R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(2
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+ ,2
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+ ,9
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+ ,6
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+ ,38
+ ,21
+ ,42
+ ,2
+ ,28
+ ,56
+ ,14
+ ,28
+ ,11
+ ,22
+ ,8
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+ ,48
+ ,24
+ ,48
+ ,2
+ ,22
+ ,44
+ ,13
+ ,26
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+ ,30
+ ,10
+ ,20
+ ,22
+ ,44
+ ,22
+ ,44
+ ,1
+ ,31
+ ,31
+ ,16
+ ,16
+ ,19
+ ,19
+ ,16
+ ,16
+ ,17
+ ,17
+ ,20
+ ,20)
+ ,dim=c(13
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'ConcernoverMistakes'
+ ,'C*G'
+ ,'Doubtsaboutactions'
+ ,'D*G'
+ ,'ParentalExpectations'
+ ,'PE*G'
+ ,'ParentalCriticism'
+ ,'PC*G'
+ ,'PersonalStandards'
+ ,'PS*G'
+ ,'Organization'
+ ,'O*G')
+ ,1:159))
> y <- array(NA,dim=c(13,159),dimnames=list(c('Gender','ConcernoverMistakes','C*G','Doubtsaboutactions','D*G','ParentalExpectations','PE*G','ParentalCriticism','PC*G','PersonalStandards','PS*G','Organization','O*G'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
ParentalExpectations Gender ConcernoverMistakes C*G Doubtsaboutactions D*G
1 11 2 24 48 14 28
2 7 1 25 25 11 11
3 17 1 17 17 6 6
4 10 1 18 18 12 12
5 12 1 18 18 8 8
6 12 1 16 16 10 10
7 11 2 20 40 10 20
8 11 2 16 32 11 22
9 12 1 18 18 16 16
10 13 1 17 17 11 11
11 14 1 23 23 13 13
12 16 2 30 60 12 24
13 11 2 23 46 8 16
14 10 2 18 36 12 24
15 11 1 15 15 11 11
16 15 2 12 24 4 8
17 9 1 21 21 9 9
18 11 1 15 15 8 8
19 17 2 20 40 8 16
20 17 2 31 62 14 28
21 11 1 27 27 15 15
22 18 2 34 68 16 32
23 14 2 21 42 9 18
24 10 1 31 31 14 14
25 11 2 19 38 11 22
26 15 2 16 32 8 16
27 15 2 20 40 9 18
28 13 2 21 42 9 18
29 16 1 22 22 9 9
30 13 2 17 34 9 18
31 9 2 24 48 10 20
32 18 2 25 50 16 32
33 18 1 26 26 11 11
34 12 2 25 50 8 16
35 17 1 17 17 9 9
36 9 1 32 32 16 16
37 9 1 33 33 11 11
38 12 1 13 13 16 16
39 18 1 32 32 12 12
40 12 2 25 50 12 24
41 18 2 29 58 14 28
42 14 1 22 22 9 9
43 15 2 18 36 10 20
44 16 2 17 34 9 18
45 10 1 20 20 10 10
46 11 1 15 15 12 12
47 14 2 20 40 14 28
48 9 1 33 33 14 14
49 12 1 29 29 10 10
50 17 1 23 23 14 14
51 5 2 26 52 16 32
52 12 2 18 36 9 18
53 12 2 20 40 10 20
54 6 2 11 22 6 12
55 24 2 28 56 8 16
56 12 1 26 26 13 13
57 12 1 22 22 10 10
58 14 2 17 34 8 16
59 7 2 12 24 7 14
60 13 2 14 28 15 30
61 12 1 17 17 9 9
62 13 2 21 42 10 20
63 14 1 19 19 12 12
64 8 2 18 36 13 26
65 11 1 10 10 10 10
66 9 1 29 29 11 11
67 11 1 31 31 8 8
68 13 1 19 19 9 9
69 10 1 9 9 13 13
70 11 2 20 40 11 22
71 12 2 28 56 8 16
72 9 2 19 38 9 18
73 15 2 30 60 9 18
74 18 2 29 58 15 30
75 15 2 26 52 9 18
76 12 1 23 23 10 10
77 13 2 13 26 14 28
78 14 1 21 21 12 12
79 10 2 19 38 12 24
80 13 2 28 56 11 22
81 13 2 23 46 14 28
82 11 1 18 18 6 6
83 13 2 21 42 12 24
84 16 1 20 20 8 8
85 8 2 23 46 14 28
86 16 2 21 42 11 22
87 11 2 21 42 10 20
88 9 1 15 15 14 14
89 16 1 28 28 12 12
90 12 2 19 38 10 20
91 14 2 26 52 14 28
92 8 2 10 20 5 10
93 9 1 16 16 11 11
94 15 2 22 44 10 20
95 11 2 19 38 9 18
96 21 2 31 62 10 20
97 14 2 31 62 16 32
98 18 1 29 29 13 13
99 12 1 19 19 9 9
100 13 1 22 22 10 10
101 15 2 23 46 10 20
102 12 1 15 15 7 7
103 19 2 20 40 9 18
104 15 2 18 36 8 16
105 11 1 23 23 14 14
106 11 1 25 25 14 14
107 10 2 21 42 8 16
108 13 2 24 48 9 18
109 15 1 25 25 14 14
110 12 2 17 34 14 28
111 12 2 13 26 8 16
112 16 2 28 56 8 16
113 9 2 21 42 8 16
114 18 2 25 50 7 14
115 8 2 9 18 6 12
116 13 1 16 16 8 8
117 17 2 19 38 6 12
118 9 2 17 34 11 22
119 15 2 25 50 14 28
120 8 2 20 40 11 22
121 7 2 29 58 11 22
122 12 1 14 14 11 11
123 14 1 22 22 14 14
124 6 1 15 15 8 8
125 8 1 19 19 20 20
126 17 2 20 40 11 22
127 10 2 15 30 8 16
128 11 1 20 20 11 11
129 14 2 18 36 10 20
130 11 2 33 66 14 28
131 13 2 22 44 11 22
132 12 1 16 16 9 9
133 11 1 17 17 9 9
134 9 1 16 16 8 8
135 12 2 21 42 10 20
136 20 1 26 26 13 13
137 12 1 18 18 13 13
138 13 1 18 18 12 12
139 12 2 17 34 8 16
140 12 2 22 44 13 26
141 9 2 30 60 14 28
142 15 1 30 30 12 12
143 24 1 24 24 14 14
