fitVsDatCorrelation=0.850672975574737 cont.fitVsDatCorrelation=0.262512384400525 fstatistic=8755.89692395776,53,715 cont.fstatistic=2589.34646117610,53,715 residuals=-0.931974668502266,-0.0929831057582796,-0.0108800604927356,0.0773965756841619,0.954870504987258 cont.residuals=-0.687122868075371,-0.217703796255046,-0.0333593409678757,0.157343703937370,1.52969330898684 predictedValues: Include Exclude Both Lung 67.0937682362547 69.6357379895541 67.8396138009748 cerebhem 69.3968940155415 90.5971494498568 66.3947288242114 cortex 64.1267509777951 69.0311850624883 63.8563043198705 heart 66.8749013779778 80.6089775738722 69.3488928956327 kidney 71.3682242737063 64.566821917177 69.7295103398358 liver 71.1485396209433 67.961386844426 71.8441413774276 stomach 66.2634716240894 82.1694277472072 69.9800263463837 testicle 68.2684455569297 71.8115285139085 68.8260978725449 diffExp=-2.5419697532994,-21.2002554343152,-4.90443408469318,-13.7340761958943,6.80140235652927,3.18715277651737,-15.9059561231177,-3.54308295697882 diffExpScore=1.35913460128469 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,-1,0,0,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,-1,0,-1,0,0,-1,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 72.768702491162 71.0033450094937 67.9526522494649 cerebhem 68.043346148827 58.9646736524087 72.4127982967414 cortex 70.756340374719 62.5918367825775 68.1607681026875 heart 72.3437342775315 66.1056717448943 75.1596133574941 kidney 68.283784992123 69.852354367046 65.4283936465484 liver 71.9893950437747 65.7564839403293 71.8775691760697 stomach 67.6556067137238 63.3421082786299 76.8813518799004 testicle 67.1124684468168 71.1990234074613 62.0671199065712 cont.diffExp=1.76535748166823,9.07867249641822,8.16450359214154,6.23806253263723,-1.568569374923,6.23291110344537,4.31349843509386,-4.08655496064449 cont.diffExpScore=1.33111593463497 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=-0.173087299642474 cont.tran.correlation=0.132577093573571 tran.covariance=-0.000813955612840583 cont.tran.covariance=0.000314208999182237 tran.mean=71.307700673858 cont.tran.mean=67.9855547294699 weightedLogRatios: wLogRatio Lung -0.157102268946023 cerebhem -1.16579243269399 cortex -0.309357587941085 heart -0.802474442409136 kidney 0.422418966412423 liver 0.194404608763455 stomach -0.925381978722323 testicle -0.214975370895211 cont.weightedLogRatios: wLogRatio Lung 0.104989726531208 cerebhem 0.59409786124893 cortex 0.514697831902348 heart 0.382009137366423 kidney -0.096183676296107 liver 0.383183230328326 stomach 0.275476177106033 testicle -0.250382346416316 varWeightedLogRatios=0.306897780815827 cont.varWeightedLogRatios=0.0877820759943318 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.14792240861625 0.087757838480389 47.2655489292067 3.45720014728713e-222 *** df.mm.trans1 0.232459263752573 0.0779306589731304 2.98289873094391 0.00295248372573427 ** df.mm.trans2 0.0238695996947047 0.070888068473535 0.336722387965981 0.736424991512091 df.mm.exp2 0.318424412442322 0.0955676504746723 3.3319267645563 0.000906833254560708 *** df.mm.exp3 0.00656187409746596 0.0955676504746723 0.0686620845534442 0.945277796099904 df.mm.exp4 