fitVsDatCorrelation=0.895664309580588 cont.fitVsDatCorrelation=0.254709840835778 fstatistic=12166.7407873687,53,715 cont.fstatistic=2562.71891471932,53,715 residuals=-0.588587143691872,-0.0816768964911206,-0.00307273839881228,0.0794194450124185,0.742824410044692 cont.residuals=-0.631545478983802,-0.233349117005977,-0.0362957793797509,0.17972628836818,1.55058461919114 predictedValues: Include Exclude Both Lung 66.7913462164745 69.9901860188792 93.9760949004512 cerebhem 63.9069780875963 64.7395486310708 124.135294990312 cortex 57.4427419199116 64.8006930320923 78.8373546618298 heart 63.7266145461767 67.8610648482391 90.177357430651 kidney 66.4233355313213 66.3887671739584 91.4335719467882 liver 69.8554479751911 69.3192913693698 105.359651446998 stomach 64.2141326902655 68.1465887440414 82.6325969294161 testicle 61.0008617205814 62.4612176751898 80.6475921425727 diffExp=-3.19883980240466,-0.832570543474432,-7.35795111218064,-4.13445030206245,0.0345683573629003,0.536156605821247,-3.93245605377591,-1.46035595460843 diffExpScore=1.00662656221030 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 70.1904145876674 74.2941433946383 64.7694767999368 cerebhem 74.3731177014167 63.2502859928136 74.5041652820372 cortex 68.521065959665 64.2834771459083 70.5837600315119 heart 73.3748656556521 65.906796777476 68.9359537006267 kidney 67.7937320833966 69.4052849087017 57.0137359723836 liver 69.7114353319986 64.0941961640793 74.2935423278257 stomach 68.3219764856975 72.1725104632197 72.6775761968754 testicle 73.768725880459 66.3472620062162 74.0740502348364 cont.diffExp=-4.10372880697088,11.1228317086032,4.23758881375673,7.46806887817618,-1.61155282530507,5.61723916791934,-3.85053397752225,7.42146387424289 cont.diffExpScore=1.66412882143535 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=0.719387286064069 cont.tran.correlation=-0.435443359227877 tran.covariance=0.00165330074766898 cont.tran.covariance=-0.000972039086439684 tran.mean=65.4418010112725 cont.tran.mean=69.1130806586879 weightedLogRatios: wLogRatio Lung -0.197650200181928 cerebhem -0.0538963518618377 cortex -0.49549545014343 heart -0.263134685305377 kidney 0.00218416100073347 liver 0.0326883488989833 stomach -0.249159738725529 testicle -0.097534644448838 cont.weightedLogRatios: wLogRatio Lung -0.243169691384729 cerebhem 0.684930193091842 cortex 0.267817082629117 heart 0.45532624401949 kidney -0.099334639642673 liver 0.353042511150049 stomach -0.233108161406078 testicle 0.450416924786291 varWeightedLogRatios=0.0301332683926141 cont.varWeightedLogRatios=0.123500083222957 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.81416937602301 0.0747148944804833 51.0496521817257 1.21554317740575e-240 *** df.mm.trans1 0.111480452543690 0.066348272277389 1.68023143206521 0.0933490995856965 . df.mm.trans2 0.511740081096442 0.0603523815950528 8.47920276833598 1.2947885190744e-16 *** df.mm.exp2 -0.400459340938659 0.081363978928885 -4.92182592604861 1.06525520446671e-06 *** df.mm.exp3 -0.0521700878814408 0.081363978928885 -0.641193911215174 0.521602174120476 df.mm.exp4 -0.0366017487493479 0.081363978928885 -0.449852001231886 0.652953506726664 