fitVsDatCorrelation=0.593745251159154 cont.fitVsDatCorrelation=0.249261992711001 fstatistic=12955.0107364753,52,692 cont.fstatistic=8939.49657197143,52,692 residuals=-0.486468081840716,-0.0765621532904414,-0.00157320399609539,0.0694591983726119,0.777975913257256 cont.residuals=-0.429859253177940,-0.0951146881387125,-0.00925187000233294,0.0760684663096963,0.8996697610289 predictedValues: Include Exclude Both Lung 45.6821518857497 43.4957920496474 46.791118672381 cerebhem 53.9730006690498 48.257131951115 53.0787685497508 cortex 45.8198531452989 45.6365506232911 46.9309776979992 heart 46.8954784834446 48.2130529979812 47.6077807550260 kidney 46.1120428307286 42.8938245707120 48.2527009131586 liver 49.2799800410853 50.0180404036903 46.4592513511157 stomach 47.7232213761559 46.4519445696942 51.3277186376673 testicle 48.6282874860002 47.7534727894682 65.6650534618717 diffExp=2.18635983610237,5.71586871793482,0.183302522007779,-1.31757451453667,3.21821826001663,-0.738060362605005,1.27127680646167,0.874814696531999 diffExpScore=1.25102614591399 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 49.3955520866677 47.816579067804 45.649776254563 cerebhem 46.9832214204057 50.6882131333734 48.8727063423033 cortex 48.4844817230253 47.2703987255066 49.2941263402147 heart 48.3042371244009 49.0413431882257 49.1062705638638 kidney 48.0910863887022 48.7185081011135 50.9019717815978 liver 48.747158601306 51.5857208427385 47.4733334417796 stomach 49.2456930138605 49.1924995275358 46.4591476345256 testicle 47.2835561579253 45.2886399467666 52.4260188651693 cont.diffExp=1.57897301886374,-3.70499171296770,1.21408299751871,-0.737106063824733,-0.627421712411305,-2.83856224143253,0.0531934863246804,1.99491621115867 cont.diffExpScore=3.1348686306599 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=0.623105403489647 cont.tran.correlation=0.116239495637801 tran.covariance=0.00188587645438485 cont.tran.covariance=9.14523182687834e-05 tran.mean=47.3021141170695 cont.tran.mean=48.5085555655849 weightedLogRatios: wLogRatio Lung 0.186227186454794 cerebhem 0.440206790552074 cortex 0.0153234520788987 heart -0.107004124157292 kidney 0.274546980290129 liver -0.0580503516629932 stomach 0.104000916005152 testicle 0.070347595647763 cont.weightedLogRatios: wLogRatio Lung 0.126170772852363 cerebhem -0.295090711726611 cortex 0.0981048589690817 heart -0.0588373753317591 kidney -0.0502876913853751 liver -0.221578091669414 stomach 0.00421090256777806 testicle 0.165296045383328 varWeightedLogRatios=0.0325095109185303 cont.varWeightedLogRatios=0.0269030791765139 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.6384601029562 0.0687904161646058 52.891962366529 2.55647524538985e-245 *** df.mm.trans1 0.0559005440885678 0.0617838535588956 0.90477593851087 0.365898984886653 df.mm.trans2 0.124458493430487 0.0568186443641833 2.19045165232667 0.0288244215114781 * df.mm.exp2 0.144572095897924 0.0778274716010198 1.85759723300621 0.0636508144681461 . df.mm.exp3 0.0480699954532961 0.0778274716010199 0.617648170555064 0.537010479526645 df.mm.exp4 0.111876389526714 0.0778274716010199 1.43749227907909 0.151030180751522 df.mm.exp5 -0.03532824690115 0.0778274716010199 -0.453930291893062 0.650021328726527 df.mm.exp6 0.222647591228445 0.0778274716010199 2.86078407339044 0.00435340037194789 ** df.mm.exp7 0.0169270402270428 0.0778274716010199 0.217494412690403 0.82788717314507 df.mm.exp8 -0.182988129758031 0.0778274716010199 -2.35120229391639 0.0189924183740299 * df.mm.trans1:exp2 0.0222041659508124 0.074514131038953 0.297985974488583 0.765803312178526 df.mm.trans2:exp2 -0.0406926655024628 0.0648562263341832 -0.627428819135832 0.530585149829032 df.mm.trans1:exp3 -0.0450601955378908 0.074514131038953 -0.604720137101714 0.545563193323809 df.mm.trans2:exp3 -2.52501699095113e-05 0.0648562263341832 -0.000389325302082877 0.999689475528 df.mm.trans1:exp4 -0.0856627983943181 0.074514131038953 -1.14961816235282 0.250698333369600 df.mm.trans2:exp4 -0.00891079497026419 0.0648562263341832 -0.137393053434064 0.890760086714964 df.mm.trans1:exp5 0.0446947234892343 0.074514131038953 0.599815402341197 