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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> x <- array(list(9
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+ ,dim=c(9
+ ,161)
+ ,dimnames=list(c('month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:161))
> y <- array(NA,dim=c(9,161),dimnames=list(c('month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),1:161))
> 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 = '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 12.0 53
2 16 9 39 32 11 18 11.0 83
3 19 9 30 35 15 11 14.0 66
4 15 9 31 33 6 12 12.0 67
5 14 9 34 37 13 16 21.0 76
6 13 9 35 29 10 18 12.0 78
7 19 9 39 31 12 14 22.0 53
8 15 9 34 36 14 14 11.0 80
9 14 9 36 35 12 15 10.0 74
10 15 9 37 38 9 15 13.0 76
11 16 9 38 31 10 17 10.0 79
12 16 9 36 34 12 19 8.0 54
13 16 9 38 35 12 10 15.0 67
14 16 9 39 38 11 16 14.0 54
15 17 9 33 37 15 18 10.0 87
16 15 9 32 33 12 14 14.0 58
17 15 9 36 32 10 14 14.0 75
18 20 9 38 38 12 17 11.0 88
19 18 9 39 38 11 14 10.0 64
20 16 9 32 32 12 16 13.0 57
21 16 9 32 33 11 18 9.5 66
22 16 9 31 31 12 11 14.0 68
23 19 9 39 38 13 14 12.0 54
24 16 9 37 39 11 12 14.0 56
25 17 9 39 32 12 17 11.0 86
26 17 9 41 32 13 9 9.0 80
27 16 9 36 35 10 16 11.0 76
28 15 9 33 37 14 14 15.0 69
29 16 9 33 33 12 15 14.0 78
30 14 9 34 33 10 11 13.0 67
31 15 9 31 31 12 16 9.0 80
32 12 9 27 32 8 13 15.0 54
33 14 9 37 31 10 17 10.0 71
34 16 9 34 37 12 15 11.0 84
35 14 9 34 30 12 14 13.0 74
36 10 9 32 33 7 16 8.0 71
37 10 9 29 31 9 9 20.0 63
38 14 9 36 33 12 15 12.0 71
39 16 9 29 31 10 17 10.0 76
40 16 9 35 33 10 13 10.0 69
41 16 9 37 32 10 15 9.0 74
42 14 9 34 33 12 16 14.0 75
43 20 9 38 32 15 16 8.0 54
44 14 9 35 33 10 12 14.0 52
45 14 9 38 28 10 15 11.0 69
46 11 9 37 35 12 11 13.0 68
47 14 9 38 39 13 15 9.0 65
48 15 9 33 34 11 15 11.0 75
49 16 9 36 38 11 17 15.0 74
50 14 9 38 32 12 13 11.0 75
51 16 9 32 38 14 16 10.0 72
52 14 9 32 30 10 14 14.0 67
53 12 9 32 33 12 11 18.0 63
54 16 10 34 38 13 12 14.0 62
55 9 10 32 32 5 12 11.0 63
56 14 10 37 35 6 15 14.5 76
57 16 10 39 34 12 16 13.0 74
58 16 10 29 34 12 15 9.0 67
59 15 10 37 36 11 12 10.0 73
60 16 10 35 34 10 12 15.0 70
61 12 10 30 28 7 8 20.0 53
62 16 10 38 34 12 13 12.0 77
63 16 10 34 35 14 11 12.0 80
64 14 10 31 35 11 14 14.0 52
65 16 10 34 31 12 15 13.0 54
66 17 10 35 37 13 10 11.0 80
67 18 10 36 35 14 11 17.0 66
68 18 10 30 27 11 12 12.0 73
69 12 10 39 40 12 15 13.0 63
70 16 10 35 37 12 15 14.0 69
71 10 10 38 36 8 14 13.0 67
72 14 10 31 38 11 16 15.0 54
73 18 10 34 39 14 15 13.0 81
74 18 10 38 41 14 15 10.0 69
75 16 10 34 27 12 13 11.0 84
76 17 10 39 30 9 12 19.0 80
77 16 10 37 37 13 17 13.0 70
78 16 10 34 31 11 13 17.0 69
79 13 10 28 31 12 15 13.0 77
80 16 10 37 27 12 13 9.0 54
81 16 10 33 36 12 15 11.0 79
82 16 10 35 37 12 15 9.0 71
83 15 10 37 33 12 16 12.0 73
84 15 10 32 34 11 15 12.0 72
85 16 10 33 31 10 14 13.0 77
86 14 10 38 39 9 15 13.0 75
87 16 10 33 34 12 14 12.0 69
88 16 10 29 32 12 13 15.0 54
89 15 10 33 33 12 7 22.0 70
90 12 10 31 36 9 17 13.0 73
91 17 10 36 32 15 13 15.0 54
92 16 10 35 41 12 15 13.0 77
93 15 10 32 28 12 14 15.0 82
94 13 10 29 30 12 13 12.5 80
95 16 10 39 36 10 16 11.0 80
96 16 10 37 35 13 12 16.0 69
97 16 10 35 31 9 14 11.0 78
98 16 10 37 34 12 17 11.0 81
99 14 10 32 36 10 15 10.0 76
100 16 10 38 36 14 17 10.0 76
101 16 10 37 35 11 12 16.0 73
102 20 10 36 37 15 16 12.0 85
103 15 10 32 28 11 11 11.0 66
104 16 10 33 39 11 15 16.0 79
105 13 10 40 32 12 9 19.0 68
106 17 10 38 35 12 16 11.0 76
107 16 10 41 39 12 15 16.0 71
108 16 10 36 35 11 10 15.0 54
109 12 11 43 42 7 10 24.0 46
110 16 11 30 34 12 15 14.0 85
111 16 11 31 33 14 11 15.0 74
112 17 11 32 41 11 13 11.0 88
113 13 11 32 33 11 14 15.0 38
114 12 11 37 34 10 18 12.0 76
115 18 11 37 32 13 16 10.0 86
116 14 11 33 40 13 14 14.0 54
117 14 11 34 40 8 14 13.0 67
118 13 11 33 35 11 14 9.0 69
119 16 11 38 36 12 14 15.0 90
120 13 11 33 37 11 12 15.0 54
121 16 11 31 27 13 14 14.0 76
122 13 11 38 39 12 15 11.0 89
123 16 11 37 38 14 15 8.0 76
124 15 11 36 31 13 15 11.0 73
125 16 11 31 33 15 13 11.0 79
