fitVsDatCorrelation=0.914622194355566 cont.fitVsDatCorrelation=0.248138603924974 fstatistic=10056.8517277641,54,738 cont.fstatistic=1740.53412217383,54,738 residuals=-0.569749265254203,-0.0875905337851774,-0.00246754911489477,0.0813369636570032,0.991101653859233 cont.residuals=-0.74381829452897,-0.259273479023938,-0.0880472114754847,0.187972854403635,1.78284222693555 predictedValues: Include Exclude Both Lung 51.4165737153107 108.658739255888 73.0112602945618 cerebhem 62.6086134776056 152.819231766156 77.2660107828959 cortex 49.1422383022559 99.108275044891 81.933980630147 heart 49.5735244385393 92.8590112374162 67.8106385016777 kidney 51.3704936648886 97.6524013538694 64.0646340296699 liver 50.9564653225837 100.764068555953 63.6157443573379 stomach 50.1956562102466 128.112477079403 104.243473735051 testicle 51.7840028560818 97.0603169873014 71.8181861070803 diffExp=-57.2421655405777,-90.2106182885508,-49.966036742635,-43.285486798877,-46.2819076889807,-49.8076032333689,-77.9168208691561,-45.2763141312195 diffExpScore=0.9978307412111 diffExp1.5=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.5Score=0.888888888888889 diffExp1.4=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.4Score=0.888888888888889 diffExp1.3=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.3Score=0.888888888888889 diffExp1.2=-1,-1,-1,-1,-1,-1,-1,-1 diffExp1.2Score=0.888888888888889 cont.predictedValues: Include Exclude Both Lung 62.7332502136273 67.8176450780197 66.6046091910035 cerebhem 57.7921368445152 63.5451856450932 75.0534085075372 cortex 59.5401159576939 64.0441371744852 56.4894788755854 heart 60.2525935185633 63.4344325959416 57.9775871927061 kidney 69.47572662803 59.8576196701989 57.3116138093958 liver 63.9395387049736 65.6037102242816 61.8941608276808 stomach 63.0020158671084 63.0210290707568 61.4022900691068 testicle 64.5392448147126 55.8903210601326 61.5619175475968 cont.diffExp=-5.08439486439239,-5.75304880057805,-4.50402121679134,-3.18183907737829,9.61810695783114,-1.66417151930809,-0.0190132036484414,8.64892375458008 cont.diffExpScore=13.0886441620479 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=0.823493794281006 cont.tran.correlation=-0.416064360347605 tran.covariance=0.0105001883983589 cont.tran.covariance=-0.00139367319192446 tran.mean=80.8801305792744 cont.tran.mean=62.7805439417584 weightedLogRatios: wLogRatio Lung -3.22802189530384 cerebhem -4.08972395106291 cortex -2.97816897848828 heart -2.64686563860012 kidney -2.73656583949160 liver -2.91260905761309 stomach -4.10811298321285 testicle -2.67710894419123 cont.weightedLogRatios: wLogRatio Lung -0.325583737077341 cerebhem -0.389492151409762 cortex -0.300666295592044 heart -0.212240192920465 kidney 0.62084072860475 liver -0.107165501542354 stomach -0.00125021168696902 testicle 0.589245705932277 varWeightedLogRatios=0.362400476552773 cont.varWeightedLogRatios=0.162185773909107 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.08000134811558 0.0785612893455939 51.9339916910924 1.17340260480938e-248 *** df.mm.trans1 -0.23129579413866 0.0692055008169938 -3.34215909729915 0.000873184300281133 *** df.mm.trans2 0.69199910665682 0.0624462367208937 11.0815181665749 1.66346675250592e-26 *** df.mm.exp2 0.481345416806876 0.0831597998111894 