144 7 1 21 21 15 15
145 17 1 21 21 13 13
146 11 2 29 58 16 32
147 17 2 31 62 9 18
148 11 1 20 20 9 9
149 12 1 16 16 9 9
150 14 1 22 22 8 8
151 11 2 20 40 7 14
152 16 2 28 56 16 32
153 21 2 38 76 11 22
154 14 1 22 22 9 9
155 20 2 20 40 11 22
156 13 2 17 34 9 18
157 11 2 28 56 14 28
158 15 2 22 44 13 26
159 19 1 31 31 16 16
PE*G ParentalCriticism PC*G PersonalStandards PS*G Organization O*G
1 22 12 24 24 48 26 52
2 7 8 8 25 25 23 23
3 17 8 8 30 30 25 25
4 10 8 8 19 19 23 23
5 12 9 9 22 22 19 19
6 12 7 7 22 22 29 29
7 22 4 8 25 50 25 50
8 22 11 22 23 46 21 42
9 12 7 7 17 17 22 22
10 13 7 7 21 21 25 25
11 14 12 12 19 19 24 24
12 32 10 20 19 38 18 36
13 22 10 20 15 30 22 44
14 20 8 16 16 32 15 30
15 11 8 8 23 23 22 22
16 30 4 8 27 54 28 56
17 9 9 9 22 22 20 20
18 11 8 8 14 14 12 12
19 34 7 14 22 44 24 48
20 34 11 22 23 46 20 40
21 11 9 9 23 23 21 21
22 36 11 22 21 42 20 40
23 28 13 26 19 38 21 42
24 10 8 8 18 18 23 23
25 22 8 16 20 40 28 56
26 30 9 18 23 46 24 48
27 30 6 12 25 50 24 48
28 26 9 18 19 38 24 48
29 16 9 9 24 24 23 23
30 26 6 12 22 44 23 46
31 18 6 12 25 50 29 58
32 36 16 32 26 52 24 48
33 18 5 5 29 29 18 18
34 24 7 14 32 64 25 50
35 17 9 9 25 25 21 21
36 9 6 6 29 29 26 26
37 9 6 6 28 28 22 22
38 12 5 5 17 17 22 22
39 18 12 12 28 28 22 22
40 24 7 14 29 58 23 46
41 36 10 20 26 52 30 60
42 14 9 9 25 25 23 23
43 30 8 16 14 28 17 34
44 32 5 10 25 50 23 46
45 10 8 8 26 26 23 23
46 11 8 8 20 20 25 25
47 28 10 20 18 36 24 48
48 9 6 6 32 32 24 24
49 12 8 8 25 25 23 23
50 17 7 7 25 25 21 21
51 10 4 8 23 46 24 48
52 24 8 16 21 42 24 48
53 24 8 16 20 40 28 56
54 12 4 8 15 30 16 32
55 48 20 40 30 60 20 40
56 12 8 8 24 24 29 29
57 12 8 8 26 26 27 27
58 28 6 12 24 48 22 44
59 14 4 8 22 44 28 56
60 26 8 16 14 28 16 32
61 12 9 9 24 24 25 25
62 26 6 12 24 48 24 48
63 14 7 7 24 24 28 28
64 16 9 18 24 48 24 48
65 11 5 5 19 19 23 23
66 9 5 5 31 31 30 30
67 11 8 8 22 22 24 24
68 13 8 8 27 27 21 21
69 10 6 6 19 19 25 25
70 22 8 16 25 50 25 50
71 24 7 14 20 40 22 44
72 18 7 14 21 42 23 46
73 30 9 18 27 54 26 52
74 36 11 22 23 46 23 46
75 30 6 12 25 50 25 50
76 12 8 8 20 20 21 21
77 26 6 12 21 42 25 50
78 14 9 9 22 22 24 24
79 20 8 16 23 46 29 58
80 26 6 12 25 50 22 44
81 26 10 20 25 50 27 54
82 11 8 8 17 17 26 26
83 26 8 16 19 38 22 44
84 16 10 10 25 25 24 24
85 16 5 10 19 38 27 54
86 32 7 14 20 40 24 48
87 22 5 10 26 52 24 48
88 9 8 8 23 23 29 29
89 16 14 14 27 27 22 22
90 24 7 14 17 34 21 42
91 28 8 16 17 34 24 48
92 16 6 12 19 38 24 48
93 9 5 5 17 17 23 23
94 30 6 12 22 44 20 40
95 22 10 20 21 42 27 54
96 42 12 24 32 64 26 52
97 28 9 18 21 42 25 50
98 18 12 12 21 21 21 21
99 12 7 7 18 18 21 21
100 13 8 8 18 18 19 19
101 30 10 20 23 46 21 42
102 12 6 6 19 19 21 21
103 38 10 20 20 40 16 32
104 30 10 20 21 42 22 44
105 11 10 10 20 20 29 29
106 11 5 5 17 17 15 15
107 20 7 14 18 36 17 34
108 26 10 20 19 38 15 30
109 15 11 11 22 22 21 21
110 24 6 12 15 30 21 42
111 24 7 14 14 28 19 38
112 32 12 24 18 36 24 48
113 18 11 22 24 48 20 40
114 36 11 22 35 70 17 34
115 16 11 22 29 58 23 46
116 13 5 5 21 21 24 24
117 34 8 16 25 50 14 28
118 18 6 12 20 40 19 38
119 30 9 18 22 44 24 48
120 16 4 8 13 26 13 26
121 14 4 8 26 52 22 44
122 12 7 7 17 17 16 16
123 14 11 11 25 25 19 19
124 6 6 6 20 20 25 25
125 8 7 7 19 19 25 25
126 34 8 16 21 42 23 46
127 20 4 8 22 44 24 48
128 11 8 8 24 24 26 26
129 28 9 18 21 42 26 52
130 22 8 16 26 52 25 50
131 26 11 22 24 48 18 36
132 12 8 8 16 16 21 21
133 11 5 5 23 23 26 26
134 9 4 4 18 18 23 23
135 24 8 16 16 32 23 46
136 20 10 10 26 26 22 22
137 12 6 6 19 19 20 20
138 13 9 9 21 21 13 13
139 24 9 18 21 42 24 48
140 24 13 26 22 44 15 30
141 18 9 18 23 46 14 28
142 15 10 10 29 29 22 22
143 24 20 20 21 21 10 10
144 7 5 5 21 21 24 24
145 17 11 11 23 23 22 22
146 22 6 12 27 54 24 48
147 34 9 18 25 50 19 38
148 11 7 7 21 21 20 20
149 12 9 9 10 10 13 13
150 14 10 10 20 20 20 20
151 22 9 18 26 52 22 44
152 32 8 16 24 48 24 48
153 42 7 14 29 58 29 58
154 14 6 6 19 19 12 12
155 40 13 26 24 48 20 40
156 26 6 12 19 38 21 42
157 22 8 16 24 48 24 48
