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BSTS_Predict_2019.R
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# need to install package first: install.packages("CausalImpact")
library(CausalImpact)
library(foreach)
library(doParallel)
library(mgcv)
library(car)
library(ggplot2)
library(lattice)
library(spdep)
library(sf)
library(tmap)
library(scales)
library(reshape2)
library(forecast)
dat <-
read.csv('D:\\Transit\\Daily_Lstaion_Final.csv') # Daily_Lstaion_Final_0806.csv
dat$date <- as.Date(dat$date)
AllCounty <- unique(dat$station_id)
dat <- dat[dat$date < '2020-01-01',]
# Setup parallel backend
cores <- detectCores()
cl <- makeCluster(cores[1] - 1)
registerDoParallel(cl)
finalMatrix <- data.frame()
# length(AllCounty)-1800
finalMatrix <-
foreach(
ccount = 1:(length(AllCounty)),
.combine = rbind,
.packages = c("CausalImpact", "reshape2", "lattice", "ggplot2", "forecast")
) %dopar%
{
eachstation <- AllCounty[ccount]
# eachstation <- 40440
# eachstation <- 40090
print(ccount)
dat_Each <- dat[dat$station_id == eachstation,]
rownames(dat_Each) <- NULL
first_enforce_day <- as.numeric(rownames(dat_Each[dat_Each$date == as.Date('2019-01-01'),]))
pre.period <- c(1, first_enforce_day - 1)
post.period <- c(first_enforce_day, nrow(dat_Each))
# We keep a copy of the actual observed response in "post.period.response
post.period.response <- dat_Each$rides[post.period[1]:post.period[2]]
dat_Each$rides[post.period[1]:post.period[2]] <- NA
response <- zoo(dat_Each$rides, dat_Each$date)
#plot(response)
# drop outliers
response_cl <- tsclean(response, replace.missing = TRUE, lambda = NULL)
response_cl[post.period[1]:post.period[2]] <- NA
response <- zoo(response_cl, dat_Each$date)
#plot(response1)
# Build a bsts model
ss <- AddSemilocalLinearTrend(list(), response)
ss <- AddSeasonal(ss, response, nseasons = 7)
ss <- AddMonthlyAnnualCycle(ss, response)
bsts.model1 <- bsts(
response ~ PRCP + TMAX + IsWeekend + Holidays,
state.specification = ss, niter = 2000, data = dat_Each, expected.model.size = 2)
#plot(bsts.model1)
#plot(bsts.model1, "components") + scale_x_date(date_breaks = "1 month", labels = date_format("%b-%Y"), limits = as.Date(c('2019-01-01', '2020-05-01')))
#plot(bsts.model1, "coef")
# Estiamting counterfactual and compare with actual post period response
impact <- CausalImpact(
bsts.model = bsts.model1,
post.period.response = post.period.response)
#impact.plot <- plot(impact) +
# theme_bw(base_size = 20) +
# scale_x_date(date_breaks = "1 month", labels = date_format("%b-%Y"), limits = as.Date(c('2019-01-01', '2020-05-01')))
#plot(impact.plot)
### Get the number of burn-ins to discard
burn <- SuggestBurn(0.1, bsts.model1)
### Get the components
components.withreg <- cbind.data.frame(
colMeans(bsts.model1$state.contributions[-(1:burn), "trend",]),
colMeans(bsts.model1$state.contributions[-(1:burn), "seasonal.7.1",]),
colMeans(bsts.model1$state.contributions[-(1:burn), "Monthly",]),
colMeans(bsts.model1$state.contributions[-(1:burn), "regression",]),
as.Date((dat_Each$date)))
names(components.withreg) <- c("Trend", "Seasonality", "Monthly", "Regression", "Date")
components.withreg$Predict <- impact$series$point.pred
components.withreg$Response <- impact$series$response
components.withreg$Predict_Lower <- impact$series$point.pred.lower
components.withreg$Predict_Upper <- impact$series$point.pred.upper
components.withreg$Raw_Response <- dat_Each$rides
#plot(impact$series$response)
#plot(components.withreg$Raw_Response)
components.withreg <- melt(components.withreg, id.vars = "Date")
names(components.withreg) <- c("Date", "Component", "Value")
# Plot different components
#ggplot(data = components.withreg, aes(x = Date, y = Value)) +
# geom_line() +
# theme_bw() +
# theme(legend.title = element_blank()) +
# ylab("") +
# xlab("") +
# facet_grid(Component ~ ., scales = "free") +
# guides(colour = FALSE) +
# scale_x_date(date_breaks = "12 month", labels = date_format("%b-%Y")) +
# theme(axis.text.x = element_text(angle = -30, hjust = 0))
#, limits = as.Date(c('2010-01-01', '2020-05-01'))
# Coefficient
#colMeans(bsts.model1$coefficients)
#plot(bsts.model1, "coef")
components.withreg$CTNAME <- eachstation
components.withreg
#write.csv(components.withreg, 'finalMatrix_Transit_temp.csv')
}
stopCluster(cl)
write.csv(finalMatrix, 'finalMatrix_Transit_2019.csv')
GetInclusionProbabilities <- function(bsts.object) {
# Pulls code from
# - BoomSpikeSlab::PlotMarginalInclusionProbabilities
# - bsts::PlotBstsCoefficients
burn <- SuggestBurn(0.1, bsts.object)
beta <- bsts.object$coefficients
beta <- beta[-(1:burn), , drop = FALSE]
inclusion.prob <- colMeans(beta != 0)
index <- order(inclusion.prob)
inclusion.prob <- inclusion.prob[index]
