# Computing confidence intervals for the estimated counts and probabilities (deprecated) This function computes the (asymptotic) Wald confidence intervals at a given significance level for the results generated by `Ipfp` and `ObtainModelEstimates` (provided that their option `compute.cov` was set to `TRUE`). ```r GetConfInt(list.est, alpha = 0.05) ``` ## Arguments - `list.est`: A list produced either by `Ipfp` or `ObtainModelEstimates` containing the estimated counts and probabilities as well as their associated standard deviations. - `alpha`: Significance level of the confidence interval corresponding to the 100(1 - $\alpha$)% confidence level. ## Details The confidence interval of the estimates $X.hat$, at significance level $\alpha$ is given by $$ \hat{X} \pm z \left( 1-\frac{\alpha}{2} \right) *\hat{\sigma}X.hat +/- z(1-\alpha/2) * \sigma.hat $$ where $\sigma.hat$ is the standart deviations of $X.hat$ and $z$ is the inverse of the cumulative distribution function of the standard normal distribution. ## Returns A list of matrices containing the upper and lower bounds for the estimated counts and probabilities. - **lower.x**: Lower bounds of the confidence interval for `list.est$x.hat`. - **upper.x**: Upper bounds of the confidence interval for `list.est$x.hat`. - **lower.p**: lower bounds of the confidence interval for `list.est$p.hat`. - **upper.p**: upper bounds of the confidence interval for `list.est$p.hat`. ## References Smithson, M. (2002). **Confidence intervals**. Sage Publications. ## Author(s) Johan Barthelemy Maintainer: Johan Barthelemy johan@uow.edu.au . ## Warning Note: this function is deprecated, instead use `confint.mipfp`. ## See Also `Estimate`, `Ipfp` and `ObtainModelEstimates` to generate the inputs for this function. The S3 method `confint.mipfp` for object of class `mipfp`. ## Examples ```r # true contingency (2-way) table true.table <- array(c(43, 44, 9, 4), dim = c(2, 2)) # generation of sample, i.e. the seed to be updated seed <- ceiling(true.table / 10) # desired targets (margins) target.row <- apply(true.table, 2, sum) target.col <- apply(true.table, 1, sum) # storing the margins in a list target.data <- list(target.col, target.row) # list of dimensions of each marginal constrain target.list <- list(1, 2) # calling the Ipfp function res <- Ipfp(seed, target.list, target.data) # addint the standart deviations to res (required by GetConfInt) cov.res <- vcov(res, seed = seed, target.list = target.list, target.data = target.data) res$p.hat.se <- cov.res$p.hat.se res$x.hat.se <- cov.res$x.hat.se # computing and printing the confidence intervals print(GetConfInt(res)) ```

GetConfInt function