mike-lawrence

#load packages
library("curl")
library("ggplot2")
library("rstan")

#retrieve the data
tmpf = tempfile()
curl_download("http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.5.0.0.annual_ns_avg.txt", tmpf)
gtemp = read.table(tmpf, colClasses = rep("numeric", 12))[, 1:2] # only want some of the variables
names(gtemp) = c("Year", "Temperature")

mike-lawrence

data {
	#N: number of cases
	int N ;
	#: K: number of outcome variables
	int K ;
	#X: matrix of outcomes for each case & variable
	matrix[N,K] X ;
	#Y: outcomes
	vector[N] Y;
}

mike-lawrence

library(rstan)
library(MASS)

# Generate some fake data -------------------------------------------------

set.seed(1)
N = 100 #number of subjects
K = 10 #number of observations per subject per condition

#population parameters

mike-lawrence

data {
    // N: num unique x values
    int<lower=1> N ;
    // x: x values
    real x[N] ;
    // y: data
    vector[N] y ;
}
transformed data{
	vector[N] zeros ;

mike-lawrence

library(rstan)

#set random seed for reproducibility
set.seed(1) 

#generate fake data

#variables setting data size
N = 30 #number of x-axis grid points
M = 2 #number of noisy replicates per grid point

mike-lawrence

library(ggplot2)
library(rstan)

N = 100 #number of x-axis grid points
M = 10 #number of noisy replicates per grid point
noise = 1 #amount of noise added to each replicate

#define the x-axis grid
x = seq(-10,10,length.out=N)

mike-lawrence

# Example model from a typical Psychology experiment where mutiple 
# human subjects each contribute multiple observations ("trials")
# in each of two conditions. We model the subject population as
# having a mean intercept and mean difference-between-conditions,
# and subjects as having potentially-correlated deviations from 
# these means. For a given subject in a given condition, trial-by-
# trial observations are sampled with normal error, with a common
# error magnitude used across all subjects/conditions.

#clear workspace to ensure a fresh start

mike-lawrence

Last login: Tue Jun 28 15:06:37 on ttys003
Mikes-MacBook-Air:~ mike$ R

R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin13.4.0 (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

mike-lawrence

library(ggplot2)
library(rstan)

#set random seed for reproducibility
set.seed(1) 

NX = 100 #number of x-axis grid points
NZ = 10 #number of noisy replicates per condition

#define the x-axis grid

mike-lawrence

#load packages
library("curl")
library("ggplot2")
library("rstan")

#retrieve the data
tmpf <- tempfile()
curl_download("http://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/time_series/HadCRUT.4.5.0.0.annual_ns_avg.txt", tmpf)
gtemp <- read.table(tmpf, colClasses = rep("numeric", 12))[, 1:2] # only want some of the variables
names(gtemp) <- c("Year", "Temperature")