-
Notifications
You must be signed in to change notification settings - Fork 0
/
causal_impact_bsts.Rmd
138 lines (99 loc) · 4.01 KB
/
causal_impact_bsts.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
---
title: "R Notebook"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
```{r}
# This part of code is from: https://google.github.io/CausalImpact/CausalImpact.html
library(CausalImpact)
set.seed(1)
x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 100)
y <- 1.2 * x1 + rnorm(100)
y[71:100] <- y[71:100] + 10
data <- cbind(y, x1)
dim(data)
head(data)
matplot(data, type = "l")
pre.period <- c(1, 70)
post.period <- c(71, 100)
impact <- CausalImpact(data, pre.period, post.period)
plot(impact)
time.points <- seq.Date(as.Date("2014-01-01"), by = 1, length.out = 100)
data <- zoo(cbind(y, x1), time.points)
head(data)
pre.period <- as.Date(c("2014-01-01", "2014-03-11"))
post.period <- as.Date(c("2014-03-12", "2014-04-10"))
impact <- CausalImpact(data, pre.period, post.period)
plot(impact)
summary(impact)
summary(impact, "report")
impact$summary
impact <- CausalImpact(data, pre.period, post.period, model.args = list(niter = 5000, nseasons = 7))
post.period <- c(71, 100)
post.period.response <- y[post.period[1] : post.period[2]]
y[post.period[1] : post.period[2]] <- NA
ss <- AddLocalLevel(list(), y)
bsts.model_temp <- bsts(y ~ x1, ss, niter = 1000)
impact <- CausalImpact(bsts.model = bsts.model_temp, post.period.response = post.period.response)
plot(impact)
summary(impact)
summary(impact, "report")
```
```{r}
# Building models using bsts
ss <- AddLocalLevel(list(), y)
ss <- AddSeasonal(ss, y, nseasons = 7)
bsts.model_1 <- bsts(y ~ x1, ss, niter = 1000)
impact <- CausalImpact(bsts.model = bsts.model_1, post.period.response = post.period.response)
plot(impact)
summary(impact)
summary(impact, "report")
```
```{r}
ss <- AddLocalLevel(list(), y)
ss <- AddSeasonal(ss, y, nseasons = 7)
bsts.model_2 <- bsts(y ~ x1, ss, niter = 1000, family="student")
impact <- CausalImpact(bsts.model = bsts.model_2, post.period.response = post.period.response)
plot(impact)
summary(impact)
summary(impact, "report")
```
```{r}
ss <- AddLocalLinearTrend(list(), y)
ss <- AddSeasonal(ss, y, nseasons = 7)
bsts.model_3 <- bsts(y ~ x1, ss, niter = 1000, family="student")
impact <- CausalImpact(bsts.model = bsts.model_3, post.period.response = post.period.response)
plot(impact)
summary(impact)
summary(impact, "report")
```
```{r}
ss <- AddStudentLocalLinearTrend(list(), y)
ss <- AddSeasonal(ss, y, nseasons = 7)
bsts.model_4 <- bsts(y ~ x1, ss, niter = 1000, family="student")
impact <- CausalImpact(bsts.model = bsts.model_4, post.period.response = post.period.response)
plot(impact)
summary(impact)
summary(impact, "report")
```
```{r}
ss <- AddSemilocalLinearTrend(list(), y)
ss <- AddSeasonal(ss, y, nseasons = 7)
bsts.model_5 <- bsts(y ~ x1, ss, niter = 1000, family="student")
impact <- CausalImpact(bsts.model = bsts.model_5, post.period.response = post.period.response)
plot(impact)
summary(impact)
summary(impact, "report")
```
```{r}
CompareBstsModels(list("Model_1" = bsts.model_1,
"Model_2" = bsts.model_2,
"Model_3" = bsts.model_3,
"Model_4" = bsts.model_4,
"Model_5" = bsts.model_5),
colors = c("black","red", "blue","green","yellow"))
```
Add a new chunk by clicking the *Insert Chunk* button on the toolbar or by pressing *Ctrl+Alt+I*.
When you save the notebook, an HTML file containing the code and output will be saved alongside it (click the *Preview* button or press *Ctrl+Shift+K* to preview the HTML file).
The preview shows you a rendered HTML copy of the contents of the editor. Consequently, unlike *Knit*, *Preview* does not run any R code chunks. Instead, the output of the chunk when it was last run in the editor is displayed.