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