# End from BoomSpikeSlab/bsts.
return(data.frame(predictor = names(inclusion.prob),
inclusion.prob = inclusion.prob))
}
# Coeffi
# Setup parallel backend
cores <- detectCores()
cl <- makeCluster(cores[1] - 1)
registerDoParallel(cl)
finalMatrix <- data.frame()
# length(AllCounty)-1800
finalMatrix <-
foreach(
ccount = 1:(length(AllCounty)),
.combine = rbind,
.packages = c("CausalImpact", "reshape2", "lattice", "ggplot2", "forecast")
) %dopar%
{
eachstation <- AllCounty[ccount]
# eachstation <- 40440
# eachstation <- 40090
print(ccount)
dat_Each <- dat[dat$station_id == eachstation,]
rownames(dat_Each) <- NULL
first_enforce_day <- as.numeric(rownames(dat_Each[dat_Each$date == as.Date('2020-03-13'),]))
pre.period <- c(1, first_enforce_day - 1)
post.period <- c(first_enforce_day, nrow(dat_Each))
# We keep a copy of the actual observed response in "post.period.response
post.period.response <- dat_Each$rides[post.period[1]:post.period[2]]
dat_Each$rides[post.period[1]:post.period[2]] <- NA
response <- zoo(dat_Each$rides, dat_Each$date)
#plot(response)
# drop outliers
response_cl <- tsclean(response, replace.missing = TRUE, lambda = NULL)
response_cl[post.period[1]:post.period[2]] <- NA
response <- zoo(response_cl, dat_Each$date)
#plot(response1)
# Build a bsts model
ss <- AddSemilocalLinearTrend(list(), response)
ss <- AddSeasonal(ss, response, nseasons = 7)
ss <- AddMonthlyAnnualCycle(ss, response)
bsts.model1 <- bsts(
response ~ PRCP + TMAX + Holidays + IsWeekend,
state.specification = ss, niter = 2000, data = dat_Each)
#plot(bsts.model1, "coef")
# Estiamting counterfactual and compare with actual post period response
impact <- CausalImpact(
bsts.model = bsts.model1,
post.period.response = post.period.response)
#impact.plot <- plot(impact) +
# theme_bw(base_size = 20) +
# scale_x_date(date_breaks = "1 month", labels = date_format("%b-%Y"), limits = as.Date(c('2019-01-01', '2020-05-01')))
#GetInclusionProbabilities(bsts.model1)
components.withreg <- as.data.frame(bsts.model1$coefficients)
# Coefficient
#colMeans(bsts.model1$coefficients)
#plot(bsts.model1, "coef")
components.withreg$CTNAME <- eachstation
components.withreg
#write.csv(components.withreg, 'finalMatrix_Transit_temp.csv')
}
stopCluster(cl)
write.csv(finalMatrix, 'finalCoeff_Transit_0810.csv')
# Causal impact
# Setup parallel backend
cores <- detectCores()
cl <- makeCluster(cores[1] - 1)
registerDoParallel(cl)
finalMatrix <- data.frame()
# length(AllCounty)-1800
finalMatrix <-
foreach(
ccount = 1:(length(AllCounty)),
.combine = rbind,
.packages = c("CausalImpact", "reshape2", "lattice", "ggplot2", "forecast")
) %dopar%
{
eachstation <- AllCounty[ccount]
# eachstation <- 40440
# eachstation <- 40090
print(ccount)
dat_Each <- dat[dat$station_id == eachstation,]
rownames(dat_Each) <- NULL
first_enforce_day <- as.numeric(rownames(dat_Each[dat_Each$date == as.Date('2020-03-13'),]))
pre.period <- c(1, first_enforce_day - 1)
post.period <- c(first_enforce_day, nrow(dat_Each))
# We keep a copy of the actual observed response in "post.period.response
post.period.response <- dat_Each$rides[post.period[1]:post.period[2]]
dat_Each$rides[post.period[1]:post.period[2]] <- NA
response <- zoo(dat_Each$rides, dat_Each$date)
#plot(response)
# drop outliers
response_cl <- tsclean(response, replace.missing = TRUE, lambda = NULL)
response_cl[post.period[1]:post.period[2]] <- NA
response <- zoo(response_cl, dat_Each$date)
#plot(response1)
# Build a bsts model
ss <- AddSemilocalLinearTrend(list(), response)
ss <- AddSeasonal(ss, response, nseasons = 7)
ss <- AddMonthlyAnnualCycle(ss, response)
bsts.model1 <- bsts(
response ~ PRCP + TMAX + Holidays + IsWeekend,
state.specification = ss, niter = 2000, data = dat_Each)
#plot(bsts.model1, "coef")
# Estiamting counterfactual and compare with actual post period response
impact <- CausalImpact(
bsts.model = bsts.model1,
post.period.response = post.period.response)
#summary(impact)
# plot(impact)
#summary(impact, "report")
components.withreg <- as.data.frame(impact$summary)
components.withreg$CTNAME <- eachstation
components.withreg
#write.csv(components.withreg, 'finalMatrix_Transit_temp.csv')
}
stopCluster(cl)
write.csv(finalMatrix, 'finalImpact_Transit_0810_old.csv')