R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
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+ ,dim=c(9
+ ,162)
+ ,dimnames=list(c('G'
+ ,'Month'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A
')
+ ,1:162))
> y <- array(NA,dim=c(9,162),dimnames=list(c('G','Month','I1','I2','I3','E1','E2','E3','A
'),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 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
G Month I1 I2 I3 E1 E2 E3 A\r t
1 1 186.590 26 21 21 23 17 23 4 1
2 1 244.665 20 16 15 24 17 20 4 2
3 1 248.180 19 19 18 22 18 20 6 3
4 2 253.568 19 18 11 20 21 21 8 4
5 1 171.242 20 16 8 24 20 24 8 5
6 1 413.971 25 23 19 27 28 22 4 6
7 2 216.890 25 17 4 28 19 23 4 7
8 1 227.901 22 12 20 27 22 20 8 8
9 1 259.823 26 19 16 24 16 25 5 9
10 1 148.438 22 16 14 23 18 23 4 10
11 2 241.013 17 19 10 24 25 27 4 11
12 2 206.248 22 20 13 27 17 27 4 12
13 1 108.908 19 13 14 27 14 22 4 13
14 1 267.952 24 20 8 28 11 24 4 14
15 1 314.219 26 27 23 27 27 25 4 15
16 2 235.115 21 17 11 23 20 22 8 16
17 1 203.027 13 8 9 24 22 28 4 17
18 2 365.415 26 25 24 28 22 28 4 18
19 2 350.933 20 26 5 27 21 27 4 19
20 1 263.304 22 13 15 25 23 25 8 20
21 2 738.751 14 19 5 19 17 16 4 21
22 1 959.073 21 15 19 24 24 28 7 22
23 1 483.828 7 5 6 20 14 21 4 23
24 2 213.016 23 16 13 28 17 24 4 24
25 1 177.341 17 14 11 26 23 27 5 25
26 1 352.622 25 24 17 23 24 14 4 26
27 1 352.622 25 24 17 23 24 14 4 27
28 1 217.307 19 9 5 20 8 27 4 28
29 2 236.184 20 19 9 11 22 20 4 29
30 1 215.701 23 19 15 24 23 21 4 30
31 2 228.383 22 25 17 25 25 22 4 31
32 1 485.625 22 19 17 23 21 21 4 32
33 1 252.502 21 18 20 18 24 12 15 33
34 2 342.515 15 15 12 20 15 20 10 34
35 2 196.931 20 12 7 20 22 24 4 35
36 2 365.315 22 21 16 24 21 19 8 36
37 1 316.664 18 12 7 23 25 28 4 37
38 2 313.523 20 15 14 25 16 23 4 38
39 2 188.124 28 28 24 28 28 27 4 39
40 1 184.083 22 25 15 26 23 22 4 40
41 1 362.962 18 19 15 26 21 27 7 41
42 1 170.161 23 20 10 23 21 26 4 42
43 1 167.484 20 24 14 22 26 22 6 43
44 2 211.752 25 26 18 24 22 21 5 44
45 2 276.469 26 25 12 21 21 19 4 45
46 1 182.097 15 12 9 20 18 24 16 46
47 2 266.904 17 12 9 22 12 19 5 47
48 2 235.328 23 15 8 20 25 26 12 48
49 1 329.450 21 17 18 25 17 22 6 49
50 2 258.118 13 14 10 20 24 28 9 50
51 1 952.515 18 16 17 22 15 21 9 51
52 1 247.141 19 11 14 23 13 23 4 52
53 1 498.726 22 20 16 25 26 28 5 53
54 1 313.308 16 11 10 23 16 10 4 54
55 2 420.188 24 22 19 23 24 24 4 55
56 1 231.156 18 20 10 22 21 21 5 56
57 1 227.844 20 19 14 24 20 21 4 57
58 1 204.385 24 17 10 25 14 24 4 58
59 2 216.563 14 21 4 21 25 24 4 59
60 2 207.190 22 23 19 12 25 25 5 60
61 1 232.417 24 18 9 17 20 25 4 61
62 1 203.109 18 17 12 20 22 23 6 62
63 1 221.070 21 27 16 23 20 21 4 63
64 2 254.623 23 25 11 23 26 16 4 64
65 1 108.981 17 19 18 20 18 17 18 65
66 2 229.417 22 22 11 28 22 25 4 66
67 2 476.376 24 24 24 24 24 24 6 67
68 2 221.910 21 20 17 24 17 23 4 68
69 1 158.946 22 19 18 24 24 25 4 69
70 1 295.746 16 11 9 24 20 23 5 70
71 1 187.976 21 22 19 28 19 28 4 71
72 2 283.760 23 22 18 25 20 26 4 72
73 2 705.401 22 16 12 21 15 22 5 73
74 1 178.690 24 20 23 25 23 19 10 74
75 1 232.635 24 24 22 25 26 26 5 75
76 1 245.185 16 16 14 18 22 18 8 76
77 1 186.030 16 16 14 17 20 18 8 77
78 2 181.142 21 22 16 26 24 25 5 78
79 2 228.478 26 24 23 28 26 27 4 79
80 2 491.849 15 16 7 21 21 12 4 80
81 2 506.461 25 27 10 27 25 15 4 81
82 1 182.828 18 11 12 22 13 21 5 82
83 0 263.654 23 21 12 21 20 23 4 83
84 1 253.718 20 20 12 25 22 22 4 84
85 2 480.937 17 20 17 22 23 21 8 85
86 2 209.894 25 27 21 23 28 24 4 86
87 1 655.920 24 20 16 26 22 27 5 87
88 1 223.408 17 12 11 19 20 22 14 88
89 1 103.138 19 8 14 25 6 28 8 89
90 1 255.664 20 21 13 21 21 26 8 90
91 1 184.661 15 18 9 13 20 10 4 91
92 2 372.945 27 24 19 24 18 19 4 92
93 1 758.600 22 16 13 25 23 22 6 93
94 1 256.445 23 18 19 26 20 21 4 94
95 1 204.868 16 20 13 25 24 24 7 95
96 1 197.384 19 20 13 25 22 25 7 96
97 2 245.311 25 19 13 22 21 21 4 97
