385 df.mm.trans1:exp3 0.0075151100769523 0.0812246121464631 0.0925225726335407 0.926309636235504 df.mm.trans2:exp3 0.170846159536872 0.0670690161072738 2.54731870918773 0.0110706725376825 * df.mm.trans1:exp4 0.0978287365553754 0.0812246121464631 1.20442232926852 0.228838161983043 df.mm.trans2:exp4 0.0909839618087378 0.0670690161072738 1.35657218622699 0.175359576326397 df.mm.trans1:exp5 -0.000821119033879237 0.0812246121464631 -0.0101092392093988 0.991937045137297 df.mm.trans2:exp5 -0.0224231412243479 0.0670690161072738 -0.334329359901197 0.738232337527548 df.mm.trans1:exp6 0.0182429807487727 0.0812246121464631 0.224599173411591 0.822357408952352 df.mm.trans2:exp6 0.00179412761437553 0.0670690161072738 0.0267504686740283 0.978666471602325 df.mm.trans1:exp7 0.0595186975735604 0.0812246121464631 0.732766780914104 0.463948815093551 df.mm.trans2:exp7 0.174529905255357 0.0670690161072738 2.60224341111855 0.00945981091203527 ** df.mm.trans1:exp8 -0.0293361582499064 0.0812246121464631 -0.361173263554744 0.718080164360468 df.mm.trans2:exp8 0.0653382499749646 0.0670690161072738 0.974194251939809 0.330300510107693 df.mm.trans1:probe2 0.143364311166736 0.0474249560969631 3.02297193219577 0.00259553617952878 ** df.mm.trans1:probe3 0.599793335098557 0.0474249560969631 12.6472090743162 3.93884922019332e-33 *** df.mm.trans1:probe4 0.0501056145115462 0.0474249560969631 1.05652421499563 0.29109747902019 df.mm.trans1:probe5 0.563819223236225 0.0474249560969631 11.8886609422150 8.63311624842428e-30 *** df.mm.trans1:probe6 -0.00853199574228381 0.0474249560969631 -0.179905190103701 0.857279668075025 df.mm.trans1:probe7 0.820210555400495 0.0474249560969631 17.2949143848131 5.73280671514481e-56 *** df.mm.trans1:probe8 0.230381921667726 0.0474249560969631 4.85782045209904 1.46891960103623e-06 *** df.mm.trans1:probe9 0.0344792426102876 0.0474249560969631 0.727027401771186 0.467455042314584 df.mm.trans1:probe10 0.104959686142141 0.0474249560969631 2.21317413404758 0.0272111646111569 * df.mm.trans1:probe11 0.212908878818236 0.0474249560969631 4.48938483744574 8.36604222997592e-06 *** df.mm.trans1:probe12 -0.023459701667566 0.0474249560969631 -0.494669971219399 0.620990109525635 df.mm.trans1:probe13 0.135152920739036 0.0474249560969631 2.84982700801468 0.00450438937754012 ** df.mm.trans1:probe14 0.0655899530605031 0.0474249560969631 1.38302612081286 0.167102927887005 df.mm.trans1:probe15 0.173225615018420 0.0474249560969631 3.65262573283673 0.000279179135456578 *** df.mm.trans1:probe16 0.0361636547791824 0.0474249560969631 0.762544823557532 0.445994743173618 df.mm.trans1:probe17 0.0329576609604836 0.0474249560969631 0.694943415300158 0.487324033884819 df.mm.trans1:probe18 0.244633420334932 0.0474249560969631 5.15832676438888 3.25745992577983e-07 *** df.mm.trans1:probe19 0.00301644225359153 0.0474249560969631 0.0636045344443596 0.949303488934923 df.mm.trans1:probe20 0.242114329043690 0.0474249560969631 5.1052093448157 4.27526051800254e-07 *** df.mm.trans2:probe2 0.119925807257764 0.0474249560969631 2.52874893574111 0.0116681237718175 * df.mm.trans2:probe3 0.224399353370145 0.0474249560969631 4.73167234802175 2.70063344071899e-06 *** df.mm.trans2:probe4 0.057826239832676 0.0474249560969631 1.21932089329607 0.223137865070021 df.mm.trans2:probe5 0.0636777489194548 0.0474249560969631 1.34270549010656 0.179807616826453 df.mm.trans2:probe6 0.101851738539758 0.0474249560969631 2.14764012288205 0.0320889913511039 * df.mm.trans3:probe2 0.071607821063202 0.0474249560969631 1.50991855251896 0.131520661382931 df.mm.trans3:probe3 0.0375385869598562 0.0474249560969631 0.79153656743733 0.428902169278643 df.mm.trans3:probe4 0.185429760576911 0.0474249560969631 3.90996167076653 0.000101399113312815 *** df.mm.trans3:probe5 0.14843786848865 0.0474249560969631 3.12995268114029 0.00182185434793610 ** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.47797754579237 0.127577375879241 35.1000913361867 8.29180266827353e-156 *** df.mm.trans1 -0.286205704459479 0.112081715576437 -2.55354500051610 0.0108765375578907 * df.mm.trans2 -0.303796511715489 0.101566560957112 -2.9911075934113 0.00287843004507164 ** df.mm.exp2 -0.166336849114389 0.135422081276149 -1.22828454227637 0.219757755525159 df.mm.exp3 -0.0780590813439848 0.135422081276149 -0.576413245228513 0.564523325088193 df.mm.exp4 -0.110315356195757 