R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
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+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('YT'
+ ,'X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5
')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('YT','X1','X2','X3','X4','X5
'),1:159))
> 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 = '2'
> #'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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
X1 YT X2 X3 X4 X5\r t
1 14 24 11 12 24 26 1
2 11 25 7 8 25 23 2
3 6 17 17 8 30 25 3
4 12 18 10 8 19 23 4
5 8 18 12 9 22 19 5
6 10 16 12 7 22 29 6
7 10 20 11 4 25 25 7
8 11 16 11 11 23 21 8
9 16 18 12 7 17 22 9
10 11 17 13 7 21 25 10
11 13 23 14 12 19 24 11
12 12 30 16 10 19 18 12
13 8 23 11 10 15 22 13
14 12 18 10 8 16 15 14
15 11 15 11 8 23 22 15
16 4 12 15 4 27 28 16
17 9 21 9 9 22 20 17
18 8 15 11 8 14 12 18
19 8 20 17 7 22 24 19
20 14 31 17 11 23 20 20
21 15 27 11 9 23 21 21
22 16 34 18 11 21 20 22
23 9 21 14 13 19 21 23
24 14 31 10 8 18 23 24
25 11 19 11 8 20 28 25
26 8 16 15 9 23 24 26
27 9 20 15 6 25 24 27
28 9 21 13 9 19 24 28
29 9 22 16 9 24 23 29
30 9 17 13 6 22 23 30
31 10 24 9 6 25 29 31
32 16 25 18 16 26 24 32
33 11 26 18 5 29 18 33
34 8 25 12 7 32 25 34
35 9 17 17 9 25 21 35
36 16 32 9 6 29 26 36
37 11 33 9 6 28 22 37
38 16 13 12 5 17 22 38
39 12 32 18 12 28 22 39
40 12 25 12 7 29 23 40
41 14 29 18 10 26 30 41
42 9 22 14 9 25 23 42
43 10 18 15 8 14 17 43
44 9 17 16 5 25 23 44
45 10 20 10 8 26 23 45
46 12 15 11 8 20 25 46
47 14 20 14 10 18 24 47
48 14 33 9 6 32 24 48
49 10 29 12 8 25 23 49
50 14 23 17 7 25 21 50
51 16 26 5 4 23 24 51
52 9 18 12 8 21 24 52
53 10 20 12 8 20 28 53
54 6 11 6 4 15 16 54
55 8 28 24 20 30 20 55
56 13 26 12 8 24 29 56
57 10 22 12 8 26 27 57
58 8 17 14 6 24 22 58
59 7 12 7 4 22 28 59
60 15 14 13 8 14 16 60
61 9 17 12 9 24 25 61
62 10 21 13 6 24 24 62
63 12 19 14 7 24 28 63
64 13 18 8 9 24 24 64
65 10 10 11 5 19 23 65
66 11 29 9 5 31 30 66
67 8 31 11 8 22 24 67
68 9 19 13 8 27 21 68
69 13 9 10 6 19 25 69
70 11 20 11 8 25 25 70
71 8 28 12 7 20 22 71
72 9 19 9 7 21 23 72
73 9 30 15 9 27 26 73
74 15 29 18 11 23 23 74
75 9 26 15 6 25 25 75
76 10 23 12 8 20 21 76
77 14 13 13 6 21 25 77
78 12 21 14 9 22 24 78
79 12 19 10 8 23 29 79
80 11 28 13 6 25 22 80
81 14 23 13 10 25 27 81
82 6 18 11 8 17 26 82
83 12 21 13 8 19 22 83
84 8 20 16 10 25 24 84
85 14 23 8 5 19 27 85
86 11 21 16 7 20 24 86
87 10 21 11 5 26 24 87
88 14 15 9 8 23 29 88
89 12 28 16 14 27 22 89
90 10 19 12 7 17 21 90
91 14 26 14 8 17 24 91
92 5 10 8 6 19 24 92
93 11 16 9 5 17 23 93
94 10 22 15 6 22 20 94
95 9 19 11 10 21 27 95
96 10 31 21 12 32 26 96
97 16 31 14 9 21 25 97
98 13 29 18 12 21 21 98
99 9 19 12 7 18 21 99
100 10 22 13 8 18 19 100
101 10 23 15 10 23 21 101
102 7 15 12 6 19 21 102
103 9 20 19 10 20 16 103
104 8 18 15 10 21 22 104
105 14 23 11 10 20 29 105
106 14 25 11 5 17 15 106
107 8 21 10 7 18 17 107
108 9 24 13 10 19 15 108
109 14 25 15 11 22 21 109
110 14 17 12 6 15 21 110
111 8 13 12 7 14 19 111
112 8 28 16 12 18 24 112
113 8 21 9 11 24 20 113
114 7 25 18 11 35 17 114
115 6 9 8 11 29 23 115
116 8 16 13 5 21 24 116
117 6 19 17 8 25 14 117
118 11 17 9 6 20 19 118
119 14 25 15 9 22 24 119
120 11 20 8 4 13 13 120
121 11 29 7 4 26 22 121
122 11 14 12 7 17 16 122
123 14 22 14 11 25 19 123
124 8 15 6 6 20 25 124
125 20 19 8 7 19 25 125
126 11 20 17 8 21 23 126
127 8 15 10 4 22 24 127
128 11 20 11 8 24 26 128
