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###### postSim ######
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)
}
# Added by Masanao
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
# Added by Masanao
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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