R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,43
+ ,16
+ ,8
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+ ,6
+ ,11
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+ ,73
+ ,43
+ ,12
+ ,8
+ ,4
+ ,6
+ ,14
+ ,17
+ ,6
+ ,13
+ ,15
+ ,69
+ ,42
+ ,14
+ ,5
+ ,9
+ ,13
+ ,14
+ ,13
+ ,3
+ ,14
+ ,13
+ ,71
+ ,42
+ ,11
+ ,9
+ ,5
+ ,12
+ ,15
+ ,14
+ ,6
+ ,13
+ ,14
+ ,77
+ ,47
+ ,13
+ ,9
+ ,9
+ ,12
+ ,13
+ ,13
+ ,5
+ ,16
+ ,11
+ ,74
+ ,44
+ ,15
+ ,14
+ ,12
+ ,12
+ ,14
+ ,16
+ ,8
+ ,13
+ ,15
+ ,82
+ ,49
+ ,14
+ ,5
+ ,6
+ ,12
+ ,11
+ ,13
+ ,6
+ ,12
+ ,16
+ ,54
+ ,33
+ ,14
+ ,12
+ ,4
+ ,12
+ ,14
+ ,14
+ ,4
+ ,9
+ ,14
+ ,54
+ ,33
+ ,14
+ ,6
+ ,6
+ ,10
+ ,11
+ ,13
+ ,3
+ ,14
+ ,13
+ ,80
+ ,47
+ ,10
+ ,6
+ ,7
+ ,12
+ ,8
+ ,14
+ ,4
+ ,15
+ ,15
+ ,76
+ ,47
+ ,8
+ ,8
+ ,9
+ ,12
+ ,12
+ ,16
+ ,7)
+ ,dim=c(11
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'Happiness'
+ ,'Belonging'
+ ,'Belonging_alternative'
+ ,'Depression'
+ ,'Weighted_popularity'
+ ,'Parental_criticism'
+ ,'Finding_Friends'
+ ,'Knowing_People'
+ ,'Perceived_Liked'
+ ,'Celebrity')
+ ,1:156))
> y <- array(NA,dim=c(11,156),dimnames=list(c('Popularity','Happiness','Belonging','Belonging_alternative','Depression','Weighted_popularity','Parental_criticism','Finding_Friends','Knowing_People','Perceived_Liked','Celebrity'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> 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
Popularity Happiness Belonging Belonging_alternative Depression
1 15 15 77 46 10
2 12 9 63 37 20
3 15 12 73 45 16
4 12 15 76 46 10
5 14 17 90 55 8
6 8 14 67 40 14
7 11 9 69 43 19
8 15 12 70 43 15
9 4 11 54 33 23
10 13 13 54 33 9
11 19 16 76 47 12
12 10 16 75 44 14
13 15 15 76 47 13
14 6 10 80 49 11
15 7 16 89 55 11
16 14 12 73 43 10
17 16 15 74 46 12
18 16 13 78 51 18
19 14 18 76 47 12
20 15 13 69 42 10
21 14 17 74 42 15
22 12 14 82 48 15
23 9 13 77 45 12
24 12 13 84 51 9
25 14 15 75 46 11
26 12 13 54 33 15
27 14 15 79 47 16
28 10 13 79 47 17
29 14 14 69 42 12
30 16 13 88 55 11
31 10 16 57 36 13
32 8 14 69 42 9
33 12 18 86 51 11
34 11 15 65 43 9
35 8 9 66 40 20
36 13 16 54 33 8
37 11 16 85 52 12
38 12 17 79 49 10
39 16 13 84 50 11
40 16 17 70 43 13
41 13 15 54 33 13
42 14 14 70 44 13
43 5 10 54 33 15
44 14 13 69 41 12
45 13 11 68 40 13
46 16 11 68 40 13
47 14 16 71 41 9
48 15 16 71 41 9
49 15 11 66 42 14
50 11 15 67 42 9
51 15 15 71 45 9
52 16 12 54 33 15
53 13 17 76 46 10
54 11 15 77 47 13
55 12 16 71 44 8
56 12 14 69 44 15
57 10 17 73 46 13
58 8 10 46 30 24
59 9 11 66 42 11
60 12 15 77 46 13
61 14 15 77 46 12
62 12 7 70 43 22
63 11 17 86 52 11
64 14 14 38 11 15
65 7 18 66 41 7
66 16 14 75 45 14
67 16 12 80 49 19
68 11 14 64 41 10
69 16 9 80 47 9
70 13 14 86 53 12
71 11 11 54 35 16
72 13 16 74 45 13
73 14 17 88 54 11
74 15 16 85 53 12
75 10 12 63 36 11
76 15 15 81 48 13
77 11 15 81 48 13
78 11 15 74 45 10
79 6 16 80 47 11
80 11 16 80 49 9
81 12 11 60 38 13
82 13 15 65 40 15
83 12 12 62 46 14
84 8 14 63 42 14
85 9 15 89 54 11
86 10 17 76 45 10
87 16 19 81 53 11
88 15 15 72 44 12
89 14 16 84 51 14
90 12 14 76 46 14
91 12 16 76 46 21
92 10 15 78 45 14
93 12 15 72 44 13
94 8 17 81 48 11
95 16 12 72 44 12
96 11 18 78 47 12
97 12 13 79 47 11
98 9 14 52 31 14
99 14 14 67 44 13
100 15 14 74 42 13
101 8 12 73 41 12
102 12 14 69 43 14
103 10 12 67 41 12
104 16 15 76 47 12
105 17 11 77 45 12
106 8 11 63 37 18
107 9 15 84 54 11
108 8 14 90 55 15
109 11 15 75 45 13
110 16 16 76 47 11
111 13 12 75 46 11
112 5 14 53 37 22
113 15 18 87 53 10
114 15 14 78 46 11
115 12 13 54 33 15
116 12 14 58 36 14
117 16 14 80 49 11
118 12 17 74 44 10
119 10 12 56 37 14
120 12 16 82 53 14
121 4 15 64 40 11
122 11 10 67 42 15
123 16 13 75 45 11
124 7 15 69 40 10
125 9 16 72 44 10
126 14 15 71 43 16
127 11 14 54 33 12
128 10 11 68 44 14
129 6 13 54 33 15
130 14 17 71 43 10
131 11 14 53 32 12
132 11 16 54 33 15
133 9 15 71 43 12
134 16 12 69 42 11
135 7 16 30 0 10
136 8 8 53 32 20
137 10 9 68 41 19
138 14 13 69 44 17
139 9 19 54 33 8
140 13 11 66 42 17
141 13 15 79 46 11
142 12 11 67 44 13
143 11 15 74 45 9
144 10 16 86 53 10
145 12 15 63 38 13
146 14 12 69 43 16
147 11 16 73 43 12
148 13 15 69 42 14
149 14 13 71 42 11
