fitVsDatCorrelation=0.838333151794808 cont.fitVsDatCorrelation=0.270314444153502 fstatistic=12066.8345403440,59,853 cont.fstatistic=3859.11401043801,59,853 residuals=-0.424716466209226,-0.090144683395558,-0.00202617163920611,0.083086652570053,0.73313380725474 cont.residuals=-0.533826866670409,-0.186751003432366,-0.0332242896542216,0.152987644706801,0.850329508086895 predictedValues: Include Exclude Both Lung 65.420517099678 72.2919800905005 61.6627536182115 cerebhem 71.3311972845518 70.5343236173185 58.219865735156 cortex 63.4313338368103 64.8714394730322 64.5239227978816 heart 60.2943306171324 64.9562752346278 58.536410593689 kidney 65.9255502151391 73.7062803081235 62.5064771415993 liver 60.968522479279 74.1730794690588 59.0083548955169 stomach 58.7531406556218 63.798068240937 58.385422037419 testicle 61.1437562600556 63.7970989123605 55.9339118445427 diffExp=-6.87146299082251,0.796873667233356,-1.44010563622188,-4.66194461749534,-7.78073009298436,-13.2045569897798,-5.04492758531526,-2.6533426523049 diffExpScore=1.01418405498469 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,-1,0,0 diffExp1.2Score=0.5 cont.predictedValues: Include Exclude Both Lung 63.783197706723 64.5456560415747 68.6870664804339 cerebhem 60.3503242176973 61.520242314159 65.3111491761565 cortex 65.9835670446842 61.161781958955 63.4247463952656 heart 62.904524906591 59.9480752425956 65.1480089867659 kidney 57.7995966057597 62.8197316354025 60.4315973134936 liver 64.2790253718011 66.027378949994 61.4847835082867 stomach 59.0481725512916 57.7549313570646 62.8053052800339 testicle 60.3478895936018 64.3800465017056 59.41601897294 cont.diffExp=-0.762458334851672,-1.1699180964618,4.82178508572918,2.95644966399539,-5.02013502964282,-1.74835357819286,1.29324119422699,-4.03215690810373 cont.diffExpScore=4.67752498329148 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=0.508831393008639 cont.tran.correlation=0.275237864224978 tran.covariance=0.00221411372094764 cont.tran.covariance=0.00055989549137493 tran.mean=65.9623058621392 cont.tran.mean=62.040883874975 weightedLogRatios: wLogRatio Lung -0.422558218248161 cerebhem 0.0478775314117323 cortex -0.0934164429140849 heart -0.30806934088559 kidney -0.473503092897544 liver -0.825025730276165 stomach -0.338948304607408 testicle -0.175632049282862 cont.weightedLogRatios: wLogRatio Lung -0.049450380474583 cerebhem -0.078907283304042 cortex 0.315026424577712 heart 0.198215999564115 kidney -0.341363967274425 liver -0.112085077550295 stomach 0.0900693616846566 testicle -0.267278614783561 varWeightedLogRatios=0.0710061019263932 cont.varWeightedLogRatios=0.0497564459685707 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.46582101721557 0.0688903801126116 64.8250308666539 0 *** df.mm.trans1 -0.253904514811485 0.0594920471498395 -4.26787321962529 2.19465239748557e-05 *** df.mm.trans2 -0.179079378806802 0.0525609547576263 -3.40708002037993 0.000687341269797545 *** df.mm.exp2 0.119337555908558 0.0676101936657845 1.7650822965909 0.0779077153653114 . df.mm.exp3 -0.184539674517252 0.0676101936657844 -2.72946525533533 0.00647437589341093 ** df.mm.exp4 -0.136565573795512 0.0676101936657844 -2.01989620782027 0.0437063533428985 * df.mm.exp5 0.0134748583039591 0.0676101936657844 0.199302169885343 0.842073879216023 df.mm.exp6 -0.000789106667406451 0.0676101936657844 -0.0116714155753971 0.9906904982381 df.mm.exp7 -0.177867759322801 0.0676101936657844 -2.63078316565768 0.00867250192626647 ** df.mm.exp8 -0.0951044232488607 0.0676101936657844 -1.40665805098840 0.159893024257099 df.mm.trans1:exp2 -0.0328397009969370 0.0624935079581428 -0.525489799979424 0.599379310449729 df.mm.trans2:exp2 -0.143951302317448 0.0461545144265796 -3.11889972423908 0.00187625288402780 ** df.mm.trans1:exp3 0.153661712042384 0.0624935079581428 2.45884279924411 0.0141362004121678 * df.mm.trans2:exp3 0.0762339340457707 0.0461545144265796 1.65171132212950 0.0989614606722394 . df.mm.trans1:exp4 0.0549677271074684 0.0624935079581428 0.879574997522702 0.379337307847653 df.mm.trans2:exp4 0.0295667310671121 0.0461545144265796 0.64060323100453 0.521952548795925 df.mm.trans1:exp5 -0.00578470640692662 0.0624935079581428 -0.0925649174759261 0.926270950765657 df.mm.trans2:exp5 0.00589995418419526 0.0461545144265796 0.127830489768896 0.898313250158568 df.mm.trans1:exp6 -0.0696891136605043 0.0624935079581428 -1.1151416513085 0.265103796794878 df.mm.trans2:exp6 0.0264771830496414 0.0461545144265796 0.573663993188793 0.566346414424963 df.mm.trans1:exp7 0.0703764425015875 0.0624935079581428 1.12614005519941 0.260422967685128 df.mm.trans2:exp7 0.0528774732904838 0.0461545144265796 1.1456619996425 0.252256344368639 df.mm.trans1:exp8 0.0274962484703633 0.0624935079581428 0.439985678012825 0.660058938516621 df.mm.trans2:exp8 -0.0299010565967991 0.0461545144265796 -0.647846845932359 0.517258342856521 df.mm.trans1:probe2 0.182401479914489 0.0427863800078793 4.26307343320232 2.24133661038004e-05 *** df.mm.trans1:probe3 -0.242593077520654 0.0427863800078793 -5.66986684725325 1.95577142833358e-08 *** df.mm.trans1:probe4 -0.165014214448445 0.0427863800078793 -3.85669959501263 0.000123576606197658 *** df.mm.trans1:probe5 -0.193027349407870 0.0427863800078793 -4.51142044202673 7.34148197102806e-06 *** df.mm.trans1:probe6 -0.00244177932701397 0.0427863800078793 -0.0570690796128185 0.954503532283369 df.mm.trans1:probe7 -0.0805328696510718 0.0427863800078793 -1.88220806799363 0.0601479747503833 . df.mm.trans1:probe8 -0.100924838141807 0.0427863800078793 -2.3588075953895 0.0185583823878903 * df.mm.trans1:probe9 -0.298892319566481 0.0427863800078793 -6.98568842494829 5.7081938355948e-12 *** df.mm.trans1:probe10 -0.0682623112185727 0.0427863800078793 -1.59542151511770 0.110988414243849 df.mm.trans1:probe11 -0.198146907395103 0.0427863800078793 -4.63107435961194 4.20449792152256e-06 *** df.mm.trans1:probe12 -0.204460552768901 0.0427863800078793 -4.77863639623751 2.07763766556826e-06 *** df.mm.trans1:probe13 -0.0405778954331636 0.0427863800078793 -0.948383467488744 0.343202873307379 df.mm.trans1:probe14 -0.287582190639084 0.0427863800078793 -6.72134895698408 3.29189143385331e-11 *** df.mm.trans1:probe15 -0.187894935289264 0.0427863800078793 -4.39146605192265 1.26735346829559e-05 *** df.mm.trans1:probe16 -0.117988571371045 0.0427863800078793 -2.75761986289368 0.00594685061684275 ** df.mm.trans1:probe17 0.0616458713079358 0.0427863800078793 1.44078258774366 0.150013070036951 df.mm.trans1:probe18 0.0835105283671392 0.0427863800078793 1.9518016796878 0.0512886629722564 . df.mm.trans1:probe19 0.133690934765995 0.0427863800078793 3.12461429878796 0.00184059524238852 ** df.mm.trans1:probe20 0.172105121853646 0.0427863800078793 4.02242774036859 6.27097265468816e-05 *** df.mm.trans1:probe21 0.379032093157754 0.0427863800078793 8.85870908190769 4.63551418433568e-18 *** df.mm.trans1:probe22 0.181375350190728 0.0427863800078793 4.23909080780675 