# Comparing deviations of mipfp objects This function compares either the margins errors from different `mipfp` objects or the absolute maximum deviation between a given table and the estimates in the mipfp objects. ```r CompareMaxDev(list.mipfp = list(), true.table = NULL, echo = FALSE) ``` ## Arguments - `list.mipfp`: The list produced by the function `Estimate`. - `true.table`: When provided, the estimates contained in the `mipfp` objects in the list `list.mipfp` are compared against this table. It is an optional argument. - `echo`: Verbose parameter. If `TRUE`, the function prints what is being compared. Default is `FALSE`. ## Returns A table with as many rows as the number of mipfp objects in `list.mipfp`. Each row details the margins errors or the maximum absolute deviation of one `mipfp` object. ## Author(s) Johan Barthelemy Maintainer: Johan Barthelemy johan@uow.edu.au . ## See Also The estimation function `Estimate`. This function is used by `error.margins.mipfp`. ## Examples ```r # loading the data data(spnamur, package = "mipfp") # subsetting the data frame, keeping only the first 3 variables spnamur.sub <- subset(spnamur, select = Household.type:Prof.status) # true table true.table <- table(spnamur.sub) # extracting the margins tgt.v1 <- apply(true.table, 1, sum) tgt.v1.v2 <- apply(true.table, c(1,2), sum) tgt.v2.v3 <- apply(true.table, c(2,3), sum) tgt.list.dims <- list(1, c(1,2), c(2,3)) tgt.data <- list(tgt.v1, tgt.v1.v2, tgt.v2.v3) # creating the seed, a 10% sample of spnamur seed.df <- spnamur.sub[sample(nrow(spnamur), round(0.10*nrow(spnamur))), ] seed.table <- table(seed.df) # applying the different fitting methods r.ipfp <- Estimate(seed=seed.table, target.list=tgt.list.dims, target.data = tgt.data, method = "ipfp") r.ml <- Estimate(seed = seed.table, target.list = tgt.list.dims, target.data = tgt.data, method = "ml") r.chi2 <- Estimate(seed = seed.table, target.list = tgt.list.dims, target.data = tgt.data, method = "chi2") r.lsq <- Estimate(seed = seed.table, target.list = tgt.list.dims, target.data = tgt.data, method = "lsq") # print the maximum absolute deviation between targets and generated margins CompareMaxDev(list(r.ipfp,r.ml,r.chi2,r.lsq), echo = TRUE) # compute the maximum absolute deviation between the true and estimated tables CompareMaxDev(list(r.ipfp,r.ml,r.chi2,r.lsq), echo = TRUE, true.table = true.table) ```

CompareMaxDev function