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View CAR_20180813.R
require(tidyverse) # ver. 1.2.1
require(CARBayes) # ver. 5.0
require(sf) # ver. 0.6-3
require(ggplot2) # ver. 3.0.0
require(ggthemes) # ver. 4.0.0
require(ggmcmc) # ver. 1.1
rmse <- function(y, pred){
return(sqrt(mean((y - pred)^2)))
}
View heatmap.R
require(tidyverse)
require(ggthemes)
require(rjson)
require(jsonlite)
# 参考
# https://www.data.jma.go.jp/gmd/risk/obsdl/index.php
# https://www.data.jma.go.jp/gmd/risk/obsdl/top/help3.html#hukajoho
# https://twitter.com/mehori/status/1020644999703089152
View logistic.ipynb
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View pull_data.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 31 22:57:58 2018
https://www.quora.com/How-can-I-extract-only-text-data-from-HTML-pages
https://qiita.com/matsu0228/items/edf7dbba9b0b0246ef8f
@author: ks
"""
import sys, re, datetime
View mcchain.py
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
import argparse
import random
from pathlib import Path
from itertools import chain
# fname = Path('~/Downloads/vpylm/out_2018-04-01T040258.txt')
View meishi.R
require(ggplot2)
require(purrr)
require(stringr)
require(glue)
require(qrencoder)
require(png)
require(gridExtra)
# given_name 必須. 名
# family_name 任意. 姓
View KFAS_crash.R
require(KFAS) # 1.2.9
require(dplyr)
require(tidyr)
require(ggplot2)
require(data.table)
require(zoo)
##### read dataset #####
# https://catalog.data.gov/dataset/allegheny-county-crash-data
# data description
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