129 10 18 14 9 21 26 129
130 14 33 11 8 26 25 130
131 11 22 13 11 24 18 131
132 9 16 12 8 16 21 132
133 9 17 11 5 23 26 133
134 8 16 9 4 18 23 134
135 10 21 12 8 16 23 135
136 13 26 20 10 26 22 136
137 13 18 12 6 19 20 137
138 12 18 13 9 21 13 138
139 8 17 12 9 21 24 139
140 13 22 12 13 22 15 140
141 14 30 9 9 23 14 141
142 12 30 15 10 29 22 142
143 14 24 24 20 21 10 143
144 15 21 7 5 21 24 144
145 13 21 17 11 23 22 145
146 16 29 11 6 27 24 146
147 9 31 17 9 25 19 147
148 9 20 11 7 21 20 148
149 9 16 12 9 10 13 149
150 8 22 14 10 20 20 150
151 7 20 11 9 26 22 151
152 16 28 16 8 24 24 152
153 11 38 21 7 29 29 153
154 9 22 14 6 19 12 154
155 11 20 20 13 24 20 155
156 9 17 13 6 19 21 156
157 14 28 11 8 24 24 157
158 13 22 15 10 22 22 158
159 16 31 19 16 17 20 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) YT X2 X3 X4 `X5\r`
7.284567 0.247876 -0.106836 0.148172 -0.191083 0.113320
t
0.001416
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.7326 -1.7386 -0.2116 1.6972 8.4438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.284567 1.697185 4.292 3.14e-05 ***
YT 0.247876 0.040274 6.155 6.39e-09 ***
X2 -0.106836 0.074214 -1.440 0.15204
X3 0.148172 0.093171 1.590 0.11384
X4 -0.191083 0.057040 -3.350 0.00102 **
`X5\r` 0.113320 0.057918 1.957 0.05223 .
t 0.001416 0.004447 0.318 0.75062
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.489 on 152 degrees of freedom
Multiple R-squared: 0.2401, Adjusted R-squared: 0.2101
F-statistic: 8.002 on 6 and 152 DF, p-value: 1.634e-07
> 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,] 0.3474228 0.6948456 0.6525772
[2,] 0.4650419 0.9300837 0.5349581
[3,] 0.3464959 0.6929918 0.6535041
[4,] 0.8247635 0.3504730 0.1752365
[5,] 0.7633543 0.4732915 0.2366457
[6,] 0.7187443 0.5625114 0.2812557
[7,] 0.7081502 0.5836995 0.2918498
[8,] 0.6275973 0.7448054 0.3724027
[9,] 0.6076290 0.7847420 0.3923710
[10,] 0.5276542 0.9446916 0.4723458
[11,] 0.6031666 0.7936667 0.3968334
[12,] 0.6454199 0.7091601 0.3545801
[13,] 0.6159398 0.7681205 0.3840602
[14,] 0.6022763 0.7954474 0.3977237
[15,] 0.5393113 0.9213773 0.4606887
[16,] 0.4692161 0.9384322 0.5307839
[17,] 0.4015275 0.8030550 0.5984725
[18,] 0.3372684 0.6745369 0.6627316
[19,] 0.3072441 0.6144882 0.6927559
[20,] 0.2531294 0.5062588 0.7468706
[21,] 0.2087548 0.4175095 0.7912452
[22,] 0.1724851 0.3449702 0.8275149
[23,] 0.2836470 0.5672939 0.7163530
[24,] 0.2609655 0.5219311 0.7390345
[25,] 0.2458753 0.4917506 0.7541247
[26,] 0.2037825 0.4075651 0.7962175
[27,] 0.2367413 0.4734825 0.7632587
[28,] 0.2268650 0.4537300 0.7731350
[29,] 0.6224550 0.7550900 0.3775450
[30,] 0.5725859 0.8548282 0.4274141
[31,] 0.5327906 0.9344189 0.4672094
[32,] 0.4884043 0.9768087 0.5115957
[33,] 0.4643532 0.9287063 0.5356468
[34,] 0.4219509 0.8439018 0.5780491
[35,] 0.3706773 0.7413545 0.6293227
[36,] 0.3211738 0.6423476 0.6788262
[37,] 0.2937007 0.5874014 0.7062993
[38,] 0.2747359 0.5494718 0.7252641
[39,] 0.2487038 0.4974077 0.7512962
[40,] 0.2624001 0.5248002 0.7375999
[41,] 0.3180589 0.6361179 0.6819411
[42,] 0.3542763 0.7085526 0.6457237
[43,] 0.3434501 0.6869002 0.6565499
[44,] 0.3295784 0.6591568 0.6704216
[45,] 0.3571970 0.7143940 0.6428030
[46,] 0.3772813 0.7545626 0.6227187
[47,] 0.3330958 0.6661916 0.6669042
[48,] 0.2938367 0.5876734 0.7061633
[49,] 0.2567729 0.5135459 0.7432271
[50,] 0.2415007 0.4830015 0.7584993
[51,] 0.3896325 0.7792650 0.6103675
[52,] 0.3463802 0.6927603 0.6536198
[53,] 0.3045151 0.6090303 0.6954849
