From edce1bfa209b5c01a640c2afe786250a0aaa733d Mon Sep 17 00:00:00 2001 From: Daniel Date: Tue, 31 Dec 2024 17:07:58 +0100 Subject: [PATCH] docs --- R/rescale_weights.R | 2 +- man/rescale_weights.Rd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/R/rescale_weights.R b/R/rescale_weights.R index 9205d5f03..aa0d260be 100644 --- a/R/rescale_weights.R +++ b/R/rescale_weights.R @@ -68,7 +68,7 @@ #' Rescaling is based on scaling the sample weights so the mean value is 1, #' which means the sum of all weights equals the sample size. Next, the design #' effect (_Kish 1965_) is calculated, which is the mean of the squared -#' weights divided by the squared mean of the weights. The scales sample +#' weights divided by the squared mean of the weights. The scaled sample #' weights are then divided by the design effect. This method is most #' appropriate when weights are based on additional variables beyond the #' grouping variables in the model (e.g., other demographic characteristics), diff --git a/man/rescale_weights.Rd b/man/rescale_weights.Rd index 52e56c377..646ae1da4 100644 --- a/man/rescale_weights.Rd +++ b/man/rescale_weights.Rd @@ -87,7 +87,7 @@ design that should be mimicked. Rescaling is based on scaling the sample weights so the mean value is 1, which means the sum of all weights equals the sample size. Next, the design effect (\emph{Kish 1965}) is calculated, which is the mean of the squared -weights divided by the squared mean of the weights. The scales sample +weights divided by the squared mean of the weights. The scaled sample weights are then divided by the design effect. This method is most appropriate when weights are based on additional variables beyond the grouping variables in the model (e.g., other demographic characteristics),