horvitzThompson {mase} | R Documentation |
Compute the Horvitz-Thompson Estimator
Description
Calculate the Horvitz-Thompson Estimator for a finite population mean/proportion or total based on sample data collected from a complex sampling design.
Usage
horvitzThompson(
y,
pi = NULL,
N = NULL,
pi2 = NULL,
var_est = FALSE,
var_method = "LinHB",
B = 1000,
fpc = TRUE,
messages = TRUE
)
Arguments
y |
A numeric vector of the sampled response variable. |
pi |
A numeric vector of inclusion probabilities for each sampled unit in y. If NULL, then simple random sampling without replacement is assumed. |
N |
A numeric value of the population size. If NULL, it is estimated with the sum of the inverse of the pis. |
pi2 |
A square matrix of the joint inclusion probabilities. Needed for the "LinHT" variance estimator. |
var_est |
A logical indicating whether or not to compute a variance estimator. Default is FALSE. |
var_method |
The method to use when computing the variance estimator. Options are a Taylor linearized technique: "LinHB"= Hajek-Berger estimator, "LinHH" = Hansen-Hurwitz estimator, "LinHTSRS" = Horvitz-Thompson estimator under simple random sampling without replacement, and "LinHT" = Horvitz-Thompson estimator or a resampling technique: "bootstrapSRS" = bootstrap variance estimator under simple random sampling without replacement. The default is "LinHB". |
B |
The number of bootstrap samples if computing the bootstrap variance estimator. Default is 1000. |
fpc |
Default to TRUE, logical for whether or not the variance calculation should include a finite population correction when calculating the "LinHTSRS" or the "SRSbootstrap" variance estimator. |
messages |
A logical indicating whether to output the messages internal to mase. Default is TRUE. |
Value
List of output containing:
* pop_total: Estimate of population total.
* pop_mean: Estimate of population mean.
* pop_total_var: Estimated variance of population total estimate.
* pop_mean_var: Estimated variance of population mean estimate.
References
Horvitz DG, Thompson DJ (1952). “A generalization of sampling without replacement from a finite universe.” Journal of the American Statistical Association, 47, 663-685.
Examples
library(dplyr)
data(IdahoSamp)
data(IdahoPop)
xsample <- filter(IdahoSamp, COUNTYFIPS == "16055")
xpop <- filter(IdahoPop, COUNTYFIPS == "16055")
horvitzThompson(y = xsample$BA_TPA_ADJ,
N = xpop$npixels,
var_est = TRUE,
var_method = "LinHTSRS")