158 30 10 20 22 44 22 44
159 19 16 16 17 17 20 20
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender ConcernoverMistakes
7.56408 -4.02282 -0.04971
`C*G` Doubtsaboutactions `D*G`
0.01392 -0.05843 0.04117
`PE*G` ParentalCriticism `PC*G`
0.55437 0.76259 -0.41336
PersonalStandards `PS*G` Organization
0.22960 -0.11795 -0.20580
`O*G`
0.10185
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.53172 -0.31898 -0.02347 0.32396 2.48734
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.56408 1.56347 4.838 3.30e-06 ***
Gender -4.02282 0.95019 -4.234 4.05e-05 ***
ConcernoverMistakes -0.04971 0.04408 -1.128 0.261255
`C*G` 0.01392 0.02709 0.514 0.608083
Doubtsaboutactions -0.05843 0.07543 -0.775 0.439833
`D*G` 0.04117 0.04733 0.870 0.385808
`PE*G` 0.55437 0.01288 43.034 < 2e-16 ***
ParentalCriticism 0.76259 0.07857 9.706 < 2e-16 ***
`PC*G` -0.41336 0.04720 -8.758 4.56e-15 ***
PersonalStandards 0.22960 0.05786 3.968 0.000113 ***
`PS*G` -0.11795 0.03458 -3.411 0.000837 ***
Organization -0.20580 0.05417 -3.800 0.000212 ***
`O*G` 0.10185 0.03327 3.061 0.002626 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7141 on 146 degrees of freedom
Multiple R-squared: 0.9603, Adjusted R-squared: 0.957
F-statistic: 294.3 on 12 and 146 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.7899628 4.200743e-01 2.100372e-01
[2,] 0.7087776 5.824448e-01 2.912224e-01
[3,] 0.6181370 7.637261e-01 3.818630e-01
[4,] 0.9247172 1.505655e-01 7.528276e-02
[5,] 0.8792576 2.414848e-01 1.207424e-01
[6,] 0.8902136 2.195728e-01 1.097864e-01
[7,] 0.9041427 1.917146e-01 9.585732e-02
[8,] 0.8596297 2.807406e-01 1.403703e-01
[9,] 0.8894693 2.210615e-01 1.105307e-01
[10,] 0.8882977 2.234045e-01 1.117023e-01
[11,] 0.8666692 2.666616e-01 1.333308e-01
[12,] 0.8208806 3.582387e-01 1.791194e-01
[13,] 0.7660818 4.678364e-01 2.339182e-01
[14,] 0.8716476 2.567048e-01 1.283524e-01
[15,] 0.8333240 3.333520e-01 1.666760e-01
[16,] 0.8885978 2.228045e-01 1.114022e-01
[17,] 0.8706720 2.586559e-01 1.293280e-01
[18,] 0.9936268 1.274644e-02 6.373220e-03
[19,] 0.9962974 7.405254e-03 3.702627e-03
[20,] 0.9980370 3.925979e-03 1.962990e-03
[21,] 0.9989774 2.045231e-03 1.022615e-03
[22,] 0.9992254 1.549269e-03 7.746344e-04
[23,] 0.9992645 1.471008e-03 7.355041e-04
[24,] 0.9996759 6.481687e-04 3.240843e-04
[25,] 0.9995349 9.302651e-04 4.651325e-04
[26,] 0.9994626 1.074834e-03 5.374172e-04
[27,] 0.9991469 1.706262e-03 8.531310e-04
[28,] 0.9988854 2.229230e-03 1.114615e-03
[29,] 0.9986899 2.620145e-03 1.310073e-03
[30,] 0.9997051 5.898370e-04 2.949185e-04
[31,] 0.9995702 8.596179e-04 4.298089e-04
[32,] 0.9993246 1.350772e-03 6.753860e-04
[33,] 0.9998023 3.953465e-04 1.976733e-04
[34,] 0.9997518 4.963954e-04 2.481977e-04
[35,] 0.9999876 2.482127e-05 1.241063e-05
[36,] 0.9999908 1.839747e-05 9.198733e-06
[37,] 0.9999841 3.185106e-05 1.592553e-05
[38,] 0.9999723 5.530953e-05 2.765476e-05
[39,] 0.9999647 7.063255e-05 3.531628e-05
[40,] 0.9999406 1.188661e-04 5.943307e-05
[41,] 0.9999220 1.559011e-04 7.795055e-05
[42,] 0.9998742 2.516020e-04 1.258010e-04
[43,] 0.9998069 3.861291e-04 1.930646e-04
[44,] 0.9997222 5.556898e-04 2.778449e-04
[45,] 0.9996256 7.488569e-04 3.744284e-04
[46,] 0.9995875 8.250367e-04 4.125184e-04
[47,] 0.9993643 1.271492e-03 6.357460e-04
[48,] 0.9998151 3.698916e-04 1.849458e-04
[49,] 0.9997714 4.572463e-04 2.286231e-04
[50,] 0.9997624 4.751618e-04 2.375809e-04
[51,] 0.9997285 5.429327e-04 2.714663e-04
[52,] 0.9998702 2.595299e-04 1.297649e-04
[53,] 0.9998417 3.165333e-04 1.582666e-04
[54,] 0.9998850 2.300859e-04 1.150429e-04
[55,] 0.9998206 3.588200e-04 1.794100e-04
[56,] 0.9997290 5.420740e-04 2.710370e-04
[57,] 0.9996163 7.673078e-04 3.836539e-04
[58,] 0.9994062 1.187530e-03 5.937651e-04
[59,] 0.9991729 1.654272e-03 8.271360e-04
[60,] 0.9987850 2.429987e-03 1.214993e-03
[61,] 0.9985271 2.945812e-03 1.472906e-03
[62,] 0.9980744 3.851102e-03 1.925551e-03
[63,] 0.9979014 4.197124e-03 2.098562e-03
[64,] 0.9969950 6.009989e-03 3.004995e-03
[65,] 0.9956409 8.718184e-03 4.359092e-03
