fitVsDatCorrelation=0.75368189051584 cont.fitVsDatCorrelation=0.235258697738119 fstatistic=6777.81832372527,47,577 cont.fstatistic=3092.64428824900,47,577 residuals=-0.485000659693744,-0.101909866165984,-0.0089189740655837,0.0743100151641528,1.33368614727493 cont.residuals=-0.562544379520797,-0.180359450397310,-0.0404785069965778,0.144079984480290,1.47991642281901 predictedValues: Include Exclude Both Lung 53.7975667161202 51.4865295968648 71.0149351311302 cerebhem 57.905363048022 59.3269736252524 66.5422501722225 cortex 50.9267399701878 50.5835451202451 67.939808549727 heart 49.60229559505 48.7029591260036 80.5749532765014 kidney 52.374702554363 59.7144277236833 87.9482860897999 liver 51.7098431821597 49.7900772028412 69.1837278298433 stomach 54.3285871435892 50.8291606912065 66.9889892392947 testicle 52.1340095556227 50.8285714397067 69.854461283136 diffExp=2.31103711925543,-1.42161057723045,0.343194849942705,0.899336469046453,-7.33972516932026,1.91976597931848,3.49942645238278,1.30543811591598 diffExpScore=7.56478716643492 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 56.8845383106426 61.2626532326277 58.3393517777854 cerebhem 58.668232762805 64.0174918877457 61.5294081880612 cortex 57.1389166637934 53.138260270611 62.1027950468 heart 61.4604697968136 55.4743199562922 61.9252086956608 kidney 61.4049403044028 58.5838148059229 62.2595406016974 liver 54.296471574072 55.1483154837315 59.1747476791904 stomach 57.2440617899888 58.9967500481379 59.5670074468824 testicle 54.6878797886126 51.8582393904974 58.6297380332734 cont.diffExp=-4.37811492198508,-5.34925912494073,4.00065639318246,5.98614984052135,2.82112549847995,-0.85184390965943,-1.75268825814909,2.82964039811514 cont.diffExpScore=6.49597040121627 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=0.626359957541009 cont.tran.correlation=0.338778450825109 tran.covariance=0.00235776126179897 cont.tran.covariance=0.00120011701868724 tran.mean=52.7525845181824 cont.tran.mean=57.5165847541686 weightedLogRatios: wLogRatio Lung 0.174019546089998 cerebhem -0.0987367652350304 cortex 0.0265536401630235 heart 0.0712660368768175 kidney -0.527747276193007 liver 0.148557860209646 stomach 0.263775337297342 testicle 0.0999428630224315 cont.weightedLogRatios: wLogRatio Lung -0.302378012036088 cerebhem -0.359112506643149 cortex 0.291020126047382 heart 0.416777828565823 kidney 0.192547116400563 liver -0.0623027378003024 stomach -0.122515763957421 testicle 0.211189049593257 varWeightedLogRatios=0.0604089105396023 cont.varWeightedLogRatios=0.081509233253833 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.46850806528583 0.0875608133400852 39.6125610644363 1.0089875360974e-166 *** df.mm.trans1 0.424926419246312 0.0742213364141781 5.72512487346078 1.66342179839314e-08 *** df.mm.trans2 0.498696671270238 0.068656545679173 7.26364349293963 1.22704143973896e-12 *** df.mm.exp2 0.280378748097649 0.0904324191590736 3.10042295345935 0.00202671368035390 ** df.mm.exp3 -0.0282659289762944 0.0904324191590736 -0.312564114054868 0.754724775782492 df.mm.exp4 -0.263069183594835 0.0904324191590737 -2.90901411287127 0.00376536058451596 ** df.mm.exp5 -0.092409864152507 0.0904324191590736 -1.02186654975972 0.307272304744006 df.mm.exp6 -0.0469600641905874 0.0904324191590737 -0.519283511679402 0.603762172534813 df.mm.exp7 0.055334267039974 0.0904324191590736 0.61188529019266 0.540854677140959 df.mm.exp8 -0.0277960575436094 0.0904324191590736 -0.307368284538703 0.75867397120731 df.mm.trans1:exp2 -0.206796979582948 0.0797538971842872 -2.59293886423007 0.00975729419427252 ** df.mm.trans2:exp2 -0.138634890540426 0.067404345536481 -2.05676487824354 0.0401578858102909 * df.mm.trans1:exp3 -0.0265741800059811 0.0797538971842872 -0.333202275301684 0.73910263941241 df.mm.trans2:exp3 0.0105720448995391 