similar to: Likelihood ratio based confidence intervals for logistic regression

Please forgive a rather na??ve question... Could someone please give a quick explanation for the differences in conf intervals achieved via confint.glm (based on profile liklihoods) and the intervals achieved using the Design library. For example, the intervals in the following two outputs are different. library(Design) x = rnorm(100) y = gl(2,50) d = data.frame(x = x, y = y) dd = datadist(d);

Hi, I found myself trying to figure out the type of confidence interval used for the coefficients of the logistic regression fit by using glm(family=binomial)... I suspect it is Wald confidence interval but am not sure...Does anybody know? Also, if so, how can I ask for likelihood ratio and/or score-based confidence intervals? Yours, Michael ~~~~~~~~~~~~~~~~~~~~~ Michael Levine Assistant

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Dear *, I would like to obtain for each factor of my anova model the "response variable vs factor" plot with means and 95% Tukey HSD intervals. I would appreciate any information on how to do that. Cheers -------------------------------------------------------------------- Max MANFRIN Tel.: +32 (0)2 650 3168 IRIDIA - CoDE, CP 194/6

Hi R People: If I have a fitted values from a model, how do I plot the (1-alpha)100% confidence intervals along with the fitted values, please? Also, if the intervals are "shaded" gray, that would be nice too, please? I check confint, but that doesn't seem to do what I want. Thanks in advance, Sincerely, Erin -- Erin Hodgess Associate Professor Department of Computer and

Hi, I am seeking help with an error when running the example from R Documentation for TukeyHSD. The error occurs with any example I run, from any text book or website. thank you... > plot(TukeyHSD(fm1, "tension")). Error in plot(confint(as.glht(x)), ylim = c(0.5, n.contrasts + 0.5), ...) : error in evaluating the argument 'x' in selecting a method for function

Hi all! I'm getting a model fit from glm() (a binary logistic regression fit, but I don't think that's important) for a formula that contains powers of the explanatory variable up to fourth. So the fit looks something like this (typing into mail; the actual fit code is complicated because it involves step-down and so forth): x_sq <- x * x x_cb <- x * x * x x_qt <- x * x * x

Hi, I cannot seem to get lme4 to work. I have installed the lme4 and Matrix package with apt-get. and both can be found in /usr/lib/R/site-library. When I tried an example for lmer, R could not find the function lmer(), > library(lme4) Attaching package: 'lme4' The following object(s) are masked from package:nlme : getCovariateFormula getResponseFormula

Hello, I'm a newcomer to R so please forgive me if this is a silly question. It's that I have a linear regression: fm <- lm (x ~ y) and I want to test whether the slope of the regression is significantly less than 1. How can I do this in R? I'm also interested in comparing the slopes of two regressions: fm1 <- lm (x ~ y) fm2 <- lm (a ~ b) and asking if the slope of fm1 is

Hi everyone, apologies if the answer to this is in an obvious place. I've been searching for about a day and haven't found anything.. I'm trying to replicate Stata's confidence intervals in R with the survey package, and the numbers are very very close but not exact. My ultimate goal is to replicate Berkeley's SDA website with R (http://sda.berkeley.edu/), which seems to

Hi - I am using R version 2.13.0. I have run several GLMMs using the glmmPQL function to model the proportion of fish caught in one net to the total caught in both nets by length. I started with a polynomial regression full model with three length terms: l, l^2, and l^3 (l=length). The length terms and intercept were the fixed effects and the random effect was a paired haul (n=18).

Dear all, It appears that MASS::polr() and Design::lrm() return the same point estimates but different st.errs when fitting proportional odds models, grade<-c(4,4,2,4,3,2,3,1,3,3,2,2,3,3,2,4,2,4,5,2,1,4,1,2,5,3,4,2,2,1) score<-c(525,533,545,582,581,576,572,609,559,543,576,525,574,582,574,471,595, 557,557,584,599,517,649,584,463,591,488,563,553,549) library(MASS) library(Design)

Hello, I am trying to plot confidence bands on the mean and prediction bands for the following nonlinear regression, using maximum likelihood via optim. A toy example with data and code of what I am trying to accomplish is: VOL<-c(0.01591475, 1.19147935 ,6.34102460, 53.68809287, 91.90143074, 116.21397007, 146.41843056, 215.64535337, 256.53149673, 315.73609232) Age <-c(1.622222, 2.833333

Hi I use gnls to fit non linear models of the form y = alpha * x**beta (alpha and beta being linear functions of a 2nd regressor z i.e. alpha=a1+a2*z and beta=b1+b2*z) with variance function varPower(fitted(.)) which sounds correct for the data set I use. My purpose is to use the fitted models for predictions with other sets of regressors x, z than those used in fitting. I therefore need to

Hello, I am doing a simple linear regression analysis that includes few variables. I am using a bootstrap analysis to obtain the variation of my variables to replacement. I am trying to obtain the coefficients 95% confidence interval from the bootstrap procedure. Here is my script for the bootstrap: N = length (data_Pb[,1]) B = 10000 stor.r2 = rep(0,B) stor.r2 = rep(0,B) stor.inter =

Greetings, We are performing a meta-analysis of mink pup survival data versus chemical concentration. We have modeled percent survival successfully using nls as shown below and the plot. What we need to do is construct a confidence interval on the concentration at which we get 50% survival (aka the EC50, although we may want other percent survivals in the future). My first question is, what seems

System: R 2.3.1 on Windows XP machine. I am building a logistic regression model for a sample of 100 cases in dataframe "d", in which there are 3 binary covariates: x1, x2 and x3. ---------------- > summary(d) y x1 x2 x3 0:54 0:50 0:64 0:78 1:46 1:50 1:36 1:22 > fit <- glm(y ~ x1 + x2 + x3, data=d, family=binomial(link=logit)) >