fitVsDatCorrelation=0.88194618189773 cont.fitVsDatCorrelation=0.232748036173028 fstatistic=8219.78286337328,53,715 cont.fstatistic=1920.46925866041,53,715 residuals=-0.61012708702388,-0.0948597137674248,-0.00394959990953235,0.0953120417099,0.886720243217023 cont.residuals=-0.697094127165915,-0.274258264670205,-0.053049717679883,0.224489385105889,1.55190097448340 predictedValues: Include Exclude Both Lung 72.3920863567295 51.1584341441023 95.8462375858475 cerebhem 83.4923411854084 61.5174081544072 86.4072644803059 cortex 105.409311281078 48.2734326207322 144.197820144737 heart 72.2770445730172 47.3101040925045 84.3897486714389 kidney 63.3890477626317 46.8599699476232 87.0193335517111 liver 63.2699594046361 50.7816970043152 75.0354011054833 stomach 60.8443423734288 49.2045043111596 72.8852882696871 testicle 68.9226884477395 50.7828572410344 75.2973586335131 diffExp=21.2336522126272,21.9749330310012,57.1358786603454,24.9669404805128,16.5290778150085,12.4882624003208,11.6398380622692,18.1398312067051 diffExpScore=0.994597760419962 diffExp1.5=0,0,1,1,0,0,0,0 diffExp1.5Score=0.666666666666667 diffExp1.4=1,0,1,1,0,0,0,0 diffExp1.4Score=0.75 diffExp1.3=1,1,1,1,1,0,0,1 diffExp1.3Score=0.857142857142857 diffExp1.2=1,1,1,1,1,1,1,1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 69.5319080713666 65.7514547507577 81.7896858010664 cerebhem 69.7692389112428 62.8067147199277 79.485622712236 cortex 66.4287747645581 70.9138904087797 63.452916468553 heart 71.8406303256041 75.225231729919 60.8065949231632 kidney 67.8095631432834 65.866082910147 71.8901946470328 liver 67.9243585627636 67.3155773041792 68.623918477261 stomach 70.9535777538863 74.1705969178261 72.9863609610106 testicle 67.0968799112038 73.0544619286088 62.2171208502484 cont.diffExp=3.78045332060894,6.96252419131508,-4.48511564422162,-3.38460140431485,1.94348023313638,0.608781258584344,-3.21701916393982,-5.957582017405 cont.diffExpScore=6.38851360194488 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=0.18981867985472 cont.tran.correlation=0.236760163228093 tran.covariance=0.00352958545320646 cont.tran.covariance=0.000385032804275354 tran.mean=62.2428268062842 cont.tran.mean=69.1536838821284 weightedLogRatios: wLogRatio Lung 1.42635061092869 cerebhem 1.30482855498558 cortex 3.33268331045491 heart 1.72420869356469 kidney 1.20797368890095 liver 0.887738020833269 stomach 0.849791615616797 testicle 1.24622355161409 cont.weightedLogRatios: wLogRatio Lung 0.235569994253767 cerebhem 0.440776051638177 cortex -0.276292819490773 heart -0.197840427922543 kidney 0.122197233498735 liver 0.0379378548472015 stomach -0.189969747856514 testicle -0.361424587649504 varWeightedLogRatios=0.62854004166725 cont.varWeightedLogRatios=0.0776965241866781 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.44424299501956 0.0909037115708977 37.8889149353744 3.87803405652867e-173 *** df.mm.trans1 0.851268757294677 0.0807242551604838 10.5453900516309 2.89582917757035e-24 *** df.mm.trans2 0.525282371381389 0.0734292074864207 7.15358900582619 2.09692197326631e-12 *** df.mm.exp2 0.43072420500507 0.0989934834846615 4.35103594542976 1.55230315865580e-05 *** df.mm.exp3 -0.0907329158380703 