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View KFAS_ARMA.R
require(KFAS) # 1.2.9
require(dplyr)
require(tidyr)
require(ggplot2)
# --- ARIMA(2, 1) with linear trend ---
# generate a dataset
set.seed(42)
t <- 100
y <- arima.sim(n = t, model = list(ar=c(.3, -0.1), ma=.2), sd=.1) + seq(from=1, to=10, length.out = t)
View bsts.R
require(bsts) # 0.7.1
data(iclaims) # bring the initial.claims data into scope
# --- model 1 ----
ss <- AddLocalLinearTrend(list(), initial.claims$iclaimsNSA)
ss <- AddSeasonal(ss, initial.claims$iclaimsNSA, nseasons = 52)
model1 <- bsts(initial.claims$iclaimsNSA,
state.specification = ss,
niter = 1000)
View bsts.R
source("common.R", encoding = "utf-8")
df$RP <- calc_RP(df$RP095, df$AP, .95)
z <- calc_Z(RP = df$RP, p = df$AP)
df <- mutate(df, z1=z$z1, z2=z$z2)
df <- mutate(df, z1E=z1*end, z2E=z2*end)
ss <- AddLocalLevel(list(), y = df$logPI) # c
ss <- AddAr(ss, lags=2, y = df$logPI) # AR(2)
# time-varying regression
View kfas.R
source("common.R", encoding = "utf-8")
# ----- KFAS ------
df$RP <- calc_RP(df$RP095, df$AP, .95)
z <- calc_Z(RP = df$RP, p = df$AP)
df <- mutate(df, z1=z$z1, z2=z$z2)
df <- mutate(df, z1E=z1*end, z2E=z2*end)
# specify model
model3KFAS <- SSModel(logPI ~ SSMtrend(1, Q=NA) +
View dlm.R
source("common.R", encoding = "utf-8")
# ---- dlm ------
# model 3
res <- data.frame()
for( a in seq(from=.1, to=.95, by=.05) ){
RP <- calc_RP(RP = df$RP095, p = df$AP, a = a)
z <- calc_Z(RP = RP, p = df$AP)
z1 <- z$z1
View common.R
# ------ common part ----
require(ggplot2)
require(dplyr)
require(tidyr)
require(dlm) # 1.1-4
require(KFAS) # 1.2.9
require(bsts) # 0.7.1
# calculate the reference price
View mlogit.R
require(mlogit) # 0.2-4
as.mldata <- function(data){
# convert HC dataset
# The alternatives are
# 1. Gas central heat with cooling (gcc)
# 2. Electric central resistence heat with cooling (ecc)
# 3. Electric room resistence heat with cooling (erc)
# 4. Electric heat pump, which provides cooling also (hpc)
# 5. Gas central heat without cooling (gc)
@Gedevan-Aleksizde
Gedevan-Aleksizde / Kalman.R
Last active May 8, 2017
Linear Kalman filter and animation
View Kalman.R
# multivariate normal Kalman filter
require(dplyr)
require(tidyr)
require(ggplot2)
require(animation)
# ARIMA(1,1) + 線形トレンド の乱数生成
N <- 50
phi1 <- .5
theta1 <- .2
View rfm_mcmc_exec.R
library(dplyr)
library(tidyr)
library(ggplot2)
library(rstan)
library(loo)
library(ggmcmc)
# read datasets
df <- read.csv("rfm.csv", stringsAsFactors = F)
colnames(df)[1] <- "ID"
View rfm_hierarchical1702.stan
/* -----------------------------------------------------------------------------
Estimate latent variable hierarchical Bayes RFM model
stan ver. 2.14
CREATED: by ill-identified, 18/APR/2016
REVISED: 25/FEB/2017, REVISED
------------------------------------------------------------------------------- */
data {
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