Package 'GECal' reference manual

Title:	Generalized Entropy Calibration
Description:	Generalized Entropy Calibration produces calibration weights using generalized entropy as the objective function for optimization. This approach, as implemented in the 'GECal' package, is based on Kwon, Kim, and Qiu (2024) <doi:10.48550/arXiv.2404.01076>. Unlike traditional methods, 'GECal' incorporates design weights into the constraints to maintain design consistency, rather than including them in the objective function itself.
Authors:	Yonghyun Kwon [aut, cre] , Jae Kwang Kim [aut] , Yumou Qiu [aut]
Maintainer:	Yonghyun Kwon <[email protected]>
License:	MIT + file LICENSE
Version:	0.1.5
Built:	2024-10-28 14:15:26 UTC
Source:	https://github.com/yonghyun-k/gecal

Performing statistical inference after calibration

Description

estimate performs statistical inference after calibration.

Usage

estimate(formula, data = NULL, calibration, pimat = NULL)
estimate(formula, data = NULL, calibration, pimat = NULL)

Arguments

`formula`	An object of class "formula" specifying the calibration model.
`data`	An optional data frame containing the variables in the model (specified by `formula`).
`calibration`	An object of class "calibration", generated by `GECalib`.
`pimat`	An optional matrix contatining the joint inclusion probability matrix used for variance estimation.

Value

A list of class estimation including the point estimates and its standard error.

References

Kwon, Y., Kim, J., & Qiu, Y. (2024). Debiased calibration estimation using generalized entropy in survey sampling. Arxiv preprint <https://arxiv.org/abs/2404.01076>

Deville, J. C., and Särndal, C. E. (1992). Calibration estimators in survey sampling. Journal of the American statistical Association, 87(418), 376-382.

Examples

set.seed(11)
N = 10000
x = data.frame(x1 = rnorm(N, 2, 1), x2= runif(N, 0, 4))
pi = pt((-x[,1] / 2 - x[,2] / 2), 3);
pi = ifelse(pi >.7, .7, pi)

delta = rbinom(N, 1, pi)
Index_S = (delta == 1)
pi_S = pi[Index_S]; d_S = 1 / pi_S
x_S = x[Index_S,,drop = FALSE]
# pimat = diag(d_S^2 - d_S) / N^2 # 1 / pi_i * (1 - 1 / pi_i)

e = rnorm(N, 0, 1)
y = x[,1] + x[,2] + e;
y_S = y[Index_S] # plot(x_S, y_S)

calibration0 <- GECal::GEcalib(~ 1, dweight = d_S, data = x_S,
                               const = N,
                               entropy = "SL", method = "DS")
GECal::estimate(y_S ~ 1, calibration = calibration0)$estimate # Hajek estimator
# sum(y_S * d_S) * N / sum(d_S)

calibration <- GECal::GEcalib(~ 0, dweight = d_S, data = x_S,
const = numeric(0),
entropy = "SL", method = "DS")
GECal::estimate(y_S ~ 1, calibration = calibration)$estimate # HT estimator

calibration1 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x)),
                               entropy = "ET", method = "DS")
GECal::estimate(y_S ~ 1, calibration = calibration1)$estimate

calibration2 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x)),
                               entropy = "ET", method = "GEC0")
GECal::estimate(y_S ~ 1, calibration = calibration2)$estimate

calibration3 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x, log(1 / pi))),
                               entropy = "ET", method = "GEC")
GECal::estimate(y_S ~ 1, calibration = calibration3)$estimate

calibration4 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x, NA)),
                               entropy = "ET", method = "GEC")
GECal::estimate(y_S ~ 1, calibration = calibration4)$estimate

calibration5 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x, NA)),
                               entropy = "ET", method = "GEC", K_alpha = "log")
GECal::estimate(y_S ~ 1, calibration = calibration5)$estimate

set.seed(11)
N = 10000
x = data.frame(x1 = rnorm(N, 2, 1), x2= runif(N, 0, 4))
pi = pt((-x[,1] / 2 - x[,2] / 2), 3);
pi = ifelse(pi >.7, .7, pi)

