|
|
|
|
| setGeneric("postSim",
|
| function(object, n.sims=1000){
|
| standardGeneric("postSim")
|
| }
|
| )
|
|
|
| setClass("postSim",
|
| slots = c(coef = "matrix",
|
| sigma = "numeric")
|
| )
|
|
|
| setClass("postSim.polr",
|
| slots = c(coef = "matrix",
|
| zeta = "matrix")
|
| )
|
|
|
| setMethod("postSim", signature(object = "lm"),
|
| function(object, n.sims=1000)
|
| {
|
| object.class <- class(object)[[1]]
|
| summ <- summary (object)
|
| coef <- summ$coef[,1:2,drop=FALSE]
|
| dimnames(coef)[[2]] <- c("coef.est","coef.sd")
|
| sigma.hat <- summ$sigma
|
| beta.hat <- coef[,1,drop = FALSE]
|
| V.beta <- summ$cov.unscaled
|
| n <- summ$df[1] + summ$df[2]
|
| k <- summ$df[1]
|
| sigma <- rep (NA, n.sims)
|
| beta <- array (NA, c(n.sims,k))
|
| dimnames(beta) <- list (NULL, rownames(beta.hat))
|
| for (s in 1:n.sims){
|
| sigma[s] <- sigma.hat*sqrt((n-k)/rchisq(1,n-k))
|
| beta[s,] <- MASS::mvrnorm (1, beta.hat, V.beta*sigma[s]^2)
|
| }
|
|
|
| ans <- new("postSim",
|
| coef = beta,
|
| sigma = sigma)
|
| return (ans)
|
| }
|
| )
|
|
|
|
|
| setMethod("postSim", signature(object = "glm"),
|
| function(object, n.sims=1000)
|
| {
|
| object.class <- class(object)[[1]]
|
| summ <- summary (object, correlation=TRUE, dispersion = object$dispersion)
|
| coef <- summ$coef[,1:2,drop=FALSE]
|
| dimnames(coef)[[2]] <- c("coef.est","coef.sd")
|
| beta.hat <- coef[,1,drop=FALSE]
|
| sd.beta <- coef[,2,drop=FALSE]
|
| corr.beta <- summ$corr
|
| n <- summ$df[1] + summ$df[2]
|
| k <- summ$df[1]
|
| V.beta <- corr.beta * array(sd.beta,c(k,k)) * t(array(sd.beta,c(k,k)))
|
| beta <- array (NA, c(n.sims,k))
|
| dimnames(beta) <- list (NULL, dimnames(beta.hat)[[1]])
|
| for (s in 1:n.sims){
|
| beta[s,] <- MASS::mvrnorm (1, beta.hat, V.beta)
|
| }
|
|
|
| beta2 <- array (0, c(n.sims,length(coefficients(object))))
|
| dimnames(beta2) <- list (NULL, names(coefficients(object)))
|
| beta2[,dimnames(beta2)[[2]]%in%dimnames(beta)[[2]]] <- beta
|
|
|
| sigma <- rep (sqrt(summ$dispersion), n.sims)
|
|
|
| ans <- new("postSim",
|
| coef = beta2,
|
| sigma = sigma)
|
| return(ans)
|
| }
|
| )
|
|
|
|
|
| setMethod("postSim", signature(object = "polr"),
|
| function(object, n.sims=1000){
|
| x <- as.matrix(model.matrix(object))
|
| coefs <- coef(object)
|
| k <- length(coefs)
|
| zeta <- object$zeta
|
| Sigma <- vcov(object)
|
|
|
| if(n.sims==1){
|
| parameters <- t(MASS::mvrnorm(n.sims, c(coefs, zeta), Sigma))
|
| }else{
|
| parameters <- MASS::mvrnorm(n.sims, c(coefs, zeta), Sigma)
|
| }
|
| ans <- new("postSim.polr",
|
| coef = parameters[,1:k,drop=FALSE],
|
| zeta = parameters[,-(1:k),drop=FALSE])
|
| return(ans)
|
| }
|
| )
|
|
|
|
|
| setMethod("postSim", signature(object = "svyglm"),
|
| function(object, n.sims=1000)
|
| {
|
| object.class <- class(object)[[2]]
|
| summ <- summary (object, correlation=TRUE, dispersion = object$dispersion)
|
| coef <- summ$coef[,1:2,drop=FALSE]
|
| dimnames(coef)[[2]] <- c("coef.est","coef.sd")
|
| beta.hat <- coef[,1,drop=FALSE]
|
| sd.beta <- coef[,2,drop=FALSE]
|
| corr.beta <- summ$corr
|
| n <- summ$df[1] + summ$df[2]
|
| k <- summ$df[1]
|
| V.beta <- corr.beta * array(sd.beta,c(k,k)) * t(array(sd.beta,c(k,k)))
|
| beta <- array (NA, c(n.sims,k))
|
| dimnames(beta) <- list (NULL, dimnames(beta.hat)[[1]])
|
| for (s in 1:n.sims){
|
| beta[s,] <- MASS::mvrnorm (1, beta.hat, V.beta)
|
| }
|
| beta2 <- array (0, c(n.sims,length(coefficients(object))))
|
| dimnames(beta2) <- list (NULL, names(coefficients(object)))
|
| beta2[,dimnames(beta2)[[2]]%in%dimnames(beta)[[2]]] <- beta
|
| sigma <- rep (sqrt(summ$dispersion), n.sims)
|
|
|
| ans <- new("postSim",
|
| coef = beta2,
|
| sigma = sigma)
|
| return(ans)
|
| }
|
| )
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