# TDI estimation and inference This function implements the estimation of the TDI and its corresponding $100(1-\alpha)\%$ upper bound (UB), where $\alpha$ is the significance level, using the methods proposed by Choudhary (2007), Escaramis **et al.** (2010), Choudhary (2010) and Perez-Jaume and Carrasco (2015) in the case of two raters. See Details and References for further information about these methods. ```r TDI(data, y, id, met, rep = NA, method = c("Choudhary P", "Escaramis et al.", "Choudhary NP", "Perez-Jaume and Carrasco"), p = 0.9, ub = TRUE, boot.type = c("differences", "cluster"), type = 8, R = 10000, dec.p = 2, dec.est = 3, choose.model.ch.p = TRUE, var.equal = TRUE, choose.model.es = TRUE, int = FALSE, tol = 10^(-8), add.es = NULL, alpha = 0.05) ``` ## Arguments - `data`: name of the dataset, of class `data.frame`, containing at least 3 columns (quantitative measurement, subject effect, rater effect). - `y`: quantitative measurement column name. - `id`: subject effect column name. The corresponding column of `data` must be a factor. - `met`: rater effect column name. The corresponding column of `data` must be a factor. - `rep`: replicate effect column name. When there are no replicates the user should use `rep = NA`. When there are replicates, the corresponding column of data must be a factor. The default value is `NA`. - `method`: name of the method(s) to estimate the TDI and UB. The options are: `"Choudhary P"` (Choudhary, 2007), `"Escaramis et al."` (Escaramis **et al.**, 2010), `"Choudhary NP"` (Choudhary, 2010) and `"Perez-Jaume and Carrasco"` (Perez-Jaume and Carrasco, 2015). This argument is not case-sensitive and is passed to `match.arg`. The default value is `c("Choudhary P", "Escaramis et al.", "Choudhary NP", "Perez-Jaume andCarrasco")`, so all approaches are executed by default. - `p`: a value or vector of the proportion(s) for estimation of the TDI, where $0

TDI function