search for: qlogi

Hi, When running z <- plot(fit, age=NA, cholesterol=NA, perim=boundaries, method='image') Legend(z, fun=plogis, at=qlogis(c(.01,.05,.1,.2,.3,.4,.5)), zlab='Probability') And after pointing the cursor to the plot() screen in R, I obtain the following message: Using function "locator(2)" to place opposite corners of image.legend Error in Legend.plot.Design(z, fun = plogis, at = qlogis(c(0.0...

...152L, 160L, 160L), Incorrect = c(126L, 103L, 66L, 8L, 0L, 0L)), .Names = c("Contr", "Correct", "Incorrect" ), row.names = c("13", "15", "17", "19", "21", "23"), class = "data.frame") q25 <- rep( qlogis( 0.25 ), nrow(c1) ) # offset defined in arguments c1.glm <- glm( cbind(Correct, Incorrect) ~ Contr - 1, binomial, c1, offset = q25 ) # offset defined in formula c1f.glm <- glm( cbind(Correct, Incorrect) ~ Contr + offset(q25) -1, binomial, c1 ) cc <- seq( 0, 1, len = 10 ) nd <- data....

Dear all, A question related to the following has been asked on R-help before, but I could not find any answer to it. Input will be much appreciated. I got an unexpected sign of the "slope" parameter associated with a covariate (diam) using zeroinfl(). It led me to compare the estimates given by zeroinfl() and hurdle(): The (significant) negative estimate here is surprising, given

Hello, I have a dataset with proportions that vary around a fixed mean, is it possible to use betareg to look at variance in the dispersion parameter while keeping the mean fixed? I am very new to R but have tried the following: svec<-c(qlogis(mean(data1$scaled)),0,0,0) f<-betareg(scaled~-1 | expt_label + grouped_hpi, data=data1, link.phi="log", control=betareg.control(start=svec)) I understood that y~-1 could be used to give a fixed mean of 0.5 however I get the following error: Error in linkinv(x %*% beta + offset) :...

...us postings on this list. I've also added the corresponding problems I encountered with each of these attempts. 1) I've used the link function as specified at https://stat.ethz.ch/pipermail/r-help/2007-August/138436.html # define link function halflogit=function(){ half.logit=function(mu) qlogis(2*mu-1) half.logit.inv=function(eta) .5*plogis(eta)+.5 half.logit.deriv=function(eta) .5*(exp(eta/2)+exp(-eta/2))^-2 half.logit.inv.indicator=function(eta) TRUE half.logit.indicator=function(mu) mu>.5 & mu<1 link <- "half.logit" structure(list(linkfun = half.logit, linkinv =...

...ils.o pbeta.o pbinom.o pcauchy.o pchisq.o pexp.o pf.o pgamma.o pgeom.o phyper.o plnorm.o plogis.o pnbeta.o pnbinom.o pnchisq.o pnf.o pnmath.o pnorm.o pnt.o polygamma.o ppois.o pt.o ptukey.o punif.o pweibull.o qbeta.o qbinom.o qcauchy.o qchisq.o qexp.o qf.o qgamma.o qgeom.o qhyper.o qlnorm.o qlogis.o qnbeta.o qnbinom.o qnchisq.o qnf.o qnorm.o qnt.o qpois.o qt.o qtukey.o qunif.o qweibull.o sign.o stirlerr.o toms708.o -lgomp -F/Library/Frameworks/R.framework/.. - framework R -Wl,-framework -Wl,CoreFoundation ld: warning, duplicate dylib /Developer/SDKs/MacOSX10.4u.sdk/usr/local/ lib/lib...

...he standard binomial weights n*p*(1-p). If you perform output <- glm(p ~ x, family = binomial, weights = n) you get a different result than if you perform the logit transformation manually on p and perform output <- lm(logit(p) ~ x, weights = w), where logit(p) is either obtained from R with qlogis(p) or from a manual computation of ln(p/1-p). The difference seems to me to be too large to be roundoff error. The only thing I can guess is that the application of the weights in glm is different than in a manual computation. Can anyone explain the difference in results? Daniel Pick Princ...

...value of x, calculated across w observations. When I use glm my.glm.obj=glm(y~x,family='binomial',weights=w) the regression comes out fine, but if I try what I understand to be the equivalent lm procedure (i.e. fitting a straight line to the logit transformed y values): my.lm.obj=lm(qlogis(y)~x,weights=w) I get an error because, of course, logit(1) = log(1/0) = log(Inf) = Inf (similarly, logit(0) = log(0/1) = log(0) = -Inf). I'd be very interested to see how glm deals with these extremes. Cheers, Mike -- Mike Lawrence http://artsweb.uwaterloo.ca/~m4lawren "The road...

...ta using this model, and I'd like to fit it. My data is a data frame with 3 columns, "response" (0/1), "subject" (a factor), and "condition" (another factor). Here is my link definition: ############################# halflogit=function(){ half.logit=function(mu) qlogis(2*mu-1) half.logit.inv=function(eta) .5*plogis(eta)+.5 half.logit.deriv=function(eta) .5*(exp(eta/2)+exp(-eta/2))^-2 half.logit.inv.indicator=function(eta) TRUE half.logit.indicator=function(mu) mu>.5 & mu<1 link <- "half.logit" structure(list(linkfun = half.logit, linkinv =...

...orrect = c(34L, 57L, 94L, 152L, 160L, 160L), Incorrect = c(126L, + 103L, 66L, 8L, 0L, 0L)), .Names = c("Contr", "Correct", "Incorrect" + ), row.names = c(NA, 6L), class = "data.frame") glm(cbind(Correct, Incorrect) ~ Contr - 1, binomial, data = c1, offset = qlogis(0.25)) Error in model.frame.default(formula = cbind(Correct, Incorrect) ~ Contr - : variable lengths differ (found for '(offset)') but not if it has the length of the number of rows of the data frame glm(cbind(Correct, Incorrect) ~ Contr - 1, binomial, data = c1, offset = rep(qlogi...

