R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:162))
> 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 = '4'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> 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
Learning month Connected Separate Software Happiness Depression Belonging
1 13 9 41 38 12 14 12 53
2 16 9 39 32 11 18 11 86
3 19 9 30 35 15 11 14 66
4 15 9 31 33 6 12 12 67
5 14 9 34 37 13 16 21 76
6 13 9 35 29 10 18 12 78
7 19 9 39 31 12 14 22 53
8 15 9 34 36 14 14 11 80
9 14 9 36 35 12 15 10 74
10 15 9 37 38 6 15 13 76
11 16 9 38 31 10 17 10 79
12 16 9 36 34 12 19 8 54
13 16 9 38 35 12 10 15 67
14 16 9 39 38 11 16 14 54
15 17 9 33 37 15 18 10 87
16 15 9 32 33 12 14 14 58
17 15 9 36 32 10 14 14 75
18 20 9 38 38 12 17 11 88
19 18 9 39 38 11 14 10 64
20 16 9 32 32 12 16 13 57
21 16 9 32 33 11 18 7 66
22 16 9 31 31 12 11 14 68
23 19 9 39 38 13 14 12 54
24 16 9 37 39 11 12 14 56
25 17 9 39 32 9 17 11 86
26 17 9 41 32 13 9 9 80
27 16 9 36 35 10 16 11 76
28 15 9 33 37 14 14 15 69
29 16 9 33 33 12 15 14 78
30 14 9 34 33 10 11 13 67
31 15 9 31 28 12 16 9 80
32 12 9 27 32 8 13 15 54
33 14 9 37 31 10 17 10 71
34 16 9 34 37 12 15 11 84
35 14 9 34 30 12 14 13 74
36 7 9 32 33 7 16 8 71
37 10 9 29 31 6 9 20 63
38 14 9 36 33 12 15 12 71
39 16 9 29 31 10 17 10 76
40 16 9 35 33 10 13 10 69
41 16 9 37 32 10 15 9 74
42 14 9 34 33 12 16 14 75
43 20 9 38 32 15 16 8 54
44 14 9 35 33 10 12 14 52
45 14 9 38 28 10 12 11 69
46 11 9 37 35 12 11 13 68
47 14 9 38 39 13 15 9 65
48 15 9 33 34 11 15 11 75
49 16 9 36 38 11 17 15 74
50 14 9 38 32 12 13 11 75
51 16 9 32 38 14 16 10 72
52 14 9 32 30 10 14 14 67
53 12 9 32 33 12 11 18 63
54 16 9 34 38 13 12 14 62
55 9 9 32 32 5 12 11 63
56 14 9 37 32 6 15 12 76
57 16 9 39 34 12 16 13 74
58 16 9 29 34 12 15 9 67
59 15 9 37 36 11 12 10 73
60 16 9 35 34 10 12 15 70
61 12 9 30 28 7 8 20 53
62 16 9 38 34 12 13 12 77
63 16 9 34 35 14 11 12 77
64 14 10 31 35 11 14 14 52
65 16 10 34 31 12 15 13 54
66 17 10 35 37 13 10 11 80
67 18 10 36 35 14 11 17 66
68 18 10 30 27 11 12 12 73
69 12 10 39 40 12 15 13 63
70 16 10 35 37 12 15 14 69
71 10 10 38 36 8 14 13 67
72 14 10 31 38 11 16 15 54
73 18 10 34 39 14 15 13 81
74 18 10 38 41 14 15 10 69
75 16 10 34 27 12 13 11 84
76 17 10 39 30 9 12 19 80
77 16 10 37 37 13 17 13 70
78 16 10 34 31 11 13 17 69
79 13 10 28 31 12 15 13 77
80 16 10 37 27 12 13 9 54
81 16 10 33 36 12 15 11 79
82 20 10 37 38 12 16 10 30
83 16 10 35 37 12 15 9 71
84 15 10 37 33 12 16 12 73
85 15 10 32 34 11 15 12 72
86 16 10 33 31 10 14 13 77
87 14 10 38 39 9 15 13 75
88 16 10 33 34 12 14 12 69
89 16 10 29 32 12 13 15 54
90 15 10 33 33 12 7 22 70
91 12 10 31 36 9 17 13 73
92 17 10 36 32 15 13 15 54
93 16 10 35 41 12 15 13 77
94 15 10 32 28 12 14 15 82
95 13 10 29 30 12 13 10 80
96 16 10 39 36 10 16 11 80
97 16 10 37 35 13 12 16 69
98 16 10 35 31 9 14 11 78
99 16 10 37 34 12 17 11 81
100 14 10 32 36 10 15 10 76
101 16 10 38 36 14 17 10 76
102 16 10 37 35 11 12 16 73
103 20 10 36 37 15 16 12 85
104 15 10 32 28 11 11 11 66
105 16 10 33 39 11 15 16 79
106 13 10 40 32 12 9 19 68
107 17 10 38 35 12 16 11 76
108 16 10 41 39 12 15 16 71
109 16 10 36 35 11 10 15 54
110 12 10 43 42 7 10 24 46
111 16 10 30 34 12 15 14 82
112 16 10 31 33 14 11 15 74
113 17 10 32 41 11 13 11 88
114 13 10 32 33 11 14 15 38
115 12 10 37 34 10 18 12 76
