fitVsDatCorrelation=0.838804957716436 cont.fitVsDatCorrelation=0.263774017322749 fstatistic=10796.2452251245,54,738 cont.fstatistic=3430.06209740856,54,738 residuals=-0.452566761416806,-0.0868972667397714,-0.0118040750892566,0.08008657004059,1.06325591861418 cont.residuals=-0.690723599199722,-0.190202046497627,-0.00860233991189814,0.162038084281601,1.34102922292037 predictedValues: Include Exclude Both Lung 61.6285752749711 75.7216522378144 49.2064758728836 cerebhem 56.9694749204042 69.7288596739285 56.8090377307682 cortex 54.3986016509525 66.7378173028478 49.3470967842959 heart 57.6951851679962 67.349871518613 52.8407461424585 kidney 61.4637744875091 83.9086080502393 50.7832432367711 liver 61.4489524544992 81.4808204176954 54.6332034709387 stomach 60.6268104361108 68.154456175012 54.2757705572204 testicle 60.5049192269828 72.9587780610681 53.2973512277141 diffExp=-14.0930769628433,-12.7593847535243,-12.3392156518953,-9.65468635061682,-22.4448335627301,-20.0318679631962,-7.52764573890123,-12.4538588340853 diffExpScore=0.991095642843186 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,-1,-1,0,0 diffExp1.3Score=0.666666666666667 diffExp1.2=-1,-1,-1,0,-1,-1,0,-1 diffExp1.2Score=0.857142857142857 cont.predictedValues: Include Exclude Both Lung 60.3004481741301 58.5305862084326 62.6057798753001 cerebhem 61.5399849787882 63.6154099517253 69.4344237824519 cortex 61.3040322153314 57.6054060367724 69.5356859370071 heart 63.090425002071 68.2466754034214 62.8597597218633 kidney 66.1315560961681 61.4278901157814 59.3958411080357 liver 63.8082020245605 69.4583552500279 62.575204588065 stomach 64.4970526307837 69.0662299882761 61.6732910183982 testicle 63.5864115884226 53.1178687655344 61.2117728257006 cont.diffExp=1.76986196569749,-2.07542497293701,3.69862617855906,-5.15625040135044,4.70366598038676,-5.65015322546746,-4.56917735749238,10.4685428228882 cont.diffExpScore=9.0917690572198 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=0.723163773024151 cont.tran.correlation=0.304654934413056 tran.covariance=0.00293830888146858 cont.tran.covariance=0.00087967116466074 tran.mean=66.2985723160403 cont.tran.mean=62.8329084018892 weightedLogRatios: wLogRatio Lung -0.869903763344122 cerebhem -0.837409050772666 cortex -0.83788122687611 heart -0.639416228444542 kidney -1.33043915977741 liver -1.20180419526149 stomach -0.487264804233478 testicle -0.785422964967712 cont.weightedLogRatios: wLogRatio Lung 0.121675917463815 cerebhem -0.137194374069570 cortex 0.254189547860970 heart -0.328681937170374 kidney 0.306546398689694 liver -0.356207784226562 stomach -0.287532443151535 testicle 0.73078073150492 varWeightedLogRatios=0.0758416189378792 cont.varWeightedLogRatios=0.147474532562448 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.17102691911245 0.0758801465560168 54.9686197038811 4.0065819003089e-263 *** df.mm.trans1 -0.283662329795452 0.0668436527483055 -4.24366889199726 2.47913092446609e-05 *** df.mm.trans2 0.0649919436968135 0.0603150690845795 1.07754073207933 0.281590850069207 df.mm.exp2 -0.304730099230857 0.0803217188745895 -3.79386924857333 0.000160465827672462 *** df.mm.exp3 -0.253933273673051 0.0803217188745895 -3.16145218542360 0.00163404588402860 ** df.mm.exp4 -0.254372436593375 0.0803217188745895 -3.16691973425693 0.0016040414351894 ** df.mm.exp5 0.0684451775477132 0.0803217188745895 0.852137858934272 0.394414020844099 df.mm.exp6 -0.0342319266053359 0.0803217188745895 -0.426185184841275 0.670097185387197 df.mm.exp7 -0.219728713020897 0.0803217188745895 -2.73560770485963 0.00637607386420766 ** df.mm.exp8 -0.135431921077480 0.0803217188745895 -1.68611831239490 0.0921956837511064 . df.mm.trans1:exp2 0.226120048594674 0.0757628362876355 2.98457739538953 0.00293348851682279 ** df.mm.trans2:exp2 0.222280240414903 0.061987775144429 3.58587221265805 0.000358085505955578 *** df.mm.trans1:exp3 0.129146074946497 0.0757628362876355 1.70460982289775 0.0886881036040819 . df.mm.trans2:exp3 0.127640895514011 0.061987775144429 2.05913013036221 0.0398321211367118 * df.mm.trans1:exp4 0.188420513351337 0.0757628362876355 2.48697808297453 0.0131032429782172 * df.mm.trans2:exp4 0.137209285315601 0.061987775144429 2.21348943393933 0.0271690258321168 * df.mm.trans1:exp5 -0.071122856318943 0.0757628362876355 -0.938756517099272 0.348162898373709 df.mm.trans2:exp5 0.0342188831275721 0.061987775144429 0.552026315637938 0.581097447189147 df.mm.trans1:exp6 0.0313130680401086 0.0757628362876355 0.413303798728281 0.679504108983877 df.mm.trans2:exp6 0.107535440393836 0.061987775144429 1.73478464331527 0.0831964594309793 . df.mm.trans1:exp7 0.203340277383994 0.0757628362876354 2.68390529377765 0.00743990175543788 ** df.mm.trans2:exp7 0.114441110308433 0.061987775144429 1.84618838217358 0.0652651252044251 . df.mm.trans1:exp8 0.117030945033395 0.0757628362876355 1.54470121193832 0.122847229403929 df.mm.trans2:exp8 0.0982623721716947 0.061987775144429 1.58518953039930 0.113351652603118 df.mm.trans1:probe2 0.659994674442913 0.0442359660424549 14.9198657447538 3.44772413085546e-44 *** df.mm.trans1:probe3 0.394892380041339 0.0442359660424549 8.92695278006016 3.43208331661273e-18 *** df.mm.trans1:probe4 0.589788970750048 0.0442359660424549 13.3327928270857 1.72228289927829e-36 *** df.mm.trans1:probe5 0.279512517222792 0.0442359660424549 6.31867103240231 4.56053444045887e-10 *** df.mm.trans1:probe6 -0.0570005711054925 0.0442359660424549 -1.28855716750454 0.197955884506247 df.mm.trans1:probe7 0.0894997117129852 0.0442359660424549 2.02323402697002 0.0434090299994663 * df.mm.trans1:probe8 -0.0176765104152636 0.0442359660424549 -0.399595894397305 0.689569798598247 df.mm.trans1:probe9 -0.0664801252232738 0.0442359660424549 -1.50285234326002 0.133304893222550 df.mm.trans1:probe10 -0.00100077357348408 0.0442359660424549 -0.0226235270305526 0.981956692123274 df.mm.trans1:probe11 0.341162822003670 0.0442359660424549 7.71234026349156 4.00489537538195e-14 *** df.mm.trans1:probe12 0.566954942587042 0.0442359660424549 12.8166058822569 4.26091532836212e-34 *** df.mm.trans1:probe13 0.426409680723673 0.0442359660424549 9.63943412729885 8.60301106554232e-21 *** df.mm.trans1:probe14 0.414576754589405 0.0442359660424549 9.37193853055047 8.49378292451053e-20 *** df.mm.trans1:probe15 0.402282459153198 0.0442359660424549 9.09401320109326 8.70051064893907e-19 *** df.mm.trans1:probe16 0.273619925871489 0.0442359660424549 6.18546287898146 1.02536630545639e-09 *** df.mm.trans1:probe17 