fitVsDatCorrelation=0.691242248959085 cont.fitVsDatCorrelation=0.278918676391307 fstatistic=16885.2903229521,58,830 cont.fstatistic=9554.83065401451,58,830 residuals=-0.323110242484661,-0.0750428937304737,-0.00516718502031335,0.0620141263788236,0.615389282598935 cont.residuals=-0.363526513889331,-0.107533914462620,-0.0205766871889986,0.0881828507075087,0.682989399830442 predictedValues: Include Exclude Both Lung 42.729788733628 44.1153272072772 46.6082469971746 cerebhem 44.3642051104967 42.5004814872710 50.6328847035718 cortex 43.0370400428251 43.7956214901126 49.2386887821656 heart 44.1962855656658 45.4824219238782 49.5581919042974 kidney 43.5089300755021 45.9760887846605 49.5793374380593 liver 44.9617914367393 48.4837954616754 51.3357037667079 stomach 44.5207557064339 45.1626228155988 48.8163591955208 testicle 45.3540527620580 42.153191063061 49.9357084195499 diffExp=-1.38553847364922,1.86372362322569,-0.758581447287519,-1.28613635821242,-2.46715870915839,-3.52200402493618,-0.641867109164899,3.20086169899704 diffExpScore=2.52236553875799 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 44.7643954282062 44.0563416373045 45.9947168749826 cerebhem 46.2823993708991 45.4619225189653 44.6190420944279 cortex 45.5807308011762 45.0421633901783 46.7526659558873 heart 47.7947982266106 44.0861397154262 44.5490687665612 kidney 44.7539042370117 49.1442914880072 45.9561316288569 liver 44.8082377612004 43.6972944927687 46.5893946402843 stomach 44.8668440748193 47.3413661904872 51.0213524864828 testicle 44.6038347492802 46.2558683569325 45.6022296787956 cont.diffExp=0.708053790901722,0.820476851933819,0.538567410997835,3.70865851118433,-4.39038725099547,1.11094326843175,-2.47452211566794,-1.65203360765227 cont.diffExpScore=5.85635699165525 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=0.0581123432460876 cont.tran.correlation=-0.376572891440988 tran.covariance=4.18883459692355e-05 cont.tran.covariance=-0.00036664858545936 tran.mean=44.3963999791802 cont.tran.mean=45.5337832774546 weightedLogRatios: wLogRatio Lung -0.120331547305800 cerebhem 0.161840965741886 cortex -0.0658860328159886 heart -0.109089228744253 kidney -0.209619930574133 liver -0.289865226881023 stomach -0.0544389528920558 testicle 0.276501653031142 cont.weightedLogRatios: wLogRatio Lung 0.0604817614422498 cerebhem 0.0684310283722377 cortex 0.0453279198318052 heart 0.309074157437846 kidney -0.360100733131382 liver 0.0951469872701875 stomach -0.205644091172513 testicle -0.138782356294199 varWeightedLogRatios=0.0347412982796167 cont.varWeightedLogRatios=0.0434587525363435 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.85639969138109 0.0539801815324113 71.4410285757486 0 *** df.mm.trans1 -0.170470007728961 0.0467332514739838 -3.64772410119722 0.000281083140559938 *** df.mm.trans2 -0.0447338652741399 0.041402895887257 -1.08045257017657 0.280254539841022 df.mm.exp2 -0.082578986020948 0.0535119108687762 -1.54318888412434 0.123166115338574 df.mm.exp3 -0.055010767741918 0.0535119108687761 -1.02800978041725 0.304244713295328 df.mm.exp4 0.00289301185831459 0.0535119108687761 0.0540629518054202 0.95689801898357 df.mm.exp5 -0.00241258815070974 0.0535119108687761 -0.0450850681939948 0.964050347676716 df.mm.exp6 0.048730104329277 0.0535119108687761 0.910640332930265 0.362749293172936 df.mm.exp7 0.0182337470328694 0.0535119108687761 0.340741841149774 