Can anyone convert this line of code to R?

[Max Coleman]

I'm wondering if anyone knows how to convert this Stata code to R code (for a student of mine).

Here is the Stata code, specifying that the data are weighted ("PWEIGHT") and also use successive difference replicate weights (PWEIGHT1-PWEIGHT80).

svyset [pweight=PWEIGHT], sdrweight(PWEIGHT1-PWEIGHT80) vce(sdr) mse

[ericmelse]

Most certainly, I am not a savvy R user, which means that I put such questions to ChatGPT's tool R Wizard.
Below is what I got as an answer (I did not test this code and you, or your student, should try ChatGPT to improve this code should it 'not work'):



To convert the Stata code that sets up a survey design with probability weights (PWEIGHT) and successive difference replicate weights (PWEIGHT1-PWEIGHT80) into R, you can use the survey package. This package supports both types of weights, and you can define the survey design using svydesign() with replicate weights and variance estimation using successive difference replication (SDR).

Here is how you can translate the Stata command into equivalent R code:

1. Install and load the survey package:

install.packages("survey")
library(survey)

2. Define the survey design in R, specifying the pweights (probability weights) and repweights (replicate weights):

# Assuming your dataset is called 'data'
svy_design <- svrepdesign(
  weights = ~PWEIGHT,
  repweights = "PWEIGHT[1-80]",
  type = "SDR",
  data = data,
  mse = TRUE
)

Explanation:

• weights = ~PWEIGHT: Specifies the main probability weight variable.
• repweights = "PWEIGHT[1-80]": Specifies the replicate weights (PWEIGHT1 to PWEIGHT80). The syntax "PWEIGHT[1-80]" is a concise way of referring to multiple replicate weight columns.
• type = "SDR": Indicates that the successive difference replication method is used for variance estimation.
• mse = TRUE: Ensures that mean squared error (MSE) is computed for standard errors.

This setup replicates the behavior of the Stata svyset command you provided.