fitVsDatCorrelation=0.700617133390356 cont.fitVsDatCorrelation=0.252729520490886 fstatistic=10324.0399748319,44,508 cont.fstatistic=5609.71147531721,44,508 residuals=-0.369234900516702,-0.0783476775684182,-0.00132011866724461,0.068023884924873,1.20304935080396 cont.residuals=-0.372713867599459,-0.106822687078146,-0.0165548272521987,0.081968463548251,1.68308481850048 predictedValues: Include Exclude Both Lung 47.9248142889725 48.3126920920618 48.226234746406 cerebhem 56.2424658880011 58.1222987175936 67.4283401066481 cortex 49.1235167167596 47.2098649535279 53.7191147726241 heart 50.4498451181822 47.5467457179581 53.7321626334457 kidney 46.5857776424722 47.4943579787212 51.6141884252486 liver 53.0647296701223 48.2876279583901 52.6195499324068 stomach 48.6181689146993 49.171942703486 51.8455554582061 testicle 50.0473208667153 51.737420169733 55.2841066170571 diffExp=-0.387877803089324,-1.87983282959246,1.91365176323161,2.90309940022412,-0.908580336249003,4.77710171173219,-0.553773788786692,-1.69009930301775 diffExpScore=2.90199458730133 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 47.4071903130645 48.6680619552734 54.2947748719346 cerebhem 50.5893707679723 51.1571108609148 49.9615180034662 cortex 50.2249555334033 50.5815485640288 49.6805086271698 heart 49.7823954904092 48.0356718935501 48.2815137041568 kidney 53.7605684743115 51.7601625998681 50.3943275073981 liver 50.2040301036298 48.1383704075511 54.0563117101934 stomach 49.8353768831849 47.6529416070498 49.1817760439825 testicle 51.1637070460373 50.685117010176 48.7841852843992 cont.diffExp=-1.26087164220898,-0.567740092942529,-0.356593030625419,1.74672359685914,2.00040587444341,2.06565969607872,2.18243527613512,0.478590035861295 cont.diffExpScore=1.46242145813688 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=0.761809879409893 cont.tran.correlation=0.667760922504847 tran.covariance=0.00314366409297741 cont.tran.covariance=0.000748868342967395 tran.mean=49.9962243373373 cont.tran.mean=49.9779112194016 weightedLogRatios: wLogRatio Lung -0.0312251723784818 cerebhem -0.133025183736958 cortex 0.153952086801505 heart 0.2306258238047 kidney -0.0743835886672465 liver 0.370211245293169 stomach -0.0440538390389233 testicle -0.130510381417794 cont.weightedLogRatios: wLogRatio Lung -0.101633816410904 cerebhem -0.0438512756263031 cortex -0.0277336894787321 heart 0.138934096378908 kidney 0.150372747077734 liver 0.163654917977762 stomach 0.174032981119957 testicle 0.0369377122700971 varWeightedLogRatios=0.034648237245481 cont.varWeightedLogRatios=0.0118958553402562 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.64619819197972 0.0691750540361633 52.7097266895312 4.26915569198356e-208 *** df.mm.trans1 0.258802952325078 0.0596089683247233 4.34167809976569 1.70735364866396e-05 *** df.mm.trans2 0.246301764176485 0.0556718014309814 4.42417449849967 1.18529036554822e-05 *** df.mm.exp2 0.0097315216616699 0.0748476548410647 0.130017723098129 0.896603870653393 df.mm.exp3 -0.106252697229047 0.0748476548410647 -1.41958619083884 0.156341521677837 df.mm.exp4 -0.0727432836024657 0.0748476548410647 -0.971884606898272 0.33157048232589 df.mm.exp5 -0.113314939770864 0.0748476548410647 -1.51394108488078 0.130662679423956 df.mm.exp6 0.0141755844595206 0.0748476548410647 0.189392499866853 0.849860848221609 df.mm.exp7 -0.040373268106384 0.0748476548410647 -0.539405919826274 0.589843131139792 df.mm.exp8 -0.0247597289270036 0.0748476548410646 -0.330801666125408 0.740930699277004 df.mm.trans1:exp2 0.150307156793541 0.0675628024917495 2.22470281353257 0.0265397315045002 * df.mm.trans2:exp2 0.175123565118044 