fitVsDatCorrelation=0.784131911853202 cont.fitVsDatCorrelation=0.247658915457250 fstatistic=3118.13197449764,49,623 cont.fstatistic=1271.88150765585,49,623 residuals=-0.736008311052412,-0.114372885364084,0.00098895749052588,0.111251376655689,1.3507705015906 cont.residuals=-0.65590228439794,-0.236798786923002,-0.0966608753693099,0.0981683098100305,2.34522806790767 predictedValues: Include Exclude Both Lung 62.5782484235646 59.504730043042 69.251650657506 cerebhem 70.394674882971 93.7696006490665 58.9658189519323 cortex 59.5950168124003 56.5529538832782 62.1592082518909 heart 58.5132500649565 55.7149827913916 59.483565768715 kidney 61.2505655857812 54.6223573649346 61.156528506975 liver 60.5548465370894 58.0390130283603 54.7045369725348 stomach 104.431078417299 58.047498818504 52.7586628631302 testicle 64.6223183745836 62.9122661875924 67.6268055205135 diffExp=3.07351838052256,-23.3749257660955,3.04206292912211,2.79826727356491,6.62820822084664,2.5158335087291,46.3835795987948,1.71005218699116 diffExpScore=2.04507557382325 diffExp1.5=0,0,0,0,0,0,1,0 diffExp1.5Score=0.5 diffExp1.4=0,0,0,0,0,0,1,0 diffExp1.4Score=0.5 diffExp1.3=0,-1,0,0,0,0,1,0 diffExp1.3Score=2 diffExp1.2=0,-1,0,0,0,0,1,0 diffExp1.2Score=2 cont.predictedValues: Include Exclude Both Lung 63.3529741062958 71.4430206617353 65.0720010647725 cerebhem 63.285750829778 73.9650821146778 61.7002077937316 cortex 76.316015052622 58.6603014047223 79.2464737108693 heart 63.0159463051094 61.8307568466441 56.7228814304436 kidney 67.056815744075 71.6471129577193 70.8326581660728 liver 68.5557078964771 73.3039859422898 63.1479157440117 stomach 67.9001469812028 72.6381713591723 55.4527068225429 testicle 64.370364898472 65.1016582752778 57.8687534263737 cont.diffExp=-8.09004655543952,-10.6793312848997,17.6557136478997,1.18518945846525,-4.59029721364423,-4.74827804581273,-4.73802437796952,-0.731293376805894 cont.diffExpScore=3.33102116064302 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=0,0,1,0,0,0,0,0 cont.diffExp1.3Score=0.5 cont.diffExp1.2=0,0,1,0,0,0,0,0 cont.diffExp1.2Score=0.5 tran.correlation=0.0925762679125128 cont.tran.correlation=-0.412493461923590 tran.covariance=0.00518563466963985 cont.tran.covariance=-0.00233345587904571 tran.mean=65.068962616551 cont.tran.mean=67.6527382110169 weightedLogRatios: wLogRatio Lung 0.207049870336178 cerebhem -1.26085884671174 cortex 0.212793793900975 heart 0.198209486748349 kidney 0.464728542506812 liver 0.173230046321032 stomach 2.55748013385319 testicle 0.111435656375576 cont.weightedLogRatios: wLogRatio Lung -0.505808030063228 cerebhem -0.658914304071807 cortex 1.10597671198388 heart 0.0784897813123299 kidney -0.280651824156047 liver -0.285360760748418 stomach -0.286791544637548 testicle -0.0471105086860943 varWeightedLogRatios=1.08987842050629 cont.varWeightedLogRatios=0.295311778265953 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.68976037533711 0.149431081714706 24.6920542433173 2.17722342616847e-94 *** df.mm.trans1 0.123516911851066 0.133719218467133 0.923703512980266 0.355998315115412 df.mm.trans2 0.462857788089813 0.123635290634985 3.74373518849349 0.000198088775863489 *** df.mm.exp2 0.73327382642484 0.169547084174545 4.32489788895426 1.77682183396180e-05 *** df.mm.exp3 0.00832385404679247 0.169547084174545 0.0490946458166349 0.96085960506877 df.mm.exp4 0.0190756848533618 0.169547084174545 0.112509660347351 0.910455552180666 df.mm.exp5 0.0172531659403138 0.169547084174545 0.101760322357134 0.918979662490515 df.mm.exp6 0.177991709356484 0.169547084174545 1.04980696201915 0.294213932624573 df.mm.exp7 0.759334380539271 0.169547084174545 4.47860477363063 8.9377848420749e-06 *** df.mm.exp8 0.111569983229608 0.169547084174545 0.658047195401773 0.51075088342246 df.mm.trans1:exp2 -0.615573955441135 0.161271445423652 -3.81700525982174 0.000148569900558906 *** df.mm.trans2:exp2 -0.278488915750732 0.141692345296934 -1.96544785229663 0.0498062323424388 * df.mm.trans1:exp3 -0.0571696424537271 0.161271445423652 -0.354493272529091 0.723089201703747 df.mm.trans2:exp3 -0.0592022238680777 0.141692345296934 -0.417822315976295 0.67622099797743 df.mm.trans1:exp4 -0.0862402078777106 0.161271445423652 -0.534751875331444 0.593012238893709 df.mm.trans2:exp4 -0.0848823891365082 0.141692345296934 -0.599061219282006 0.549349829792449 df.mm.trans1:exp5 -0.0386978309570265 0.161271445423652 -0.239954635833822 0.810444354153276 df.mm.trans2:exp5 -0.102865697367054 0.141692345296934 -0.725979213284147 0.468124151306808 df.mm.trans1:exp6 -0.210859949112318 0.161271445423652 -1.30748471038006 0.191530448619893 df.mm.trans2:exp6 -0.202932092423506 0.141692345296934 -1.43220222657926 0.152587481490947 df.mm.trans1:exp7 -0.247224811853451 0.161271445423652 -1.532973250187 0.125790077991595 df.mm.trans2:exp7 -0.784128565912456 0.141692345296934 -5.53402206921777 4.61395653565708e-08 *** df.mm.trans1:exp8 -0.079427894885303 0.161271445423652 -0.492510590927302 0.622531955874665 df.mm.trans2:exp8 -0.055884633517473 0.141692345296934 -0.394408275199057 0.693414609695427 df.mm.trans1:probe2 0.494514954188776 0.080635722711826 6.13270319354687 1.53501485073567e-09 *** df.mm.trans1:probe3 0.457350163458856 0.080635722711826 5.67180584582001 2.16426542554898e-08 *** df.mm.trans1:probe4 0.380699534088544 0.080635722711826 4.7212267874014 2.89793352664024e-06 *** df.mm.trans1:probe5 0.0486561886489544 0.080635722711826 0.603407361063045 0.546457343574233 df.mm.trans1:probe6 0.0668121548033424 0.080635722711826 0.828567693776542 0.407666385799933 df.mm.trans1:probe7 0.172419204975662 0.080635722711826 2.13824839881264 0.0328849558473555 * df.mm.trans1:probe8 0.129977717990921 0.080635722711826 1.61191235868788 0.107487460262191 df.mm.trans1:probe9 0.0663162790845102 0.080635722711826 0.82241811512634 0.411153626407892 df.mm.trans1:probe10 0.141800368497262 0.080635722711826 1.75853038490180 0.0791482518477462 . df.mm.trans1:probe11 0.140575709315136 0.080635722711826 1.74334283351712 0.0817670582106155 . df.mm.trans1:probe12 0.0688201902783311 0.080635722711826 0.853470248220866 0.393726599739343 df.mm.trans1:probe13 0.0593746899492967 0.080635722711826 0.73633233450996 0.461805633178277 df.mm.trans1:probe14 0.514661263512595 0.080635722711826 6.38254667043636 3.40223029878196e-10 *** df.mm.trans1:probe15 0.863251517498566 0.080635722711826 10.7055717796892 1.1487893316998e-24 *** df.mm.trans1:probe16 0.83793291752513 0.080635722711826 10.3915843914454 1.95617779154463e-23 *** df.mm.trans1:probe17 0.658891542122428 0.080635722711826 8.17121146761663 1.70694920560695e-15 *** df.mm.trans1:probe18 0.84175410858641 0.080635722711826 10.4389727068566 1.27996189594486e-23 *** df.mm.trans1:probe19 1.16528164329020 0.080635722711826 14.4511837198341 4.94028776728138e-41 *** df.mm.trans2:probe2 -0.0828040994099557 0.080635722711826 -1.02689101833785 0.304870316211257 df.mm.trans2:probe3 -0.159893524479976 0.080635722711826 -1.98291178032099 0.0478160219848145 * df.mm.trans2:probe4 -0.112070573735592 0.080635722711826 -1.38983777867418 0.165074559288066 df.mm.trans2:probe5 -0.170762558170531 0.080635722711826 -2.11770357389612 0.0345956594526318 * df.mm.trans2:probe6 -0.0735304618236597 0.080635722711826 -0.911884452086342 0.362182257618342 df.mm.trans3:probe2 0.00993931810621304 0.080635722711826 0.123261970897612 0.901939408115554 df.mm.trans3:probe3 -0.125103600961641 0.080635722711826 -1.55146623300857 0.121297815130473 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.22341681109174 0.233285939291165 18.1040350049580 3.53971123274682e-59 *** df.mm.trans1 -0.099597367300119 0.208757195112478 -0.477096692386847 0.633460582882045 df.mm.trans2 0.0775400687841655 0.193014562796142 0.401731701799422 0.688019262924203 df.mm.exp2 0.0868383127191979 0.264690252736384 0.328075219323182 0.742965018671238 df.mm.exp3 -0.20804460772861 0.264690252736384 -0.785992704974332 0.432170599284387 df.mm.exp4 -0.0125166528426333 0.264690252736384 -0.0472879250869094 