GECal (version 0.1.7)

IAdata: 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