delta = rbinom(N, 1, pi)
Index_S = (delta == 1)
pi_S = pi[Index_S]; d_S = 1 / pi_S
x_S = x[Index_S,,drop = FALSE]
# pimat = diag(d_S^2 - d_S) / N^2 # 1 / pi_i * (1 - 1 / pi_i)

e = rnorm(N, 0, 1)
y = x[,1] + x[,2] + e;
y_S = y[Index_S] # plot(x_S, y_S)

calibration0 <- GECal::GEcalib(~ 1, dweight = d_S, data = x_S,
                               const = N,
                               entropy = "SL", method = "DS")
GECal::estimate(y_S ~ 1, calibration = calibration0)$estimate # Hajek estimator
# sum(y_S * d_S) * N / sum(d_S)

calibration <- GECal::GEcalib(~ 0, dweight = d_S, data = x_S,
const = numeric(0),
entropy = "SL", method = "DS")
GECal::estimate(y_S ~ 1, calibration = calibration)$estimate # HT estimator

calibration1 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x)),
                               entropy = "ET", method = "DS")
GECal::estimate(y_S ~ 1, calibration = calibration1)$estimate

calibration2 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x)),
                               entropy = "ET", method = "GEC0")
GECal::estimate(y_S ~ 1, calibration = calibration2)$estimate

calibration3 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x, log(1 / pi))),
                               entropy = "ET", method = "GEC")
GECal::estimate(y_S ~ 1, calibration = calibration3)$estimate

calibration4 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x, NA)),
                               entropy = "ET", method = "GEC")
GECal::estimate(y_S ~ 1, calibration = calibration4)$estimate

calibration5 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                               const = colSums(cbind(1, x, NA)),
                               entropy = "ET", method = "GEC", K_alpha = "log")
GECal::estimate(y_S ~ 1, calibration = calibration5)$estimate

Debiasing covariate for GECalib

Description

It returns the debiasing covariate, which is equivalent to the first order derivatie of the generalized entropy $G$ .

Usage

g(x, entropy = NULL, del = NULL)
g(x, entropy = NULL, del = NULL)

Arguments

`x`	A vector of design weights
`entropy`	An optional data frame containing the variables in the model (specified by `formula`).
`del`	The optional vector for threshold ( $\delta$ ) when `entropy == "PH"`.

Value

A vector of debiasing covariate.

Examples

set.seed(11)
N = 10000
x = data.frame(x1 = rnorm(N, 2, 1), x2= runif(N, 0, 4))
pi = pt((-x[,1] / 2 - x[,2] / 2), 3);
pi = ifelse(pi >.7, .7, pi)

g_EL <- g(1 / pi, entropy = 1)
g_ET <- g(1 / pi, entropy = 0)
g_EL <- g(1 / pi, entropy = -1)

set.seed(11)
N = 10000
x = data.frame(x1 = rnorm(N, 2, 1), x2= runif(N, 0, 4))
pi = pt((-x[,1] / 2 - x[,2] / 2), 3);
pi = ifelse(pi >.7, .7, pi)

g_EL <- g(1 / pi, entropy = 1)
g_ET <- g(1 / pi, entropy = 0)
g_EL <- g(1 / pi, entropy = -1)

Generalized Entropy Calibration

Description

GEcalib computes the calibration weights. Generalized entropy calibration weights maximize the generalized entropy:

$H(\bm{\omega}) = -\sum_{i \in A} G(\omega_i),$

subject to the calibration constraints $\sum_{i \in A} \omega_i \bm{z}_i = \sum_{i \in U} \bm{z}_i$ , where $A$ denotes the sample index, and $U$ represents the population index. The auxiliary variables, whose population totals are known, are defined as $\bm{z}_i^T = (\bm{x}_i^T, g(d_i))$ , where $g$ is the first-order derivative of the gerenalized entropy $G$ , and $d_i$ is the design weight for each sampled unit $i \in A$ .