5.78819835905989 1.05189176904418e-08 *** df.mm.exp3 -0.252540978201542 0.0831597998111894 -3.03681561012562 0.00247511052495819 ** df.mm.exp4 -0.119738873154403 0.0831597998111895 -1.43986485569066 0.150329683118961 df.mm.exp5 0.023026697563501 0.0831597998111895 0.276896981664002 0.781936745363194 df.mm.exp6 0.0533334590580747 0.0831597998111894 0.641337030382058 0.521502854332404 df.mm.exp7 -0.215451182983245 0.0831597998111894 -2.59080930296150 0.00976413276137189 ** df.mm.exp8 -0.0892828709735988 0.0831597998111894 -1.07363018160591 0.283339380765504 df.mm.trans1:exp2 -0.284403118987110 0.078439834045943 -3.62574860651199 0.000307930609895556 *** df.mm.trans2:exp2 -0.140301824055069 0.0641780460375897 -2.1861342424307 0.0291192401539138 * df.mm.trans1:exp3 0.207299327443884 0.078439834045943 2.64278131086390 0.00839718033405186 ** df.mm.trans2:exp3 0.160541779613429 0.0641780460375897 2.50150619293392 0.0125821259796708 * df.mm.trans1:exp4 0.0832352165823692 0.078439834045943 1.06113453189635 0.288975900435134 df.mm.trans2:exp4 -0.0373909305998923 0.0641780460375897 -0.58261248056683 0.560332160492237 df.mm.trans1:exp5 -0.0239233094560963 0.078439834045943 -0.304989292074283 0.760460299710995 df.mm.trans2:exp5 -0.129824587510271 0.0641780460375897 -2.02288158530461 0.0434454552357784 * df.mm.trans1:exp6 -0.0623223783113423 0.078439834045943 -0.794524606908775 0.427145509756335 df.mm.trans2:exp6 -0.128763768111884 0.0641780460375897 -2.00635226626355 0.0451830766319765 * df.mm.trans1:exp7 0.191419109950855 0.078439834045943 2.44033037906148 0.0149080757843691 * df.mm.trans2:exp7 0.38014764982642 0.0641780460375897 5.92332851024732 4.83630504650452e-09 *** df.mm.trans1:exp8 0.0964035810607554 0.078439834045943 1.22901306757343 0.219458632784856 df.mm.trans2:exp8 -0.0235966575690477 0.0641780460375897 -0.367674914179016 0.713221066440684 df.mm.trans1:probe2 0.00772478361504272 0.0457989959887287 0.168667095168283 0.866104717266354 df.mm.trans1:probe3 0.144398799317278 0.0457989959887287 3.15288132850806 0.00168212354448838 ** df.mm.trans1:probe4 0.00226444996618671 0.0457989959887287 0.049443222876414 0.960579461236093 df.mm.trans1:probe5 -0.000591390493200912 0.0457989959887287 -0.0129127392518922 0.989700900853199 df.mm.trans1:probe6 0.0851141791001352 0.0457989959887287 1.85842892977571 0.0635060790735897 . df.mm.trans1:probe7 0.0663498095109971 0.0457989959887287 1.44871755545309 0.147841214903694 df.mm.trans1:probe8 0.229899438722288 0.0457989959887287 5.01974844118564 6.49136474504331e-07 *** df.mm.trans1:probe9 0.122589347374745 0.0457989959887287 2.67668198239355 0.00760059408039151 ** df.mm.trans1:probe10 0.0775066939776692 0.0457989959887287 1.69232299321024 0.0910065022590357 . df.mm.trans1:probe11 0.0471371193608158 0.0457989959887287 1.02921730800423 0.303714762258958 df.mm.trans1:probe12 0.02003036318596 0.0457989959887287 0.43735376187918 0.661982763794498 df.mm.trans1:probe13 -0.0615015766121964 0.0457989959887287 -1.34285862134035 0.179730608328765 df.mm.trans1:probe14 -0.115535675878789 0.0457989959887287 -2.52266831148924 0.0118559435916603 * df.mm.trans1:probe15 -0.0465934020551188 0.0457989959887287 -1.01734549086154 0.309322499025473 df.mm.trans1:probe16 -0.078488945902403 0.0457989959887287 -1.71377001193911 0.08699089249009 . df.mm.trans1:probe17 0.354990627343152 0.0457989959887287 7.75105697580174 3.02262166816857e-14 *** df.mm.trans1:probe18 0.3356704718513 0.0457989959887287 7.32921027207474 6.09095830135907e-13 *** df.mm.trans1:probe19 0.33176452442676 0.0457989959887287 7.24392570763797 1.09906432593933e-12 *** df.mm.trans1:probe20 0.244357524465212 0.0457989959887287 5.3354340895453 1.26914303763329e-07 *** df.mm.trans1:probe21 0.363742345962086 0.0457989959887287 7.94214672416844 7.41147997309957e-15 *** df.mm.trans1:probe22 0.333055845749928 0.0457989959887287 7.27212111444308 9.04800854493998e-13 *** df.mm.trans2:probe2 -0.108505115664481 0.0457989959887287 -2.36915926478354 0.0180850294043390 * df.mm.trans2:probe3 -0.0498635379410919 0.0457989959887287 -1.08874740296411 0.276620694994095 df.mm.trans2:probe4 -0.214531350011428 0.0457989959887287 -4.68419329681867 3.34592342308132e-06 *** df.mm.trans2:probe5 -0.332747266482418 0.0457989959887287 -7.26538342815 9.4790134100374e-13 *** df.mm.trans2:probe6 -0.216024209946718 0.0457989959887287 -4.71678920646825 2.86556415503629e-06 *** df.mm.trans3:probe2 -0.430450649662821 0.0457989959887287 -9.39869183526984 6.77022326996832e-20 *** df.mm.trans3:probe3 -0.697622583575688 0.0457989959887287 -15.2322680555568 9.26698459901504e-46 *** df.mm.trans3:probe4 -0.565411924377547 0.0457989959887287 -12.3455091573775 5.78154433929851e-32 *** df.mm.trans3:probe5 0.443030253764482 0.0457989959887287 9.67336170149915 6.41216671970322e-21 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.12597252624606 0.188232610382753 21.9195415600744 2.07276842579249e-82 *** df.mm.trans1 -0.0125681829535414 0.165816169517324 -0.0757958828148439 0.939602033410695 df.mm.trans2 0.0881465018078884 0.149620993296649 0.589131911677149 0.555953074007778 df.mm.exp2 -0.266536254326527 0.199250627475163 -1.33769342512987 0.181408469282608 df.mm.exp3 0.0552281416759905 0.199250627475163 0.277179260993168 0.781720082625676 df.mm.exp4 0.0315556798785044 0.199250627475163 0.158371796758522 0.874207161220154 df.mm.exp5 0.127502639370961 0.199250627475163 0.639910854919914 0.522429156992471 df.mm.exp6 0.0592040945969472 0.199250627475163 0.297133792486185 0.76644797822893 df.mm.exp7 0.0122478129738993 0.199250627475163 0.0614693822001943 0.951002040532857 df.mm.exp8 -0.0863189987180456 0.199250627475163 -0.433218202681974 0.664982843130187 df.mm.trans1:exp2 0.184497366484598 0.187941603854111 0.981673896045995 0.32658223310246 df.mm.trans2:exp2 0.201465079155834 0.153770403153364 1.31016811443813 0.190546604448473 df.mm.trans1:exp3 -0.107469452044280 0.187941603854111 -0.571823640111653 0.567615580544871 df.mm.trans2:exp3 -0.112478065634583 0.153770403153364 -0.73146758627148 0.46472578472615 df.mm.trans1:exp4 -0.0719016759964768 0.187941603854111 -0.382574557851971 0.70214539064249 df.mm.trans2:exp4 -0.0983712779537707 0.153770403153364 -0.639728295799938 0.522547790490177 df.mm.trans1:exp5 -0.0254168184688891 0.187941603854111 -0.135237850202762 0.892460698110227 df.mm.trans2:exp5 -0.252356315660183 0.153770403153364 -1.64112410766390 0.101197725005133 df.mm.trans1:exp6 -0.0401577790032018 0.187941603854111 -0.213671577658633 0.830862193095069 df.mm.trans2:exp6 -0.092394254924185 0.153770403153364 -0.600858507420542 0.548118718619751 df.mm.trans1:exp7 -0.00797270271722341 0.187941603854111 -0.0424211699470873 0.966174426566814 df.mm.trans2:exp7 -0.0856017604804271 0.153770403153364 -0.556685543674174 0.57791106036507 df.mm.trans1:exp8 0.114700870717291 0.187941603854111 0.610300584676965 0.541850592780352 df.mm.trans2:exp8 -0.107112196491252 0.153770403153364 -0.696572255093999 0.486289857504556 df.mm.trans1:probe2 0.135051595375459 0.109734255123337 1.23071501441065 0.218821571737802 df.mm.trans1:probe3 0.117267401040003 0.109734255123337 1.06864899122156 0.285577280216095 df.mm.trans1:probe4 -0.120332230909601 0.109734255123337 -1.09657855493121 0.273183354901523 df.mm.trans1:probe5 -0.0169383850681682 0.109734255123337 -0.154358227056175 0.877369490636724 df.mm.trans1:probe6 -0.0519756775376185 0.109734255123337 -0.47365043376109 0.635889286026194 df.mm.trans1:probe7 -0.0146581245641034 0.109734255123337 -0.133578384868320 0.893772393307288 df.mm.trans1:probe8 0.150689769924038 0.109734255123337 1.37322452095445 0.170099613194267 df.mm.trans1:probe9 -0.0206123606789865 0.109734255123337 -0.187838890014965 0.851054581991256 df.mm.trans1:probe10 0.00693618270911374 0.109734255123337 0.0632089104839484 0.949617265034853 df.mm.trans1:probe11 0.0911980976367448 0.109734255123337 0.831081393264501 0.406196445956239 df.mm.trans1:probe12 0.189187682588061 0.109734255123337 1.72405309878326 0.0851170293481669 . df.mm.trans1:probe13 -0.0448062207183675 0.109734255123337 -0.408315713885398 0.683160344467797 df.mm.trans1:probe14 0.105813310253298 0.109734255123337 0.964268724787606 0.335227071199141 df.mm.trans1:probe15 -0.0576622021932008 0.109734255123337 -0.525471304547435 0.599413434571968 df.mm.trans1:probe16 0.00544459346669656 0.109734255123337 0.0496161700881559 0.960441685391003 df.mm.trans1:probe17 -0.0286620386235868 0.109734255123337 -0.261194998693634 0.794015006384115 df.mm.trans1:probe18 0.0141853284949692 0.109734255123337 0.12926982990886 0.897179365428245 df.mm.trans1:probe19 0.0420153532142627 0.109734255123337 0.382882748573262 0.701916954631586 df.mm.trans1:probe20 0.087661136492634 0.109734255123337 0.798849332818696 0.424634788996630 df.mm.trans1:probe21 0.0813836211114745 0.109734255123337 0.741642808073038 0.458539721411295 df.mm.trans1:probe22 0.0169694639546123 0.109734255123337 0.154641446606981 0.877146274347716 df.mm.trans2:probe2 -0.0382439180969710 0.109734255123337 -0.348513944474189 0.72755364004696 df.mm.trans2:probe3 -0.0187227587504157 0.109734255123337 -0.170619089994934 0.86457005095425 df.mm.trans2:probe4 0.0160422410598085 0.109734255123337 0.146191734219890 0.883809952826896 df.mm.trans2:probe5 -0.0203327610832192 0.109734255123337 -0.185290919962650 0.853051770037406 df.mm.trans2:probe6 0.090994431001251 0.109734255123337 0.829225394558673 0.407244997316293 df.mm.trans3:probe2 0.0365137311087178 0.109734255123337 0.332746880795590 0.739419855621632 df.mm.trans3:probe3 -0.00678133185240556 0.109734255123337 -0.0617977662926097 0.950740614363356 df.mm.trans3:probe4 -0.143579424741351 0.109734255123337 -1.30842848096954 0.191135354989607 df.mm.trans3:probe5 0.0999617111018565 0.109734255123337 0.91094354255655 0.362622557205653