0.121060794850778 0.0955676504746723 1.26675495577724 0.205655407698516 df.mm.exp5 -0.0412929812567492 0.0955676504746723 -0.432081159803054 0.665812698668631 df.mm.exp6 -0.0230123478523527 0.0955676504746723 -0.240796417386567 0.809781935593027 df.mm.exp7 0.121989453080108 0.0955676504746723 1.27647224216774 0.202203073357981 df.mm.exp8 0.0336869063673758 0.0955676504746723 0.352492775537091 0.724572613358335 df.mm.trans1:exp2 -0.284673467631979 0.090745967705991 -3.13703710289691 0.00177665545106942 ** df.mm.trans2:exp2 -0.0552795753318731 0.0764149432721796 -0.723413156703851 0.469662680521846 df.mm.trans1:exp3 -0.0517914321643189 0.090745967705991 -0.570729845893744 0.568362103785665 df.mm.trans2:exp3 -0.0152814265924859 0.0764149432721796 -0.199979558161229 0.84155343107734 df.mm.trans1:exp4 -0.124328231239124 0.090745967705991 -1.37006893399316 0.171095428960336 df.mm.trans2:exp4 0.0252713203326036 0.0764149432721796 0.330711759381808 0.740959106494186 df.mm.trans1:exp5 0.103054546478416 0.090745967705991 1.13563774880118 0.256488722524294 df.mm.trans2:exp5 -0.0342842449195651 0.0764149432721796 -0.448658906903186 0.653813670087739 df.mm.trans1:exp6 0.0816909799402831 0.090745967705991 0.900216086790266 0.368308361574114 df.mm.trans2:exp6 -0.00132586119482581 0.0764149432721796 -0.0173508104311911 0.986161591249163 df.mm.trans1:exp7 -0.134441830497951 0.090745967705991 -1.48151850596305 0.138909007473868 df.mm.trans2:exp7 0.0435159421665816 0.0764149432721796 0.569469010944413 0.569216753253079 df.mm.trans1:exp8 -0.0163304111284015 0.090745967705991 -0.179957429968797 0.85723697733977 df.mm.trans2:exp8 -0.00291979135934463 0.0764149432721796 -0.038209690857778 0.96953116001873 df.mm.trans1:probe2 -0.459545198410947 0.0497036135152066 -9.2457100381716 2.63902671403802e-19 *** df.mm.trans1:probe3 0.529710391720966 0.0497036135152066 10.6573819136692 1.02873552550434e-24 *** df.mm.trans1:probe4 -0.354272414996978 0.0497036135152066 -7.12769937518908 2.49999274986565e-12 *** df.mm.trans1:probe5 -0.475131142647044 0.0497036135152066 -9.5592877266696 1.85915769684308e-20 *** df.mm.trans1:probe6 -0.515063752122709 0.0497036135152066 -10.3627023408495 1.54055070548765e-23 *** df.mm.trans1:probe7 0.030615185832652 0.0497036135152066 0.615954930988778 0.538120295455166 df.mm.trans1:probe8 -0.138088073219049 0.0497036135152066 -2.77823006121682 0.00560893556269585 ** df.mm.trans1:probe9 -0.0839783617644514 0.0497036135152066 -1.68958262438522 0.0915437704691057 . df.mm.trans1:probe10 0.501790029924339 0.0497036135152066 10.0956448522766 1.70718659198700e-22 *** df.mm.trans1:probe11 -0.438138310257613 0.0497036135152066 -8.81501925656907 9.02944787280695e-18 *** df.mm.trans1:probe12 -0.419148654298528 0.0497036135152066 -8.4329614016955 1.85641383630855e-16 *** df.mm.trans1:probe13 -0.440598213844279 0.0497036135152066 -8.86451070020251 6.05684235286652e-18 *** df.mm.trans1:probe14 -0.417093586158972 0.0497036135152066 -8.3916149483048 2.55865742720124e-16 *** df.mm.trans1:probe15 -0.456532443848957 0.0497036135152066 -9.1850956411707 4.37290150156236e-19 *** df.mm.trans1:probe16 -0.438760967206214 0.0497036135152066 -8.82754665457025 