df.mm.exp5 -0.0309245290240007 0.081363978928885 -0.380076410115463 0.704001569314777 df.mm.exp6 -0.0791165456380049 0.081363978928885 -0.972378031157444 0.331191352626143 df.mm.exp7 0.062592068355712 0.081363978928885 0.769284752045124 0.441978336348522 df.mm.exp8 -0.0515432275244087 0.081363978928885 -0.63348951468388 0.526616606037341 df.mm.trans1:exp2 0.356314375115666 0.0772589152044531 4.61195156795481 4.72599885392273e-06 *** df.mm.trans2:exp2 0.322476584896816 0.0650578287042572 4.95676832933888 8.95801657369208e-07 *** df.mm.trans1:exp3 -0.0986147776363922 0.0772589152044532 -1.27641939283543 0.202221734358750 df.mm.trans2:exp3 -0.0248686463679859 0.0650578287042573 -0.382254478258641 0.702386206827656 df.mm.trans1:exp4 -0.0103694896521564 0.0772589152044532 -0.134217386106383 0.89326844825863 df.mm.trans2:exp4 0.00570916733181242 0.0650578287042573 0.0877552701269113 0.930095761232088 df.mm.trans1:exp5 0.0253994379979735 0.0772589152044531 0.32875737292917 0.742435350206897 df.mm.trans2:exp5 -0.0219026303613643 0.0650578287042573 -0.336664023340376 0.736468974945325 df.mm.trans1:exp6 0.123971099156394 0.0772589152044532 1.60461868806111 0.109019324864598 df.mm.trans2:exp6 0.069484755343025 0.0650578287042573 1.06804602500480 0.285860214978794 df.mm.trans1:exp7 -0.101942271016650 0.0772589152044532 -1.31948877028465 0.187428113947556 df.mm.trans2:exp7 -0.089285999069242 0.0650578287042573 -1.37240975986337 0.170366270939675 df.mm.trans1:exp8 -0.039142306328162 0.0772589152044531 -0.506638052379822 0.612564997780647 df.mm.trans2:exp8 -0.0622659580208346 0.0650578287042573 -0.957086322445312 0.338847180856817 df.mm.trans1:probe2 0.350360626489676 0.0423164506258579 8.27953718489765 6.06698124780229e-16 *** df.mm.trans1:probe3 0.456572661213521 0.0423164506258578 10.7894838640963 3.00473438835654e-25 *** df.mm.trans1:probe4 0.0129656081731992 0.0423164506258578 0.306396400960823 0.75939204721604 df.mm.trans1:probe5 0.696649565483668 0.0423164506258578 16.4628544024903 7.1265005465382e-52 *** df.mm.trans1:probe6 0.283712533628981 0.0423164506258578 6.70454467312095 4.10283518813530e-11 *** df.mm.trans1:probe7 0.0252832951580178 0.0423164506258579 0.597481470777423 0.550375144090238 df.mm.trans1:probe8 1.17563384911683 0.0423164506258578 27.7819578846824 8.84472384646299e-116 *** df.mm.trans1:probe9 0.492199002449762 0.0423164506258579 11.6313867342409 9.22525468255373e-29 *** df.mm.trans1:probe10 0.315612065677499 0.0423164506258578 7.45837755789097 2.54372014691752e-13 *** df.mm.trans1:probe11 0.341765669278667 0.0423164506258578 8.07642569790171 2.83273418351945e-15 *** df.mm.trans1:probe12 0.496542276221616 0.0423164506258579 11.7340246849106 3.34614282293862e-29 *** df.mm.trans1:probe13 0.52722945338356 0.0423164506258578 12.4592078396431 2.19341363418926e-32 *** df.mm.trans1:probe14 0.577144156563496 0.0423164506258578 13.638765728873 8.17486319064564e-38 *** df.mm.trans1:probe15 0.452630138639746 0.0423164506258578 10.6963162539715 7.16564393050704e-25 *** df.mm.trans1:probe16 0.55800017398654 0.0423164506258578 13.1863652488276 1.07010172675997e-35 *** df.mm.trans1:probe17 