[54,] 0.2837138 0.5674276 0.7162862
[55,] 0.2933225 0.5866450 0.7066775
[56,] 0.2686565 0.5373131 0.7313435
[57,] 0.2373163 0.4746326 0.7626837
[58,] 0.4206837 0.8413675 0.5793163
[59,] 0.3752325 0.7504649 0.6247675
[60,] 0.4729918 0.9459836 0.5270082
[61,] 0.4309976 0.8619952 0.5690024
[62,] 0.5496010 0.9007980 0.4503990
[63,] 0.5225497 0.9549005 0.4774503
[64,] 0.5460923 0.9078155 0.4539077
[65,] 0.5544398 0.8911203 0.4455602
[66,] 0.5360622 0.9278756 0.4639378
[67,] 0.5029308 0.9941385 0.4970692
[68,] 0.6671006 0.6657989 0.3328994
[69,] 0.6361210 0.7277580 0.3638790
[70,] 0.6000958 0.7998085 0.3999042
[71,] 0.5547954 0.8904092 0.4452046
[72,] 0.5647033 0.8705934 0.4352967
[73,] 0.7194968 0.5610064 0.2805032
[74,] 0.6859938 0.6280123 0.3140062
[75,] 0.6630913 0.6738174 0.3369087
[76,] 0.6402618 0.7194765 0.3597382
[77,] 0.5997696 0.8004609 0.4002304
[78,] 0.5567924 0.8864152 0.4432076
[79,] 0.6439900 0.7120200 0.3560100
[80,] 0.5993238 0.8013524 0.4006762
[81,] 0.5573479 0.8853041 0.4426521
[82,] 0.5240917 0.9518166 0.4759083
[83,] 0.5686906 0.8626188 0.4313094
[84,] 0.5317954 0.9364093 0.4682046
[85,] 0.4877584 0.9755169 0.5122416
[86,] 0.4704154 0.9408309 0.5295846
[87,] 0.4289976 0.8579952 0.5710024
[88,] 0.4291753 0.8583505 0.5708247
[89,] 0.3849513 0.7699026 0.6150487
[90,] 0.3514278 0.7028555 0.6485722
[91,] 0.3125972 0.6251945 0.6874028
[92,] 0.2719548 0.5439096 0.7280452
[93,] 0.2538156 0.5076313 0.7461844
[94,] 0.2162299 0.4324599 0.7837701
[95,] 0.1967947 0.3935894 0.8032053
[96,] 0.1727959 0.3455918 0.8272041
[97,] 0.1806144 0.3612288 0.8193856
[98,] 0.1811123 0.3622246 0.8188877
[99,] 0.1740473 0.3480946 0.8259527
[100,] 0.1712347 0.3424695 0.8287653
[101,] 0.2166968 0.4333937 0.7833032
[102,] 0.1878011 0.3756021 0.8121989
[103,] 0.3729959 0.7459919 0.6270041
[104,] 0.4352384 0.8704768 0.5647616
[105,] 0.4042445 0.8084889 0.5957555
[106,] 0.3984470 0.7968939 0.6015530
[107,] 0.3576691 0.7153382 0.6423309
[108,] 0.3609397 0.7218793 0.6390603
[109,] 0.3182321 0.6364641 0.6817679
[110,] 0.2889469 0.5778937 0.7110531
[111,] 0.2434425 0.4868850 0.7565575
[112,] 0.2293675 0.4587350 0.7706325
[113,] 0.2060183 0.4120366 0.7939817
[114,] 0.2105543 0.4211085 0.7894457
[115,] 0.2280426 0.4560851 0.7719574
[116,] 0.7593834 0.4812332 0.2406166
[117,] 0.7197675 0.5604650 0.2802325
[118,] 0.6678588 0.6642825 0.3321412
[119,] 0.6079095 0.7841810 0.3920905
[120,] 0.5453568 0.9092864 0.4546432
[121,] 0.4827112 0.9654223 0.5172888
[122,] 0.4274796 0.8549592 0.5725204
[123,] 0.3745502 0.7491005 0.6254498
[124,] 0.3149158 0.6298317 0.6850842
[125,] 0.2832496 0.5664991 0.7167504
[126,] 0.2857470 0.5714940 0.7142530
[127,] 0.2413916 0.4827833 0.7586084
[128,] 0.2502191 0.5004382 0.7497809
[129,] 0.2575096 0.5150193 0.7424904
[130,] 0.2874911 0.5749821 0.7125089
[131,] 0.2271001 0.4542002 0.7728999
[132,] 0.1730739 0.3461479 0.8269261
[133,] 0.1318546 0.2637093 0.8681454
[134,] 0.1370435 0.2740870 0.8629565
[135,] 0.1275009 0.2550019 0.8724991
[136,] 0.1441704 0.2883409 0.8558296
[137,] 0.4157265 0.8314529 0.5842735
[138,] 0.3052196 0.6104392 0.6947804
[139,] 0.2117381 0.4234762 0.7882619
[140,] 0.1234477 0.2468954 0.8765523
> postscript(file="/var/www/html/rcomp/tmp/1b6in1290372194.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23fzq1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/33fzq1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43fzq1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wozt1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
