similar to: glm(family=binomial(link=logit))

Hi All, When modeling with glm and family = binomial (link = logit) and response values of 0 and 1, I get the predicted probabilities of assigning to my class one, then I would like to compare it with my vector y which does have the original labels. How should I change the probabilities into values of zero and 1 and then compare it with my vector y to find out about the accuracy of my

Dear R-help list, I?m using the mgcv package to plot predictions based on the gam function. I predict the chance of being a (frequent) participant at theater plays vs. not being a participant by age. Because my outcome variable is dichotomous, I use the binomial family with logit link function. Dataset in attachment, code to read it in R: data <- read.spss("pas_r.sav") attach(data)

Hi all, Trying to understand the logistic regression performed by glm (i.e. when family='binomial'), and I'm curious to know how it treats perfect success. That is, lets say I have the following summary data x=c(1,2,3,4,5,6) y=c(0,.04,.26,.76,.94,1) w=c(100,100,100,100,100,100) where x is y is the probability of success at each value of x, calculated across w observations.

Dear all, I have a question about the glm. When the events of an observation is 0, the logit function on it is Inf. I wonder how the glm solve it. An example: Treat Events Trials A 0 50 B 7 50 C 10 50 D 15 50 E 17 50 Program: treat <- factor(c("A", "B", "C", "D", "E")) events <- c(0, 7, 10, 15,

Hei, I just wonder the use of family=binomial in glm function. As I learned from book (e.g. Andy Field) that logistic regression (binary logit) can use glm funtion with family = binomial. Here the y is a factor variable (e.g. value = 1 or 2). But I have also seen i many other cases, same function glm with family=binomial, but y is a variable with several column , like y= cbind(y1, y2), and

Dear all, I'm using a binomial distribution with a logit link function to fit a GAM model. I have 2 questions about it. First i am not sure if i've chosen the most adequate distribution. I don't have presence/absence data (0/1) but I do have a rate which values vary between 0 and 1. This means the response variable is continuous even if within a limited interval. Should i use

Dear R-helpers, i have a problem with a glm-model. I am trying to fit models with the log as link function instead of the logit. However, in some cases glm fails to estimate those models and suggests to give start values. However, when I set start = coef(logistic_model) within the function call, glm still says it cannot find starting values? This seems to be more of a problem, when I include a

Does anyone know what algorithm R uses in glm, family=binomial (i.e. a logit model)? I assume that it's in the source somewhere, but I wasn't able to find it. I'd like to know what file it's in (in a unix distribution of R). Thanks for your help. --------------------------- Barnet Wagman wagman at enteract.com 1361 N. Hoyne, 2nd floor Chicago, IL 60622 773-645-8369

Hi all, I am using glm function with family binomial(logit) to fit logistic regression model. My data is very big and the algorithm is such that it has to run glm function hundreds of times. Now *I need only the **estimates of the coefficients and std. error in my output, *but apparently glm function is computing several other statistics and parameters (mentioned below) which increases the

Hi All, I am using "glm" function to build logistic regression. I noticed that glm function glm function is computing many other statistics which are not required for our analysis. As our dataset is very big and we have to run logistic regression on several samples the run time drastically increases if all those statistics are computed. Is these any way to skip computation in glm

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

Hello, Could anybody provide me with codes (procedure) how to obtain Conditional Logit (McFadden) and Mixed Logit (say, assuming normal distribution) estimates in R? Thanks, David U. -- View this message in context: http://www.nabble.com/Conditional-Logit-and-Mixed-Logit-tf4489238.html#a12802959 Sent from the R help mailing list archive at Nabble.com.

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +

Good morning, I'd like to estimate a simple multinomial logit model in R (not a McFadden conditional logit). For instance, I'd like to estimate the probability of someone having one of eight titles in a company with the independent variables being the company characteristics. A binary logit is well documented. What about the multinomial? Thanks, Walt Paczkowski

Hi I am carrying out some logit regressions and want to (a) make sure I'm taking the right approach and (b) work out how to carry out some additional analysis. So, to carry out a logit regression where the dependent variable is a factor db, I use something like: res1_l <- glm(formula = db ~ y1 + + y5, family = binomial(link = "logit")) summary(res1_l) ...which is, I hope

Dear Listers, I have a general statistical question. Are hurdle logit-poisson model and posson model nested? Thank you so much?

# Hello R Users, # # I would like to fit a glm model with quasi family and # logistical link function, but this does not seam to work # with binary data. # # Please don't suggest to use the quasibinomial family. This # works out, but when applied to the true data, the # variance function does not seams to be # appropriate. # # I couldn't see in the # theory why this does not work. # Is

Dear R-Help, I'm hoping to find a Multinomial Nested Logit package in R. It would be great to find something analogous to "PROC MDC" in SAS: > The MDC (Multinomial Discrete Choice) procedure analyzes models > where the > choice set consists of multiple alternatives. This procedure > supports conditional logit, > mixed logit, heteroscedastic extreme value,

Hi everyone? Does anybody know how can I estimate a Latent class multinomial (or conditional) logit using R? I have tried flexmix, poLCA, and they do not seem to support this model. thanks in advance adan -- View this message in context: http://r.789695.n4.nabble.com/Latent-class-multinomial-or-conditional-logit-using-R-tp4230083p4230083.html Sent from the R help mailing list archive at

Hello, I'd like to fit a logit function to my data. The data is distributed like a logit (like in this plot on wikipedia http://en.wikipedia.org/wiki/File:Logit.png) but the values on the x-axis are not between 0 and 1. I don't think using a glm is the solution because I simply want to infer the parameters of the logit function (offset, compression, slope...), so I can apply it to all