fitVsDatCorrelation=0.81651328610077 cont.fitVsDatCorrelation=0.25905710378544 fstatistic=10494.4616786493,52,692 cont.fstatistic=3740.94557464084,52,692 residuals=-0.505320888470547,-0.0822114087437868,-0.00583507355078688,0.0735077568783322,0.755716828075533 cont.residuals=-0.552486111459264,-0.170574876959087,-0.0245182631647914,0.123622854836196,1.20969696023976 predictedValues: Include Exclude Both Lung 52.6014544829677 51.2297641936416 69.5685980332321 cerebhem 54.6637931973422 48.0874414079308 58.438337099481 cortex 52.2337515001829 49.6444671959996 63.2820434464864 heart 53.6265037380209 66.9412763822616 80.517266730144 kidney 53.2874536174365 57.9534921599771 73.5339710629104 liver 54.5345385704347 61.0502986519679 86.1864131531206 stomach 55.8134254187095 51.9247625385036 73.8675829137539 testicle 53.7088208031882 50.9132110137471 79.6701227291929 diffExp=1.37169028932610,6.57635178941143,2.58928430418332,-13.3147726442408,-4.66603854254056,-6.51576008153325,3.88866288020594,2.79560978944114 diffExpScore=5.04148766100943 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,-1,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 53.3034237787656 61.4354097696309 58.2777728660294 cerebhem 53.9237056775865 53.4628706484693 56.7372893003699 cortex 57.7154869966695 56.3695768398375 56.7008701173815 heart 55.9973957861829 52.6396478151641 53.3899805861058 kidney 59.1705604374947 49.7454218853053 55.2830802953138 liver 54.5129546302223 52.3717404529174 57.7752576292094 stomach 55.3294077871164 50.1663272629509 70.7041051875577 testicle 57.0356440120209 52.2893800145879 55.8634652254763 cont.diffExp=-8.13198599086529,0.460835029117199,1.34591015683205,3.35774797101877,9.4251385521894,2.14121417730489,5.16308052416554,4.74626399743298 cont.diffExpScore=1.78243859123580 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=0.0370576949603633 cont.tran.correlation=-0.482165445057091 tran.covariance=0.000111477056656229 cont.tran.covariance=-0.00118879033540763 tran.mean=54.2634034295195 cont.tran.mean=54.7168096121826 weightedLogRatios: wLogRatio Lung 0.104358790691923 cerebhem 0.504660993694524 cortex 0.199824393358517 heart -0.907698591522591 kidney -0.337242963048023 liver -0.457692977578826 stomach 0.287856657793946 testicle 0.211512658599714 cont.weightedLogRatios: wLogRatio Lung -0.574615646575118 cerebhem 0.0341876188262277 cortex 0.0954155844578024 heart 0.246995298156622 kidney 0.692924696832418 liver 0.159419498061600 stomach 0.388347283302888 testicle 0.347552862314788 varWeightedLogRatios=0.222969820088005 cont.varWeightedLogRatios=0.133862443598213 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.29271627956040 0.0792438844354468 41.5516768646379 3.32528323339012e-190 *** df.mm.trans1 0.715835428949221 0.0711725967710716 10.0577393747726 2.64822895811263e-22 *** df.mm.trans2 0.545431474407407 0.0654528688560362 8.33319431126976 4.22718075423676e-16 *** df.mm.exp2 0.149499313970964 0.089654220882988 1.66751004580234 0.095865466491752 . df.mm.exp3 0.0562630211538919 0.089654220882988 0.627555742493411 0.530502026193352 df.mm.exp4 0.140636348454037 0.089654220882988 1.56865284276563 0.117185977064012 df.mm.exp5 0.0808430721432693 0.089654220882988 0.901720759458513 0.367518955246340 df.mm.exp6 -0.00273139489061483 0.089654220882988 -0.0304658817366747 0.975704287539329 df.mm.exp7 0.0127849874152663 0.089654220882988 0.142603296190065 0.886645003948147 df.mm.exp8 -0.120946135112387 0.089654220882988 -1.34902890149745 0.177768932796598 df.mm.trans1:exp2 -0.111041510788444 0.0858373813981471 -1.29362649442194 0.196226088104151 df.mm.trans2:exp2 -0.212798959384864 0.0747118507358234 -2.84826245487223 0.00452633270993791 ** df.mm.trans1:exp3 -0.0632779258739183 0.0858373813981472 -0.737183786867993 0.461260458814777 df.mm.trans2:exp3 -0.0876967680497053 0.0747118507358234 -1.17379996862607 0.240878977723494 df.mm.trans1:exp4 -0.121336700993171 0.0858373813981471 -1.4135648014513 0.157939399252756 df.mm.trans2:exp4 0.126858719659712 0.0747118507358234 1.69797319180697 0.0899623937996617 . df.mm.trans1:exp5 -0.0678859316022102 0.0858373813981472 -0.790866758706545 0.429292708697564 df.mm.trans2:exp5 0.0424770623866814 0.0747118507358234 0.568545176813752 0.569849290867606 df.mm.trans1:exp6 0.0388218599429638 0.0858373813981472 0.452272183873981 0.651214650746296 df.mm.trans2:exp6 0.178108792314901 0.0747118507358234 2.38394298308421 0.0173968566184175 * df.mm.trans1:exp7 0.0464856808055211 0.0858373813981471 0.541555206465379 0.588299267206163 df.mm.trans2:exp7 0.000690114128715778 0.0747118507358234 0.00923701021884707 0.992632699191636 df.mm.trans1:exp8 0.141779612776081 0.0858373813981472 1.65172341544825 0.0990446118742076 . df.mm.trans2:exp8 0.114747878351090 0.0747118507358234 1.53587251849552 0.125026721972908 df.mm.trans1:probe2 0.273219035972527 0.0429186906990736 6.3659685680585 3.53350146348638e-10 *** df.mm.trans1:probe3 0.306162539967877 0.0429186906990736 7.13354799461498 2.47222596252919e-12 *** df.mm.trans1:probe4 0.252696546374267 0.0429186906990736 5.88779718715236 6.10329180869817e-09 *** df.mm.trans1:probe5 -0.272015252335290 0.0429186906990736 -6.33792056338664 4.19800880120114e-10 *** df.mm.trans1:probe6 0.0175885697373314 0.0429186906990736 0.409811423667475 0.682071115426994 df.mm.trans1:probe7 -0.237740585959284 0.0429186906990736 -5.53932522374025 4.31668756753745e-08 *** df.mm.trans1:probe8 -0.00900156529789349 0.0429186906990736 -0.209735319304319 0.833935989456328 df.mm.trans1:probe9 -0.113342287639246 0.0429186906990736 -2.64086079498442 0.00845615246137181 ** df.mm.trans1:probe10 -0.241657305903224 0.0429186906990736 -5.63058429712163 2.61187299384649e-08 *** df.mm.trans1:probe11 -0.0224121933194282 0.0429186906990736 -0.522201235740678 0.6016973432118 df.mm.trans1:probe12 0.0997514112956657 0.0429186906990736 2.32419511571491 0.0204036549311401 * df.mm.trans1:probe13 -0.098038296436651 0.0429186906990736 -2.28427975876644 0.0226570716631465 * df.mm.trans1:probe14 0.0680453371050023 0.0429186906990736 1.58544764522537 0.113321501058463 df.mm.trans1:probe15 0.0880108944682462 0.0429186906990736 2.05064257633903 0.0406778974963579 * df.mm.trans1:probe16 0.0411368744346145 0.0429186906990736 0.958483909097964 0.338153551319726 df.mm.trans1:probe17 -0.260696780089102 0.0429186906990736 -6.07420160873475 2.05577941143850e-09 *** df.mm.trans1:probe18 -0.176861283134687 0.0429186906990736 -4.12084526004668 4.23229324991638e-05 *** df.mm.trans1:probe19 -0.179882693580222 0.0429186906990736 -4.1912437367085 3.13401595732901e-05 *** df.mm.trans1:probe20 -0.238223022776182 0.0429186906990736 -5.55056593982547 4.05919197280018e-08 *** df.mm.trans1:probe21 -0.257024486109495 0.0429186906990736 -5.98863762903752 3.39988268529261e-09 *** df.mm.trans1:probe22 -0.184913891295055 0.0429186906990736 -4.30846999950644 1.88202503114482e-05 *** df.mm.trans2:probe2 0.107225081380180 0.0429186906990736 2.49833067210727 0.012708872811144 * df.mm.trans2:probe3 0.217582790780369 0.0429186906990736 5.06965117612653 5.1217858108067e-07 *** df.mm.trans2:probe4 0.188400377889231 0.0429186906990736 4.38970469090516 1.31240194932211e-05 *** df.mm.trans2:probe5 0.183492602140955 0.0429186906990736 4.27535414413085 2.17632673476167e-05 *** df.mm.trans2:probe6 0.186855617500344 0.0429186906990736 4.35371197156248 1.54079292897146e-05 *** df.mm.trans3:probe2 -0.576488189325564 0.0429186906990736 -13.4321010248807 1.01624264476856e-36 *** df.mm.trans3:probe3 -0.358521495888556 0.0429186906990736 -8.35350496599132 3.61618052394175e-16 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.99912124085876 0.132574268145279 30.1651391088683 2.97571580741807e-128 *** df.mm.trans1 -0.0201447032323768 0.119071080325578 -0.169182165621534 0.86570279855988 df.mm.trans2 0.0757714173885406 0.109502029695006 0.691963588251151 0.489192298029591 df.mm.exp2 -0.100640232231890 0.149990662426193 -0.670976650172559 0.502459294710895 df.mm.exp3 0.0208994074616484 0.149990662426193 0.139338056940262 0.889223559542899 df.mm.exp4 -0.0176144906562753 0.149990662426193 -0.117437248234989 0.906547644903214 df.mm.exp5 -0.0538905085021101 0.149990662426193 -0.359292422810843 0.719485947590808 df.mm.exp6 -0.128521274912610 0.149990662426193 -0.856861839488522 0.391817874740392 df.mm.exp7 -0.358621226720974 0.149990662426193 -2.39095701639056 0.0170707781412534 * df.mm.exp8 -0.0512070694656276 0.149990662426193 -0.341401715528961 0.732904823001084 df.mm.trans1:exp2 0.112209856840871 0.143605126117167 0.781377795311566 0.434847566223732 df.mm.trans2:exp2 -0.0383587366385365 0.124992218688494 -0.306888997099365 0.759020187036204 df.mm.trans1:exp3 0.0586255704181927 0.143605126117167 0.40824148833211 0.683222677841775 df.mm.trans2:exp3 -0.106956187496779 0.124992218688494 -0.855702767892576 0.392458392512790 df.mm.trans1:exp4 0.0669191113838145 0.143605126117167 0.465993890282268 0.641366513165735 df.mm.trans2:exp4 -0.136902288058136 0.124992218688494 -1.09528648658781 0.273772202326833 df.mm.trans1:exp5 0.158314071687141 0.143605126117167 1.10242632674527 0.270659628626107 df.mm.trans2:exp5 -0.157177429801215 0.124992218688494 -1.25749771826143 0.208997670941749 df.mm.trans1:exp6 0.150959082864722 0.143605126117167 1.05120956992548 0.293529297551134 df.mm.trans2:exp6 -0.0310979588386007 0.124992218688494 -0.248799158578847 0.80358996352566 df.mm.trans1:exp7 0.395925215178349 0.143605126117167 2.75704096283662 0.00598645692635805 ** df.mm.trans2:exp7 0.155978881428653 0.124992218688494 1.24790873436197 0.212486574617914 df.mm.trans1:exp8 0.118882910296745 0.143605126117167 0.827845868118582 0.408043195219157 df.mm.trans2:exp8 -0.109986014344940 0.124992218688494 -0.879942891637499 0.379195722553578 df.mm.trans1:probe2 0.0182698274178608 0.0718025630585837 0.25444533787679 0.7992271199475 df.mm.trans1:probe3 0.0162802608884894 0.0718025630585837 0.226736486763103 0.820695614607391 df.mm.trans1:probe4 -0.0877258507153549 0.0718025630585836 -1.22176489220558 0.222212580552777 df.mm.trans1:probe5 -0.0520583552569562 0.0718025630585837 -0.725020849387811 0.468684323270606 df.mm.trans1:probe6 0.0123462540850011 0.0718025630585837 0.171947261477669 0.863529262214928 df.mm.trans1:probe7 -0.0579315305250609 0.0718025630585837 -0.806817027935264 0.420049110376504 df.mm.trans1:probe8 0.0239855304000442 0.0718025630585837 0.334048387387988 0.738444252884367 df.mm.trans1:probe9 -0.0233532292449657 0.0718025630585837 -0.325242278968674 0.745096021960242 df.mm.trans1:probe10 -0.00540458955138521 0.0718025630585837 -0.0752701480443757 0.94002152659714 df.mm.trans1:probe11 0.0394633562854675 0.0718025630585837 0.549609298114739 0.582764703727339 df.mm.trans1:probe12 -0.0155066656972944 0.0718025630585837 -0.215962565077831 0.829080569258997 df.mm.trans1:probe13 0.0108432102027413 0.0718025630585836 0.151014249921612 0.880008485159727 df.mm.trans1:probe14 0.0800121012544996 0.0718025630585837 1.11433489065311 0.265522416781748 df.mm.trans1:probe15 -0.0152021120710196 0.0718025630585836 -0.211721022529742 0.832387026283059 df.mm.trans1:probe16 -0.0127045796766098 0.0718025630585836 -0.176937690458824 0.859609106007557 df.mm.trans1:probe17 0.0265712292549291 0.0718025630585837 0.370059620758241 0.711451297277523 df.mm.trans1:probe18 0.0450377331595724 0.0718025630585837 0.627244087691217 0.530706144516376 df.mm.trans1:probe19 0.0475708348956373 0.0718025630585837 0.662522796809137 0.507856820952909 df.mm.trans1:probe20 -0.123632880196692 0.0718025630585837 -1.72184494439035 0.0855444877323146 . df.mm.trans1:probe21 0.0109280017934763 0.0718025630585837 0.152195149141963 0.879077381708905 df.mm.trans1:probe22 -0.0121878596592346 0.0718025630585837 -0.169741289726532 0.865263209764216 df.mm.trans2:probe2 0.0859612155573181 0.0718025630585837 1.19718867816992 0.231642968748559 df.mm.trans2:probe3 0.103049126456407 0.0718025630585837 1.43517337079359 0.151689507721286 df.mm.trans2:probe4 0.0838968221192614 0.0718025630585836 1.16843770675442 0.243032624785987 df.mm.trans2:probe5 0.0372531807173488 0.0718025630585837 0.518828007392354 0.604046556111174 df.mm.trans2:probe6 0.077683108333071 0.0718025630585837 1.08189882121185 0.279674353188217 df.mm.trans3:probe2 0.0399489862655451 0.0718025630585837 0.55637270542767 0.578135956739722 df.mm.trans3:probe3 -0.00853055265494304 0.0718025630585836 -0.118805684526651 0.905463783929012