similar to: generalised linear models

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

hello, I have a problem in how to generate data in a simulation study. I have a logistic model to evaluate p by 3 covariates. I need to generate 4 variables: the binary outcome Y and 3 covariates: gender (binary) and aps and tiss (continuous variables). I have the logistic model which is the expected model: log(p(y=1)/(1-p(y=1))=-1.659-0.05*sex+0.063*aps+0.04*tiss0) I generate the outcome y like

Hi All, I have data with 400 individuals and the following information Grade: pass or fail coded as 1 for pass and 0 for fail Sex: male or female ( coded as 1 for male and 2 for female ) Age Teaching.method : can be 1,2,3 I want to fit a logistic regression where the outcome if (1=pass or 0 for fail) and the rest of the variables are the regressors. My question is that I am not sure

I have a question about some strange results I get when using lmer to build a logistic mixed effects model. I have a data set of about 30k points, and I'm trying to do backwards selection to reduce the number of fixed effects in my model. I've got 3 crossed random effects and about 20 or so fixed effects. At a certain point, I get a model (m17) where the fixed effects are like this

Dear R-helpers I have a question related to logistic GAM models. Consider the following example: # Load package library(mgcv) # Simulation of dataset n <- 1000 set.seed(0) age <- rnorm(n, 50, 10) blood.pressure <- rnorm(n, 120, 15) cholesterol <- rnorm(n, 200, 25) sex <- factor(sample(c('female','male'), n,TRUE)) L <-

Hi All: I came across the following problem while working with a dataset, and wondered if there could be a solution I sought here. My dataset consists of information on 402 individuals with the followng five variables (age,sex, status = a binary variable with levels "case" or "control", mma, dma). During data check, I found that in the raw data, the data entry

Hi, I'm trying to build a hierarchical logistic regression model with lme4 package, but I have a problem on selecting the variables to include in this model. In a simple logistic regression, using Forward selection, i use a likelihood ratio test to check which variables i should include in the model, using the function add1(). The problem is that this function doesn't work with the

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Hello I am running R 1.8.1 on a Windows platform I am attempting to fit an ordinal logistic regression model, using the polr function, as described in Venables and Ripley. But when I try model4 <- polr(ypsxcat~committed + as.factor(sex) + as.factor(drugusey) + anycsw + as.factor(sex)*committed + as.factor(sex)*as.factor(drugusey)+as.factor(sex)*anycsw, data = duhray) I get a message

Dear Masters, first I'd like to wish u all a great 2011 and happy holydays by now, second (here it come the boring stuff) I have a question to which I hope u would answer: I run a logistic regression by glm(), on the following data type (y1=1,x1=x1); (y2=0,x2=x2);......(yn=0,xn=xn), where the response (y) is abinary outcome on 0,1 amd x is any explanatory variable (continuous or not)

dear professor: I have a problem about the nomogram.I have got the result through analysing the dataset "exp2.sav" through multinominal logistic regression by SPSS 17.0. and I want to deveop the nomogram through R-Projject,just like this : > n<-100 > set.seed(10) > T.Grade<-factor(0:3,labels=c("G0", "G1", "G2","G3")) >

Dear all: "predict.glm" provides an example to perform logistic regression when the response variable is a tow-columned matrix. I find some paradox about the degree of freedom . > summary(budworm.lg) Call: glm(formula = SF ~ sex * ldose, family = binomial) Deviance Residuals: Min 1Q Median 3Q Max -1.39849 -0.32094 -0.07592 0.38220 1.10375

I am hoping for some advie regarding the following scenario. I have data from 26 studies that I wanted to analyse using logistic regression. Given the data was from studies and not individuals I was unsure how I would perform this in R. When analysed in SPSS, weighting was used so that each study was included twice. Where "use" occurred, a value of 1 was assigned and was weighted

Hi ! This is Madhavi from Mumbai, India. Incidently this is my first post. I am working on Credit Scoring Model and using R, I have run the logistic regression. I have received following Output. I have two questions (a) What is the significance of "family = binomial(link = logit)". Why do I have to mention Binomial? Is it because my dependent variable assumes only two values 0 and 1?

Hi I have a small problem, I'm new in using R, so I hope you can help me... I'm running a logistic regression model and want to do a nice plot. The plot I have made is done a plot with the command jitter: plot(jitter(overto$age[overto$sex=="F"]),jitter(overto$neg.pos[overto$sex=="F"]),xlab="age",ylab="neg and pos") my responsvariable is positive

I'm trying to figure out how to use lmer to fit models with factors that have some nesting and some non-nested groupings. For example, in this paper: http://www.stat.columbia.edu/~gelman/research/published/parkgelmanbafumi.pdf we have a logistic regression of survey respondents' political preferences (1=Republican, 0=Democrat), regressing on sex, ethnicity, state (51 states within 5

I have been trying to run a logistic regression using a number of studies. Below is the syntax, error message & data. Any advice regarding what I am doing wrong or solutions are appreciated, regards Bob Green > logreg <- read.csv("c:\\logregtest.csv",header=T) > attach(logreg) > names(logreg) [1] "medyear" "where" "who"

Hello, I would like some help with my Generalised Linear Model in R-Studio I am a little confused about what family to use and for my data My outcome variable (response) is categories 0,1,2,3 but my data i dont have any individuals that have fallen into the top group "3" What i have read about Generalised Models is that Biomial data is only two option not the 4 that i have. What

Dear all R-users, I am a new user of R and I am trying to build a discrete choice model (with more than two alternatives A, B, C and D) using logistic regression. I have data that describes the observed choice probabilities and some background information. An example below describes the data: Sex Age pr(A) pr(B) pr(C) pr(D) ... 1 11 0.5 0.5 0 0 1 40 1 0 0 0 0 34 0 0 0 1 0 64 0.1 0.5 0.2 0.2 ...

Hello, I was hoping for some advice in changing 2 defaults in a logistic regression. 1. It looks like the first category is the reference category? In the following syntax 'where' has 4 levels, how can I make the reference category the third category? model<- glm(cbind(sucesses, failures) ~ where + who + firstep + dxnarrow + age + sex + medyear, family = binomial, data=life.use)