98 1 301.707 19 17 14 21 18 20 6 98
99 2 501.478 19 16 12 23 21 21 4 99
100 2 278.731 26 26 22 25 23 24 7 100
101 1 205.920 21 15 11 24 23 23 4 101
102 2 177.140 20 22 5 21 15 18 4 102
103 1 139.753 24 17 18 21 21 24 8 103
104 1 366.460 22 23 19 25 24 24 4 104
105 2 435.522 20 21 14 22 23 19 4 105
106 1 239.906 18 19 15 20 21 20 10 106
107 2 178.722 18 14 12 20 21 18 8 107
108 1 340.040 24 17 19 23 20 20 6 108
109 1 236.948 24 12 15 28 11 27 4 109
110 1 221.152 22 24 17 23 22 23 4 110
111 1 263.303 23 18 8 28 27 26 4 111
112 1 222.814 22 20 10 24 25 23 5 112
113 1 317.990 20 16 12 18 18 17 4 113
114 1 149.593 18 20 12 20 20 21 6 114
115 1 221.983 25 22 20 28 24 25 4 115
116 2 201.551 18 12 12 21 10 23 5 116
117 1 266.901 16 16 12 21 27 27 7 117
118 1 185.218 20 17 14 25 21 24 8 118
119 2 347.536 19 22 6 19 21 20 5 119
120 1 395.593 15 12 10 18 18 27 8 120
121 1 238.217 19 14 18 21 15 21 10 121
122 1 254.697 19 23 18 22 24 24 8 122
123 1 157.584 16 15 7 24 22 21 5 123
124 1 807.302 17 17 18 15 14 15 12 124
125 1 252.391 28 28 9 28 28 25 4 125
126 2 189.194 23 20 17 26 18 25 5 126
127 1 267.834 25 23 22 23 26 22 4 127
128 1 173.150 20 13 11 26 17 24 6 128
129 2 267.633 17 18 15 20 19 21 4 129
130 2 283.284 23 23 17 22 22 22 4 130
131 1 209.475 16 19 15 20 18 23 7 131
132 2 135.135 23 23 22 23 24 22 7 132
133 2 285.012 11 12 9 22 15 20 10 133
134 2 178.495 18 16 13 24 18 23 4 134
135 2 256.852 24 23 20 23 26 25 5 135
136 1 301.828 23 13 14 22 11 23 8 136
137 1 158.403 21 22 14 26 26 22 11 137
138 2 355.963 16 18 12 23 21 25 7 138
139 2 364.117 24 23 20 27 23 26 4 139
140 1 233.203 23 20 20 23 23 22 8 140
141 1 257.634 18 10 8 21 15 24 6 141
142 1 208.570 20 17 17 26 22 24 7 142
143 1 212.503 9 18 9 23 26 25 5 143
144 2 251.059 24 15 18 21 16 20 4 144
145 1 276.803 25 23 22 27 20 26 8 145
146 1 198.339 20 17 10 19 18 21 4 146
147 2 301.018 21 17 13 23 22 26 8 147
148 2 369.761 25 22 15 25 16 21 6 148
149 2 162.768 22 20 18 23 19 22 4 149
150 2 199.968 21 20 18 22 20 16 9 150
151 1 406.676 21 19 12 22 19 26 5 151
152 1 364.156 22 18 12 25 23 28 6 152
153 1 202.391 27 22 20 25 24 18 4 153
154 2 319.491 24 20 12 28 25 25 4 154
155 2 185.546 24 22 16 28 21 23 4 155
156 2 243.559 21 18 16 20 21 21 5 156
157 1 220.928 18 16 18 25 23 20 6 157
158 1 193.714 16 16 16 19 27 25 16 158
159 1 372.943 22 16 13 25 23 22 6 159
160 1 163.822 20 16 17 22 18 21 6 160
161 2 217.566 18 17 13 18 16 16 4 161
162 1 232.527 20 18 17 20 16 18 4 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month I1 I2 I3 E1
1.3662892 0.0002237 -0.0017324 0.0428417 -0.0160961 -0.0113654
E2 E3 `A\r` t
-0.0136010 0.0015081 -0.0165383 0.0003344
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.5775 -0.3844 -0.1940 0.5209 0.8176
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.3662892 0.4245244 3.218 0.00158 **
Month 0.0002237 0.0002757 0.811 0.41854
I1 -0.0017324 0.0156439 -0.111 0.91197
I2 0.0428417 0.0134573 3.184 0.00176 **
I3 -0.0160961 0.0106333 -1.514 0.13217
E1 -0.0113654 0.0151411 -0.751 0.45403
E2 -0.0136010 0.0115651 -1.176 0.24142
E3 0.0015081 0.0119350 0.126 0.89961
`A\r` -0.0165383 0.0166895 -0.991 0.32329
t 0.0003344 0.0008348 0.401 0.68926
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4858 on 152 degrees of freedom
Multiple R-squared: 0.1094, Adjusted R-squared: 0.05669
F-statistic: 2.075 on 9 and 152 DF, p-value: 0.03501
> 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,] 0.8064575 0.3870850 0.1935425
[2,] 0.7741426 0.4517147 0.2258574
[3,] 0.6707286 0.6585427 0.3292714
[4,] 0.6446969 0.7106061 0.3553031
[5,] 0.6143786 0.7712428 0.3856214
[6,] 0.7421482 0.5157036 0.2578518
[7,] 0.6720905 0.6558189 0.3279095
[8,] 0.6282007 0.7435985 0.3717993
[9,] 0.5602464 0.8795072 0.4397536
[10,] 0.4808846 0.9617693 0.5191154
[11,] 0.4016497 0.8032993 0.5983503
[12,] 0.4353071 0.8706143 0.5646929
[13,] 0.4568281 0.9136561 0.5431719
[14,] 0.4704266 0.9408531 0.5295734
