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(1
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+ ,4)
+ ,dim=c(8
+ ,162)
+ ,dimnames=list(c('G'
+ ,'I1'
+ ,'I2'
+ ,'I3'
+ ,'E1'
+ ,'E2'
+ ,'E3'
+ ,'A')
+ ,1:162))
> y <- array(NA,dim=c(8,162),dimnames=list(c('G','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 = '8'
> 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
A G I1 I2 I3 E1 E2 E3 t
1 4 1 26 21 21 23 17 23 1
2 4 1 20 16 15 24 17 20 2
3 6 1 19 19 18 22 18 20 3
4 8 2 19 18 11 20 21 21 4
5 8 1 20 16 8 24 20 24 5
6 4 1 25 23 19 27 28 22 6
7 4 2 25 17 4 28 19 23 7
8 8 1 22 12 20 27 22 20 8
9 5 1 26 19 16 24 16 25 9
10 4 1 22 16 14 23 18 23 10
11 4 2 17 19 10 24 25 27 11
12 4 2 22 20 13 27 17 27 12
13 4 1 19 13 14 27 14 22 13
14 4 1 24 20 8 28 11 24 14
15 4 1 26 27 23 27 27 25 15
16 8 2 21 17 11 23 20 22 16
17 4 1 13 8 9 24 22 28 17
18 4 2 26 25 24 28 22 28 18
19 4 2 20 26 5 27 21 27 19
20 8 1 22 13 15 25 23 25 20
21 4 2 14 19 5 19 17 16 21
22 7 1 21 15 19 24 24 28 22
23 4 1 7 5 6 20 14 21 23
24 4 2 23 16 13 28 17 24 24
25 5 1 17 14 11 26 23 27 25
26 4 1 25 24 17 23 24 14 26
27 4 1 25 24 17 23 24 14 27
28 4 1 19 9 5 20 8 27 28
29 4 2 20 19 9 11 22 20 29
30 4 1 23 19 15 24 23 21 30
31 4 2 22 25 17 25 25 22 31
32 4 1 22 19 17 23 21 21 32
33 15 1 21 18 20 18 24 12 33
34 10 2 15 15 12 20 15 20 34
35 4 2 20 12 7 20 22 24 35
36 8 2 22 21 16 24 21 19 36
37 4 1 18 12 7 23 25 28 37
38 4 2 20 15 14 25 16 23 38
39 4 2 28 28 24 28 28 27 39
40 4 1 22 25 15 26 23 22 40
41 7 1 18 19 15 26 21 27 41
42 4 1 23 20 10 23 21 26 42
43 6 1 20 24 14 22 26 22 43
44 5 2 25 26 18 24 22 21 44
45 4 2 26 25 12 21 21 19 45
46 16 1 15 12 9 20 18 24 46
47 5 2 17 12 9 22 12 19 47
48 12 2 23 15 8 20 25 26 48
49 6 1 21 17 18 25 17 22 49
50 9 2 13 14 10 20 24 28 50
51 9 1 18 16 17 22 15 21 51
52 4 1 19 11 14 23 13 23 52
53 5 1 22 20 16 25 26 28 53
54 4 1 16 11 10 23 16 10 54
55 4 2 24 22 19 23 24 24 55
56 5 1 18 20 10 22 21 21 56
57 4 1 20 19 14 24 20 21 57
58 4 1 24 17 10 25 14 24 58
59 4 2 14 21 4 21 25 24 59
60 5 2 22 23 19 12 25 25 60
61 4 1 24 18 9 17 20 25 61
62 6 1 18 17 12 20 22 23 62
63 4 1 21 27 16 23 20 21 63
64 4 2 23 25 11 23 26 16 64
65 18 1 17 19 18 20 18 17 65
66 4 2 22 22 11 28 22 25 66
67 6 2 24 24 24 24 24 24 67
68 4 2 21 20 17 24 17 23 68
69 4 1 22 19 18 24 24 25 69
70 5 1 16 11 9 24 20 23 70
71 4 1 21 22 19 28 19 28 71
72 4 2 23 22 18 25 20 26 72
73 5 2 22 16 12 21 15 22 73
74 10 1 24 20 23 25 23 19 74
75 5 1 24 24 22 25 26 26 75
76 8 1 16 16 14 18 22 18 76
77 8 1 16 16 14 17 20 18 77
78 5 2 21 22 16 26 24 25 78
79 4 2 26 24 23 28 26 27 79
80 4 2 15 16 7 21 21 12 80
81 4 2 25 27 10 27 25 15 81
82 5 1 18 11 12 22 13 21 82
83 4 0 23 21 12 21 20 23 83
84 4 1 20 20 12 25 22 22 84
85 8 2 17 20 17 22 23 21 85