1.80180144 -0.75110417 -1.97236889 1.15520717 -1.75417943 -0.09669580
7 8 9 10 11 12
0.27459259 1.29858793 5.24112503 1.01879574 0.62724915 -0.91936543
13 14 15 16 17 18
-4.93744104 1.47435490 1.86774927 -3.28558824 -1.94862979 -1.72305123
19 20 21 22 23 24
-2.00582933 1.31778767 2.84988407 2.29599612 -2.70220367 -0.28658977
25 26 27 28 29 30
-0.39108531 -1.34317018 -0.50940792 -2.56339012 -1.42343705 -0.44362988
31 32 33 34 35 36
-1.71419437 4.27399882 1.90777101 -2.00311970 0.33200773 3.40000723
37 38 39 40 41 42
-1.58708942 6.73578630 -0.26955236 1.64177354 1.47886444 -1.46443491
43 44 45 46 47 48
-0.64133500 0.57847688 -0.06101990 1.91064255 2.42516203 1.93502794
49 50 51 52 53 54
-2.27498205 4.11985361 3.81516364 -1.43024335 -1.57177457 -2.98621129
55 56 57 58 59 60
-3.23625825 0.58773273 -0.81337161 -0.88095630 -2.15658543 5.22574485
61 62 63 64 65 66
-0.88335346 -0.21160180 1.78811975 2.55049653 1.60319281 -0.82178504
67 68 69 70 71 72
-5.58962970 -0.10748067 4.36375677 0.59269243 -4.75218325 -1.76545742
73 74 75 76 77 78
-3.34229947 2.50395104 -2.17795596 -1.55473423 5.06359864 1.04589407
79 80 81 82 83 84
0.88554228 -0.55450222 2.52417525 -5.57053392 0.73353939 -2.07597549
85 86 87 88 89 90
1.57869115 0.16241638 0.06966352 3.75746702 -0.04994978 -1.00813107
91 92 93 94 95 96
0.98085930 -4.01704146 0.48044607 -0.22000650 -2.48214994 -1.47082722
97 98 99 100 101 102
2.23582249 0.16626650 -1.82979212 -1.38953373 -0.99272101 -2.50327909
103 104 105 106 107 108
-0.83122988 -2.25307380 1.09446127 2.35138109 -3.09726673 -2.54859706
109 110 111 112 113 114
2.16094221 3.22530644 -1.89722030 -5.73256376 -2.99874797 -1.58826641
115 116 117 118 119 120
-1.51844261 -1.47376402 -2.33844977 1.07552542 2.10316716 -0.13909215
121 122 123 124 125 126
-1.01402625 1.75253623 3.57779997 -2.43764574 8.44384971 0.61671810
127 128 129 130 131 132
-1.22271740 0.20615879 -0.70041796 0.47642045 0.38187173 -1.66323151
133 134 135 136 137 138
-0.80385862 -2.23835601 -2.13350101 2.20820026 2.81685007 2.65315842
139 140 141 142 143 144
-2.45373512 1.92373894 1.51589556 0.24726698 3.04408301 3.60618701
145 146 147 148 149 150
2.39290632 4.04601704 -3.07021867 -1.56732111 -2.07541848 -3.38099737
151 152 153 154 155 156
-3.13913676 3.94998401 -2.45902384 -1.07849727 1.06850941 -0.96866184
157 158 159
1.40872218 1.87003775 1.44726842
> postscript(file="/var/www/html/rcomp/tmp/6wozt1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 1.80180144 NA
1 -0.75110417 1.80180144
2 -1.97236889 -0.75110417
3 1.15520717 -1.97236889
4 -1.75417943 1.15520717
5 -0.09669580 -1.75417943
6 0.27459259 -0.09669580
7 1.29858793 0.27459259
8 5.24112503 1.29858793
9 1.01879574 5.24112503
10 0.62724915 1.01879574
11 -0.91936543 0.62724915
12 -4.93744104 -0.91936543
13 1.47435490 -4.93744104
14 1.86774927 1.47435490
15 -3.28558824 1.86774927
16 -1.94862979 -3.28558824
17 -1.72305123 -1.94862979
18 -2.00582933 -1.72305123
19 1.31778767 -2.00582933
20 2.84988407 1.31778767
21 2.29599612 2.84988407
22 -2.70220367 2.29599612
23 -0.28658977 -2.70220367
24 -0.39108531 -0.28658977
25 -1.34317018 -0.39108531
26 -0.50940792 -1.34317018
27 -2.56339012 -0.50940792
28 -1.42343705 -2.56339012
29 -0.44362988 -1.42343705
30 -1.71419437 -0.44362988
31 4.27399882 -1.71419437
32 1.90777101 4.27399882
33 -2.00311970 1.90777101
34 0.33200773 -2.00311970
35 3.40000723 0.33200773
36 -1.58708942 3.40000723
