fitVsDatCorrelation=0.774716344388063 cont.fitVsDatCorrelation=0.264297749868882 fstatistic=11048.2552317969,56,784 cont.fstatistic=4741.00369523875,56,784 residuals=-0.465735045017317,-0.0879757192666817,-0.00384227033504777,0.0785553989239929,0.834227439577283 cont.residuals=-0.505448332748768,-0.150902577950679,-0.0202584474095614,0.130049897811511,1.04203874380695 predictedValues: Include Exclude Both Lung 55.0221400011913 61.5448949349264 81.1136945248592 cerebhem 59.7980246663267 63.190303848352 69.8746774699532 cortex 51.4894617118423 66.6525680147045 84.088573350905 heart 51.3624271908842 58.3406033390592 77.9387847250087 kidney 54.437472023372 59.3109655747551 77.1336244397464 liver 52.6414284251367 54.2251583694495 74.4141453364406 stomach 53.4384520735949 56.2199007622375 68.317668491894 testicle 53.3105896684823 56.6049938856544 75.8948739979111 diffExp=-6.5227549337351,-3.39227918202536,-15.1631063028622,-6.97817614817497,-4.87349355138311,-1.58372994431283,-2.78144868864256,-3.29440421717202 diffExpScore=0.978065073147713 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,-1,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 60.1284918880616 57.312274001402 58.7298842891951 cerebhem 61.5879149745906 58.0108558700714 67.851281955366 cortex 60.3551190328584 62.0250509760409 61.7242738791348 heart 61.37409382941 60.6612237004089 58.4159364956218 kidney 57.2297319478856 62.8798033546303 61.4352800147066 liver 63.2855093537147 63.2045173872755 60.7311489061627 stomach 58.8262764823624 56.1671408461664 60.5437721277151 testicle 61.2413646157492 55.0717317766158 57.2631456677811 cont.diffExp=2.81621788665957,3.5770591045192,-1.66993194318247,0.712870129001118,-5.65007140674471,0.0809919664391714,2.65913563619598,6.16963283913339 cont.diffExpScore=2.40678026531482 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=0.253005187683404 cont.tran.correlation=0.0342280300611001 tran.covariance=0.000841814371321466 cont.tran.covariance=3.89955231347089e-05 tran.mean=56.7243365306231 cont.tran.mean=59.9600687523277 weightedLogRatios: wLogRatio Lung -0.455267166823052 cerebhem -0.227254993509100 cortex -1.05064618851901 heart -0.509897108303199 kidney -0.346388957739182 liver -0.11792371526854 stomach -0.203158870268809 testicle -0.240215617531775 cont.weightedLogRatios: wLogRatio Lung 0.195353707528279 cerebhem 0.244759990411950 cortex -0.112278749015749 heart 0.0480311233093339 kidney -0.385470166225117 liver 0.00531069589737693 stomach 0.187407306115041 testicle 0.431300064925385 varWeightedLogRatios=0.0878183749227158 cont.varWeightedLogRatios=0.0622218086999834 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.78816293631535 0.0722881597001781 52.4036433079374 2.22020215375378e-258 *** df.mm.trans1 0.0934273715164325 0.0630056114099787 1.48284207431143 0.138518226182444 df.mm.trans2 0.318446360784319 0.0562257090385077 5.66371445073681 2.07955632136281e-08 *** df.mm.exp2 0.258769431148844 0.0735606873743055 3.51776798702443 0.00046024197737261 *** df.mm.exp3 -0.0226507029871829 0.0735606873743055 -0.307918587980660 0.758226067009312 df.mm.exp4 -0.0823692350703979 0.0735606873743055 -1.11974531520173 0.263165227547546 df.mm.exp5 0.00265689585269097 0.0735606873743055 0.0361184206881 0.971197128362833 df.mm.exp6 -0.0846484001036392 