0.135422081276149 -0.814603904741388 0.415579318100476 df.mm.exp5 -0.245418207322515 0.135422081276149 -1.81224660712505 0.0703813819773463 . df.mm.exp6 -0.267128671433629 0.135422081276149 -1.9725636241619 0.0489429851635608 * df.mm.exp7 -0.253723100453814 0.135422081276149 -1.87357259660209 0.0614106014405637 . df.mm.exp8 -0.143413845217095 0.135422081276149 -1.0590137432953 0.28996303003798 df.mm.trans1:exp2 0.121234649009603 0.127037172849148 0.954324205195943 0.340252797623014 df.mm.trans2:exp2 0.105471425781658 0.104897493098255 1.00547136701223 0.315021379214397 df.mm.trans1:exp3 0.0369733631958981 0.127037172849148 0.291043655700703 0.77110514162249 df.mm.trans2:exp3 0.0238287177590883 0.104897493098255 0.227161937385563 0.820364916123199 df.mm.trans1:exp4 0.0415614574954381 0.127037172849148 0.327159811284456 0.743645954619364 df.mm.trans2:exp4 0.107326725105236 0.104897493098255 1.02315815121250 0.306590614495632 df.mm.trans1:exp5 0.155905234062848 0.127037172849148 1.22724105524593 0.220149340165018 df.mm.trans2:exp5 0.217975305725175 0.104897493098255 2.07798393733779 0.0380789194114329 * df.mm.trans1:exp6 0.220796684430328 0.127037172849148 1.73804784440942 0.0826472123935619 . df.mm.trans2:exp6 0.126994391966682 0.104897493098255 1.21065230651156 0.226442014567404 df.mm.trans1:exp7 0.233511982094106 0.127037172849148 1.83813900181322 0.0664703848604848 . df.mm.trans2:exp7 0.134377535077553 0.104897493098255 1.28103666835665 0.200609923895444 df.mm.trans1:exp8 0.0703594117239347 0.127037172849148 0.553849004554627 0.579861102041676 df.mm.trans2:exp8 0.110711569173569 0.104897493098255 1.05542626333184 0.291598751992436 df.mm.trans1:probe2 -0.0998750285260757 0.074173728699245 -1.34650138637418 0.178581731340254 df.mm.trans1:probe3 -0.0514705568458023 0.074173728699245 -0.693918962258213 0.487965900044227 df.mm.trans1:probe4 -0.152767189453622 0.074173728699245 -2.05958621917273 0.0398116379602979 * df.mm.trans1:probe5 -0.112793930569029 0.074173728699245 -1.52067224537652 0.128798879018442 df.mm.trans1:probe6 -0.141930515645369 0.074173728699245 -1.91348767460323 0.0560979635144525 . df.mm.trans1:probe7 -0.106826029423441 0.074173728699245 -1.44021382363819 0.150259167750506 df.mm.trans1:probe8 -0.143968517323946 0.074173728699245 -1.94096373269436 0.0526683847810734 . df.mm.trans1:probe9 -0.0488960780670672 0.074173728699245 -0.659210193751051 0.509980102038103 df.mm.trans1:probe10 -0.131912348754954 0.074173728699245 -1.77842412762913 0.0757731980329717 . df.mm.trans1:probe11 -0.0639890756184023 0.074173728699245 -0.862691909124067 0.388605754404252 df.mm.trans1:probe12 -0.140970894975966 0.074173728699245 -1.90055020083413 0.0577762506371555 . df.mm.trans1:probe13 -0.104968069404653 0.074173728699245 -1.41516506242084 0.157469952435143 df.mm.trans1:probe14 -0.0983128196153898 0.074173728699245 -1.32543990088489 0.185462704687042 df.mm.trans1:probe15 -0.161662213696991 0.074173728699245 -2.17950771158463 0.0296304330472317 * df.mm.trans1:probe16 -0.134912666294119 0.074173728699245 -1.81887399568591 0.0693627406782365 . df.mm.trans1:probe17 -0.195090769135620 0.074173728699245 -2.6301868944281 0.00872355621494289 ** df.mm.trans1:probe18 -0.116122397697445 0.074173728699245 -1.56554618102443 0.117912052521910 df.mm.trans1:probe19 -0.0971484162366957 0.074173728699245 -1.30974157481832 0.190717917882179 df.mm.trans1:probe20 -0.0898638361244562 0.074173728699245 -1.21153186849795 0.226105171594501 df.mm.trans2:probe2 -0.109472732651996 0.074173728699245 -1.47589631223582 0.140426594825680 df.mm.trans2:probe3 -0.0491257502859159 0.074173728699245 -0.66230660299023 0.507995252873314 df.mm.trans2:probe4 -0.100690253027089 0.074173728699245 -1.35749213087778 0.175067420607242 df.mm.trans2:probe5 -0.171670417471489 0.074173728699245 -2.31443693720142 0.0209356532972786 * df.mm.trans2:probe6 -0.0701822671188562 0.074173728699245 -0.946187664414537 0.344383144392209 df.mm.trans3:probe2 0.109007622003325 0.074173728699245 1.46962575449486 0.142117617405810 df.mm.trans3:probe3 0.158040753851159 0.074173728699245 2.13068368845219 0.0334671754197591 * df.mm.trans3:probe4 0.0763517885751478 0.074173728699245 1.02936430342789 0.303668164134659 df.mm.trans3:probe5 0.0596132084233596 0.074173728699245 0.803697069956878 0.421847930232259