150 13 14 77 47 13
151 16 11 74 44 15
152 13 15 82 49 14
153 12 16 54 33 14
154 9 14 54 33 14
155 14 13 80 47 10
156 15 15 76 47 8
Weighted_popularity Parental_criticism Finding_Friends Knowing_People
1 5 4 11 12
2 6 4 12 7
3 4 10 12 13
4 6 6 11 11
5 3 5 11 16
6 10 8 10 10
7 8 9 11 15
8 3 6 9 5
9 4 8 10 4
10 3 11 12 7
11 5 6 12 15
12 5 8 12 5
13 6 11 13 16
14 5 5 9 15
15 3 10 12 13
16 4 7 12 13
17 8 7 12 15
18 8 13 12 15
19 8 10 13 10
20 5 8 11 17
21 8 6 12 14
22 2 8 12 9
23 0 7 15 6
24 5 5 11 11
25 2 9 12 13
26 7 9 10 12
27 5 11 11 10
28 2 11 13 4
29 12 11 6 13
30 7 9 12 15
31 0 7 12 8
32 2 6 10 10
33 3 6 12 8
34 0 6 12 7
35 9 5 11 9
36 2 4 9 14
37 3 10 10 5
38 1 8 12 7
39 10 6 12 16
40 1 5 11 14
41 4 9 12 16
42 6 10 11 15
43 6 6 14 4
44 4 9 10 12
45 4 10 10 8
46 7 6 11 17
47 7 6 11 15
48 7 6 11 16
49 0 13 10 12
50 3 8 10 12
51 8 10 12 13
52 8 5 11 14
53 10 8 8 14
54 11 6 12 15
55 6 9 10 14
56 2 9 7 11
57 6 7 11 13
58 1 20 7 4
59 5 8 11 8
60 4 8 8 13
61 6 7 11 15
62 6 7 12 15
63 4 10 8 8
64 1 5 14 17
65 6 8 14 12
66 7 9 11 13
67 7 9 12 14
68 2 20 14 7
69 7 6 9 16
70 8 10 13 11
71 5 11 8 10
72 4 7 11 14
73 2 12 9 19
74 0 12 12 14
75 7 8 7 8
76 0 6 11 15
77 5 6 12 8
78 3 9 11 8
79 3 5 12 6
80 3 11 9 7
81 3 6 11 16
82 7 6 13 15
83 6 10 12 10
84 3 8 12 8
85 0 7 11 9
86 2 8 12 8
87 0 9 12 14
88 9 8 11 14
89 10 10 11 14
90 3 13 8 15
91 7 7 9 7
92 3 7 11 7
93 6 7 12 12
94 5 8 13 7
95 0 9 12 12
96 0 9 6 6
97 4 8 12 10
98 0 7 11 12
99 0 6 13 13
100 7 8 11 14
101 3 8 12 8
102 9 4 10 14
103 4 8 10 10
104 4 10 11 14
105 15 7 11 15
106 7 8 11 10
107 8 7 9 6
108 2 10 7 9
109 8 9 11 11
110 7 8 12 16
111 3 8 12 14
112 3 5 15 8
113 6 8 11 16
114 8 9 10 16
115 5 11 13 14
116 6 7 13 12
117 10 8 11 16
118 0 4 12 15
119 5 16 12 11
120 0 9 12 6
121 0 16 8 6
122 5 12 5 16
123 10 8 11 16
124 0 4 12 8
125 5 11 12 11
126 6 11 11 12
127 1 8 12 13
128 5 8 10 11
129 3 12 7 9
130 3 8 12 15
131 6 6 12 11
132 2 8 9 12
133 5 6 11 15
134 6 14 12 8
135 2 10 12 7
136 3 5 11 10
137 7 8 11 9
138 6 12 12 13
139 3 11 12 11
140 6 8 11 12
141 9 8 12 5
142 2 9 12 12
143 5 6 8 14
144 10 5 15 15
145 9 8 11 14
146 8 7 11 13
147 8 4 6 14
148 5 9 13 14
149 9 5 12 15
150 9 9 12 13
151 14 12 12 14
152 5 6 12 11
153 12 4 12 14
154 6 6 10 11
155 6 7 12 8
156 8 9 12 12
Perceived_Liked Celebrity t
1 13 6 1
2 11 4 2
3 14 6 3
4 12 5 4
5 12 5 5
6 6 4 6
7 10 5 7
8 11 3 8
9 10 2 9
10 12 5 10
11 15 6 11
12 13 6 12
13 18 8 13
14 11 6 14
15 12 3 15
16 13 6 16
17 14 6 17
18 16 7 18
19 16 8 19
20 16 6 20
21 15 7 21
22 13 4 22
23 8 4 23
24 14 2 24
25 15 6 25
26 13 6 26
27 16 6 27
28 13 6 28
29 12 6 29
30 15 7 30
31 11 4 31
32 14 3 32
33 13 5 33
34 13 6 34
35 12 4 35
36 14 6 36
37 13 3 37
38 12 3 38
39 14 6 39
40 15 6 40
41 16 6 41
42 15 8 42
43 5 2 43
44 15 6 44
45 8 4 45
46 16 7 46
47 16 6 47
48 14 6 48
49 16 6 49
50 14 5 50
51 13 6 51
52 14 6 52
53 14 5 53
54 12 6 54
55 13 7 55
56 15 5 56
57 15 6 57
58 13 6 58
59 10 4 59
60 13 5 60
61 14 6 61
62 13 6 62
63 13 4 63
64 18 6 64
65 12 4 65
66 14 7 66
67 16 8 67
68 13 6 68
69 16 6 69
70 15 6 70
71 14 5 71
72 13 6 72
73 12 6 73
74 16 4 74
75 9 5 75
76 15 8 76
77 16 6 77
78 12 6 78
79 11 2 79
80 13 2 80
81 13 4 81
82 14 6 82
83 15 6 83
84 14 5 84
85 12 4 85
86 16 4 86
87 14 6 87
88 13 5 88
89 12 6 89
90 13 7 90
91 12 6 91
92 9 4 92
93 13 4 93
94 10 3 94
95 15 8 95
96 9 4 96
97 13 4 97
98 13 5 98
99 13 5 99
100 15 7 100
101 13 4 101
102 14 5 102
103 11 5 103
104 15 8 104
105 14 5 105
106 15 2 106
107 12 5 107
108 15 4 108
109 14 5 109
110 16 7 110
111 14 6 111
112 12 3 112
113 11 5 113
114 13 6 114
115 12 5 115
116 12 6 116
117 16 7 117
118 13 6 118
119 12 6 119
120 14 5 120
121 4 4 121
122 14 6 122
123 15 6 123
124 12 3 124
125 11 4 125
126 12 4 126
127 11 4 127
128 12 5 128
129 11 4 129
130 13 6 130
131 12 6 131
132 12 4 132
133 15 7 133
134 14 4 134
135 12 4 135
136 12 4 136
137 12 4 137
138 13 5 138
139 11 4 139
140 13 7 140
141 12 3 141
142 14 5 142
143 15 5 143
144 15 6 144
145 13 5 145
146 16 6 146
147 17 6 147
148 13 3 148
149 14 6 149
150 13 5 150
151 16 8 151
152 13 6 152
153 14 4 153
154 13 3 154
155 14 4 155
156 16 7 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Happiness Belonging
