fitVsDatCorrelation=0.87535731799991 cont.fitVsDatCorrelation=0.287350741806753 fstatistic=4711.0165754505,57,807 cont.fstatistic=1189.75741344632,57,807 residuals=-0.852780009863602,-0.130416941584279,-0.000836136973680635,0.140696919610797,0.831302367022292 cont.residuals=-0.979614327406547,-0.363040910619785,-0.0791725585633232,0.253025075159636,2.09723429411713 predictedValues: Include Exclude Both Lung 87.3756995369607 72.413914882027 95.5552598269787 cerebhem 84.1907103955257 95.363672836895 159.600894510847 cortex 133.708409753156 200.170190047636 334.894470875837 heart 85.5595226886943 93.9558445956014 115.809082776580 kidney 74.1096238433648 66.8636159157295 76.2027228163495 liver 69.2837943663148 63.4245561325471 63.9737961339514 stomach 74.4223465266055 80.0453307763208 83.0521985902876 testicle 69.4944280749629 63.555411086143 77.12483243395 diffExp=14.9617846549336,-11.1729624413693,-66.4617802944805,-8.39632190690709,7.24600792763535,5.8592382337677,-5.62298424971526,5.93901698881993 diffExpScore=2.14261516791587 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,-1,0,0,0,0,0 diffExp1.4Score=0.5 diffExp1.3=0,0,-1,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=1,0,-1,0,0,0,0,0 diffExp1.2Score=2 cont.predictedValues: Include Exclude Both Lung 92.5190388951751 105.293721768416 80.6321514874078 cerebhem 83.727520760518 116.731518741897 84.6120017367956 cortex 103.353842801355 103.115155212649 78.2093077985404 heart 98.9315607244955 125.768872073696 108.749360281310 kidney 91.1247286937234 108.611387196177 111.344337630673 liver 97.4866631761572 106.125380363137 105.222035537889 stomach 88.891556659214 103.679369054449 111.399568922819 testicle 83.0923818500914 95.1513836022438 93.5816519477559 cont.diffExp=-12.7746828732411,-33.0039979813788,0.238687588705176,-26.8373113492006,-17.4866585024537,-8.63871718697933,-14.7878123952346,-12.0590017521524 cont.diffExpScore=0.995863657192641 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,-1,0,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,-1,0,-1,0,0,0,0 cont.diffExp1.2Score=0.666666666666667 tran.correlation=0.971667626167424 cont.tran.correlation=0.235190305230352 tran.covariance=0.077591966842301 cont.tran.covariance=0.00163951844541059 tran.mean=88.3710669661553 cont.tran.mean=100.225255098337 weightedLogRatios: wLogRatio Lung 0.821952586219421 cerebhem -0.560184765461488 cortex -2.05684032220989 heart -0.420884118685977 kidney 0.437706234603734 liver 0.370584267222853 stomach -0.316561697744285 testicle 0.374899053774943 cont.weightedLogRatios: wLogRatio Lung -0.593937156202654 cerebhem -1.52653457494268 cortex 0.0107211778092565 heart -1.13154772751966 kidney -0.807516879963124 liver -0.392448203657563 stomach -0.702391057290761 testicle -0.608157156040145 varWeightedLogRatios=0.81989180288756 cont.varWeightedLogRatios=0.213924961683077 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.95931495751384 0.122601079595645 32.2942911316294 1.58323231020051e-147 *** df.mm.trans1 0.198582767611826 0.10646289811147 1.86527674085956 0.062505325822764 . df.mm.trans2 0.321274142335921 0.094630222640303 3.395047938935 0.000719676422237539 *** df.mm.exp2 -0.274804949346648 0.122990359007541 -2.23436171391125 0.0257323789047569 * df.mm.exp3 0.188102902326350 0.122990359007541 1.52941176726558 0.126554259009470 df.mm.exp4 0.0471832769722969 