86 4 2 25 27 21 23 28 24 86
87 5 1 24 20 16 26 22 27 87
88 14 1 17 12 11 19 20 22 88
89 8 1 19 8 14 25 6 28 89
90 8 1 20 21 13 21 21 26 90
91 4 1 15 18 9 13 20 10 91
92 4 2 27 24 19 24 18 19 92
93 6 1 22 16 13 25 23 22 93
94 4 1 23 18 19 26 20 21 94
95 7 1 16 20 13 25 24 24 95
96 7 1 19 20 13 25 22 25 96
97 4 2 25 19 13 22 21 21 97
98 6 1 19 17 14 21 18 20 98
99 4 2 19 16 12 23 21 21 99
100 7 2 26 26 22 25 23 24 100
101 4 1 21 15 11 24 23 23 101
102 4 2 20 22 5 21 15 18 102
103 8 1 24 17 18 21 21 24 103
104 4 1 22 23 19 25 24 24 104
105 4 2 20 21 14 22 23 19 105
106 10 1 18 19 15 20 21 20 106
107 8 2 18 14 12 20 21 18 107
108 6 1 24 17 19 23 20 20 108
109 4 1 24 12 15 28 11 27 109
110 4 1 22 24 17 23 22 23 110
111 4 1 23 18 8 28 27 26 111
112 5 1 22 20 10 24 25 23 112
113 4 1 20 16 12 18 18 17 113
114 6 1 18 20 12 20 20 21 114
115 4 1 25 22 20 28 24 25 115
116 5 2 18 12 12 21 10 23 116
117 7 1 16 16 12 21 27 27 117
118 8 1 20 17 14 25 21 24 118
119 5 2 19 22 6 19 21 20 119
120 8 1 15 12 10 18 18 27 120
121 10 1 19 14 18 21 15 21 121
122 8 1 19 23 18 22 24 24 122
123 5 1 16 15 7 24 22 21 123
124 12 1 17 17 18 15 14 15 124
125 4 1 28 28 9 28 28 25 125
126 5 2 23 20 17 26 18 25 126
127 4 1 25 23 22 23 26 22 127
128 6 1 20 13 11 26 17 24 128
129 4 2 17 18 15 20 19 21 129
130 4 2 23 23 17 22 22 22 130
131 7 1 16 19 15 20 18 23 131
132 7 2 23 23 22 23 24 22 132
133 10 2 11 12 9 22 15 20 133
134 4 2 18 16 13 24 18 23 134
135 5 2 24 23 20 23 26 25 135
136 8 1 23 13 14 22 11 23 136
137 11 1 21 22 14 26 26 22 137
138 7 2 16 18 12 23 21 25 138
139 4 2 24 23 20 27 23 26 139
140 8 1 23 20 20 23 23 22 140
141 6 1 18 10 8 21 15 24 141
142 7 1 20 17 17 26 22 24 142
143 5 1 9 18 9 23 26 25 143
144 4 2 24 15 18 21 16 20 144
145 8 1 25 23 22 27 20 26 145
146 4 1 20 17 10 19 18 21 146
147 8 2 21 17 13 23 22 26 147
148 6 2 25 22 15 25 16 21 148
149 4 2 22 20 18 23 19 22 149
150 9 2 21 20 18 22 20 16 150
151 5 1 21 19 12 22 19 26 151
152 6 1 22 18 12 25 23 28 152
153 4 1 27 22 20 25 24 18 153
154 4 2 24 20 12 28 25 25 154
155 4 2 24 22 16 28 21 23 155
156 5 2 21 18 16 20 21 21 156
157 6 1 18 16 18 25 23 20 157
158 16 1 16 16 16 19 27 25 158
159 6 1 22 16 13 25 23 22 159
160 6 1 20 16 17 22 18 21 160
161 4 2 18 17 13 18 16 16 161
162 4 1 20 18 17 20 16 18 162
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) G I1 I2 I3 E1
12.148919 -0.398705 -0.183317 -0.141158 0.192189 -0.177151
E2 E3 t
0.083791 0.000321 0.002720
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.6137 -1.4632 -0.5042 1.0508 10.4413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.148919 1.803111 6.738 3.07e-10 ***
G -0.398705 0.389667 -1.023 0.307831
I1 -0.183317 0.074029 -2.476 0.014365 *
I2 -0.141158 0.066149 -2.134 0.034441 *