-1.400332 -0.060189 0.071908
Belonging_alternative Depression Weighted_popularity
-0.039993 -0.076095 0.094136
Parental_criticism Finding_Friends Knowing_People
0.083878 0.118164 0.230029
Perceived_Liked Celebrity t
0.344271 0.522588 -0.006399
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.191 -1.266 0.144 1.046 6.647
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.400332 2.564480 -0.546 0.585877
Happiness -0.060189 0.085851 -0.701 0.484374
Belonging 0.071908 0.051482 1.397 0.164632
Belonging_alternative -0.039993 0.073107 -0.547 0.585191
Depression -0.076095 0.063844 -1.192 0.235270
Weighted_popularity 0.094136 0.058400 1.612 0.109169
Parental_criticism 0.083878 0.065932 1.272 0.205353
Finding_Friends 0.118164 0.093894 1.258 0.210256
Knowing_People 0.230029 0.064589 3.561 0.000500 ***
Perceived_Liked 0.344271 0.094193 3.655 0.000360 ***
Celebrity 0.522588 0.158855 3.290 0.001261 **
t -0.006399 0.003744 -1.709 0.089591 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.024 on 144 degrees of freedom
Multiple R-squared: 0.5587, Adjusted R-squared: 0.525
F-statistic: 16.58 on 11 and 144 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,] 0.9336493 0.1327014852 6.635074e-02
[2,] 0.9999306 0.0001387563 6.937813e-05
[3,] 0.9998253 0.0003493392 1.746696e-04
[4,] 0.9995788 0.0008423515 4.211758e-04
[5,] 0.9994425 0.0011150741 5.575370e-04
[6,] 0.9989417 0.0021165835 1.058292e-03
[7,] 0.9990348 0.0019304894 9.652447e-04
[8,] 0.9995673 0.0008654991 4.327495e-04
[9,] 0.9991578 0.0016843443 8.421722e-04
[10,] 0.9984453 0.0031093480 1.554674e-03
[11,] 0.9972355 0.0055289651 2.764483e-03
[12,] 0.9952679 0.0094642354 4.732118e-03
[13,] 0.9959427 0.0081146225 4.057311e-03
[14,] 0.9935169 0.0129661352 6.483068e-03
[15,] 0.9965825 0.0068350846 3.417542e-03
[16,] 0.9952172 0.0095656475 4.782824e-03
[17,] 0.9929213 0.0141573260 7.078663e-03
[18,] 0.9962843 0.0074313309 3.715665e-03
[19,] 0.9943309 0.0113382765 5.669138e-03
[20,] 0.9922774 0.0154451259 7.722563e-03
[21,] 0.9912794 0.0174412878 8.720644e-03
[22,] 0.9877578 0.0244843692 1.224218e-02
[23,] 0.9848936 0.0302127147 1.510636e-02
[24,] 0.9860231 0.0279538815 1.397694e-02
[25,] 0.9852618 0.0294764797 1.473824e-02
[26,] 0.9898364 0.0203272883 1.016364e-02
[27,] 0.9860230 0.0279540329 1.397702e-02
[28,] 0.9807785 0.0384429152 1.922146e-02
[29,] 0.9734890 0.0530219268 2.651096e-02
[30,] 0.9676904 0.0646191715 3.230959e-02
[31,] 0.9912926 0.0174148901 8.707445e-03
[32,] 0.9880137 0.0239726436 1.198632e-02
[33,] 0.9839869 0.0320262115 1.601311e-02
[34,] 0.9790217 0.0419565609 2.097828e-02
[35,] 0.9765208 0.0469583356 2.347917e-02
[36,] 0.9720103 0.0559794819 2.798974e-02
[37,] 0.9694320 0.0611360987 3.056805e-02
[38,] 0.9821390 0.0357220227 1.786101e-02
[39,] 0.9760674 0.0478652476 2.393262e-02
[40,] 0.9767379 0.0465241293 2.326206e-02
[41,] 0.9733760 0.0532479788 2.662399e-02
[42,] 0.9663856 0.0672287074 3.361435e-02
[43,] 0.9750728 0.0498543741 2.492719e-02
[44,] 0.9686047 0.0627906292 3.139531e-02
[45,] 0.9587840 0.0824319366 4.121597e-02
[46,] 0.9467447 0.1065105780 5.325529e-02
[47,] 0.9328431 0.1343138176 6.715691e-02
[48,] 0.9198991 0.1602017685 8.010088e-02
[49,] 0.9002519 0.1994961004 9.974805e-02
[50,] 0.8846228 0.2307544507 1.153772e-01
[51,] 0.9361582 0.1276835455 6.384177e-02
[52,] 0.9413024 0.1173951317 5.869757e-02
[53,] 0.9281614 0.1436771948 7.183860e-02
[54,] 0.9155660 0.1688679911 8.443400e-02
[55,] 0.8984462 0.2031075464 1.015538e-01
[56,] 0.8854602 0.2290795272 1.145398e-01
[57,] 0.8627781 0.2744438401 1.372219e-01
[58,] 0.8368683 0.3262633251 1.631317e-01
[59,] 0.8207122 0.3585755769 1.792878e-01
[60,] 0.8151383 0.3697233315 1.848617e-01
[61,] 0.7902476 0.4195048979 2.097524e-01
[62,] 0.7561521 0.4876957838 2.438479e-01
[63,] 0.7466586 0.5066828501 2.533414e-01
[64,] 0.7057337 0.5885326299 2.942663e-01
[65,] 0.7113378 0.5773244990 2.886622e-01
[66,] 0.6972041 0.6055918586 3.027959e-01
[67,] 0.6618987 0.6762026235 3.381013e-01
[68,] 0.6199649 0.7600702958 3.800351e-01
[69,] 0.5726545 0.8546910868 4.273455e-01
[70,] 0.5814237 0.8371526822 4.185763e-01
[71,] 0.5630902 0.8738195925 4.369098e-01