0.381632190732671 0.0442359660424549 8.62719241547487 3.83034929360888e-17 *** df.mm.trans1:probe18 0.381981504325631 0.0442359660424549 8.63508901238935 3.59750494347121e-17 *** df.mm.trans1:probe19 0.204131125634227 0.0442359660424549 4.61459630921850 4.64374386045172e-06 *** df.mm.trans1:probe20 0.367390500206159 0.0442359660424549 8.3052441954938 4.75105344746937e-16 *** df.mm.trans1:probe21 0.375140229368134 0.0442359660424549 8.48043487980115 1.21852349616048e-16 *** df.mm.trans1:probe22 0.304736156826389 0.0442359660424549 6.88887762808034 1.20555395668624e-11 *** df.mm.trans2:probe2 0.197761334185623 0.0442359660424549 4.47060055150201 9.02653260182591e-06 *** df.mm.trans2:probe3 0.106306815455348 0.0442359660424549 2.40317607969321 0.0164991582968490 * df.mm.trans2:probe4 0.473495994163462 0.0442359660424549 10.7038691934303 5.9261017414638e-25 *** df.mm.trans2:probe5 0.0738434534421232 0.0442359660424549 1.66930803254648 0.0954804474766592 . df.mm.trans2:probe6 0.150090522967714 0.0442359660424549 3.39295230545359 0.000728326277909737 *** df.mm.trans3:probe2 -0.272341566476554 0.0442359660424549 -6.15656423587942 1.22002102829395e-09 *** df.mm.trans3:probe3 -0.0624773371032108 0.0442359660424549 -1.41236515651651 0.158264007298826 df.mm.trans3:probe4 0.0910760467650855 0.0442359660424549 2.05886871957711 0.0398572351213486 * df.mm.trans3:probe5 -0.0379136169081429 0.0442359660424549 -0.857076725119009 0.391680707368242 cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.98950189110586 0.134438843878883 29.6752171916922 5.4692552913497e-128 *** df.mm.trans1 0.0528262098574862 0.118428651023885 0.44605937330851 0.655685195707071 df.mm.trans2 0.0807175880934697 0.106861788283709 0.755345660875258 0.450282603972650 df.mm.exp2 0.000128706419685743 0.142308093934595 0.000904420937187846 0.999278620989875 df.mm.exp3 -0.104409441005113 0.142308093934595 -0.73368589317976 0.463373204882339 df.mm.exp4 0.194760135946970 0.142308093934595 1.36858087661887 0.171546777298901 df.mm.exp5 0.193254401041000 0.142308093934595 1.35800006660070 0.174878688289134 df.mm.exp6 0.228208610842594 0.142308093934595 1.6036235503757 0.109224910445203 df.mm.exp7 0.247803082917818 0.142308093934595 1.74131404663257 0.082045240116127 . df.mm.exp8 -0.0214577517358915 0.142308093934595 -0.150783775838875 0.880187488033749 df.mm.trans1:exp2 0.0202188832732710 0.134231002202607 0.150627522267567 0.880310708939768 df.mm.trans2:exp2 0.0831775706424072 0.109825365438496 0.75736211129648 0.449074688235748 df.mm.trans1:exp3 0.120915524062053 0.134231002202607 0.900801767683627 0.367987586357889 df.mm.trans2:exp3 0.0884764003341697 0.109825365438496 0.805609887851615 0.420727309923494 df.mm.trans1:exp4 -0.149530657258131 0.134231002202607 -1.11398004041146 0.265650416785381 df.mm.trans2:exp4 -0.0411808738799298 0.109825365438496 -0.374966873231044 0.707792833503686 df.mm.trans1:exp5 -0.100947904877602 0.134231002202607 -0.752046123631203 0.452263100769485 df.mm.trans2:exp5 -0.144939891382432 0.109825365438496 -1.31973056318761 0.187334225562418 df.mm.trans1:exp6 -0.171666406488400 0.134231002202607 -1.27888791465096 0.201338519642322 df.mm.trans2:exp6 -0.0570307016762779 0.109825365438496 -0.519285334936725 0.603717471133053 df.mm.trans1:exp7 -0.180523091919253 0.134231002202607 -1.34486883772777 0.179080743873365 df.mm.trans2:exp7 -0.0822866425579348 0.109825365438496 -0.749249886211545 0.453945354099692 df.mm.trans1:exp8 0.0745180088722375 0.134231002202607 0.555147526647837 0.578961979533743 df.mm.trans2:exp8 -0.0755783238053343 0.109825365438496 -0.688168197789053 0.49156311261162 df.mm.trans1:probe2 0.0752664999532412 0.0783740201163543 0.960350124205703 0.337193556779059 df.mm.trans1:probe3 0.0866800010323847 0.0783740201163543 1.10597875295537 0.269096111226457 df.mm.trans1:probe4 -0.0164717120116699 0.0783740201163543 -0.210168012145044 0.833594553439676 df.mm.trans1:probe5 0.0198591274641388 0.0783740201163543 0.253389164351349 0.800038069758948 df.mm.trans1:probe6 0.138842397727667 0.0783740201163543 1.77153599523849 0.0768842299237666 . df.mm.trans1:probe7 0.101191014315689 0.0783740201163543 1.29112956264666 0.197063028646140 df.mm.trans1:probe8 0.0912260031538152 0.0783740201163543 1.16398269501017 0.244807185476722 df.mm.trans1:probe9 0.0703549343379957 0.0783740201163543 0.897681836832493 0.369647949828375 df.mm.trans1:probe10 0.110113959537041 0.0783740201163543 1.40498036688134 0.160447889224963 df.mm.trans1:probe11 -0.0106725860445802 0.0783740201163543 -0.136175049190224 0.891720035650055 df.mm.trans1:probe12 0.105795883805996 0.0783740201163543 1.34988461289762 0.177466877324786 df.mm.trans1:probe13 0.0128882269071902 0.0783740201163543 0.164445142510954 0.869425756988148 df.mm.trans1:probe14 0.0898722135518176 0.0783740201163543 1.14670924648746 0.251873448728662 df.mm.trans1:probe15 0.099476454390214 0.0783740201163543 1.26925292644847 0.204750996603208 df.mm.trans1:probe16 0.044325207818249 0.0783740201163543 0.565559961737878 0.571864852125651 df.mm.trans1:probe17 0.0413276144494437 0.0783740201163543 0.527312678207505 0.598134949699605 df.mm.trans1:probe18 0.0499613922789836 0.0783740201163543 0.637473900213499 0.52401392021835 df.mm.trans1:probe19 0.0361218366372103 0.0783740201163543 0.460890440270689 0.645012998296968 df.mm.trans1:probe20 0.185669874630364 0.0783740201163543 2.3690232344177 0.0180916307962434 * df.mm.trans1:probe21 0.111892854369236 0.0783740201163543 1.42767787339630 0.153807597157346 df.mm.trans1:probe22 0.0955875507178478 0.0783740201163543 1.21963312046439 0.222993613784769 df.mm.trans2:probe2 0.0220074794547413 0.0783740201163543 0.280800696736864 0.7789419706045 df.mm.trans2:probe3 -0.00219177165521952 0.0783740201163543 -0.027965538222559 0.977697197271127 df.mm.trans2:probe4 -0.0662023763992065 0.0783740201163543 -0.84469797901042 0.39855323289388 df.mm.trans2:probe5 0.0330142969699197 0.0783740201163543 0.421240315616152 0.673702260573503 df.mm.trans2:probe6 0.00600214694865418 0.0783740201163543 0.0765833746915543 0.938975741500335 df.mm.trans3:probe2 -0.0531381489887356 0.0783740201163543 -0.678007187966708 0.497979650667714 df.mm.trans3:probe3 0.139502299631461 0.0783740201163543 1.77995590151373 0.0754945617589358 . df.mm.trans3:probe4 -0.047482837021311 0.0783740201163543 -0.605849195317757 0.544801179241917 df.mm.trans3:probe5 0.0301782616956645 0.0783740201163543 0.385054405156987 0.700308053277142