0.195819982295010 0.0423164506258578 4.62751434486691 4.39406628573997e-06 *** df.mm.trans1:probe18 0.090855510291167 0.0423164506258578 2.14704940862051 0.0321250007645121 * df.mm.trans1:probe19 0.0168901475359224 0.0423164506258578 0.399139041344870 0.689909940587542 df.mm.trans1:probe20 0.0394617843662654 0.0423164506258578 0.932540035438414 0.351372292144072 df.mm.trans1:probe21 0.0463809699362882 0.0423164506258578 1.09605057253896 0.273425675732381 df.mm.trans1:probe22 0.0223066241946918 0.0423164506258579 0.527138355527889 0.598261025390209 df.mm.trans2:probe2 -0.248420729599190 0.0423164506258578 -5.87054740946042 6.64829904843816e-09 *** df.mm.trans2:probe3 -0.220075354880075 0.0423164506258578 -5.20070449258322 2.59504916808087e-07 *** df.mm.trans2:probe4 -0.0841301361908688 0.0423164506258579 -1.98811892175712 0.0471794482066429 * df.mm.trans2:probe5 -0.224264858846536 0.0423164506258578 -5.29970863646813 1.5466947079451e-07 *** df.mm.trans2:probe6 0.00134683322377759 0.0423164506258579 0.0318276510401512 0.974618377696696 df.mm.trans3:probe2 0.369664114249597 0.0423164506258578 8.73570700713992 1.70602533837342e-17 *** df.mm.trans3:probe3 0.194144493060687 0.0423164506258578 4.58792006865655 5.28627615235484e-06 *** df.mm.trans3:probe4 -0.141206484530150 0.0423164506258579 -3.33691702497999 0.000890954327882727 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.57832382056456 0.162459221987449 28.1813723133444 4.23629786433539e-118 *** df.mm.trans1 -0.246079070149011 0.144266933244638 -1.70572053217301 0.088494430883345 . df.mm.trans2 -0.278785040589998 0.131229536322012 -2.12440772408060 0.0339785887730701 * df.mm.exp2 -0.243070255979548 0.176916916051359 -1.37392320307564 0.169896082891297 df.mm.exp3 -0.2547656536409 0.176916916051359 -1.44002992662918 0.150296627887274 df.mm.exp4 -0.137764272037152 0.176916916051359 -0.778694740514009 0.436417181177654 df.mm.exp5 0.0247311119030700 0.176916916051359 0.139789413330552 0.888865745130121 df.mm.exp6 -0.291715275645438 0.176916916051359 -1.64888288896441 0.0996108978588553 . df.mm.exp7 -0.171151610105909 0.176916916051359 -0.967412353357006 0.333665045541311 df.mm.exp8 -0.197637331350558 0.176916916051359 -1.11711946919301 0.264318487908463 df.mm.trans1:exp2 0.300953053935322 0.167990911892251 1.79148413771545 0.0736384433339879 . df.mm.trans2:exp2 0.082137780345424 0.141461007326286 0.580639017761055 0.561666725705409 df.mm.trans1:exp3 0.230695126515095 0.167990911892251 1.37325956455943 0.170102138238745 df.mm.trans2:exp3 0.110036161934841 0.141461007326286 0.777855071263827 0.436911764655716 df.mm.trans1:exp4 0.182133961882140 0.167990911892251 1.08418937566670 0.278646287247284 df.mm.trans2:exp4 0.0179737211164607 0.141461007326286 0.127057776953359 0.898930392652362 df.mm.trans1:exp5 -0.0594731257512804 0.167990911892251 -0.354025852240306 0.723423882584307 df.mm.trans2:exp5 -0.0928002207245111 0.141461007326286 -0.656012723778103 0.512026979860303 df.mm.trans1:exp6 0.284867887602707 0.167990911892251 1.69573392032910 0.0903716180995512 . df.mm.trans2:exp6 0.144036967153612 0.141461007326286 1.01820968107052 