[72,] 0.5395088 0.9209823005 4.604912e-01
[73,] 0.5917909 0.8164181443 4.082091e-01
[74,] 0.6207437 0.7585125497 3.792563e-01
[75,] 0.5858615 0.8282770488 4.141385e-01
[76,] 0.5699168 0.8601663479 4.300832e-01
[77,] 0.5582096 0.8835807283 4.417904e-01
[78,] 0.5264658 0.9470683168 4.735342e-01
[79,] 0.4829475 0.9658949509 5.170525e-01
[80,] 0.4654655 0.9309310527 5.345345e-01
[81,] 0.4793073 0.9586146299 5.206927e-01
[82,] 0.6190567 0.7618866917 3.809433e-01
[83,] 0.5739056 0.8521887993 4.260944e-01
[84,] 0.5379113 0.9241773380 4.620887e-01
[85,] 0.6049848 0.7900304075 3.950152e-01
[86,] 0.5705912 0.8588175732 4.294088e-01
[87,] 0.5908218 0.8183563560 4.091782e-01
[88,] 0.5444238 0.9111524825 4.555762e-01
[89,] 0.4942123 0.9884246177 5.057877e-01
[90,] 0.5002204 0.9995592047 4.997796e-01
[91,] 0.5519344 0.8961312671 4.480656e-01
[92,] 0.5548922 0.8902156980 4.451078e-01
[93,] 0.5119420 0.9761159236 4.880580e-01
[94,] 0.6097246 0.7805508746 3.902754e-01
[95,] 0.5930667 0.8138665182 4.069333e-01
[96,] 0.5505773 0.8988454775 4.494227e-01
[97,] 0.4948266 0.9896531993 5.051734e-01
[98,] 0.6168093 0.7663813134 3.831907e-01
[99,] 0.6261544 0.7476912184 3.738456e-01
[100,] 0.6152355 0.7695290238 3.847645e-01
[101,] 0.5599876 0.8800247408 4.400124e-01
[102,] 0.5211389 0.9577222447 4.788611e-01
[103,] 0.4770162 0.9540323637 5.229838e-01
[104,] 0.4638834 0.9277668332 5.361166e-01
[105,] 0.4947394 0.9894787443 5.052606e-01
[106,] 0.4415309 0.8830617167 5.584691e-01
[107,] 0.4134830 0.8269659044 5.865170e-01
[108,] 0.3685878 0.7371756658 6.314122e-01
[109,] 0.4260640 0.8521280640 5.739360e-01
[110,] 0.3873901 0.7747802074 6.126099e-01
[111,] 0.4151332 0.8302663369 5.848668e-01
[112,] 0.4448626 0.8897251009 5.551374e-01
[113,] 0.4320321 0.8640642461 5.679679e-01
[114,] 0.3616136 0.7232271893 6.383864e-01
[115,] 0.5980661 0.8038677623 4.019339e-01
[116,] 0.7384688 0.5230623367 2.615312e-01
[117,] 0.7418200 0.5163599779 2.581800e-01
[118,] 0.7892672 0.4214655637 2.107328e-01
[119,] 0.7626227 0.4747545478 2.373773e-01
[120,] 0.8089580 0.3820839551 1.910420e-01
[121,] 0.7326375 0.5347249417 2.673625e-01
[122,] 0.6681749 0.6636502375 3.318251e-01
[123,] 0.7582452 0.4835096413 2.417548e-01
[124,] 0.7042372 0.5915255521 2.957628e-01
[125,] 0.6469509 0.7060981706 3.530491e-01
[126,] 0.5030940 0.9938120983 4.969060e-01
[127,] 0.4021277 0.8042553331 5.978723e-01
> postscript(file="/var/www/rcomp/tmp/131v31321616175.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/20co91321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3n7e41321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4pe551321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5x0vn1321616175.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 = 156
Frequency = 1
1 2 3 4 5 6
1.895881119 2.620427765 1.330717095 -0.178019636 0.365933615 -1.121711250
7 8 9 10 11 12
-1.123213570 6.647304648 -2.330299894 2.390933319 4.619595624 -1.448810891
13 14 15 16 17 18
-2.291412675 -7.190631352 -5.132371829 0.473251186 1.679605980 0.220125052
19 20 21 22 23 24
-1.184223080 -0.206106818 -0.659061264 0.634972150 -0.078438783 0.435413441
25 26 27 28 29 30
0.095636326 -0.039586069 0.255082604 -1.323734745 1.041293627 -0.020941910
31 32 33 34 35 36
1.169255572 -2.710391435 0.255145411 0.108760171 -2.316616832 0.876211275
37 38 39 40 41 42
0.984433103 2.214215114 0.730369634 3.021527893 -0.882268859 -1.271526173
43 44 45 46 47 48
-0.553508690 0.682492275 3.967834104 0.516979380 -0.673137972 0.791774017
49 50 51 52 53 54
1.698770513 -1.158338499 1.397298101 3.385471213 -0.312550828 -2.693270860
55 56 57 58 59 60
-1.877145284 0.463107197 -3.373848399 -1.038388722 -0.604058129 -0.092649403
61 62 63 64 65 66
0.051852785 -1.052834208 -0.007501405 0.090599448 -4.545998168 1.955246027
67 68 69 70 71 72
0.462847129 -1.264760180 0.645387855 -1.418359962 0.179267112 0.196823950
73 74 75 76 77 78
-0.336219969 1.513831060 0.640340593 0.275525580 -1.995810234 -0.402432914
79 80 81 82 83 84
-2.499181981 1.367662582 0.443356298 -0.209140654 -0.321061732 -2.649083341
85 86 87 88 89 90
-1.734921254 -1.646741065 2.884120930 2.234770084 0.430392812 -1.643370365