[15,] 0.4232685 0.8465371 0.5767315
[16,] 0.3660314 0.7320629 0.6339686
[17,] 0.3666699 0.7333398 0.6333301
[18,] 0.3222586 0.6445172 0.6777414
[19,] 0.3027955 0.6055911 0.6972045
[20,] 0.2607365 0.5214730 0.7392635
[21,] 0.2202468 0.4404937 0.7797532
[22,] 0.2271776 0.4543551 0.7728224
[23,] 0.2630039 0.5260077 0.7369961
[24,] 0.2618447 0.5236894 0.7381553
[25,] 0.2741624 0.5483249 0.7258376
[26,] 0.3273571 0.6547142 0.6726429
[27,] 0.2954859 0.5909719 0.7045141
[28,] 0.4089517 0.8179035 0.5910483
[29,] 0.4509267 0.9018535 0.5490733
[30,] 0.4990536 0.9981072 0.5009464
[31,] 0.5362093 0.9275814 0.4637907
[32,] 0.5224333 0.9551333 0.4775667
[33,] 0.4837139 0.9674278 0.5162861
[34,] 0.4717672 0.9435343 0.5282328
[35,] 0.5095089 0.9809821 0.4904911
[36,] 0.5592329 0.8815343 0.4407671
[37,] 0.5257463 0.9485073 0.4742537
[38,] 0.5794378 0.8411243 0.4205622
[39,] 0.5599911 0.8800179 0.4400089
[40,] 0.5139407 0.9721186 0.4860593
[41,] 0.4994268 0.9988536 0.5005732
[42,] 0.4552177 0.9104355 0.5447823
[43,] 0.4733167 0.9466334 0.5266833
[44,] 0.4996613 0.9993227 0.5003387
[45,] 0.4827782 0.9655565 0.5172218
[46,] 0.4705028 0.9410055 0.5294972
[47,] 0.4499512 0.8999025 0.5500488
[48,] 0.4579135 0.9158269 0.5420865
[49,] 0.4751515 0.9503030 0.5248485
[50,] 0.4526191 0.9052382 0.5473809
[51,] 0.4990858 0.9981716 0.5009142
[52,] 0.4870558 0.9741115 0.5129442
[53,] 0.4476107 0.8952215 0.5523893
[54,] 0.4561910 0.9123820 0.5438090
[55,] 0.4906373 0.9812746 0.5093627
[56,] 0.5180971 0.9638057 0.4819029
[57,] 0.4840131 0.9680261 0.5159869
[58,] 0.4413115 0.8826230 0.5586885
[59,] 0.4286202 0.8572404 0.5713798
[60,] 0.4457382 0.8914764 0.5542618
[61,] 0.4586146 0.9172293 0.5413854
[62,] 0.4158077 0.8316155 0.5841923
[63,] 0.3947944 0.7895888 0.6052056
[64,] 0.3594328 0.7188655 0.6405672
[65,] 0.3247974 0.6495948 0.6752026
[66,] 0.3516265 0.7032529 0.6483735
[67,] 0.3954707 0.7909415 0.6045293
[68,] 0.3973089 0.7946177 0.6026911
[69,] 0.3608292 0.7216583 0.6391708
[70,] 0.3206449 0.6412898 0.6793551
[71,] 0.7049038 0.5901924 0.2950962
[72,] 0.6943778 0.6112445 0.3056222
[73,] 0.7298011 0.5403978 0.2701989
[74,] 0.7438107 0.5123786 0.2561893
[75,] 0.7309526 0.5380948 0.2690474
[76,] 0.7034856 0.5930289 0.2965144
[77,] 0.6638336 0.6723328 0.3361664
[78,] 0.6482994 0.7034011 0.3517006
[79,] 0.6520908 0.6958183 0.3479092
[80,] 0.6462756 0.7074489 0.3537244
[81,] 0.6141855 0.7716290 0.3858145
[82,] 0.5803591 0.8392817 0.4196409
[83,] 0.5525908 0.8948183 0.4474092
[84,] 0.5243300 0.9513400 0.4756700
[85,] 0.5631215 0.8737570 0.4368785
[86,] 0.5383841 0.9232319 0.4616159
[87,] 0.5881578 0.8236845 0.4118422
[88,] 0.6174700 0.7650599 0.3825300
[89,] 0.5748463 0.8503073 0.4251537
[90,] 0.5371383 0.9257233 0.4628617
[91,] 0.4896251 0.9792502 0.5103749
[92,] 0.4695744 0.9391489 0.5304256
[93,] 0.4907424 0.9814848 0.5092576
[94,] 0.4515746 0.9031492 0.5484254
[95,] 0.6032988 0.7934025 0.3967012
[96,] 0.5551886 0.8896228 0.4448114
[97,] 0.5176240 0.9647520 0.4823760
[98,] 0.5247788 0.9504423 0.4752212
[99,] 0.4874722 0.9749444 0.5125278
[100,] 0.4499675 0.8999349 0.5500325
[101,] 0.4157076 0.8314153 0.5842924
[102,] 0.4139029 0.8278057 0.5860971
[103,] 0.3909703 0.7819406 0.6090297
[104,] 0.4150548 0.8301095 0.5849452
[105,] 0.3653815 0.7307630 0.6346185
[106,] 0.3216938 0.6433876 0.6783062
[107,] 0.3125335 0.6250671 0.6874665
[108,] 0.2674149 0.5348298 0.7325851
[109,] 0.2319278 0.4638556 0.7680722
[110,] 0.2249171 0.4498342 0.7750829
[111,] 0.1953692 0.3907383 0.8046308
[112,] 0.2062319 0.4124639 0.7937681
[113,] 0.2346406 0.4692812 0.7653594
[114,] 0.2336358 0.4672715 0.7663642
[115,] 0.2940159 0.5880318 0.7059841
[116,] 0.2578082 0.5156164 0.7421918
[117,] 0.2292957 0.4585914 0.7707043
[118,] 0.1940856 0.3881712 0.8059144
[119,] 0.2295390 0.4590780 0.7704610
[120,] 0.2095320 0.4190639 0.7904680
[121,] 0.2304358 0.4608715 0.7695642
[122,] 0.2497620 0.4995240 0.7502380