Usage

GEcalib(
  formula,
  dweight,
  data = NULL,
  const,
  method = c("GEC", "GEC0", "DS"),
  entropy = c("SL", "EL", "ET", "CE", "HD", "PH"),
  weight.scale = 1,
  G.scale = 1,
  K_alpha = NULL,
  is.total = TRUE,
  del = NULL
)
GEcalib(
  formula,
  dweight,
  data = NULL,
  const,
  method = c("GEC", "GEC0", "DS"),
  entropy = c("SL", "EL", "ET", "CE", "HD", "PH"),
  weight.scale = 1,
  G.scale = 1,
  K_alpha = NULL,
  is.total = TRUE,
  del = NULL
)

Arguments

`formula`	An object of class "formula" specifying the calibration model.
`dweight`	A vector of sampling weights.
`data`	An optional data frame containing the variables in the model (specified by `formula`).
`const`	A vector used in the calibration constraint for population totals( or means).
`method`	The method to be used in calibration. See "Details" for more information.
`entropy`	The generalized entropy used in calibration, which can be either a numeric value or a string. If numeric, `entropy` represents the order of Renyi's entropy, where $G(\omega) = r^{-1}(r+1)^{-1}\omega^{r+1}$ if $r \neq 0, -1$ . If a string, valid options include: "SL" (Squared-loss, $r = 1$ ), "EL" (Empirical Likelihood, $r = -1$ ), "ET" (Exponential Tilting, $r = 0$ ), "HD" (Hellinger Distance, $r = -1/2$ ), "CE" (Cross-Entropy), and "PH" (Pseudo-Huber). See "Summary" for details.
`weight.scale`	Positive scaling factor for the calibration weights $\omega_i$ . Asymptotics justify setting `weight.scale` to the finite population correction ( $fpc = n / N$ ).
`G.scale`	Positive scaling factor for the generalized entropy function $G$ . Asymptotics justify setting `G.scale` to the variance of the error term in a linear super-population model.
`K_alpha`	The $K$ function used in joint optimization when the `const` of the debiasing covariate $g(d_i)$ is not available. `K_alpha` can be `NULL`, `"log"`, or custom functions. See "Details".
`is.total`	Logical, `TRUE` if `sum(const[1])` equals the population size.
`del`	The optional threshold ( $\delta$ ) used when Pseudo-Huber (PH) entropy is selected. `del = quantile(dweight, 0.75)` if not specified.

Details

The GEcal object returns the calibration weights and necessary information for estimating population totals(or mean).

The terms to the right of the ~ symbol in the formula argument define the calibration constraints. When method == "GEC", the debiasing covariate g(dweight) must be included in the formula. If the population total(mean) of g(dweight) is unavailable, const that corresponds to g(dweight) can be set to NA. In this case, GECalib performs joint optimization over both the calibration weights $\omega_i$ and the missing value of const.

The length of the const vector should match the number of columns in the model.matrix generated by formula. Additionally, the condition number of the model.matrix must exceed .Machine$double.eps to ensure its invertibility.

Both weight.scale and G.scale are positive scaling factors used for calibration. Note that weight.scale is not supported when method == "DS".

Let $q_i$ be the scaling factor for the generalized entropy function $G$ , and $\phi_i$ be the scaling factor for the calibration weights $\omega_i$ .

If method == "GEC", GEcalib minimizes the negative entropy:

$\sum_{i \in A} q_iG(\phi_i\omega_i),$

with respect to $\bm \omega$ subject to the calibration constraints $\sum_{i \in A} \omega_i \bm{z}_i = \sum_{i \in U} \bm{z}_i$ , where $\bm{z}_i^T = (\bm{x}_i^T, q_i \phi_i g(\phi_i d_i))$ , $A$ denotes the sample index, and $U$ represents the population index.

