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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Type 'q()' to quit R.
> x <- array(list(9
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+ ,4)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('month'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('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 = '2'
> 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
I1 month I2 I3 E1 E2 E3 A t
1 26 9 21 21 23 17 23 4 1
2 20 9 16 15 24 17 20 4 2
3 19 9 19 18 22 18 20 6 3
4 19 9 18 11 20 21 21 8 4
5 20 9 16 8 24 20 24 8 5
6 25 9 23 19 27 28 22 4 6
7 25 9 17 4 28 19 23 4 7
8 22 9 12 20 27 22 20 8 8
9 26 9 19 16 24 16 25 5 9
10 22 9 16 14 23 18 23 4 10
11 17 9 19 10 24 25 27 4 11
12 22 9 20 13 27 17 27 4 12
13 19 9 13 14 27 14 22 4 13
14 24 9 20 8 28 11 24 4 14
15 26 9 27 23 27 27 25 4 15
16 21 9 17 11 23 20 22 8 16
17 13 9 8 9 24 22 28 4 17
18 26 9 25 24 28 22 28 4 18
19 20 9 26 5 27 21 27 4 19
20 22 9 13 15 25 23 25 8 20
21 14 9 19 5 19 17 16 4 21
22 21 9 15 19 24 24 28 7 22
23 7 9 5 6 20 14 21 4 23
24 23 9 16 13 28 17 24 4 24
25 17 9 14 11 26 23 27 5 25
26 25 9 24 17 23 24 14 4 26
27 25 9 24 17 23 24 14 4 27
28 19 9 9 5 20 8 27 4 28
29 20 9 19 9 11 22 20 4 29
30 23 9 19 15 24 23 21 4 30
31 22 9 25 17 25 25 22 4 31
32 22 9 19 17 23 21 21 4 32
33 21 9 18 20 18 24 12 15 33
34 15 9 15 12 20 15 20 10 34
35 20 9 12 7 20 22 24 4 35
36 22 9 21 16 24 21 19 8 36
37 18 9 12 7 23 25 28 4 37
38 20 9 15 14 25 16 23 4 38
39 28 9 28 24 28 28 27 4 39
40 22 9 25 15 26 23 22 4 40
41 18 9 19 15 26 21 27 7 41
42 23 9 20 10 23 21 26 4 42
43 20 9 24 14 22 26 22 6 43
44 25 9 26 18 24 22 21 5 44
45 26 9 25 12 21 21 19 4 45
46 15 9 12 9 20 18 24 16 46
47 17 9 12 9 22 12 19 5 47
48 23 9 15 8 20 25 26 12 48
49 21 9 17 18 25 17 22 6 49
50 13 9 14 10 20 24 28 9 50
51 18 9 16 17 22 15 21 9 51
52 19 9 11 14 23 13 23 4 52
53 22 9 20 16 25 26 28 5 53
54 16 9 11 10 23 16 10 4 54
55 24 10 22 19 23 24 24 4 55
56 18 10 20 10 22 21 21 5 56
57 20 10 19 14 24 20 21 4 57
58 24 10 17 10 25 14 24 4 58
59 14 10 21 4 21 25 24 4 59
60 22 10 23 19 12 25 25 5 60
61 24 10 18 9 17 20 25 4 61
62 18 10 17 12 20 22 23 6 62
63 21 10 27 16 23 20 21 4 63
64 23 10 25 11 23 26 16 4 64
65 17 10 19 18 20 18 17 18 65
66 22 10 22 11 28 22 25 4 66
67 24 10 24 24 24 24 24 6 67
68 21 10 20 17 24 17 23 4 68
69 22 10 19 18 24 24 25 4 69
70 16 10 11 9 24 20 23 5 70
71 21 10 22 19 28 19 28 4 71
72 23 10 22 18 25 20 26 4 72
73 22 10 16 12 21 15 22 5 73
74 24 10 20 23 25 23 19 10 74
75 24 10 24 22 25 26 26 5 75
76 16 10 16 14 18 22 18 8 76
77 16 10 16 14 17 20 18 8 77
78 21 10 22 16 26 24 25 5 78
79 26 10 24 23 28 26 27 4 79
80 15 10 16 7 21 21 12 4 80
81 25 10 27 10 27 25 15 4 81
82 18 10 11 12 22 13 21 5 82
83 23 10 21 12 21 20 23 4 83
84 20 10 20 12 25 22 22 4 84
85 17 10 20 17 22 23 21 8 85
86 25 10 27 21 23 28 24 4 86
87 24 10 20 16 26 22 27 5 87
88 17 10 12 11 19 20 22 14 88
89 19 10 8 14 25 6 28 8 89
90 20 10 21 13 21 21 26 8 90
91 15 10 18 9 13 20 10 4 91
92 27 10 24 19 24 18 19 4 92
93 22 10 16 13 25 23 22 6 93
94 23 10 18 19 26 20 21 4 94
95 16 10 20 13 25 24 24 7 95
96 19 10 20 13 25 22 25 7 96
97 25 10 19 13 22 21 21 4 97
98 19 10 17 14 21 18 20 6 98
99 19 10 16 12 23 21 21 4 99
100 26 10 26 22 25 23 24 7 100
101 21 10 15 11 24 23 23 4 101
102 20 10 22 5 21 15 18 4 102
103 24 10 17 18 21 21 24 8 103
104 22 10 23 19 25 24 24 4 104
105 20 10 21 14 22 23 19 4 105
106 18 10 19 15 20 21 20 10 106
107 18 10 14 12 20 21 18 8 107
108 24 10 17 19 23 20 20 6 108
109 24 11 12 15 28 11 27 4 109
110 22 11 24 17 23 22 23 4 110
111 23 11 18 8 28 27 26 4 111
112 22 11 20 10 24 25 23 5 112
113 20 11 16 12 18 18 17 4 113
114 18 11 20 12 20 20 21 6 114
115 25 11 22 20 28 24 25 4 115
116 18 11 12 12 21 10 23 5 116
117 16 11 16 12 21 27 27 7 117