91 92 93 94 95 96
1.731725238 1.179667937 0.573625387 -1.341345661 1.947923493 3.249126235
97 98 99 100 101 102
0.507725519 -1.299834978 2.689280074 0.558208537 -2.705091434 -0.298513190
103 104 105 106 107 108
-0.412757339 1.216109797 2.728003330 -2.079417059 -1.583871667 -3.375546707
109 110 111 112 113 114
-1.374423718 0.724039971 -0.430672756 -2.711190258 2.215321290 1.059160783
115 116 117 118 119 120
0.602708093 0.604397220 0.276561128 -0.436944958 -1.625554236 1.433900843
121 122 123 124 125 126
-2.222932114 -1.917694464 1.321189884 -1.797126117 -1.712521813 3.171897099
127 128 129 130 131 132
1.354626875 -0.781159058 -2.477331713 1.197647551 0.179825573 1.881395676
133 134 135 136 137 138
-4.864827869 4.627642073 -1.445963415 -0.780907989 0.092752887 2.089274082
139 140 141 142 143 144
-0.551870034 0.755961494 3.415973130 0.347863586 -1.535168116 -4.902001440
145 146 147 148 149 150
0.082795565 0.758068132 -2.318632146 2.008122239 0.457112763 0.435810507
151 152 153 154 155 156
-0.043345997 0.870986398 0.830734395 -0.092914275 2.742232173 0.472002081
> postscript(file="/var/www/rcomp/tmp/68aov1321616175.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.895881119 NA
1 2.620427765 1.895881119
2 1.330717095 2.620427765
3 -0.178019636 1.330717095
4 0.365933615 -0.178019636
5 -1.121711250 0.365933615
6 -1.123213570 -1.121711250
7 6.647304648 -1.123213570
8 -2.330299894 6.647304648
9 2.390933319 -2.330299894
10 4.619595624 2.390933319
11 -1.448810891 4.619595624
12 -2.291412675 -1.448810891
13 -7.190631352 -2.291412675
14 -5.132371829 -7.190631352
15 0.473251186 -5.132371829
16 1.679605980 0.473251186
17 0.220125052 1.679605980
18 -1.184223080 0.220125052
19 -0.206106818 -1.184223080
20 -0.659061264 -0.206106818
21 0.634972150 -0.659061264
22 -0.078438783 0.634972150
23 0.435413441 -0.078438783
24 0.095636326 0.435413441
25 -0.039586069 0.095636326
26 0.255082604 -0.039586069
27 -1.323734745 0.255082604
28 1.041293627 -1.323734745
29 -0.020941910 1.041293627
30 1.169255572 -0.020941910
31 -2.710391435 1.169255572
32 0.255145411 -2.710391435
33 0.108760171 0.255145411
34 -2.316616832 0.108760171
35 0.876211275 -2.316616832
36 0.984433103 0.876211275
37 2.214215114 0.984433103
38 0.730369634 2.214215114
39 3.021527893 0.730369634
40 -0.882268859 3.021527893
41 -1.271526173 -0.882268859
42 -0.553508690 -1.271526173
43 0.682492275 -0.553508690
44 3.967834104 0.682492275
45 0.516979380 3.967834104
46 -0.673137972 0.516979380
47 0.791774017 -0.673137972
48 1.698770513 0.791774017
49 -1.158338499 1.698770513
50 1.397298101 -1.158338499
51 3.385471213 1.397298101
52 -0.312550828 3.385471213
53 -2.693270860 -0.312550828
54 -1.877145284 -2.693270860
55 0.463107197 -1.877145284
56 -3.373848399 0.463107197
57 -1.038388722 -3.373848399
58 -0.604058129 -1.038388722
59 -0.092649403 -0.604058129
60 0.051852785 -0.092649403
61 -1.052834208 0.051852785
62 -0.007501405 -1.052834208
63 0.090599448 -0.007501405
64 -4.545998168 0.090599448
65 1.955246027 -4.545998168
66 0.462847129 1.955246027
67 -1.264760180 0.462847129
68 0.645387855 -1.264760180
69 -1.418359962 0.645387855
70 0.179267112 -1.418359962
71 0.196823950 0.179267112
72 -0.336219969 0.196823950
73 1.513831060 -0.336219969
74 0.640340593 1.513831060
75 0.275525580 0.640340593
76 -1.995810234 0.275525580
77 -0.402432914 -1.995810234
78 -2.499181981 -0.402432914
79 1.367662582 -2.499181981
80 0.443356298 1.367662582
81 -0.209140654 0.443356298
82 -0.321061732 -0.209140654
83 -2.649083341 -0.321061732
84 -1.734921254 -2.649083341
85 -1.646741065 -1.734921254
86 2.884120930 -1.646741065
87 2.234770084 2.884120930
88 0.430392812 2.234770084
89 -1.643370365 0.430392812
90 1.731725238 -1.643370365
91 1.179667937 1.731725238
92 0.573625387 1.179667937
93 -1.341345661 0.573625387
94 1.947923493 -1.341345661
95 3.249126235 1.947923493
96 0.507725519 3.249126235
97 -1.299834978 0.507725519
98 2.689280074 -1.299834978
99 0.558208537 2.689280074
100 -2.705091434 0.558208537
101 -0.298513190 -2.705091434
102 -0.412757339 -0.298513190
103 1.216109797 -0.412757339
104 2.728003330 1.216109797
105 -2.079417059 2.728003330
106 -1.583871667 -2.079417059