126 15 11 39 32 10 17 8.0 90
127 17 11 44 39 11 17 10.0 74
128 15 11 33 36 9 19 11.0 81
129 12 11 35 33 11 15 13.0 72
130 16 11 32 33 10 13 11.0 71
131 10 11 28 32 11 9 20.0 66
132 16 11 40 37 8 15 10.0 77
133 12 11 27 30 11 15 15.0 65
134 14 11 37 38 12 15 12.0 74
135 15 11 32 29 12 16 14.0 85
136 13 11 28 22 9 11 23.0 54
137 15 11 34 35 11 14 14.0 63
138 11 11 30 35 10 11 16.0 54
139 12 11 35 34 8 15 11.0 64
140 11 11 31 35 9 13 12.0 69
141 16 11 32 34 8 15 10.0 54
142 15 11 30 37 9 16 14.0 84
143 17 11 30 35 15 14 12.0 86
144 16 11 31 23 11 15 12.0 77
145 10 11 40 31 8 16 11.0 89
146 18 11 32 27 13 16 12.0 76
147 13 11 36 36 12 11 13.0 60
148 16 11 32 31 12 12 11.0 75
149 13 11 35 32 9 9 19.0 73
150 10 11 38 39 7 16 12.0 85
151 15 11 42 37 13 13 17.0 79
152 16 11 34 38 9 16 9.0 71
153 16 11 35 39 6 12 12.0 72
154 14 11 38 34 8 9 19.0 69
155 10 11 33 31 8 13 18.0 78
156 17 11 36 32 15 13 15.0 54
157 13 11 32 37 6 14 14.0 69
158 15 11 33 36 9 19 11.0 81
159 16 11 34 32 11 13 9.0 84
160 12 11 32 38 8 12 18.0 84
161 13 11 34 36 8 13 16.0 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 48 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 45 82
83 43 83
84 44 84
85 47 85
86 45 86
87 42 87
88 33 88
89 43 89
90 46 90
91 33 91
92 46 92
93 48 93
94 47 94
95 47 95
96 43 96
97 46 97
98 48 98
99 46 99
100 45 100
101 45 101
102 52 102
103 42 103
104 47 104
105 41 105
106 47 106
107 43 107
108 33 108
109 30 109
110 52 110
111 44 111
112 55 112
113 11 113
114 47 114
115 53 115
116 33 116
117 44 117
118 42 118
119 55 119
120 33 120
121 46 121
122 54 122
123 47 123
124 45 124
125 47 125
126 55 126
127 44 127
128 53 128
129 44 129
130 42 130
131 40 131
132 46 132
133 40 133
134 46 134
135 53 135
136 33 136
137 42 137
138 35 138
139 40 139
140 41 140
141 33 141
142 51 142
143 53 143
144 46 144
145 55 145
146 47 146
147 38 147
148 46 148
149 46 149
150 53 150
151 47 151
152 41 152
153 44 153
154 43 154
155 51 155
156 33 156
157 43 157
158 53 158
159 51 159
160 50 160
161 46 161
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month Connected Separate
5.456741 0.147564 0.087991 -0.020540
Software Happiness Depression Belonging
0.522499 0.030456 -0.087567 -0.024164
Belonging_Final t
0.064957 -0.006402
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.449 -1.055 0.190 1.111 4.053
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.456741 4.962242 1.100 0.2732
month 0.147564 0.506087 0.292 0.7710
Connected 0.087991 0.043569 2.020 0.0452 *
Separate -0.020540 0.042088 -0.488 0.6262
Software 0.522499 0.067502 7.740 1.32e-12 ***
Happiness 0.030456 0.071749 0.424 0.6718
Depression -0.087567 0.053313 -1.643 0.1026
Belonging -0.024164 0.047810 -0.505 0.6140
Belonging_Final 0.064957 0.074513 0.872 0.3847
t -0.006402 0.008885 -0.721 0.4723
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.723 on 151 degrees of freedom
Multiple R-squared: 0.3796, Adjusted R-squared: 0.3426
F-statistic: 10.26 on 9 and 151 DF, p-value: 2.89e-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,] 0.37311773 0.74623547 0.6268823
[2,] 0.37260560 0.74521120 0.6273944
[3,] 0.24257952 0.48515903 0.7574205
[4,] 0.19467043 0.38934087 0.8053296
[5,] 0.20777345 0.41554690 0.7922265
[6,] 0.41270168 0.82540336 0.5872983
[7,] 0.36390196 0.72780392 0.6360980
[8,] 0.30819167 0.61638334 0.6918083
[9,] 0.25515715 0.51031430 0.7448429
[10,] 0.29687623 0.59375245 0.7031238
[11,] 0.45574194 0.91148388 0.5442581
[12,] 0.59856262 0.80287475 0.4014374
[13,] 0.52711669 0.94576662 0.4728833
[14,] 0.46959347 0.93918694 0.5304065
[15,] 0.46025477 0.92050954 0.5397452
[16,] 0.58998798 0.82002404 0.4100120
[17,] 0.53541961 0.92916078 0.4645804
[18,] 0.64854103 0.70291794 0.3514590
[19,] 0.60673400 0.78653199 0.3932660
[20,] 0.57503934 0.84992132 0.4249607
[21,] 0.55754243 0.88491513 0.4424576
[22,] 0.51433800 0.97132400 0.4856620
[23,] 0.46403160 0.92806320 0.5359684
[24,] 0.63016569 0.73966862 0.3698343
[25,] 0.66316022 0.67367956 0.3368398
[26,] 0.64380109 0.71239782 0.3561989
[27,] 0.68446981 0.63106038 0.3155302