118 20 11 17 14 25 21 24 8 118
119 19 11 22 6 19 21 20 5 119
120 15 11 12 10 18 18 27 8 120
121 19 11 14 18 21 15 21 10 121
122 19 11 23 18 22 24 24 8 122
123 16 11 15 7 24 22 21 5 123
124 17 11 17 18 15 14 15 12 124
125 28 11 28 9 28 28 25 4 125
126 23 11 20 17 26 18 25 5 126
127 25 11 23 22 23 26 22 4 127
128 20 11 13 11 26 17 24 6 128
129 17 11 18 15 20 19 21 4 129
130 23 11 23 17 22 22 22 4 130
131 16 11 19 15 20 18 23 7 131
132 23 11 23 22 23 24 22 7 132
133 11 11 12 9 22 15 20 10 133
134 18 11 16 13 24 18 23 4 134
135 24 11 23 20 23 26 25 5 135
136 23 11 13 14 22 11 23 8 136
137 21 11 22 14 26 26 22 11 137
138 16 11 18 12 23 21 25 7 138
139 24 11 23 20 27 23 26 4 139
140 23 11 20 20 23 23 22 8 140
141 18 11 10 8 21 15 24 6 141
142 20 11 17 17 26 22 24 7 142
143 9 11 18 9 23 26 25 5 143
144 24 11 15 18 21 16 20 4 144
145 25 11 23 22 27 20 26 8 145
146 20 11 17 10 19 18 21 4 146
147 21 11 17 13 23 22 26 8 147
148 25 11 22 15 25 16 21 6 148
149 22 11 20 18 23 19 22 4 149
150 21 11 20 18 22 20 16 9 150
151 21 11 19 12 22 19 26 5 151
152 22 11 18 12 25 23 28 6 152
153 27 11 22 20 25 24 18 4 153
154 24 11 20 12 28 25 25 4 154
155 24 11 22 16 28 21 23 4 155
156 21 11 18 16 20 21 21 5 156
157 18 11 16 18 25 23 20 6 157
158 16 11 16 16 19 27 25 16 158
159 22 11 16 13 25 23 22 6 159
160 20 11 16 17 22 18 21 6 160
161 18 11 17 13 18 16 16 4 161
162 20 11 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 I2 I3 E1 E2
11.556084 -0.497696 0.365101 0.253316 0.257114 -0.118528
E3 A t
0.044458 -0.212687 0.005517
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.6020 -1.4249 -0.0079 1.7955 7.3726
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.556084 6.618093 1.746 0.082795 .
month -0.497696 0.739787 -0.673 0.502117
I2 0.365101 0.063579 5.742 4.87e-08 ***
I3 0.253316 0.050846 4.982 1.68e-06 ***
E1 0.257114 0.075436 3.408 0.000835 ***
E2 -0.118528 0.059202 -2.002 0.047043 *
E3 0.044458 0.062072 0.716 0.474945
A -0.212687 0.084646 -2.513 0.013018 *
t 0.005517 0.012972 0.425 0.671192
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.509 on 153 degrees of freedom
Multiple R-squared: 0.5526, Adjusted R-squared: 0.5292
F-statistic: 23.62 on 8 and 153 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.93384776 0.13230448 0.06615224
[2,] 0.92191565 0.15616869 0.07808435
[3,] 0.86667103 0.26665794 0.13332897
[4,] 0.79583012 0.40833975 0.20416988
[5,] 0.76842525 0.46314950 0.23157475
[6,] 0.68989854 0.62020291 0.31010146
[7,] 0.59939536 0.80120929 0.40060464
[8,] 0.62127023 0.75745953 0.37872977
[9,] 0.66409583 0.67180833 0.33590417
[10,] 0.59847099 0.80305802 0.40152901
[11,] 0.53157131 0.93685737 0.46842869
[12,] 0.58410747 0.83178505 0.41589253
[13,] 0.56102282 0.87795437 0.43897718
[14,] 0.49538175 0.99076350 0.50461825
[15,] 0.58704979 0.82590041 0.41295021
[16,] 0.56347991 0.87304018 0.43652009
[17,] 0.81089420 0.37821161 0.18910580
[18,] 0.91470190 0.17059619 0.08529810
[19,] 0.89698387 0.20603227 0.10301613
[20,] 0.89569737 0.20860527 0.10430263
[21,] 0.86420548 0.27158904 0.13579452
[22,] 0.86493222 0.27013556 0.13506778
[23,] 0.90405365 0.19189271 0.09594635
[24,] 0.94453202 0.11093596 0.05546798
[25,] 0.92722814 0.14554373 0.07277186
[26,] 0.91297529 0.17404942 0.08702471
[27,] 0.88924370 0.22151260 0.11075630
[28,] 0.86013793 0.27972413 0.13986207
[29,] 0.84893828 0.30212343 0.15106172
[30,] 0.87723324 0.24553352 0.12276676
[31,] 0.87529360 0.24941280 0.12470640
[32,] 0.86119026 0.27761949 0.13880974
[33,] 0.83025152 0.33949696 0.16974848
[34,] 0.85992311 0.28015379 0.14007689
[35,] 0.82908988 0.34182025 0.17091012
[36,] 0.79517455 0.40965089 0.20482545
[37,] 0.94600734 0.10798531 0.05399266
[38,] 0.93127464 0.13745072 0.06872536
[39,] 0.94871650 0.10256701 0.05128350
[40,] 0.94234031 0.11531938 0.05765969
[41,] 0.92988403 0.14023195 0.07011597
