search for: cloglog

I'm having trouble using binomial(link="cloglog") with GLMM in lme4, Version: 0.5-2, Date: 2004/03/11. The example in the Help file works fine, even simplified as follows: fm0 <- GLMM(immun~1, data=guImmun, family=binomial, random=~1|comm) However, for another application, I need binomial(link="cloglog"), and...

...nomial model (dichotomous response variable with far more zeros than ones). I am aware that there are several relevant posts on this list, but I am afraid I need a little more help. The two suggested approaches seem to be: 1) modify the make.link function in GLM, or 2) use the loglog or cloglog functions in the VGAM package. Below are my questions for each. Responses to either are much appreciated. 1) Modifying the make.link function: In his Sat, 24 Jan 2009 post, Re: [R] glm binomial loglog (NOT cloglog) link, William Simpson suggested a loglog={} insertion that seems reasonab...

I would like to do an R glm() with family = binomial(link="loglog") Right now, the cloglog link exists, which is nice when the data have a heavy tail to the left. I have the opposite case and the loglog link is what I need. Can someone suggest how to add the loglog link onto glm()? It would be lovely to have it there by default, and it certainly makes sense to have the two opposite cases...

I'm having trouble getting fun="cloglog" to work with plot on a survfit object. Here are the data I used for the commands that follow. days status 2 0 2 0 5 1 9 0 14 1 16 0 16 0 17 0 29 1 30 0 37 1 37 0 39 1 44 0 44 0 58 0 60 1 67 1 68 1 82 1 82 1 86 0 86 0 89 1 93 0 97 1 100 0 100 0 100 0 > library(survival) Loading require...

...weights=rep(N, n)) Error in eval(expr, envir, enclos) : ..1 used in an incorrect context, no ... to look in Is there something I can do to get past this short of working through the code for GLMM line by line? Also, I gather GLMM does not currently accept binomial(link="cloglog"): > fit <- GLMM(1-yield~1, random=~1|nest, family=binomial(link="cloglog"), + data=DF,weights=rep(N, n)) Error in getClass(thisClass) : "family" is not a defined class Error in GLMM(1 - yield ~ 1, random = ~1 | nest, family = binomial(link = "c...

...(x)=1.36 Is there anyone tell me what I did wrong? Here are the code and results, 1) SAS Genmod: % r: # of failure % k: size of a risk set data bin_data; input r k y x; os=log(y); cards; 1 3 5 0.5 0 2 5 0.5 0 2 4 1.0 1 2 4 1.0 ; proc genmod data=nelson; model r/k = x / dist = binomial link =cloglog offset = os ; <Results from SAS> Log Likelihood -4.7514 Parameter DF Estimate Error Limits Square Pr > ChiSq Intercept 1 -3.6652 1.9875 -7.5605 0.2302 3.40 0.0652 x...

Hello, I'm not a Stata user so I'm trying to reproduce Stata results that are given to me in R. I would like to use a GLM with a complementary log-log function. The stata code I have is: glm c IndA fia, family(binomial s) link(cloglog) offset(offset) The R code is: glmt <- glm(data=dataset, c ~ IndA + fia, offset = offset, family = binomial(link = cloglog)) Which yields different results from the Stata output. I think the difference is in the variable s that is included in the binomial family in Stata (bold in the code)...

Hello everybody, does anybody know how the function plot.survfit exactly works? I'd like to plot the log of the cummulative hazard against the log time by using plot.survfit(...fun="cloglog") which does not work correctly. The scales are wrong and there is an error message about infinit numbers. It must have something to do with the censored data, doesn't it? #Example: motorette <- read.table("http://stat.ethz.ch/Teaching/Datasets/NDK/motorette.dat",header=T)...

...(sorry, I don't want to upgrade yet, but it should be easy to check)? > glm(1 ~ 2, family=quasibinomial(link=foo)) Error in quasibinomial(link = foo) : ?foo? link not available for quasibinomial family, available links are "logit", ", ""probit" and "cloglog" > glm(1 ~ 2, family=binomial(link=foo)) Error in binomial(link = foo) : link "foo" not available for binomial family, available links are "logit", ""probit", "cloglog", "cauchit" and "log" I hope this is helpful, Yan

I would like to get horizontal numbers on the both axes: X and Y. I got horizontal numbers only on the Y axis when adding las=2, How to obtain a horizontal orientation for number on scale also for the X axis (now they are vertical)? Here is my code: plot(survfit(Y~addicts$clinic), fun="cloglog", las=2) [[alternative HTML version deleted]]

...warning message. I would appreciate help getting two lines, and an explanation of the warning message. My problem may the that I have very few events in one of my strata, but I don't know. Thanks, John fit0<-coxph(Surv(Time30,Died)~strata(Rx)+Age,data=GVHDdata) plot(survfit(fit0),fun="cloglog") WARNING: Warning in xy.coords (x, y, xlabel, ylabel, log) : 2 x values <=0 omitted from logarithmic plot > print(survfit(fit0),fun="cloglog") Call: survfit.coxph(object = fit0) n events median 0.95LCL 0.95UCL Rx=0 31 5 Inf Inf Inf Rx=1 32 15...

