fitVsDatCorrelation=0.869888549027346 cont.fitVsDatCorrelation=0.233537995855424 fstatistic=5175.1046955909,58,830 cont.fstatistic=1321.07463347247,58,830 residuals=-0.715508176016015,-0.126051977041932,0.00648244018167108,0.128708565001132,0.863619768813714 cont.residuals=-0.861053910239166,-0.303482696258563,-0.134699755944125,0.190231153433358,1.86024144511217 predictedValues: Include Exclude Both Lung 61.2062231295847 61.703190387608 73.513575597743 cerebhem 54.0066780990934 64.5623760269616 69.7860166928447 cortex 58.9690015090613 76.2314158041194 87.2516556651914 heart 96.4850342918283 132.046204999901 143.664017814342 kidney 56.0390097539931 61.1741662138359 65.252034301951 liver 51.245199729919 58.2610589075508 62.4684348675147 stomach 64.845944310747 57.6931332212082 78.7924556043184 testicle 49.3377230198891 55.0552199207055 56.037128954223 diffExp=-0.496967258023311,-10.5556979278682,-17.262414295058,-35.5611707080723,-5.13515645984282,-7.0158591776318,7.15281108953878,-5.71749690081638 diffExpScore=1.17601903232815 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,-1,0,0,0,0 diffExp1.3Score=0.5 diffExp1.2=0,0,-1,-1,0,0,0,0 diffExp1.2Score=0.666666666666667 cont.predictedValues: Include Exclude Both Lung 66.1106673300664 79.9257707934613 73.5059119192126 cerebhem 68.7301045860311 62.5845244093645 68.8705182529786 cortex 62.294170340778 77.4181567368769 68.0396689059814 heart 73.9810761411867 61.3248338687132 77.0593318065091 kidney 71.4201852239644 63.7281237402879 69.0141500701122 liver 69.1715345725417 65.1831860077053 72.3897803222585 stomach 64.971829298389 63.0448240626012 72.5793103892357 testicle 65.7417013774325 71.3778288243725 75.8397062252645 cont.diffExp=-13.8151034633949,6.14558017666666,-15.1239863960989,12.6562422724735,7.69206148367645,3.9883485648364,1.92700523578776,-5.63612744693992 cont.diffExpScore=21.1575765084773 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=-1,0,-1,1,0,0,0,0 cont.diffExp1.2Score=1.5 tran.correlation=0.932636286800547 cont.tran.correlation=-0.683475827167464 tran.covariance=0.0538548434429072 cont.tran.covariance=-0.0039628652851359 tran.mean=66.1788487078754 cont.tran.mean=67.9380323321108 weightedLogRatios: wLogRatio Lung -0.0333036746978596 cerebhem -0.728087370664061 cortex -1.079784014384 heart -1.48293329362466 kidney -0.356835149990936 liver -0.513347727174085 stomach 0.480779207901395 testicle -0.433493662237081 cont.weightedLogRatios: wLogRatio Lung -0.813387258691505 cerebhem 0.391851604120712 cortex -0.921697161526203 heart 0.78989839828356 kidney 0.479931426956074 liver 0.249838585737649 stomach 0.125215384192802 testicle -0.34767554832134 varWeightedLogRatios=0.36520866022116 cont.varWeightedLogRatios=0.388236791378688 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.25474282523502 0.107387033845113 39.6206382920656 1.51112492821583e-193 *** df.mm.trans1 -0.343366330492755 0.0915847718485767 -3.74916401015297 0.000189730317036259 *** df.mm.trans2 -0.117284179128308 0.0814064853472887 -1.44072279533948 0.150040129751250 df.mm.exp2 -0.0278085376281943 0.104093288868355 -0.267150148972267 0.789419920499983 df.mm.exp3 0.00287461336001200 0.104093288868355 0.0276157415263099 0.977975264858038 df.mm.exp4 0.545948030757652 0.104093288868355 5.24479567023866 1.98709693742219e-07 *** df.mm.exp5 0.0224013919845160 0.104093288868355 0.215204959205839 0.829660392448682 