0.96229890612239 df.mm.exp5 -0.0251547574007506 0.264690252736384 -0.095034694858232 0.924317827463384 df.mm.exp6 0.134654105673933 0.264690252736384 0.508723325781251 0.611126274188364 df.mm.exp7 0.245870525500301 0.264690252736384 0.92889905449285 0.353301158708024 df.mm.exp8 0.0402980964902127 0.264690252736384 0.152246242820083 0.879042013610831 df.mm.trans1:exp2 -0.0878999671675621 0.251770650354583 -0.349127140291243 0.727111829292564 df.mm.trans2:exp2 -0.05214541236618 0.221204527757294 -0.23573392866259 0.813716612217384 df.mm.trans1:exp3 0.394205566950643 0.251770650354583 1.56573280640718 0.117918954386803 df.mm.trans2:exp3 0.0109075912556315 0.221204527757294 0.0493099818806571 0.960688068772542 df.mm.trans1:exp4 0.00718261027612189 0.251770650354583 0.0285283859179225 0.977249864599641 df.mm.trans2:exp4 -0.131982641067934 0.221204527757295 -0.596654337983284 0.550954930586366 df.mm.trans1:exp5 0.0819731607425934 0.251770650354583 0.32558664255403 0.744846440398421 df.mm.trans2:exp5 0.0280073990081439 0.221204527757294 0.126613136232336 0.899287450943874 df.mm.trans1:exp6 -0.055729289838129 0.251770650354583 -0.221349429568705 0.824892894557811 df.mm.trans2:exp6 -0.108939337957254 0.221204527757294 -0.492482405589737 0.6225518646745 df.mm.trans1:exp7 -0.176554179150099 0.251770650354583 -0.701250042057908 0.483408831339571 df.mm.trans2:exp7 -0.229280183717798 0.221204527757294 -1.03650764314085 0.300367514466990 df.mm.trans1:exp8 -0.0243665945594074 0.251770650354583 -0.0967809175735556 0.922931503321213 df.mm.trans2:exp8 -0.13324829303505 0.221204527757295 -0.602375974786782 0.547143076748831 df.mm.trans1:probe2 -0.0471472529078258 0.125885325177292 -0.374525409069132 0.708140901483926 df.mm.trans1:probe3 0.0694879495094417 0.125885325177292 0.551994042288708 0.581150386498987 df.mm.trans1:probe4 0.0170112623158084 0.125885325177292 0.135133005311385 0.892550362485846 df.mm.trans1:probe5 0.100792762957120 0.125885325177292 0.80067126819721 0.423627205173595 df.mm.trans1:probe6 -0.142934886721892 0.125885325177292 -1.13543724433796 0.256628784624352 df.mm.trans1:probe7 0.138772355737999 0.125885325177292 1.10237119014911 0.270725999182123 df.mm.trans1:probe8 0.116104770351870 0.125885325177292 0.922305838177343 0.356726104377163 df.mm.trans1:probe9 -0.0185994412288967 0.125885325177292 -0.14774908197363 0.882588599112047 df.mm.trans1:probe10 -0.0372041178750683 0.125885325177292 -0.295539752728696 0.767680061954042 df.mm.trans1:probe11 0.112965039339879 0.125885325177292 0.897364638656519 0.369871016078823 df.mm.trans1:probe12 -0.0645458365183228 0.125885325177292 -0.512735193140416 0.608318310703051 df.mm.trans1:probe13 -0.0825417338601922 0.125885325177292 -0.655689880801784 0.512265739881139 df.mm.trans1:probe14 0.188650668452857 0.125885325177292 1.49859142189265 0.134486172126657 df.mm.trans1:probe15 0.0111863152715626 0.125885325177292 0.0888611540368844 0.929220813279659 df.mm.trans1:probe16 0.00542501622329115 0.125885325177292 0.0430949057457713 0.965639688227179 df.mm.trans1:probe17 0.0479245448545719 0.125885325177292 0.38070001238887 0.703555632852408 df.mm.trans1:probe18 0.161807992238446 0.125885325177292 1.28536024362302 0.199144034086352 df.mm.trans1:probe19 -0.0293024081144599 0.125885325177292 -0.232770643227807 0.816015960476978 df.mm.trans2:probe2 0.0300877327205539 0.125885325177292 0.239009055886217 0.811177163292489 df.mm.trans2:probe3 -0.0475941747153365 0.125885325177292 -0.378075638668024 0.705503187676568 df.mm.trans2:probe4 -0.141139548370617 0.125885325177292 -1.12117554744242 0.262645119488085 df.mm.trans2:probe5 -0.0864020984469776 0.125885325177292 -0.686355604398626 0.492744253440741 df.mm.trans2:probe6 -0.0434618665096949 0.125885325177292 -0.345249666301334 0.73002321723681 df.mm.trans3:probe2 -0.0551850419597372 0.125885325177292 -0.438375496762763 0.661266071586043 df.mm.trans3:probe3 -0.0224103991840604 0.125885325177292 -0.178022332249597 0.858763306203135