[123,] 0.2193194 0.4386388 0.7806806
[124,] 0.1872548 0.3745096 0.8127452
[125,] 0.1778075 0.3556151 0.8221925
[126,] 0.1861243 0.3722486 0.8138757
[127,] 0.2112534 0.4225069 0.7887466
[128,] 0.1799933 0.3599865 0.8200067
[129,] 0.1577729 0.3155458 0.8422271
[130,] 0.1344388 0.2688775 0.8655612
[131,] 0.0973002 0.1946004 0.9026998
[132,] 0.1369560 0.2739121 0.8630440
[133,] 0.1171387 0.2342773 0.8828613
[134,] 0.6221998 0.7556003 0.3778002
[135,] 0.5078616 0.9842768 0.4921384
[136,] 0.5379152 0.9241696 0.4620848
[137,] 0.3862212 0.7724424 0.6137788
> postscript(file="/var/wessaorg/rcomp/tmp/1g1n61321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/23k361321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3ppk81321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4htm81321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5aohh1321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
-0.400885962 -0.291082633 -0.350225924 0.628044335 -0.287412231 -0.376443763
7 8 9 10 11 12
0.570360583 0.134226802 -0.403452035 -0.287155034 0.590772614 0.537610929
13 14 15 16 17 18
-0.163422962 -0.619591038 -0.480517693 0.693452135 0.003184294 0.547643455
19 20 21 22 23 24
0.168028374 -0.017697656 0.241375436 -0.215848367 -0.135499737 0.721072923
25 26 27 28 29 30
-0.157296236 -0.532245154 -0.532579595 -0.334887656 0.396937339 -0.337198584
31 32 33 34 35 36
0.470099106 -0.406349282 -0.085673283 0.688786446 0.767666617 0.597978090
37 38 39 40 41 42
-0.194381107 0.701461635 0.538328838 -0.571030973 -0.346382610 -0.500456048
43 44 45 46 47 48
-0.516622907 0.413800829 0.285769517 -0.065108909 0.685797015 0.817591794
49 50 51 52 53 54
-0.277160666 0.803302975 -0.405817874 -0.182272258 -0.378523399 -0.206913615
55 56 57 58 59 60
0.544005362 -0.514729636 -0.411040885 -0.452664370 0.363159176 0.447279496
61 62 63 64 65 66
-0.529700921 -0.345353531 -0.731706980 0.358268357 -0.163038476 0.478879993
67 68 69 70 71 72
0.566663351 0.549967162 -0.283423102 -0.161730594 -0.431814353 0.516316892
73 74 75 76 77 78
0.489519246 -0.142365108 -0.394673699 -0.269991516 -0.295662148 0.585421756
79 80 81 82 83 84
0.640529110 0.522485218 0.231308233 -0.203656012 -1.577536846 -0.463832611
85 86 87 88 89 90
0.607459770 0.454794281 -0.463119724 -0.066962259 -0.047775884 -0.491962259
91 92 93 94 95 96
-0.567484116 0.399011783 -0.342164565 -0.278562257 -0.373618177 -0.395791536
97 98 99 100 101 102
0.555110794 -0.384036559 0.610544822 0.499726162 -0.258254722 0.214283582
103 104 105 106 107 108
-0.208590954 -0.483940604 0.461859420 -0.348618086 0.800592609 -0.256879636
109 110 111 112 113 114
-0.193546485 -0.576904687 -0.352441212 -0.450544205 -0.442961663 -0.503485425
115 116 117 118 119 120
-0.358580990 0.682758780 -0.248763137 -0.249629028 0.257246322 -0.281075816
121 122 123 124 125 126
-0.160776452 -0.454198919 -0.321893934 -0.460725637 -0.743232542 0.591205886
127 128 129 130 131 132
-0.408600125 -0.203310682 0.550657503 0.437226215 -0.448579690 0.638356545
133 134 135 136 137 138
0.764576573 0.652991011 0.569270033 -0.273765273 -0.330460294 0.582750682
139 140 141 142 143 144
0.530552503 -0.286983639 -0.233812052 -0.206162434 -0.412320430 0.710358847
145 146 147 148 149 150
-0.392649883 -0.496936750 0.688261348 0.413053264 0.571278932 0.654867281
151 152 153 154 155 156
-0.540271802 -0.384499312 -0.386982140 0.575349575 0.532287791 0.613778277
157 158 159 160 161 162
-0.166740430 -0.052591229 -0.277977526 -0.271211459 0.507542681 -0.451416106
> postscript(file="/var/wessaorg/rcomp/tmp/6b5b11321804256.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 -0.400885962 NA
1 -0.291082633 -0.400885962
2 -0.350225924 -0.291082633
3 0.628044335 -0.350225924
4 -0.287412231 0.628044335
5 -0.376443763 -0.287412231
6 0.570360583 -0.376443763
7 0.134226802 0.570360583
8 -0.403452035 0.134226802
9 -0.287155034 -0.403452035
10 0.590772614 -0.287155034
11 0.537610929 0.590772614