0.733384165007916 df.mm.exp8 -0.0548525022167037 0.0535119108687761 -1.02505220475522 0.305637204266744 df.mm.trans1:exp2 0.120115633680016 0.049606869053327 2.42135083250048 0.0156763967433095 * df.mm.trans2:exp2 0.0452871132095326 0.0372427137902635 1.21599928148555 0.224330915945299 df.mm.trans1:exp3 0.062175603564146 0.0496068690533269 1.25336681694843 0.210425187283683 df.mm.trans2:exp3 0.04773733635835 0.0372427137902635 1.28178995298753 0.200274275417807 df.mm.trans1:exp4 0.0308514312909577 0.0496068690533269 0.62191853426171 0.534166227725064 df.mm.trans2:exp4 0.0276256303202013 0.0372427137902635 0.741772752538338 0.45843486988802 df.mm.trans1:exp5 0.0204824888412402 0.049606869053327 0.41289622248124 0.679789287018018 df.mm.trans2:exp5 0.0437267626732625 0.0372427137902635 1.17410248134748 0.240690749002532 df.mm.trans1:exp6 0.00218664015837880 0.049606869053327 0.0440793825554315 0.964851729272025 df.mm.trans2:exp6 0.0456922458034457 0.0372427137902635 1.22687745207738 0.220216553838836 df.mm.trans1:exp7 0.0228254483412681 0.0496068690533269 0.460126768265316 0.64554574288953 df.mm.trans2:exp7 0.00522879106273564 0.0372427137902635 0.140397692074271 0.888379845009098 df.mm.trans1:exp8 0.114455735665516 0.049606869053327 2.30725578634033 0.0212858399896227 * df.mm.trans2:exp8 0.00935561331772503 0.0372427137902635 0.251206541242193 0.80171660558812 df.mm.trans1:probe2 0.0811387300705397 0.0332772994062509 2.43826066171999 0.0149669090779264 * df.mm.trans1:probe3 0.0170636654146687 0.0332772994062509 0.512771941206965 0.608247295533269 df.mm.trans1:probe4 0.268932975149783 0.0332772994062509 8.0815745252232 2.25125240492730e-15 *** df.mm.trans1:probe5 0.180889201247790 0.0332772994062509 5.43581373715112 7.17518961050833e-08 *** df.mm.trans1:probe6 0.369547615248723 0.0332772994062509 11.1050963221885 8.23513649455572e-27 *** df.mm.trans1:probe7 0.0400174020655907 0.0332772994062509 1.20254355911086 0.229495940904209 df.mm.trans1:probe8 0.123857617137781 0.0332772994062509 3.72198523761563 0.000210991747407641 *** df.mm.trans1:probe9 0.225425832376028 0.0332772994062509 6.77416245903907 2.36945502640161e-11 *** df.mm.trans1:probe10 0.122732748562275 0.0332772994062509 3.6881823571062 0.000240566338999231 *** df.mm.trans1:probe11 0.0318801814461357 0.0332772994062509 0.958015885151643 0.338333715706337 df.mm.trans1:probe12 0.0706703604435766 0.0332772994062509 2.12368075849032 0.0339920558023143 * df.mm.trans1:probe13 0.0726138645752688 0.0332772994062509 2.18208405943028 0.0293830891211511 * df.mm.trans1:probe14 0.0977532288008603 0.0332772994062509 2.93753491253855 0.00339987740664348 ** df.mm.trans1:probe15 0.037557241293821 0.0332772994062509 1.12861445982501 0.259386614424496 df.mm.trans1:probe16 0.0609205263284237 0.0332772994062509 1.83069321776094 0.0675047452819166 . df.mm.trans1:probe17 0.0779614957077156 0.0332772994062509 2.34278313140612 0.0193758465041129 * df.mm.trans1:probe18 0.0505833847304302 0.0332772994062509 1.52005678444353 0.128877535456229 df.mm.trans1:probe19 0.0224529793446784 0.0332772994062509 0.674723602735047 0.500039279934835 df.mm.trans1:probe20 0.0257167745637187 0.0332772994062509 0.772802331396159 0.439859384003692 df.mm.trans1:probe21 0.077299387138935 0.0332772994062509 2.32288642762925 0.0204261606199504 * df.mm.trans1:probe22 0.0829500633799616 0.0332772994062509 