# Extract simulation model from a glarma fit Extracts required components from a fitted glarma model to create an object of class `"glarmaSimModel"` to be used as input to simulate a glarma model using the function `glarmaSim`. ```r extractGlarmaSimModel(object, ...) ``` ## Arguments - `object`: An object of class `"glarma"` obtained from a call to `glarma` - ``...``: Further arguments for the call, currently unused. ## Returns An object of class `"glarmaSimModel"` ## Author(s) "William T.M. Dunsmuir" and "David J Scott" ## See Also `glarmaSim` ## Examples ```r ### Extract model from a glarma fit to use in simulation ### Example with asthma data data(Asthma) y <- Asthma[,1] X <- as.matrix(Asthma[,2:16]) ### Pearson Residuals, Fisher Scoring glarmamod <- glarma(y, X, thetaLags = 7, type = "Poi", method = "FS", residuals = "Pearson", maxit = 100, grad = 1e-6) mod <- extractGlarmaSimModel(glarmamod) str(mod) glarmamod <- glarma(y, X, thetaLags = 7, type = "NegBin", method = "NR", residuals = "Pearson", alphaInit = 0, maxit = 100, grad = 1e-6) mod <- extractGlarmaSimModel(glarmamod) str(mod) data(DriverDeaths) y <- DriverDeaths[, "Deaths"] X <- as.matrix(DriverDeaths[, 2:5]) Population <- DriverDeaths[, "Population"] ### Offset included glarmamodOffset <- glarma(y, X, offset = log(Population/100000), phiLags = c(12), type = "Poi", method = "FS", residuals = "Pearson", maxit = 100, grad = 1e-6) mod <- extractGlarmaSimModel(glarmamodOffset) str(mod) data(OxBoatRace) y1 <- OxBoatRace$Camwin n1 <- rep(1, length(OxBoatRace$Year)) Y <- cbind(y1, n1 - y1) X <- cbind(OxBoatRace$Intercept, OxBoatRace$Diff) colnames(X) <- c("Intercept", "Weight Diff") oxcamglm <- glm(Y ~ Diff + I(Diff^2), data = OxBoatRace, family = binomial(link = "logit"), x = TRUE) summary(oxcamglm) X <- oxcamglm$x glarmamod <- glarma(Y, X, thetaLags = c(1, 2), type = "Bin", method = "NR", residuals = "Pearson", maxit = 100, grad = 1e-6) mod <- extractGlarmaSimModel(glarmamod) str(mod) data(RobberyConvict) datalen <- dim(RobberyConvict)[1] monthmat <- matrix(0, nrow = datalen, ncol = 12) dimnames(monthmat) <- list(NULL, c("Jan","Feb","Mar","Apr","May","Jun", "Jul","Aug","Sep","Oct","Nov","Dec")) monthNames <- months(strptime(RobberyConvict$Date, format = "%m/%d/%Y"), abbreviate=TRUE) monthNamesUnique <- unique(monthNames) for (j in 1:12) { monthmat[monthNames == monthNamesUnique[j], j] <- 1 } RobberyConvict <- cbind(rep(1, datalen), RobberyConvict, monthmat) rm(monthmat) ### Lower court robbery y1 <- RobberyConvict$LC.Y n1 <- RobberyConvict$LC.N Y <- cbind(y1, n1-y1) glm.LCRobbery <- glm(Y ~ Step.2001 + I(Feb + Mar + Apr + May + Jun + Jul) + I(Aug + Sep + Oct + Nov + Dec), data = RobberyConvict, family = binomial(link = logit), na.action = na.omit, x = TRUE) summary(glm.LCRobbery, corr = FALSE) X <- glm.LCRobbery$x ### Newton Raphson glarmamod <- glarma(Y, X, phiLags = c(1), type = "Bin", method = "NR", residuals = "Pearson", maxit = 100, grad = 1e-6) mod <- extractGlarmaSimModel(glarmamod) str(mod) ```

extractGlarmaSimModel function