[28,] 0.66481586 0.67036829 0.3351841
[29,] 0.62381659 0.75236683 0.3761834
[30,] 0.58407442 0.83185116 0.4159256
[31,] 0.61685728 0.76628544 0.3831427
[32,] 0.56287186 0.87425629 0.4371281
[33,] 0.53000470 0.93999059 0.4699953
[34,] 0.74869696 0.50260608 0.2513030
[35,] 0.82280358 0.35439283 0.1771964
[36,] 0.79156878 0.41686243 0.2084312
[37,] 0.80586545 0.38826910 0.1941346
[38,] 0.80247590 0.39504820 0.1975241
[39,] 0.76960850 0.46078300 0.2303915
[40,] 0.73570211 0.52859579 0.2642979
[41,] 0.76544055 0.46911890 0.2345595
[42,] 0.72540374 0.54919252 0.2745963
[43,] 0.73687682 0.52624636 0.2631232
[44,] 0.74540584 0.50918832 0.2545942
[45,] 0.70682356 0.58635288 0.2931764
[46,] 0.67970350 0.64059301 0.3202965
[47,] 0.64108705 0.71782589 0.3589129
[48,] 0.64960607 0.70078785 0.3503939
[49,] 0.60902963 0.78194074 0.3909704
[50,] 0.56373838 0.87252324 0.4362616
[51,] 0.52094345 0.95811309 0.4790565
[52,] 0.47275880 0.94551759 0.5272412
[53,] 0.43137788 0.86275576 0.5686221
[54,] 0.39993519 0.79987038 0.6000648
[55,] 0.40054209 0.80108419 0.5994579
[56,] 0.54530330 0.90939340 0.4546967
[57,] 0.69330660 0.61338680 0.3066934
[58,] 0.65696035 0.68607931 0.3430397
[59,] 0.80075609 0.39848782 0.1992439
[60,] 0.76665340 0.46669321 0.2333466
[61,] 0.76385520 0.47228960 0.2361448
[62,] 0.74710915 0.50578170 0.2528908
[63,] 0.70756304 0.58487393 0.2924370
[64,] 0.78941052 0.42117896 0.2105895
[65,] 0.75413114 0.49173773 0.2458689
[66,] 0.74043525 0.51912951 0.2595648
[67,] 0.76475777 0.47048447 0.2352422
[68,] 0.72665196 0.54669608 0.2733480
[69,] 0.69155254 0.61689493 0.3084475
[70,] 0.65031181 0.69937638 0.3496882
[71,] 0.61579379 0.76841242 0.3842062
[72,] 0.57139769 0.85720462 0.4286023
[73,] 0.55951172 0.88097656 0.4404883
[74,] 0.51547914 0.96904173 0.4845209
[75,] 0.47298036 0.94596072 0.5270196
[76,] 0.45318303 0.90636606 0.5468170
[77,] 0.41732555 0.83465109 0.5826745
[78,] 0.42526380 0.85052759 0.5747362
[79,] 0.38060568 0.76121136 0.6193943
[80,] 0.34528862 0.69057724 0.6547114
[81,] 0.30366924 0.60733848 0.6963308
[82,] 0.34655049 0.69310099 0.6534495
[83,] 0.31670164 0.63340327 0.6832984
[84,] 0.27477185 0.54954370 0.7252282
[85,] 0.26985668 0.53971335 0.7301433
[86,] 0.23275847 0.46551695 0.7672415
[87,] 0.21932863 0.43865726 0.7806714
[88,] 0.21491588 0.42983176 0.7850841
[89,] 0.18982852 0.37965704 0.8101715
[90,] 0.21162104 0.42324208 0.7883790
[91,] 0.18150036 0.36300073 0.8184996
[92,] 0.16543630 0.33087260 0.8345637
[93,] 0.18973820 0.37947641 0.8102618
[94,] 0.16077932 0.32155864 0.8392207
[95,] 0.13238673 0.26477347 0.8676133
[96,] 0.11688470 0.23376940 0.8831153
[97,] 0.11931025 0.23862049 0.8806898
[98,] 0.10722643 0.21445285 0.8927736
[99,] 0.09210815 0.18421630 0.9078919
[100,] 0.11796833 0.23593666 0.8820317
[101,] 0.10264325 0.20528651 0.8973567
[102,] 0.13878247 0.27756494 0.8612175
[103,] 0.15126874 0.30253747 0.8487313
[104,] 0.13068647 0.26137294 0.8693135
[105,] 0.14121654 0.28243307 0.8587835
[106,] 0.13907780 0.27815561 0.8609222
[107,] 0.16855008 0.33710016 0.8314499
[108,] 0.13979273 0.27958546 0.8602073
[109,] 0.12689640 0.25379280 0.8731036
[110,] 0.13186652 0.26373303 0.8681335
[111,] 0.10573909 0.21147818 0.8942609
[112,] 0.08579847 0.17159693 0.9142015
[113,] 0.06593820 0.13187641 0.9340618
[114,] 0.04914669 0.09829338 0.9508533
[115,] 0.04747943 0.09495885 0.9525206
[116,] 0.05691121 0.11382242 0.9430888
[117,] 0.06182525 0.12365050 0.9381748
[118,] 0.06386774 0.12773548 0.9361323
[119,] 0.08098168 0.16196336 0.9190183
[120,] 0.14559020 0.29118040 0.8544098
[121,] 0.17310834 0.34621668 0.8268917
[122,] 0.13758924 0.27517848 0.8624108
[123,] 0.11244280 0.22488560 0.8875572
[124,] 0.08326603 0.16653207 0.9167340
[125,] 0.10318641 0.20637282 0.8968136
[126,] 0.10859853 0.21719707 0.8914015
[127,] 0.08212842 0.16425685 0.9178716
[128,] 0.24736465 0.49472931 0.7526353
[129,] 0.20758210 0.41516421 0.7924179
[130,] 0.16725411 0.33450821 0.8327459
[131,] 0.12516540 0.25033079 0.8748346
[132,] 0.08204348 0.16408696 0.9179565