116 18 10 37 32 13 16 10 86
117 14 10 33 40 13 14 14 54
118 14 10 34 40 8 14 13 70
119 13 10 33 35 11 14 9 69
120 16 10 38 36 12 14 15 90
121 13 10 33 37 11 12 15 54
122 16 10 31 27 13 14 14 76
123 13 10 38 39 12 15 11 89
124 16 10 37 38 14 15 8 76
125 15 10 33 31 13 15 11 73
126 16 10 31 33 15 13 11 79
127 15 10 39 32 10 17 8 90
128 17 10 44 39 11 17 10 74
129 15 10 33 36 9 19 11 81
130 12 10 35 33 11 15 13 72
131 16 10 32 33 10 13 11 71
132 10 10 28 32 11 9 20 66
133 16 10 40 37 8 15 10 77
134 12 10 27 30 11 15 15 65
135 14 10 37 38 12 15 12 74
136 15 10 32 29 12 16 14 82
137 13 10 28 22 9 11 23 54
138 15 10 34 35 11 14 14 63
139 11 10 30 35 10 11 16 54
140 12 10 35 34 8 15 11 64
141 8 10 31 35 9 13 12 69
142 16 10 32 34 8 15 10 54
143 15 10 30 34 9 16 14 84
144 17 10 30 35 15 14 12 86
145 16 10 31 23 11 15 12 77
146 10 10 40 31 8 16 11 89
147 18 10 32 27 13 16 12 76
148 13 10 36 36 12 11 13 60
149 16 10 32 31 12 12 11 75
150 13 10 35 32 9 9 19 73
151 10 10 38 39 7 16 12 85
152 15 10 42 37 13 13 17 79
153 16 10 34 38 9 16 9 71
154 16 10 35 39 6 12 12 72
155 14 10 35 34 8 9 19 69
156 10 9 33 31 8 13 18 78
157 17 10 36 32 15 13 15 54
158 13 10 32 37 6 14 14 69
159 15 10 33 36 9 19 11 81
160 16 10 34 32 11 13 9 84
161 12 10 32 35 8 12 18 84
162 13 10 34 36 8 13 16 69
Belonging_Final t
1 32 1
2 51 2
3 42 3
4 41 4
5 46 5
6 47 6
7 37 7
8 49 8
9 45 9
10 47 10
11 49 11
12 33 12
13 42 13
14 33 14
15 53 15
16 36 16
17 45 17
18 54 18
19 41 19
20 36 20
21 41 21
22 44 22
23 33 23
24 37 24
25 52 25
26 47 26
27 43 27
28 44 28
29 45 29
30 44 30
31 49 31
32 33 32
33 43 33
34 54 34
35 42 35
36 44 36
37 37 37
38 43 38
39 46 39
40 42 40
41 45 41
42 44 42
43 33 43
44 31 44
45 42 45
46 40 46
47 43 47
48 46 48
49 42 49
50 45 50
51 44 51
52 40 52
53 37 53
54 46 54
55 36 55
56 47 56
57 45 57
58 42 58
59 43 59
60 43 60
61 32 61
62 45 62
63 45 63
64 31 64
65 33 65
66 49 66
67 42 67
68 41 68
69 38 69
70 42 70
71 44 71
72 33 72
73 48 73
74 40 74
75 50 75
76 49 76
77 43 77
78 44 78
79 47 79
80 33 80
81 46 81
82 0 82
83 45 83
84 43 84
85 44 85
86 47 86
87 45 87
88 42 88
89 33 89
90 43 90
91 46 91
92 33 92
93 46 93
94 48 94
95 47 95
96 47 96
97 43 97
98 46 98
99 48 99
100 46 100
101 45 101
102 45 102
103 52 103
104 42 104
105 47 105
106 41 106
107 47 107
108 43 108
109 33 109
110 30 110
111 49 111
112 44 112
113 55 113
114 11 114
115 47 115
116 53 116
117 33 117
118 44 118
119 42 119
120 55 120
121 33 121
122 46 122
123 54 123
124 47 124
125 45 125
126 47 126
127 55 127
128 44 128
129 53 129
130 44 130
131 42 131
132 40 132
133 46 133
134 40 134
135 46 135
136 53 136
137 33 137
138 42 138
139 35 139
140 40 140
141 41 141
142 33 142
143 51 143
144 53 144
145 46 145
146 55 146
147 47 147
148 38 148
149 46 149
150 46 150
151 53 151
152 47 152
153 41 153
154 44 154
155 43 155
156 51 156
157 33 157
158 43 158
159 53 159
160 51 160
161 50 161
162 46 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month Connected Separate
-2.09744 0.98345 0.10524 -0.02193
Software Happiness Depression Belonging
0.49865 0.03942 -0.07390 0.03217
Belonging_Final t
-0.03779 -0.01291
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0838 -1.0971 0.1465 1.1567 4.1920
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.097441 5.099384 -0.411 0.6814
month 0.983452 0.548529 1.793 0.0750 .
