mtcars
R
Clojure
10
15
20
25
30
-4
-2
0
2
4
Fitted values
Residuals
lm(data = mtcars)
Residuals vs Fitted
Fiat 128
Toyota Corolla
Chrysler Imperial
Datsun 710
Ford Pantera L
Residuals vs Fitted
Residuals
Fitted values
Fiat 128
Toyota Corolla
Chrysler Imperial
Datsun 710
Ford Pantera L
10
12
14
16
18
20
22
24
26
28
30
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
-2
-1
0
1
2
-2
-1
0
1
2
Theoretical Quantiles
Standardized residuals
lm(data = mtcars)
Q-Q Residuals
Ford Pantera L
Fiat 128
Chrysler Imperial
Toyota Corolla
Datsun 710
Q-Q Residuals
Standardised residuals
Theoretical Quantiles
Ford Pantera L
Fiat 128
Chrysler Imperial
Toyota Corolla
Datsun 710
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
10
15
20
25
30
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Fitted values
S
t
a
n
d
a
r
d
i
z
e
d
r
e
s
i
d
u
a
l
s
lm(data = mtcars)
Scale-Location
Ford Pantera L
Fiat 128
Chrysler Imperial
Toyota Corolla
Datsun 710
Scale Location
(sqrt (abs standardised-residuals))
Fitted values
Ford Pantera L
Fiat 128
Chrysler Imperial
Toyota Corolla
Datsun 710
10
12
14
16
18
20
22
24
26
28
30
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
0
5
10
15
20
25
30
0.0
0.2
0.4
0.6
Obs. number
Cook's distance
lm(data = mtcars)
Cook's distance
Ford Pantera L
Merc 230
Chrysler Imperial
Lotus Europa
Toyota Corolla
Cooks distance
Cook's distance
Obs. number
Ford Pantera L
Merc 230
Chrysler Imperial
Lotus Europa
Toyota Corolla
0
8
16
24
0.0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.0
0.2
0.4
0.6
-2
-1
0
1
2
Leverage
Standardized residuals
lm(data = mtcars)
Cook's distance
1
0.5
0.5
1
Residuals vs Leverage
Ford Pantera L
Merc 230
Chrysler Imperial
Lotus Europa
Toyota Corolla
Residual vs Leverage
Standardised residuals
Leverage
Ford Pantera L
Merc 230
Chrysler Imperial
Lotus Europa
Toyota Corolla
1.0
0.5
0.5
1.0
··· Cook's distance
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0.0
0.2
0.4
0.6
Leverage
h
i
i
Cook's distance
0.1
0.3
0.4
0.5
0.6
0.7
lm(data = mtcars)
0
0.5
1
1.5
2
Cook's dist vs Leverage*
h
i
i
(
1
−
h
i
i
)
Ford Pantera L
Merc 230
Chrysler Imperial
Lotus Europa
Toyota Corolla
Cook's dist vs Leverage* hᵢᵢ / (1 - hᵢᵢ)
Cook's distance
Leverage hᵢᵢ
0
0.5
1
1.5
2
Ford Pantera L
Merc 230
Chrysler Imperial
Lotus Europa
Toyota Corolla
0.6
0.4
0.2
-0.0
-0.05
0.0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
iris
R
Clojure
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
-0.5
0.0
0.5
Fitted values
Residuals
lm(data = iris)
Residuals vs Fitted
85
136
15
142
107
Residuals vs Fitted
Residuals
Fitted values
85
136
15
142
107
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
-2
-1
0
1
2
-2
-1
0
1
2
3
Theoretical Quantiles
Standardized residuals
lm(data = iris)
Q-Q Residuals
85
136
15
142
107
Q-Q Residuals
Standardised residuals
Theoretical Quantiles
85
136
15
142
107
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
0.0
0.5
1.0
1.5
Fitted values
S
t
a
n
d
a
r
d
i
z
e
d
r
e
s
i
d
u
a
l
s
lm(data = iris)
Scale-Location
85
136
15
142
107
Scale Location
(sqrt (abs standardised-residuals))
Fitted values
85
136
15
142
107
4.5
5.0
5.5
6.0
6.5
7.0
7.5
8.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
0
50
100
150
0.00
0.02
0.04
0.06
Obs. number
Cook's distance
lm(data = iris)
Cook's distance
