1d:T7cc,# Fit linear models on the (replication) data. This function fit linear models for a subset of data frames. ```r meechua_reg(x) ``` ## Arguments - `x`: Data to be used in the regression. ## Details The data used for the regression must be sorted by `mu`. A set of linear `models` will be estimated and model coefficients are saved and stored in `mod_coef`. The estimated standard errror for the `after` measure is also stored in `se_after` to be used further in other functions. ## Returns A table containing the estimations for each `mu`. The variables `models`, `mod_coef`, `se_after` are stored globally for further analysis if `to_global` is set to TRUE. In any case the values will be returned. The models are saved in an object called `mee_chua`, which is not automatically printed but is saved in the environment. ## References Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology. ## Author(s) Daniela Recchia, Thomas Ostermann. ## See Also lm ,dlply ## Examples ```r # Generate example data language_test <- data.frame( Before = rnorm(100, mean = 50, sd = 10), After = rnorm(100, mean = 55, sd = 10) ) # Replicate data replicate_data <- function(start, end, Before, After, data) { mu <- seq(start * 100, end * 100, by = (end - start)) mu <- rep(mu, each = nrow(data)) before <- data[[Before]] - mu / 100 after <- data[[After]] mee_chua <- data.frame(mu = mu, before = before, after = after) return(mee_chua) } mee_chua <- replicate_data(0, 1, "Before", "After", data = language_test) mee_chua <- mee_chua[order(mee_chua$mu), ] # Sortieren nach 'mu' # Regression ausführen und Ergebnisse erhalten reg_results <- meechua_reg(mee_chua) # Zugriff auf Ergebnisse mod_coef <- reg_results$mod_coef se_after <- reg_results$se_after # Anzeigen der Ergebnisse print(mod_coef) print(se_after) ```

meechua_reg function