fitVsDatCorrelation=0.821148381615502 cont.fitVsDatCorrelation=0.277139433070853 fstatistic=8183.48717294471,52,692 cont.fstatistic=2878.63328208167,52,692 residuals=-0.602087962276743,-0.0975574821282559,-0.00993503792314776,0.0854019841429411,1.10207489234899 cont.residuals=-0.788835010363981,-0.183859221082121,0.00558275341819489,0.175446776599571,1.2257633892863 predictedValues: Include Exclude Both Lung 90.709530839214 80.7568637363126 69.8415004649034 cerebhem 84.7876469759145 75.5894995471943 60.0985400480369 cortex 83.9023841854612 79.809015829588 71.5161846770336 heart 88.4514684513916 69.0564637664845 63.6713409792876 kidney 91.6165513670826 84.5654983364232 74.9014574411935 liver 93.8581618324617 69.0651664921842 58.2482839436035 stomach 95.2539497981573 68.121630290572 61.3561935047183 testicle 95.1514970034784 98.9825376912456 93.4558742192788 diffExp=9.95266710290142,9.19814742872022,4.09336835587320,19.3950046849071,7.05105303065933,24.7929953402775,27.1323195075852,-3.8310406877672 diffExpScore=1.06744054360653 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=0,0,0,0,0,1,1,0 diffExp1.3Score=0.666666666666667 diffExp1.2=0,0,0,1,0,1,1,0 diffExp1.2Score=0.75 cont.predictedValues: Include Exclude Both Lung 79.5779699609621 77.062189998849 79.3455551278015 cerebhem 79.1043253300852 76.8205589295985 77.2249209694833 cortex 76.534927272544 80.3727203061493 72.8830375396773 heart 67.5299874037731 76.3847521214131 79.1827166981349 kidney 75.8676916535326 79.0012903224614 85.428951933928 liver 67.668778535089 67.0891848904432 80.1073218620739 stomach 77.0145021141245 81.9468234735717 78.496830293948 testicle 86.0918763883618 85.3294513912843 82.9583071880898 cont.diffExp=2.51577996211309,2.28376640048667,-3.83779303360528,-8.85476471764,-3.13359866892877,0.579593644645769,-4.93232135944727,0.762424997077488 cont.diffExpScore=1.72249427085825 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=0.151339867057462 cont.tran.correlation=0.765490115363164 tran.covariance=0.00067112736975697 cont.tran.covariance=0.00440452337842753 tran.mean=84.3548666339479 cont.tran.mean=77.0873143807652 weightedLogRatios: wLogRatio Lung 0.517124674673319 cerebhem 0.503280175371926 cortex 0.220309571568367 heart 1.07890433340929 kidney 0.358588744193424 liver 1.34607787661413 stomach 1.47139307187741 testicle -0.180597694651868 cont.weightedLogRatios: wLogRatio Lung 0.140084666539016 cerebhem 0.127613649946391 cortex -0.213432588041987 heart -0.526627126591417 kidney -0.176027424861237 liver 0.0362174070565772 stomach -0.271587992758562 testicle 0.0395931031109680 varWeightedLogRatios=0.334306277466958 cont.varWeightedLogRatios=0.0539066385878762 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 5.0099601429583 0.0860611310208006 58.2139705060047 7.55681906073858e-269 *** df.mm.trans1 -0.528182611904505 0.0720350719424828 -7.33229797183015 6.33840865215979e-13 *** df.mm.trans2 -0.639227457615288 0.0661217829485745 -9.66742621130529 8.00590762806297e-21 *** df.mm.exp2 0.0166046939745075 0.0855904193903968 0.194001783059034 0.846231419734016 df.mm.exp3 -0.113510269968364 