df.mm.exp6 -0.0722199262428216 0.104093288868355 -0.693800023305602 0.488001748867156 df.mm.exp7 -0.0787791397574203 0.104093288868355 -0.756812860981371 0.449376757115436 df.mm.exp8 -0.0581032626229973 0.104093288868355 -0.558184521352566 0.576868921477021 df.mm.trans1:exp2 -0.0973326244247523 0.0939314741776882 -1.03620884561686 0.300406514626545 df.mm.trans2:exp2 0.0731047266647716 0.0692714944888543 1.05533635738917 0.291578607214339 df.mm.trans1:exp3 -0.0401115751483987 0.0939314741776881 -0.427030188757823 0.669468087071048 df.mm.trans2:exp3 0.208563407622641 0.0692714944888543 3.01081143349951 0.00268445322024377 ** df.mm.trans1:exp4 -0.0908089889710478 0.0939314741776881 -0.966757838797104 0.333946708661723 df.mm.trans2:exp4 0.214868230818302 0.0692714944888543 3.1018275613048 0.00198837004614153 ** df.mm.trans1:exp5 -0.110602210487199 0.0939314741776881 -1.17747763947551 0.239342439507517 df.mm.trans2:exp5 -0.0310120499126957 0.0692714944888543 -0.447688477656354 0.654494857573785 df.mm.trans1:exp6 -0.105406993404480 0.0939314741776881 -1.12216905278293 0.26211508896182 df.mm.trans2:exp6 0.0148182155195833 0.0692714944888543 0.213915054510156 0.830665837842227 df.mm.trans1:exp7 0.136544639550267 0.0939314741776882 1.45366226545076 0.146417960048866 df.mm.trans2:exp7 0.0115816602519866 0.0692714944888543 0.167192296592505 0.867259469948813 df.mm.trans1:exp8 -0.157456645579239 0.0939314741776882 -1.67629271186974 0.0940574578138677 . df.mm.trans2:exp8 -0.0558956948951954 0.0692714944888543 -0.8069075931975 0.419950878992295 df.mm.trans1:probe2 0.060853126755522 0.0672878490444865 0.904370218689704 0.366061541134181 df.mm.trans1:probe3 0.0580045515511275 0.0672878490444865 0.862036049224556 0.388916664308281 df.mm.trans1:probe4 0.358340701795954 0.0672878490444865 5.32548902787844 1.29725284827922e-07 *** df.mm.trans1:probe5 0.239052884428676 0.0672878490444865 3.55269023788574 0.000402819641524319 *** df.mm.trans1:probe6 0.233522258384426 0.0672878490444865 3.4704967048365 0.000546297356858587 *** df.mm.trans1:probe7 0.0378808265377260 0.0672878490444865 0.562966822028767 0.57360951062289 df.mm.trans1:probe8 0.107523364688915 0.0672878490444865 1.59796109127857 0.110432238022551 df.mm.trans1:probe9 0.222011535915147 0.0672878490444865 3.29942982377645 0.00101010173464542 ** df.mm.trans1:probe10 0.0974217154648724 0.0672878490444865 1.44783518641625 0.148040762615950 df.mm.trans1:probe11 0.121440894879615 0.0672878490444865 1.80479680364468 0.071468813786487 . df.mm.trans1:probe12 0.141285085601325 0.0672878490444865 2.09971172518706 0.0360555709467807 * df.mm.trans1:probe13 0.577574282402801 0.0672878490444865 8.58363420148777 4.49237797158176e-17 *** df.mm.trans1:probe14 0.74096353242931 0.0672878490444865 11.0118475022055 2.03767208391462e-26 *** df.mm.trans1:probe15 0.845290632738887 0.0672878490444865 12.5623072329156 2.92071365235942e-33 *** df.mm.trans1:probe16 0.89535194655292 0.0672878490444865 13.3062946619228 9.2729462590474e-37 *** df.mm.trans1:probe17 0.694675020298525 0.0672878490444865 10.3239296568872 1.36719219348491e-23 *** df.mm.trans1:probe18 0.857851256215649 0.0672878490444865 12.7489772432537 3.98265682573685e-34 *** df.mm.trans2:probe2 0.0112484357958011 0.0672878490444865 0.167168901302883 0.86727787179262 df.mm.trans2:probe3 -0.0100552595000189 0.0672878490444865 -0.149436482854000 0.881245528094396 