Connected 0.105236 0.046886 2.244 0.0262 *
Separate -0.021934 0.044830 -0.489 0.6253
Software 0.498650 0.071170 7.006 7.42e-11 ***
Happiness 0.039419 0.076218 0.517 0.6058
Depression -0.073903 0.056407 -1.310 0.1921
Belonging 0.032173 0.044736 0.719 0.4731
Belonging_Final -0.037795 0.064240 -0.588 0.5572
t -0.012912 0.005824 -2.217 0.0281 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.833 on 152 degrees of freedom
Multiple R-squared: 0.3769, Adjusted R-squared: 0.34
F-statistic: 10.22 on 9 and 152 DF, p-value: 3.148e-12
> 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,] 0.74224497 0.51551005 0.25775503
[2,] 0.71802316 0.56395367 0.28197684
[3,] 0.59739687 0.80520627 0.40260313
[4,] 0.48485711 0.96971422 0.51514289
[5,] 0.40653230 0.81306461 0.59346770
[6,] 0.56449838 0.87100324 0.43550162
[7,] 0.47949243 0.95898487 0.52050757
[8,] 0.39081623 0.78163247 0.60918377
[9,] 0.31969152 0.63938304 0.68030848
[10,] 0.31233089 0.62466177 0.68766911
[11,] 0.44408976 0.88817953 0.55591024
[12,] 0.51887436 0.96225129 0.48112564
[13,] 0.46518800 0.93037601 0.53481200
[14,] 0.39645709 0.79291418 0.60354291
[15,] 0.36825316 0.73650632 0.63174684
[16,] 0.47365600 0.94731200 0.52634400
[17,] 0.41638681 0.83277363 0.58361319
[18,] 0.52800241 0.94399518 0.47199759
[19,] 0.47695773 0.95391545 0.52304227
[20,] 0.43956776 0.87913553 0.56043224
[21,] 0.42727143 0.85454286 0.57272857
[22,] 0.39626956 0.79253912 0.60373044
[23,] 0.34500024 0.69000048 0.65499976
[24,] 0.88009128 0.23981743 0.11990872
[25,] 0.85522932 0.28954136 0.14477068
[26,] 0.83676959 0.32646082 0.16323041
[27,] 0.86653386 0.26693228 0.13346614
[28,] 0.85666569 0.28666862 0.14333431
[29,] 0.83288758 0.33422483 0.16711242
[30,] 0.80451118 0.39097765 0.19548882
[31,] 0.83862704 0.32274592 0.16137296
[32,] 0.80340847 0.39318305 0.19659153
[33,] 0.77596706 0.44806588 0.22403294
[34,] 0.89300195 0.21399610 0.10699805
[35,] 0.91112964 0.17774072 0.08887036
[36,] 0.89115234 0.21769533 0.10884766
[37,] 0.89031818 0.21936365 0.10968182
[38,] 0.87993593 0.24012813 0.12006407
[39,] 0.85320285 0.29359431 0.14679715
[40,] 0.82432835 0.35134331 0.17567165
[41,] 0.82775877 0.34448247 0.17224123
[42,] 0.80303958 0.39392084 0.19696042
[43,] 0.81528722 0.36942555 0.18471278
[44,] 0.79682324 0.40635352 0.20317676
[45,] 0.76078412 0.47843175 0.23921588
[46,] 0.75011329 0.49977343 0.24988671
[47,] 0.71652522 0.56694955 0.28347478
[48,] 0.73401452 0.53197096 0.26598548
[49,] 0.70400688 0.59198624 0.29599312
[50,] 0.67153509 0.65692983 0.32846491
[51,] 0.63438693 0.73122615 0.36561307
[52,] 0.58914846 0.82170309 0.41085154
[53,] 0.54479366 0.91041268 0.45520634
[54,] 0.50052102 0.99895796 0.49947898
[55,] 0.47969459 0.95938918 0.52030541
[56,] 0.55011644 0.89976713 0.44988356
[57,] 0.72989484 0.54021032 0.27010516
[58,] 0.69006875 0.61986250 0.30993125
[59,] 0.82557600 0.34884801 0.17442400
[60,] 0.79504515 0.40990970 0.20495485
[61,] 0.78329582 0.43340837 0.21670418
[62,] 0.76226815 0.47546370 0.23773185
[63,] 0.72491660 0.55016680 0.27508340
[64,] 0.75264204 0.49471592 0.24735796
[65,] 0.71544584 0.56910832 0.28455416
[66,] 0.69202806 0.61594387 0.30797194
[67,] 0.70860774 0.58278451 0.29139226
[68,] 0.66654185 0.66691631 0.33345815
[69,] 0.62600548 0.74798904 0.37399452
[70,] 0.76315594 0.47368812 0.23684406
[71,] 0.72625405 0.54749191 0.27374595
[72,] 0.69808017 0.60383966 0.30191983
[73,] 0.65591806 0.68816388 0.34408194
[74,] 0.63847854 0.72304291 0.36152146
[75,] 0.59405181 0.81189638 0.40594819
[76,] 0.55126848 0.89746303 0.44873152
[77,] 0.52871931 0.94256138 0.47128069
[78,] 0.48865921 0.97731841 0.51134079
[79,] 0.47831632 0.95663264 0.52168368
[80,] 0.43641430 0.87282860 0.56358570
[81,] 0.39429736 0.78859472 0.60570264
[82,] 0.35064534 0.70129069 0.64935466
[83,] 0.37806852 0.75613705 0.62193148
[84,] 0.34135975 0.68271950 0.65864025
[85,] 0.29966920 0.59933839 0.70033080
[86,] 0.29195019 0.58390038 0.70804981
[87,] 0.25138813 0.50277627 0.74861187
[88,] 0.21798131 0.43596262 0.78201869
[89,] 0.19302711 0.38605421 0.80697289
[90,] 0.17546135 0.35092270 0.82453865
[91,] 0.22387641 0.44775281 0.77612359
[92,] 0.18952506 0.37905011 0.81047494
[93,] 0.18429044 0.36858088 0.81570956
[94,] 0.19369911 0.38739822 0.80630089
[95,] 0.17583504 0.35167009 0.82416496
[96,] 0.15534604 0.31069207 0.84465396
[97,] 0.15403673 0.30807346 0.84596327