37 6.73578630 -1.58708942
38 -0.26955236 6.73578630
39 1.64177354 -0.26955236
40 1.47886444 1.64177354
41 -1.46443491 1.47886444
42 -0.64133500 -1.46443491
43 0.57847688 -0.64133500
44 -0.06101990 0.57847688
45 1.91064255 -0.06101990
46 2.42516203 1.91064255
47 1.93502794 2.42516203
48 -2.27498205 1.93502794
49 4.11985361 -2.27498205
50 3.81516364 4.11985361
51 -1.43024335 3.81516364
52 -1.57177457 -1.43024335
53 -2.98621129 -1.57177457
54 -3.23625825 -2.98621129
55 0.58773273 -3.23625825
56 -0.81337161 0.58773273
57 -0.88095630 -0.81337161
58 -2.15658543 -0.88095630
59 5.22574485 -2.15658543
60 -0.88335346 5.22574485
61 -0.21160180 -0.88335346
62 1.78811975 -0.21160180
63 2.55049653 1.78811975
64 1.60319281 2.55049653
65 -0.82178504 1.60319281
66 -5.58962970 -0.82178504
67 -0.10748067 -5.58962970
68 4.36375677 -0.10748067
69 0.59269243 4.36375677
70 -4.75218325 0.59269243
71 -1.76545742 -4.75218325
72 -3.34229947 -1.76545742
73 2.50395104 -3.34229947
74 -2.17795596 2.50395104
75 -1.55473423 -2.17795596
76 5.06359864 -1.55473423
77 1.04589407 5.06359864
78 0.88554228 1.04589407
79 -0.55450222 0.88554228
80 2.52417525 -0.55450222
81 -5.57053392 2.52417525
82 0.73353939 -5.57053392
83 -2.07597549 0.73353939
84 1.57869115 -2.07597549
85 0.16241638 1.57869115
86 0.06966352 0.16241638
87 3.75746702 0.06966352
88 -0.04994978 3.75746702
89 -1.00813107 -0.04994978
90 0.98085930 -1.00813107
91 -4.01704146 0.98085930
92 0.48044607 -4.01704146
93 -0.22000650 0.48044607
94 -2.48214994 -0.22000650
95 -1.47082722 -2.48214994
96 2.23582249 -1.47082722
97 0.16626650 2.23582249
98 -1.82979212 0.16626650
99 -1.38953373 -1.82979212
100 -0.99272101 -1.38953373
101 -2.50327909 -0.99272101
102 -0.83122988 -2.50327909
103 -2.25307380 -0.83122988
104 1.09446127 -2.25307380
105 2.35138109 1.09446127
106 -3.09726673 2.35138109
107 -2.54859706 -3.09726673
108 2.16094221 -2.54859706
109 3.22530644 2.16094221
110 -1.89722030 3.22530644
111 -5.73256376 -1.89722030
112 -2.99874797 -5.73256376
113 -1.58826641 -2.99874797
114 -1.51844261 -1.58826641
115 -1.47376402 -1.51844261
116 -2.33844977 -1.47376402
117 1.07552542 -2.33844977
118 2.10316716 1.07552542
119 -0.13909215 2.10316716
120 -1.01402625 -0.13909215
121 1.75253623 -1.01402625
122 3.57779997 1.75253623
123 -2.43764574 3.57779997
124 8.44384971 -2.43764574
125 0.61671810 8.44384971
126 -1.22271740 0.61671810
127 0.20615879 -1.22271740
128 -0.70041796 0.20615879
129 0.47642045 -0.70041796
130 0.38187173 0.47642045
131 -1.66323151 0.38187173
132 -0.80385862 -1.66323151
133 -2.23835601 -0.80385862
134 -2.13350101 -2.23835601
135 2.20820026 -2.13350101
136 2.81685007 2.20820026
137 2.65315842 2.81685007
138 -2.45373512 2.65315842
139 1.92373894 -2.45373512
140 1.51589556 1.92373894
141 0.24726698 1.51589556
142 3.04408301 0.24726698
143 3.60618701 3.04408301
144 2.39290632 3.60618701
145 4.04601704 2.39290632
146 -3.07021867 4.04601704
147 -1.56732111 -3.07021867
148 -2.07541848 -1.56732111
149 -3.38099737 -2.07541848
150 -3.13913676 -3.38099737
151 3.94998401 -3.13913676
152 -2.45902384 3.94998401
153 -1.07849727 -2.45902384
154 1.06850941 -1.07849727
155 -0.96866184 1.06850941
156 1.40872218 -0.96866184
157 1.87003775 1.40872218
158 1.44726842 1.87003775
159 NA 1.44726842
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.75110417 1.80180144
[2,] -1.97236889 -0.75110417
[3,] 1.15520717 -1.97236889
[4,] -1.75417943 1.15520717
[5,] -0.09669580 -1.75417943
