-
Notifications
You must be signed in to change notification settings - Fork 0
/
hw_week03.R
104 lines (74 loc) · 1.75 KB
/
hw_week03.R
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
###statistical rethinking course 2023###
##########################
########## Week 03 #######
### Homework Questions ###
##########################
packages <- c('rstan','rethinking','ggplot2','ggdag', 'dagitty', 'data.table', 'tidyr' )
lapply(packages, require, character.only=TRUE)
data(foxes)
setDT(foxes)
d$W <- standardize(d$weight)
d$A <- standardize(d$area)
d$F <- standardize(d$avgfood)
d$G <- standardize(d$groupsize)
foxes[, `:=`(W = standardize(weight),
A = standardize(area),
F = standardize(avgfood),
G = standardize(groupsize))
]
### Question 1
m1 <- quap(
alist(
F ~ dnorm( mu , sigma ),
mu <- a + bA*A,
a ~ dnorm(0,0.2),
bA ~ dnorm(0,0.5),
sigma ~ dexp(1)
), data = foxes )
precis(m1)
#### Question 2
m2 <- quap(
alist(
W ~ dnorm( mu , sigma ),
mu <- a + bF*F,
a ~ dnorm(0,0.2),
bF ~ dnorm(0,0.5),
sigma ~ dexp(1)
), data = foxes )
precis(m2)
###Question 3
m3 <- quap(
alist(
W ~ dnorm( mu , sigma ),
mu <- a + bF*F + bG*G,
a ~ dnorm(0,0.2),
bF ~ dnorm(0,0.5),
c(bF,bG) ~ dnorm(0,0.5),
sigma ~ dexp(1)
), data = foxes )
precis(m3)
m3b <- quap(
alist(
G ~ dnorm( mu , sigma ),
mu <- a + bF*F,
a ~ dnorm(0,0.2),
bF ~ dnorm(0,0.5),
sigma ~ dexp(1)
), data = foxes )
precis(m3b)
###Question 4
coords <- data.frame(
name = c('A', 'F', 'G', 'W', 'U'),
x = c(1, 1, 2, 1.5, 2),
y = c(3, 2, 2, 1, 3)
)
dag <- dagify(
W ~ F + G,
F ~ A + U,
G ~ F + U,
coords = coords,
latent = "U"
)
dag |> ggdag(seed = 2) + theme_dag()
adjustmentSets(dag, exposure = 'F', outcome = 'W', effect = 'total')
adjustmentSets(dag, exposure = 'F', outcome = 'W', effect = 'direct')