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# 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")) |
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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 |
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## 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 |
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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, |
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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 |
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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)) |
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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, |
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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) : |
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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)) |
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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 |