[98,] 0.14603303 0.29206605 0.85396697
[99,] 0.13338139 0.26676279 0.86661861
[100,] 0.11426584 0.22853168 0.88573416
[101,] 0.15876063 0.31752127 0.84123937
[102,] 0.13915560 0.27831120 0.86084440
[103,] 0.15364488 0.30728976 0.84635512
[104,] 0.16364125 0.32728249 0.83635875
[105,] 0.13638396 0.27276792 0.86361604
[106,] 0.13877492 0.27754984 0.86122508
[107,] 0.12334042 0.24668083 0.87665958
[108,] 0.13620997 0.27241995 0.86379003
[109,] 0.10993194 0.21986387 0.89006806
[110,] 0.09690425 0.19380849 0.90309575
[111,] 0.09053933 0.18107865 0.90946067
[112,] 0.06965363 0.13930726 0.93034637
[113,] 0.05251662 0.10503324 0.94748338
[114,] 0.03933550 0.07867100 0.96066450
[115,] 0.02837267 0.05674533 0.97162733
[116,] 0.02970952 0.05941905 0.97029048
[117,] 0.03183282 0.06366564 0.96816718
[118,] 0.03001582 0.06003164 0.96998418
[119,] 0.03634420 0.07268840 0.96365580
[120,] 0.03693665 0.07387330 0.96306335
[121,] 0.09293727 0.18587453 0.90706273
[122,] 0.09484935 0.18969870 0.90515065
[123,] 0.07456200 0.14912400 0.92543800
[124,] 0.05767681 0.11535362 0.94232319
[125,] 0.04103510 0.08207019 0.95896490
[126,] 0.04557189 0.09114378 0.95442811
[127,] 0.04038235 0.08076470 0.95961765
[128,] 0.02661873 0.05323745 0.97338127
[129,] 0.54258059 0.91483881 0.45741941
[130,] 0.48329618 0.96659236 0.51670382
[131,] 0.40475496 0.80950992 0.59524504
[132,] 0.31269335 0.62538671 0.68730665
[133,] 0.22969430 0.45938860 0.77030570
[134,] 0.36739448 0.73478896 0.63260552
[135,] 0.29207049 0.58414097 0.70792951
[136,] 0.44991922 0.89983844 0.55008078
[137,] 0.46248880 0.92497759 0.53751120
> postscript(file="/var/fisher/rcomp/tmp/1wnw91355315942.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/fisher/rcomp/tmp/26uq81355315942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/33pt11355315942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4281e1355315942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5elrk1355315942.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 162
Frequency = 1
1 2 3 4 5
-3.3664139710 -0.3511633092 2.4810144691 2.5754813483 -1.7232541325
6 7 8 9 10
-2.2656211047 3.6959938327 -1.8806055339 -2.1742644041 2.0240706897
11 12 13 14 15
0.4621283693 -0.2730304009 0.3453472217 0.5851511099 -0.4673124255
16 17 18 19 20
-0.1971663136 0.1633868455 3.6820741145 2.4135657200 0.7119774724
21 22 23 24 25
0.6226356947 1.0405593856 2.6350875881 1.1911837118 2.0203210727
26 27 28 29 30
-0.0002319443 0.9499975717 -1.0346619396 0.5227319386 -0.1724151012
31 32 33 34 35
-0.6727476299 -0.3631045001 -1.1179646952 0.4951986653 -1.5900143486
36 37 38 39 40
-6.0838245771 -1.1448327229 -1.6749573229 1.7539121686 1.4109842202
41 42 43 44 45
0.9912698758 -1.3953469414 2.4951998580 -0.0711435079 -0.8365136237
46 47 48 49 50
-4.4183169847 -2.1649415449 0.2012420581 1.0839538450 -1.8005884479
51 52 53 54 55
-0.1553942166 0.0607793519 -2.4286294054 1.0221243831 -2.5287255028
56 57 58 59 60
1.4125013824 0.2901491419 1.2110472804 -0.0384876704 2.1057114197
61 62 63 64 65
0.6675316959 0.4077795207 -0.0548941946 -0.9090369341 0.0997017053
66 67 68 69 70
0.4578493370 1.4128611148 2.7057241925 -4.2780001671 0.1220975260
71 72 73 74 75
-4.1025809789 -0.7336303483 1.0794302042 0.5772718256 -0.2505555665
76 77 78 79 80
2.5194733714 -0.6437611904 1.0738084670 -2.2989641923 -0.3268420585
81 82 83 84 85
0.0604064194 3.4208251720 -0.0305278254 -1.2734682885 -0.1044073224
86 87 88 89 90
1.3019592592 -0.5878450729 0.3907894482 1.1843411258 0.4152672864
91 92 93 94 95
-1.8420498259 -0.0095241538 0.3266998493 -0.5278763174 -2.4589360504
96 97 98 99 100
0.5861722087 0.0215826906 1.5273052582 -0.2395875020 -0.5691198055
101 102 103 104 105
-1.2988558348 1.0303409514 2.6229580732 0.2105284082 1.3420532205
106 107 108 109 110
-2.4485210393 0.9428924321 0.1464548911 1.3886368682 -0.3778323218
111 112 113 114 115
0.9581733117 0.1466012969 2.3165767300 -1.6441271171 -2.8781464171
116 117 118 119 120
1.4310190672 -1.3115701637 0.9164336801 -1.9100717909 0.3590720086
121 122 123 124 125
-1.1756844001 0.4618464262 -2.8770440848 -0.8461606455 -0.8245581211
126 127 128 129 130
-0.5932149573 -0.3817989797 1.0066518441 1.2186622960 -2.7871167247
131 132 133 134 135
1.9277669421 -3.2508834141 2.0021448784 -1.7375513286 -1.3846669147
136 137 138 139 140
0.0725831898 0.8560125194 0.7925679508 -1.9838728206 -1.1817186816
141 142 143 144 145