[42,] 0.91144479 0.17711041 0.08855521
[43,] 0.89390785 0.21218431 0.10609215
[44,] 0.87123880 0.25752239 0.12876120
[45,] 0.85940892 0.28118217 0.14059108
[46,] 0.83296654 0.33406692 0.16703346
[47,] 0.86570893 0.26858214 0.13429107
[48,] 0.90622159 0.18755682 0.09377841
[49,] 0.89539019 0.20921961 0.10460981
[50,] 0.96708003 0.06583994 0.03291997
[51,] 0.95848244 0.08303511 0.04151756
[52,] 0.96451124 0.07097752 0.03548876
[53,] 0.95920527 0.08158946 0.04079473
[54,] 0.95328417 0.09343166 0.04671583
[55,] 0.94094222 0.11811557 0.05905778
[56,] 0.92590678 0.14818643 0.07409322
[57,] 0.91424386 0.17151228 0.08575614
[58,] 0.89489355 0.21021290 0.10510645
[59,] 0.87367719 0.25264563 0.12632281
[60,] 0.90263500 0.19473000 0.09736500
[61,] 0.88413468 0.23173065 0.11586532
[62,] 0.89269833 0.21460334 0.10730167
[63,] 0.88130152 0.23739696 0.11869848
[64,] 0.85703851 0.28592298 0.14296149
[65,] 0.83624183 0.32751634 0.16375817
[66,] 0.81235855 0.37528289 0.18764145
[67,] 0.79576627 0.40846745 0.20423373
[68,] 0.76745338 0.46509325 0.23254662
[69,] 0.75394065 0.49211869 0.24605935
[70,] 0.74106676 0.51786648 0.25893324
[71,] 0.70839630 0.58320741 0.29160370
[72,] 0.70375902 0.59248196 0.29624098
[73,] 0.67503277 0.64993447 0.32496723
[74,] 0.71984381 0.56031239 0.28015619
[75,] 0.67923974 0.64152051 0.32076026
[76,] 0.65610591 0.68778818 0.34389409
[77,] 0.66566566 0.66866869 0.33433434
[78,] 0.63209249 0.73581501 0.36790751
[79,] 0.58747001 0.82505998 0.41252999
[80,] 0.54846504 0.90306993 0.45153496
[81,] 0.54247336 0.91505329 0.45752664
[82,] 0.53922004 0.92155992 0.46077996
[83,] 0.50066737 0.99866525 0.49933263
[84,] 0.62659122 0.74681756 0.37340878
[85,] 0.62342780 0.75314439 0.37657220
[86,] 0.70916702 0.58166597 0.29083298
[87,] 0.67191785 0.65616429 0.32808215
[88,] 0.63544719 0.72910562 0.36455281
[89,] 0.59125428 0.81749145 0.40874572
[90,] 0.56621888 0.86756225 0.43378112
[91,] 0.51842342 0.96315316 0.48157658
[92,] 0.59319677 0.81360646 0.40680323
[93,] 0.58901718 0.82196563 0.41098282
[94,] 0.56679714 0.86640572 0.43320286
[95,] 0.54346220 0.91307560 0.45653780
[96,] 0.49720906 0.99441811 0.50279094
[97,] 0.46852991 0.93705982 0.53147009
[98,] 0.45669178 0.91338355 0.54330822
[99,] 0.42227489 0.84454978 0.57772511
[100,] 0.45216089 0.90432178 0.54783911
[101,] 0.45166190 0.90332379 0.54833810
[102,] 0.46598610 0.93197221 0.53401390
[103,] 0.42469965 0.84939931 0.57530035
[104,] 0.37394225 0.74788450 0.62605775
[105,] 0.32528639 0.65057277 0.67471361
[106,] 0.29104511 0.58209023 0.70895489
[107,] 0.24904547 0.49809093 0.75095453
[108,] 0.22338973 0.44677945 0.77661027
[109,] 0.19860772 0.39721545 0.80139228
[110,] 0.16566718 0.33133437 0.83433282
[111,] 0.15898857 0.31797715 0.84101143
[112,] 0.13017556 0.26035113 0.86982444
[113,] 0.10473206 0.20946411 0.89526794
[114,] 0.23926992 0.47853984 0.76073008
[115,] 0.19665078 0.39330155 0.80334922
[116,] 0.18601667 0.37203335 0.81398333
[117,] 0.16752631 0.33505261 0.83247369
[118,] 0.14830050 0.29660100 0.85169950
[119,] 0.13098823 0.26197647 0.86901177
[120,] 0.14963893 0.29927786 0.85036107
[121,] 0.11525081 0.23050163 0.88474919
[122,] 0.19905078 0.39810156 0.80094922
[123,] 0.18867673 0.37735346 0.81132327
[124,] 0.18487839 0.36975679 0.81512161
[125,] 0.16520348 0.33040697 0.83479652
[126,] 0.13616231 0.27232463 0.86383769
[127,] 0.13971582 0.27943165 0.86028418
[128,] 0.10456119 0.20912239 0.89543881
[129,] 0.08533326 0.17066652 0.91466674
[130,] 0.06298483 0.12596966 0.93701517
[131,] 0.04590142 0.09180284 0.95409858
[132,] 0.92696498 0.14607004 0.07303502
[133,] 0.96431731 0.07136538 0.03568269
[134,] 0.93376142 0.13247716 0.06623858
[135,] 0.88494841 0.23010318 0.11505159
[136,] 0.82471152 0.35057697 0.17528848
[137,] 0.79101370 0.41797259 0.20898630