0.548825615831695 df.mm.trans2:exp5 0.0213919141480675 0.0648562263341832 0.329835936458003 0.741623750949159 df.mm.trans1:exp6 -0.146837349083572 0.074514131038953 -1.97059734893522 0.0491681464405751 * df.mm.trans2:exp6 -0.0829280417267644 0.0648562263341832 -1.27864426307295 0.201451008204069 df.mm.trans1:exp7 0.0267833884413991 0.074514131038953 0.359440391613744 0.719375317795588 df.mm.trans2:exp7 0.0488270889497041 0.0648562263341832 0.752851217369847 0.45179535527231 df.mm.trans1:exp8 0.245485866307977 0.074514131038953 3.29448740641754 0.00103625424755783 ** df.mm.trans2:exp8 0.276375723690734 0.0648562263341832 4.26135992351234 2.31346280328389e-05 *** df.mm.trans1:probe2 0.149580103188267 0.0372570655194765 4.01481171699023 6.59942570164557e-05 *** df.mm.trans1:probe3 0.0697625876329402 0.0372570655194765 1.87246597820408 0.0615636436836283 . df.mm.trans1:probe4 0.0443093693600104 0.0372570655194765 1.18928768925312 0.234734391460644 df.mm.trans1:probe5 0.212249591800947 0.0372570655194765 5.69689504102226 1.80504491854636e-08 *** df.mm.trans1:probe6 0.0315599200495118 0.0372570655194765 0.847085502024133 0.397240324333157 df.mm.trans1:probe7 0.052071007879168 0.0372570655194765 1.39761430894087 0.162676799156362 df.mm.trans1:probe8 0.283579908855813 0.0372570655194765 7.61143973369478 8.89874913505852e-14 *** df.mm.trans1:probe9 0.214513166482474 0.0372570655194765 5.75765062254661 1.28249297237568e-08 *** df.mm.trans1:probe10 0.0901524796436835 0.0372570655194765 2.41974182310615 0.0157882044394673 * df.mm.trans1:probe11 0.179392974160459 0.0372570655194765 4.81500546699471 1.80897402716372e-06 *** df.mm.trans1:probe12 0.161298708068846 0.0372570655194765 4.32934547635067 1.71646127934201e-05 *** df.mm.trans1:probe13 0.101864918228008 0.0372570655194765 2.73411007570517 0.00641487507748828 ** df.mm.trans1:probe14 0.0809348421987869 0.0372570655194765 2.17233539652975 0.0301691413446235 * df.mm.trans1:probe15 0.140416363454535 0.0372570655194765 3.76885193443726 0.000177986342943834 *** df.mm.trans1:probe16 0.120842232007517 0.0372570655194765 3.24347154888905 0.00123755947328372 ** df.mm.trans1:probe17 0.239878184506079 0.0372570655194765 6.43846156860315 2.25676249494809e-10 *** df.mm.trans1:probe18 0.250278098415220 0.0372570655194765 6.71760094160891 3.85838997092489e-11 *** df.mm.trans1:probe19 0.185873058747635 0.0372570655194765 4.98893447876265 7.6871407719557e-07 *** df.mm.trans1:probe20 0.178610508062016 0.0372570655194765 4.79400364928489 2.00234926650927e-06 *** df.mm.trans1:probe21 0.252716773681772 0.0372570655194765 6.78305631853003 2.52688963721413e-11 *** df.mm.trans1:probe22 0.143790829203819 0.0372570655194765 3.85942443933624 0.000124287855465925 *** df.mm.trans2:probe2 0.0429092865507993 0.0372570655194765 1.15170870149095 0.249838543254034 df.mm.trans2:probe3 -0.0296641995382938 0.0372570655194765 -0.79620332746782 0.426186915049194 df.mm.trans2:probe4 0.0363013563873709 0.0372570655194765 0.974348244587165 0.330224124841017 df.mm.trans2:probe5 -0.00747441991333894 0.0372570655194765 -0.200617515338979 0.841056607876459 df.mm.trans2:probe6 0.0456383993555274 0.0372570655194765 1.22495958066449 0.221007245703027 df.mm.trans3:probe2 0.0355438469810325 0.0372570655194765 0.954016278132575 0.340408528861948 df.mm.trans3:probe3 0.0450789704640718 0.0372570655194765 1.20994420348286 0.226713455041565 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.93557070791026 0.0827923528661679 47.5354374125836 3.76233660019257e-220 *** df.mm.trans1 -0.0624916293004438 0.0743596403464067 -0.840397142984079 0.400976021822873 df.mm.trans2 -0.0683888789417418 0.0683837882637663 -1.00007444276055 0.317624051601341 df.mm.exp2 -0.0599692377236375 0.0936688546272908 -0.640226017092401 0.522237591876342 df.mm.exp3 -0.106910968162084 0.0936688546272909 -1.14137157529558 0.254110141828898 df.mm.exp4 -0.0700378713400493 0.0936688546272908 -0.7477178152624 0.454884393949153 df.mm.exp5 -0.116979911826626 0.0936688546272909 -1.24886668351066 0.212136145829723 df.mm.exp6 0.0234895673231233 0.0936688546272909 0.250772441027367 