-5.1949069827 3.1430718008 1.8391251576 0.8243450391 1.4489813193
146 147 148 149 150
-3.9730436216 2.4905543218 -1.7758089049 1.1809162874 0.1698352963
151 152 153 154 155
-2.8968837656 -0.8866436251 2.3058178911 4.1919766145 1.7922194995
156 157 158 159 160
-1.2855246057 0.8297394325 1.6431536148 1.6060147205 1.3452530941
161 162
-0.2028551958 0.7657039167
> postscript(file="/var/fisher/rcomp/tmp/6tqrc1355315942.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 = 162
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.3664139710 NA
1 -0.3511633092 -3.3664139710
2 2.4810144691 -0.3511633092
3 2.5754813483 2.4810144691
4 -1.7232541325 2.5754813483
5 -2.2656211047 -1.7232541325
6 3.6959938327 -2.2656211047
7 -1.8806055339 3.6959938327
8 -2.1742644041 -1.8806055339
9 2.0240706897 -2.1742644041
10 0.4621283693 2.0240706897
11 -0.2730304009 0.4621283693
12 0.3453472217 -0.2730304009
13 0.5851511099 0.3453472217
14 -0.4673124255 0.5851511099
15 -0.1971663136 -0.4673124255
16 0.1633868455 -0.1971663136
17 3.6820741145 0.1633868455
18 2.4135657200 3.6820741145
19 0.7119774724 2.4135657200
20 0.6226356947 0.7119774724
21 1.0405593856 0.6226356947
22 2.6350875881 1.0405593856
23 1.1911837118 2.6350875881
24 2.0203210727 1.1911837118
25 -0.0002319443 2.0203210727
26 0.9499975717 -0.0002319443
27 -1.0346619396 0.9499975717
28 0.5227319386 -1.0346619396
29 -0.1724151012 0.5227319386
30 -0.6727476299 -0.1724151012
31 -0.3631045001 -0.6727476299
32 -1.1179646952 -0.3631045001
33 0.4951986653 -1.1179646952
34 -1.5900143486 0.4951986653
35 -6.0838245771 -1.5900143486
36 -1.1448327229 -6.0838245771
37 -1.6749573229 -1.1448327229
38 1.7539121686 -1.6749573229
39 1.4109842202 1.7539121686
40 0.9912698758 1.4109842202
41 -1.3953469414 0.9912698758
42 2.4951998580 -1.3953469414
43 -0.0711435079 2.4951998580
44 -0.8365136237 -0.0711435079
45 -4.4183169847 -0.8365136237
46 -2.1649415449 -4.4183169847
47 0.2012420581 -2.1649415449
48 1.0839538450 0.2012420581
49 -1.8005884479 1.0839538450
50 -0.1553942166 -1.8005884479
51 0.0607793519 -0.1553942166
52 -2.4286294054 0.0607793519
53 1.0221243831 -2.4286294054
54 -2.5287255028 1.0221243831
55 1.4125013824 -2.5287255028
56 0.2901491419 1.4125013824
57 1.2110472804 0.2901491419
58 -0.0384876704 1.2110472804
59 2.1057114197 -0.0384876704
60 0.6675316959 2.1057114197
61 0.4077795207 0.6675316959
62 -0.0548941946 0.4077795207
63 -0.9090369341 -0.0548941946
64 0.0997017053 -0.9090369341
65 0.4578493370 0.0997017053
66 1.4128611148 0.4578493370
67 2.7057241925 1.4128611148
68 -4.2780001671 2.7057241925
69 0.1220975260 -4.2780001671
70 -4.1025809789 0.1220975260
71 -0.7336303483 -4.1025809789
72 1.0794302042 -0.7336303483
73 0.5772718256 1.0794302042
74 -0.2505555665 0.5772718256
75 2.5194733714 -0.2505555665
76 -0.6437611904 2.5194733714
77 1.0738084670 -0.6437611904
78 -2.2989641923 1.0738084670
79 -0.3268420585 -2.2989641923
80 0.0604064194 -0.3268420585
81 3.4208251720 0.0604064194
82 -0.0305278254 3.4208251720
83 -1.2734682885 -0.0305278254
84 -0.1044073224 -1.2734682885
85 1.3019592592 -0.1044073224
86 -0.5878450729 1.3019592592
87 0.3907894482 -0.5878450729
88 1.1843411258 0.3907894482
89 0.4152672864 1.1843411258
90 -1.8420498259 0.4152672864
91 -0.0095241538 -1.8420498259
92 0.3266998493 -0.0095241538
93 -0.5278763174 0.3266998493
94 -2.4589360504 -0.5278763174
95 0.5861722087 -2.4589360504
96 0.0215826906 0.5861722087
97 1.5273052582 0.0215826906
98 -0.2395875020 1.5273052582
99 -0.5691198055 -0.2395875020
100 -1.2988558348 -0.5691198055
101 1.0303409514 -1.2988558348
102 2.6229580732 1.0303409514
103 0.2105284082 2.6229580732
104 1.3420532205 0.2105284082
105 -2.4485210393 1.3420532205
106 0.9428924321 -2.4485210393
107 0.1464548911 0.9428924321
108 1.3886368682 0.1464548911
109 -0.3778323218 1.3886368682
110 0.9581733117 -0.3778323218
111 0.1466012969 0.9581733117
112 2.3165767300 0.1466012969
113 -1.6441271171 2.3165767300
114 -2.8781464171 -1.6441271171
115 1.4310190672 -2.8781464171
116 -1.3115701637 1.4310190672
117 0.9164336801 -1.3115701637
118 -1.9100717909 0.9164336801
119 0.3590720086 -1.9100717909
120 -1.1756844001 0.3590720086
121 0.4618464262 -1.1756844001
122 -2.8770440848 0.4618464262
123 -0.8461606455 -2.8770440848
124 -0.8245581211 -0.8461606455
125 -0.5932149573 -0.8245581211
126 -0.3817989797 -0.5932149573
127 1.0066518441 -0.3817989797