135
107
142
15
101
Cooks distance
Cook's distance
Obs. number
135
107
142
15
101
0
38
76
114
0.0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055
0.06
0.065
0.07
0.00
0.02
0.04
0.06
0.08
0.10
0.12
-3
-2
-1
0
1
2
3
Leverage
Standardized residuals
lm(data = iris)
Cook's distance
Residuals vs Leverage
135
107
142
15
101
Residual vs Leverage
Standardised residuals
Leverage
135
107
142
15
101
1.0
0.5
0.5
1.0
··· Cook's distance
0.0
0.02
0.04
0.06
0.08
0.1
0.12
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
0.00
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Leverage
h
i
i
Cook's distance
0.02
0.04
0.06
0.08
0.1
0.12
lm(data = iris)
0
0.5
1
1.5
2
2.5
3
Cook's dist vs Leverage*
h
i
i
(
1
−
h
i
i
)
135
107
142
15
101
Cook's dist vs Leverage* hᵢᵢ / (1 - hᵢᵢ)
Cook's distance
Leverage hᵢᵢ
0
0.5
1
1.5
2
2.5
3
135
107
142
15
101
0.12
0.1
0.08
0.06
0.04
0.02
-0.0
-0.005
0.0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0.055
0.06
0.065
0.07
0.075
rock
R
Clojure
2000
4000
6000
8000
10000
-4000
-2000
0
2000
4000
Fitted values
Residuals
lm(data = rock)
Residuals vs Fitted
48
42
32
35
36
Residuals vs Fitted
Residuals
Fitted values
48
42
32
35
36
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
-3500
-3000
-2500
-2000
-1500
-1000
-500
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
-2
-1
0
1
2
-2
-1
0
1
2
3
4
Theoretical Quantiles
Standardized residuals
lm(data = rock)
Q-Q Residuals
48
42
35
32
36
Q-Q Residuals
Standardised residuals
Theoretical Quantiles
48
42
35
32
36
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
2000
4000
6000
8000
10000
0.0
0.5
1.0
1.5
2.0
Fitted values
S
t
a
n
d
a
r
d
i
z
e
d
r
e
s
i
d
u
a
l
s
lm(data = rock)
Scale-Location
48
42
35
32
36
Scale Location
(sqrt (abs standardised-residuals))
Fitted values
48
42
35
32
36
2000
3000
4000
5000
6000
7000
8000
9000
10000
11000
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
0
10
20
30
40
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Obs. number
Cook's distance
lm(data = rock)
Cook's distance
42
48
38
35
43
Cooks distance
Cook's distance
Obs. number
42
48
38
35
43
0
12
24
36
0.0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.0
0.1
0.2
0.3
0.4
0.5
-2
0
2
4
Leverage
Standardized residuals
lm(data = rock)
Cook's distance
1
0.5
0.5
1
Residuals vs Leverage
42
48
38
35
43
Residual vs Leverage
Standardised residuals
Leverage
42
48
38
35
43
1.0
0.5
0.5
1.0
··· Cook's distance
0.0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0.00
0.05
0.10
0.15
0.20
0.25
0.30
Leverage
h
i
i
Cook's distance
0
0.1
0.2
0.3
0.4
0.5
lm(data = rock)
0
1
2
3
4
Cook's dist vs Leverage*
h
i
i
(
1
−
h
i
i
)
42
48
38
35
43
Cook's dist vs Leverage* hᵢᵢ / (1 - hᵢᵢ)
Cook's distance
Leverage hᵢᵢ
0
1
2
3
4
42
48
38
35
43
0.5
0.4
0.3
0.2
0.1
-0.0
-0.02
0.0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
ToothGrowth
R
Clojure
10
15
20
25
-5
0
5
10
Fitted values
Residuals
lm(data = ToothGrowth)
Residuals vs Fitted
23
50
26
44
32
Residuals vs Fitted
Residuals
Fitted values
23
50
26
44
32
10
12
14
16
18
20
22
24
26
28
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
-2
-1
0
1
2
-1
0
1
2
Theoretical Quantiles
Standardized residuals
lm(data = ToothGrowth)
Q-Q Residuals
23
50
26
32
44
Q-Q Residuals
Standardised residuals
Theoretical Quantiles