0.802064489114388 df.mm.exp7 0.00775559710843998 0.0936688546272908 0.0827980350491053 0.934036075393825 df.mm.exp8 -0.23641829645306 0.0936688546272909 -2.52397979449808 0.0118261169827395 * df.mm.trans1:exp2 0.0098994032332977 0.089681100572656 0.110384497626427 0.912136452013365 df.mm.trans2:exp2 0.118290216931891 0.0780573788560757 1.51542645506964 0.130121069569675 df.mm.trans1:exp3 0.0882943690235385 0.089681100572656 0.98453708150031 0.325195606143625 df.mm.trans2:exp3 0.0954228262121574 0.0780573788560757 1.2224702854563 0.221946035341047 df.mm.trans1:exp4 0.0476967718851034 0.089681100572656 0.531848645707257 0.595001522025915 df.mm.trans2:exp4 0.0953291304544678 0.0780573788560757 1.22126994079878 0.222399743721033 df.mm.trans1:exp5 0.090216376192631 0.089681100572656 1.00596865578764 0.314782269895306 df.mm.trans2:exp5 0.135666491076922 0.0780573788560757 1.73803544347892 0.082649398962692 . df.mm.trans1:exp6 -0.0367030380880487 0.089681100572656 -0.409261682268421 0.682474271693882 df.mm.trans2:exp6 0.0523829172301125 0.0780573788560757 0.671082196171326 0.502392099500382 df.mm.trans1:exp7 -0.0107940662183899 0.089681100572656 -0.120360545861555 0.904232480477213 df.mm.trans2:exp7 0.0206131443568686 0.0780573788560757 0.264076819628746 0.79179934932236 df.mm.trans1:exp8 0.192720500159819 0.089681100572656 2.14895333497480 0.0319843198367066 * df.mm.trans2:exp8 0.182102101906820 0.0780573788560757 2.33292617015214 0.0199377092043237 * df.mm.trans1:probe2 -0.00199330682473381 0.044840550286328 -0.0444532195079144 0.964555964606915 df.mm.trans1:probe3 0.0184943693077058 0.044840550286328 0.412447420685306 0.68013925570633 df.mm.trans1:probe4 0.0754897672138604 0.044840550286328 1.68351562886322 0.0927263297513476 . df.mm.trans1:probe5 0.0649192750254781 0.044840550286328 1.44778051587097 0.148131332151934 df.mm.trans1:probe6 0.0457451897874308 0.044840550286328 1.02017458517628 0.308002188144490 df.mm.trans1:probe7 -0.00351184807490069 0.044840550286328 -0.0783185766560823 0.937597281190917 df.mm.trans1:probe8 0.0902358641434213 0.044840550286328 2.01237191709787 0.0445677274358489 * df.mm.trans1:probe9 -0.0390442168132312 0.044840550286328 -0.870734559766004 0.38420106059628 df.mm.trans1:probe10 0.00720224047758103 0.044840550286328 0.160618913719643 0.872440433328227 df.mm.trans1:probe11 0.0259681385817446 0.044840550286328 0.579121764026663 0.562695474835835 df.mm.trans1:probe12 -0.0201562930893795 0.044840550286328 -0.449510386484379 0.653204274193879 df.mm.trans1:probe13 0.0257790423908579 0.044840550286328 0.574904683957861 0.565542621263945 df.mm.trans1:probe14 0.0375096388415715 0.044840550286328 0.836511563797831 0.403155939527506 df.mm.trans1:probe15 0.0491061206344033 0.044840550286328 1.09512752008701 0.273841780779664 df.mm.trans1:probe16 0.0115668781738264 0.044840550286328 0.257955758793469 0.796517745935129 df.mm.trans1:probe17 0.043764344914396 0.044840550286328 0.975999282679185 0.329405877452881 df.mm.trans1:probe18 0.0740935516638146 0.044840550286328 1.65237828685626 0.0989110765312307 . df.mm.trans1:probe19 0.0424756982630275 0.044840550286328 0.947260860801222 0.343836530061576 df.mm.trans1:probe20 0.0471171313516105 0.044840550286328 1.05077058713030 0.293730771557584 df.mm.trans1:probe21 0.0528352787825327 0.044840550286328 1.17829238145283 0.239085086448357 df.mm.trans1:probe22 0.0219357052217408 0.044840550286328 0.489193488520347 0.62485973372555 df.mm.trans2:probe2 -0.0085839910911561 0.044840550286328 -0.191433669666034 0.848241997757376 df.mm.trans2:probe3 0.006730129657912 0.044840550286328 0.150090255693495 0.880737142794015 df.mm.trans2:probe4 -0.0112927072245094 0.044840550286328 -0.251841405879281 0.801238425663246 df.mm.trans2:probe5 -2.50181606086782e-05 0.044840550286328 -0.000557936074578155 0.999554992216218 df.mm.trans2:probe6 0.0148869221591574 0.044840550286328 0.331996865874691 0.739992156053248 df.mm.trans3:probe2 0.0615099156243872 0.044840550286328 1.37174756401555 0.17058660096447 df.mm.trans3:probe3 -0.0125843975134708 0.044840550286328 -0.280647704658251 0.779064506007474