[133,] 0.15617842 0.31235684 0.8438216
[134,] 0.17096497 0.34192993 0.8290350
[135,] 0.29176647 0.58353294 0.7082335
[136,] 0.17735662 0.35471323 0.8226434
> postscript(file="/var/fisher/rcomp/tmp/1jfi81355065236.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/2xyyv1355065236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3wlzq1355065236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4bfxu1355065236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5r0cl1355065236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 161
Frequency = 1
1 2 3 4 5
-3.0490104618 -0.1860219269 2.2336464364 2.6969964224 -1.5769323250
6 7 8 9 10
-2.1209943344 3.5724916793 -2.0137390005 -2.1620417775 0.5666024473
11 12 13 14 15
0.4376967412 -0.1641331339 0.3034268977 0.8061316553 -0.6829613161
16 17 18 19 20
-0.2276273945 0.2774450791 3.5615578519 2.2707720852 0.6047977754
21 22 23 24 25
0.6795326949 0.6710519727 2.7045349633 0.9770042032 0.4767288854
26 27 28 29 30
0.0329684114 1.2335548499 -1.3679090284 0.6358276313 -0.5673550620
31 32 33 34 35
-0.8962595562 -0.3995386159 -1.1370275805 -0.0403222945 -1.4342652606
36 37 38 39 40
-3.2789234544 -2.5692353189 -1.7848933482 1.5312657607 1.2633072028
41 42 43 44 45
0.8506564963 -1.4069230427 2.3411548846 -0.0266194648 -1.0447004096
46 47 48 49 50
-4.4488116565 -2.7101982421 -0.0996382114 1.2499734127 -1.9644976463
51 52 53 54 55
-0.5383768845 -0.0561112677 -2.4932319603 -0.2196790652 -2.5695101721
56 57 58 59 60
1.3507802823 -0.0545534288 0.5376690969 -0.3373127577 1.6918339378
61 62 63 64 65
0.4458467903 0.1417451844 -0.5858112863 -0.2364998541 0.7016597750
66 67 68 69 70
0.7869160975 1.7530968962 3.4564346851 -3.6351315346 0.6343384796
71 72 73 74 75
-3.7891318494 -0.1785908244 1.5502804247 1.2127840206 0.1899565287
76 77 78 79 80
3.0848156559 -0.2085982734 1.3665071225 -2.0343823104 0.1622065979
81 82 83 84 85
0.5793196641 0.1267883720 -0.7144634313 0.2162683612 1.6395310906
86 87 88 89 90
-0.0560716933 0.7128642581 1.5454606236 0.7532046255 -1.6503435998
91 92 93 94 95
0.3812342125 0.7032721837 -0.0968770977 -1.9572539543 1.1147559775
96 97 98 99 100
0.2627887784 1.9768645915 0.1526182446 -0.3325070655 -0.9400016902
101 102 103 104 105
1.3065412813 2.7151955376 0.2338617841 1.6835759135 -2.0228622262
106 107 108 109 110
1.1109796286 0.5428707485 1.7330617333 -0.0005969502 0.8582651654
111 112 113 114 115
0.1743834377 2.0372001774 -0.1509986333 -2.8462612594 1.2892418835
116 117 118 119 120
-1.2509980380 0.7919448838 -1.9558849040 0.2970126255 -1.0935310256
121 122 123 124 125
0.3771429081 -2.9620919368 -1.0553726530 -1.2621382249 -0.7437130668
126 127 128 129 130
-0.4876652396 1.2031011888 0.7719710113 -2.8401289874 1.9442742062
131 132 133 134 135
-3.3213728124 2.1169849858 -1.9063969972 -1.5730496432 -0.3557695740
136 137 138 139 140
0.9167535120 0.3706222762 -2.2447853712 -1.2966873506 -2.2359344356
141 142 143 144 145
3.1055819409 1.7025986012 0.3371204573 1.3058100622 -4.1605574799
146 147 148 149 150
2.1482093231 -2.0521548949 0.8407255844 -0.0852293404 -3.1449173603
151 152 153 154 155
-0.8925878118 2.3328001663 4.0530681344 1.3446038444 -2.7822320473
156 157 158 159 160
0.6498330614 1.4082571067 0.9640459858 0.9653107609 -0.2780470851
161
0.2030662964
> postscript(file="/var/fisher/rcomp/tmp/6ru6m1355065236.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 = 161
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.0490104618 NA
1 -0.1860219269 -3.0490104618
2 2.2336464364 -0.1860219269
3 2.6969964224 2.2336464364
4 -1.5769323250 2.6969964224
5 -2.1209943344 -1.5769323250
6 3.5724916793 -2.1209943344
7 -2.0137390005 3.5724916793
8 -2.1620417775 -2.0137390005
9 0.5666024473 -2.1620417775
10 0.4376967412 0.5666024473
11 -0.1641331339 0.4376967412
12 0.3034268977 -0.1641331339
13 0.8061316553 0.3034268977
14 -0.6829613161 0.8061316553
15 -0.2276273945 -0.6829613161
16 0.2774450791 -0.2276273945
17 3.5615578519 0.2774450791
18 2.2707720852 3.5615578519
19 0.6047977754 2.2707720852
20 0.6795326949 0.6047977754