0.0855904193903967 -1.32620298833470 0.185210014369387 df.mm.exp4 -0.0892330424900194 0.0855904193903968 -1.04255877147894 0.297516772947334 df.mm.exp5 -0.0139120345604567 0.0855904193903967 -0.162541960414995 0.870926539179081 df.mm.exp6 0.0592435998064895 0.0855904193903968 0.692175598956542 0.489059245917344 df.mm.exp7 0.00826815407762576 0.0855904193903968 0.0966013969380485 0.923070916664657 df.mm.exp8 -0.0399526123062828 0.0855904193903968 -0.466788369432449 0.640798222114419 df.mm.trans1:exp2 -0.0841172667596928 0.0738808468206975 -1.13855309433361 0.255283605458525 df.mm.trans2:exp2 -0.08273027363517 0.0599252798279887 -1.38055715171696 0.167860915063292 df.mm.trans1:exp3 0.0355018675098297 0.0738808468206976 0.480528703142638 0.631003372028105 df.mm.trans2:exp3 0.101703790042001 0.0599252798279887 1.69717672297793 0.0901129097515285 . df.mm.trans1:exp4 0.0640246324908406 0.0738808468206976 0.86659310560182 0.386465362236534 df.mm.trans2:exp4 -0.0672854304149504 0.0599252798279887 -1.12282213129565 0.261902450197031 df.mm.trans1:exp5 0.0238615491801834 0.0738808468206976 0.322973412014257 0.746812943518354 df.mm.trans2:exp5 0.0599954385875522 0.0599252798279887 1.00117077066248 0.317094206298978 df.mm.trans1:exp6 -0.0251213062916142 0.0738808468206976 -0.340024612232469 0.733941175987828 df.mm.trans2:exp6 -0.215636057319158 0.0599252798279887 -3.5984155257702 0.000342991129331046 *** df.mm.trans1:exp7 0.0406158942486703 0.0738808468206976 0.54974862899503 0.582669173746169 df.mm.trans2:exp7 -0.178416324263194 0.0599252798279887 -2.97731316024432 0.00300942883903727 ** df.mm.trans1:exp8 0.0877605064963664 0.0738808468206976 1.18786546544808 0.235293960873176 df.mm.trans2:exp8 0.243453101604767 0.0599252798279887 4.06261100997078 5.4083989044461e-05 *** df.mm.trans1:probe2 0.416957697731695 0.0529245741288576 7.87833826903381 1.28799012747241e-14 *** df.mm.trans1:probe3 -0.0402186908931528 0.0529245741288576 -0.759924695761005 0.447558410469113 df.mm.trans1:probe4 -0.0702467378072378 0.0529245741288576 -1.32729906595385 0.184847503730773 df.mm.trans1:probe5 -0.0322797970376788 0.0529245741288576 -0.609920770625115 0.54211451608608 df.mm.trans1:probe6 0.343576183833537 0.0529245741288576 6.49180819097417 1.61810566844232e-10 *** df.mm.trans1:probe7 0.196831741335291 0.0529245741288576 3.7190992006106 0.000216127748312188 *** df.mm.trans1:probe8 -0.184235300459948 0.0529245741288576 -3.48109178944705 0.000530735055889467 *** df.mm.trans1:probe9 -0.0481982393574966 0.0529245741288576 -0.910696782181872 0.362772268100881 df.mm.trans1:probe10 -0.0523845885998882 0.0529245741288576 -0.989797073706165 0.322619274323946 df.mm.trans1:probe11 0.219815192957027 0.0529245741288576 4.15336725094536 3.68584055814756e-05 *** df.mm.trans1:probe12 -0.102494925098342 0.0529245741288576 -1.93662257628741 0.05319827172098 . df.mm.trans2:probe2 0.0261480558638053 0.0529245741288576 0.494062659817376 0.621418714218281 df.mm.trans2:probe3 0.283403988648054 0.0529245741288576 5.35486573700222 1.16632460949882e-07 *** df.mm.trans2:probe4 0.0474681479914168 0.0529245741288576 