If method == "GEC", but an element of const corresponding to the debiasing covariate $g(d_i)$ is NA, GEcalib minimizes the negative adjusted entropy:

$\sum_{i \in A} q_iG(\phi_i\omega_i) - K(\alpha),$

with respect to $\bm \omega$ and $\alpha$ subject to the calibration constraints $\sum_{i \in A} \omega_i (\bm{x}_i^T, q_i \phi_i g(\phi_i d_i)) = \left(\sum_{i \in U} \bm x_i, \alpha \right)$ , where the solution $\hat \alpha$ is an estimate of population total for $g(d_i)$ . Examples of $K(\alpha)$ includes $K(\alpha) = \alpha$ when K_alpha == NULL, and

$K(\alpha) = \left(\sum_{i \in A} d_i g(d_i) + N \right) \log \left| \frac{1}{N}\sum_{i \in A}q_i \phi_i \omega_i g(\phi_i \omega_i) + 1 \right|$

when K_alpha == "log".

If method == "GEC0", GEcalib minimizes the negative adjusted entropy:

$\sum_{i \in A} q_iG(\phi_i\omega_i) - q_i\phi_i\omega_i g(\phi_i \omega_i)$

with respect to $\bm \omega$ subject to the calibration constraints $\sum_{i \in A} \omega_i \bm{x}_i = \sum_{i \in U} \bm{x}_i$ .

If method == "DS", GEcalib minimizes the divergence between $\bm \omega$ and $\bm d$ :

$\sum_{i \in A} q_id_i \tilde G(\omega_i / d_i)$

with respect to $\bm \omega$ subject to the calibration constraints $\sum_{i \in A} \omega_i \bm{x}_i = \sum_{i \in U} \bm{x}_i$ . When method == "DS", weight.scale, the scaling factor for the calibration weights $\phi_i$ , is not applicable.

Examples of $G$ and $\tilde G$ are given in "Summary".

Value

A list of class calibration including the calibration weights and data needed for estimation.

Summary

The table below provides a comparison between the GEC and DS methods.

GEC	DS

$\min_{\bm \omega} \left(-H(\bm \omega)\right) = \sum_{i \in A}G(\omega_i) \quad$	$\quad \min_{\bm \omega} D(\bm \omega, \bm d) = \sum_{i \in A}d_i \tilde G(\omega_i / d_i)$

s.t. $\sum_{i \in A} \omega_i (\bm{x}_i^T, g(d_i)) = \sum_{i \in U} (\bm{x}_i^T, g(d_i))$	s.t. $\sum_{i \in A} \omega_i \bm{x}_i^T = \sum_{i \in U} \bm{x}_i^T$

$G(\omega) = \begin{cases} \frac{1}{r(r+1)} \omega^{r+1} & r \neq 0, -1\\ \omega \log \omega - \omega & r = 0\text{(ET)} \\ -\log \omega & r = -1\text{(EL)} \end{cases}$	$\tilde G(\omega) = \begin{cases} \frac{1}{r(r+1)} \left(\omega^{r+1} - (r+1)\omega + r\right) & r \neq 0, -1 \\ \omega \log \omega - \omega + 1 & r = 0\text{(ET)} \\ -\log \omega + \omega - 1 & r = -1\text{(EL)} \end{cases}$

If method == "GEC", further examples include

$G(\omega) = (\omega - 1) \log (\omega-1) - \omega \log \omega$

when entropy == "CE", and

$G(\omega) = \delta^2 \left(1 + (\omega / \delta)^2 \right)^{1/2}$

for a threshold $\delta$ when entropy == "PH".

Author(s)

Yonghyun Kwon

References

Kwon, Y., Kim, J., & Qiu, Y. (2024). Debiased calibration estimation using generalized entropy in survey sampling. Arxiv preprint <https://arxiv.org/abs/2404.01076>

Deville, J. C., and Särndal, C. E. (1992). Calibration estimators in survey sampling. Journal of the American statistical Association, 87(418), 376-382.