[138,] 0.67168651 0.65662698 0.32831349
[139,] 0.53894363 0.92211275 0.46105637
> postscript(file="/var/wessaorg/rcomp/tmp/1vgf01353254643.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/23o4v1353254643.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3e7vr1353254643.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4kjgh1353254643.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5j5im1353254643.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
1.860486185 -0.923371135 -2.726009710 0.657513123 1.861790308 0.929136943
7 8 9 10 11 12
5.545642779 1.909397992 3.561259786 1.528075034 -3.164731950 -1.014861971
13 14 15 16 17 18
-1.851282077 1.405774171 -0.846087871 2.022079604 -3.328447016 -1.368878555
19 20 21 22 23 24
-2.743453309 3.155135528 -4.126947463 0.430216794 -7.114864713 1.255593779
25 26 27 28 29 30
-2.208380021 1.870324281 1.864806938 2.672547272 3.287369954 1.493548838
31 32 33 34 35 36
-2.273723490 -0.004059333 2.976407767 -2.907353903 3.824752437 0.179535080
37 38 39 40 41 42
1.220129854 -1.012592512 0.175577826 -2.310917393 -3.947108176 2.126589114
43 44 45 46 47 48
-1.899639038 0.194809098 3.603329116 0.335566734 -1.012618279 7.372574433
49 50 51 52 53 54
-1.228389114 -3.625495416 -2.404201490 -0.470786069 -0.251812651 -1.535026506
55 56 57 58 59 60
0.987013259 -1.760869057 -1.259991089 3.376304145 -4.237462262 1.709334565
61 62 63 64 65 66
5.971586379 -0.448773151 -3.463423281 1.461295465 -1.370540913 -0.614233113
67 68 69 70 71 72
-0.907715611 -1.890229063 -0.043181163 -1.020554870 -4.157302299 -0.930719642
73 74 75 76 77 78
2.600598971 1.464779177 -0.766883210 -1.505656541 -1.491115746 -1.983049732
79 80 81 82 83 84
-0.070754590 -2.228388737 1.788088398 -0.073262616 2.055413391 -1.331944661
85 86 87 88 89 90
-3.818965041 -0.042051141 1.371526099 2.252612474 0.202611570 -0.400619174
91 92 93 94 95 96
-1.498616235 2.306675762 2.369391078 0.120161870 -4.859748605 -2.146779099
97 98 99 100 101 102
4.405386391 -0.751881708 -0.514142409 0.523686832 1.984266860 -0.011658351
103 104 105 106 107 108
3.810396461 -2.162665420 -1.296300086 -1.316092247 0.927384618 2.649193880
109 110 111 112 113 114
2.891245682 -1.234913715 3.403716647 2.298824445 2.014126258 -1.481423502
115 116 117 118 119 120
0.570326420 -0.315642262 -1.519047079 0.210140413 0.488093855 -0.651284066
121 122 123 124 125 126
0.151664373 -2.888875093 -1.443080931 -0.843916943 4.585130486 0.015533611
127 128 129 130 131 132
1.288382482 1.218719812 -3.137831000 0.321411997 -4.083347804 -0.338197885
133 134 135 136 137 138
-5.117155156 -2.164481496 0.830190185 4.201754000 0.342306795 -3.501593378
139 140 141 142 143 144
-0.833062644 1.313758361 2.050758930 -1.033496553 -9.601968203 3.546525522
145 146 147 148 149 150
0.122367531 1.538640470 1.847293664 2.081156588 -0.554126735 0.146179211
151 152 153 154 155 156
0.611805843 1.797932053 3.443211255 2.230406922 0.096227467 0.909627904
157 158 159 160 161 162
-2.663686937 -0.241192823 2.502942427 -0.292678421 -1.061721462 -1.048745915
> postscript(file="/var/wessaorg/rcomp/tmp/6y2271353254643.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 1.860486185 NA
1 -0.923371135 1.860486185
2 -2.726009710 -0.923371135
3 0.657513123 -2.726009710
4 1.861790308 0.657513123
5 0.929136943 1.861790308
6 5.545642779 0.929136943
7 1.909397992 5.545642779
8 3.561259786 1.909397992
9 1.528075034 3.561259786
10 -3.164731950 1.528075034
11 -1.014861971 -3.164731950
12 -1.851282077 -1.014861971
13 1.405774171 -1.851282077
14 -0.846087871 1.405774171
15 2.022079604 -0.846087871
16 -3.328447016 2.022079604
17 -1.368878555 -3.328447016
18 -2.743453309 -1.368878555
19 3.155135528 -2.743453309
20 -4.126947463 3.155135528
21 0.430216794 -4.126947463
22 -7.114864713 0.430216794
23 1.255593779 -7.114864713
24 -2.208380021 1.255593779