[6,] 0.27459259 -0.09669580
[7,] 1.29858793 0.27459259
[8,] 5.24112503 1.29858793
[9,] 1.01879574 5.24112503
[10,] 0.62724915 1.01879574
[11,] -0.91936543 0.62724915
[12,] -4.93744104 -0.91936543
[13,] 1.47435490 -4.93744104
[14,] 1.86774927 1.47435490
[15,] -3.28558824 1.86774927
[16,] -1.94862979 -3.28558824
[17,] -1.72305123 -1.94862979
[18,] -2.00582933 -1.72305123
[19,] 1.31778767 -2.00582933
[20,] 2.84988407 1.31778767
[21,] 2.29599612 2.84988407
[22,] -2.70220367 2.29599612
[23,] -0.28658977 -2.70220367
[24,] -0.39108531 -0.28658977
[25,] -1.34317018 -0.39108531
[26,] -0.50940792 -1.34317018
[27,] -2.56339012 -0.50940792
[28,] -1.42343705 -2.56339012
[29,] -0.44362988 -1.42343705
[30,] -1.71419437 -0.44362988
[31,] 4.27399882 -1.71419437
[32,] 1.90777101 4.27399882
[33,] -2.00311970 1.90777101
[34,] 0.33200773 -2.00311970
[35,] 3.40000723 0.33200773
[36,] -1.58708942 3.40000723
[37,] 6.73578630 -1.58708942
[38,] -0.26955236 6.73578630
[39,] 1.64177354 -0.26955236
[40,] 1.47886444 1.64177354
[41,] -1.46443491 1.47886444
[42,] -0.64133500 -1.46443491
[43,] 0.57847688 -0.64133500
[44,] -0.06101990 0.57847688
[45,] 1.91064255 -0.06101990
[46,] 2.42516203 1.91064255
[47,] 1.93502794 2.42516203
[48,] -2.27498205 1.93502794
[49,] 4.11985361 -2.27498205
[50,] 3.81516364 4.11985361
[51,] -1.43024335 3.81516364
[52,] -1.57177457 -1.43024335
[53,] -2.98621129 -1.57177457
[54,] -3.23625825 -2.98621129
[55,] 0.58773273 -3.23625825
[56,] -0.81337161 0.58773273
[57,] -0.88095630 -0.81337161
[58,] -2.15658543 -0.88095630
[59,] 5.22574485 -2.15658543
[60,] -0.88335346 5.22574485
[61,] -0.21160180 -0.88335346
[62,] 1.78811975 -0.21160180
[63,] 2.55049653 1.78811975
[64,] 1.60319281 2.55049653
[65,] -0.82178504 1.60319281
[66,] -5.58962970 -0.82178504
[67,] -0.10748067 -5.58962970
[68,] 4.36375677 -0.10748067
[69,] 0.59269243 4.36375677
[70,] -4.75218325 0.59269243
[71,] -1.76545742 -4.75218325
[72,] -3.34229947 -1.76545742
[73,] 2.50395104 -3.34229947
[74,] -2.17795596 2.50395104
[75,] -1.55473423 -2.17795596
[76,] 5.06359864 -1.55473423
[77,] 1.04589407 5.06359864
[78,] 0.88554228 1.04589407
[79,] -0.55450222 0.88554228
[80,] 2.52417525 -0.55450222
[81,] -5.57053392 2.52417525
[82,] 0.73353939 -5.57053392
[83,] -2.07597549 0.73353939
[84,] 1.57869115 -2.07597549
[85,] 0.16241638 1.57869115
[86,] 0.06966352 0.16241638
[87,] 3.75746702 0.06966352
[88,] -0.04994978 3.75746702
[89,] -1.00813107 -0.04994978
[90,] 0.98085930 -1.00813107
[91,] -4.01704146 0.98085930
[92,] 0.48044607 -4.01704146
[93,] -0.22000650 0.48044607
[94,] -2.48214994 -0.22000650
[95,] -1.47082722 -2.48214994
[96,] 2.23582249 -1.47082722
[97,] 0.16626650 2.23582249
[98,] -1.82979212 0.16626650
[99,] -1.38953373 -1.82979212
[100,] -0.99272101 -1.38953373
[101,] -2.50327909 -0.99272101
[102,] -0.83122988 -2.50327909
[103,] -2.25307380 -0.83122988
[104,] 1.09446127 -2.25307380
[105,] 2.35138109 1.09446127
[106,] -3.09726673 2.35138109
[107,] -2.54859706 -3.09726673
[108,] 2.16094221 -2.54859706
[109,] 3.22530644 2.16094221
[110,] -1.89722030 3.22530644
[111,] -5.73256376 -1.89722030
[112,] -2.99874797 -5.73256376
[113,] -1.58826641 -2.99874797
[114,] -1.51844261 -1.58826641
[115,] -1.47376402 -1.51844261
[116,] -2.33844977 -1.47376402
[117,] 1.07552542 -2.33844977
[118,] 2.10316716 1.07552542
[119,] -0.13909215 2.10316716
[120,] -1.01402625 -0.13909215
[121,] 1.75253623 -1.01402625
[122,] 3.57779997 1.75253623
[123,] -2.43764574 3.57779997
[124,] 8.44384971 -2.43764574