107 -3.375546707 -1.583871667
108 -1.374423718 -3.375546707
109 0.724039971 -1.374423718
110 -0.430672756 0.724039971
111 -2.711190258 -0.430672756
112 2.215321290 -2.711190258
113 1.059160783 2.215321290
114 0.602708093 1.059160783
115 0.604397220 0.602708093
116 0.276561128 0.604397220
117 -0.436944958 0.276561128
118 -1.625554236 -0.436944958
119 1.433900843 -1.625554236
120 -2.222932114 1.433900843
121 -1.917694464 -2.222932114
122 1.321189884 -1.917694464
123 -1.797126117 1.321189884
124 -1.712521813 -1.797126117
125 3.171897099 -1.712521813
126 1.354626875 3.171897099
127 -0.781159058 1.354626875
128 -2.477331713 -0.781159058
129 1.197647551 -2.477331713
130 0.179825573 1.197647551
131 1.881395676 0.179825573
132 -4.864827869 1.881395676
133 4.627642073 -4.864827869
134 -1.445963415 4.627642073
135 -0.780907989 -1.445963415
136 0.092752887 -0.780907989
137 2.089274082 0.092752887
138 -0.551870034 2.089274082
139 0.755961494 -0.551870034
140 3.415973130 0.755961494
141 0.347863586 3.415973130
142 -1.535168116 0.347863586
143 -4.902001440 -1.535168116
144 0.082795565 -4.902001440
145 0.758068132 0.082795565
146 -2.318632146 0.758068132
147 2.008122239 -2.318632146
148 0.457112763 2.008122239
149 0.435810507 0.457112763
150 -0.043345997 0.435810507
151 0.870986398 -0.043345997
152 0.830734395 0.870986398
153 -0.092914275 0.830734395
154 2.742232173 -0.092914275
155 0.472002081 2.742232173
156 NA 0.472002081
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.620427765 1.895881119
[2,] 1.330717095 2.620427765
[3,] -0.178019636 1.330717095
[4,] 0.365933615 -0.178019636
[5,] -1.121711250 0.365933615
[6,] -1.123213570 -1.121711250
[7,] 6.647304648 -1.123213570
[8,] -2.330299894 6.647304648
[9,] 2.390933319 -2.330299894
[10,] 4.619595624 2.390933319
[11,] -1.448810891 4.619595624
[12,] -2.291412675 -1.448810891
[13,] -7.190631352 -2.291412675
[14,] -5.132371829 -7.190631352
[15,] 0.473251186 -5.132371829
[16,] 1.679605980 0.473251186
[17,] 0.220125052 1.679605980
[18,] -1.184223080 0.220125052
[19,] -0.206106818 -1.184223080
[20,] -0.659061264 -0.206106818
[21,] 0.634972150 -0.659061264
[22,] -0.078438783 0.634972150
[23,] 0.435413441 -0.078438783
[24,] 0.095636326 0.435413441
[25,] -0.039586069 0.095636326
[26,] 0.255082604 -0.039586069
[27,] -1.323734745 0.255082604
[28,] 1.041293627 -1.323734745
[29,] -0.020941910 1.041293627
[30,] 1.169255572 -0.020941910
[31,] -2.710391435 1.169255572
[32,] 0.255145411 -2.710391435
[33,] 0.108760171 0.255145411
[34,] -2.316616832 0.108760171
[35,] 0.876211275 -2.316616832
[36,] 0.984433103 0.876211275
[37,] 2.214215114 0.984433103
[38,] 0.730369634 2.214215114
[39,] 3.021527893 0.730369634
[40,] -0.882268859 3.021527893
[41,] -1.271526173 -0.882268859
[42,] -0.553508690 -1.271526173
[43,] 0.682492275 -0.553508690
[44,] 3.967834104 0.682492275
[45,] 0.516979380 3.967834104
[46,] -0.673137972 0.516979380
[47,] 0.791774017 -0.673137972
[48,] 1.698770513 0.791774017
[49,] -1.158338499 1.698770513
[50,] 1.397298101 -1.158338499
[51,] 3.385471213 1.397298101
[52,] -0.312550828 3.385471213
[53,] -2.693270860 -0.312550828
[54,] -1.877145284 -2.693270860
[55,] 0.463107197 -1.877145284
[56,] -3.373848399 0.463107197
[57,] -1.038388722 -3.373848399
[58,] -0.604058129 -1.038388722
[59,] -0.092649403 -0.604058129
[60,] 0.051852785 -0.092649403
[61,] -1.052834208 0.051852785
[62,] -0.007501405 -1.052834208
[63,] 0.090599448 -0.007501405
[64,] -4.545998168 0.090599448
[65,] 1.955246027 -4.545998168
[66,] 0.462847129 1.955246027
[67,] -1.264760180 0.462847129
[68,] 0.645387855 -1.264760180
[69,] -1.418359962 0.645387855
[70,] 0.179267112 -1.418359962
[71,] 0.196823950 0.179267112
[72,] -0.336219969 0.196823950
[73,] 1.513831060 -0.336219969
[74,] 0.640340593 1.513831060
[75,] 0.275525580 0.640340593
[76,] -1.995810234 0.275525580
[77,] -0.402432914 -1.995810234
[78,] -2.499181981 -0.402432914
[79,] 1.367662582 -2.499181981
[80,] 0.443356298 1.367662582
[81,] -0.209140654 0.443356298
[82,] -0.321061732 -0.209140654
[83,] -2.649083341 -0.321061732
[84,] -1.734921254 -2.649083341
[85,] -1.646741065 -1.734921254
[86,] 2.884120930 -1.646741065