8.16276853403897e-18 *** df.mm.trans1:probe17 -0.344999573603780 0.0497036135152066 -6.94113665394226 8.73659477323983e-12 *** df.mm.trans1:probe18 -0.111044844793769 0.0497036135152066 -2.23414027553138 0.0257822091332486 * df.mm.trans1:probe19 -0.00772411918452835 0.0497036135152066 -0.155403574071434 0.876547027560967 df.mm.trans1:probe20 -0.194405486087916 0.0497036135152066 -3.91129482021339 0.000100552626314973 *** df.mm.trans1:probe21 -0.158199680861479 0.0497036135152066 -3.18286075544745 0.00152142918538128 ** df.mm.trans1:probe22 -0.140943926838401 0.0497036135152066 -2.83568772711625 0.0047019274749997 ** df.mm.trans2:probe2 0.246763861447634 0.0497036135152066 4.96470666810045 8.61100770450971e-07 *** df.mm.trans2:probe3 -0.00102502525727973 0.0497036135152066 -0.0206227512405336 0.983552345021372 df.mm.trans2:probe4 0.135850760654337 0.0497036135152066 2.73321698457144 0.0064269242903404 ** df.mm.trans2:probe5 0.0152358998472482 0.0497036135152066 0.306535053886712 0.759286536801935 df.mm.trans2:probe6 0.318033544375046 0.0497036135152066 6.39860005908313 2.83501761820248e-10 *** df.mm.trans3:probe2 -0.333783266846199 0.0497036135152066 -6.71547284472748 3.82373317943333e-11 *** df.mm.trans3:probe3 -0.458125331440392 0.0497036135152066 -9.21714336323314 3.34924128942368e-19 *** df.mm.trans3:probe4 0.0434887047333334 0.0497036135152066 0.874960624744683 0.381889141644928 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.42010549360429 0.161082017342812 27.4400927336134 8.57526469517045e-114 *** df.mm.trans1 -0.0690144594286458 0.143043948866765 -0.482470317517084 0.62961954849761 df.mm.trans2 -0.128228238979248 0.130117072992906 -0.98548358051552 0.324719950272406 df.mm.exp2 -0.316501365900836 0.175417150168450 -1.80427834790900 0.0716083832671282 . df.mm.exp3 -0.157193904155127 0.175417150168451 -0.896114798377332 0.370493049548258 df.mm.exp4 -0.178132403651404 0.175417150168451 -1.01547883704841 0.310221125745766 df.mm.exp5 -0.0421019653678207 0.175417150168450 -0.24001054245489 0.810390876037337 df.mm.exp6 -0.143688913636113 0.175417150168450 -0.819126941112264 0.412986930495662 df.mm.exp7 -0.310484562162129 0.175417150168450 -1.76997837363094 0.0771568655994862 . df.mm.exp8 0.012430800648561 0.175417150168450 0.0708642264261156 0.943525630660373 df.mm.trans1:exp2 0.249360359432526 0.166566813824537 1.49705907021314 0.134819182140238 df.mm.trans2:exp2 0.130712890087799 0.140261809435632 0.931920746023064 0.351692049185276 df.mm.trans1:exp3 0.129150101338154 0.166566813824537 0.775365142507927 0.438380287303255 df.mm.trans2:exp3 0.0311017821824539 0.140261809435632 0.221740916558808 0.824578891528366 df.mm.trans1:exp4 0.172275298113175 0.166566813824537 1.03427143833496 0.301358971318669 df.mm.trans2:exp4 0.106659963606986 0.140261809435632 0.760434818545058 0.447245399945176 df.mm.trans1:exp5 -0.0215116567773922 0.166566813824537 -0.129147315023104 0.89727740338211 df.mm.trans2:exp5 0.0257587678036188 0.140261809435632 0.183647764899539 0.85434185651539 df.mm.trans1:exp6 0.132921778816295 0.166566813824537 0.798008773562278 0.425130353183266 df.mm.trans2:exp6 0.0669202061525724 0.140261809435632 