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...ion: > data(swiss) > lm1 <- lm(Fertility ~ . , data = swiss) > AIC(lm1) [1] 326.0716 Clearly this includes the estimation of the residual standard error as an estimated parameter, as this gives the correct score: > -2*logLik(lm1) + 2*(length(coef(lm1))+1) [1] 326.0716 attr(,"nall") [1] 47 attr(,"nobs") [1] 47 attr(,"df") [1] 7 attr(,"class") [1] "logLik" I thought the same would have held for nls() objects. > -----Original Message----- > From: Adaikalavan Ramasamy [mailto:ramasamy at cancer.org.uk] > Sent: Friday, 16...

...( density ~ SSlogis( log(conc), Asym, xmid, scal ), DNase1 ) Using AIC() function: > AIC(fm1DNase1) [1] -78.41642 Using number of estimable coefficients (including residual error): > -2*logLik(fm1DNase1) + 2*(length(coef(fm1DNase1))+1) [1] -76.41642 attr(,"df") [1] 3 attr(,"nall") [1] 16 attr(,"nobs") [1] 16 attr(,"class") [1] "logLik" Based on the difference in AIC of 2 between the two approaches, it appears that when applied to a nls() object, AIC() doesn't include the estimate of residual error in the number of estimated parameter...

...GB2DCPOD[[8]]<- GB3ACPOD[[9]]<- GB3CCPOD[[10]]<- GB3DCPOD[[11]]<- GB4ACPOD[[12]]<- GB4CCPOD[[13]]<- GB4DCPOD[[14]]<- GB5ACPOD[[15]]<- GB5BCPOD[[16]]<- GB5CCPOD[[17]]<- GB5D Each each file you can find a txt document that contains the columns: file, chuckend, Nfiltered, Nall, MinsOn and Presyabs. What I want to do is to create a loop for all the CPODs. If the row of the column Nfiltered is 0 then the row of the column Presyabs must be 0 if it is different than 0 then it must be 1. I create this loop without success#Creating the loop (0-1 presence, absence) for(i in 1...

Dear all, I try to compare the performances of several parameters to diagnose lameness in dogs. I have several ROC curves from the same dataset. I plotted the ROC curves and calculated AUC with the ROCR package. I would like to compare the AUC. I used the following program I found on R-help archives : From: Bernardo Rangel Tura Date: Thu 16 Dec 2004 - 07:30:37 EST

...R that will do the LR test between a glm and a glmer fit, so I try to do it using the liklihoods from both models #form the likelihood ratio test between the glm and glmer fits x2<--2*(logLik(ph.fit.2)-logLik(ph.fit.3)) > ML 79.60454 attr(,"nobs") n 45243 attr(,"nall") n 45243 attr(,"df") [1] 14 attr(,"REML") [1] FALSE attr(,"class") [1] "logLik" #Get the associated p-value dchisq(x2,14) ML >5.94849e-15 Which looks like an improvement in model fit to me. Am I seeing this correctly or are the tw...

...oduct: netfilter/iptables Version: unspecified Hardware: All OS: All Status: NEW Severity: enhancement Priority: P5 Component: unknown Assignee: netfilter-buglog at lists.netfilter.org Reporter: joe at nall.com The kernel and nftables userspace are both limited to 255 byte (NFT_SECMARK_CTX_MAXLEN) SELinux secmark contexts. If we start with 44 characters of non category SELinux packet context system_u:object_r:http_client_packet_t:s10: we are left with 211 bytes for category bit representation. I...

Hi there, I'm a PhD student investigating growth patterns in fish. I've been using the minpack.lm package to fit extended von Bertalanffy growth models that include explanatory covariates (temperature and density). I found the nls.lm comand a powerful tool to fit models with a lot of parameters. However, in order to select the best model over the possible candidates (without covariates,

...= qsec, y = mpg, colour = cyl)) pushViewport(viewport(layout.pos.col = 1, layout.pos.row = 2)) print(p, vp = current.viewport()) upViewport() pushViewport(viewport(layout.pos.col = 2, layout.pos.row = 2)) grid.rect() grid.text("We want to remove\nall other legends\nand place a\nsingle one here") ------------------------------- END CODE See how there are 3 legends when there only needs to be one? I would like to remove the 3 legends and place them with a single legend in the space in the lower right. Thanks for your help. Regards, K...

Hello All, I would like to retrieve some of the results from the lmer(...) function in library lme4. If I run a model, say fm.1 <- lmer(y ~ 1 + (1 | x), data = dog) and try names(fm.1), I get NULL. Is there anyway to retrieve the information? Thanks

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