[66,] 0.9937807 1.243866e-02 6.219330e-03
[67,] 0.9920071 1.598582e-02 7.992912e-03
[68,] 0.9888758 2.224845e-02 1.112422e-02
[69,] 0.9887447 2.251063e-02 1.125532e-02
[70,] 0.9848352 3.032957e-02 1.516478e-02
[71,] 0.9814339 3.713212e-02 1.856606e-02
[72,] 0.9749862 5.002750e-02 2.501375e-02
[73,] 0.9785890 4.282200e-02 2.141100e-02
[74,] 0.9885135 2.297297e-02 1.148649e-02
[75,] 0.9840991 3.180182e-02 1.590091e-02
[76,] 0.9786601 4.267985e-02 2.133992e-02
[77,] 0.9721576 5.568478e-02 2.784239e-02
[78,] 0.9639193 7.216139e-02 3.608069e-02
[79,] 0.9541915 9.161706e-02 4.580853e-02
[80,] 0.9423136 1.153728e-01 5.768641e-02
[81,] 0.9281540 1.436919e-01 7.184597e-02
[82,] 0.9086517 1.826966e-01 9.134832e-02
[83,] 0.9363803 1.272395e-01 6.361973e-02
[84,] 0.9216146 1.567709e-01 7.838543e-02
[85,] 0.9034384 1.931232e-01 9.656162e-02
[86,] 0.8787995 2.424010e-01 1.212005e-01
[87,] 0.8654605 2.690790e-01 1.345395e-01
[88,] 0.8462128 3.075744e-01 1.537872e-01
[89,] 0.8127072 3.745856e-01 1.872928e-01
[90,] 0.7860633 4.278733e-01 2.139367e-01
[91,] 0.7465908 5.068185e-01 2.534092e-01
[92,] 0.7074576 5.850847e-01 2.925424e-01
[93,] 0.6622635 6.754729e-01 3.377365e-01
[94,] 0.6134204 7.731592e-01 3.865796e-01
[95,] 0.5644487 8.711026e-01 4.355513e-01
[96,] 0.5107946 9.784108e-01 4.892054e-01
[97,] 0.4568453 9.136905e-01 5.431547e-01
[98,] 0.4407431 8.814862e-01 5.592569e-01
[99,] 0.3915859 7.831719e-01 6.084141e-01
[100,] 0.3679316 7.358633e-01 6.320684e-01
[101,] 0.5604106 8.791788e-01 4.395894e-01
[102,] 0.5333623 9.332755e-01 4.666377e-01
[103,] 0.4747681 9.495362e-01 5.252319e-01
[104,] 0.4153185 8.306371e-01 5.846815e-01
[105,] 0.3659622 7.319244e-01 6.340378e-01
[106,] 0.3258788 6.517577e-01 6.741212e-01
[107,] 0.2981127 5.962255e-01 7.018873e-01
[108,] 0.3050381 6.100763e-01 6.949619e-01
[109,] 0.6100258 7.799483e-01 3.899742e-01
[110,] 0.5841931 8.316137e-01 4.158069e-01
[111,] 0.5387895 9.224211e-01 4.612105e-01
[112,] 0.4698291 9.396583e-01 5.301709e-01
[113,] 0.4935460 9.870920e-01 5.064540e-01
[114,] 0.4217282 8.434563e-01 5.782718e-01
[115,] 0.3836726 7.673452e-01 6.163274e-01
[116,] 0.3140078 6.280157e-01 6.859922e-01
[117,] 0.2490584 4.981168e-01 7.509416e-01
[118,] 0.1978787 3.957573e-01 8.021213e-01
[119,] 0.1500273 3.000546e-01 8.499727e-01
[120,] 0.1125493 2.250986e-01 8.874507e-01
[121,] 0.6352727 7.294545e-01 3.647273e-01
[122,] 0.7484534 5.030932e-01 2.515466e-01
[123,] 0.7235066 5.529868e-01 2.764934e-01
[124,] 0.6289477 7.421046e-01 3.710523e-01
[125,] 0.5147412 9.705177e-01 4.852588e-01
[126,] 0.3938825 7.877651e-01 6.061175e-01
[127,] 0.3401855 6.803710e-01 6.598145e-01
[128,] 0.9950881 9.823881e-03 4.911941e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1vbt71322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xour1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3xuxd1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4n84x1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mcxr1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
0.45041929 -2.53171769 1.20159056 -0.75814137 -1.03592279 0.66497490
7 8 9 10 11 12
-0.05024465 0.26633144 0.67076503 0.85951886 -0.07239221 -0.09071249
13 14 15 16 17 18
0.37877472 0.14592298 -0.98771597 -0.49780784 -2.14424037 -1.07407131
19 20 21 22 23 24
-0.48342394 -0.13193076 -0.94238491 -0.23546333 0.20036745 -0.14670460
25 26 27 28 29 30
0.13534113 -0.21886603 -0.33512271 0.05887485 1.09950086 -0.20420238
31 32 33 34 35 36
0.39132562 -0.07179002 2.48734386 0.23455964 1.04663251 -0.73994283
37 38 39 40 41 42
-1.09459727 1.19028588 0.80216067 0.11587585 -0.30871618 0.09658055
43 44 45 46 47 48
-0.35833269 -0.57567833 -1.50268668 -0.32363670 -0.13342543 -1.28154548
49 50 51 52 53 54
-0.17766525 2.04613826 0.57525155 0.05046350 0.07237707 0.31055438
55 56 57 58 59 60
-0.19406832 0.50210095 -0.12405064 -0.27855069 0.26892577 -0.34995284
61 62 63 64 65 66
-0.65407274 -0.12597911 1.37086392 0.47277231 0.41435460 -0.39190224
67 68 69 70 71 72
-0.14732041 -0.53839712 -0.15661793 0.18236127 0.21841740 0.33231653