25 1.870324281 -2.208380021
26 1.864806938 1.870324281
27 2.672547272 1.864806938
28 3.287369954 2.672547272
29 1.493548838 3.287369954
30 -2.273723490 1.493548838
31 -0.004059333 -2.273723490
32 2.976407767 -0.004059333
33 -2.907353903 2.976407767
34 3.824752437 -2.907353903
35 0.179535080 3.824752437
36 1.220129854 0.179535080
37 -1.012592512 1.220129854
38 0.175577826 -1.012592512
39 -2.310917393 0.175577826
40 -3.947108176 -2.310917393
41 2.126589114 -3.947108176
42 -1.899639038 2.126589114
43 0.194809098 -1.899639038
44 3.603329116 0.194809098
45 0.335566734 3.603329116
46 -1.012618279 0.335566734
47 7.372574433 -1.012618279
48 -1.228389114 7.372574433
49 -3.625495416 -1.228389114
50 -2.404201490 -3.625495416
51 -0.470786069 -2.404201490
52 -0.251812651 -0.470786069
53 -1.535026506 -0.251812651
54 0.987013259 -1.535026506
55 -1.760869057 0.987013259
56 -1.259991089 -1.760869057
57 3.376304145 -1.259991089
58 -4.237462262 3.376304145
59 1.709334565 -4.237462262
60 5.971586379 1.709334565
61 -0.448773151 5.971586379
62 -3.463423281 -0.448773151
63 1.461295465 -3.463423281
64 -1.370540913 1.461295465
65 -0.614233113 -1.370540913
66 -0.907715611 -0.614233113
67 -1.890229063 -0.907715611
68 -0.043181163 -1.890229063
69 -1.020554870 -0.043181163
70 -4.157302299 -1.020554870
71 -0.930719642 -4.157302299
72 2.600598971 -0.930719642
73 1.464779177 2.600598971
74 -0.766883210 1.464779177
75 -1.505656541 -0.766883210
76 -1.491115746 -1.505656541
77 -1.983049732 -1.491115746
78 -0.070754590 -1.983049732
79 -2.228388737 -0.070754590
80 1.788088398 -2.228388737
81 -0.073262616 1.788088398
82 2.055413391 -0.073262616
83 -1.331944661 2.055413391
84 -3.818965041 -1.331944661
85 -0.042051141 -3.818965041
86 1.371526099 -0.042051141
87 2.252612474 1.371526099
88 0.202611570 2.252612474
89 -0.400619174 0.202611570
90 -1.498616235 -0.400619174
91 2.306675762 -1.498616235
92 2.369391078 2.306675762
93 0.120161870 2.369391078
94 -4.859748605 0.120161870
95 -2.146779099 -4.859748605
96 4.405386391 -2.146779099
97 -0.751881708 4.405386391
98 -0.514142409 -0.751881708
99 0.523686832 -0.514142409
100 1.984266860 0.523686832
101 -0.011658351 1.984266860
102 3.810396461 -0.011658351
103 -2.162665420 3.810396461
104 -1.296300086 -2.162665420
105 -1.316092247 -1.296300086
106 0.927384618 -1.316092247
107 2.649193880 0.927384618
108 2.891245682 2.649193880
109 -1.234913715 2.891245682
110 3.403716647 -1.234913715
111 2.298824445 3.403716647
112 2.014126258 2.298824445
113 -1.481423502 2.014126258
114 0.570326420 -1.481423502
115 -0.315642262 0.570326420
116 -1.519047079 -0.315642262
117 0.210140413 -1.519047079
118 0.488093855 0.210140413
119 -0.651284066 0.488093855
120 0.151664373 -0.651284066
121 -2.888875093 0.151664373
122 -1.443080931 -2.888875093
123 -0.843916943 -1.443080931
124 4.585130486 -0.843916943
125 0.015533611 4.585130486
126 1.288382482 0.015533611
127 1.218719812 1.288382482
128 -3.137831000 1.218719812
129 0.321411997 -3.137831000
130 -4.083347804 0.321411997
131 -0.338197885 -4.083347804
132 -5.117155156 -0.338197885
133 -2.164481496 -5.117155156
134 0.830190185 -2.164481496
135 4.201754000 0.830190185
136 0.342306795 4.201754000
137 -3.501593378 0.342306795
138 -0.833062644 -3.501593378
139 1.313758361 -0.833062644
140 2.050758930 1.313758361
141 -1.033496553 2.050758930
142 -9.601968203 -1.033496553
143 3.546525522 -9.601968203
144 0.122367531 3.546525522
145 1.538640470 0.122367531
146 1.847293664 1.538640470
147 2.081156588 1.847293664
148 -0.554126735 2.081156588
149 0.146179211 -0.554126735
150 0.611805843 0.146179211
151 1.797932053 0.611805843
152 3.443211255 1.797932053
153 2.230406922 3.443211255
154 0.096227467 2.230406922
155 0.909627904 0.096227467
156 -2.663686937 0.909627904
157 -0.241192823 -2.663686937
158 2.502942427 -0.241192823