12 -0.163422962 0.537610929
13 -0.619591038 -0.163422962
14 -0.480517693 -0.619591038
15 0.693452135 -0.480517693
16 0.003184294 0.693452135
17 0.547643455 0.003184294
18 0.168028374 0.547643455
19 -0.017697656 0.168028374
20 0.241375436 -0.017697656
21 -0.215848367 0.241375436
22 -0.135499737 -0.215848367
23 0.721072923 -0.135499737
24 -0.157296236 0.721072923
25 -0.532245154 -0.157296236
26 -0.532579595 -0.532245154
27 -0.334887656 -0.532579595
28 0.396937339 -0.334887656
29 -0.337198584 0.396937339
30 0.470099106 -0.337198584
31 -0.406349282 0.470099106
32 -0.085673283 -0.406349282
33 0.688786446 -0.085673283
34 0.767666617 0.688786446
35 0.597978090 0.767666617
36 -0.194381107 0.597978090
37 0.701461635 -0.194381107
38 0.538328838 0.701461635
39 -0.571030973 0.538328838
40 -0.346382610 -0.571030973
41 -0.500456048 -0.346382610
42 -0.516622907 -0.500456048
43 0.413800829 -0.516622907
44 0.285769517 0.413800829
45 -0.065108909 0.285769517
46 0.685797015 -0.065108909
47 0.817591794 0.685797015
48 -0.277160666 0.817591794
49 0.803302975 -0.277160666
50 -0.405817874 0.803302975
51 -0.182272258 -0.405817874
52 -0.378523399 -0.182272258
53 -0.206913615 -0.378523399
54 0.544005362 -0.206913615
55 -0.514729636 0.544005362
56 -0.411040885 -0.514729636
57 -0.452664370 -0.411040885
58 0.363159176 -0.452664370
59 0.447279496 0.363159176
60 -0.529700921 0.447279496
61 -0.345353531 -0.529700921
62 -0.731706980 -0.345353531
63 0.358268357 -0.731706980
64 -0.163038476 0.358268357
65 0.478879993 -0.163038476
66 0.566663351 0.478879993
67 0.549967162 0.566663351
68 -0.283423102 0.549967162
69 -0.161730594 -0.283423102
70 -0.431814353 -0.161730594
71 0.516316892 -0.431814353
72 0.489519246 0.516316892
73 -0.142365108 0.489519246
74 -0.394673699 -0.142365108
75 -0.269991516 -0.394673699
76 -0.295662148 -0.269991516
77 0.585421756 -0.295662148
78 0.640529110 0.585421756
79 0.522485218 0.640529110
80 0.231308233 0.522485218
81 -0.203656012 0.231308233
82 -1.577536846 -0.203656012
83 -0.463832611 -1.577536846
84 0.607459770 -0.463832611
85 0.454794281 0.607459770
86 -0.463119724 0.454794281
87 -0.066962259 -0.463119724
88 -0.047775884 -0.066962259
89 -0.491962259 -0.047775884
90 -0.567484116 -0.491962259
91 0.399011783 -0.567484116
92 -0.342164565 0.399011783
93 -0.278562257 -0.342164565
94 -0.373618177 -0.278562257
95 -0.395791536 -0.373618177
96 0.555110794 -0.395791536
97 -0.384036559 0.555110794
98 0.610544822 -0.384036559
99 0.499726162 0.610544822
100 -0.258254722 0.499726162
101 0.214283582 -0.258254722
102 -0.208590954 0.214283582
103 -0.483940604 -0.208590954
104 0.461859420 -0.483940604
105 -0.348618086 0.461859420
106 0.800592609 -0.348618086
107 -0.256879636 0.800592609
108 -0.193546485 -0.256879636
109 -0.576904687 -0.193546485
110 -0.352441212 -0.576904687
111 -0.450544205 -0.352441212
112 -0.442961663 -0.450544205
113 -0.503485425 -0.442961663
114 -0.358580990 -0.503485425
115 0.682758780 -0.358580990
116 -0.248763137 0.682758780
117 -0.249629028 -0.248763137
118 0.257246322 -0.249629028
119 -0.281075816 0.257246322
120 -0.160776452 -0.281075816
121 -0.454198919 -0.160776452
122 -0.321893934 -0.454198919
123 -0.460725637 -0.321893934
124 -0.743232542 -0.460725637
125 0.591205886 -0.743232542
126 -0.408600125 0.591205886
127 -0.203310682 -0.408600125
128 0.550657503 -0.203310682
129 0.437226215 0.550657503
130 -0.448579690 0.437226215
131 0.638356545 -0.448579690
132 0.764576573 0.638356545
133 0.652991011 0.764576573
134 0.569270033 0.652991011
135 -0.273765273 0.569270033
136 -0.330460294 -0.273765273
137 0.582750682 -0.330460294
138 0.530552503 0.582750682
139 -0.286983639 0.530552503
140 -0.233812052 -0.286983639
141 -0.206162434 -0.233812052
142 -0.412320430 -0.206162434
143 0.710358847 -0.412320430
144 -0.392649883 0.710358847
145 -0.496936750 -0.392649883
146 0.688261348 -0.496936750
147 0.413053264 0.688261348
148 0.571278932 0.413053264
149 0.654867281 0.571278932