[87,] 2.234770084 2.884120930
[88,] 0.430392812 2.234770084
[89,] -1.643370365 0.430392812
[90,] 1.731725238 -1.643370365
[91,] 1.179667937 1.731725238
[92,] 0.573625387 1.179667937
[93,] -1.341345661 0.573625387
[94,] 1.947923493 -1.341345661
[95,] 3.249126235 1.947923493
[96,] 0.507725519 3.249126235
[97,] -1.299834978 0.507725519
[98,] 2.689280074 -1.299834978
[99,] 0.558208537 2.689280074
[100,] -2.705091434 0.558208537
[101,] -0.298513190 -2.705091434
[102,] -0.412757339 -0.298513190
[103,] 1.216109797 -0.412757339
[104,] 2.728003330 1.216109797
[105,] -2.079417059 2.728003330
[106,] -1.583871667 -2.079417059
[107,] -3.375546707 -1.583871667
[108,] -1.374423718 -3.375546707
[109,] 0.724039971 -1.374423718
[110,] -0.430672756 0.724039971
[111,] -2.711190258 -0.430672756
[112,] 2.215321290 -2.711190258
[113,] 1.059160783 2.215321290
[114,] 0.602708093 1.059160783
[115,] 0.604397220 0.602708093
[116,] 0.276561128 0.604397220
[117,] -0.436944958 0.276561128
[118,] -1.625554236 -0.436944958
[119,] 1.433900843 -1.625554236
[120,] -2.222932114 1.433900843
[121,] -1.917694464 -2.222932114
[122,] 1.321189884 -1.917694464
[123,] -1.797126117 1.321189884
[124,] -1.712521813 -1.797126117
[125,] 3.171897099 -1.712521813
[126,] 1.354626875 3.171897099
[127,] -0.781159058 1.354626875
[128,] -2.477331713 -0.781159058
[129,] 1.197647551 -2.477331713
[130,] 0.179825573 1.197647551
[131,] 1.881395676 0.179825573
[132,] -4.864827869 1.881395676
[133,] 4.627642073 -4.864827869
[134,] -1.445963415 4.627642073
[135,] -0.780907989 -1.445963415
[136,] 0.092752887 -0.780907989
[137,] 2.089274082 0.092752887
[138,] -0.551870034 2.089274082
[139,] 0.755961494 -0.551870034
[140,] 3.415973130 0.755961494
[141,] 0.347863586 3.415973130
[142,] -1.535168116 0.347863586
[143,] -4.902001440 -1.535168116
[144,] 0.082795565 -4.902001440
[145,] 0.758068132 0.082795565
[146,] -2.318632146 0.758068132
[147,] 2.008122239 -2.318632146
[148,] 0.457112763 2.008122239
[149,] 0.435810507 0.457112763
[150,] -0.043345997 0.435810507
[151,] 0.870986398 -0.043345997
[152,] 0.830734395 0.870986398
[153,] -0.092914275 0.830734395
[154,] 2.742232173 -0.092914275
[155,] 0.472002081 2.742232173
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.620427765 1.895881119
2 1.330717095 2.620427765
3 -0.178019636 1.330717095
4 0.365933615 -0.178019636
5 -1.121711250 0.365933615
6 -1.123213570 -1.121711250
7 6.647304648 -1.123213570
8 -2.330299894 6.647304648
9 2.390933319 -2.330299894
10 4.619595624 2.390933319
11 -1.448810891 4.619595624
12 -2.291412675 -1.448810891
13 -7.190631352 -2.291412675
14 -5.132371829 -7.190631352
15 0.473251186 -5.132371829
16 1.679605980 0.473251186
17 0.220125052 1.679605980
18 -1.184223080 0.220125052
19 -0.206106818 -1.184223080
20 -0.659061264 -0.206106818
21 0.634972150 -0.659061264
22 -0.078438783 0.634972150
23 0.435413441 -0.078438783
24 0.095636326 0.435413441
25 -0.039586069 0.095636326
26 0.255082604 -0.039586069
27 -1.323734745 0.255082604
28 1.041293627 -1.323734745
29 -0.020941910 1.041293627
30 1.169255572 -0.020941910
31 -2.710391435 1.169255572
32 0.255145411 -2.710391435
33 0.108760171 0.255145411
34 -2.316616832 0.108760171
35 0.876211275 -2.316616832
36 0.984433103 0.876211275
37 2.214215114 0.984433103
38 0.730369634 2.214215114
39 3.021527893 0.730369634
40 -0.882268859 3.021527893
41 -1.271526173 -0.882268859
42 -0.553508690 -1.271526173
43 0.682492275 -0.553508690
44 3.967834104 0.682492275
45 0.516979380 3.967834104
46 -0.673137972 0.516979380
47 0.791774017 -0.673137972
48 1.698770513 0.791774017
49 -1.158338499 1.698770513
50 1.397298101 -1.158338499
51 3.385471213 1.397298101
52 -0.312550828 3.385471213
53 -2.693270860 -0.312550828
54 -1.877145284 -2.693270860
55 0.463107197 -1.877145284
56 -3.373848399 0.463107197
57 -1.038388722 -3.373848399
58 -0.604058129 -1.038388722
59 -0.092649403 -0.604058129
60 0.051852785 -0.092649403
61 -1.052834208 0.051852785
62 -0.007501405 -1.052834208
63 0.090599448 -0.007501405
64 -4.545998168 0.090599448
65 1.955246027 -4.545998168
66 0.462847129 1.955246027
67 -1.264760180 0.462847129
68 0.645387855 -1.264760180
69 -1.418359962 0.645387855