2.48913577383012e-05 *** df.mm.trans2:probe2 -0.220853982732666 0.0427863800078793 -5.16178238710531 3.04491861235702e-07 *** df.mm.trans2:probe3 -0.148896163557938 0.0427863800078793 -3.47998974277605 0.000526855893526862 *** df.mm.trans2:probe4 0.0400663419299747 0.0427863800078793 0.93642747815067 0.349318173879574 df.mm.trans2:probe5 0.0415509060144562 0.0427863800078793 0.971124596350623 0.331761523223622 df.mm.trans2:probe6 0.191677844917007 0.0427863800078793 4.47987992631555 8.48535803033035e-06 *** df.mm.trans3:probe2 -0.0511772676147784 0.0427863800078793 -1.19611118316983 0.231985546577283 df.mm.trans3:probe3 -0.212681933620305 0.0427863800078793 -4.97078588048671 8.06036738078547e-07 *** df.mm.trans3:probe4 -0.0116302886734713 0.0427863800078793 -0.271822217054341 0.7858244967686 df.mm.trans3:probe5 0.0435550223628148 0.0427863800078793 1.01796465031148 0.308983389463404 df.mm.trans3:probe6 0.615795860783645 0.0427863800078793 14.3923337442953 3.32121307195343e-42 *** df.mm.trans3:probe7 -0.243132191413210 0.0427863800078793 -5.682466975903 1.82184252142129e-08 *** df.mm.trans3:probe8 -0.179295038851077 0.0427863800078793 -4.19046993033903 3.07390413658184e-05 *** df.mm.trans3:probe9 0.558269241680827 0.0427863800078793 13.0478260039297 1.30795293859872e-35 *** df.mm.trans3:probe10 0.368726154943306 0.0427863800078793 8.61783948245689 3.27924010742522e-17 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08108314768107 0.121663268187112 33.544086136208 5.60352432983037e-158 *** df.mm.trans1 0.0611465737918468 0.105065422422690 0.581985703591872 0.560730088677313 df.mm.trans2 0.0823494131003711 0.092824825823208 0.887148587353257 0.375248966501991 df.mm.exp2 -0.0529318708523659 0.119402405832234 -0.44330656893746 0.657656415428249 df.mm.exp3 0.0597724413570098 0.119402405832234 0.500596624836795 0.616784172414885 df.mm.exp4 -0.0348665636944176 0.119402405832234 -0.292008887521133 0.770350832358129 df.mm.exp5 0.0024372209992101 0.119402405832234 0.0204118248893117 0.983719624446602 df.mm.exp6 0.141211450364560 0.119402405832234 1.18265163402963 0.237276777004483 df.mm.exp7 -0.0987789121554523 0.119402405832234 -0.827277402552852 0.408311215774173 df.mm.exp8 0.0870641607879456 0.119402405832234 0.729165883895795 0.466100355390438 df.mm.trans1:exp2 -0.00239160616551865 0.110366126681787 -0.0216697481140589 0.98271646345415 df.mm.trans2:exp2 0.00492531582572561 0.0815107865212506 0.0604253257259581 0.951831042393708 df.mm.trans1:exp3 -0.0258565113406647 0.110366126681787 -0.234279412697117 0.814824286904473 df.mm.trans2:exp3 -0.113622743704047 0.0815107865212506 -1.39395960403872 0.163692816625009 df.mm.trans1:exp4 0.0209948660780717 0.110366126681787 0.190229255200784 0.849174748233662 df.mm.trans2:exp4 -0.0390274803955738 0.0815107865212506 -0.478801420783727 0.632202612578886 df.mm.trans1:exp5 -0.100945221321264 0.110366126681787 -0.914639521710438 0.360639373617400 df.mm.trans2:exp5 -0.0295408178619469 0.0815107865212506 -0.362416057097490 0.717130914243929 df.mm.trans1:exp6 -0.133467868677968 0.110366126681787 -1.20931913342206 0.226875332639958 df.mm.trans2:exp6 -0.118514780784706 0.0815107865212506 -1.45397665563941 0.146320603662205 df.mm.trans1:exp7 0.0216427100027727 0.110366126681787 0.196099207732224 0.844579222999303 df.mm.trans2:exp7 -0.0123851696269578 0.0815107865212506 -0.151945161561272 0.879266117638369 df.mm.trans1:exp8 -0.142427980144277 0.110366126681787 -1.29050447294334 0.197225248356982 df.mm.trans2:exp8 -0.089633231620047 0.0815107865212506 -1.09964871454993 0.271795457347329 df.mm.trans1:probe2 -0.0799935185142694 0.0755625214601096 -1.05864014287161 0.290063385854175 df.mm.trans1:probe3 -0.0982924047007565 0.0755625214601096 -1.30080895662867 0.193675146338041 df.mm.trans1:probe4 0.109583834863283 0.0755625214601096 1.45024057887128 0.14735903273902 df.mm.trans1:probe5 -0.0371977494875005 0.0755625214601096 -0.492277769041067 0.622649665348546 df.mm.trans1:probe6 0.0633102080088787 0.0755625214601096 0.837851977217317 0.402348540430959 df.mm.trans1:probe7 0.0648344828684493 0.0755625214601096 0.858024343492511 0.391120041633385 df.mm.trans1:probe8 0.000173705364839022 0.0755625214601095 0.00229882965102909 0.99816633842291 df.mm.trans1:probe9 0.0612965399707159 0.0755625214601095 0.811202945405616 0.417475419600509 df.mm.trans1:probe10 0.0967894939752295 0.0755625214601096 1.28091932488417 0.200570149594393 df.mm.trans1:probe11 0.082423333823312 0.0755625214601096 1.09079649845756 0.275670460438023 df.mm.trans1:probe12 0.0416270995326103 0.0755625214601096 0.550896115273043 0.58184914313258 df.mm.trans1:probe13 -0.0729676134540468 0.0755625214601095 -0.965658795446197 0.334488634962862 df.mm.trans1:probe14 0.0840510534599762 0.0755625214601096 1.11233786056687 0.266306298099678 df.mm.trans1:probe15 -0.0189477115082039 0.0755625214601095 -0.250755416072327 0.802063600342032 df.mm.trans1:probe16 0.0225301569231052 0.0755625214601095 0.298165763764238 0.765649261936265 df.mm.trans1:probe17 0.0167728248460683 0.0755625214601096 0.221972805062135 0.824388185336247 df.mm.trans1:probe18 0.0597236558213644 0.0755625214601096 0.790387280192777 0.429521306460286 df.mm.trans1:probe19 -0.0283883247123797 0.0755625214601096 -0.375693189743096 0.707238409532978 df.mm.trans1:probe20 0.0114122850437911 0.0755625214601096 0.151031024683524 0.879986951172683 df.mm.trans1:probe21 0.0436284484376144 0.0755625214601095 0.577382114765009 0.563833713087782 df.mm.trans1:probe22 0.00195261108614244 0.0755625214601096 0.0258409995909580 0.979390203770418 df.mm.trans2:probe2 -0.0240697378865164 0.0755625214601095 -0.318540692150183 0.750152809741058 df.mm.trans2:probe3 0.0316711464269623 0.0755625214601095 0.419138295215333 0.675220589628062 df.mm.trans2:probe4 -0.0954522615643465 0.0755625214601096 -1.26322229221449 0.206854468128224 df.mm.trans2:probe5 0.122807969662336 0.0755625214601095 1.62524975727773 0.104478743070077 df.mm.trans2:probe6 0.0280870149064056 0.0755625214601096 0.371705633476420 0.710204342997147 df.mm.trans3:probe2 -0.075308416999767 0.0755625214601095 -0.996637162770214 0.319223292576897 df.mm.trans3:probe3 0.0448710969758945 0.0755625214601095 0.593827417466245 0.552784987781422 df.mm.trans3:probe4 -0.0475574455685173 0.0755625214601095 -0.629378753508425 0.529269647373102 df.mm.trans3:probe5 0.0272860634029297 0.0755625214601095 0.361105782015684 0.718109786073018 df.mm.trans3:probe6 0.0361266834481858 0.0755625214601095 0.478103201826815 0.632699255439406 df.mm.trans3:probe7 -0.011023529131821 0.0755625214601095 -0.145886200179814 0.88404569282096 df.mm.trans3:probe8 0.0846687503131517 0.0755625214601095 1.12051250642621 0.262810800082999 df.mm.trans3:probe9 0.0356865299676203 0.0755625214601095 0.472278178097321 0.636849042597287 df.mm.trans3:probe10 -0.0449318714698373 0.0755625214601095 -0.59463171161589 0.552247365725702