[125,] 0.61671810 8.44384971
[126,] -1.22271740 0.61671810
[127,] 0.20615879 -1.22271740
[128,] -0.70041796 0.20615879
[129,] 0.47642045 -0.70041796
[130,] 0.38187173 0.47642045
[131,] -1.66323151 0.38187173
[132,] -0.80385862 -1.66323151
[133,] -2.23835601 -0.80385862
[134,] -2.13350101 -2.23835601
[135,] 2.20820026 -2.13350101
[136,] 2.81685007 2.20820026
[137,] 2.65315842 2.81685007
[138,] -2.45373512 2.65315842
[139,] 1.92373894 -2.45373512
[140,] 1.51589556 1.92373894
[141,] 0.24726698 1.51589556
[142,] 3.04408301 0.24726698
[143,] 3.60618701 3.04408301
[144,] 2.39290632 3.60618701
[145,] 4.04601704 2.39290632
[146,] -3.07021867 4.04601704
[147,] -1.56732111 -3.07021867
[148,] -2.07541848 -1.56732111
[149,] -3.38099737 -2.07541848
[150,] -3.13913676 -3.38099737
[151,] 3.94998401 -3.13913676
[152,] -2.45902384 3.94998401
[153,] -1.07849727 -2.45902384
[154,] 1.06850941 -1.07849727
[155,] -0.96866184 1.06850941
[156,] 1.40872218 -0.96866184
[157,] 1.87003775 1.40872218
[158,] 1.44726842 1.87003775
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.75110417 1.80180144
2 -1.97236889 -0.75110417
3 1.15520717 -1.97236889
4 -1.75417943 1.15520717
5 -0.09669580 -1.75417943
6 0.27459259 -0.09669580
7 1.29858793 0.27459259
8 5.24112503 1.29858793
9 1.01879574 5.24112503
10 0.62724915 1.01879574
11 -0.91936543 0.62724915
12 -4.93744104 -0.91936543
13 1.47435490 -4.93744104
14 1.86774927 1.47435490
15 -3.28558824 1.86774927
16 -1.94862979 -3.28558824
17 -1.72305123 -1.94862979
18 -2.00582933 -1.72305123
19 1.31778767 -2.00582933
20 2.84988407 1.31778767
21 2.29599612 2.84988407
22 -2.70220367 2.29599612
23 -0.28658977 -2.70220367
24 -0.39108531 -0.28658977
25 -1.34317018 -0.39108531
26 -0.50940792 -1.34317018
27 -2.56339012 -0.50940792
28 -1.42343705 -2.56339012
29 -0.44362988 -1.42343705
30 -1.71419437 -0.44362988
31 4.27399882 -1.71419437
32 1.90777101 4.27399882
33 -2.00311970 1.90777101
34 0.33200773 -2.00311970
35 3.40000723 0.33200773
36 -1.58708942 3.40000723
37 6.73578630 -1.58708942
38 -0.26955236 6.73578630
39 1.64177354 -0.26955236
40 1.47886444 1.64177354
41 -1.46443491 1.47886444
42 -0.64133500 -1.46443491
43 0.57847688 -0.64133500
44 -0.06101990 0.57847688
45 1.91064255 -0.06101990
46 2.42516203 1.91064255
47 1.93502794 2.42516203
48 -2.27498205 1.93502794
49 4.11985361 -2.27498205
50 3.81516364 4.11985361
51 -1.43024335 3.81516364
52 -1.57177457 -1.43024335
53 -2.98621129 -1.57177457
54 -3.23625825 -2.98621129
55 0.58773273 -3.23625825
56 -0.81337161 0.58773273
57 -0.88095630 -0.81337161
58 -2.15658543 -0.88095630
59 5.22574485 -2.15658543
60 -0.88335346 5.22574485
61 -0.21160180 -0.88335346
62 1.78811975 -0.21160180
63 2.55049653 1.78811975
64 1.60319281 2.55049653
65 -0.82178504 1.60319281
66 -5.58962970 -0.82178504
67 -0.10748067 -5.58962970
68 4.36375677 -0.10748067
69 0.59269243 4.36375677
70 -4.75218325 0.59269243
71 -1.76545742 -4.75218325
72 -3.34229947 -1.76545742
73 2.50395104 -3.34229947
74 -2.17795596 2.50395104
75 -1.55473423 -2.17795596
76 5.06359864 -1.55473423
77 1.04589407 5.06359864
78 0.88554228 1.04589407
79 -0.55450222 0.88554228
80 2.52417525 -0.55450222
81 -5.57053392 2.52417525
82 0.73353939 -5.57053392
83 -2.07597549 0.73353939
84 1.57869115 -2.07597549
85 0.16241638 1.57869115
86 0.06966352 0.16241638
87 3.75746702 0.06966352
88 -0.04994978 3.75746702
89 -1.00813107 -0.04994978
90 0.98085930 -1.00813107
91 -4.01704146 0.98085930
92 0.48044607 -4.01704146