128 1.2186622960 1.0066518441
129 -2.7871167247 1.2186622960
130 1.9277669421 -2.7871167247
131 -3.2508834141 1.9277669421
132 2.0021448784 -3.2508834141
133 -1.7375513286 2.0021448784
134 -1.3846669147 -1.7375513286
135 0.0725831898 -1.3846669147
136 0.8560125194 0.0725831898
137 0.7925679508 0.8560125194
138 -1.9838728206 0.7925679508
139 -1.1817186816 -1.9838728206
140 -5.1949069827 -1.1817186816
141 3.1430718008 -5.1949069827
142 1.8391251576 3.1430718008
143 0.8243450391 1.8391251576
144 1.4489813193 0.8243450391
145 -3.9730436216 1.4489813193
146 2.4905543218 -3.9730436216
147 -1.7758089049 2.4905543218
148 1.1809162874 -1.7758089049
149 0.1698352963 1.1809162874
150 -2.8968837656 0.1698352963
151 -0.8866436251 -2.8968837656
152 2.3058178911 -0.8866436251
153 4.1919766145 2.3058178911
154 1.7922194995 4.1919766145
155 -1.2855246057 1.7922194995
156 0.8297394325 -1.2855246057
157 1.6431536148 0.8297394325
158 1.6060147205 1.6431536148
159 1.3452530941 1.6060147205
160 -0.2028551958 1.3452530941
161 0.7657039167 -0.2028551958
162 NA 0.7657039167
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.3511633092 -3.3664139710
[2,] 2.4810144691 -0.3511633092
[3,] 2.5754813483 2.4810144691
[4,] -1.7232541325 2.5754813483
[5,] -2.2656211047 -1.7232541325
[6,] 3.6959938327 -2.2656211047
[7,] -1.8806055339 3.6959938327
[8,] -2.1742644041 -1.8806055339
[9,] 2.0240706897 -2.1742644041
[10,] 0.4621283693 2.0240706897
[11,] -0.2730304009 0.4621283693
[12,] 0.3453472217 -0.2730304009
[13,] 0.5851511099 0.3453472217
[14,] -0.4673124255 0.5851511099
[15,] -0.1971663136 -0.4673124255
[16,] 0.1633868455 -0.1971663136
[17,] 3.6820741145 0.1633868455
[18,] 2.4135657200 3.6820741145
[19,] 0.7119774724 2.4135657200
[20,] 0.6226356947 0.7119774724
[21,] 1.0405593856 0.6226356947
[22,] 2.6350875881 1.0405593856
[23,] 1.1911837118 2.6350875881
[24,] 2.0203210727 1.1911837118
[25,] -0.0002319443 2.0203210727
[26,] 0.9499975717 -0.0002319443
[27,] -1.0346619396 0.9499975717
[28,] 0.5227319386 -1.0346619396
[29,] -0.1724151012 0.5227319386
[30,] -0.6727476299 -0.1724151012
[31,] -0.3631045001 -0.6727476299
[32,] -1.1179646952 -0.3631045001
[33,] 0.4951986653 -1.1179646952
[34,] -1.5900143486 0.4951986653
[35,] -6.0838245771 -1.5900143486
[36,] -1.1448327229 -6.0838245771
[37,] -1.6749573229 -1.1448327229
[38,] 1.7539121686 -1.6749573229
[39,] 1.4109842202 1.7539121686
[40,] 0.9912698758 1.4109842202
[41,] -1.3953469414 0.9912698758
[42,] 2.4951998580 -1.3953469414
[43,] -0.0711435079 2.4951998580
[44,] -0.8365136237 -0.0711435079
[45,] -4.4183169847 -0.8365136237
[46,] -2.1649415449 -4.4183169847
[47,] 0.2012420581 -2.1649415449
[48,] 1.0839538450 0.2012420581
[49,] -1.8005884479 1.0839538450
[50,] -0.1553942166 -1.8005884479
[51,] 0.0607793519 -0.1553942166
[52,] -2.4286294054 0.0607793519
[53,] 1.0221243831 -2.4286294054
[54,] -2.5287255028 1.0221243831
[55,] 1.4125013824 -2.5287255028
[56,] 0.2901491419 1.4125013824
[57,] 1.2110472804 0.2901491419
[58,] -0.0384876704 1.2110472804
[59,] 2.1057114197 -0.0384876704
[60,] 0.6675316959 2.1057114197
[61,] 0.4077795207 0.6675316959
[62,] -0.0548941946 0.4077795207
[63,] -0.9090369341 -0.0548941946
[64,] 0.0997017053 -0.9090369341
[65,] 0.4578493370 0.0997017053
[66,] 1.4128611148 0.4578493370
[67,] 2.7057241925 1.4128611148
[68,] -4.2780001671 2.7057241925
[69,] 0.1220975260 -4.2780001671
[70,] -4.1025809789 0.1220975260
[71,] -0.7336303483 -4.1025809789
[72,] 1.0794302042 -0.7336303483
[73,] 0.5772718256 1.0794302042
[74,] -0.2505555665 0.5772718256
[75,] 2.5194733714 -0.2505555665
[76,] -0.6437611904 2.5194733714
[77,] 1.0738084670 -0.6437611904
[78,] -2.2989641923 1.0738084670
[79,] -0.3268420585 -2.2989641923
[80,] 0.0604064194 -0.3268420585
[81,] 3.4208251720 0.0604064194
[82,] -0.0305278254 3.4208251720
[83,] -1.2734682885 -0.0305278254
[84,] -0.1044073224 -1.2734682885
[85,] 1.3019592592 -0.1044073224
[86,] -0.5878450729 1.3019592592
[87,] 0.3907894482 -0.5878450729
[88,] 1.1843411258 0.3907894482
[89,] 0.4152672864 1.1843411258
[90,] -1.8420498259 0.4152672864
[91,] -0.0095241538 -1.8420498259
[92,] 0.3266998493 -0.0095241538
[93,] -0.5278763174 0.3266998493
[94,] -2.4589360504 -0.5278763174
[95,] 0.5861722087 -2.4589360504
[96,] 0.0215826906 0.5861722087
[97,] 1.5273052582 0.0215826906
[98,] -0.2395875020 1.5273052582
[99,] -0.5691198055 -0.2395875020
