The main function for the not missing at random non-response

Here is the part where the description will go

Usage

mnar(
  response,
  outcome,
  calibration,
  target,
  data,
  svydesign,
  dweights,
  pop_totals,
  theta_0,
  method = c("gencalib", "emplik", "gmm"),
  tol = 1e-08,
  eps = .Machine$double.eps,
  control = NULL,
  maxit = 100,
  ...
)

Arguments

response

response variables (instruments)

outcome

outcome variables (for modelling E(Y|X))

calibration

calibration variables (for which totals should be reproduced)

target

target variable

data

data.frame

svydesign

surveydesign object

dweights

design weights

pop_totals

population totals (for both calibration and response)

method

methods c("gencalib", "emplik", "gmm")

tol

tolerance 1e-8

eps

eps for inverse .Machine$double.eps

control

control for methods

maxit

maxit 50

...

TBA

N

population size

theta0

vector of initial moment conditions for GMM and EL

Value

Returns a list with containing:

• g – g-weight that sums up to sample size,

• Xs – matrix used for calibration (i.e. Intercept, X and X_q transformed for calibration of quantiles),

• totals – a vector of totals (i.e. N, pop_totals and pop_quantiles),

• method – selected method,

• backend – selected backend.

Details

Imports for the function

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.

add others

See also

sampling::calib() – for standard calibration.

laeken::calibWeights() – for standard calibration.

survey::calibrate() – for standard and more advanced calibration.

ebal::ebalance() – for standard entropy balancing.

Author

Maciej Ostapiuk, Maciej Beręsewicz

Examples

# Set seed for reproducibility
set.seed(123)

# Define sample size
n <- 10000

# Generate variables
x1 <- rlnorm(n, 0, 1)
x2 <- rexp(n, 1)
x3 <-  rpois(n,2)
y <- 1 + x1 + x2 + rnorm(n)

# Calculate probabilities
pr <- plogis(1 + 0.5*x1 - 0.5*y)

# Create population data frame
pop_data <- data.frame(x1, x2, y, pr)

# Calculate population totals
totals <- c(N = n, colSums(pop_data[, c("x1", "x2", "y")]))

# Generate flag variable based on probabilities
flag <- rbinom(n, 1, pop_data$pr)

# Create sample based on flag
sample <- pop_data[flag == 1, ]
sample$d <- n/nrow(sample)


# Case when dim(response) == dim(calibration)
g <- mnar(response = ~ x1 + y,
            calibration =  ~ x1 + x2,
            data = sample, dweights = sample$d,
            pop_totals = totals,
            method = "gencalib")
#> Error in gencal(Xs = Xs, Zs = Zs, d = dweights, pop_totals = pop_totals,     method = "raking", eps = eps, maxit = maxit, tol = tol): object 'totals' not found

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

## Case when dim(response) < dim(calibration)
g1 <- mnar(response = ~ y,
             calibration =  ~ x1 + x2,
             data = sample, dweights = sample$d,
             pop_totals = totals,
             method = "gencalib")
#> Error in sample$d: object of type 'closure' is not subsettable

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