21 0.6710519727 0.6795326949
22 2.7045349633 0.6710519727
23 0.9770042032 2.7045349633
24 0.4767288854 0.9770042032
25 0.0329684114 0.4767288854
26 1.2335548499 0.0329684114
27 -1.3679090284 1.2335548499
28 0.6358276313 -1.3679090284
29 -0.5673550620 0.6358276313
30 -0.8962595562 -0.5673550620
31 -0.3995386159 -0.8962595562
32 -1.1370275805 -0.3995386159
33 -0.0403222945 -1.1370275805
34 -1.4342652606 -0.0403222945
35 -3.2789234544 -1.4342652606
36 -2.5692353189 -3.2789234544
37 -1.7848933482 -2.5692353189
38 1.5312657607 -1.7848933482
39 1.2633072028 1.5312657607
40 0.8506564963 1.2633072028
41 -1.4069230427 0.8506564963
42 2.3411548846 -1.4069230427
43 -0.0266194648 2.3411548846
44 -1.0447004096 -0.0266194648
45 -4.4488116565 -1.0447004096
46 -2.7101982421 -4.4488116565
47 -0.0996382114 -2.7101982421
48 1.2499734127 -0.0996382114
49 -1.9644976463 1.2499734127
50 -0.5383768845 -1.9644976463
51 -0.0561112677 -0.5383768845
52 -2.4932319603 -0.0561112677
53 -0.2196790652 -2.4932319603
54 -2.5695101721 -0.2196790652
55 1.3507802823 -2.5695101721
56 -0.0545534288 1.3507802823
57 0.5376690969 -0.0545534288
58 -0.3373127577 0.5376690969
59 1.6918339378 -0.3373127577
60 0.4458467903 1.6918339378
61 0.1417451844 0.4458467903
62 -0.5858112863 0.1417451844
63 -0.2364998541 -0.5858112863
64 0.7016597750 -0.2364998541
65 0.7869160975 0.7016597750
66 1.7530968962 0.7869160975
67 3.4564346851 1.7530968962
68 -3.6351315346 3.4564346851
69 0.6343384796 -3.6351315346
70 -3.7891318494 0.6343384796
71 -0.1785908244 -3.7891318494
72 1.5502804247 -0.1785908244
73 1.2127840206 1.5502804247
74 0.1899565287 1.2127840206
75 3.0848156559 0.1899565287
76 -0.2085982734 3.0848156559
77 1.3665071225 -0.2085982734
78 -2.0343823104 1.3665071225
79 0.1622065979 -2.0343823104
80 0.5793196641 0.1622065979
81 0.1267883720 0.5793196641
82 -0.7144634313 0.1267883720
83 0.2162683612 -0.7144634313
84 1.6395310906 0.2162683612
85 -0.0560716933 1.6395310906
86 0.7128642581 -0.0560716933
87 1.5454606236 0.7128642581
88 0.7532046255 1.5454606236
89 -1.6503435998 0.7532046255
90 0.3812342125 -1.6503435998
91 0.7032721837 0.3812342125
92 -0.0968770977 0.7032721837
93 -1.9572539543 -0.0968770977
94 1.1147559775 -1.9572539543
95 0.2627887784 1.1147559775
96 1.9768645915 0.2627887784
97 0.1526182446 1.9768645915
98 -0.3325070655 0.1526182446
99 -0.9400016902 -0.3325070655
100 1.3065412813 -0.9400016902
101 2.7151955376 1.3065412813
102 0.2338617841 2.7151955376
103 1.6835759135 0.2338617841
104 -2.0228622262 1.6835759135
105 1.1109796286 -2.0228622262
106 0.5428707485 1.1109796286
107 1.7330617333 0.5428707485
108 -0.0005969502 1.7330617333
109 0.8582651654 -0.0005969502
110 0.1743834377 0.8582651654
111 2.0372001774 0.1743834377
112 -0.1509986333 2.0372001774
113 -2.8462612594 -0.1509986333
114 1.2892418835 -2.8462612594
115 -1.2509980380 1.2892418835
116 0.7919448838 -1.2509980380
117 -1.9558849040 0.7919448838
118 0.2970126255 -1.9558849040
119 -1.0935310256 0.2970126255
120 0.3771429081 -1.0935310256
121 -2.9620919368 0.3771429081
122 -1.0553726530 -2.9620919368
123 -1.2621382249 -1.0553726530
124 -0.7437130668 -1.2621382249
125 -0.4876652396 -0.7437130668
126 1.2031011888 -0.4876652396
127 0.7719710113 1.2031011888
128 -2.8401289874 0.7719710113
129 1.9442742062 -2.8401289874
130 -3.3213728124 1.9442742062
131 2.1169849858 -3.3213728124
132 -1.9063969972 2.1169849858
133 -1.5730496432 -1.9063969972
134 -0.3557695740 -1.5730496432
135 0.9167535120 -0.3557695740
136 0.3706222762 0.9167535120
137 -2.2447853712 0.3706222762
138 -1.2966873506 -2.2447853712
139 -2.2359344356 -1.2966873506
140 3.1055819409 -2.2359344356
141 1.7025986012 3.1055819409
142 0.3371204573 1.7025986012
143 1.3058100622 0.3371204573
144 -4.1605574799 1.3058100622
145 2.1482093231 -4.1605574799
146 -2.0521548949 2.1482093231
147 0.8407255844 -2.0521548949
148 -0.0852293404 0.8407255844
149 -3.1449173603 -0.0852293404
150 -0.8925878118 -3.1449173603
151 2.3328001663 -0.8925878118
152 4.0530681344 2.3328001663
153 1.3446038444 4.0530681344