0.0593910188990825 2.94865399456465 0.00333859211615776 ** df.mm.trans1:exp3 0.130957159012030 0.0675628024917495 1.93830264853241 0.053139956040921 . df.mm.trans2:exp3 0.0831612688630675 0.0593910188990825 1.40023307235014 0.162053982068044 df.mm.trans1:exp4 0.124089546501354 0.0675628024917496 1.83665481485181 0.0668449546214696 . df.mm.trans2:exp4 0.0567623287200744 0.0593910188990825 0.955739264492587 0.339658562194141 df.mm.trans1:exp5 0.0849768196241829 0.0675628024917496 1.25774563058659 0.209061612291410 df.mm.trans2:exp5 0.0962315619892212 0.0593910188990825 1.62030495137217 0.105787329139602 df.mm.trans1:exp6 0.0877034845355858 0.0675628024917496 1.29810311741133 0.194840947324019 df.mm.trans2:exp6 -0.0146945089148654 0.0593910188990825 -0.247419714078898 0.804683384117357 df.mm.trans1:exp7 0.0547371612415833 0.0675628024917496 0.810167121890296 0.41822347876341 df.mm.trans2:exp7 0.0580021563123074 0.0593910188990825 0.976614939219429 0.329224650430121 df.mm.trans1:exp8 0.0680952902292219 0.0675628024917495 1.00788137433372 0.313991236143026 df.mm.trans2:exp8 0.0932467406420446 0.0593910188990825 1.57004783501846 0.11702660815006 df.mm.trans1:probe2 0.00919005771818052 0.0394481778033154 0.23296532894374 0.81588216337885 df.mm.trans1:probe3 0.107105486846636 0.0394481778033154 2.71509339114859 0.00685121937793601 ** df.mm.trans1:probe4 0.132094732040629 0.0394481778033154 3.34856359396980 0.000872623978700277 *** df.mm.trans1:probe5 -0.0250000542145314 0.0394481778033154 -0.633744208393582 0.526533001876081 df.mm.trans1:probe6 -0.0209787447070810 0.0394481778033154 -0.53180516503649 0.59509342434482 df.mm.trans1:probe7 -0.189200756960343 0.0394481778033154 -4.79618495697517 2.12816241201026e-06 *** df.mm.trans1:probe8 -0.0990173381899365 0.0394481778033154 -2.51006114106529 0.0123808067097259 * df.mm.trans1:probe9 -0.110612245901758 0.0394481778033154 -2.80398872802844 0.00524065368251706 ** df.mm.trans1:probe10 -0.116295543383934 0.0394481778033154 -2.94805869015729 0.00334491814064934 ** df.mm.trans1:probe11 -0.135205560178710 0.0394481778033154 -3.42742219559116 0.000658790217404858 *** df.mm.trans1:probe12 -0.153331450125459 0.0394481778033154 -3.88690831018745 0.000114988438685634 *** df.mm.trans2:probe2 -0.0473019621471635 0.0394481778033154 -1.19909118193003 0.231051504904697 df.mm.trans2:probe3 -0.0639929701509578 0.0394481778033154 -1.62220345056292 0.105380207887981 df.mm.trans2:probe4 -0.0465656131081074 0.0394481778033154 -1.18042494485496 0.238383662547494 df.mm.trans2:probe5 0.0334333639861463 0.0394481778033154 0.847526193803972 0.397101012889901 df.mm.trans2:probe6 -0.0384350102054513 0.0394481778033154 -0.974316491805638 0.330363129290898 df.mm.trans3:probe2 -0.430102330462706 0.0394481778033154 -10.9029707939148 5.149149899744e-25 *** df.mm.trans3:probe3 -0.219086678767234 0.0394481778033154 -5.55378450836381 4.51392068010757e-08 *** df.mm.trans3:probe4 -0.404579873829184 0.0394481778033154 -10.2559838339398 1.47466194640046e-22 *** df.mm.trans3:probe5 -0.323229847700484 0.0394481778033154 -8.19378398951848 2.07126402043286e-15 *** cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.72047547950771 0.0937994581458742 39.6641468197155 1.10814303709788e-157 *** df.mm.trans1 0.126933916798669 0.0808281107604285 1.57041795984687 0.116940525971967 df.mm.trans2 0.164921083430667 0.0754894214538787 2.18469131507952 0.0293677754436013 * df.mm.exp2 0.1980210044447 0.101491347790072 1.95111217612651 0.0515930194099558 . df.mm.exp3 0.185117287384926 0.101491347790072 1.82397112084696 0.0687437867843282 . df.mm.exp4 