0.067404345536481 0.156845153163266 0.875421750716954 df.mm.trans1:exp4 0.181878060592993 0.0797538971842872 2.28049119872761 0.0229423939510437 * df.mm.trans2:exp4 0.207488761947698 0.067404345536481 3.07826981029583 0.00218094962830657 ** df.mm.trans1:exp5 0.065605325405705 0.0797538971842872 0.822597110886141 0.41107695497083 df.mm.trans2:exp5 0.240663313536268 0.067404345536481 3.57044210756434 0.000386083390315553 *** df.mm.trans1:exp6 0.00737998013361307 0.0797538971842872 0.0925344139178574 0.926305568721657 df.mm.trans2:exp6 0.0134555631820305 0.067404345536481 0.199624565373875 0.84184454374505 df.mm.trans1:exp7 -0.0455119497223113 0.0797538971842872 -0.57065486865359 0.568455778109453 df.mm.trans2:exp7 -0.0681842600235684 0.067404345536481 -1.01157068555269 0.312167366588919 df.mm.trans1:exp8 -0.00361466990163662 0.0797538971842872 -0.0453227996280132 0.963865695559615 df.mm.trans2:exp8 0.0149344717084448 0.067404345536481 0.221565413766415 0.824730599294549 df.mm.trans1:probe2 0.0107274916545964 0.0522111815449602 0.205463491481394 0.837282484815656 df.mm.trans1:probe3 -0.0442018142151262 0.0522111815449602 -0.846596704138233 0.397570930387432 df.mm.trans1:probe4 0.0844712224535854 0.0522111815449602 1.61787609385636 0.106235794961444 df.mm.trans1:probe5 0.0684073112626098 0.0522111815449602 1.31020423668640 0.190648085892559 df.mm.trans1:probe6 0.123228751469437 0.0522111815449602 2.36019848283498 0.0185969858031312 * df.mm.trans1:probe7 0.244235716471372 0.0522111815449602 4.67784312180438 3.61486735251993e-06 *** df.mm.trans1:probe8 0.367938383968375 0.0522111815449602 7.0471185114157 5.22966348974634e-12 *** df.mm.trans1:probe9 0.118466329066761 0.0522111815449602 2.26898387589154 0.0236377260721836 * df.mm.trans1:probe10 0.150381987061656 0.0522111815449602 2.88026400881502 0.00412098372945646 ** df.mm.trans1:probe11 0.425108170452689 0.0522111815449602 8.14209059962795 2.40704189587034e-15 *** df.mm.trans1:probe12 0.287111516111315 0.0522111815449602 5.49904268808927 5.74285659434942e-08 *** df.mm.trans2:probe2 -0.0320476721743599 0.0522111815449602 -0.613808598580802 0.539583723970798 df.mm.trans2:probe3 -0.155678775049544 0.0522111815449602 -2.98171331203231 0.00298734304871550 ** df.mm.trans2:probe4 -0.0901676834962441 0.0522111815449602 -1.72698032927293 0.0847063820515391 . df.mm.trans2:probe5 -0.0551814527873953 0.0522111815449602 -1.05688956186286 0.29100428204031 df.mm.trans2:probe6 -0.0293077570481527 0.0522111815449602 -0.561331044058353 0.574789821096155 df.mm.trans3:probe2 0.00661199731179118 0.0522111815449602 0.126639488250183 0.899269858369304 df.mm.trans3:probe3 -0.194550941168579 0.0522111815449602 -3.72623134377923 0.000213484755091879 *** df.mm.trans3:probe4 -0.166455138752736 0.0522111815449602 -3.18811284914895 0.00150959693769259 ** df.mm.trans3:probe5 -0.356275164365913 0.0522111815449602 -6.82373303617188 2.24861599715504e-11 *** df.mm.trans3:probe6 -0.239853216374072 0.0522111815449602 -4.59390516124461 5.34562301322074e-06 *** df.mm.trans3:probe7 -0.17968221402961 0.0522111815449602 -3.44145082935695 0.000620472109693622 *** df.mm.trans3:probe8 0.0962771019969085 0.0522111815449602 1.84399393287053 0.0656965299170379 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.01064864238474 0.129485888295739 30.9736350051105 8.28835959351061e-125 *** df.mm.trans1 -0.0073273737862102 0.109759323942769 -0.0667585542894821 0.946797043561434 df.mm.trans2 0.0948529392874206 0.101530050549620 0.934235123236382 0.350573414257786 df.mm.exp2 0.0216223871746839 0.133732450383539 0.161683922732828 0.871611364362168 df.mm.exp3 -0.200325486231231 0.133732450383539 -1.49795719480729 0.134691218925178 df.mm.exp4 -0.0815300626894195 0.133732450383539 -0.609650555684836 0.542333307257524 