fitVsDatCorrelation=0.890930603717331 cont.fitVsDatCorrelation=0.232099156392727 fstatistic=10342.6055215055,49,623 cont.fstatistic=2244.59574260469,49,623 residuals=-0.45736325656305,-0.0914395852790342,-0.0101526902250044,0.0794299415765147,1.28522786856424 cont.residuals=-0.626951968095219,-0.209298695145638,-0.093760000812809,0.130451630630437,1.20174348406651 predictedValues: Include Exclude Both Lung 46.2605936784973 53.2381850175255 88.0961781238001 cerebhem 50.7234479727408 47.968103425954 61.6093206568792 cortex 46.5325423822661 50.5021196471359 70.8812288576262 heart 47.5845212385813 53.127441792296 81.5661711419838 kidney 45.4248470935898 49.3066939224977 62.8836441926639 liver 46.6925195498121 53.8183428871918 68.4125616927548 stomach 50.3476716626257 52.223414686995 74.5056927196417 testicle 47.1844018096181 52.252700241703 74.7070785034775 diffExp=-6.97759133902817,2.75534454678682,-3.96957726486981,-5.54292055371469,-3.88184682890789,-7.12582333737971,-1.87574302436931,-5.06829843208485 diffExpScore=1.13799871914354 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 55.7417343994098 55.9295070350175 52.0974564970282 cerebhem 59.6660055843898 49.951546196238 54.6995761397672 cortex 53.3158932991009 54.0272611352477 56.5393380061992 heart 56.1332970537195 57.7515877557449 59.0355097066041 kidney 55.3694106744794 54.7191226903105 59.0166740803297 liver 55.910009532771 55.3934670166834 62.0094953026534 stomach 58.8172376312592 50.3856724326368 59.201053083728 testicle 54.3927673292136 45.4362247381679 56.8187272482641 cont.diffExp=-0.187772635607679,9.71445938815185,-0.711367836146849,-1.61829070202543,0.650287984168919,0.51654251608759,8.43156519862242,8.95654259104571 cont.diffExpScore=1.15082488784187 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=-0.283900665272838 cont.tran.correlation=-0.175183429007594 tran.covariance=-0.000454743641798489 cont.tran.covariance=-0.000443765951034266 tran.mean=49.5742216880644 cont.tran.mean=54.5587965315244 weightedLogRatios: wLogRatio Lung -0.548529955249693 cerebhem 0.217736955560197 cortex -0.317718717592665 heart -0.431664989782245 kidney -0.316281209053063 liver -0.555992022388989 stomach -0.144018695339774 testicle -0.398428028613497 cont.weightedLogRatios: wLogRatio Lung -0.0135271954537943 cerebhem 0.710819553723054 cortex -0.0527899732574948 heart -0.114878708423268 kidney 0.0473521207404656 liver 0.0373043557911961 stomach 0.618459233552553 testicle 0.702822048314219 varWeightedLogRatios=0.0637368402490961 cont.varWeightedLogRatios=0.133316579540564 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.46723539049152 0.0702512008789745 49.3548202323931 1.92006178749742e-217 *** df.mm.trans1 0.308250016068257 0.0591730382282931 5.20929844567065 2.57982307514644e-07 *** df.mm.trans2 0.538681966602515 0.0545271739149037 9.87914700004799 1.76216547590453e-21 *** df.mm.exp2 0.345474156240529 0.0712113985047375 4.8513884503693 1.55091421165348e-06 *** df.mm.exp3 0.170524327590986 0.0712113985047375 2.39462124282872 0.0169320185020895 * df.mm.exp4 0.103149265236204 0.0712113985047376 1.44849374400844 0.147982108245358 df.mm.exp5 0.242195756463091 0.0712113985047375 3.40108130929318 0.000714133328592655 *** df.mm.exp6 0.273004625852747 0.0712113985047376 3.8337208871777 0.000139040742107188 *** df.mm.exp7 0.232970550060387 0.0712113985047375 3.27153454295506 0.00112871351365759 ** df.mm.exp8 0.165942848166178 0.0712113985047376 2.33028492138289 0.0201094904558816 * df.mm.trans1:exp2 -0.253376358124983 0.0621584045698333 -4.07630086194252 5.16824309448392e-05 *** df.mm.trans2:exp2 -0.449713780543763 0.0515390263813124 -8.72569414130077 2.41114438637499e-17 *** df.mm.trans1:exp3 -0.164662913829016 0.0621584045698333 -2.64908526801104 0.00827565393962088 ** df.mm.trans2:exp3 -0.223284921345811 0.0515390263813124 -4.33234651531508 1.71944628797662e-05 *** df.mm.trans1:exp4 -0.0749322311840123 0.0621584045698333 -1.20550441573557 0.228466297738532 df.mm.trans2:exp4 -0.105231578264696 0.0515390263813124 -2.04178436523302 0.0415935928985579 * df.mm.trans1:exp5 -0.260426998553494 0.0621584045698333 -4.18973106462073 3.19651001036572e-05 *** df.mm.trans2:exp5 -0.318911807636524 0.0515390263813124 -6.18777322794283 1.10601678050026e-09 *** df.mm.trans1:exp6 -0.263711145192404 0.0621584045698333 -4.24256618260096 2.54572590818997e-05 *** df.mm.trans2:exp6 -0.262166173301158 0.0515390263813124 -5.08675059869228 4.82670614321395e-07 *** df.mm.trans1:exp7 -0.148308665249184 0.0621584045698333 -2.38597927787164 0.0173313911776884 * df.mm.trans2:exp7 -0.252215500887286 0.0515390263813124 -4.89367996634754 1.2618666562294e-06 *** df.mm.trans1:exp8 -0.146169970565129 0.0621584045698333 -2.35157210962375 0.0190047342695694 * df.mm.trans2:exp8 -0.184627181755443 0.0515390263813124 -3.58227919149027 0.000367189412756723 *** df.mm.trans1:probe2 0.051778431809522 0.0425569504017874 1.21668567227381 0.224184558756967 df.mm.trans1:probe3 0.0163345761982871 0.0425569504017874 0.383828635371417 0.701236418982128 df.mm.trans1:probe4 0.00157407612622204 0.0425569504017874 0.036987521694128 0.970506803492354 df.mm.trans1:probe5 0.0759578139382734 0.0425569504017874 1.78485096373548 0.074771951636572 . df.mm.trans1:probe6 0.0402786106314912 0.0425569504017874 0.946463744493297 0.344279050292895 df.mm.trans1:probe7 0.725483791462072 0.0425569504017874 17.0473632300401 9.06583818528047e-54 *** df.mm.trans1:probe8 0.143632097122250 0.0425569504017874 3.37505614867125 0.000783855258178516 *** df.mm.trans1:probe9 0.0562160880620212 0.0425569504017874 1.32096138307082 0.186999222168183 df.mm.trans1:probe10 0.064109635857846 0.0425569504017874 1.50644337182472 0.132460250126056 df.mm.trans1:probe11 0.0591658929137665 0.0425569504017874 1.39027567424760 0.164941658118306 df.mm.trans1:probe12 0.0591808267983443 0.0425569504017874 1.39062658953727 0.164835213732601 df.mm.trans2:probe2 -0.118944489060665 0.0425569504017874 -2.79494860270037 0.00535063451182849 ** df.mm.trans2:probe3 -0.111292593755299 0.0425569504017874 -2.61514494587997 0.00913487026790136 ** df.mm.trans2:probe4 -0.0754695398994596 0.0425569504017874 -1.77337753732208 0.0766546423256394 . df.mm.trans2:probe5 -0.116339951804706 0.0425569504017874 -2.73374738336089 0.00643970909778664 ** df.mm.trans2:probe6 -0.076216701129998 0.0425569504017874 -1.79093427537507 0.0737891938565023 . df.mm.trans3:probe2 -0.0121871661143372 0.0425569504017874 -0.286373107078306 0.774687573802568 df.mm.trans3:probe3 0.281653621660982 0.0425569504017874 6.61827548736088 7.84294678846446e-11 *** df.mm.trans3:probe4 0.0848779044191916 0.0425569504017874 1.99445457481903 0.0465377097264547 * df.mm.trans3:probe5 0.285845972522401 0.0425569504017874 6.71678703064202 4.19269512448443e-11 *** df.mm.trans3:probe6 0.632153768279833 0.0425569504017874 14.8543014081498 5.98733782060568e-43 *** df.mm.trans3:probe7 -0.0151405918656330 0.0425569504017874 -0.355772481878708 0.722131390900733 df.mm.trans3:probe8 -0.184868095472406 0.0425569504017874 -4.34401651732642 1.63309788932039e-05 *** df.mm.trans3:probe9 0.00256822689343014 0.0425569504017874 0.0603480011885972 0.951897825476144 df.mm.trans3:probe10 0.567714136918671 0.0425569504017874 13.3401038269609 6.87538257701087e-36 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.04525323948427 0.150466529795831 26.8847380541925 2.78992507039758e-106 *** df.mm.trans1 -0.0391356216153797 0.126738925574041 -0.308789280310861 0.757585054569318 df.mm.trans2 -0.052841457699608 0.116788247544461 -0.452455266780944 0.651098545566657 df.mm.exp2 -0.0937451679194184 0.152523115346812 -0.61462924951577 0.53902399669339 df.mm.exp3 -0.160918520671262 0.152523115346812 -1.05504349491787 0.291814406555773 df.mm.exp4 -0.0859642171891118 0.152523115346812 -0.563614354412074 0.573219423038586 df.mm.exp5 -0.153284592012885 0.152523115346812 -1.00499253286521 0.315290618504041 df.mm.exp6 -0.180787539260517 0.152523115346812 -1.18531239576006 0.236345874452371 df.mm.exp7 -0.178502608191468 0.152523115346812 -1.17033151195202 0.242315073199718 df.mm.exp8 -0.319030200683603 0.152523115346812 -2.09168426673020 0.0368710879842501 * df.mm.trans1:exp2 0.161778467985101 0.133133089772809 1.21516347484442 0.224764062416319 df.mm.trans2:exp2 -0.0192934674165544 0.110388126489277 -0.174778466037545 0.861310493416773 df.mm.trans1:exp3 0.116423855664403 0.133133089772809 0.874492253301412 0.382187221478757 df.mm.trans2:exp3 0.126315180855634 0.110388126489277 1.14428231434749 0.252946021798563 df.mm.trans1:exp4 0.0929642465632561 0.133133089772809 0.698280545594631 0.485262315446774 df.mm.trans2:exp4 0.118022964987962 0.110388126489277 1.06916358435910 0.285410036636418 df.mm.trans1:exp5 0.146582741928413 0.133133089772809 1.10102411187599 0.271311372056166 df.mm.trans2:exp5 0.131405737661258 0.110388126489277 1.19039739001297 0.23434363022935 df.mm.trans1:exp6 0.183801827346781 0.133133089772809 1.38058710768628 0.167901052528239 df.mm.trans2:exp6 0.171157107342526 0.110388126489277 1.55050287368679 0.121528683238638 df.mm.trans1:exp7 0.232208439673786 0.133133089772809 1.74418275779559 0.0816204111736328 . df.mm.trans2:exp7 0.0741173708930836 0.110388126489277 0.67142520894476 0.502198564310597 df.mm.trans1:exp8 0.294532254744947 0.133133089772809 2.21231442346576 0.0273068981671499 * df.mm.trans2:exp8 0.11124779446723 0.110388126489277 1.00778768519127 0.313947650028984 df.mm.trans1:probe2 0.0420313019126696 0.0911499955236599 0.461122369465827 0.644871752359125 df.mm.trans1:probe3 0.00826253062274451 0.0911499955236598 0.0906476251071214 0.927801724343071 df.mm.trans1:probe4 -0.00259135330375663 0.0911499955236598 -0.0284295494351833 0.977328661052036 df.mm.trans1:probe5 -0.00629952587865968 0.0911499955236599 -0.0691116422164224 0.944922930278197 df.mm.trans1:probe6 0.137602019455345 0.0911499955236598 1.50962179059710 0.131646938483455 df.mm.trans1:probe7 0.0387048981013905 0.0911499955236599 0.42462863414342 0.671254018529968 df.mm.trans1:probe8 -0.0126980292587916 0.0911499955236598 -0.139309159433755 0.889250873434073 df.mm.trans1:probe9 -0.00693593438921052 0.0911499955236598 -0.0760936338983161 0.93936903370715 df.mm.trans1:probe10 -0.00904619346212663 0.0911499955236599 -0.0992451333667756 0.920975565498064 df.mm.trans1:probe11 0.137137867767360 0.0911499955236598 1.5045296160411 0.132951830976234 df.mm.trans1:probe12 -0.00471415068692223 0.0911499955236598 -0.0517186057973922 0.958769489739314 df.mm.trans2:probe2 0.0492382959665615 0.0911499955236599 0.540189779315795 0.589259205490514 df.mm.trans2:probe3 0.159955431273725 0.0911499955236598 1.75485945286970 0.0797748837211041 . df.mm.trans2:probe4 0.0528860209063632 0.0911499955236598 0.580208705469827 0.561983668430785 df.mm.trans2:probe5 0.0747416681871569 0.0911499955236598 0.81998542904762 0.412538014960713 df.mm.trans2:probe6 0.17006359240887 0.0911499955236598 1.86575535667170 0.0625453477630908 . df.mm.trans3:probe2 -0.00267253469275653 0.0911499955236599 -0.0293201845749166 0.976618618691885 df.mm.trans3:probe3 -0.0486234495866404 0.0911499955236599 -0.533444344207556 0.593916281664458 df.mm.trans3:probe4 0.00573877614195729 0.0911499955236598 0.0629596974633715 0.949818791480957 df.mm.trans3:probe5 0.0113115864641442 0.0911499955236599 0.124098595936936 0.901277237841713 df.mm.trans3:probe6 -0.0188719354976636 0.0911499955236599 -0.207042637679176 0.836044212045448 df.mm.trans3:probe7 0.0989123365219331 0.0911499955236599 1.08516008095972 0.278270490144128 df.mm.trans3:probe8 -0.0196750367223448 0.0911499955236598 -0.215853403056259 0.82917267118554 df.mm.trans3:probe9 -0.0487308394166216 0.0911499955236598 -0.534622510255336 0.593101655285144 df.mm.trans3:probe10 0.0210107744234352 0.0911499955236599 0.230507684643620 0.81777296427216