IAdata {GECal} | R Documentation |
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
[Package GECal version 0.1.5 Index]