154 -2.7822320473 1.3446038444
155 0.6498330614 -2.7822320473
156 1.4082571067 0.6498330614
157 0.9640459858 1.4082571067
158 0.9653107609 0.9640459858
159 -0.2780470851 0.9653107609
160 0.2030662964 -0.2780470851
161 NA 0.2030662964
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1860219269 -3.0490104618
[2,] 2.2336464364 -0.1860219269
[3,] 2.6969964224 2.2336464364
[4,] -1.5769323250 2.6969964224
[5,] -2.1209943344 -1.5769323250
[6,] 3.5724916793 -2.1209943344
[7,] -2.0137390005 3.5724916793
[8,] -2.1620417775 -2.0137390005
[9,] 0.5666024473 -2.1620417775
[10,] 0.4376967412 0.5666024473
[11,] -0.1641331339 0.4376967412
[12,] 0.3034268977 -0.1641331339
[13,] 0.8061316553 0.3034268977
[14,] -0.6829613161 0.8061316553
[15,] -0.2276273945 -0.6829613161
[16,] 0.2774450791 -0.2276273945
[17,] 3.5615578519 0.2774450791
[18,] 2.2707720852 3.5615578519
[19,] 0.6047977754 2.2707720852
[20,] 0.6795326949 0.6047977754
[21,] 0.6710519727 0.6795326949
[22,] 2.7045349633 0.6710519727
[23,] 0.9770042032 2.7045349633
[24,] 0.4767288854 0.9770042032
[25,] 0.0329684114 0.4767288854
[26,] 1.2335548499 0.0329684114
[27,] -1.3679090284 1.2335548499
[28,] 0.6358276313 -1.3679090284
[29,] -0.5673550620 0.6358276313
[30,] -0.8962595562 -0.5673550620
[31,] -0.3995386159 -0.8962595562
[32,] -1.1370275805 -0.3995386159
[33,] -0.0403222945 -1.1370275805
[34,] -1.4342652606 -0.0403222945
[35,] -3.2789234544 -1.4342652606
[36,] -2.5692353189 -3.2789234544
[37,] -1.7848933482 -2.5692353189
[38,] 1.5312657607 -1.7848933482
[39,] 1.2633072028 1.5312657607
[40,] 0.8506564963 1.2633072028
[41,] -1.4069230427 0.8506564963
[42,] 2.3411548846 -1.4069230427
[43,] -0.0266194648 2.3411548846
[44,] -1.0447004096 -0.0266194648
[45,] -4.4488116565 -1.0447004096
[46,] -2.7101982421 -4.4488116565
[47,] -0.0996382114 -2.7101982421
[48,] 1.2499734127 -0.0996382114
[49,] -1.9644976463 1.2499734127
[50,] -0.5383768845 -1.9644976463
[51,] -0.0561112677 -0.5383768845
[52,] -2.4932319603 -0.0561112677
[53,] -0.2196790652 -2.4932319603
[54,] -2.5695101721 -0.2196790652
[55,] 1.3507802823 -2.5695101721
[56,] -0.0545534288 1.3507802823
[57,] 0.5376690969 -0.0545534288
[58,] -0.3373127577 0.5376690969
[59,] 1.6918339378 -0.3373127577
[60,] 0.4458467903 1.6918339378
[61,] 0.1417451844 0.4458467903
[62,] -0.5858112863 0.1417451844
[63,] -0.2364998541 -0.5858112863
[64,] 0.7016597750 -0.2364998541
[65,] 0.7869160975 0.7016597750
[66,] 1.7530968962 0.7869160975
[67,] 3.4564346851 1.7530968962
[68,] -3.6351315346 3.4564346851
[69,] 0.6343384796 -3.6351315346
[70,] -3.7891318494 0.6343384796
[71,] -0.1785908244 -3.7891318494
[72,] 1.5502804247 -0.1785908244
[73,] 1.2127840206 1.5502804247
[74,] 0.1899565287 1.2127840206
[75,] 3.0848156559 0.1899565287
[76,] -0.2085982734 3.0848156559
[77,] 1.3665071225 -0.2085982734
[78,] -2.0343823104 1.3665071225
[79,] 0.1622065979 -2.0343823104
[80,] 0.5793196641 0.1622065979
[81,] 0.1267883720 0.5793196641
[82,] -0.7144634313 0.1267883720
[83,] 0.2162683612 -0.7144634313
[84,] 1.6395310906 0.2162683612
[85,] -0.0560716933 1.6395310906
[86,] 0.7128642581 -0.0560716933
[87,] 1.5454606236 0.7128642581
[88,] 0.7532046255 1.5454606236
[89,] -1.6503435998 0.7532046255
[90,] 0.3812342125 -1.6503435998
[91,] 0.7032721837 0.3812342125
[92,] -0.0968770977 0.7032721837
[93,] -1.9572539543 -0.0968770977
[94,] 1.1147559775 -1.9572539543
[95,] 0.2627887784 1.1147559775
[96,] 1.9768645915 0.2627887784
[97,] 0.1526182446 1.9768645915
[98,] -0.3325070655 0.1526182446
[99,] -0.9400016902 -0.3325070655
[100,] 1.3065412813 -0.9400016902
[101,] 2.7151955376 1.3065412813
[102,] 0.2338617841 2.7151955376
[103,] 1.6835759135 0.2338617841
[104,] -2.0228622262 1.6835759135
[105,] 1.1109796286 -2.0228622262
[106,] 0.5428707485 1.1109796286
[107,] 1.7330617333 0.5428707485
[108,] -0.0005969502 1.7330617333
[109,] 0.8582651654 -0.0005969502
[110,] 0.1743834377 0.8582651654
[111,] 2.0372001774 0.1743834377
[112,] -0.1509986333 2.0372001774
[113,] -2.8462612594 -0.1509986333
[114,] 1.2892418835 -2.8462612594