0.0989934834846616 -0.91655443009164 0.35968520617312 df.mm.exp4 0.0475053905930672 0.0989934834846616 0.479884017824545 0.631456629810698 df.mm.exp5 -0.123954571230607 0.0989934834846615 -1.25214879674189 0.210925081409988 df.mm.exp6 0.102707476511415 0.0989934834846616 1.03751755061058 0.299845462368384 df.mm.exp7 0.0611380546164197 0.0989934834846616 0.617596759547235 0.537037822788197 df.mm.exp8 0.184820062016580 0.0989934834846615 1.86699220504971 0.0623117130556466 . df.mm.trans1:exp2 -0.288066288637889 0.0939989568727906 -3.06456899333182 0.00226181228696502 ** df.mm.trans2:exp2 -0.246331380032593 0.0791542052904257 -3.11204413118388 0.00193194622267974 ** df.mm.trans1:exp3 0.466486901416153 0.0939989568727907 4.96268168217497 8.69827289195216e-07 *** df.mm.trans2:exp3 0.0326869066963137 0.0791542052904257 0.412952243994893 0.679765408067822 df.mm.trans1:exp4 -0.0490958032432564 0.0939989568727906 -0.52230157521533 0.601622151406662 df.mm.trans2:exp4 -0.125708869937887 0.0791542052904257 -1.58815150094233 0.112694166415166 df.mm.trans1:exp5 -0.00885131942481974 0.0939989568727906 -0.0941640175517936 0.925005248545687 df.mm.trans2:exp5 0.036190993779966 0.0791542052904257 0.457221364893718 0.647650829831014 df.mm.trans1:exp6 -0.237393823926319 0.0939989568727906 -2.5254942376391 0.0117685688650433 * df.mm.trans2:exp6 -0.110098851247020 0.0791542052904257 -1.3909412752368 0.164675930831111 df.mm.trans1:exp7 -0.234916205065496 0.0939989568727906 -2.49913629768689 0.012672836245411 * df.mm.trans2:exp7 -0.100080253476358 0.0791542052904257 -1.26437064346932 0.206509020241141 df.mm.trans1:exp8 -0.233931631832433 0.0939989568727906 -2.48866199812211 0.0130489579944834 * df.mm.trans2:exp8 -0.192188589475833 0.0791542052904257 -2.4280275289313 0.0154269265282425 * df.mm.trans1:probe2 0.195898845773056 0.0514853490611827 3.80494352947397 0.000153974776292561 *** df.mm.trans1:probe3 0.319412738204731 0.0514853490611827 6.20395401855305 9.31759478790025e-10 *** df.mm.trans1:probe4 0.312099477772365 0.0514853490611827 6.06190855191602 2.17677673399206e-09 *** df.mm.trans1:probe5 -0.340061394328988 0.0514853490611827 -6.60501289259738 7.75921683460103e-11 *** df.mm.trans1:probe6 -0.427112900630249 0.0514853490611827 -8.29581440970108 5.35507897640534e-16 *** df.mm.trans1:probe7 0.62426398493041 0.0514853490611827 12.1250801696724 6.65593392046065e-31 *** df.mm.trans1:probe8 -0.388548861722099 0.0514853490611827 -7.54678503316285 1.36098996995743e-13 *** df.mm.trans1:probe9 0.0618454442460561 0.0514853490611827 1.20122414189252 0.23006202322282 df.mm.trans1:probe10 0.147334032874095 0.0514853490611827 2.86166910705044 0.00433728534420124 ** df.mm.trans1:probe11 0.0229527020589714 0.0514853490611827 0.445810361151393 0.655869196653503 df.mm.trans1:probe12 0.0227888386395972 0.0514853490611827 0.442627641749423 0.658168957241133 df.mm.trans1:probe13 -0.0139051825297429 0.0514853490611827 -0.270080377880291 0.787176351015868 df.mm.trans1:probe14 0.0510297102801041 0.0514853490611827 0.991150127378236 0.321947574916079 df.mm.trans1:probe15 -0.0408635487976489 0.0514853490611827 -0.793692760033319 0.42763763477347 df.mm.trans1:probe16 