159 -0.292678421 2.502942427
160 -1.061721462 -0.292678421
161 -1.048745915 -1.061721462
162 NA -1.048745915
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.923371135 1.860486185
[2,] -2.726009710 -0.923371135
[3,] 0.657513123 -2.726009710
[4,] 1.861790308 0.657513123
[5,] 0.929136943 1.861790308
[6,] 5.545642779 0.929136943
[7,] 1.909397992 5.545642779
[8,] 3.561259786 1.909397992
[9,] 1.528075034 3.561259786
[10,] -3.164731950 1.528075034
[11,] -1.014861971 -3.164731950
[12,] -1.851282077 -1.014861971
[13,] 1.405774171 -1.851282077
[14,] -0.846087871 1.405774171
[15,] 2.022079604 -0.846087871
[16,] -3.328447016 2.022079604
[17,] -1.368878555 -3.328447016
[18,] -2.743453309 -1.368878555
[19,] 3.155135528 -2.743453309
[20,] -4.126947463 3.155135528
[21,] 0.430216794 -4.126947463
[22,] -7.114864713 0.430216794
[23,] 1.255593779 -7.114864713
[24,] -2.208380021 1.255593779
[25,] 1.870324281 -2.208380021
[26,] 1.864806938 1.870324281
[27,] 2.672547272 1.864806938
[28,] 3.287369954 2.672547272
[29,] 1.493548838 3.287369954
[30,] -2.273723490 1.493548838
[31,] -0.004059333 -2.273723490
[32,] 2.976407767 -0.004059333
[33,] -2.907353903 2.976407767
[34,] 3.824752437 -2.907353903
[35,] 0.179535080 3.824752437
[36,] 1.220129854 0.179535080
[37,] -1.012592512 1.220129854
[38,] 0.175577826 -1.012592512
[39,] -2.310917393 0.175577826
[40,] -3.947108176 -2.310917393
[41,] 2.126589114 -3.947108176
[42,] -1.899639038 2.126589114
[43,] 0.194809098 -1.899639038
[44,] 3.603329116 0.194809098
[45,] 0.335566734 3.603329116
[46,] -1.012618279 0.335566734
[47,] 7.372574433 -1.012618279
[48,] -1.228389114 7.372574433
[49,] -3.625495416 -1.228389114
[50,] -2.404201490 -3.625495416
[51,] -0.470786069 -2.404201490
[52,] -0.251812651 -0.470786069
[53,] -1.535026506 -0.251812651
[54,] 0.987013259 -1.535026506
[55,] -1.760869057 0.987013259
[56,] -1.259991089 -1.760869057
[57,] 3.376304145 -1.259991089
[58,] -4.237462262 3.376304145
[59,] 1.709334565 -4.237462262
[60,] 5.971586379 1.709334565
[61,] -0.448773151 5.971586379
[62,] -3.463423281 -0.448773151
[63,] 1.461295465 -3.463423281
[64,] -1.370540913 1.461295465
[65,] -0.614233113 -1.370540913
[66,] -0.907715611 -0.614233113
[67,] -1.890229063 -0.907715611
[68,] -0.043181163 -1.890229063
[69,] -1.020554870 -0.043181163
[70,] -4.157302299 -1.020554870
[71,] -0.930719642 -4.157302299
[72,] 2.600598971 -0.930719642
[73,] 1.464779177 2.600598971
[74,] -0.766883210 1.464779177
[75,] -1.505656541 -0.766883210
[76,] -1.491115746 -1.505656541
[77,] -1.983049732 -1.491115746
[78,] -0.070754590 -1.983049732
[79,] -2.228388737 -0.070754590
[80,] 1.788088398 -2.228388737
[81,] -0.073262616 1.788088398
[82,] 2.055413391 -0.073262616
[83,] -1.331944661 2.055413391
[84,] -3.818965041 -1.331944661
[85,] -0.042051141 -3.818965041
[86,] 1.371526099 -0.042051141
[87,] 2.252612474 1.371526099
[88,] 0.202611570 2.252612474
[89,] -0.400619174 0.202611570
[90,] -1.498616235 -0.400619174
[91,] 2.306675762 -1.498616235
[92,] 2.369391078 2.306675762
[93,] 0.120161870 2.369391078
[94,] -4.859748605 0.120161870
[95,] -2.146779099 -4.859748605
[96,] 4.405386391 -2.146779099
[97,] -0.751881708 4.405386391
[98,] -0.514142409 -0.751881708
[99,] 0.523686832 -0.514142409
[100,] 1.984266860 0.523686832
[101,] -0.011658351 1.984266860
[102,] 3.810396461 -0.011658351
[103,] -2.162665420 3.810396461
[104,] -1.296300086 -2.162665420
[105,] -1.316092247 -1.296300086
[106,] 0.927384618 -1.316092247
[107,] 2.649193880 0.927384618
[108,] 2.891245682 2.649193880
[109,] -1.234913715 2.891245682
[110,] 3.403716647 -1.234913715
[111,] 2.298824445 3.403716647
[112,] 2.014126258 2.298824445
[113,] -1.481423502 2.014126258
[114,] 0.570326420 -1.481423502
[115,] -0.315642262 0.570326420
[116,] -1.519047079 -0.315642262
[117,] 0.210140413 -1.519047079
[118,] 0.488093855 0.210140413
