search for: numalive

Dear all, I'm trying to use tcltk to build a small user-interface and couter the problem: It seems that sometimes the dialog box will be minimized automatically though I want it to be on top of the screen all the time. For example, when runing the following code, if you type in "a1", "a2", "a3", etc when being asked for an integer, the program is supposed to

...01 ?*? 0.05 ?.? 0.1 ? ? 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 124.8756 on 11 degrees of freedom Residual deviance: 4.9937 on 8 degrees of freedom AIC: 43.104 Number of Fisher Scoring iterations: 4 This is the data set used in regression: numdead numalive sex ldose 1 1 19 M 0 2 4 16 M 1 3 9 11 M 2 4 13 7 M 3 5 18 2 M 4 6 20 0 M 5 7 0 20 F 0 8 2 18 F 1 9 6 14 F 2 10 10 10...

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

...predict.glm example > ## example from Venables and Ripley (2002, pp. 190-2.) > ldose <- rep(0:5, 2) > numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16) > sex <- factor(rep(c("M", "F"), c(6, 6))) > SF <- cbind(numdead, numalive=20-numdead) > budworm.lg <- glm(SF ~ sex*ldose, family=binomial) > ld <- seq(0, 5, 0.1) > row.names(predict(budworm.lg, data.frame(ldose=ld, > sex=factor(rep("M", length(ld)), levels=levels(sex))), > type = "response")) [...

Goodness, how many prereleases has there been in the last week... have lost count... Anyway, I have implemented the already-promised chainbooting API, and have included a sample program to chainboot floppies (it works for harddisks too, but only for the MBR.) As a bonus, in the process I actually discovered that booting .bs/.bss images under SYSLINUX was actually broken... that has been

Dear R-help list members, I am currently trying to fit a generalized linear model using a binomial with the canonical link. The usual solution is to use the R function glm() in the package "stats". However, I run into problem when I want to fit a glm without an intercept. It is indicated that the solution is in changing the function glm.fit (also in "stats"), by specifying

Hi. I'm trying to plot the ratio of used versus unused bird houses (coded 1 or 0) versus a continuous environmental gradient (proportion of urban cover [purban2]) that I would like to convert into bins (0 - 0.25, 0.26 - 0.5, 0.51 - 0.75, 0.76 - 1.0) and I'm not having much luck figuring this out. I ran a logistic regression and purban2 ends up driving the probability of a box being