73 74 75 76 77 78
0.09267930 -0.30202177 -0.20183696 -0.04201038 -0.41330023 0.55149561
79 80 81 82 83 84
0.24112849 0.00526780 0.09120581 0.11908406 -0.08116619 0.65372018
85 86 87 88 89 90
0.27652181 -0.43712063 0.03994206 -1.09955428 -0.81906702 -0.04714279
91 92 93 94 95 96
-0.13677902 0.26528857 -0.02159860 -0.34254229 0.31560657 -0.33796090
97 98 99 100 101 102
0.01610571 1.38970707 0.37011995 0.38324919 -0.05578095 0.43001726
103 104 105 106 107 108
-0.56175355 -0.12777226 -0.28547320 0.41195898 0.25978607 0.16974657
109 110 111 112 113 114
0.16449331 -0.26320815 -0.15357116 0.09572531 0.66904764 -0.13533497
115 116 117 118 119 120
0.60094112 1.36646356 -0.39542870 0.16197570 -0.17181372 0.15146858
121 122 123 124 125 126
0.55758587 -0.18238928 -0.93137461 -1.92237292 -0.91839571 -0.49940126
127 128 129 130 131 132
-0.02397617 -0.50463319 -0.12260042 0.40117313 0.18005226 0.13683453
133 134 135 136 137 138
0.51283363 0.16419361 0.05861756 2.41773125 0.53700246 -1.03328190
139 140 141 142 143 144
0.11663449 0.35037090 0.57527497 -0.01950325 -0.03548773 -1.00755236
145 146 147 148 149 150
0.88763058 0.14183322 -0.13016795 -0.47864317 -0.37404469 -0.02347335
151 152 153 154 155 156
0.34212440 -0.31432347 -0.54203384 0.67078692 -0.49239078 -0.22725518
157 158 159
0.27717695 -0.15356247 0.90444863
> postscript(file="/var/wessaorg/rcomp/tmp/6fpwn1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 0.45041929 NA
1 -2.53171769 0.45041929
2 1.20159056 -2.53171769
3 -0.75814137 1.20159056
4 -1.03592279 -0.75814137
5 0.66497490 -1.03592279
6 -0.05024465 0.66497490
7 0.26633144 -0.05024465
8 0.67076503 0.26633144
9 0.85951886 0.67076503
10 -0.07239221 0.85951886
11 -0.09071249 -0.07239221
12 0.37877472 -0.09071249
13 0.14592298 0.37877472
14 -0.98771597 0.14592298
15 -0.49780784 -0.98771597
16 -2.14424037 -0.49780784
17 -1.07407131 -2.14424037
18 -0.48342394 -1.07407131
19 -0.13193076 -0.48342394
20 -0.94238491 -0.13193076
21 -0.23546333 -0.94238491
22 0.20036745 -0.23546333
23 -0.14670460 0.20036745
24 0.13534113 -0.14670460
25 -0.21886603 0.13534113
26 -0.33512271 -0.21886603
27 0.05887485 -0.33512271
28 1.09950086 0.05887485
29 -0.20420238 1.09950086
30 0.39132562 -0.20420238
31 -0.07179002 0.39132562
32 2.48734386 -0.07179002
33 0.23455964 2.48734386
34 1.04663251 0.23455964
35 -0.73994283 1.04663251
36 -1.09459727 -0.73994283
37 1.19028588 -1.09459727
38 0.80216067 1.19028588
39 0.11587585 0.80216067
40 -0.30871618 0.11587585
41 0.09658055 -0.30871618
42 -0.35833269 0.09658055
43 -0.57567833 -0.35833269
44 -1.50268668 -0.57567833
45 -0.32363670 -1.50268668
46 -0.13342543 -0.32363670
47 -1.28154548 -0.13342543
48 -0.17766525 -1.28154548
49 2.04613826 -0.17766525
50 0.57525155 2.04613826
51 0.05046350 0.57525155
52 0.07237707 0.05046350
53 0.31055438 0.07237707
54 -0.19406832 0.31055438
55 0.50210095 -0.19406832
56 -0.12405064 0.50210095
57 -0.27855069 -0.12405064
58 0.26892577 -0.27855069
59 -0.34995284 0.26892577
60 -0.65407274 -0.34995284
61 -0.12597911 -0.65407274
62 1.37086392 -0.12597911
63 0.47277231 1.37086392
64 0.41435460 0.47277231
65 -0.39190224 0.41435460
66 -0.14732041 -0.39190224
67 -0.53839712 -0.14732041
68 -0.15661793 -0.53839712
69 0.18236127 -0.15661793
70 0.21841740 0.18236127
71 0.33231653 0.21841740
72 0.09267930 0.33231653
73 -0.30202177 0.09267930
74 -0.20183696 -0.30202177
75 -0.04201038 -0.20183696
76 -0.41330023 -0.04201038
77 0.55149561 -0.41330023
78 0.24112849 0.55149561
79 0.00526780 0.24112849
80 0.09120581 0.00526780
81 0.11908406 0.09120581
82 -0.08116619 0.11908406
83 0.65372018 -0.08116619
84 0.27652181 0.65372018
85 -0.43712063 0.27652181
86 0.03994206 -0.43712063
87 -1.09955428 0.03994206
88 -0.81906702 -1.09955428
89 -0.04714279 -0.81906702
90 -0.13677902 -0.04714279
91 0.26528857 -0.13677902
92 -0.02159860 0.26528857
93 -0.34254229 -0.02159860
94 0.31560657 -0.34254229
95 -0.33796090 0.31560657
96 0.01610571 -0.33796090
97 1.38970707 0.01610571
98 0.37011995 1.38970707
99 0.38324919 0.37011995