Examples

set.seed(11)
N = 10000
x = data.frame(x1 = rnorm(N, 2, 1), x2= runif(N, 0, 4))
pi = pt((-x[,1] / 2 - x[,2] / 2), 3);
pi = ifelse(pi >.7, .7, pi)

delta = rbinom(N, 1, pi)
Index_S = (delta == 1)
pi_S = pi[Index_S]; d_S = 1 / pi_S
x_S = x[Index_S,]

# Deville & Sarndal(1992)'s calibration using divergence
w1 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x)),
                    entropy = "ET", method = "DS")$w

# Generalized entropy calibration without debiasing covariate 
w2 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x)),
                    entropy = "ET", method = "GEC0")$w
all.equal(w1, w2)

# Generalized entropy calibration with debiasing covariate
w3 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x, log(1 / pi))),
                    entropy = "ET", method = "GEC")$w
                    
# Generalized entropy calibration with debiasing covariate
# when its population total is unknown
w4 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x, NA)),
                    entropy = "ET", method = "GEC")$w
all.equal(w1, w4)

w5 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
const = colSums(cbind(1, x, NA)),
entropy = "ET", method = "GEC", K_alpha = "log")$w 
set.seed(11)
N = 10000
x = data.frame(x1 = rnorm(N, 2, 1), x2= runif(N, 0, 4))
pi = pt((-x[,1] / 2 - x[,2] / 2), 3);
pi = ifelse(pi >.7, .7, pi)

delta = rbinom(N, 1, pi)
Index_S = (delta == 1)
pi_S = pi[Index_S]; d_S = 1 / pi_S
x_S = x[Index_S,]

# Deville & Sarndal(1992)'s calibration using divergence
w1 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x)),
                    entropy = "ET", method = "DS")$w

# Generalized entropy calibration without debiasing covariate 
w2 <- GECal::GEcalib(~ ., dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x)),
                    entropy = "ET", method = "GEC0")$w
all.equal(w1, w2)

# Generalized entropy calibration with debiasing covariate
w3 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x, log(1 / pi))),
                    entropy = "ET", method = "GEC")$w
                    
# Generalized entropy calibration with debiasing covariate
# when its population total is unknown
w4 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
                    const = colSums(cbind(1, x, NA)),
                    entropy = "ET", method = "GEC")$w
all.equal(w1, w4)

w5 <- GECal::GEcalib(~ . + g(d_S), dweight = d_S, data = x_S,
const = colSums(cbind(1, x, NA)),
entropy = "ET", method = "GEC", K_alpha = "log")$w

Synthetic pesticides data in Iowa

Description

A synthetic proprietary pesticide usage survey data in Iowa CRD(Crop Reporting District) collected from GfK Kynetec in 2020.

Format

A data frame with 1197 rows on the following 32 variables:

Corn10, Corn20, Corn30, Corn40, Corn50, Corn60, Corn70: Haversted acres of corn in each CRD
Soybean10, Soybean20, Soybean30, Soybean40, Soybean50, Soybean60, Soybean70, Soybean90: Haversted acres of soybean in each CRD
Alfalfa10, Alfalfa30, Alfalfa40, Alfalfa50, Alfalfa70, Alfalfa80: Haversted acres of alfalfa in each CRD
Pasture10, Pasture20, Pasture30, Pasture40, Pasture50, Pasture60, Pasture70, Pasture80, Pasture90: Acres of pasture in each CRD
d: Design weights, or inverse first-order inclusion probabilities of the sample
y: Pesticide usage($) which is of an interest.

Details

The original data is contaminated by adding noise and creating missing values and imputation.