[115,] -1.2509980380 1.2892418835
[116,] 0.7919448838 -1.2509980380
[117,] -1.9558849040 0.7919448838
[118,] 0.2970126255 -1.9558849040
[119,] -1.0935310256 0.2970126255
[120,] 0.3771429081 -1.0935310256
[121,] -2.9620919368 0.3771429081
[122,] -1.0553726530 -2.9620919368
[123,] -1.2621382249 -1.0553726530
[124,] -0.7437130668 -1.2621382249
[125,] -0.4876652396 -0.7437130668
[126,] 1.2031011888 -0.4876652396
[127,] 0.7719710113 1.2031011888
[128,] -2.8401289874 0.7719710113
[129,] 1.9442742062 -2.8401289874
[130,] -3.3213728124 1.9442742062
[131,] 2.1169849858 -3.3213728124
[132,] -1.9063969972 2.1169849858
[133,] -1.5730496432 -1.9063969972
[134,] -0.3557695740 -1.5730496432
[135,] 0.9167535120 -0.3557695740
[136,] 0.3706222762 0.9167535120
[137,] -2.2447853712 0.3706222762
[138,] -1.2966873506 -2.2447853712
[139,] -2.2359344356 -1.2966873506
[140,] 3.1055819409 -2.2359344356
[141,] 1.7025986012 3.1055819409
[142,] 0.3371204573 1.7025986012
[143,] 1.3058100622 0.3371204573
[144,] -4.1605574799 1.3058100622
[145,] 2.1482093231 -4.1605574799
[146,] -2.0521548949 2.1482093231
[147,] 0.8407255844 -2.0521548949
[148,] -0.0852293404 0.8407255844
[149,] -3.1449173603 -0.0852293404
[150,] -0.8925878118 -3.1449173603
[151,] 2.3328001663 -0.8925878118
[152,] 4.0530681344 2.3328001663
[153,] 1.3446038444 4.0530681344
[154,] -2.7822320473 1.3446038444
[155,] 0.6498330614 -2.7822320473
[156,] 1.4082571067 0.6498330614
[157,] 0.9640459858 1.4082571067
[158,] 0.9653107609 0.9640459858
[159,] -0.2780470851 0.9653107609
[160,] 0.2030662964 -0.2780470851
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1860219269 -3.0490104618
2 2.2336464364 -0.1860219269
3 2.6969964224 2.2336464364
4 -1.5769323250 2.6969964224
5 -2.1209943344 -1.5769323250
6 3.5724916793 -2.1209943344
7 -2.0137390005 3.5724916793
8 -2.1620417775 -2.0137390005
9 0.5666024473 -2.1620417775
10 0.4376967412 0.5666024473
11 -0.1641331339 0.4376967412
12 0.3034268977 -0.1641331339
13 0.8061316553 0.3034268977
14 -0.6829613161 0.8061316553
15 -0.2276273945 -0.6829613161
16 0.2774450791 -0.2276273945
17 3.5615578519 0.2774450791
18 2.2707720852 3.5615578519
19 0.6047977754 2.2707720852
20 0.6795326949 0.6047977754
21 0.6710519727 0.6795326949
22 2.7045349633 0.6710519727
23 0.9770042032 2.7045349633
24 0.4767288854 0.9770042032
25 0.0329684114 0.4767288854
26 1.2335548499 0.0329684114
27 -1.3679090284 1.2335548499
28 0.6358276313 -1.3679090284
29 -0.5673550620 0.6358276313
30 -0.8962595562 -0.5673550620
31 -0.3995386159 -0.8962595562
32 -1.1370275805 -0.3995386159
33 -0.0403222945 -1.1370275805
34 -1.4342652606 -0.0403222945
35 -3.2789234544 -1.4342652606
36 -2.5692353189 -3.2789234544
37 -1.7848933482 -2.5692353189
38 1.5312657607 -1.7848933482
39 1.2633072028 1.5312657607
40 0.8506564963 1.2633072028
41 -1.4069230427 0.8506564963
42 2.3411548846 -1.4069230427
43 -0.0266194648 2.3411548846
44 -1.0447004096 -0.0266194648
45 -4.4488116565 -1.0447004096
46 -2.7101982421 -4.4488116565
47 -0.0996382114 -2.7101982421
48 1.2499734127 -0.0996382114
49 -1.9644976463 1.2499734127
50 -0.5383768845 -1.9644976463
51 -0.0561112677 -0.5383768845
52 -2.4932319603 -0.0561112677
53 -0.2196790652 -2.4932319603
54 -2.5695101721 -0.2196790652
55 1.3507802823 -2.5695101721
56 -0.0545534288 1.3507802823
57 0.5376690969 -0.0545534288
58 -0.3373127577 0.5376690969
59 1.6918339378 -0.3373127577
60 0.4458467903 1.6918339378
61 0.1417451844 0.4458467903
62 -0.5858112863 0.1417451844
63 -0.2364998541 -0.5858112863
64 0.7016597750 -0.2364998541
65 0.7869160975 0.7016597750
66 1.7530968962 0.7869160975
67 3.4564346851 1.7530968962
68 -3.6351315346 3.4564346851
69 0.6343384796 -3.6351315346
70 -3.7891318494 0.6343384796
71 -0.1785908244 -3.7891318494
72 1.5502804247 -0.1785908244
73 1.2127840206 1.5502804247
74 0.1899565287 1.2127840206
75 3.0848156559 0.1899565287
76 -0.2085982734 3.0848156559
77 1.3665071225 -0.2085982734
78 -2.0343823104 1.3665071225
79 0.1622065979 -2.0343823104
80 0.5793196641 0.1622065979
81 0.1267883720 0.5793196641