100 -0.05578095 0.38324919
101 0.43001726 -0.05578095
102 -0.56175355 0.43001726
103 -0.12777226 -0.56175355
104 -0.28547320 -0.12777226
105 0.41195898 -0.28547320
106 0.25978607 0.41195898
107 0.16974657 0.25978607
108 0.16449331 0.16974657
109 -0.26320815 0.16449331
110 -0.15357116 -0.26320815
111 0.09572531 -0.15357116
112 0.66904764 0.09572531
113 -0.13533497 0.66904764
114 0.60094112 -0.13533497
115 1.36646356 0.60094112
116 -0.39542870 1.36646356
117 0.16197570 -0.39542870
118 -0.17181372 0.16197570
119 0.15146858 -0.17181372
120 0.55758587 0.15146858
121 -0.18238928 0.55758587
122 -0.93137461 -0.18238928
123 -1.92237292 -0.93137461
124 -0.91839571 -1.92237292
125 -0.49940126 -0.91839571
126 -0.02397617 -0.49940126
127 -0.50463319 -0.02397617
128 -0.12260042 -0.50463319
129 0.40117313 -0.12260042
130 0.18005226 0.40117313
131 0.13683453 0.18005226
132 0.51283363 0.13683453
133 0.16419361 0.51283363
134 0.05861756 0.16419361
135 2.41773125 0.05861756
136 0.53700246 2.41773125
137 -1.03328190 0.53700246
138 0.11663449 -1.03328190
139 0.35037090 0.11663449
140 0.57527497 0.35037090
141 -0.01950325 0.57527497
142 -0.03548773 -0.01950325
143 -1.00755236 -0.03548773
144 0.88763058 -1.00755236
145 0.14183322 0.88763058
146 -0.13016795 0.14183322
147 -0.47864317 -0.13016795
148 -0.37404469 -0.47864317
149 -0.02347335 -0.37404469
150 0.34212440 -0.02347335
151 -0.31432347 0.34212440
152 -0.54203384 -0.31432347
153 0.67078692 -0.54203384
154 -0.49239078 0.67078692
155 -0.22725518 -0.49239078
156 0.27717695 -0.22725518
157 -0.15356247 0.27717695
158 0.90444863 -0.15356247
159 NA 0.90444863
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.53171769 0.45041929
[2,] 1.20159056 -2.53171769
[3,] -0.75814137 1.20159056
[4,] -1.03592279 -0.75814137
[5,] 0.66497490 -1.03592279
[6,] -0.05024465 0.66497490
[7,] 0.26633144 -0.05024465
[8,] 0.67076503 0.26633144
[9,] 0.85951886 0.67076503
[10,] -0.07239221 0.85951886
[11,] -0.09071249 -0.07239221
[12,] 0.37877472 -0.09071249
[13,] 0.14592298 0.37877472
[14,] -0.98771597 0.14592298
[15,] -0.49780784 -0.98771597
[16,] -2.14424037 -0.49780784
[17,] -1.07407131 -2.14424037
[18,] -0.48342394 -1.07407131
[19,] -0.13193076 -0.48342394
[20,] -0.94238491 -0.13193076
[21,] -0.23546333 -0.94238491
[22,] 0.20036745 -0.23546333
[23,] -0.14670460 0.20036745
[24,] 0.13534113 -0.14670460
[25,] -0.21886603 0.13534113
[26,] -0.33512271 -0.21886603
[27,] 0.05887485 -0.33512271
[28,] 1.09950086 0.05887485
[29,] -0.20420238 1.09950086
[30,] 0.39132562 -0.20420238
[31,] -0.07179002 0.39132562
[32,] 2.48734386 -0.07179002
[33,] 0.23455964 2.48734386
[34,] 1.04663251 0.23455964
[35,] -0.73994283 1.04663251
[36,] -1.09459727 -0.73994283
[37,] 1.19028588 -1.09459727
[38,] 0.80216067 1.19028588
[39,] 0.11587585 0.80216067
[40,] -0.30871618 0.11587585
[41,] 0.09658055 -0.30871618
[42,] -0.35833269 0.09658055
[43,] -0.57567833 -0.35833269
[44,] -1.50268668 -0.57567833
[45,] -0.32363670 -1.50268668
[46,] -0.13342543 -0.32363670
[47,] -1.28154548 -0.13342543
[48,] -0.17766525 -1.28154548
[49,] 2.04613826 -0.17766525
[50,] 0.57525155 2.04613826
[51,] 0.05046350 0.57525155
[52,] 0.07237707 0.05046350
[53,] 0.31055438 0.07237707
[54,] -0.19406832 0.31055438
[55,] 0.50210095 -0.19406832
[56,] -0.12405064 0.50210095
[57,] -0.27855069 -0.12405064
[58,] 0.26892577 -0.27855069
[59,] -0.34995284 0.26892577
[60,] -0.65407274 -0.34995284
[61,] -0.12597911 -0.65407274
[62,] 1.37086392 -0.12597911
[63,] 0.47277231 1.37086392
[64,] 0.41435460 0.47277231
[65,] -0.39190224 0.41435460
[66,] -0.14732041 -0.39190224
[67,] -0.53839712 -0.14732041
[68,] -0.15661793 -0.53839712
[69,] 0.18236127 -0.15661793
[70,] 0.21841740 0.18236127
[71,] 0.33231653 0.21841740
[72,] 0.09267930 0.33231653
[73,] -0.30202177 0.09267930
[74,] -0.20183696 -0.30202177
[75,] -0.04201038 -0.20183696
[76,] -0.41330023 -0.04201038
[77,] 0.55149561 -0.41330023
[78,] 0.24112849 0.55149561
[79,] 0.00526780 0.24112849
[80,] 0.09120581 0.00526780
[81,] 0.11908406 0.09120581
[82,] -0.08116619 0.11908406
