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); options(datadist = 'dd') m1 = lrm(y~x, data = d) summary(m1) m2 = glm(y~x, family = binomial, data = d) confint(m2) I have spent time trying to figure this out via archives, but have not had much luck. Regards Stephen
On Wed, 8 Feb 2006, Cox, Stephen wrote:> 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.Well, the Design library is not giving you confidence intervals for parameters, is it? (Since there is no summary method for lrm it is a long haul to find out what it is giving you, which I leave to you.)> 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); options(datadist = 'dd') > m1 = lrm(y~x, data = d) > summary(m1) > > m2 = glm(y~x, family = binomial, data = d) > confint(m2) > > I have spent time trying to figure this out via archives, but have not had much luck. > > Regards > > Stephen-- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
Cox, Stephen wrote:> 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); options(datadist = 'dd') > m1 = lrm(y~x, data = d) > summary(m1) > > m2 = glm(y~x, family = binomial, data = d) > confint(m2) > > I have spent time trying to figure this out via archives, but have not had much luck. > > Regards > > StephenDesign uses Wald(large sample normality of parameter estimates) -based confidence intervals. These are good for most situations but profile confidence intervals are preferred. Someday I'll make Design do those. One advantage to Wald statistics is that they extend readily to cluster sampling (e.g., using cluster sandwich covariance estimators) and other complications (e.g., adjustment of variances for multiple imputation), whereas likelihood ratio statistics do not (unless e.g. you have an explicit model for the correlation structure or other facits of the model). Also note that confint is probably giving a confidence interval for a one-unit change in x whereas summary.Design is computing an interquartile-range effect (difference in x-values is shown in the summary output). When posting a nice simulated example it's best to do set.seed(something) so everyone will get the same data. Frank -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University