0.153187649086079 0.101491347790072 1.50936658564173 0.131826821256459 df.mm.exp5 0.261913523543721 0.101491347790072 2.58064878678595 0.010140935929223 * df.mm.exp6 0.0507796531970339 0.101491347790072 0.50033479998776 0.617056030534064 df.mm.exp7 0.127777404321527 0.101491347790072 1.25899800430108 0.208609270513243 df.mm.exp8 0.223887643815492 0.101491347790072 2.20597763938053 0.0278324479720013 * df.mm.trans1:exp2 -0.133053425656904 0.0916132896872013 -1.45233760419688 0.147025103418106 df.mm.trans2:exp2 -0.148142504888766 0.080532577373239 -1.83953512629031 0.0664198425261736 . df.mm.trans1:exp3 -0.127379173597240 0.0916132896872013 -1.39040060707519 0.165016097066599 df.mm.trans2:exp3 -0.146553433421423 0.080532577373239 -1.81980309337675 0.0693773895503447 . df.mm.trans1:exp4 -0.104300143326516 0.0916132896872013 -1.13848267737827 0.255455599159464 df.mm.trans2:exp4 -0.166266752682267 0.080532577373239 -2.06458998464288 0.0394687921896587 * df.mm.trans1:exp5 -0.136147164650837 0.0916132896872013 -1.48610714794424 0.13787110961667 df.mm.trans2:exp5 -0.200315734879690 0.080532577373239 -2.48738760652474 0.0131882635619702 * df.mm.trans1:exp6 0.00654173966205484 0.0916132896872013 0.0714060119922616 0.94310272746354 df.mm.trans2:exp6 -0.0617230754684304 0.080532577373239 -0.766436111716213 0.443772855824633 df.mm.trans1:exp7 -0.0778262048754108 0.0916132896872013 -0.849507807667814 0.395998949485999 df.mm.trans2:exp7 -0.148856045448645 0.080532577373239 -1.84839539853234 0.065126116078169 . df.mm.trans1:exp8 -0.147631121442814 0.0916132896872013 -1.61145966864498 0.107700665652927 df.mm.trans2:exp8 -0.183278329350843 0.080532577373239 -2.27582843277715 0.0232717666310797 * df.mm.trans1:probe2 0.080654578595964 0.0534906369694875 1.5078261012665 0.132220660866071 df.mm.trans1:probe3 0.0302214413956555 0.0534906369694875 0.564985633147248 0.572332944584958 df.mm.trans1:probe4 0.0106261204165861 0.0534906369694875 0.198653839599023 0.842613012191237 df.mm.trans1:probe5 0.0332539291587505 0.0534906369694875 0.621677569061656 0.534432787610472 df.mm.trans1:probe6 0.0768218853137825 0.0534906369694875 1.43617443474460 0.151568215727819 df.mm.trans1:probe7 -0.0126709279439079 0.0534906369694875 -0.236881231216891 0.81284442162919 df.mm.trans1:probe8 0.0230828250711358 0.0534906369694875 0.431530196290295 0.66626597690813 df.mm.trans1:probe9 0.00573218456947833 0.0534906369694875 0.107162391293791 0.914702458340822 df.mm.trans1:probe10 -0.0327198115583307 0.0534906369694875 -0.611692314993275 0.541014995097854 df.mm.trans1:probe11 -0.00707476079471255 0.0534906369694875 -0.132261666630521 0.894829695614844 df.mm.trans1:probe12 -0.0147307048269833 0.0534906369694875 -0.275388472853410 0.783129775883383 df.mm.trans2:probe2 -0.0244105439679366 0.0534906369694875 -0.456351715943503 0.648332121466115 df.mm.trans2:probe3 0.044565123912699 0.0534906369694875 0.833138777878456 0.405157996313165 df.mm.trans2:probe4 -0.0269432344624532 0.0534906369694875 -0.503700011608056 0.614690553903811 df.mm.trans2:probe5 -0.00791960117000335 0.0534906369694875 -0.148055839651356 0.882357447735157 df.mm.trans2:probe6 0.0105990957394089 0.0534906369694875 0.198148617027217 0.84300805576696 df.mm.trans3:probe2 -0.0265074692366335 0.0534906369694875 -0.495553441469656 0.62042380660842 df.mm.trans3:probe3 -0.0665015276011577 0.0534906369694875 -1.24323678626396 0.214354055278400 df.mm.trans3:probe4 -0.0039779766092443 0.0534906369694875 -0.0743677180646297 0.940747069277164 df.mm.trans3:probe5 0.00747455112968431 0.0534906369694875 0.139735691200462 0.888924208161688