[83,] 0.65372018 -0.08116619
[84,] 0.27652181 0.65372018
[85,] -0.43712063 0.27652181
[86,] 0.03994206 -0.43712063
[87,] -1.09955428 0.03994206
[88,] -0.81906702 -1.09955428
[89,] -0.04714279 -0.81906702
[90,] -0.13677902 -0.04714279
[91,] 0.26528857 -0.13677902
[92,] -0.02159860 0.26528857
[93,] -0.34254229 -0.02159860
[94,] 0.31560657 -0.34254229
[95,] -0.33796090 0.31560657
[96,] 0.01610571 -0.33796090
[97,] 1.38970707 0.01610571
[98,] 0.37011995 1.38970707
[99,] 0.38324919 0.37011995
[100,] -0.05578095 0.38324919
[101,] 0.43001726 -0.05578095
[102,] -0.56175355 0.43001726
[103,] -0.12777226 -0.56175355
[104,] -0.28547320 -0.12777226
[105,] 0.41195898 -0.28547320
[106,] 0.25978607 0.41195898
[107,] 0.16974657 0.25978607
[108,] 0.16449331 0.16974657
[109,] -0.26320815 0.16449331
[110,] -0.15357116 -0.26320815
[111,] 0.09572531 -0.15357116
[112,] 0.66904764 0.09572531
[113,] -0.13533497 0.66904764
[114,] 0.60094112 -0.13533497
[115,] 1.36646356 0.60094112
[116,] -0.39542870 1.36646356
[117,] 0.16197570 -0.39542870
[118,] -0.17181372 0.16197570
[119,] 0.15146858 -0.17181372
[120,] 0.55758587 0.15146858
[121,] -0.18238928 0.55758587
[122,] -0.93137461 -0.18238928
[123,] -1.92237292 -0.93137461
[124,] -0.91839571 -1.92237292
[125,] -0.49940126 -0.91839571
[126,] -0.02397617 -0.49940126
[127,] -0.50463319 -0.02397617
[128,] -0.12260042 -0.50463319
[129,] 0.40117313 -0.12260042
[130,] 0.18005226 0.40117313
[131,] 0.13683453 0.18005226
[132,] 0.51283363 0.13683453
[133,] 0.16419361 0.51283363
[134,] 0.05861756 0.16419361
[135,] 2.41773125 0.05861756
[136,] 0.53700246 2.41773125
[137,] -1.03328190 0.53700246
[138,] 0.11663449 -1.03328190
[139,] 0.35037090 0.11663449
[140,] 0.57527497 0.35037090
[141,] -0.01950325 0.57527497
[142,] -0.03548773 -0.01950325
[143,] -1.00755236 -0.03548773
[144,] 0.88763058 -1.00755236
[145,] 0.14183322 0.88763058
[146,] -0.13016795 0.14183322
[147,] -0.47864317 -0.13016795
[148,] -0.37404469 -0.47864317
[149,] -0.02347335 -0.37404469
[150,] 0.34212440 -0.02347335
[151,] -0.31432347 0.34212440
[152,] -0.54203384 -0.31432347
[153,] 0.67078692 -0.54203384
[154,] -0.49239078 0.67078692
[155,] -0.22725518 -0.49239078
[156,] 0.27717695 -0.22725518
[157,] -0.15356247 0.27717695
[158,] 0.90444863 -0.15356247
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.53171769 0.45041929
2 1.20159056 -2.53171769
3 -0.75814137 1.20159056
4 -1.03592279 -0.75814137
5 0.66497490 -1.03592279
6 -0.05024465 0.66497490
7 0.26633144 -0.05024465
8 0.67076503 0.26633144
9 0.85951886 0.67076503
10 -0.07239221 0.85951886
11 -0.09071249 -0.07239221
12 0.37877472 -0.09071249
13 0.14592298 0.37877472
14 -0.98771597 0.14592298
15 -0.49780784 -0.98771597
16 -2.14424037 -0.49780784
17 -1.07407131 -2.14424037
18 -0.48342394 -1.07407131
19 -0.13193076 -0.48342394
20 -0.94238491 -0.13193076
21 -0.23546333 -0.94238491
22 0.20036745 -0.23546333
23 -0.14670460 0.20036745
24 0.13534113 -0.14670460
25 -0.21886603 0.13534113
26 -0.33512271 -0.21886603
27 0.05887485 -0.33512271
28 1.09950086 0.05887485
29 -0.20420238 1.09950086
30 0.39132562 -0.20420238
31 -0.07179002 0.39132562
32 2.48734386 -0.07179002
33 0.23455964 2.48734386
34 1.04663251 0.23455964
35 -0.73994283 1.04663251
36 -1.09459727 -0.73994283
37 1.19028588 -1.09459727
38 0.80216067 1.19028588
39 0.11587585 0.80216067
40 -0.30871618 0.11587585
41 0.09658055 -0.30871618
42 -0.35833269 0.09658055
43 -0.57567833 -0.35833269
44 -1.50268668 -0.57567833
45 -0.32363670 -1.50268668
46 -0.13342543 -0.32363670
47 -1.28154548 -0.13342543
48 -0.17766525 -1.28154548
49 2.04613826 -0.17766525
50 0.57525155 2.04613826
51 0.05046350 0.57525155
52 0.07237707 0.05046350
53 0.31055438 0.07237707
54 -0.19406832 0.31055438
55 0.50210095 -0.19406832
56 -0.12405064 0.50210095
57 -0.27855069 -0.12405064
58 0.26892577 -0.27855069
59 -0.34995284 0.26892577
60 -0.65407274 -0.34995284
61 -0.12597911 -0.65407274
62 1.37086392 -0.12597911
63 0.47277231 1.37086392
64 0.41435460 0.47277231
65 -0.39190224 0.41435460
66 -0.14732041 -0.39190224
