fitVsDatCorrelation=0.64361835286771 cont.fitVsDatCorrelation=0.315019790705792 fstatistic=8591.70122684912,42,462 cont.fstatistic=5583.23672562636,42,462 residuals=-0.472187442430298,-0.0802706868505615,-0.00346054915935068,0.0660599185664966,1.09208926632721 cont.residuals=-0.461659617859067,-0.101608912156841,-0.0154060756129463,0.0761699801959866,1.34547826757643 predictedValues: Include Exclude Both Lung 48.5473055590507 41.0441764804029 53.2328736012982 cerebhem 59.2461687860163 46.1929136271013 67.5810142021884 cortex 54.4021674928182 46.269486367618 61.2780049372371 heart 49.1536892792614 46.1858290818447 49.2569992455599 kidney 47.5945276307119 44.6478100370662 51.7061931417255 liver 49.8476593811525 50.2108296819103 49.3204394707642 stomach 48.5727122107637 43.8697395370907 52.0925654623189 testicle 52.9395774841203 47.2485132970094 51.2091364141621 diffExp=7.50312907864779,13.0532551589150,8.13268112520024,2.96786019741664,2.94671759364569,-0.363170300757794,4.70297267367305,5.69106418711083 diffExpScore=0.994003235704725 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=0,1,0,0,0,0,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 51.2539004614781 50.9535943775825 50.0845311205199 cerebhem 50.4750377969238 51.9957366969788 49.4614776009571 cortex 48.1510373668451 48.4831668788004 49.5864134273916 heart 51.3509602411504 53.7363684643785 48.4430503030053 kidney 52.9791515939076 56.4068364815753 60.8011300257695 liver 50.6701842677475 50.6660511806304 54.6227146067672 stomach 51.064130984596 47.9709901837043 56.3494719872863 testicle 50.5324064632656 51.5162980084217 52.8804743386061 cont.diffExp=0.300306083895606,-1.52069890005503,-0.332129511955351,-2.38540822322805,-3.42768488766767,0.00413308711707572,3.09314080089170,-0.98389154515607 cont.diffExpScore=1.92689441591198 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=0.300890199575525 cont.tran.correlation=0.732587407702447 tran.covariance=0.00143450216623167 cont.tran.covariance=0.00100396188333105 tran.mean=48.4983191208711 cont.tran.mean=51.1378657154991 weightedLogRatios: wLogRatio Lung 0.637744904195234 cerebhem 0.984862888016133 cortex 0.633993656040774 heart 0.240633928984079 kidney 0.244833661158101 liver -0.0284023231847702 stomach 0.390254433463954 testicle 0.444944415038125 cont.weightedLogRatios: wLogRatio Lung 0.0231169650436735 cerebhem -0.116841070227580 cortex -0.026655782010384 heart -0.179872270428813 kidney -0.25084559634291 liver 0.000320193341745297 stomach 0.243809774057138 testicle -0.0758272015949682 varWeightedLogRatios=0.096177790706387 cont.varWeightedLogRatios=0.0224358399906040 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.58384902538662 0.0796975282361229 44.9681326975237 7.25708285364231e-171 *** df.mm.trans1 0.275148051451324 0.0702275240543647 3.91795175974488 0.000102801026438433 *** df.mm.trans2 0.0621880643283363 0.0663989780582156 0.936581648497852 0.349463109949438 df.mm.exp2 0.0786889027511493 0.0914838431751513 0.860139889406424 0.390158055710779 df.mm.exp3 0.0929542040258536 0.0914838431751513 1.01607235550749 0.310126736054082 df.mm.exp4 0.208061926076099 0.0914838431751513 2.27430242166097 0.0234055448288396 * df.mm.exp5 0.0934339570112124 0.0914838431751512 1.02131648352734 0.307639014079731 df.mm.exp6 0.304352161001709 0.0914838431751513 3.32684057029621 0.000948461361720192 *** df.mm.exp7 0.088752906607138 0.0914838431751513 0.970148427599563 0.332480131156765 df.mm.exp8 0.266142916492610 