Examples

data(IAdata)
data(IApimat)

total <- c(
Corn10 = 2093000, Corn20 = 1993600, Corn30 = 1803200, Corn40 = 2084600, 
Corn50 = 2056600, Corn60 = 1429400, Corn70 = 2539600,
Soybean10 = 1472980, Soybean20 = 1192860, Soybean30 = 721920, 
Soybean40 = 1477680, Soybean50 = 1353600, Soybean60 = 918380,
Soybean70 = 1485200, Soybean90 = 777380, Alfalfa10 = 60590, 
Alfalfa30 = 154395, Alfalfa40 = 57816, Alfalfa50 = 150453,
Alfalfa70 = 66065, Alfalfa80 = 240681, Pasture10 = 141947, 
Pasture20 = 61476, Pasture30 = 188310, Pasture40 = 213635,
Pasture50 = 160737, Pasture60 = 222214, Pasture70 = 250807, 
Pasture80 = 570647, Pasture90 = 232630
)

calibration <- GECal::GEcalib(~ 0, dweight = d, data = IAdata,
                              const = numeric(0),
                              entropy = "EL", method = "DS")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate


calibration <- GECal::GEcalib(~ 0 + . -y -d, dweight = d, data = IAdata,
                              const = total,
                              entropy = "SL", method = "DS")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate

calibration <- GECal::GEcalib(~ 0 + . -y -d, dweight = d, data = IAdata,
                              const = c(total),
                              entropy = "ET", method = "DS")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate


calibration <- GECal::GEcalib(~ 0 + . -y -d + g(d), dweight = d, data = IAdata,
                              const = c(total, NA),
                              entropy = "HD", method = "GEC")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate

calibration <- GECal::GEcalib(~ 0 + . -y -d, dweight = d, data = IAdata,
                              const = total,
                              entropy = "HD", method = "GEC0")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate
data(IAdata)
data(IApimat)

total <- c(
Corn10 = 2093000, Corn20 = 1993600, Corn30 = 1803200, Corn40 = 2084600, 
Corn50 = 2056600, Corn60 = 1429400, Corn70 = 2539600,
Soybean10 = 1472980, Soybean20 = 1192860, Soybean30 = 721920, 
Soybean40 = 1477680, Soybean50 = 1353600, Soybean60 = 918380,
Soybean70 = 1485200, Soybean90 = 777380, Alfalfa10 = 60590, 
Alfalfa30 = 154395, Alfalfa40 = 57816, Alfalfa50 = 150453,
Alfalfa70 = 66065, Alfalfa80 = 240681, Pasture10 = 141947, 
Pasture20 = 61476, Pasture30 = 188310, Pasture40 = 213635,
Pasture50 = 160737, Pasture60 = 222214, Pasture70 = 250807, 
Pasture80 = 570647, Pasture90 = 232630
)

calibration <- GECal::GEcalib(~ 0, dweight = d, data = IAdata,
                              const = numeric(0),
                              entropy = "EL", method = "DS")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate


calibration <- GECal::GEcalib(~ 0 + . -y -d, dweight = d, data = IAdata,
                              const = total,
                              entropy = "SL", method = "DS")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate

calibration <- GECal::GEcalib(~ 0 + . -y -d, dweight = d, data = IAdata,
                              const = c(total),
                              entropy = "ET", method = "DS")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate


calibration <- GECal::GEcalib(~ 0 + . -y -d + g(d), dweight = d, data = IAdata,
                              const = c(total, NA),
                              entropy = "HD", method = "GEC")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate

calibration <- GECal::GEcalib(~ 0 + . -y -d, dweight = d, data = IAdata,
                              const = total,
                              entropy = "HD", method = "GEC0")
GECal::estimate(y ~ 1, data = IAdata, calibration = calibration, pimat = IApimat)$estimate

A matrix used for variance estimation in IAdata

Description

The matrix that is used for variance estimation in IAdata. The sample is collected from a straitified random sampling.

Examples

data(IApimat) 
data(IApimat)

Package 'GECal'

Help Index

Performing statistical inference after calibration

Description

Usage

Arguments

Value

References

Examples

Debiasing covariate for GECalib

Description

Usage

Arguments

Value

Examples

Generalized Entropy Calibration

Description

Usage

Arguments

Details

Value

Summary

Author(s)

References

Examples

Synthetic pesticides data in Iowa

Description

Format

Details

Examples

A matrix used for variance estimation in IAdata

Description

Examples