67 -0.53839712 -0.14732041
68 -0.15661793 -0.53839712
69 0.18236127 -0.15661793
70 0.21841740 0.18236127
71 0.33231653 0.21841740
72 0.09267930 0.33231653
73 -0.30202177 0.09267930
74 -0.20183696 -0.30202177
75 -0.04201038 -0.20183696
76 -0.41330023 -0.04201038
77 0.55149561 -0.41330023
78 0.24112849 0.55149561
79 0.00526780 0.24112849
80 0.09120581 0.00526780
81 0.11908406 0.09120581
82 -0.08116619 0.11908406
83 0.65372018 -0.08116619
84 0.27652181 0.65372018
85 -0.43712063 0.27652181
86 0.03994206 -0.43712063
87 -1.09955428 0.03994206
88 -0.81906702 -1.09955428
89 -0.04714279 -0.81906702
90 -0.13677902 -0.04714279
91 0.26528857 -0.13677902
92 -0.02159860 0.26528857
93 -0.34254229 -0.02159860
94 0.31560657 -0.34254229
95 -0.33796090 0.31560657
96 0.01610571 -0.33796090
97 1.38970707 0.01610571
98 0.37011995 1.38970707
99 0.38324919 0.37011995
100 -0.05578095 0.38324919
101 0.43001726 -0.05578095
102 -0.56175355 0.43001726
103 -0.12777226 -0.56175355
104 -0.28547320 -0.12777226
105 0.41195898 -0.28547320
106 0.25978607 0.41195898
107 0.16974657 0.25978607
108 0.16449331 0.16974657
109 -0.26320815 0.16449331
110 -0.15357116 -0.26320815
111 0.09572531 -0.15357116
112 0.66904764 0.09572531
113 -0.13533497 0.66904764
114 0.60094112 -0.13533497
115 1.36646356 0.60094112
116 -0.39542870 1.36646356
117 0.16197570 -0.39542870
118 -0.17181372 0.16197570
119 0.15146858 -0.17181372
120 0.55758587 0.15146858
121 -0.18238928 0.55758587
122 -0.93137461 -0.18238928
123 -1.92237292 -0.93137461
124 -0.91839571 -1.92237292
125 -0.49940126 -0.91839571
126 -0.02397617 -0.49940126
127 -0.50463319 -0.02397617
128 -0.12260042 -0.50463319
129 0.40117313 -0.12260042
130 0.18005226 0.40117313
131 0.13683453 0.18005226
132 0.51283363 0.13683453
133 0.16419361 0.51283363
134 0.05861756 0.16419361
135 2.41773125 0.05861756
136 0.53700246 2.41773125
137 -1.03328190 0.53700246
138 0.11663449 -1.03328190
139 0.35037090 0.11663449
140 0.57527497 0.35037090
141 -0.01950325 0.57527497
142 -0.03548773 -0.01950325
143 -1.00755236 -0.03548773
144 0.88763058 -1.00755236
145 0.14183322 0.88763058
146 -0.13016795 0.14183322
147 -0.47864317 -0.13016795
148 -0.37404469 -0.47864317
149 -0.02347335 -0.37404469
150 0.34212440 -0.02347335
151 -0.31432347 0.34212440
152 -0.54203384 -0.31432347
153 0.67078692 -0.54203384
154 -0.49239078 0.67078692
155 -0.22725518 -0.49239078
156 0.27717695 -0.22725518
157 -0.15356247 0.27717695
158 0.90444863 -0.15356247
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7uwhi1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/84s1z1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94u711322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10j1tg1322161093.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11wquk1322161093.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12zyim1322161093.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13bfdq1322161093.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/145ad01322161093.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/156vja1322161093.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16nkas1322161093.tab")
+ }
>
> try(system("convert tmp/1vbt71322161093.ps tmp/1vbt71322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xour1322161093.ps tmp/2xour1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xuxd1322161093.ps tmp/3xuxd1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n84x1322161093.ps tmp/4n84x1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mcxr1322161093.ps tmp/5mcxr1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fpwn1322161093.ps tmp/6fpwn1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uwhi1322161093.ps tmp/7uwhi1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/84s1z1322161093.ps tmp/84s1z1322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/94u711322161093.ps tmp/94u711322161093.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j1tg1322161093.ps tmp/10j1tg1322161093.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.804 0.602 6.494