0.0914838431751513 2.90917944913030 0.00379856645588927 ** df.mm.trans1:exp2 0.120473518405569 0.084697580815995 1.42239621539246 0.155586057848222 df.mm.trans2:exp2 0.0394885371007461 0.0773179592983955 0.510729168993543 0.60978466322037 df.mm.trans1:exp3 0.0209110977318219 0.084697580815995 0.246891322400945 0.805102000598324 df.mm.trans2:exp3 0.0268795362903692 0.0773179592983955 0.347649324093413 0.728261886480664 df.mm.trans1:exp4 -0.195648715595385 0.084697580815995 -2.30996816804521 0.0213298232396620 * df.mm.trans2:exp4 -0.0900378666462075 0.0773179592983955 -1.16451426632617 0.244816632956323 df.mm.trans1:exp5 -0.113254863052720 0.084697580815995 -1.33716762582353 0.181825910324198 df.mm.trans2:exp5 -0.00927765999009547 0.0773179592983955 -0.119993596239263 0.90454038335788 df.mm.trans1:exp6 -0.277919313861327 0.084697580815995 -3.28131348243706 0.00111148673672394 ** df.mm.trans2:exp6 -0.102770388631591 0.0773179592983955 -1.32919168540089 0.184440743639716 df.mm.trans1:exp7 -0.0882297054612565 0.084697580815995 -1.04170278077877 0.298094303879300 df.mm.trans2:exp7 -0.0221770903761901 0.0773179592983955 -0.286829742758748 0.774371278190849 df.mm.trans1:exp8 -0.179530395686832 0.084697580815995 -2.11966379626428 0.0345670326003982 * df.mm.trans2:exp8 -0.125370689437821 0.0773179592983955 -1.62149506499486 0.105593732805724 df.mm.trans1:probe2 0.081637015837124 0.0423487904079975 1.92772957741213 0.054501424593222 . df.mm.trans1:probe3 0.0284500426364926 0.0423487904079975 0.67180295735483 0.502044935266154 df.mm.trans1:probe4 0.0261170236602875 0.0423487904079975 0.616712387973077 0.537728224504082 df.mm.trans1:probe5 0.0380653831782647 0.0423487904079975 0.898854083234362 0.369198610349545 df.mm.trans1:probe6 -0.0112181085071613 0.0423487904079975 -0.264897967547209 0.791206207159473 df.mm.trans1:probe7 -0.0261056104781660 0.0423487904079975 -0.616442883649304 0.537905877975053 df.mm.trans1:probe8 0.0374231722177453 0.0423487904079975 0.883689282673773 0.377323580660667 df.mm.trans1:probe9 0.052990679561834 0.0423487904079975 1.25129145487534 0.211461392605371 df.mm.trans1:probe10 -0.0272594688754852 0.0423487904079975 -0.64368943275266 0.52009623792721 df.mm.trans1:probe11 0.0529442072702332 0.0423487904079975 1.25019408488784 0.211861523443443 df.mm.trans1:probe12 0.100079934856718 0.0423487904079975 2.36323006849843 0.0185296266759142 * df.mm.trans2:probe2 0.198111299625271 0.0423487904079975 4.6780863801923 3.80841129729024e-06 *** df.mm.trans2:probe3 0.0135378603545181 0.0423487904079975 0.319675254572596 0.749359026288647 df.mm.trans2:probe4 0.285376762674943 0.0423487904079975 6.73872287556647 4.77221971338202e-11 *** df.mm.trans2:probe5 0.046389890932096 0.0423487904079975 1.09542422546584 0.273901541364603 df.mm.trans2:probe6 0.0740910349660536 0.0423487904079975 1.74954312159201 0.08086113613263 . df.mm.trans3:probe2 0.237879752707285 0.0423487904079975 5.61715577742599 3.35792983799505e-08 *** df.mm.trans3:probe3 -0.0774067851648488 0.0423487904079975 -1.82783934131518 0.0682184328998024 . cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.00601001339770 0.0988306956463584 40.5340667410885 3.04428885686807e-154 *** df.mm.trans1 -0.0221909780212226 0.0870872059576422 -0.25481329636429 0.798980690852011 df.mm.trans2 -0.0333883588990824 0.0823395321904901 -0.405496096599619 0.685300601399743 df.mm.exp2 0.0174516796555545 0.113446578099828 0.153831697243418 0.877809611702236 df.mm.exp3 -0.102152353521254 