I3 0.192189 0.049213 3.905 0.000141 ***
E1 -0.177151 0.071866 -2.465 0.014807 *
E2 0.083791 0.055707 1.504 0.134606
E3 0.000321 0.057493 0.006 0.995552
t 0.002720 0.004010 0.678 0.498532
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.347 on 153 degrees of freedom
Multiple R-squared: 0.2412, Adjusted R-squared: 0.2015
F-statistic: 6.078 on 8 and 153 DF, p-value: 8.689e-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,] 0.600391894 0.799216213 0.399608106
[2,] 0.435142110 0.870284220 0.564857890
[3,] 0.392240859 0.784481717 0.607759141
[4,] 0.283089843 0.566179686 0.716910157
[5,] 0.208445269 0.416890538 0.791554731
[6,] 0.178978389 0.357956777 0.821021611
[7,] 0.122032393 0.244064786 0.877967607
[8,] 0.078232378 0.156464757 0.921767622
[9,] 0.057903259 0.115806518 0.942096741
[10,] 0.107052853 0.214105706 0.892947147
[11,] 0.073516959 0.147033917 0.926483041
[12,] 0.061814766 0.123629532 0.938185234
[13,] 0.045033252 0.090066503 0.954966748
[14,] 0.028712546 0.057425091 0.971287454
[15,] 0.023276963 0.046553925 0.976723037
[16,] 0.015073028 0.030146057 0.984926972
[17,] 0.011067598 0.022135196 0.988932402
[18,] 0.012736884 0.025473768 0.987263116
[19,] 0.008080128 0.016160256 0.991919872
[20,] 0.004878493 0.009756986 0.995121507
[21,] 0.003026684 0.006053367 0.996973316
[22,] 0.364957303 0.729914607 0.635042697
[23,] 0.479873677 0.959747355 0.520126323
[24,] 0.484259625 0.968519249 0.515740375
[25,] 0.467388648 0.934777295 0.532611352
[26,] 0.425855427 0.851710854 0.574144573
[27,] 0.407562725 0.815125451 0.592437275
[28,] 0.352701930 0.705403860 0.647298070
[29,] 0.302314594 0.604629188 0.697685406
[30,] 0.325672288 0.651344577 0.674327712
[31,] 0.277959121 0.555918242 0.722040879
[32,] 0.240594167 0.481188334 0.759405833
[33,] 0.201060145 0.402120291 0.798939855
[34,] 0.170170099 0.340340197 0.829829901
[35,] 0.839957105 0.320085790 0.160042895
[36,] 0.822831681 0.354336638 0.177168319
[37,] 0.947509425 0.104981150 0.052490575
[38,] 0.932835787 0.134328426 0.067164213
[39,] 0.921497735 0.157004531 0.078502265
[40,] 0.912030810 0.175938379 0.087969190
[41,] 0.922607499 0.154785001 0.077392501
[42,] 0.906650190 0.186699620 0.093349810
[43,] 0.919208986 0.161582027 0.080791014
[44,] 0.915727024 0.168545952 0.084272976
[45,] 0.896295775 0.207408450 0.103704225
[46,] 0.884865042 0.230269916 0.115134958
[47,] 0.859347164 0.281305672 0.140652836
[48,] 0.837778536 0.324442929 0.162221464
[49,] 0.850223725 0.299552550 0.149776275
[50,] 0.829022545 0.341954910 0.170977455
[51,] 0.798967737 0.402064526 0.201032263
[52,] 0.768727048 0.462545903 0.231272952
[53,] 0.734967262 0.530065476 0.265032738
[54,] 0.997241903 0.005516193 0.002758097
[55,] 0.996153451 0.007693098 0.003846549
[56,] 0.994652315 0.010695370 0.005347685