150 -0.540271802 0.654867281
151 -0.384499312 -0.540271802
152 -0.386982140 -0.384499312
153 0.575349575 -0.386982140
154 0.532287791 0.575349575
155 0.613778277 0.532287791
156 -0.166740430 0.613778277
157 -0.052591229 -0.166740430
158 -0.277977526 -0.052591229
159 -0.271211459 -0.277977526
160 0.507542681 -0.271211459
161 -0.451416106 0.507542681
162 NA -0.451416106
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.291082633 -0.400885962
[2,] -0.350225924 -0.291082633
[3,] 0.628044335 -0.350225924
[4,] -0.287412231 0.628044335
[5,] -0.376443763 -0.287412231
[6,] 0.570360583 -0.376443763
[7,] 0.134226802 0.570360583
[8,] -0.403452035 0.134226802
[9,] -0.287155034 -0.403452035
[10,] 0.590772614 -0.287155034
[11,] 0.537610929 0.590772614
[12,] -0.163422962 0.537610929
[13,] -0.619591038 -0.163422962
[14,] -0.480517693 -0.619591038
[15,] 0.693452135 -0.480517693
[16,] 0.003184294 0.693452135
[17,] 0.547643455 0.003184294
[18,] 0.168028374 0.547643455
[19,] -0.017697656 0.168028374
[20,] 0.241375436 -0.017697656
[21,] -0.215848367 0.241375436
[22,] -0.135499737 -0.215848367
[23,] 0.721072923 -0.135499737
[24,] -0.157296236 0.721072923
[25,] -0.532245154 -0.157296236
[26,] -0.532579595 -0.532245154
[27,] -0.334887656 -0.532579595
[28,] 0.396937339 -0.334887656
[29,] -0.337198584 0.396937339
[30,] 0.470099106 -0.337198584
[31,] -0.406349282 0.470099106
[32,] -0.085673283 -0.406349282
[33,] 0.688786446 -0.085673283
[34,] 0.767666617 0.688786446
[35,] 0.597978090 0.767666617
[36,] -0.194381107 0.597978090
[37,] 0.701461635 -0.194381107
[38,] 0.538328838 0.701461635
[39,] -0.571030973 0.538328838
[40,] -0.346382610 -0.571030973
[41,] -0.500456048 -0.346382610
[42,] -0.516622907 -0.500456048
[43,] 0.413800829 -0.516622907
[44,] 0.285769517 0.413800829
[45,] -0.065108909 0.285769517
[46,] 0.685797015 -0.065108909
[47,] 0.817591794 0.685797015
[48,] -0.277160666 0.817591794
[49,] 0.803302975 -0.277160666
[50,] -0.405817874 0.803302975
[51,] -0.182272258 -0.405817874
[52,] -0.378523399 -0.182272258
[53,] -0.206913615 -0.378523399
[54,] 0.544005362 -0.206913615
[55,] -0.514729636 0.544005362
[56,] -0.411040885 -0.514729636
[57,] -0.452664370 -0.411040885
[58,] 0.363159176 -0.452664370
[59,] 0.447279496 0.363159176
[60,] -0.529700921 0.447279496
[61,] -0.345353531 -0.529700921
[62,] -0.731706980 -0.345353531
[63,] 0.358268357 -0.731706980
[64,] -0.163038476 0.358268357
[65,] 0.478879993 -0.163038476
[66,] 0.566663351 0.478879993
[67,] 0.549967162 0.566663351
[68,] -0.283423102 0.549967162
[69,] -0.161730594 -0.283423102
[70,] -0.431814353 -0.161730594
[71,] 0.516316892 -0.431814353
[72,] 0.489519246 0.516316892
[73,] -0.142365108 0.489519246
[74,] -0.394673699 -0.142365108
[75,] -0.269991516 -0.394673699
[76,] -0.295662148 -0.269991516
[77,] 0.585421756 -0.295662148
[78,] 0.640529110 0.585421756
[79,] 0.522485218 0.640529110
[80,] 0.231308233 0.522485218
[81,] -0.203656012 0.231308233
[82,] -1.577536846 -0.203656012
[83,] -0.463832611 -1.577536846
[84,] 0.607459770 -0.463832611
[85,] 0.454794281 0.607459770
[86,] -0.463119724 0.454794281
[87,] -0.066962259 -0.463119724
[88,] -0.047775884 -0.066962259
[89,] -0.491962259 -0.047775884
[90,] -0.567484116 -0.491962259
[91,] 0.399011783 -0.567484116
[92,] -0.342164565 0.399011783
[93,] -0.278562257 -0.342164565
[94,] -0.373618177 -0.278562257
[95,] -0.395791536 -0.373618177
[96,] 0.555110794 -0.395791536
[97,] -0.384036559 0.555110794
[98,] 0.610544822 -0.384036559
[99,] 0.499726162 0.610544822
[100,] -0.258254722 0.499726162
[101,] 0.214283582 -0.258254722
[102,] -0.208590954 0.214283582
[103,] -0.483940604 -0.208590954
[104,] 0.461859420 -0.483940604
[105,] -0.348618086 0.461859420
[106,] 0.800592609 -0.348618086
[107,] -0.256879636 0.800592609
[108,] -0.193546485 -0.256879636
[109,] -0.576904687 -0.193546485
[110,] -0.352441212 -0.576904687
[111,] -0.450544205 -0.352441212
[112,] -0.442961663 -0.450544205