2.49269216132305 0.0128717898528758 * df.mm.trans2:probe2 -0.0970535902312925 0.0332772994062509 -2.91651041289311 0.0036351113644873 ** df.mm.trans2:probe3 -0.117863460477541 0.0332772994062509 -3.54185774027688 0.000419469200379182 *** df.mm.trans2:probe4 -0.0529024878143166 0.0332772994062509 -1.58974702750005 0.112272643700099 df.mm.trans2:probe5 -0.0516914058067547 0.0332772994062509 -1.55335338891848 0.120719932135573 df.mm.trans2:probe6 -0.0533672804263222 0.0332772994062509 -1.60371428506898 0.109157479545011 df.mm.trans3:probe2 0.0254027088718997 0.0332772994062509 0.763364495471288 0.445462966283996 df.mm.trans3:probe3 0.185277918319380 0.0332772994062509 5.56769694732431 3.48620708448214e-08 *** df.mm.trans3:probe4 0.214833709813773 0.0332772994062509 6.4558637163152 1.82916538115867e-10 *** df.mm.trans3:probe5 0.368942758178525 0.0332772994062509 11.0869200554544 9.8300295773783e-27 *** df.mm.trans3:probe6 0.168647745936346 0.0332772994062509 5.06795169516271 4.95937022433061e-07 *** df.mm.trans3:probe7 0.377128022474544 0.0332772994062509 11.3328914666586 8.79852990439217e-28 *** df.mm.trans3:probe8 0.265393737701292 0.0332772994062509 7.97521861558993 5.03157421296697e-15 *** df.mm.trans3:probe9 0.199608530258596 0.0332772994062509 5.99833922283671 2.97384959777042e-09 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.70543571684531 0.0717358229793511 51.653909622141 1.68500127418578e-261 *** df.mm.trans1 0.0779845991342427 0.0621051682268666 1.2556861427276 0.209583154480725 df.mm.trans2 0.0764789570575555 0.0550215046686616 1.38998301696964 0.164906697016493 df.mm.exp2 0.0951202870843815 0.0711135245639079 1.33758364063223 0.181398597940706 df.mm.exp3 0.0238569467460653 0.0711135245639079 0.335476927804719 0.737350122065282 df.mm.exp4 0.0981151544261572 0.0711135245639079 1.37969753331497 0.168051357357741 df.mm.exp5 0.109896254626449 0.0711135245639079 1.54536363231003 0.122639507317749 df.mm.exp6 -0.0200505877573139 0.0711135245639079 -0.281951821123631 0.778050738792629 df.mm.exp7 -0.0295165216044331 0.0711135245639079 -0.415061998198491 0.678203783330413 df.mm.exp8 0.0536957180351683 0.0711135245639079 0.755070408399085 0.450420936551978 df.mm.trans1:exp2 -0.0617716199797219 0.0659240016603618 -0.937012596686214 0.349024638725161 df.mm.trans2:exp2 -0.0637144857565966 0.0494929184727705 -1.28734549755134 0.198332750681383 df.mm.trans1:exp3 -0.0057849682209658 0.0659240016603618 -0.0877520792923003 0.930094892634017 df.mm.trans2:exp3 -0.00172723797134241 0.0494929184727705 -0.0348986890375577 0.972168916895856 df.mm.trans1:exp4 -0.0326114230992221 0.0659240016603618 -0.494682092680524 0.620955455423881 df.mm.trans2:exp4 -0.097439020085493 0.0494929184727705 -1.96874670341174 0.0493145441254941 * df.mm.trans1:exp5 -0.110130646729108 0.0659240016603618 -1.67056980698013 0.095183684660374 . df.mm.trans2:exp5 -0.000604866023139773 0.0494929184727705 -0.0122212640071438 0.990252021739446 df.mm.trans1:exp6 0.0210295103352103 0.0659240016603618 0.318996265480873 0.749809606044394 df.mm.trans2:exp6 0.0118674704768763 0.0494929184727705 0.239781181693811 0.810559099070301 df.mm.trans1:exp7 0.0318025257207648 0.0659240016603617 0.482411942840035 0.629640533919916 df.mm.trans2:exp7 0.101431677843702 0.0494929184727705 2.04941799703137 0.0407351033245001 * df.mm.trans1:exp8 -0.0572889603174028 0.0659240016603618 -0.869015212586055 0.385090026622357 df.mm.trans2:exp8 -0.0049766852774131 0.0494929184727705 -0.100553481810759 0.91992920884036 df.mm.trans1:probe2 0.0485371555251192 0.0442231647184134 1.09755047686376 0.272719132178483 df.mm.trans1:probe3 0.00851580822410399 0.0442231647184134 0.192564423607572 0.847347214724935 df.mm.trans1:probe4 -0.0157428443881174 0.0442231647184134 -0.355986381534618 0.721941225327292 df.mm.trans1:probe5 0.0249580540898578 0.0442231647184134 0.56436607937889 0.572657493138486 df.mm.trans1:probe6 0.0810660394219283 0.0442231647184134 1.83311257658985 0.06714384661534 . df.mm.trans1:probe7 -0.0213262520419981 0.0442231647184134 -0.482241652712802 0.629761434391575 df.mm.trans1:probe8 0.0660063715986732 0.0442231647184134 1.49257458209882 0.135928577359591 df.mm.trans1:probe9 -0.0193822567474926 0.0442231647184134 -0.438282897004483 0.661295267651243 df.mm.trans1:probe10 0.0898016610143367 0.0442231647184134 2.03064754831862 0.0426090920991128 * df.mm.trans1:probe11 0.0323479840502282 0.0442231647184134 0.731471486859902 0.464697673825843 df.mm.trans1:probe12 0.0231563371943821 0.0442231647184134 0.523624605833341 0.600679440000712 df.mm.trans1:probe13 0.0549927270391356 0.0442231647184134 1.24352762605970 0.214024582038667 df.mm.trans1:probe14 0.0512333597574841 0.0442231647184134 1.15851861990673 0.246985680427912 df.mm.trans1:probe15 0.0232506517743083 0.0442231647184134 0.525757302136889 0.599197279141028 df.mm.trans1:probe16 0.0406707836669661 0.0442231647184134 0.919671487238267 0.358011654801674 df.mm.trans1:probe17 -0.00340022905450916 0.0442231647184134 -0.0768879630428936 0.938731203099692 df.mm.trans1:probe18 0.0146046447477903 0.0442231647184134 0.330248747252348 0.741295354541573 df.mm.trans1:probe19 0.0547351414938678 0.0442231647184134 1.23770295143707 0.216176231028304 df.mm.trans1:probe20 -0.0176620088882537 0.0442231647184134 -0.399383648834606 0.689713283723993 df.mm.trans1:probe21 0.0105929474674044 0.0442231647184134 0.239533907960997 0.81075074545337 df.mm.trans1:probe22 0.0108195633564253 0.0442231647184134 0.244658278649161 0.806781505591649 df.mm.trans2:probe2 -0.0339275030419967 0.0442231647184134 -0.767188491778612 0.443187625699075 df.mm.trans2:probe3 0.0378417696856319 0.0442231647184134 0.855700172671622 0.392410595743265 df.mm.trans2:probe4 0.00660293238814396 0.0442231647184134 0.149309359250689 0.881345801236517 df.mm.trans2:probe5 0.0106783614349803 0.0442231647184134 0.241465338425545 0.809254117825585 df.mm.trans2:probe6 0.032123929162231 0.0442231647184134 0.72640502702095 0.467795308414016 df.mm.trans3:probe2 -0.0110152815734453 0.0442231647184134 -0.249083973152624 0.803357450249242 df.mm.trans3:probe3 -0.0165740909703586 0.0442231647184134 -0.374783014193862 0.707917586679453 df.mm.trans3:probe4 -0.00661900928021295 0.0442231647184134 -0.149672899313263 0.881059051501633 df.mm.trans3:probe5 -0.0601209592158515 0.0442231647184134 -1.35949020380305 0.174360523849988 df.mm.trans3:probe6 -0.00942626996557644 0.0442231647184134 -0.213152315660747 0.831260502690048 df.mm.trans3:probe7 -0.0448309655854081 0.0442231647184134 -1.01374394778992 0.311000367663118 df.mm.trans3:probe8 -0.0948566739471592 0.0442231647184134 -2.14495444980362 0.0322461383045484 * df.mm.trans3:probe9 -0.0389113526512548 0.0442231647184134 -0.879886206675143 0.379175638765768