...etween zero and one.") if (POD.ceiling - POD.floor < difference.criterion) stop (paste("POD.ceiling-POD.floor difference must be greater than ",difference.criterion," to discourage answer-shopping.", sep="")) linkfun <- function(mu) { mu <- qlogis( (mu - POD.floor)/(POD.ceiling - POD.floor) ) } linkinv <- function(eta) { eta <- POD.floor + (POD.ceiling - POD.floor)*plogis(eta) } mu.eta <- function(eta) { (POD.ceiling - POD.floor)*dlogis(eta)# derivitaive of mu with respect to eta } valideta &...

This post about length 1 offsets on R help seems to have been ignored (sorry deleted original email - is there a way to continue thread in this case?) https://stat.ethz.ch/pipermail/r-help/2009-February/189352.html It does seem to be a bug, in that glm does not behave as documented. In fact the same bug applies to lm as well. I don't think the suggested fix works though - Y isn't

...inom.o rbinom.o rmultinom.o dcauchy.o pcauchy.o qcauchy.o rcauchy.o dexp.o pexp.o qexp.o rexp.o dgeom.o pgeom.o qgeom.o rgeom.o dhyper.o phyper.o qhyper.o rhyper.o dnbinom.o pnbinom.o qnbinom.o rnbinom.o dpois.o ppois.o qpois.o rpois.o dweibull.o pweibull.o qweibull.o rweibull.o dlogis.o plogis.o qlogis.o rlogis.o dnchisq.o pnchisq.o qnchisq.o dnbeta.o pnbeta.o qnbeta.o pnf.o pnt.o qnf.o qnt.o ptukey.o qtukey.o toms708.o wilcox.o signrank.o ranlib libnmath.a config.status: creating src/unix/Makefile make: /usr/local/R-devel/srcunix: No such file or directory *** Error code 2 Stop in /usr/local...

...im=boundaries, method='image') # draws image() plot # don't show estimates where data are sparse # doesn't make sense here since vars don't interact if(!.R.)Legend(z, fun=plogis, at=qlogis(c(.01,.05,.1,.2,.3,.4,.5)), zlab='Probability') # gray scale or color legend for prob. _______________________________________________________________________ Ir. Jan Verbesselt Research Associate Group of Geomatics Engineering Department Biosystems ~ M³-BIORES Vital Decosterstraat 1...

...ustom link function, but my understanding is that lmer does not currently allow a custom link. Therefore, I was investigating if other procedures for mixed models will allow a custom link function. here is the custom link function: logexp <- function(days = 1) { linkfun <- function(mu) qlogis(mu^(1/days)) linkinv <- function(eta) plogis(eta)^days mu.eta <- function(eta) days * plogis(eta)^(days-1) * .Call("logit_mu_eta", eta, PACKAGE = "stats") valideta <- function(eta) TRUE link <- paste("logexp(", days, ")", se...

Hello, Frank was nice enough to point me to the val.prob function of the Design library. It creates a beautiful graph that really helps me visualize how well my model is predicting probabilities. By default, there are two lines on the graph 1) fitted logistic calibration curve 2) nonparametric fit using lowess Right now, the nonparametric line doesn't look very good. The

Hello, Suppose one is interested in fitting a GLM with a log link to binomial data. How does R choose starting values for the estimation procedure? Assuming I don't supply them. Thanks, Tyler

...s with glm() is shown below to illustrate what I'm hoping to scale up to a mixed model. Thanks very much for your time and thoughts. ----------------- library(MASS) logexp <- function(days = 1) ##Custom link function from Shaffer (2004) Auk 121:526-540. { linkfun <- function(mu) qlogis(mu^(1/days)) linkinv <- function(eta) plogis(eta)^days mu.eta <- function(eta) days * plogis(eta)^(days-1) * .Call("logit_mu_eta", eta, PACKAGE = "stats") valideta <- function(eta) TRUE link <- paste("logexp(", days, ")", se...

...nction. Actually, glmmPQL seems to work, but as I want to evaluate a suite of competing models, I'd like to use ML or REML estimation methods in order to end up with meaningful log-likelihoods. Here's the link function I use: logexp <- function(days = 1) { linkfun <- function(mu) qlogis(mu^(1/days)) linkinv <- function(eta) plogis(eta)^days mu.eta <- function(eta) days*.Call("logit_mu_eta", eta, PACKAGE = "stats")*plogis(eta)^(days-1) valideta <- function(eta) TRUE link <- paste("logexp(", days, ")",...

...cc -g -I../include -c dlnorm.c -o dlnorm.o gcc -g -I../include -c plnorm.c -o plnorm.o gcc -g -I../include -c qlnorm.c -o qlnorm.o gcc -g -I../include -c rlnorm.c -o rlnorm.o gcc -g -I../include -c dlogis.c -o dlogis.o gcc -g -I../include -c plogis.c -o plogis.o gcc -g -I../include -c qlogis.c -o qlogis.o gcc -g -I../include -c rlogis.c -o rlogis.o gcc -g -I../include -c dnbinom.c -o dnbinom.o gcc -g -I../include -c pnbinom.c -o pnbinom.o gcc -g -I../include -c qnbinom.c -o qnbinom.o gcc -g -I../include -c rnbinom.c -o rnbinom.o gcc -g -I../include -c pnchisq.c -o pnchisq....