[113,] -0.503485425 -0.442961663
[114,] -0.358580990 -0.503485425
[115,] 0.682758780 -0.358580990
[116,] -0.248763137 0.682758780
[117,] -0.249629028 -0.248763137
[118,] 0.257246322 -0.249629028
[119,] -0.281075816 0.257246322
[120,] -0.160776452 -0.281075816
[121,] -0.454198919 -0.160776452
[122,] -0.321893934 -0.454198919
[123,] -0.460725637 -0.321893934
[124,] -0.743232542 -0.460725637
[125,] 0.591205886 -0.743232542
[126,] -0.408600125 0.591205886
[127,] -0.203310682 -0.408600125
[128,] 0.550657503 -0.203310682
[129,] 0.437226215 0.550657503
[130,] -0.448579690 0.437226215
[131,] 0.638356545 -0.448579690
[132,] 0.764576573 0.638356545
[133,] 0.652991011 0.764576573
[134,] 0.569270033 0.652991011
[135,] -0.273765273 0.569270033
[136,] -0.330460294 -0.273765273
[137,] 0.582750682 -0.330460294
[138,] 0.530552503 0.582750682
[139,] -0.286983639 0.530552503
[140,] -0.233812052 -0.286983639
[141,] -0.206162434 -0.233812052
[142,] -0.412320430 -0.206162434
[143,] 0.710358847 -0.412320430
[144,] -0.392649883 0.710358847
[145,] -0.496936750 -0.392649883
[146,] 0.688261348 -0.496936750
[147,] 0.413053264 0.688261348
[148,] 0.571278932 0.413053264
[149,] 0.654867281 0.571278932
[150,] -0.540271802 0.654867281
[151,] -0.384499312 -0.540271802
[152,] -0.386982140 -0.384499312
[153,] 0.575349575 -0.386982140
[154,] 0.532287791 0.575349575
[155,] 0.613778277 0.532287791
[156,] -0.166740430 0.613778277
[157,] -0.052591229 -0.166740430
[158,] -0.277977526 -0.052591229
[159,] -0.271211459 -0.277977526
[160,] 0.507542681 -0.271211459
[161,] -0.451416106 0.507542681
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.291082633 -0.400885962
2 -0.350225924 -0.291082633
3 0.628044335 -0.350225924
4 -0.287412231 0.628044335
5 -0.376443763 -0.287412231
6 0.570360583 -0.376443763
7 0.134226802 0.570360583
8 -0.403452035 0.134226802
9 -0.287155034 -0.403452035
10 0.590772614 -0.287155034
11 0.537610929 0.590772614
12 -0.163422962 0.537610929
13 -0.619591038 -0.163422962
14 -0.480517693 -0.619591038
15 0.693452135 -0.480517693
16 0.003184294 0.693452135
17 0.547643455 0.003184294
18 0.168028374 0.547643455
19 -0.017697656 0.168028374
20 0.241375436 -0.017697656
21 -0.215848367 0.241375436
22 -0.135499737 -0.215848367
23 0.721072923 -0.135499737
24 -0.157296236 0.721072923
25 -0.532245154 -0.157296236
26 -0.532579595 -0.532245154
27 -0.334887656 -0.532579595
28 0.396937339 -0.334887656
29 -0.337198584 0.396937339
30 0.470099106 -0.337198584
31 -0.406349282 0.470099106
32 -0.085673283 -0.406349282
33 0.688786446 -0.085673283
34 0.767666617 0.688786446
35 0.597978090 0.767666617
36 -0.194381107 0.597978090
37 0.701461635 -0.194381107
38 0.538328838 0.701461635
39 -0.571030973 0.538328838
40 -0.346382610 -0.571030973
41 -0.500456048 -0.346382610
42 -0.516622907 -0.500456048
43 0.413800829 -0.516622907
44 0.285769517 0.413800829
45 -0.065108909 0.285769517
46 0.685797015 -0.065108909
47 0.817591794 0.685797015
48 -0.277160666 0.817591794
49 0.803302975 -0.277160666
50 -0.405817874 0.803302975
51 -0.182272258 -0.405817874
52 -0.378523399 -0.182272258
53 -0.206913615 -0.378523399
54 0.544005362 -0.206913615
55 -0.514729636 0.544005362
56 -0.411040885 -0.514729636
57 -0.452664370 -0.411040885
58 0.363159176 -0.452664370
59 0.447279496 0.363159176
60 -0.529700921 0.447279496
61 -0.345353531 -0.529700921
62 -0.731706980 -0.345353531
63 0.358268357 -0.731706980
64 -0.163038476 0.358268357
65 0.478879993 -0.163038476
66 0.566663351 0.478879993
67 0.549967162 0.566663351
68 -0.283423102 0.549967162
69 -0.161730594 -0.283423102
70 -0.431814353 -0.161730594
71 0.516316892 -0.431814353
72 0.489519246 0.516316892
73 -0.142365108 0.489519246
74 -0.394673699 -0.142365108
75 -0.269991516 -0.394673699
76 -0.295662148 -0.269991516
77 0.585421756 -0.295662148
78 0.640529110 0.585421756
79 0.522485218 0.640529110
80 0.231308233 0.522485218
81 -0.203656012 0.231308233