0.477109246072310 0.633430124586103 df.mm.trans1:exp7 0.237628839667563 0.166566813824537 1.42662775502138 0.154123723567333 df.mm.trans2:exp7 0.196307899083623 0.140261809435632 1.39958196656312 0.162072239312829 df.mm.trans1:exp8 -0.093346906982374 0.166566813824537 -0.560417197393872 0.575370455645074 df.mm.trans2:exp8 -0.00967868725335235 0.140261809435632 -0.0690044374323721 0.945005380540176 df.mm.trans1:probe2 -0.0876661615194568 0.0912324012634623 -0.960910381677812 0.336922066676774 df.mm.trans1:probe3 -0.150896291155878 0.0912324012634623 -1.65397697601006 0.0985711051164605 . df.mm.trans1:probe4 -0.0201778884653131 0.0912324012634623 -0.221170200344098 0.825023056627326 df.mm.trans1:probe5 -0.169069416377758 0.0912324012634623 -1.85317293019085 0.0642693689174336 . df.mm.trans1:probe6 -0.0648911700973167 0.0912324012634623 -0.711273288860643 0.47714687278819 df.mm.trans1:probe7 -0.114513119898321 0.0912324012634623 -1.25518037794082 0.209823363539828 df.mm.trans1:probe8 -0.0879669561559283 0.0912324012634623 -0.96420739712743 0.335267946577722 df.mm.trans1:probe9 -0.101728860263971 0.0912324012634623 -1.11505187691154 0.265202832736801 df.mm.trans1:probe10 -0.0403032620834764 0.0912324012634623 -0.441764784498964 0.658792998283252 df.mm.trans1:probe11 -0.0203962258006826 0.0912324012634623 -0.223563399825267 0.82316090479772 df.mm.trans1:probe12 -0.058639682199599 0.0912324012634623 -0.6427506169684 0.520591984675685 df.mm.trans1:probe13 -0.096471591439377 0.0912324012634623 -1.05742685825823 0.29067394452832 df.mm.trans1:probe14 -0.134488697776092 0.0912324012634624 -1.47413304827649 0.140885906537331 df.mm.trans1:probe15 -0.174311096726013 0.0912324012634623 -1.91062708327314 0.0564522119265112 . df.mm.trans1:probe16 -0.00461425999225217 0.0912324012634623 -0.0505769872145208 0.959676726425404 df.mm.trans1:probe17 0.0781362132722113 0.0912324012634623 0.85645244661015 0.392034503925276 df.mm.trans1:probe18 -0.0699719150133748 0.0912324012634623 -0.766963425760425 0.443356427777133 df.mm.trans1:probe19 -0.109329826233566 0.0912324012634623 -1.19836620235219 0.231171539103521 df.mm.trans1:probe20 0.0496039006040323 0.0912324012634623 0.543709251505782 0.586811048502856 df.mm.trans1:probe21 -0.154758864744196 0.0912324012634623 -1.69631471495836 0.0902615735971897 . df.mm.trans1:probe22 -0.126476966351909 0.0912324012634623 -1.38631631526027 0.166082472129532 df.mm.trans2:probe2 0.0523903484929958 0.0912324012634623 0.574251557203917 0.565978195460554 df.mm.trans2:probe3 -0.0245445148698124 0.0912324012634623 -0.269032871325313 0.787981999492404 df.mm.trans2:probe4 -0.154552522036314 0.0912324012634623 -1.69405298880597 0.0906907177858146 . df.mm.trans2:probe5 -0.089229664351419 0.0912324012634623 -0.978047964491697 0.328381400491698 df.mm.trans2:probe6 -0.0755663060727758 0.0912324012634623 -0.828283647325628 0.40778626903621 df.mm.trans3:probe2 0.0614877163292617 0.0912324012634623 0.673967970564499 0.500549498030794 df.mm.trans3:probe3 -0.0781929364597357 0.0912324012634623 -0.857074190494328 0.391691054192392 df.mm.trans3:probe4 0.000498728162007369 0.0912324012634623 0.0054665684022405 0.995639856019886