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Generalized calibration function based on sampling::gencalib

gencal.Rd

Importing

Usage

gencal(Xs, Zs, d, pop_totals, method = "raking", eps, maxit, tol)

Arguments

Xs

A matrix of calibration variables

Zs

A matrix of instrumental variables

d

A vector of initial weights, derived from sampling design

pop_totals

A vector of population totals (including totals for both Xs and Zs)

method

a method upon which the sampling::gencalib() function will be called

eps

describes desired accuracy level in any convergence-related

maxit

maximum number of iterations for sampling::gencalib() since it works iteratively

tol

desired tolerance

References

Andridge, Rebecca R, and Roderick JA Little. 2011. “Proxy Pattern-Mixture Analysis for Survey Nonresponse.” Journal of Official Statistics 27 (2): 153.

Chang, T., and P. S. Kott. 2008. “Using Calibration Weighting to Adjust for Nonresponse Under a Plausible Model.” Biometrika 95 (3): 555–71. https://doi.org/10.1093/biomet/asn022.

Deville, Jean-Claude, and Carl-Erik Särndal. 1992. “Calibration Estimators in Survey Sampling.” Journal of the American Statistical Association 87 (418): 376–82.

Estevao, Victor, and Carl Särndal. n.d. “A Functional Form Approach to Calibration.” Journal of Of®cial Statistics 16 (4): 379±399. https://www.proquest.com/scholarly-journals/functional-form-approach-calibration/docview/1266846662/se-2.

Isaki, C. T., and W. A. Fuller. 1982. “Survey Design Under the Regression Super-Population Model.” Journal of the American Statistical Association 77 (377): 89–96.

Kott, Phillip S., and Ted Chang. 2010. “Using Calibration Weighting to Adjust for Nonignorable Unit Nonresponse.” Journal of the American Statistical Association 105 (491): 1265–75. https://doi.org/10.1198/jasa.2010.tm09016.

Kott, Phillip S., and Dan Liao. 2017. “Calibration Weighting for Nonresponse That Is Not Missing at Random: Allowing More Calibration Than Response-Model Variables.” Journal of Survey Statistics and Methodology 5 (2): 159–74. https://doi.org/10.1093/jssam/smx003.

Author

Maciej Ostapiuk and Maciej Beręsewicz based on Kott and Chang (2010) and Kott and Liao (2017)

Examples

## Generate the data to formulate response model, calibration variables and target variable

set.seed(123)
n <- 10000
x1 <- rlnorm(n, 0, 1)
x2 <- rexp(n, 1)
x3 <-  rpois(n,2)
y <- 1 + x1 + x2 + rnorm(n)
pr <- plogis(1 + 0.5*x1 - 0.5*y)
pop_data <- data.frame(x1, x2, y, pr)
totals <- c(N = n, colSums(pop_data[, c("x1", "x2", "y")]))
flag <- rbinom(n, 1, pop_data$pr)
sample <- pop_data[flag == 1, ]
sample$d <- n/nrow(sample)

## case when dim(Xs) == dim(Zs)
g <- gencal(Xs = stats::model.matrix(~ x1 + x2, data = sample),
            Zs = stats::model.matrix(~ x1 + y, data = sample),
            d = sample$d,
            pop_totals = totals,
            method = "raking",
            eps = .Machine$double.eps,
            maxit = 100,
            tol = 1e-8)
#> Error in gencal(Xs = stats::model.matrix(~x1 + x2, data = sample), Zs = stats::model.matrix(~x1 +     y, data = sample), d = sample$d, pop_totals = totals, method = "raking",     eps = .Machine$double.eps, maxit = 100, tol = 1e-08): object 'totals' not found

head(g) #display only couple of first output values
#> Error: object 'g' not found



## case when dim(Xs) > dim(Zs)
g1 <-gencal(Xs = stats::model.matrix(~ x1 + x2, data = sample),
            Zs = stats::model.matrix(~ y, data = sample),
            d = sample$d,
            pop_totals = totals,
            method = "raking",
            eps = .Machine$double.eps,
            maxit = 100,
            tol = 1e-8)
#> Error in rep(1, nrow(sample)): invalid 'times' argument

head(g1) #display only couple of first output values
#> Error: object 'g1' not found