similar to: why does glm.predict give values over 1 ?

Dear gurus, I've analyzed a (fake) data set ("data") using logistic regression (glm): logreg1 <- glm(z ~ x1 + x2 + y, data=data, family=binomial("logit"), na.action=na.pass) Then, I created a data frame with 2 fixed levels (0 and 1) for each predictor: attach(data) x1<-c(0,1) x2<-c(0,1) y<-c(0,1) newdata1<-data.frame(expand.grid(x1,x2,y))

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Hello R community, I have a question about the logistic regression function. Specifically, when the predictor variable has not just 0's and 1's, but also fractional values (between zero and one). I get a warning when I use the "glm(formula = ... , family = binomial(link = "logit"))" which says: "In eval(expr, envir, enclos) : non-integer #successes in a binomial

I have a puzzle.... When I include an interaction in the model, many predicted probabilities are above 1. Is that a problem with my model? I thought the predicted prob can't be bigger than 1... Any help would be really appreciated! Thanks! K. reg1<-glm(pyea~male+edu+married+inc+relig+factor(time)+ factor(time)*male, data=mydata, family=binomial(link="logit"))

How do I construct a figure showing predicted value plots for the dependent variable as a function of each explanatory variable (separately) using the results of a logistic regression? It would also be helpful to know how to show uncertainty in the prediction (95% CI or SE). Thanks- This email has been processed by SmoothZap - www.smoothwall.net

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

Hi All, Sorry for this is a very naive question. I am trying to do binary classification (male vs female) using glm using following data: X1 X2 X3 Class 2.3 4.5 2.1 Male 0.9 3.2 1.6 Male 1.7 1.8 2.6 Feamle I am trying to use predict.glm for prediction with type="respose" which gives the predicted probabilities as per documentation. My

Say I fit a logistic model and want to calculate an odds ratio between 2 sets of predictors. It is easy to obtain the difference in the predicted logodds using the predict() function, and thus get a point-estimate OR. But I can't see how to obtain the confidence interval for such an OR. For example: model <- glm(chd ~age.cat + male + lowed, family=binomial(logit)) pred1 <-

Dear R Wizards, After much frustration and days of confusion I have finally broken down and am asking for help, which I don’t like doing, but I just can’t figure this one out on my own. I’ve conducted a laboratory experiment testing the effects of temperature and salinity on whether or not a biological event will occur (Go or NoGo). I’ve coded the factors temperature and salinity as factors for

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Hello all, I've come across an online posting http://www.biostat.wustl.edu/archives/html/s-news/2001-10/msg00119.html that described how to get confidence intervals for predicted values from predict.glm. These instructions were meant for S-Plus. Yet, it generally seems to work with R too, but I am encountering some problems. I am explaining my procedure in the following and would be most

Problems with predict and lines in plotting binomial glm Dear R-helpers I have found quite a lot of tips on how to work with glm through this mailing list, but still have a problem that I can't solve. I have got a data set of which the x-variable is count data and the y-variable is proportional data, and I want to know what the relationship between the variables are. The data was

Dear R helpers, running the following code of a glm model of the family poisson, gives predicted values < 0. Why? library(MASS) library(stats) library(mvtnorm) library(pscl) data(bioChemists) poisson_glm <- glm(art ~ fem + mar + kid5 + phd + ment, data = bioChemists, family = poisson) predicted.values = predict(poisson_glm) range(predicted.values) Thank you in advance for any hints.

Hi, I have a question about logistic regression in R. Suppose I have a small list of proteins P1, P2, P3 that predict a two-class target T, say cancer/noncancer. Lets further say I know that I can build a simple logistic regression model in R model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of the Proteins). This works fine. T is a factored vector with levels cancer,

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, I have fitted a glm logistic function to dichotomous forced choices responses varying according to time interval between two stimulus. x values are time separation in miliseconds, and the y values are proportion responses for one of the stimulus. Now I am trying to extrapolate x values for the y value (proportion) at .25, .5, and .75. I have tried several predict parameters, and they

Dear All, I know this may be a trivial question. In the past I have used glm to make logistic regressions on data. The output creates an object with the results of the logistic regression. This object can then be used to make predictions. Great. I have a different problem. I need to make predictions from a logistic regression taken from a paper. Thus I need to (by hand) enter the reported odds

Hey, all. I had a quick question about fitting new glm values and then looking at the error around them. I'm working with a glm using a Gamma distribution and a log link with two types of treatments. However, when I then look at the predicted values for each category, I find for the one that is close to 0, the error (using se.fit=T with predicted) actually makes it overlap 0.

I am having a couple of problems someone may be able to cast some light on. Question 1: I am making a logistic model but when i do this: glm.model = glm(as.factor(form$finished) ~ ., family=binomial, data=form[1:150000,]) I get this: Error in model.frame(formula, rownames, variables, varnames, extras, extranames, : variable lengths differ (found for 'barrier') which is

I am not sure whether it is a design decision or just an oversight. When I ask for the standard errors of the predictions with predict(budwm.lgt,se=TRUE) where budwm.lgt is a logistic fit of the budworm data in MASS, I got Error in match.arg(type) : ARG should be one of response, terms If one is to construct a CI for the fitted binomial probability, wouldn't it be more natural to do