The "likelihood" method uses the (Rao-Scott) scaled chi-squared distribution for the loglikelihood from a binomial distribution.. To find the 95% confidence for the slope of regression line we can use confint function with regression model object. Details. This means that, according to our model, a car with a speed of 19 mph has, on average, a stopping distance ranging between 51.83 and 62.44 ft. confint is a generic function in package stats.. Produces this, which is the 95% confidence ellipse for x and the interaction term. confint.fderiv.Rd Calculates point-wise confidence or simultaneous intervals for the first derivatives of smooth terms in a fitted GAM. But the confidence interval provides the range of the slope values that we expect 95% of the times when the sample size is same. The "logit" method fits a logistic regression model and computes a Wald-type interval on the log-odds scale, which is then transformed to the probability scale.. These confint methods call the appropriate profile method, then find the confidence intervals by interpolation in the profile traces. Notice how the confidence limits produced by confint(...) are well with the ellipse. For example, the 95% confidence interval associated with a speed of 19 is (51.83, 62.44). As you can see, manually computing the 95% CI around the x-coefficient yielded (0.4539815,1.138661) whereas computing it using confint yielded (0.4843258,1.173998). So the 90% CI is (7414,21906) and the 95% is (6358,23737). Example. Details. In that sense, the ellipse provides a more conservative estimate of the confidence limits. Consider the below data frame − Note: this method of using the sample quantiles to find the bootstrap confidence interval is called the Percentile Method. If the profile object is already available it should be used as the main argument rather than the fitted model object itself. So my question is, how is confint computing this confidence interval, and why does my estimate differ?

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