0.122990359007541 0.383633947839798 0.701350820943827 df.mm.exp5 -0.0181077978493226 0.122990359007541 -0.147229408836935 0.882987722486652 df.mm.exp6 0.0366776093441456 0.122990359007541 0.298215320616284 0.765615592669316 df.mm.exp7 0.0799690883590454 0.122990359007541 0.650206154403879 0.515744117415845 df.mm.exp8 -0.145177555895937 0.122990359007541 -1.18039785449391 0.238189819006796 df.mm.trans1:exp2 0.237672329996855 0.114398789352727 2.07757731827072 0.0380639957692196 * df.mm.trans2:exp2 0.550104191981749 0.0874545325462225 6.29017360181992 5.19365984688823e-10 *** df.mm.trans1:exp3 0.237341273309209 0.114398789352727 2.07468343548124 0.0383323123028406 * df.mm.trans2:exp3 0.82866657717917 0.0874545325462225 9.47539885072501 2.86747435634085e-20 *** df.mm.trans1:exp4 -0.0681881781145635 0.114398789352727 -0.596056815814006 0.551304413224412 df.mm.trans2:exp4 0.213243181163612 0.0874545325462225 2.43833195324561 0.0149699440891515 * df.mm.trans1:exp5 -0.146564008520573 0.114398789352727 -1.28116747869307 0.200502872799904 df.mm.trans2:exp5 -0.0616357161504711 0.0874545325462225 -0.704774405121824 0.481153983546777 df.mm.trans1:exp6 -0.268683784690207 0.114398789352727 -2.34865933643557 0.0190815081519422 * df.mm.trans2:exp6 -0.16922497761544 0.0874545325462225 -1.93500522715617 0.0533387785852272 . df.mm.trans1:exp7 -0.240430041744996 0.114398789352727 -2.10168344530007 0.0358904590443414 * df.mm.trans2:exp7 0.0202255451142106 0.0874545325462225 0.231269261013096 0.817164248648916 df.mm.trans1:exp8 -0.0837930729978955 0.114398789352727 -0.73246468316667 0.464097668357693 df.mm.trans2:exp8 0.0146912213889934 0.0874545325462225 0.167986963754321 0.866635624031875 df.mm.trans1:probe2 0.551189619397093 0.0748915873743111 7.35983357706421 4.54012137462769e-13 *** df.mm.trans1:probe3 0.437718425718368 0.0748915873743111 5.84469419149355 7.36265575204713e-09 *** df.mm.trans1:probe4 0.0928258812089856 0.0748915873743111 1.23947007218632 0.215531791837554 df.mm.trans1:probe5 0.344044120394031 0.0748915873743111 4.59389542211844 5.04592914811266e-06 *** df.mm.trans1:probe6 0.465968663729018 0.0748915873743111 6.22190929670229 7.88261246664194e-10 *** df.mm.trans1:probe7 1.06978077894426 0.0748915873743111 14.2843918315879 1.96143384684948e-41 *** df.mm.trans1:probe8 -0.166114562438005 0.0748915873743111 -2.21806705214777 0.0268279562089339 * df.mm.trans1:probe9 0.603843331092957 0.0748915873743111 8.0628993490941 2.68350532331337e-15 *** df.mm.trans1:probe10 0.549669998069099 0.0748915873743111 7.33954262875784 5.23425041636767e-13 *** df.mm.trans1:probe11 0.622102827630795 0.0748915873743111 8.30671173414312 4.13902113014158e-16 *** df.mm.trans1:probe12 0.818080230134305 0.0748915873743111 10.9235263774756 5.30361451601939e-26 *** df.mm.trans1:probe13 0.772751579769846 0.0748915873743111 10.3182694727460 1.56708208130212e-23 *** df.mm.trans1:probe14 0.78627233346631 0.0748915873743111 10.4988071562229 2.94498361821342e-24 *** df.mm.trans1:probe15 0.752982720092384 0.0748915873743111 10.0543031132315 1.73600021656846e-22 *** df.mm.trans1:probe16 0.836409098087859 0.0748915873743111 11.1682650536896 4.97185754423198e-27 *** df.mm.trans1:probe17 0.115190862469697 0.0748915873743111 1.53810149455063 0.124415778399575 df.mm.trans1:probe18 0.196723682350106 0.0748915873743111 2.6267794454252 0.00878320551913002 ** df.mm.trans1:probe19 