0.308922723421259 df.mm.trans1:exp7 0.144171331620031 0.167990911892251 0.858209113791236 0.391064597487243 df.mm.trans2:exp7 0.142178717140545 0.141461007326286 1.00507355226591 0.3152014480022 df.mm.trans1:exp8 0.247360447210734 0.167990911892251 1.47246326854509 0.141335854013691 df.mm.trans2:exp8 0.0845077005003949 0.141461007326286 0.597392186706788 0.550434705727566 df.mm.trans1:probe2 -0.151077627962899 0.0920124118992489 -1.64192661451289 0.101044936680719 df.mm.trans1:probe3 0.0339125898991676 0.0920124118992489 0.368565383725633 0.712560801281283 df.mm.trans1:probe4 -0.114820875988723 0.0920124118992489 -1.2478846453286 0.212481819505926 df.mm.trans1:probe5 -0.184141700937843 0.0920124118992489 -2.00127023231902 0.0457407499944006 * df.mm.trans1:probe6 -0.186848357209605 0.0920124118992489 -2.03068644058803 0.0426563726786604 * df.mm.trans1:probe7 -0.0750221984982617 0.0920124118992489 -0.81534867905005 0.415144264392992 df.mm.trans1:probe8 -0.164693383035794 0.0920124118992489 -1.78990398834593 0.0738924062802129 . df.mm.trans1:probe9 -0.0294192229906983 0.0920124118992489 -0.319731027406515 0.749265637506162 df.mm.trans1:probe10 -0.00669329655291778 0.0920124118992488 -0.0727434094461817 0.942030648193557 df.mm.trans1:probe11 -0.0784746907952338 0.0920124118992489 -0.852870707064624 0.394016610342779 df.mm.trans1:probe12 -0.0740555194954422 0.0920124118992489 -0.80484271596457 0.421177990195469 df.mm.trans1:probe13 -0.155711404112363 0.0920124118992489 -1.69228695236098 0.0910269510727848 . df.mm.trans1:probe14 -0.0724449379023673 0.0920124118992489 -0.787338755794083 0.431344496576259 df.mm.trans1:probe15 -0.154595482499494 0.0920124118992488 -1.68015900581730 0.0933631930164193 . df.mm.trans1:probe16 -0.127483730819969 0.0920124118992488 -1.38550580501639 0.166329893921630 df.mm.trans1:probe17 -0.0492497589050163 0.0920124118992489 -0.535251254569258 0.592642558179574 df.mm.trans1:probe18 -0.0558970440262553 0.0920124118992489 -0.607494607221698 0.543715603009874 df.mm.trans1:probe19 -0.115223329472734 0.0920124118992489 -1.25225854962807 0.210885122709456 df.mm.trans1:probe20 -0.0887101081194611 0.0920124118992489 -0.964110235655993 0.335316617626737 df.mm.trans1:probe21 -0.130458523163258 0.0920124118992489 -1.41783614265113 0.156674260641536 df.mm.trans1:probe22 -0.125749216503612 0.0920124118992489 -1.3666549317423 0.172163072117487 df.mm.trans2:probe2 0.0459640873336471 0.0920124118992489 0.499542250712616 0.617551109076968 df.mm.trans2:probe3 -0.0673777542807098 0.0920124118992489 -0.732268102639094 0.464244904528101 df.mm.trans2:probe4 0.0387835344603418 0.0920124118992489 0.421503291347353 0.673514294473964 df.mm.trans2:probe5 0.0912162847157135 0.0920124118992489 0.991347610967886 0.32185123546092 df.mm.trans2:probe6 -0.0236527034041934 0.0920124118992489 -0.257059921764604 0.797206471190338 df.mm.trans3:probe2 0.210922347914307 0.0920124118992489 2.29232495443399 0.0221765411602133 * df.mm.trans3:probe3 0.0476819387270844 0.0920124118992489 0.518212029691112 0.604470658787888 df.mm.trans3:probe4 0.210894695446096 0.0920124118992489 2.29202442467240 0.0221939733380966 *