df.mm.exp5 -0.0332801607800405 0.133732450383539 -0.248856284952488 0.803560503929746 df.mm.exp6 -0.165926573821580 0.133732450383539 -1.24073531402221 0.215207781557976 df.mm.exp7 -0.0522127111315459 0.133732450383539 -0.39042663902293 0.696365188778372 df.mm.exp8 -0.211003231757662 0.133732450383539 -1.57780128272916 0.115159322081518 df.mm.trans1:exp2 0.0092524438889061 0.117940935311376 0.0784498093429452 0.937497450998505 df.mm.trans2:exp2 0.0223635581091696 0.0996782832850704 0.224357376272342 0.822558656809449 df.mm.trans1:exp3 0.204787353751855 0.117940935311376 1.73635517821862 0.0830348053205324 . df.mm.trans2:exp3 0.0580522759442307 0.0996782832850704 0.582396426092199 0.560527189784232 df.mm.trans1:exp4 0.158900693536102 0.117940935311376 1.34729043072778 0.178415545041429 df.mm.trans2:exp4 -0.0177201386079796 0.0996782832850704 -0.177773312540924 0.858963398357522 df.mm.trans1:exp5 0.109746883909394 0.117940935311376 0.93052411039179 0.352488933303177 df.mm.trans2:exp5 -0.0114317901170563 0.0996782832850704 -0.114686867994731 0.908733220199249 df.mm.trans1:exp6 0.119362248659805 0.117940935311376 1.01205106051326 0.311937837691466 df.mm.trans2:exp6 0.0607823633093648 0.0996782832850704 0.609785414697934 0.542244019439857 df.mm.trans1:exp7 0.0585130542231431 0.117940935311376 0.496121673689144 0.619997495532976 df.mm.trans2:exp7 0.0145246581109571 0.0996782832850704 0.145715371816928 0.884196975429535 df.mm.trans1:exp8 0.171621770786747 0.117940935311376 1.45515015913388 0.146171504136343 df.mm.trans2:exp8 0.0443466505356755 0.0996782832850704 0.444897815995168 0.656560443032019 df.mm.trans1:probe2 0.0576084202984132 0.0772104662283246 0.7461219069453 0.455897643542493 df.mm.trans1:probe3 0.087076673603961 0.0772104662283246 1.12778328972215 0.259880189251664 df.mm.trans1:probe4 0.0195019545416551 0.0772104662283246 0.252581748230667 0.800681388011375 df.mm.trans1:probe5 0.121518733618760 0.0772104662283246 1.57386348710042 0.116067141748609 df.mm.trans1:probe6 0.0800742820216434 0.0772104662283246 1.03709103096010 0.30012785386781 df.mm.trans1:probe7 0.0565909508648462 0.0772104662283246 0.732944037632114 0.463890212818236 df.mm.trans1:probe8 0.133846125033564 0.0772104662283246 1.73352307752886 0.083536932567216 . df.mm.trans1:probe9 0.0388921147656772 0.0772104662283246 0.503715579836891 0.614653514481683 df.mm.trans1:probe10 0.0932467403862385 0.0772104662283246 1.20769560062611 0.227659158631237 df.mm.trans1:probe11 0.00195956367471792 0.0772104662283246 0.0253795083806793 0.9797610304989 df.mm.trans1:probe12 0.0637304646337634 0.0772104662283246 0.825412249749943 0.409478709312675 df.mm.trans2:probe2 0.0348786751423431 0.0772104662283246 0.451735067098298 0.65162960417219 df.mm.trans2:probe3 0.0642233643017317 0.0772104662283246 0.831796094998472 0.405868150692563 df.mm.trans2:probe4 9.73289895179906e-05 0.0772104662283246 0.00126056730741882 0.998994648754516 df.mm.trans2:probe5 0.0147157087917532 0.0772104662283246 0.190592150398837 0.848912178477664 df.mm.trans2:probe6 0.0214485400741448 0.0772104662283246 0.277793168748882 0.781270642207702 df.mm.trans3:probe2 -0.0147317541643051 0.0772104662283246 -0.190799963838332 0.848749430526709 df.mm.trans3:probe3 -0.0126184355903934 0.0772104662283246 -0.163429081662045 0.870237831745668 df.mm.trans3:probe4 -0.0656681989116321 0.0772104662283246 -0.8505090322527 0.395394931717034 df.mm.trans3:probe5 0.0169592266847688 0.0772104662283246 0.219649323637257 0.826221957115765 df.mm.trans3:probe6 -0.0920523545799319 0.0772104662283246 -1.19222637909888 0.233662580272157 df.mm.trans3:probe7 0.0152757021490222 0.0772104662283246 0.19784496707803 0.843236042776446 df.mm.trans3:probe8 -0.102343022540204 0.0772104662283246 -1.32550711761743 0.185527578937850