# Method for non-implemented model classes Uses user defined functions to produce the (half-)normal plot with simulated envelope. UTF-8 ```r newhnp(object, sim=99, conf=.95, halfnormal=T, plot.sim=T, verb.sim=F, how.many.out=F, print.on=F, paint.out=F, col.paint.out, diagfun, simfun, fitfun, ...) ``` ## Arguments - `object`: fitted model object or numeric vector. - `sim`: number of simulations used to compute envelope. Default is 99. - `conf`: confidence level of the simulated envelope. Default is 0.95. - `halfnormal`: logical. If `TRUE`, a half-normal plot is produced. If `FALSE`, a normal plot is produced. Default is `TRUE`. - `plot.sim`: logical. Should the (half-)normal plot be plotted? Default is `TRUE`. - `verb.sim`: logical. If `TRUE`, prints each step of the simulation procedure. Default is `FALSE`. - `how.many.out`: logical. If `TRUE`, the number of points out of the envelope is printed. Default is `FALSE`. - `print.on`: logical. If `TRUE`, the number of points out of the envelope is printed on the plot. Default is `FALSE`. - `paint.out`: logical. If `TRUE`, points out of the simulation envelope are plotted in a different color. Default is `FALSE`. - `col.paint.out`: If `paint.out=TRUE`, sets the color of points out of the envelope. Default is `"red"`. - `diagfun`: user-defined function used to obtain the diagnostic measures from the fitted model object (only used when `newclass=TRUE`). Default is `resid`. - `simfun`: user-defined function used to simulate a random sample from the model estimated parameters (only used when `newclass=TRUE`). - `fitfun`: user-defined function used to re-fit the model to simulated data (only used when `newclass=TRUE`). - ``...``: extra graphical arguments passed to `plot.hnp`. ## Details By providing three user-defined functions, `newhnp` produces the half-normal plot with simulated envelope for a model whose class is not yet implemented in the package. The first function, `diagfun`, must extract the desired model diagnostics from a model fit object. The second function, `simfun`, must return the response variable (numeric vector or matrix), simulated using the same error distributions and estimated parameters from the fitted model. The third and final function, `fitfun`, must return a fitted model object. See the Examples section. ## Author(s) Rafael A. Moral , John Hinde and Clarice G. B. ## Examples ```r ## Implementing new classes ## Users provide three functions - diagfun, simfun and fitfun, ## in the following way: ## ## diagfun <- function(obj) { ## userfunction(obj, other_argumens) ## # e.g., resid(obj, type="pearson") ## } ## ## simfun <- function(n, obj) { ## userfunction(n, other_arguments) # e.g., rpois(n, fitted(obj)) ## } ## ## fitfun <- function(y.) { ## userfunction(y. ~ linear_predictor, other_arguments, data=data) ## # e.g., glm(y. ~ block + factor1 * factor2, family=poisson, ## # data=mydata) ## } ## ## when response is binary: ## fitfun <- function(y.) { ## userfunction(cbind(y., m-y.) ~ linear_predictor, ## other_arguments, data=data) ## #e.g., glm(cbind(y., m-y.) ~ treatment - 1, ## # family=binomial, data=data) ## } ## Not run: ## Example no. 1: Using Cook's distance as a diagnostic measure y <- rpois(30, lambda=rep(c(.5, 1.5, 5), each=10)) tr <- gl(3, 10) fit1 <- glm(y ~ tr, family=poisson) # diagfun d.fun <- function(obj) cooks.distance(obj) # simfun s.fun <- function(n, obj) { lam <- fitted(obj) rpois(n, lambda=lam) } # fitfun my.data <- data.frame(y, tr) f.fun <- function(y.) glm(y. ~ tr, family=poisson, data=my.data) # hnp call hnp(fit1, newclass=TRUE, diagfun=d.fun, simfun=s.fun, fitfun=f.fun) ## Example no. 2: Implementing gamma model using package gamlss # load package require(gamlss) # model fitting y <- rGA(30, mu=rep(c(.5, 1.5, 5), each=10), sigma=.5) tr <- gl(3, 10) fit2 <- gamlss(y ~ tr, family=GA) # diagfun d.fun <- function(obj) resid(obj) # this is the default if no # diagfun is provided # simfun s.fun <- function(n, obj) { mu <- obj$mu.fv sig <- obj$sigma.fv rGA(n, mu=mu, sigma=sig) } # fitfun my.data <- data.frame(y, tr) f.fun <- function(y.) gamlss(y. ~ tr, family=GA, data=my.data) # hnp call hnp(fit2, newclass=TRUE, diagfun=d.fun, simfun=s.fun, fitfun=f.fun, data=data.frame(y, tr)) ## Example no. 3: Implementing binomial model in gamlss # model fitting y <- rBI(30, bd=50, mu=rep(c(.2, .5, .9), each=10)) m <- 50 tr <- gl(3, 10) fit3 <- gamlss(cbind(y, m-y) ~ tr, family=BI) # diagfun d.fun <- function(obj) resid(obj) # simfun s.fun <- function(n, obj) { mu <- obj$mu.fv bd <- obj$bd rBI(n, bd=bd, mu=mu) } # fitfun my.data <- data.frame(y, tr, m) f.fun <- function(y.) gamlss(cbind(y., m-y.) ~ tr, family=BI, data=my.data) # hnp call hnp(fit3, newclass=TRUE, diagfun=d.fun, simfun=s.fun, fitfun=f.fun) ## End(Not run) ```

newhnp function