0.239240993754642 0.0514853490611827 4.64677812459501 4.01412338824258e-06 *** df.mm.trans1:probe17 -0.203081181207802 0.0514853490611827 -3.94444603971647 8.78650815669935e-05 *** df.mm.trans1:probe18 -0.148143824091252 0.0514853490611827 -2.87739768288654 0.00412921897750227 ** df.mm.trans1:probe19 -0.287914019922404 0.0514853490611827 -5.59215437347548 3.19341045127606e-08 *** df.mm.trans1:probe20 -0.134423124904586 0.0514853490611827 -2.61090052521240 0.0092196667461373 ** df.mm.trans1:probe21 -0.131738265709204 0.0514853490611827 -2.55875250166125 0.0107096683864881 * df.mm.trans1:probe22 -0.229858312096751 0.0514853490611827 -4.46453828687455 9.32165452972053e-06 *** df.mm.trans2:probe2 -0.0604441506223579 0.0514853490611827 -1.17400681406528 0.240783186049149 df.mm.trans2:probe3 -0.0473443565446262 0.0514853490611827 -0.919569497108088 0.358107895565389 df.mm.trans2:probe4 0.0175438094746474 0.0514853490611827 0.340753433637969 0.7333893071326 df.mm.trans2:probe5 -0.140094038271425 0.0514853490611827 -2.72104668271636 0.0066658281152922 ** df.mm.trans2:probe6 -0.115641235970812 0.0514853490611827 -2.24609987267232 0.0250021498054513 * df.mm.trans3:probe2 -0.441003929973451 0.0514853490611827 -8.56561989022125 6.57597238068285e-17 *** df.mm.trans3:probe3 -0.332272389475181 0.0514853490611827 -6.45372704146037 2.01254119651624e-10 *** df.mm.trans3:probe4 -0.258919313037472 0.0514853490611827 -5.02899014493978 6.24076093178442e-07 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98369178536026 0.187561700285277 21.2393669885758 5.4305751397135e-78 *** df.mm.trans1 0.210102601514017 0.166558419788550 1.26143488741516 0.207563593886994 df.mm.trans2 0.247062714585771 0.151506542128503 1.63070657619670 0.103392633238055 df.mm.exp2 -0.0138373667614573 0.204253333100316 -0.0677461001562275 0.946006690706074 df.mm.exp3 0.283782082164683 0.204253333100316 1.38936328654822 0.165154814461737 df.mm.exp4 0.463722241511149 0.204253333100316 2.27032888263027 0.0234843842797049 * df.mm.exp5 0.105670580411488 0.204253333100316 0.517350580318754 0.605071460050296 df.mm.exp6 0.175628835448705 0.204253333100316 0.859857867594492 0.390155602838718 df.mm.exp7 0.254604637643053 0.204253333100316 1.24651399210219 0.212983973526751 df.mm.exp8 0.343196136671365 0.204253333100316 1.68024742344258 0.0933459880525448 . df.mm.trans1:exp2 0.0172448199612208 0.193948122375097 0.0889146012348045 0.929174677946127 df.mm.trans2:exp2 -0.0319824394882458 0.163318934644634 -0.195828117283745 0.844800341690058 df.mm.trans1:exp3 -0.329437520818492 0.193948122375098 -1.69858577017496 0.089832308453066 . df.mm.trans2:exp3 -0.208197548617856 0.163318934644634 -1.27479124861347 0.202797245451354 df.mm.trans1:exp4 -0.431057799688604 0.193948122375097 -2.22254175193784 0.026558776390631 * df.mm.trans2:exp4 -0.329117334808922 0.163318934644634 -2.01518173949058 0.044259380144747 * df.mm.trans1:exp5 -0.130753102253702 0.193948122375097 -0.674165342012564 0.500424096847914 df.mm.trans2:exp5 -0.103928742805219 0.163318934644634 -0.636354523321854 0.524749028517623 df.mm.trans1:exp6 -0.199019879649849 0.193948122375098 -1.02615007153791 0.305167878673522 