23
50
26
32
44
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-3.0
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
10
15
20
25
0.0
0.5
1.0
1.5
Fitted values
S
t
a
n
d
a
r
d
i
z
e
d
r
e
s
i
d
u
a
l
s
lm(data = ToothGrowth)
Scale-Location
23
50
26
32
44
Scale Location
(sqrt (abs standardised-residuals))
Fitted values
23
50
26
32
44
10
12
14
16
18
20
22
24
26
28
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
0
10
20
30
40
50
60
0.00
0.02
0.04
0.06
0.08
0.10
Obs. number
Cook's distance
lm(data = ToothGrowth)
Cook's distance
23
26
32
22
53
Cooks distance
Cook's distance
Obs. number
23
26
32
22
53
0
15
30
45
0.0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.00
0.01
0.02
0.03
0.04
0.05
0.06
-2
-1
0
1
2
Leverage
Standardized residuals
lm(data = ToothGrowth)
Cook's distance
Residuals vs Leverage
23
26
32
22
53
Residual vs Leverage
Standardised residuals
Leverage
23
26
32
22
53
1.0
0.5
0.5
1.0
··· Cook's distance
0.0
0.01
0.02
0.03
0.04
0.05
0.06
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
0.00
0.02
0.04
0.06
0.08
0.10
Leverage
h
i
i
Cook's distance
0.03
0.04
0.05
0.06
lm(data = ToothGrowth)
0
0.5
1
1.5
2
2.5
Cook's dist vs Leverage*
h
i
i
(
1
−
h
i
i
)
23
26
32
22
53
Cook's dist vs Leverage* hᵢᵢ / (1 - hᵢᵢ)
Cook's distance
Leverage hᵢᵢ
0
0.5
1
1.5
2
2.5
23
26
32
22
53
0.06
0.05
0.04
0.03
0.02
0.01
-0.0
-0.01
0.0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
0.11
airquality
R
Clojure
-20
0
20
40
60
80
100
-50
0
50
100
Fitted values
Residuals
lm(data = airquality)
Residuals vs Fitted
117
62
30
101
86
Residuals vs Fitted
Residuals
Fitted values
117
62
30
101
86
-20
0
20
40
60
80
100
-40
-30
-20
-10
0
10
20
30
40
50
60
70
80
90
100
-2
-1
0
1
2
-2
-1
0
1
2
3
4
5
Theoretical Quantiles
Standardized residuals
lm(data = airquality)
Q-Q Residuals
117
62
30
101
86
Q-Q Residuals
Standardised residuals
Theoretical Quantiles
117
62
30
101
86
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
-20
0
20
40
60
80
100
0.0
0.5
1.0
1.5
2.0
Fitted values
S
t
a
n
d
a
r
d
i
z
e
d
r
e
s
i
d
u
a
l
s
lm(data = airquality)
Scale-Location
117
62
30
101
86
Scale Location
(sqrt (abs standardised-residuals))
Fitted values
117
62
30
101
86
-20
0
20
40
60
80
100
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
0
20
40
60
80
100
0.00
0.05
0.10
0.15
0.20
0.25
Obs. number
Cook's distance
lm(data = airquality)
Cook's distance
117
62
9
30
48
Cooks distance
Cook's distance
Obs. number
117
62
9
30
48
0
28
56
84
0.0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.00
0.02
0.04
0.06
0.08
0.10
0.12
-2
0
2
4
Leverage
Standardized residuals
lm(data = airquality)
Cook's distance
0.5
Residuals vs Leverage
117
62
9
30
48
Residual vs Leverage
Standardised residuals
Leverage
117
62
9
30
48
1.0
0.5
0.5
1.0
··· Cook's distance
0.0
0.02
0.04
0.06
0.08
0.1
0.12
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
0.00
0.05
0.10
0.15
0.20
0.25
Leverage
h
i
i
Cook's distance
0
0.02
0.04
0.06
0.08
0.1
0.12
lm(data = airquality)
0
1
2
3
4
5
Cook's dist vs Leverage*
h
i
i
(
1
−
h
i
i
)
117
62
9
30
48
Cook's dist vs Leverage* hᵢᵢ / (1 - hᵢᵢ)
Cook's distance
Leverage hᵢᵢ
0
1
2
3
4
5
117
62
9
30
48
0.12
0.1
0.08
0.06
0.04
0.02
-0.0
-0.02
0.0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
source: notebooks/plot_lm.clj