jrnold

## Check whether columns could be a key to a data.frame
## 
## @param x \code{data.frame} to check
## @param i \code{character} or \code{integer} Column names
## @return \code{logical}. \code{TRUE} if no duplicated values
## of \code{i} in \code{x}.
is_key <- function(x, i) {
    !any(duplicated(x[ , i]))
}

jrnold

library(ggplot2)
library(plyr)
library(grid)

notnull <- function(x) !is.null(x)

as.character.element <- function(x) {
    .f <- function(x) {
        if (class(x) %in% c("rel", "unit")) {
            as.character(x)

jrnold

setOldClass("dlm")
setOldClass("dlmFiltered")

##' Mean Squared Error Generic function
mse <- function(x, na.rm=FALSE, ...) {
    mean((x - mean(x, na.rm=na.rm))^2, na.rm=na.rm)
}
##' @export
setGeneric("mse")

jrnold

##' Bayesian normal linear regression with reference prior
##'
##' Given an lm object, simulate the Bayesian posterior.
##'
##' @export
bayeslm <- function(..., niter=100) {
    llik <- function(y, b, sigma2, X) {
        sum(dnorm(y, X %*% b, sqrt(sigma2), log = TRUE))
    }

jrnold

For ($i=1; $i -le 4; $i++) {
  model --refresh=0 --seed=12345 --chain_id=$i --samples=samples$i.csv
}

jrnold

library(KFAS)
library(rstan)
data(GlobalTemp)

model_dlm1a <- stan_model("../stan/dlm1a.stan")

y <- as.matrix(GlobalTemp)
data <-
    within(list(),
       {

jrnold

data {
  int n;
  int m;
  row_vector[m] alpha;
}
parameters {
  row_vector[m] beta[n-1];
}
transformed parameters {
  matrix[n, m] alpha_beta;

jrnold

library("stringr")
library("ggplot2")

shinyServer(function(input, output) {

    output$main_plot <- 
        renderPlot({
                    x <- as.numeric(str_split(input$data, ",")[[1]])
                    xseq <- seq(min(x), max(x), length.out=100)
                    xvar <- var(x)

jrnold

quantile.ordered <- function(x, probs = seq(0, 1, 0.25)) {
  tab <- table(x)
  cdf <- cumsum(tab / sum(tab))
  idx <- sapply(probs, function(p) min(which(cdf >= p)))
  levels(x)[idx] 
}

quantile.factor <- quantile.ordinal
median.ordered <- function(x) quantile(x, 0.5)
median.factor <- median.ordered

jrnold

library("ggplot2")

#' Draw Normal Distribution Density with an area shaded in.
#'
#' @param lb Lower bound of the shaded area. Use \code{-Inf} for a left tail.
#' @param ub Upper bound of the shaded area. Use \code{Inf} for a right tail.
#' @param mean Mean of the normal distribution
#' @param sd Standard deviation of the normal distribution
#' @param limits Lower and upper bounds on the x-axis of the area displayed.
#' @return ggplot object.