82 -1.577536846 -0.203656012
83 -0.463832611 -1.577536846
84 0.607459770 -0.463832611
85 0.454794281 0.607459770
86 -0.463119724 0.454794281
87 -0.066962259 -0.463119724
88 -0.047775884 -0.066962259
89 -0.491962259 -0.047775884
90 -0.567484116 -0.491962259
91 0.399011783 -0.567484116
92 -0.342164565 0.399011783
93 -0.278562257 -0.342164565
94 -0.373618177 -0.278562257
95 -0.395791536 -0.373618177
96 0.555110794 -0.395791536
97 -0.384036559 0.555110794
98 0.610544822 -0.384036559
99 0.499726162 0.610544822
100 -0.258254722 0.499726162
101 0.214283582 -0.258254722
102 -0.208590954 0.214283582
103 -0.483940604 -0.208590954
104 0.461859420 -0.483940604
105 -0.348618086 0.461859420
106 0.800592609 -0.348618086
107 -0.256879636 0.800592609
108 -0.193546485 -0.256879636
109 -0.576904687 -0.193546485
110 -0.352441212 -0.576904687
111 -0.450544205 -0.352441212
112 -0.442961663 -0.450544205
113 -0.503485425 -0.442961663
114 -0.358580990 -0.503485425
115 0.682758780 -0.358580990
116 -0.248763137 0.682758780
117 -0.249629028 -0.248763137
118 0.257246322 -0.249629028
119 -0.281075816 0.257246322
120 -0.160776452 -0.281075816
121 -0.454198919 -0.160776452
122 -0.321893934 -0.454198919
123 -0.460725637 -0.321893934
124 -0.743232542 -0.460725637
125 0.591205886 -0.743232542
126 -0.408600125 0.591205886
127 -0.203310682 -0.408600125
128 0.550657503 -0.203310682
129 0.437226215 0.550657503
130 -0.448579690 0.437226215
131 0.638356545 -0.448579690
132 0.764576573 0.638356545
133 0.652991011 0.764576573
134 0.569270033 0.652991011
135 -0.273765273 0.569270033
136 -0.330460294 -0.273765273
137 0.582750682 -0.330460294
138 0.530552503 0.582750682
139 -0.286983639 0.530552503
140 -0.233812052 -0.286983639
141 -0.206162434 -0.233812052
142 -0.412320430 -0.206162434
143 0.710358847 -0.412320430
144 -0.392649883 0.710358847
145 -0.496936750 -0.392649883
146 0.688261348 -0.496936750
147 0.413053264 0.688261348
148 0.571278932 0.413053264
149 0.654867281 0.571278932
150 -0.540271802 0.654867281
151 -0.384499312 -0.540271802
152 -0.386982140 -0.384499312
153 0.575349575 -0.386982140
154 0.532287791 0.575349575
155 0.613778277 0.532287791
156 -0.166740430 0.613778277
157 -0.052591229 -0.166740430
158 -0.277977526 -0.052591229
159 -0.271211459 -0.277977526
160 0.507542681 -0.271211459
161 -0.451416106 0.507542681
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7nm261321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8s3v81321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/95jf91321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/103ift1321804256.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/118z1p1321804256.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12tox41321804256.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13dflp1321804256.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14kxq81321804256.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15jsfb1321804256.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16wak21321804256.tab")
+ }
>
> try(system("convert tmp/1g1n61321804256.ps tmp/1g1n61321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/23k361321804256.ps tmp/23k361321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ppk81321804256.ps tmp/3ppk81321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/4htm81321804256.ps tmp/4htm81321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/5aohh1321804256.ps tmp/5aohh1321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b5b11321804256.ps tmp/6b5b11321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nm261321804256.ps tmp/7nm261321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/8s3v81321804256.ps tmp/8s3v81321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/95jf91321804256.ps tmp/95jf91321804256.png",intern=TRUE))
character(0)
> try(system("convert tmp/103ift1321804256.ps tmp/103ift1321804256.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.460 0.529 6.042