0.0688922539516771 0.0748915873743111 0.91989309302994 0.35790351045281 df.mm.trans1:probe20 0.107427312962895 0.0748915873743111 1.43443765487262 0.151834793403388 df.mm.trans1:probe21 0.183691084193889 0.0748915873743111 2.45275992449985 0.0143871390864579 * df.mm.trans1:probe22 0.160134205026398 0.0748915873743111 2.1382135249189 0.0327991514545983 * df.mm.trans2:probe2 0.228446641620157 0.0748915873743111 3.05036452864021 0.00236040543797864 ** df.mm.trans2:probe3 0.00659648922743919 0.0748915873743111 0.0880805102243283 0.92983453870784 df.mm.trans2:probe4 -0.0793141412095487 0.0748915873743111 -1.05905274531215 0.289892639910711 df.mm.trans2:probe5 -0.0512178536878542 0.0748915873743111 -0.683893284727233 0.494238878524278 df.mm.trans2:probe6 -0.0791798778574736 0.0748915873743111 -1.05725997583319 0.290709332782112 df.mm.trans3:probe2 -0.586078436474952 0.0748915873743111 -7.82569120274763 1.58264564474656e-14 *** df.mm.trans3:probe3 -0.0380101056223477 0.0748915873743111 -0.507535051064837 0.611918122503307 df.mm.trans3:probe4 0.201621153944131 0.0748915873743111 2.69217359403026 0.00724589368568171 ** df.mm.trans3:probe5 -0.121005338793876 0.0748915873743111 -1.61574007223384 0.106541369969591 df.mm.trans3:probe6 0.812753077646876 0.0748915873743111 10.852394856911 1.04777890843711e-25 *** df.mm.trans3:probe7 0.325843997522983 0.0748915873743111 4.35087583194627 1.52987325526661e-05 *** df.mm.trans3:probe8 0.0491384486653155 0.0748915873743111 0.656127749298724 0.511928958136452 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.97450118143456 0.242887547975907 20.4806760284311 2.14993752481968e-75 *** df.mm.trans1 -0.474886614082581 0.210915861083676 -2.2515452922442 0.024619241155541 * df.mm.trans2 -0.258668171062209 0.187473901676260 -1.37975562864686 0.168044056401796 df.mm.exp2 -0.0449025813612532 0.243658757512924 -0.184284701356862 0.853836412315238 df.mm.exp3 0.120345404845842 0.243658757512924 0.493909622105246 0.621504409207805 df.mm.exp4 -0.0544423808309515 0.243658757512924 -0.223436996013015 0.823251960279132 df.mm.exp5 -0.306892843373885 0.243658757512924 -1.25951903599282 0.208207069842970 df.mm.exp6 -0.206006704678362 0.243658757512924 -0.84547219554559 0.398097852566705 df.mm.exp7 -0.378673920939813 0.243658757512924 -1.55411578391443 0.120548863516149 df.mm.exp8 -0.357682952448324 0.243658757512924 -1.46796674209156 0.142502893889577 df.mm.trans1:exp2 -0.054944142024225 0.226637820229138 -0.242431479303299 0.808507442653065 df.mm.trans2:exp2 0.14802537263569 0.173257992829988 0.854363889468244 0.393156943290058 df.mm.trans1:exp3 -0.00960138623919055 0.226637820229138 -0.0423644483938438 0.96621864820008 df.mm.trans2:exp3 -0.141252824382863 0.173257992829988 -0.815274505237223 0.415155741564768 df.mm.trans1:exp4 0.121456236857396 0.226637820229138 0.535904540268698 0.592172195099933 df.mm.trans2:exp4 0.232134459653058 0.173257992829988 1.33981962887474 0.180681139188827 df.mm.trans1:exp5 0.291707607167394 0.226637820229138 1.28710912800198 0.198425297325296 df.mm.trans2:exp5 0.337915304823424 0.173257992829988 1.95035911073383 0.0514791937694964 . df.mm.trans1:exp6 0.258307836156070 0.226637820229138 1.13973844213165 0.254733454535654 df.mm.trans2:exp6 0.213874138378170 0.173257992829988 1.23442581138543 0.217403605184131 df.mm.trans1:exp7 0.338676634004394 0.226637820229138 