# Auxiliary for Controlling robustbetareg Fitting Several parameters that control fitting of robust beta regression models using `robustbetareg.` ```r robustbetareg.control( start = NULL, alpha.optimal = TRUE, tolerance = 0.001, maxit = 5000, L = 0.02, M = 3, ... ) ``` ## Arguments - `start`: an optional vector with starting values for the parameter estimates. - `alpha.optimal`: a logical value. If `TRUE` the tuning constant will be select via the data-driven algorithm. - `tolerance`: numeric tolerance for convergence. - `maxit`: argument passed to `optim`. - `L`: numeric specifying the threshold for the data-driven algorithm. Default is `L = 0.02`. - `M`: integer specifying the number of grid spacing for the data-driven algorithm. Default is `M = 3`. - `...`: currently not used. ## Returns A list with components named as the arguments. ## Details The `robustbetareg.control` controls the fitting process of the robust estimation in beta regression via the LMDPDE, LSMLE, MDPDE, and SMLE. The arguments `L` and `M` are passed to the data-driven algorithm for selecting the optimum alpha value; details can be found in Ribeiro and Ferrari (2022). Starting values for the parameters associated to the mean and precision submodels may be supplied via `start`. We do not recommend changing the arguments passed to the data-driven algorithm. ## Examples ```r data("Firm") # Using a robust start value for the parameters associated with the # mean submodel # using the robustbase package # robust regression to obtain a starting value for beta fit_lm_Rob <- robustbase::lmrob(FIRMCOST ~ SIZELOG + INDCOST, data = Firm) initials_beta_rob <- as.numeric(coef(fit_lm_Rob)) etarob <- model.matrix(fit_lm_Rob)%*%initials_beta_rob muhat_Rob <- set.link(link.mu = "logit", link.phi = "log")$linkfun.mu$inv.link(etarob) T_1_Rob <- 1/set.link(link.mu = "logit", link.phi = "log")$linkfun.mu$d.linkfun(muhat_Rob) #estimate of variance of y based on robustbase package sigma2hat_Rob <- ((fit_lm_Rob$scale^2)*(T_1_Rob^2)) #phi estimate from robust method phihat_Rob <- mean((muhat_Rob*(1-muhat_Rob))/sigma2hat_Rob) gama1hat_rob <- log(phihat_Rob) #gamma estimates from robustbase package initials_gama_rob <- as.numeric(gama1hat_rob) #robust starting values for beta and gamma thetaStart <- c(initials_beta_rob, initials_gama_rob) fit_LSMLE <- robustbetareg(FIRMCOST ~ SIZELOG + INDCOST, data = Firm, type = "LSMLE", link.phi = "log", control = robustbetareg.control(start = thetaStart)) ``` ## References Maluf, Y.S., Ferrari, S.L.P., and Queiroz, F.F. (2022). Robust beta regression through the logit transformation. **arXiv**:2209.11315. Ribeiro, K.A.T. and Ferrari, S.L.P. (2022). Robust estimation in beta regression via maximum Lq-likelihood. **Statistical Papers**. DOI: 10.1007/s00362-022-01320-0. ## See Also `robustbetareg` ## Author(s) Yuri S. Maluf (yurimaluf@gmail.com ), Francisco F. Queiroz (ffelipeq@outlook.com ) and Silvia L. P. Ferrari.

robustbetareg.control function