1d:T77a,# An internal function to create an A matrix for calibration of quantiles `joint_calib_create_matrix` is function that creates an matrix for calibration of quantiles. Function allows to create matrix using `logistic` interpolation (using `stats::plogis`, default) or `linear` (as in Harms and Duchesne (2006), i.e. slightly modified Heavyside function). In case of `logistic` interpolation elements of are created as follows where is the th row of the auxiliary variable , is the population size, is the known population th quantile, and is set to -1000 (by default). In case of `linear` interpolation elements of are created as follows , , where is the set of respondents, is the auxiliary variable index and , , . ```r joint_calib_create_matrix(X_q, N, pop_quantiles, control = control_calib()) ``` ## Arguments - `X_q`: matrix of variables for calibration of quantiles, - `N`: population size for calibration of quantiles, - `pop_quantiles`: a vector of population quantiles for `X_q`, - `control`: a control parameter for creation of `X_q` matrix. ## Returns Return matrix A ## Examples ```r # Create matrix for one variable and 3 quantiles set.seed(123) N <- 1000 x <- as.matrix(rnorm(N)) quants <- list(quantile(x, c(0.25,0.5,0.75))) A <- joint_calib_create_matrix(x, N, quants) head(A) colSums(A) # Create matrix with linear interpolation A <- joint_calib_create_matrix(x, N, quants, control_calib(interpolation="linear")) head(A) colSums(A) # Create matrix for two variables and different number of quantiles set.seed(123) x1 <- rnorm(N) x2 <- rchisq(N, 1) x <- cbind(x1, x2) quants <- list(quantile(x1, 0.5), quantile(x2, c(0.1, 0.75, 0.9))) B <- joint_calib_create_matrix(x, N, quants) head(B) colSums(B) ``` ## References Harms, T. and Duchesne, P. (2006). On calibration estimation for quantiles. Survey Methodology, 32(1), 37. ## Author(s) Maciej Beręsewicz 1e:T49a,M7.49933 0.25C3.49635 0.25 0.25 3.49593 0.25 7.50024C0.25 10.703 2.32715 13.4206 5.2081 14.3797C5.57084 14.446 5.70302

joint_calib_create_matrix function