0.896901841398746 0.370083211091664 df.mm.trans2:probe5 -0.046090361147475 0.0529245741288576 -0.870868814839339 0.384127794271137 df.mm.trans2:probe6 0.082565355225708 0.0529245741288576 1.56005705449198 0.119203588557389 df.mm.trans3:probe2 0.416183759599211 0.0529245741288576 7.86371485174186 1.43386843157999e-14 *** df.mm.trans3:probe3 0.591885913739582 0.0529245741288576 11.1835744260973 8.32639161712164e-27 *** df.mm.trans3:probe4 0.331459783935414 0.0529245741288576 6.26287106493092 6.63613701625621e-10 *** df.mm.trans3:probe5 0.418790077505850 0.0529245741288576 7.91296074459106 9.98416947022909e-15 *** df.mm.trans3:probe6 0.45336072680493 0.0529245741288576 8.566166743281 6.92709995666508e-17 *** df.mm.trans3:probe7 0.415372349667938 0.0529245741288576 7.84838341177038 1.60430600447938e-14 *** df.mm.trans3:probe8 0.343908359838127 0.0529245741288576 6.49808459489535 1.5557592787028e-10 *** df.mm.trans3:probe9 0.18647240149138 0.0529245741288576 3.52336139800328 0.000454133185782179 *** df.mm.trans3:probe10 0.745462524480468 0.0529245741288576 14.0853759666627 8.43747647253378e-40 *** df.mm.trans3:probe11 0.163837863019921 0.0529245741288576 3.09568599684937 0.00204282347799174 ** df.mm.trans3:probe12 0.412551374794058 0.0529245741288576 7.79508161538727 2.36740244561412e-14 *** df.mm.trans3:probe13 0.768533053488884 0.0529245741288576 14.5212893280484 6.70266510905583e-42 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.30865044184705 0.144888651449672 29.7376668133577 7.90074430054065e-126 *** df.mm.trans1 0.0139034987460525 0.121275008903890 0.114644384459052 0.908760253473167 df.mm.trans2 0.0331423104728651 0.111319661375951 0.297721984267777 0.766004721584476 df.mm.exp2 0.0179800142934001 0.144096182508793 0.124777866979945 0.90073564774207 df.mm.exp3 0.088028466415695 0.144096182508793 0.610900753115534 0.541465886118654 df.mm.exp4 -0.170940821830739 0.144096182508793 -1.18629667250420 0.235912294237069 df.mm.exp5 -0.0967675736506383 0.144096182508793 -0.671548489112357 0.50209529397875 df.mm.exp6 -0.310257131222676 0.144096182508793 -2.15312526550621 0.0316537337294181 * df.mm.exp7 0.0394684163056619 0.144096182508793 0.273903275010450 0.784240690967146 df.mm.exp8 0.136058936722492 0.144096182508793 0.94422304847798 0.345385227109357 df.mm.trans1:exp2 -0.0239497544795359 0.124382472515071 -0.192549271575495 0.847368469753095 df.mm.trans2:exp2 -0.0211204737604063 0.100887507275764 -0.209346769790594 0.834239156280188 df.mm.trans1:exp3 -0.127018559384441 0.124382472515071 -1.02119339498619 0.307519688888749 df.mm.trans2:exp3 -0.0459664056733884 0.100887507275764 -0.455620392599697 0.648805906986161 df.mm.trans1:exp4 0.00677528362858144 0.124382472515071 0.0544713695714685 0.956575340417082 df.mm.trans2:exp4 0.162111160329210 0.100887507275764 1.60685068653841 0.108543293098306 df.mm.trans1:exp5 0.0490212022541116 0.124382472515071 0.394116640897068 0.693616344375209 df.mm.trans2:exp5 0.121619000987292 0.100887507275764 1.20549118787187 0.228425781600577 df.mm.trans1:exp6 0.148144735726475 0.124382472515071 1.19104189465702 0.23404550667273 