0.0735606873743055 -1.15072878088965 0.250194678193696 df.mm.exp7 0.0519821902820542 0.0735606873743055 0.706657212398635 0.479989486511461 df.mm.exp8 -0.0487676919906496 0.0735606873743055 -0.662958622755936 0.507551916228229 df.mm.trans1:exp2 -0.175532452782494 0.0686897401522531 -2.55543917320725 0.0107930357939763 * df.mm.trans2:exp2 -0.232385469321853 0.05357762781915 -4.3373601777642 1.63032143605027e-05 *** df.mm.trans1:exp3 -0.0437077869455787 0.0686897401522531 -0.636307356072376 0.524761829316888 df.mm.trans2:exp3 0.102377371287939 0.05357762781915 1.91082314494235 0.0563917733212005 . df.mm.trans1:exp4 0.0135405020159698 0.0686897401522531 0.197125538485905 0.843780390821875 df.mm.trans2:exp4 0.0289006337951433 0.05357762781915 0.539416076663503 0.589752945628203 df.mm.trans1:exp5 -0.0133398048710671 0.0686897401522531 -0.194203746316393 0.846066657779066 df.mm.trans2:exp5 -0.0396295974351849 0.05357762781915 -0.739666891728646 0.45972355778806 df.mm.trans1:exp6 0.0404161727794307 0.0686897401522531 0.588387329604784 0.55644180029929 df.mm.trans2:exp6 -0.0419735300240017 0.05357762781915 -0.783415237525677 0.433619904633247 df.mm.trans1:exp7 -0.0811872768708774 0.0686897401522531 -1.18194182553207 0.237587149055934 df.mm.trans2:exp7 -0.142478297408276 0.05357762781915 -2.65928715413097 0.0079904243565481 ** df.mm.trans1:exp8 0.0171670341784489 0.0686897401522531 0.249921373124976 0.802713624563202 df.mm.trans2:exp8 -0.0349020029317874 0.05357762781915 -0.651428671862783 0.514960705596311 df.mm.trans1:probe2 0.083692724915091 0.0436515680496098 1.91729022929886 0.055564194631247 . df.mm.trans1:probe3 0.11808646240429 0.0436515680496098 2.70520550991629 0.00697439119415853 ** df.mm.trans1:probe4 -0.0193469230407874 0.0436515680496098 -0.443212555819295 0.657734270379576 df.mm.trans1:probe5 -0.0251107686359930 0.0436515680496098 -0.575254676016559 0.565284123407038 df.mm.trans1:probe6 0.0946691334117674 0.0436515680496098 2.16874530839709 0.0304020074396104 * df.mm.trans1:probe7 0.408571775488123 0.0436515680496098 9.35984189671681 8.2219030660736e-20 *** df.mm.trans1:probe8 -0.0464813224267403 0.0436515680496098 -1.06482595021362 0.287282530735994 df.mm.trans1:probe9 0.0108270542399396 0.0436515680496098 0.248033569553211 0.804173382250505 df.mm.trans1:probe10 0.171368509572209 0.0436515680496098 3.9258271175379 9.40336564344591e-05 *** df.mm.trans1:probe11 0.235644835338784 0.0436515680496098 5.39831318478582 8.92100905550104e-08 *** df.mm.trans1:probe12 0.178260559187319 0.0436515680496098 4.08371490766899 4.88729776720702e-05 *** df.mm.trans1:probe13 0.299222859886176 0.0436515680496098 6.85480208972358 1.44663910947402e-11 *** df.mm.trans1:probe14 0.158304491468606 0.0436515680496098 3.62654764861354 0.000305813165519273 *** df.mm.trans1:probe15 0.225429777243962 0.0436515680496098 5.16429964183102 3.06276853532082e-07 *** df.mm.trans1:probe16 0.125276671437767 0.0436515680496098 2.86992374009085 0.00421619056894366 ** df.mm.trans1:probe17 0.313393155511728 0.0436515680496098 7.17942492135812 1.62703896989422e-12 *** df.mm.trans1:probe18 0.327311277404962 0.0436515680496098 7.49827078452197 1.75186506802261e-13 *** df.mm.trans1:probe19 0.185639701243642 0.0436515680496098 4.25276134485394 2.36626844740676e-05 *** df.mm.trans1:probe20 0.449026918274343 0.0436515680496098 10.2866159988577 2.28836804258145e-23 *** df.mm.trans1:probe21 0.155076209499865 0.0436515680496098 3.55259195554263 0.00040425374988908 *** df.mm.trans1:probe22 0.209351812013866 0.0436515680496098 4.7959746091123 1.93754378002224e-06 *** df.mm.trans2:probe2 0.0435967757352645 0.0436515680496098 0.998744780158114 0.318226492262604 df.mm.trans2:probe3 -0.08201209227657 0.0436515680496098 -1.87878914643716 0.0606440989860584 . df.mm.trans2:probe4 0.027622661164529 0.0436515680496098 0.632798829428899 0.527049540402481 df.mm.trans2:probe5 0.0693519762899329 0.0436515680496098 1.58876254367574 0.112517179639230 df.mm.trans2:probe6 0.112489613442793 0.0436515680496098 2.57698906291176 0.0101482198870805 * df.mm.trans3:probe2 0.239899301680224 0.0436515680496098 5.49577741188083 5.26286777519478e-08 *** df.mm.trans3:probe3 0.329266997384591 0.0436515680496098 7.54307375648867 1.27257557783006e-13 *** df.mm.trans3:probe4 0.165429020690466 0.0436515680496098 3.78976124070633 0.000162351129153408 *** df.mm.trans3:probe5 0.388902026209086 0.0436515680496098 8.90923381645994 3.54340017425934e-18 *** df.mm.trans3:probe6 0.0655607426854844 0.0436515680496098 1.50191036919854 0.13352290182493 df.mm.trans3:probe7 0.181647877275902 0.0436515680496098 4.1613139090321 3.51432541150888e-05 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.05851171452859 0.110258879968704 36.8089329011919 4.907836566841e-173 *** df.mm.trans1 0.0653454361879375 0.0961004979877847 0.67996979782814 0.49672431122398 df.mm.trans2 -0.0372037593903134 0.085759323930013 -0.433815912782555 0.664541471907894 df.mm.exp2 -0.108272362347663 0.112199826821693 -0.96499580627453 0.334844472037374 df.mm.exp3 0.0330569351428197 0.112199826821693 0.294625545147707 0.768357939919395 df.mm.exp4 0.0826538475645846 0.112199826821693 0.736666445091196 0.46154559092922 df.mm.exp5 -0.00173560106980527 0.112199826821693 -0.0154688391147294 0.987662079840992 df.mm.exp6 0.115525519728532 0.112199826821693 1.02964080249539 0.303496194183713 df.mm.exp7 -0.0724959834899347 0.112199826821693 -0.646132757451974 0.518382464265974 df.mm.exp8 0.00375225719344194 0.112199826821693 0.0334426290996419 0.973330127671765 df.mm.trans1:exp2 0.132254224282992 0.104770322635701 1.26232525543380 0.207207135419576 df.mm.trans2:exp2 0.120387718813552 0.0817202880695964 1.47316806704138 0.141107079798218 df.mm.trans1:exp3 -0.0292949726594519 0.104770322635701 -0.279611362478229 0.77984943842682 df.mm.trans2:exp3 0.0459666095574964 0.0817202880695964 0.562487120925826 0.573944969039492 df.mm.trans1:exp4 -0.0621498299289909 0.104770322635701 -0.593200711475266 0.553217917185789 df.mm.trans2:exp4 -0.0258639791449617 0.0817202880695964 -0.316493979107549 0.751711843003381 df.mm.trans1:exp5 -0.0476746501692363 0.104770322635701 -0.455039642619093 0.649206697964036 df.mm.trans2:exp5 0.0944458151339846 0.0817202880695964 1.15572053605037 0.248147566117239 df.mm.trans1:exp6 -0.0643529408269366 0.104770322635701 -0.614228716758845 0.539242394258834 df.mm.trans2:exp6 -0.0176645502233841 0.0817202880695964 -0.216158687648534 0.828920282559159 df.mm.trans1:exp7 0.0506008136457656 0.104770322635701 0.482968958888395 0.629252611417402 df.mm.trans2:exp7 0.0523130802035616 