df.mm.trans2:probe4 -0.150095665947116 0.0672878490444865 -2.23065037861267 0.0259708354961786 * df.mm.trans2:probe5 -0.0629022334276667 0.0672878490444865 -0.934823067179331 0.350151376524693 df.mm.trans2:probe6 -0.0755324377982793 0.0672878490444865 -1.1225271556584 0.261962977384382 df.mm.trans3:probe2 0.209925796138934 0.0672878490444865 3.11981730906786 0.00187218716504155 ** df.mm.trans3:probe3 0.558957555312023 0.0672878490444865 8.30696126045692 3.97790956014607e-16 *** df.mm.trans3:probe4 1.02433496106922 0.0672878490444865 15.2231788594103 2.45420412594452e-46 *** df.mm.trans3:probe5 0.243089381190091 0.0672878490444865 3.61267873237225 0.000321273113055284 *** df.mm.trans3:probe6 0.090867202493756 0.0672878490444865 1.35042513297877 0.177247740132245 df.mm.trans3:probe7 0.577559170860817 0.0672878490444865 8.58340962094019 4.50044405056682e-17 *** df.mm.trans3:probe8 0.0806527322585088 0.0672878490444865 1.19862253592303 0.231016864906710 df.mm.trans3:probe9 0.250625967766769 0.0672878490444865 3.72468389650962 0.000208784424252773 *** df.mm.trans3:probe10 1.07372916948329 0.0672878490444865 15.9572520853417 3.38969475143472e-50 *** df.mm.trans3:probe11 0.744194539737784 0.0672878490444865 11.0598651956577 1.27886591690740e-26 *** df.mm.trans3:probe12 1.10909401361304 0.0672878490444865 16.4828275738131 5.1107788383841e-53 *** df.mm.trans3:probe13 0.580880592350916 0.0672878490444865 8.63277100694472 3.03124884700919e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.19148930099701 0.211693638108849 19.7997886872813 9.25220405094293e-72 *** df.mm.trans1 -0.0594040001147951 0.180542406785886 -0.329030731185749 0.742215464284757 df.mm.trans2 0.164697584488998 0.160477801013465 1.02629512274523 0.305051496452081 df.mm.exp2 -0.140585471288772 0.205200630227299 -0.685112278325106 0.493464355461438 df.mm.exp3 -0.0140643348008373 0.205200630227299 -0.0685394327749304 0.94537273895584 df.mm.exp4 -0.199644071422775 0.205200630227299 -0.972921336555502 0.330875849586043 df.mm.exp5 -0.0861676704433703 0.205200630227299 -0.419919131573442 0.674653206671284 df.mm.exp6 -0.143336770544756 0.205200630227299 -0.698520128256832 0.485047638851157 df.mm.exp7 -0.241942770482835 0.205200630227299 -1.17905471447547 0.238714261674293 df.mm.exp8 -0.149963852702241 0.205200630227299 -0.730815751082867 0.465097945690366 df.mm.trans1:exp2 0.179442662582743 0.185168495577245 0.969077714993297 0.332788714944708 df.mm.trans2:exp2 -0.103994833934367 0.136555915183703 -0.761554955671212 0.446541992725003 df.mm.trans1:exp3 -0.0453979333007988 0.185168495577245 -0.245170935580997 0.806384683649015 df.mm.trans2:exp3 -0.0178126678089059 0.136555915183703 -0.130442301125831 0.89624810826402 df.mm.trans1:exp4 0.312123288568786 0.185168495577245 1.68561767268116 0.0922453263771751 . df.mm.trans2:exp4 -0.0652693863255798 0.136555915183703 -0.477968209855837 0.632798687245152 df.mm.trans1:exp5 0.163418090520603 0.185168495577245 0.882537226492891 0.37774188118353 df.mm.trans2:exp5 -0.140304700283907 0.136555915183703 -1.02745238165012 0.304506825487476 df.mm.trans1:exp6 0.188596082900515 0.185168495577245 1.01851063979639 0.308732132799593 df.mm.trans2:exp6 -0.0605600159565411 0.136555915183703 -0.443481455014764 0.657533106495601 df.mm.trans1:exp7 0.224566435493714 0.185168495577245 1.21276805103185 0.225563574798838 df.mm.trans2:exp7 0.00469039808145786 