df.mm.trans2:exp6 -0.152118961194663 0.163318934644634 -0.93142268853063 0.351949344629855 df.mm.trans1:exp7 -0.234364565050015 0.193948122375097 -1.20838790383725 0.227297583106448 df.mm.trans2:exp7 -0.134118630258355 0.163318934644634 -0.821206864655243 0.411802169416756 df.mm.trans1:exp8 -0.378844349015726 0.193948122375097 -1.95332826312718 0.0511703736947988 . df.mm.trans2:exp8 -0.237872716166142 0.163318934644634 -1.45649196575847 0.145695648425061 df.mm.trans1:probe2 0.0560967933845433 0.106229761610613 0.528070406391072 0.597614316904628 df.mm.trans1:probe3 0.166568265682006 0.106229761610613 1.56799999507261 0.117323563426539 df.mm.trans1:probe4 0.130303381717600 0.106229761610613 1.22661841410535 0.220369819679933 df.mm.trans1:probe5 0.0671126270264348 0.106229761610613 0.631768592990325 0.527740034759646 df.mm.trans1:probe6 0.046257827395375 0.106229761610613 0.435450731452582 0.663366711937931 df.mm.trans1:probe7 -0.025817656955693 0.106229761610613 -0.243036005769532 0.808047209657893 df.mm.trans1:probe8 0.0200709213568181 0.106229761610613 0.188938778102397 0.850194416874268 df.mm.trans1:probe9 0.118461913199997 0.106229761610613 1.11514806588968 0.265161645805515 df.mm.trans1:probe10 0.135036663611762 0.106229761610613 1.2711754367551 0.204079624924485 df.mm.trans1:probe11 -0.0227492190392031 0.106229761610613 -0.214151088115877 0.830490293593518 df.mm.trans1:probe12 0.185203893800983 0.106229761610613 1.74342755733417 0.0816887817116361 . df.mm.trans1:probe13 0.0828772321804637 0.106229761610613 0.780169614653296 0.435549231310175 df.mm.trans1:probe14 0.0155913364329188 0.106229761610613 0.146769946543503 0.88335497948256 df.mm.trans1:probe15 0.0030429286318964 0.106229761610613 0.0286447845289374 0.977155887050142 df.mm.trans1:probe16 0.0883330302555929 0.106229761610613 0.831528085127206 0.405952976735461 df.mm.trans1:probe17 0.0060498237131368 0.106229761610613 0.056950365146375 0.954600644757087 df.mm.trans1:probe18 -0.0448756113630267 0.106229761610613 -0.422439160953019 0.672831501399532 df.mm.trans1:probe19 0.115795817681543 0.106229761610613 1.09005062165153 0.276058071916085 df.mm.trans1:probe20 0.0324261966676737 0.106229761610613 0.30524587625954 0.7602677324472 df.mm.trans1:probe21 0.00778089049231569 0.106229761610613 0.0732458623115116 0.941630956694709 df.mm.trans1:probe22 0.0642085453738992 0.106229761610613 0.604430852525647 0.545748973872923 df.mm.trans2:probe2 -0.139258418047996 0.106229761610613 -1.31091716611819 0.190306624656156 df.mm.trans2:probe3 -0.0673677889022868 0.106229761610613 -0.634170574054608 0.52617234435682 df.mm.trans2:probe4 -0.0190021402283174 0.106229761610613 -0.178877745183784 0.858084371442076 df.mm.trans2:probe5 0.0128495743799613 0.106229761610613 0.120960211009996 0.903756534451695 df.mm.trans2:probe6 -0.235948263662501 0.106229761610613 -2.22111261557164 0.0266558498830265 * df.mm.trans3:probe2 -0.109430566156313 0.106229761610613 -1.03013095856727 0.303296867735549 df.mm.trans3:probe3 -0.0583317848260463 0.106229761610613 -0.54910962748709 0.583101658282356 df.mm.trans3:probe4 0.0209385259706442 0.106229761610613 0.197106024274013 0.843800585278831