1.49435179733895 0.135474587612023 df.mm.trans2:exp7 0.363223272986253 0.173257992829988 2.0964301101114 0.0363548706466454 * df.mm.trans1:exp8 0.250221526386974 0.226637820229138 1.10405900539457 0.269896631142538 df.mm.trans2:exp8 0.25639829232878 0.173257992829988 1.4798641502235 0.139299764682583 df.mm.trans1:probe2 0.152024271471206 0.148369280934260 1.02463441565486 0.305842754845959 df.mm.trans1:probe3 0.084691300599783 0.148369280934260 0.570814255258866 0.568284467159846 df.mm.trans1:probe4 0.133741047574124 0.148369280934260 0.90140658990848 0.367641115055949 df.mm.trans1:probe5 0.0181587139486561 0.148369280934260 0.122388636207666 0.902621708653344 df.mm.trans1:probe6 0.0407107243777394 0.148369280934260 0.274387825575415 0.78385682042648 df.mm.trans1:probe7 0.0399374773055275 0.148369280934260 0.269176186971097 0.787862908907276 df.mm.trans1:probe8 0.173601551855176 0.148369280934260 1.17006398333963 0.242320651457128 df.mm.trans1:probe9 -0.122873372822669 0.148369280934260 -0.828159117904684 0.407825238590709 df.mm.trans1:probe10 0.0172092079437712 0.148369280934260 0.115989023033659 0.907690097539674 df.mm.trans1:probe11 -0.0877226502303036 0.148369280934260 -0.591245368838663 0.554521594028543 df.mm.trans1:probe12 0.0803501524516955 0.148369280934260 0.541555178711808 0.588274451500208 df.mm.trans1:probe13 0.124550579587730 0.148369280934260 0.839463390288432 0.401457932555602 df.mm.trans1:probe14 0.192818336867310 0.148369280934260 1.29958395466475 0.194114712277218 df.mm.trans1:probe15 0.0930106576690647 0.148369280934260 0.626886219865664 0.5309111220067 df.mm.trans1:probe16 -0.138259474508307 0.148369280934260 -0.931860514775751 0.351687310130646 df.mm.trans1:probe17 -0.0811662036219174 0.148369280934260 -0.547055314353656 0.584491969975704 df.mm.trans1:probe18 -0.0947296636695926 0.148369280934260 -0.638472216574035 0.523347479590645 df.mm.trans1:probe19 -0.0991640188679667 0.148369280934260 -0.66835950301535 0.504095255323032 df.mm.trans1:probe20 -0.0457385335297726 0.148369280934260 -0.308274955851802 0.757952675273462 df.mm.trans1:probe21 0.205274857304483 0.148369280934260 1.38354015070975 0.166881885946364 df.mm.trans1:probe22 0.147571494722617 0.148369280934260 0.99462296907676 0.320217803118848 df.mm.trans2:probe2 -0.142707263429004 0.148369280934260 -0.961838343694851 0.336418917155664 df.mm.trans2:probe3 -0.207682740234664 0.148369280934260 -1.39976913635300 0.161966755390410 df.mm.trans2:probe4 -0.363331375274233 0.148369280934260 -2.44883154374267 0.0145438022730492 * df.mm.trans2:probe5 -0.026557284436462 0.148369280934260 -0.178994494475101 0.857986952565698 df.mm.trans2:probe6 -0.0868303514154552 0.148369280934260 -0.585231328673275 0.558555782653262 df.mm.trans3:probe2 0.203771972403398 0.148369280934260 1.37341079716957 0.170006103211627 df.mm.trans3:probe3 0.171073112390851 0.148369280934260 1.15302245393135 0.249242570952077 df.mm.trans3:probe4 0.367822598568343 0.148369280934260 2.47910211771748 0.0133746087731105 * df.mm.trans3:probe5 -0.0334323879571715 0.148369280934260 -0.225332277319487 0.821777840062059 df.mm.trans3:probe6 0.226592187186672 0.148369280934260 1.5272176676995 0.127098719286854 df.mm.trans3:probe7 0.0585478569213342 0.148369280934260 0.394609022519128 0.693235717953591 df.mm.trans3:probe8 0.197233264578667 0.148369280934260 1.32934030101594 0.184111392579903