mages

# Original code by Max Gordon
# Source: http://gforge.se/2013/06/visualizing-transitions-with-the-transitionplot-function/
# Create the transition matrix
library(Gmisc)
library(grid)
library(RColorBrewer)
set.seed(9730)
b4 <- sample(1:3, replace = TRUE, size = 500, prob = c(0.1, 0.4, 0.5))
after <- sample(1:3, replace = TRUE, size = 500, prob = c(0.3, 0.5, 0.2))
b4 <- factor(b4, labels = c("None", "Moderate", "Major"))

mages

library(formattable)
DF <- data.frame(Ticker=c("", "", "", "IBM", "AAPL", "MSFT"),
                 Name=c("Dow Jones", "S&P 500", "Technology", 
                        "IBM", "Apple", "Microsoft"),
                 Value=accounting(c(15988.08, 1880.33, NA, 
                                    130.00, 97.05, 50.99)),
                 Change=percent(c(-0.0239, -0.0216, 0.021, 
                                  -0.0219, -0.0248, -0.0399)))
DF
##   Ticker       Name     Value Change

mages

## Markus Gesmann, March 2012

library(simecol)
library(latticeExtra)

HaresLynxObservations <- "Year	 Hares.x.1000	 Lynx.x.1000
1900	30	4
1901	47.2	6.1
1902	70.2	9.8
1903	77.4	35.2

mages

HaresLynxObservations <- structure(
  list(Year = 1900:1920, 
       Hares.x.1000 = c(30, 47.2, 70.2, 77.4, 
                        36.3, 20.6, 18.1, 21.4, 
                        22, 25.4, 27.1, 40.3, 57, 
                        76.6, 52.3, 19.5, 11.2, 
                        7.6, 14.6, 16.2, 24.7), 
       Lynx.x.1000 = c(4, 6.1, 9.8, 35.2, 59.4, 
                       41.7, 19, 13, 8.3, 9.1, 7.4, 
                       8, 12.3, 19.5, 45.7, 51.1, 

mages

stanmodel <- "
data {
  int<lower=0> N; 
  real x[N]; 
  real Y[N]; 
} 
parameters {
  real alpha; 
  real beta;  
  real<lower=.5,upper= 1> lambda; // original gamma in the JAGS example  

mages

dat <- list(
  "N" = 27,  
  "x" =
    c(1, 1.5, 1.5, 1.5, 2.5, 4, 5, 5, 7, 8, 8.5, 9, 9.5, 9.5, 10, 
      12, 12, 13, 13, 14.5, 15.5, 15.5, 16.5, 17, 22.5, 29, 31.5),
  "Y" =
    c(1.8, 1.85, 1.87, 1.77, 2.02, 2.27, 2.15, 2.26, 2.47, 2.19, 
      2.26, 2.4, 2.39, 2.41, 2.5, 2.32, 2.32, 2.43, 2.47, 2.56, 2.65, 
      2.47, 2.64, 2.56, 2.7, 2.72, 2.57))

mages

library(lattice)
library(data.table)
library(arm)
library(ChainLadder)
dat <- data.table( # Page D5.17
  originf=factor(rep(0:6, each=7)),  
  devf=factor(rep(0:6, 7)),
  inc.value= c(3511,  3215,  2266,  1712, 1059, 587, 340,
               4001,  3702,  2278, 1180, 956, 629,  NA,

mages

library(rstan)
rtools <- "C:\\Rtools\\bin"
gcc <- "C:\\Rtools\\gcc-4.6.3\\bin"
path <- strsplit(Sys.getenv("PATH"), ";")[[1]]
new_path <- c(rtools, gcc, path)
new_path <- new_path[!duplicated(tolower(new_path))]
Sys.setenv(PATH = paste(new_path, collapse = ";"))

# The above overcomes:
# Error in compileCode(f, code, language = language, verbose = verbose) : 

mages

A <- function(samples){
  as.matrix(samples[,c("b_Intercept" ,"b_temp")])
}
x <- c(1, 35)
prob <- 0.975 
lin.samples <- posterior_samples(lin.mod)
n <- nrow(lin.samples)
mu <- A(lin.samples) %*% x
sigma <- lin.samples[,"sigma_units"]
(lin.q <- quantile(rnorm(n, mu, sigma), prob))

mages

log.lin.mod
##  Family: gaussian (identity) 
## Formula: log_units ~ temp 
##    Data: NULL (Number of observations: 12) 
## Samples: 2 chains, each with n.iter = 2000; n.warmup = 500; n.thin = 1; 
##          total post-warmup samples = 3000
##    WAIC: -9.76
##  
## Fixed Effects: 
##           Estimate Est.Error l-95% CI u-95% CI Eff.Sample Rhat