[119,] -0.651284066 0.488093855
[120,] 0.151664373 -0.651284066
[121,] -2.888875093 0.151664373
[122,] -1.443080931 -2.888875093
[123,] -0.843916943 -1.443080931
[124,] 4.585130486 -0.843916943
[125,] 0.015533611 4.585130486
[126,] 1.288382482 0.015533611
[127,] 1.218719812 1.288382482
[128,] -3.137831000 1.218719812
[129,] 0.321411997 -3.137831000
[130,] -4.083347804 0.321411997
[131,] -0.338197885 -4.083347804
[132,] -5.117155156 -0.338197885
[133,] -2.164481496 -5.117155156
[134,] 0.830190185 -2.164481496
[135,] 4.201754000 0.830190185
[136,] 0.342306795 4.201754000
[137,] -3.501593378 0.342306795
[138,] -0.833062644 -3.501593378
[139,] 1.313758361 -0.833062644
[140,] 2.050758930 1.313758361
[141,] -1.033496553 2.050758930
[142,] -9.601968203 -1.033496553
[143,] 3.546525522 -9.601968203
[144,] 0.122367531 3.546525522
[145,] 1.538640470 0.122367531
[146,] 1.847293664 1.538640470
[147,] 2.081156588 1.847293664
[148,] -0.554126735 2.081156588
[149,] 0.146179211 -0.554126735
[150,] 0.611805843 0.146179211
[151,] 1.797932053 0.611805843
[152,] 3.443211255 1.797932053
[153,] 2.230406922 3.443211255
[154,] 0.096227467 2.230406922
[155,] 0.909627904 0.096227467
[156,] -2.663686937 0.909627904
[157,] -0.241192823 -2.663686937
[158,] 2.502942427 -0.241192823
[159,] -0.292678421 2.502942427
[160,] -1.061721462 -0.292678421
[161,] -1.048745915 -1.061721462
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.923371135 1.860486185
2 -2.726009710 -0.923371135
3 0.657513123 -2.726009710
4 1.861790308 0.657513123
5 0.929136943 1.861790308
6 5.545642779 0.929136943
7 1.909397992 5.545642779
8 3.561259786 1.909397992
9 1.528075034 3.561259786
10 -3.164731950 1.528075034
11 -1.014861971 -3.164731950
12 -1.851282077 -1.014861971
13 1.405774171 -1.851282077
14 -0.846087871 1.405774171
15 2.022079604 -0.846087871
16 -3.328447016 2.022079604
17 -1.368878555 -3.328447016
18 -2.743453309 -1.368878555
19 3.155135528 -2.743453309
20 -4.126947463 3.155135528
21 0.430216794 -4.126947463
22 -7.114864713 0.430216794
23 1.255593779 -7.114864713
24 -2.208380021 1.255593779
25 1.870324281 -2.208380021
26 1.864806938 1.870324281
27 2.672547272 1.864806938
28 3.287369954 2.672547272
29 1.493548838 3.287369954
30 -2.273723490 1.493548838
31 -0.004059333 -2.273723490
32 2.976407767 -0.004059333
33 -2.907353903 2.976407767
34 3.824752437 -2.907353903
35 0.179535080 3.824752437
36 1.220129854 0.179535080
37 -1.012592512 1.220129854
38 0.175577826 -1.012592512
39 -2.310917393 0.175577826
40 -3.947108176 -2.310917393
41 2.126589114 -3.947108176
42 -1.899639038 2.126589114
43 0.194809098 -1.899639038
44 3.603329116 0.194809098
45 0.335566734 3.603329116
46 -1.012618279 0.335566734
47 7.372574433 -1.012618279
48 -1.228389114 7.372574433
49 -3.625495416 -1.228389114
50 -2.404201490 -3.625495416
51 -0.470786069 -2.404201490
52 -0.251812651 -0.470786069
53 -1.535026506 -0.251812651
54 0.987013259 -1.535026506
55 -1.760869057 0.987013259
56 -1.259991089 -1.760869057
57 3.376304145 -1.259991089
58 -4.237462262 3.376304145
59 1.709334565 -4.237462262
60 5.971586379 1.709334565
61 -0.448773151 5.971586379
62 -3.463423281 -0.448773151
63 1.461295465 -3.463423281
64 -1.370540913 1.461295465
65 -0.614233113 -1.370540913
66 -0.907715611 -0.614233113
67 -1.890229063 -0.907715611
68 -0.043181163 -1.890229063
69 -1.020554870 -0.043181163
70 -4.157302299 -1.020554870
71 -0.930719642 -4.157302299
72 2.600598971 -0.930719642
73 1.464779177 2.600598971
74 -0.766883210 1.464779177
75 -1.505656541 -0.766883210
76 -1.491115746 -1.505656541
77 -1.983049732 -1.491115746
78 -0.070754590 -1.983049732
79 -2.228388737 -0.070754590
80 1.788088398 -2.228388737
81 -0.073262616 1.788088398
82 2.055413391 -0.073262616
83 -1.331944661 2.055413391
84 -3.818965041 -1.331944661
85 -0.042051141 -3.818965041