0.113446578099828 -0.900444554893178 0.368352840025738 df.mm.exp4 0.0883899478743638 0.113446578099828 0.77913278086347 0.436300403312389 df.mm.exp5 -0.0591143650473215 0.113446578099828 -0.521076669190529 0.602563026235207 df.mm.exp6 -0.103850899898009 0.113446578099828 -0.915416768292694 0.360450421561927 df.mm.exp7 -0.18188892580021 0.113446578099828 -1.60330023916769 0.109551784020673 df.mm.exp8 -0.0575159248730274 0.113446578099828 -0.506986864094005 0.612405927278875 df.mm.trans1:exp2 -0.0327644883202785 0.105031122255237 -0.311950283085209 0.755219040406994 df.mm.trans2:exp2 0.00279474463213486 0.0958798581654606 0.0291484018187840 0.976758820025007 df.mm.trans1:exp3 0.0397033143280865 0.105031122255237 0.378014758631285 0.705593247055149 df.mm.trans2:exp3 0.0524537122203392 0.0958798581654606 0.547077490767868 0.58458964610083 df.mm.trans1:exp4 -0.086498033437591 0.105031122255237 -0.823546693401896 0.410622087827066 df.mm.trans2:exp4 -0.0352152273321805 0.0958798581654606 -0.367284933519710 0.713574671178965 df.mm.trans1:exp5 0.09222111337001 0.105031122255237 0.878036065785363 0.38038053416381 df.mm.trans2:exp5 0.160789426081721 0.0958798581654606 1.67698856838363 0.094221132746216 . df.mm.trans1:exp6 0.0923968343154152 0.105031122255237 0.879709102706535 0.379474259885026 df.mm.trans2:exp6 0.0981916799155848 0.0958798581654606 1.02411165175208 0.306318464229270 df.mm.trans1:exp7 0.178179517169144 0.105031122255237 1.69644495215569 0.0904752493074714 . df.mm.trans2:exp7 0.121570078493305 0.0958798581654606 1.26794178484819 0.205457354299959 df.mm.trans1:exp8 0.0433390458010331 0.105031122255237 0.412630512465766 0.680068757253192 df.mm.trans2:exp8 0.0684988443247112 0.0958798581654606 0.714423713544739 0.475326018170531 df.mm.trans1:probe2 -0.047268032181203 0.0525155611276186 -0.900076685200724 0.368548355665327 df.mm.trans1:probe3 -0.073925934597801 0.0525155611276186 -1.40769579550246 0.159893636770192 df.mm.trans1:probe4 -0.106737275485210 0.0525155611276187 -2.03248852708299 0.042675636256256 * df.mm.trans1:probe5 -0.088302774510524 0.0525155611276186 -1.68145922112378 0.093349555034719 . df.mm.trans1:probe6 -0.0762338164044393 0.0525155611276187 -1.45164242307499 0.147279760231999 df.mm.trans1:probe7 -0.0370707723435927 0.0525155611276187 -0.705900718712814 0.480605672604731 df.mm.trans1:probe8 -0.0318784124257736 0.0525155611276187 -0.607027931174638 0.544130577226071 df.mm.trans1:probe9 -0.0492112294699843 0.0525155611276186 -0.937078999315946 0.349207512181380 df.mm.trans1:probe10 -0.0435153940031122 0.0525155611276187 -0.828619042979755 0.407747875622914 df.mm.trans1:probe11 -0.0813221631527536 0.0525155611276186 -1.54853459444395 0.122178349023927 df.mm.trans1:probe12 -0.0699439007529008 0.0525155611276187 -1.33187000673818 0.183559586119007 df.mm.trans2:probe2 -0.0594112648958608 0.0525155611276187 -1.13130781848612 0.258512447164113 df.mm.trans2:probe3 -0.0593902137874891 0.0525155611276187 -1.13090696380763 0.258680969099714 df.mm.trans2:probe4 -0.109400861088968 0.0525155611276187 -2.08320845745343 0.0377814782403487 * df.mm.trans2:probe5 -0.105533355797239 0.0525155611276186 -2.00956351853084 0.0450584061921902 * df.mm.trans2:probe6 -0.0416214558931282 0.0525155611276187 -0.792554720913739 0.428444082207823 df.mm.trans3:probe2 -0.112422455440340 0.0525155611276186 -2.14074558143139 0.0328173424722930 * df.mm.trans3:probe3 0.00226702704362258 0.0525155611276187 0.0431686721981976 0.96558572835866