df.mm.trans2:exp6 0.171667225036907 0.100887507275764 1.70157068672214 0.0892850674287625 . df.mm.trans1:exp7 -0.0722119683021175 0.124382472515071 -0.580563859537106 0.561723442590191 df.mm.trans2:exp7 0.0219893681778293 0.100887507275764 0.217959277333753 0.827525095849714 df.mm.trans1:exp8 -0.0573811758912611 0.124382472515071 -0.461328471214531 0.644707929820387 df.mm.trans2:exp8 -0.0341520316468919 0.100887507275764 -0.338515962670595 0.735077083240994 df.mm.trans1:probe2 0.123341085780497 0.0891014338659458 1.3842772268521 0.166719816773861 df.mm.trans1:probe3 0.178463476770223 0.0891014338659458 2.00292485796271 0.045574932017847 * df.mm.trans1:probe4 0.144662828445329 0.0891014338659458 1.62357464036972 0.104922027872672 df.mm.trans1:probe5 0.0903746485899044 0.0891014338659459 1.01428949758400 0.310799133507522 df.mm.trans1:probe6 0.111105552956205 0.0891014338659458 1.24695583601230 0.212835571286605 df.mm.trans1:probe7 0.0394690493886223 0.0891014338659458 0.442967612036457 0.657927599623212 df.mm.trans1:probe8 0.0462416651454949 0.0891014338659458 0.518977789011409 0.603942156359371 df.mm.trans1:probe9 0.249270569575639 0.0891014338659458 2.79760446897711 0.00529142545686379 ** df.mm.trans1:probe10 0.062449024490204 0.0891014338659458 0.70087564005041 0.483616225607257 df.mm.trans1:probe11 0.150713556496030 0.0891014338659458 1.69148295326853 0.0911948379300061 . df.mm.trans1:probe12 0.158492409059841 0.0891014338659458 1.77878629089510 0.0757137235039917 . df.mm.trans2:probe2 0.0507683240211743 0.0891014338659458 0.569781223695634 0.569011011502708 df.mm.trans2:probe3 -0.0508124040247981 0.0891014338659459 -0.570275940802994 0.568675662903846 df.mm.trans2:probe4 0.088872934176938 0.0891014338659459 0.997435510528915 0.318901806952133 df.mm.trans2:probe5 0.0164599264755751 0.0891014338659459 0.184732453355792 0.853493015381654 df.mm.trans2:probe6 -0.0517086690660861 0.0891014338659459 -0.580334870299421 0.561877736496003 df.mm.trans3:probe2 0.0279238840679290 0.0891014338659458 0.313394328871754 0.754075528968261 df.mm.trans3:probe3 -0.0311880602504544 0.0891014338659458 -0.350028713313157 0.726423656486399 df.mm.trans3:probe4 0.0377986770817994 0.0891014338659458 0.424220749788022 0.671536674790175 df.mm.trans3:probe5 -0.0696763981381077 0.0891014338659458 -0.781989639391625 0.434488141905598 df.mm.trans3:probe6 -0.000827159567352882 0.0891014338659459 -0.00928334743296447 0.99259574238542 df.mm.trans3:probe7 -0.0657299826920652 0.0891014338659458 -0.737698371846145 0.460947831306085 df.mm.trans3:probe8 0.0994735661484308 0.0891014338659458 1.11640814106415 0.264634965259799 df.mm.trans3:probe9 0.0821885631230109 0.0891014338659458 0.922415718322385 0.356633261530608 df.mm.trans3:probe10 0.0525138873058927 0.0891014338659458 0.589371966616166 0.555804136767767 df.mm.trans3:probe11 -0.0133013751097663 0.0891014338659458 -0.149283513549046 0.881373418744288 df.mm.trans3:probe12 0.057839322009854 0.0891014338659458 0.649140193376394 0.516463145933922 df.mm.trans3:probe13 0.0584953939825035 0.0891014338659458 0.65650339668507 0.511718528718694