70 0.179267112 -1.418359962
71 0.196823950 0.179267112
72 -0.336219969 0.196823950
73 1.513831060 -0.336219969
74 0.640340593 1.513831060
75 0.275525580 0.640340593
76 -1.995810234 0.275525580
77 -0.402432914 -1.995810234
78 -2.499181981 -0.402432914
79 1.367662582 -2.499181981
80 0.443356298 1.367662582
81 -0.209140654 0.443356298
82 -0.321061732 -0.209140654
83 -2.649083341 -0.321061732
84 -1.734921254 -2.649083341
85 -1.646741065 -1.734921254
86 2.884120930 -1.646741065
87 2.234770084 2.884120930
88 0.430392812 2.234770084
89 -1.643370365 0.430392812
90 1.731725238 -1.643370365
91 1.179667937 1.731725238
92 0.573625387 1.179667937
93 -1.341345661 0.573625387
94 1.947923493 -1.341345661
95 3.249126235 1.947923493
96 0.507725519 3.249126235
97 -1.299834978 0.507725519
98 2.689280074 -1.299834978
99 0.558208537 2.689280074
100 -2.705091434 0.558208537
101 -0.298513190 -2.705091434
102 -0.412757339 -0.298513190
103 1.216109797 -0.412757339
104 2.728003330 1.216109797
105 -2.079417059 2.728003330
106 -1.583871667 -2.079417059
107 -3.375546707 -1.583871667
108 -1.374423718 -3.375546707
109 0.724039971 -1.374423718
110 -0.430672756 0.724039971
111 -2.711190258 -0.430672756
112 2.215321290 -2.711190258
113 1.059160783 2.215321290
114 0.602708093 1.059160783
115 0.604397220 0.602708093
116 0.276561128 0.604397220
117 -0.436944958 0.276561128
118 -1.625554236 -0.436944958
119 1.433900843 -1.625554236
120 -2.222932114 1.433900843
121 -1.917694464 -2.222932114
122 1.321189884 -1.917694464
123 -1.797126117 1.321189884
124 -1.712521813 -1.797126117
125 3.171897099 -1.712521813
126 1.354626875 3.171897099
127 -0.781159058 1.354626875
128 -2.477331713 -0.781159058
129 1.197647551 -2.477331713
130 0.179825573 1.197647551
131 1.881395676 0.179825573
132 -4.864827869 1.881395676
133 4.627642073 -4.864827869
134 -1.445963415 4.627642073
135 -0.780907989 -1.445963415
136 0.092752887 -0.780907989
137 2.089274082 0.092752887
138 -0.551870034 2.089274082
139 0.755961494 -0.551870034
140 3.415973130 0.755961494
141 0.347863586 3.415973130
142 -1.535168116 0.347863586
143 -4.902001440 -1.535168116
144 0.082795565 -4.902001440
145 0.758068132 0.082795565
146 -2.318632146 0.758068132
147 2.008122239 -2.318632146
148 0.457112763 2.008122239
149 0.435810507 0.457112763
150 -0.043345997 0.435810507
151 0.870986398 -0.043345997
152 0.830734395 0.870986398
153 -0.092914275 0.830734395
154 2.742232173 -0.092914275
155 0.472002081 2.742232173
> 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/www/rcomp/tmp/75oco1321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/834p61321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/95ka61321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10xzfk1321616175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/116tpw1321616175.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/www/rcomp/tmp/127u151321616175.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/www/rcomp/tmp/13vxs41321616175.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/www/rcomp/tmp/147hzd1321616175.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/www/rcomp/tmp/156b1a1321616175.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/www/rcomp/tmp/16grhm1321616175.tab")
+ }
>
> try(system("convert tmp/131v31321616175.ps tmp/131v31321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/20co91321616175.ps tmp/20co91321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n7e41321616175.ps tmp/3n7e41321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pe551321616175.ps tmp/4pe551321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x0vn1321616175.ps tmp/5x0vn1321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/68aov1321616175.ps tmp/68aov1321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/75oco1321616175.ps tmp/75oco1321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/834p61321616175.ps tmp/834p61321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/95ka61321616175.ps tmp/95ka61321616175.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xzfk1321616175.ps tmp/10xzfk1321616175.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.680 0.300 6.035