ramhiser

# For info about census regions, see:
# http://en.wikipedia.org/wiki/List_of_regions_of_the_United_States#Census_Bureau-designated_regions_and_divisions

# Region - Northeast

# Division - New England
new_england <- data.frame(
  region = "Northeast",
  division = "New England",
  state = c("ME", "NH", "VT", "MA", "RI", "CT")

ramhiser

import numpy as np
import pandas as pd
from itertools import chain, izip, repeat

np.random.seed(42)
num_rows = 10
num_features = 5
num_feature_values = 3

# Builds tuples of features with many values per feature

ramhiser

library(dplyr)
group_size <- 20
foo <- iris[1:119, ]
filter(group_by(foo, Species), n() >= group_size)

ramhiser

# Comment thread beginning here:
# http://ramhiser.com/blog/2013/07/02/a-brief-look-at-mixture-discriminant-analysis/#comment-1374749931
#
# I'm using version 0.4-4 of the `mda` package
library(mda)
test_data <- read.csv("ts2.csv")
colnames(test_data)[16] <- "filter"
mda_out <- mda(formula=fol_up_u ~ . - filter,
               data=test_data,
               CV=TRUE,

ramhiser

library(clusteval)
adjacency_matrix <- function(cluster_labels, names=NULL, force_symmetric=FALSE) {
  adj_matrix <- diag(length(cluster_labels))
  adj_matrix[lower.tri(adj_matrix)] <- clusteval::comembership(cluster_labels)
  if (force_symmetric) {
    adj_matrix <- adj_matrix + t(adj_matrix)
  }
  diag(adj_matrix) <- 0
  if (!is.null(names))
  rownames(adj_matrix) <- colnames(adj_matrix) <- names

ramhiser

# For the standard conjugate beta prior for a binomial likelihood, a typical
# approach is to weight each prior observation equally, there are times where
# the prior Bernoulli trials should be weighted over time, so that the more
# recent trials are weighted near 1 and the oldest trials should be weighted
# near 0.

# Gompertz Function
# http://en.wikipedia.org/wiki/Gompertz_function
gompertz <- function(x, a=1, b=1, c=1) {
  a * exp(-b * exp(-c * x))

ramhiser

library(dplyr)

objects <- ls()
object_sizes <- sapply(objects, function(x) object.size(get(x)))
object_sizes <- data.frame(objects, object_sizes, row.names=NULL)
object_sizes$units_MB <- utils:::format.object_size(object_sizes$object_sizes, units="Mb")
dplyr::arrange(object_sizes, object_sizes)

ramhiser

#' Cuts a vector into factors with pretty levels
#' 
#' @param x numeric vectory
#' @param breaks numeric vector of two ore more unique cut points
#' @param collapse character string to collapse factor labels
#' @param ... arguments passed to \code{\link[base]{cut}}
#' @return A \code{\link{factor}} is returned
#' 
#' @examples
#' set.seed(42)

ramhiser

import pandas as pd

df = pd.DataFrame([['01-02-2015', 'a', 17],
                  ['01-09-2015', 'a', 42],
                  ['01-30-2015', 'a', 19],
                  ['01-02-2015', 'b', 23],
                  ['01-23-2015', 'b', 1],
                  ['01-30-2015', 'b', 13]])
df.columns = ['date', 'group', 'response']
df.set_index(['date', 'group'], inplace=True)

ramhiser

# SAT scores data from Table 5.2 on page 120 of Gelman's BDA3 text
y <- c(28, 8, -3, 7, -1, 1, 18, 12)
sigma <- c(15, 10, 16, 11, 9, 11, 10, 18)

# Goal: Replicate calculations in Section 5.5
# Instructions for posterior simulation given on page 118
library(itertools2)

# Equation 5.21 on page 117
tau_posterior <- function(tau, y, sigma) {