survey-analysis

Complex survey design setup in R requires defining stratification, clustering, and weights using the survey package. You can configure designs for datasets like NHIS by specifying these structural elements to ensure accurate statistical inference.

Weighted regression for national health survey data is performed using svyglm from the survey package. This function enables accurate model fitting and significance testing by applying survey weights to your dataset.

Calculating weighted means and totals for social science survey data is best done through the survey package in R. It provides specialized functions to generate accurate descriptive statistics from complex survey designs.

Calibration and weighting techniques for public health survey data are applied using the anesrake package. This process adjusts sample weights to match population totals, ensuring your survey analysis yields representative results.

The survey package supports marginaleffects for post-regression analysis in R. This integration allows you to compute marginal effects and predicted values from svyglm models while preserving the complex survey design structure.