[100,] -1.2988558348 -0.5691198055
[101,] 1.0303409514 -1.2988558348
[102,] 2.6229580732 1.0303409514
[103,] 0.2105284082 2.6229580732
[104,] 1.3420532205 0.2105284082
[105,] -2.4485210393 1.3420532205
[106,] 0.9428924321 -2.4485210393
[107,] 0.1464548911 0.9428924321
[108,] 1.3886368682 0.1464548911
[109,] -0.3778323218 1.3886368682
[110,] 0.9581733117 -0.3778323218
[111,] 0.1466012969 0.9581733117
[112,] 2.3165767300 0.1466012969
[113,] -1.6441271171 2.3165767300
[114,] -2.8781464171 -1.6441271171
[115,] 1.4310190672 -2.8781464171
[116,] -1.3115701637 1.4310190672
[117,] 0.9164336801 -1.3115701637
[118,] -1.9100717909 0.9164336801
[119,] 0.3590720086 -1.9100717909
[120,] -1.1756844001 0.3590720086
[121,] 0.4618464262 -1.1756844001
[122,] -2.8770440848 0.4618464262
[123,] -0.8461606455 -2.8770440848
[124,] -0.8245581211 -0.8461606455
[125,] -0.5932149573 -0.8245581211
[126,] -0.3817989797 -0.5932149573
[127,] 1.0066518441 -0.3817989797
[128,] 1.2186622960 1.0066518441
[129,] -2.7871167247 1.2186622960
[130,] 1.9277669421 -2.7871167247
[131,] -3.2508834141 1.9277669421
[132,] 2.0021448784 -3.2508834141
[133,] -1.7375513286 2.0021448784
[134,] -1.3846669147 -1.7375513286
[135,] 0.0725831898 -1.3846669147
[136,] 0.8560125194 0.0725831898
[137,] 0.7925679508 0.8560125194
[138,] -1.9838728206 0.7925679508
[139,] -1.1817186816 -1.9838728206
[140,] -5.1949069827 -1.1817186816
[141,] 3.1430718008 -5.1949069827
[142,] 1.8391251576 3.1430718008
[143,] 0.8243450391 1.8391251576
[144,] 1.4489813193 0.8243450391
[145,] -3.9730436216 1.4489813193
[146,] 2.4905543218 -3.9730436216
[147,] -1.7758089049 2.4905543218
[148,] 1.1809162874 -1.7758089049
[149,] 0.1698352963 1.1809162874
[150,] -2.8968837656 0.1698352963
[151,] -0.8866436251 -2.8968837656
[152,] 2.3058178911 -0.8866436251
[153,] 4.1919766145 2.3058178911
[154,] 1.7922194995 4.1919766145
[155,] -1.2855246057 1.7922194995
[156,] 0.8297394325 -1.2855246057
[157,] 1.6431536148 0.8297394325
[158,] 1.6060147205 1.6431536148
[159,] 1.3452530941 1.6060147205
[160,] -0.2028551958 1.3452530941
[161,] 0.7657039167 -0.2028551958
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.3511633092 -3.3664139710
2 2.4810144691 -0.3511633092
3 2.5754813483 2.4810144691
4 -1.7232541325 2.5754813483
5 -2.2656211047 -1.7232541325
6 3.6959938327 -2.2656211047
7 -1.8806055339 3.6959938327
8 -2.1742644041 -1.8806055339
9 2.0240706897 -2.1742644041
10 0.4621283693 2.0240706897
11 -0.2730304009 0.4621283693
12 0.3453472217 -0.2730304009
13 0.5851511099 0.3453472217
14 -0.4673124255 0.5851511099
15 -0.1971663136 -0.4673124255
16 0.1633868455 -0.1971663136
17 3.6820741145 0.1633868455
18 2.4135657200 3.6820741145
19 0.7119774724 2.4135657200
20 0.6226356947 0.7119774724
21 1.0405593856 0.6226356947
22 2.6350875881 1.0405593856
23 1.1911837118 2.6350875881
24 2.0203210727 1.1911837118
25 -0.0002319443 2.0203210727
26 0.9499975717 -0.0002319443
27 -1.0346619396 0.9499975717
28 0.5227319386 -1.0346619396
29 -0.1724151012 0.5227319386
30 -0.6727476299 -0.1724151012
31 -0.3631045001 -0.6727476299
32 -1.1179646952 -0.3631045001
33 0.4951986653 -1.1179646952
34 -1.5900143486 0.4951986653
35 -6.0838245771 -1.5900143486
36 -1.1448327229 -6.0838245771
37 -1.6749573229 -1.1448327229
38 1.7539121686 -1.6749573229
39 1.4109842202 1.7539121686
40 0.9912698758 1.4109842202
41 -1.3953469414 0.9912698758
42 2.4951998580 -1.3953469414
43 -0.0711435079 2.4951998580
44 -0.8365136237 -0.0711435079
45 -4.4183169847 -0.8365136237
46 -2.1649415449 -4.4183169847
47 0.2012420581 -2.1649415449
48 1.0839538450 0.2012420581
49 -1.8005884479 1.0839538450
50 -0.1553942166 -1.8005884479
51 0.0607793519 -0.1553942166
52 -2.4286294054 0.0607793519
53 1.0221243831 -2.4286294054
54 -2.5287255028 1.0221243831
55 1.4125013824 -2.5287255028
56 0.2901491419 1.4125013824
57 1.2110472804 0.2901491419
58 -0.0384876704 1.2110472804
59 2.1057114197 -0.0384876704
60 0.6675316959 2.1057114197
61 0.4077795207 0.6675316959
62 -0.0548941946 0.4077795207
63 -0.9090369341 -0.0548941946
64 0.0997017053 -0.9090369341
65 0.4578493370 0.0997017053
66 1.4128611148 0.4578493370
67 2.7057241925 1.4128611148
68 -4.2780001671 2.7057241925
69 0.1220975260 -4.2780001671
70 -4.1025809789 0.1220975260
71 -0.7336303483 -4.1025809789
72 1.0794302042 -0.7336303483
73 0.5772718256 1.0794302042
74 -0.2505555665 0.5772718256
75 2.5194733714 -0.2505555665