Hi, I would like apply different link functions from Aranda-Ordaz (1981) family to large binary dataset (n = 2000). The existing links in glm for binomial data (logit, probit, cloglog) are not adequate for my data, and I need to test some other transformations. Is it possible to do this in R? And how? Thank you for your help, /Sharon +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ SHARON K?HLMANN-BERENZON Tel. +46-31-772 53 60 Dept. Mathematical Sta...

Hello, The following code: > binomial(identity) Generates an error message: Error in binomial(identity) : link "identity" not available for binomial family; available links are ?logit?, ?probit?, ?cloglog?, ?cauchit?, ?log? While : > binomial("identity") Yields an identity-binomial object that works as expected with stats::glm The error in the first example mislead me during years. I thought identity-binomial models were unsupported by R. The documentation is correct but misleading to...

...set.seed(8) N. <- 1000 z <- rnorm(N.) pr.good <- exp(-1e-4*exp(2+2*z)) quantile(pr.good) DF. <- data.frame(yield=rbinom(N., N., pr.good)/N., Offset=rep(-10, N.), nest=1:N.) fit <- glmmPQL(fixed=1-yield~offset(Offset), random=~1|nest, family=binomial(link="cloglog"), data=DF., weights=rep(N., N.)) A year on can anyone suggest what the problem might be? Or in particular, Spencer - did you come up with a solution? Thanks in advance, Kechi Nzerem

...ying an unavailable link to `quasibinomial', the error message looks strange. E.g. > quasibinomial("x") Error in quasibinomial("x") : 'x' link not available for quasibinomial family, available links are "logit", ", ""probit" and "cloglog" ^^^^^^^^ Clearly, something's missing. A quick peek in the code reveals that `log' is allowed, but is missing from the above list. Furthermore, shouldn't `quasibinomial' also allow the `cauchit' link?

-- Error in binomial(link = "identity") : identity link not available for binomial family, available links are "logit", "probbit", "cloglog" and "log" Hi, I have a question, dealing with this error response. I'm trying to make anova on percentages. The variablethat has a biological significance is actually the percentage itself. Is it really not possible, to test a simple linear probability model with glm, using...

...n advance, Jin [I]for 1) and 2) (working) % data set alpha<-1 verpi<-c(5^alpha,10^alpha-5^alpha,14^alpha-10^alpha,18^alpha-14^alpha) r<-c(1,0,0,1) k<-c(3,2,2,2) x<-c(0.5,0.5,1.0,1.0) % estimate lambda (lambda=beta0+beta1*x) GLM_results <- glm(r/k ~ x, family=binomial(link='cloglog'), offset=log(verpi),weights=k) beta0<-GLM_results$coefficients[[1]] beta1]<-GLM_results$coefficients[[2]] lambda1<-beta0+beta1*x[1] lambda2<-beta0+beta1*x[2] % using lambda, estimate alpha (a=alpha) through ML estimation L<-function(a){ s1_f1<-(exp(-lambda1*(0^a-0^a))-exp(-l...

...re a particular reason (and if so, what is it) that the inverse link is not in the list of allowable link functions for the binomial family? I initially thought this might have something to do with the properties of canonical vs non-canonical link functions, but since other link functions (probit, cloglog, cauchit, log) are allowed, I can't think of any good reason. In fact, it's sort of a mystery to me why the sets of link functions for each family are restricted. Is this from painful experience that some link functions just don't work well? I can go ahead and hack my own version th...

Hi all, I'm trying to estimate a nested model (purchase decision, cloglog formula, & quantity bought given a purchase, truncated Poisson). Some of the parameters are mixed (6) and 4 are fixed for all the respondent. The simulated ML (500 simulations) method forwards highly correlated estimates. After some research, Hybrid Monte Carlo seems to be a good alternative to...

...rror is loaded. Is there some specific 'type= ' syntax, or an additional parameter that I must specify? > leukemia.surv <- survfit(Surv(time, status) ~ x, data = aml) > plot(leukemia.surv, lty = 2:3) ## works fine > plot(leukemia.surv, lty = 2:3, fun="cloglog") ## works fine > plot(leukemia.surv, type="p") ## generates error below: Error in plot.default(tempx, tempy * yscale, type = "n", log = logax, : formal argument "type" matched by multiple actual arguments > search() [1] &qu...