0.136555915183703 0.0343478206355842 0.972608050308959 df.mm.trans1:exp8 0.144367185470035 0.185168495577245 0.779653066899879 0.435817364572071 df.mm.trans2:exp8 0.0368528143003221 0.136555915183703 0.269873437930137 0.787324705183988 df.mm.trans1:probe2 0.193446886083074 0.132645525765163 1.45837475457373 0.145115563950258 df.mm.trans1:probe3 0.202331362448327 0.132645525765163 1.52535384274126 0.127551801359836 df.mm.trans1:probe4 0.0923610324114114 0.132645525765163 0.696299644323685 0.486436134001577 df.mm.trans1:probe5 0.100719665434301 0.132645525765163 0.759314457485858 0.44788006178093 df.mm.trans1:probe6 0.0718007797731252 0.132645525765163 0.541298165610515 0.588447339002374 df.mm.trans1:probe7 0.123879819299183 0.132645525765163 0.933916305013566 0.350618674932947 df.mm.trans1:probe8 0.0242859488597324 0.132645525765163 0.183089091921038 0.854772827903953 df.mm.trans1:probe9 0.0647675502124738 0.132645525765163 0.48827542307864 0.625483735137851 df.mm.trans1:probe10 -0.0172176437965836 0.132645525765163 -0.129801919041475 0.896754594139077 df.mm.trans1:probe11 0.175958035344369 0.132645525765163 1.32652823628508 0.185029653809889 df.mm.trans1:probe12 0.126636640165072 0.132645525765163 0.954699673694763 0.340007557427949 df.mm.trans1:probe13 -0.0348064783197265 0.132645525765163 -0.262402204061886 0.793076486588637 df.mm.trans1:probe14 0.0857418959735978 0.132645525765163 0.646398704208056 0.51819987023814 df.mm.trans1:probe15 0.0458678145050777 0.132645525765163 0.345792398503380 0.729586364238462 df.mm.trans1:probe16 0.139076133099867 0.132645525765163 1.04847963998491 0.294722882974556 df.mm.trans1:probe17 0.0535250423613381 0.132645525765163 0.403519395415562 0.68667010138022 df.mm.trans1:probe18 0.38821477030194 0.132645525765163 2.92670836850720 0.00351921897882042 ** df.mm.trans2:probe2 0.111763445002203 0.132645525765163 0.842572294523296 0.399710415718895 df.mm.trans2:probe3 0.194563568633553 0.132645525765163 1.46679330125322 0.142811070359431 df.mm.trans2:probe4 0.0255604908145819 0.132645525765163 0.192697723252531 0.847242845828671 df.mm.trans2:probe5 0.0972566983015536 0.132645525765163 0.733207529922552 0.463638892187738 df.mm.trans2:probe6 0.0441734113327551 0.132645525765163 0.333018479725883 0.739204433077758 df.mm.trans3:probe2 0.110112468208784 0.132645525765163 0.830125762430371 0.406706382407592 df.mm.trans3:probe3 -0.0344913762291253 0.132645525765163 -0.260026684127960 0.794907679032386 df.mm.trans3:probe4 0.0444460052848553 0.132645525765163 0.335073535488433 0.737654280388999 df.mm.trans3:probe5 0.201890940476341 0.132645525765163 1.52203355003297 0.128381546741939 df.mm.trans3:probe6 -0.134021276985551 0.132645525765163 -1.01037163683021 0.312611720850410 df.mm.trans3:probe7 0.0207852120482151 0.132645525765163 0.156697422912051 0.875521424811121 df.mm.trans3:probe8 0.00135418698338471 0.132645525765163 0.0102090664240132 0.991856938281738 df.mm.trans3:probe9 -0.0301663675562286 0.132645525765163 -0.227420920398290 0.820152460199596 df.mm.trans3:probe10 -0.0565082910095898 0.132645525765163 -0.426009778193594 0.670211170620447 df.mm.trans3:probe11 0.0455597178669614 0.132645525765163 0.343469691903673 0.731332120438753 df.mm.trans3:probe12 -0.02847964419061 0.132645525765163 -0.214704898837151 0.830050142741618 df.mm.trans3:probe13 -0.132903200354478 0.132645525765163 -1.00194258033076 0.316663334479542