82 -0.7144634313 0.1267883720
83 0.2162683612 -0.7144634313
84 1.6395310906 0.2162683612
85 -0.0560716933 1.6395310906
86 0.7128642581 -0.0560716933
87 1.5454606236 0.7128642581
88 0.7532046255 1.5454606236
89 -1.6503435998 0.7532046255
90 0.3812342125 -1.6503435998
91 0.7032721837 0.3812342125
92 -0.0968770977 0.7032721837
93 -1.9572539543 -0.0968770977
94 1.1147559775 -1.9572539543
95 0.2627887784 1.1147559775
96 1.9768645915 0.2627887784
97 0.1526182446 1.9768645915
98 -0.3325070655 0.1526182446
99 -0.9400016902 -0.3325070655
100 1.3065412813 -0.9400016902
101 2.7151955376 1.3065412813
102 0.2338617841 2.7151955376
103 1.6835759135 0.2338617841
104 -2.0228622262 1.6835759135
105 1.1109796286 -2.0228622262
106 0.5428707485 1.1109796286
107 1.7330617333 0.5428707485
108 -0.0005969502 1.7330617333
109 0.8582651654 -0.0005969502
110 0.1743834377 0.8582651654
111 2.0372001774 0.1743834377
112 -0.1509986333 2.0372001774
113 -2.8462612594 -0.1509986333
114 1.2892418835 -2.8462612594
115 -1.2509980380 1.2892418835
116 0.7919448838 -1.2509980380
117 -1.9558849040 0.7919448838
118 0.2970126255 -1.9558849040
119 -1.0935310256 0.2970126255
120 0.3771429081 -1.0935310256
121 -2.9620919368 0.3771429081
122 -1.0553726530 -2.9620919368
123 -1.2621382249 -1.0553726530
124 -0.7437130668 -1.2621382249
125 -0.4876652396 -0.7437130668
126 1.2031011888 -0.4876652396
127 0.7719710113 1.2031011888
128 -2.8401289874 0.7719710113
129 1.9442742062 -2.8401289874
130 -3.3213728124 1.9442742062
131 2.1169849858 -3.3213728124
132 -1.9063969972 2.1169849858
133 -1.5730496432 -1.9063969972
134 -0.3557695740 -1.5730496432
135 0.9167535120 -0.3557695740
136 0.3706222762 0.9167535120
137 -2.2447853712 0.3706222762
138 -1.2966873506 -2.2447853712
139 -2.2359344356 -1.2966873506
140 3.1055819409 -2.2359344356
141 1.7025986012 3.1055819409
142 0.3371204573 1.7025986012
143 1.3058100622 0.3371204573
144 -4.1605574799 1.3058100622
145 2.1482093231 -4.1605574799
146 -2.0521548949 2.1482093231
147 0.8407255844 -2.0521548949
148 -0.0852293404 0.8407255844
149 -3.1449173603 -0.0852293404
150 -0.8925878118 -3.1449173603
151 2.3328001663 -0.8925878118
152 4.0530681344 2.3328001663
153 1.3446038444 4.0530681344
154 -2.7822320473 1.3446038444
155 0.6498330614 -2.7822320473
156 1.4082571067 0.6498330614
157 0.9640459858 1.4082571067
158 0.9653107609 0.9640459858
159 -0.2780470851 0.9653107609
160 0.2030662964 -0.2780470851
> 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/712bj1355065236.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/8mtrc1355065236.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/9mebr1355065236.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/10oijg1355065236.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11joy91355065236.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12hm8a1355065236.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13us2e1355065236.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/149rb51355065236.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/1585ry1355065236.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/16s9wq1355065237.tab")
+ }
>
> try(system("convert tmp/1jfi81355065236.ps tmp/1jfi81355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xyyv1355065236.ps tmp/2xyyv1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wlzq1355065236.ps tmp/3wlzq1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/4bfxu1355065236.ps tmp/4bfxu1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/5r0cl1355065236.ps tmp/5r0cl1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ru6m1355065236.ps tmp/6ru6m1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/712bj1355065236.ps tmp/712bj1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mtrc1355065236.ps tmp/8mtrc1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mebr1355065236.ps tmp/9mebr1355065236.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oijg1355065236.ps tmp/10oijg1355065236.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.528 1.593 10.125