76 -0.6437611904 2.5194733714
77 1.0738084670 -0.6437611904
78 -2.2989641923 1.0738084670
79 -0.3268420585 -2.2989641923
80 0.0604064194 -0.3268420585
81 3.4208251720 0.0604064194
82 -0.0305278254 3.4208251720
83 -1.2734682885 -0.0305278254
84 -0.1044073224 -1.2734682885
85 1.3019592592 -0.1044073224
86 -0.5878450729 1.3019592592
87 0.3907894482 -0.5878450729
88 1.1843411258 0.3907894482
89 0.4152672864 1.1843411258
90 -1.8420498259 0.4152672864
91 -0.0095241538 -1.8420498259
92 0.3266998493 -0.0095241538
93 -0.5278763174 0.3266998493
94 -2.4589360504 -0.5278763174
95 0.5861722087 -2.4589360504
96 0.0215826906 0.5861722087
97 1.5273052582 0.0215826906
98 -0.2395875020 1.5273052582
99 -0.5691198055 -0.2395875020
100 -1.2988558348 -0.5691198055
101 1.0303409514 -1.2988558348
102 2.6229580732 1.0303409514
103 0.2105284082 2.6229580732
104 1.3420532205 0.2105284082
105 -2.4485210393 1.3420532205
106 0.9428924321 -2.4485210393
107 0.1464548911 0.9428924321
108 1.3886368682 0.1464548911
109 -0.3778323218 1.3886368682
110 0.9581733117 -0.3778323218
111 0.1466012969 0.9581733117
112 2.3165767300 0.1466012969
113 -1.6441271171 2.3165767300
114 -2.8781464171 -1.6441271171
115 1.4310190672 -2.8781464171
116 -1.3115701637 1.4310190672
117 0.9164336801 -1.3115701637
118 -1.9100717909 0.9164336801
119 0.3590720086 -1.9100717909
120 -1.1756844001 0.3590720086
121 0.4618464262 -1.1756844001
122 -2.8770440848 0.4618464262
123 -0.8461606455 -2.8770440848
124 -0.8245581211 -0.8461606455
125 -0.5932149573 -0.8245581211
126 -0.3817989797 -0.5932149573
127 1.0066518441 -0.3817989797
128 1.2186622960 1.0066518441
129 -2.7871167247 1.2186622960
130 1.9277669421 -2.7871167247
131 -3.2508834141 1.9277669421
132 2.0021448784 -3.2508834141
133 -1.7375513286 2.0021448784
134 -1.3846669147 -1.7375513286
135 0.0725831898 -1.3846669147
136 0.8560125194 0.0725831898
137 0.7925679508 0.8560125194
138 -1.9838728206 0.7925679508
139 -1.1817186816 -1.9838728206
140 -5.1949069827 -1.1817186816
141 3.1430718008 -5.1949069827
142 1.8391251576 3.1430718008
143 0.8243450391 1.8391251576
144 1.4489813193 0.8243450391
145 -3.9730436216 1.4489813193
146 2.4905543218 -3.9730436216
147 -1.7758089049 2.4905543218
148 1.1809162874 -1.7758089049
149 0.1698352963 1.1809162874
150 -2.8968837656 0.1698352963
151 -0.8866436251 -2.8968837656
152 2.3058178911 -0.8866436251
153 4.1919766145 2.3058178911
154 1.7922194995 4.1919766145
155 -1.2855246057 1.7922194995
156 0.8297394325 -1.2855246057
157 1.6431536148 0.8297394325
158 1.6060147205 1.6431536148
159 1.3452530941 1.6060147205
160 -0.2028551958 1.3452530941
161 0.7657039167 -0.2028551958
> 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/fisher/rcomp/tmp/7aky01355315942.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/fisher/rcomp/tmp/89yag1355315942.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/fisher/rcomp/tmp/9tc1v1355315942.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/fisher/rcomp/tmp/10mskd1355315942.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11fr571355315942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/123xam1355315942.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13otwd1355315943.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14w09z1355315943.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/fisher/rcomp/tmp/15v5ft1355315943.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/fisher/rcomp/tmp/161v9m1355315943.tab")
+ }
>
> try(system("convert tmp/1wnw91355315942.ps tmp/1wnw91355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/26uq81355315942.ps tmp/26uq81355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/33pt11355315942.ps tmp/33pt11355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/4281e1355315942.ps tmp/4281e1355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/5elrk1355315942.ps tmp/5elrk1355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tqrc1355315942.ps tmp/6tqrc1355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aky01355315942.ps tmp/7aky01355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/89yag1355315942.ps tmp/89yag1355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/9tc1v1355315942.ps tmp/9tc1v1355315942.png",intern=TRUE))
character(0)
> try(system("convert tmp/10mskd1355315942.ps tmp/10mskd1355315942.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.820 1.725 10.566