0.0817202880695964 0.640148015129482 0.522263407457654 df.mm.trans1:exp8 0.0145867925468036 0.104770322635701 0.139226378041458 0.889307020415424 df.mm.trans2:exp8 -0.0436305143291869 0.0817202880695964 -0.533900642788108 0.593561618853224 df.mm.trans1:probe2 -0.0928762397762686 0.0665803780590055 -1.39494911990375 0.163426240856172 df.mm.trans1:probe3 0.0261295643427350 0.0665803780590055 0.392451426448469 0.694831363821588 df.mm.trans1:probe4 -0.0195493690658500 0.0665803780590055 -0.293620577650141 0.769125558708152 df.mm.trans1:probe5 -0.0378414385988031 0.0665803780590055 -0.568357220279928 0.569955181540437 df.mm.trans1:probe6 -0.0739631796186968 0.0665803780590055 -1.11088554578570 0.266958189623391 df.mm.trans1:probe7 -0.0394486688193549 0.0665803780590055 -0.592496918302181 0.553688727225844 df.mm.trans1:probe8 -0.0484164976880174 0.0665803780590055 -0.727188686809637 0.467327458910664 df.mm.trans1:probe9 0.0821659963732966 0.0665803780590056 1.23408726067128 0.217540187939748 df.mm.trans1:probe10 -0.0319275920875432 0.0665803780590056 -0.47953455685169 0.631692128280865 df.mm.trans1:probe11 -0.0886640084858475 0.0665803780590055 -1.33168376435578 0.183351158687459 df.mm.trans1:probe12 -0.119630798224012 0.0665803780590055 -1.79678760787438 0.0727541497971944 . df.mm.trans1:probe13 -0.0844813210681317 0.0665803780590055 -1.26886214123419 0.204866822783887 df.mm.trans1:probe14 -0.0230886145367184 0.0665803780590055 -0.346778062994124 0.728851114603283 df.mm.trans1:probe15 -0.0749047394909993 0.0665803780590055 -1.12502724788700 0.260921789789656 df.mm.trans1:probe16 -0.114630522611460 0.0665803780590056 -1.72168626783512 0.0855208217007684 . df.mm.trans1:probe17 -0.0548022878767759 0.0665803780590055 -0.823099680031983 0.410701599948490 df.mm.trans1:probe18 -0.00347284233527594 0.0665803780590055 -0.0521601474265917 0.958414374293962 df.mm.trans1:probe19 -0.0359747842894432 0.0665803780590055 -0.540321117695686 0.589129050547913 df.mm.trans1:probe20 -7.07247849672258e-05 0.0665803780590055 -0.00106224667130228 0.999152720176924 df.mm.trans1:probe21 0.0429909357606032 0.0665803780590055 0.645699784439543 0.518662733510595 df.mm.trans1:probe22 -0.00136992538255382 0.0665803780590055 -0.0205755122228316 0.983589509993607 df.mm.trans2:probe2 0.112754013460947 0.0665803780590055 1.69350215105449 0.0907571521761397 . df.mm.trans2:probe3 0.0222476547976187 0.0665803780590055 0.334147318567374 0.738357756558193 df.mm.trans2:probe4 0.0552222874451237 0.0665803780590055 0.829407838390224 0.407126122519664 df.mm.trans2:probe5 0.100454761073257 0.0665803780590055 1.50877426655989 0.131759347803016 df.mm.trans2:probe6 0.0630103540457938 0.0665803780590055 0.946380238182969 0.34424626705289 df.mm.trans3:probe2 0.0352676348454175 0.0665803780590055 0.529700128980377 0.596469822063796 df.mm.trans3:probe3 -0.0264514290000225 0.0665803780590055 -0.397285653388457 0.691264959429191 df.mm.trans3:probe4 0.0495263762476567 0.0665803780590055 0.74385844135287 0.457184990831363 df.mm.trans3:probe5 -0.0966314802761998 0.0665803780590055 -1.45135072964834 0.147082231402872 df.mm.trans3:probe6 -0.0403307799468784 0.0665803780590055 -0.605745733542337 0.544858928302776 df.mm.trans3:probe7 -0.0173131659403279 0.0665803780590055 -0.260034058758038 0.794905775717782