86 1.371526099 -0.042051141
87 2.252612474 1.371526099
88 0.202611570 2.252612474
89 -0.400619174 0.202611570
90 -1.498616235 -0.400619174
91 2.306675762 -1.498616235
92 2.369391078 2.306675762
93 0.120161870 2.369391078
94 -4.859748605 0.120161870
95 -2.146779099 -4.859748605
96 4.405386391 -2.146779099
97 -0.751881708 4.405386391
98 -0.514142409 -0.751881708
99 0.523686832 -0.514142409
100 1.984266860 0.523686832
101 -0.011658351 1.984266860
102 3.810396461 -0.011658351
103 -2.162665420 3.810396461
104 -1.296300086 -2.162665420
105 -1.316092247 -1.296300086
106 0.927384618 -1.316092247
107 2.649193880 0.927384618
108 2.891245682 2.649193880
109 -1.234913715 2.891245682
110 3.403716647 -1.234913715
111 2.298824445 3.403716647
112 2.014126258 2.298824445
113 -1.481423502 2.014126258
114 0.570326420 -1.481423502
115 -0.315642262 0.570326420
116 -1.519047079 -0.315642262
117 0.210140413 -1.519047079
118 0.488093855 0.210140413
119 -0.651284066 0.488093855
120 0.151664373 -0.651284066
121 -2.888875093 0.151664373
122 -1.443080931 -2.888875093
123 -0.843916943 -1.443080931
124 4.585130486 -0.843916943
125 0.015533611 4.585130486
126 1.288382482 0.015533611
127 1.218719812 1.288382482
128 -3.137831000 1.218719812
129 0.321411997 -3.137831000
130 -4.083347804 0.321411997
131 -0.338197885 -4.083347804
132 -5.117155156 -0.338197885
133 -2.164481496 -5.117155156
134 0.830190185 -2.164481496
135 4.201754000 0.830190185
136 0.342306795 4.201754000
137 -3.501593378 0.342306795
138 -0.833062644 -3.501593378
139 1.313758361 -0.833062644
140 2.050758930 1.313758361
141 -1.033496553 2.050758930
142 -9.601968203 -1.033496553
143 3.546525522 -9.601968203
144 0.122367531 3.546525522
145 1.538640470 0.122367531
146 1.847293664 1.538640470
147 2.081156588 1.847293664
148 -0.554126735 2.081156588
149 0.146179211 -0.554126735
150 0.611805843 0.146179211
151 1.797932053 0.611805843
152 3.443211255 1.797932053
153 2.230406922 3.443211255
154 0.096227467 2.230406922
155 0.909627904 0.096227467
156 -2.663686937 0.909627904
157 -0.241192823 -2.663686937
158 2.502942427 -0.241192823
159 -0.292678421 2.502942427
160 -1.061721462 -0.292678421
161 -1.048745915 -1.061721462
> 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/7vlcw1353254643.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/8lal71353254643.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/9dd5p1353254643.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/10t20s1353254643.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11r4wm1353254643.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/125jih1353254643.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1324yf1353254643.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14bi7k1353254643.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/1588it1353254643.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/160yti1353254643.tab")
+ }
>
> try(system("convert tmp/1vgf01353254643.ps tmp/1vgf01353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/23o4v1353254643.ps tmp/23o4v1353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e7vr1353254643.ps tmp/3e7vr1353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kjgh1353254643.ps tmp/4kjgh1353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j5im1353254643.ps tmp/5j5im1353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y2271353254643.ps tmp/6y2271353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vlcw1353254643.ps tmp/7vlcw1353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/8lal71353254643.ps tmp/8lal71353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dd5p1353254643.ps tmp/9dd5p1353254643.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t20s1353254643.ps tmp/10t20s1353254643.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
13.024 1.507 14.593