93 -0.22000650 0.48044607
94 -2.48214994 -0.22000650
95 -1.47082722 -2.48214994
96 2.23582249 -1.47082722
97 0.16626650 2.23582249
98 -1.82979212 0.16626650
99 -1.38953373 -1.82979212
100 -0.99272101 -1.38953373
101 -2.50327909 -0.99272101
102 -0.83122988 -2.50327909
103 -2.25307380 -0.83122988
104 1.09446127 -2.25307380
105 2.35138109 1.09446127
106 -3.09726673 2.35138109
107 -2.54859706 -3.09726673
108 2.16094221 -2.54859706
109 3.22530644 2.16094221
110 -1.89722030 3.22530644
111 -5.73256376 -1.89722030
112 -2.99874797 -5.73256376
113 -1.58826641 -2.99874797
114 -1.51844261 -1.58826641
115 -1.47376402 -1.51844261
116 -2.33844977 -1.47376402
117 1.07552542 -2.33844977
118 2.10316716 1.07552542
119 -0.13909215 2.10316716
120 -1.01402625 -0.13909215
121 1.75253623 -1.01402625
122 3.57779997 1.75253623
123 -2.43764574 3.57779997
124 8.44384971 -2.43764574
125 0.61671810 8.44384971
126 -1.22271740 0.61671810
127 0.20615879 -1.22271740
128 -0.70041796 0.20615879
129 0.47642045 -0.70041796
130 0.38187173 0.47642045
131 -1.66323151 0.38187173
132 -0.80385862 -1.66323151
133 -2.23835601 -0.80385862
134 -2.13350101 -2.23835601
135 2.20820026 -2.13350101
136 2.81685007 2.20820026
137 2.65315842 2.81685007
138 -2.45373512 2.65315842
139 1.92373894 -2.45373512
140 1.51589556 1.92373894
141 0.24726698 1.51589556
142 3.04408301 0.24726698
143 3.60618701 3.04408301
144 2.39290632 3.60618701
145 4.04601704 2.39290632
146 -3.07021867 4.04601704
147 -1.56732111 -3.07021867
148 -2.07541848 -1.56732111
149 -3.38099737 -2.07541848
150 -3.13913676 -3.38099737
151 3.94998401 -3.13913676
152 -2.45902384 3.94998401
153 -1.07849727 -2.45902384
154 1.06850941 -1.07849727
155 -0.96866184 1.06850941
156 1.40872218 -0.96866184
157 1.87003775 1.40872218
158 1.44726842 1.87003775
> 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/html/rcomp/tmp/77fyw1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87fyw1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90pfz1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100pfz1290372194.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11l7e51290372194.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/html/rcomp/tmp/1268cs1290372194.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/html/rcomp/tmp/13dr9m1290372194.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/html/rcomp/tmp/14oi8p1290372194.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/html/rcomp/tmp/15ripd1290372194.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/html/rcomp/tmp/166s5m1290372194.tab")
+ }
>
> try(system("convert tmp/1b6in1290372194.ps tmp/1b6in1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/23fzq1290372194.ps tmp/23fzq1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/33fzq1290372194.ps tmp/33fzq1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/43fzq1290372194.ps tmp/43fzq1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wozt1290372194.ps tmp/5wozt1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wozt1290372194.ps tmp/6wozt1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/77fyw1290372194.ps tmp/77fyw1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/87fyw1290372194.ps tmp/87fyw1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/90pfz1290372194.ps tmp/90pfz1290372194.png",intern=TRUE))
character(0)
> try(system("convert tmp/100pfz1290372194.ps tmp/100pfz1290372194.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.035 1.746 9.365