[57,] 0.993416207 0.013167587 0.006583793
[58,] 0.993506768 0.012986463 0.006493232
[59,] 0.992318279 0.015363442 0.007681721
[60,] 0.990488712 0.019022575 0.009511288
[61,] 0.987746519 0.024506962 0.012253481
[62,] 0.983563603 0.032872794 0.016436397
[63,] 0.988246253 0.023507495 0.011753747
[64,] 0.985254849 0.029490303 0.014745151
[65,] 0.980289525 0.039420951 0.019710475
[66,] 0.973959755 0.052080491 0.026040245
[67,] 0.965990303 0.068019393 0.034009697
[68,] 0.959037915 0.081924170 0.040962085
[69,] 0.955979597 0.088040807 0.044020403
[70,] 0.955985302 0.088029397 0.044014698
[71,] 0.949767910 0.100464179 0.050232090
[72,] 0.941775876 0.116448248 0.058224124
[73,] 0.930180879 0.139638242 0.069819121
[74,] 0.916726973 0.166546053 0.083273027
[75,] 0.906232426 0.187535147 0.093767574
[76,] 0.887713154 0.224573692 0.112286846
[77,] 0.976741967 0.046516066 0.023258033
[78,] 0.972221160 0.055557681 0.027778840
[79,] 0.969124778 0.061750444 0.030875222
[80,] 0.976881545 0.046236909 0.023118455
[81,] 0.969918569 0.060162861 0.030081431
[82,] 0.961140836 0.077718327 0.038859164
[83,] 0.957374583 0.085250835 0.042625417
[84,] 0.946117338 0.107765324 0.053882662
[85,] 0.935745840 0.128508319 0.064254160
[86,] 0.920216019 0.159567962 0.079783981
[87,] 0.901508787 0.196982425 0.098491213
[88,] 0.890494926 0.219010148 0.109505074
[89,] 0.886188878 0.227622244 0.113811122
[90,] 0.876858905 0.246282191 0.123141095
[91,] 0.856668745 0.286662511 0.143331255
[92,] 0.834875786 0.330248428 0.165124214
[93,] 0.829248713 0.341502573 0.170751287
[94,] 0.810501944 0.378996112 0.189498056
[95,] 0.823825538 0.352348924 0.176174462
[96,] 0.805727580 0.388544840 0.194272420
[97,] 0.769005738 0.461988524 0.230994262
[98,] 0.735993731 0.528012538 0.264006269
[99,] 0.719436858 0.561126285 0.280563142
[100,] 0.675312489 0.649375021 0.324687511
[101,] 0.626873093 0.746253813 0.373126907
[102,] 0.636696134 0.726607731 0.363303866
[103,] 0.590752803 0.818494394 0.409247197
[104,] 0.579902131 0.840195738 0.420097869
[105,] 0.538749664 0.922500672 0.461250336
[106,] 0.509797690 0.980404620 0.490202310
[107,] 0.471556508 0.943113016 0.528443492
[108,] 0.420987061 0.841974123 0.579012939
[109,] 0.373506100 0.747012200 0.626493900
[110,] 0.349951139 0.699902278 0.650048861
[111,] 0.302276748 0.604553497 0.697723252
[112,] 0.277600326 0.555200652 0.722399674
[113,] 0.375880100 0.751760200 0.624119900
[114,] 0.339740627 0.679481254 0.660259373
[115,] 0.286818280 0.573636560 0.713181720
[116,] 0.312103039 0.624206078 0.687896961
[117,] 0.264668611 0.529337222 0.735331389
[118,] 0.279257958 0.558515916 0.720742042
[119,] 0.252645140 0.505290281 0.747354860
[120,] 0.208577168 0.417154336 0.791422832
[121,] 0.166790486 0.333580972 0.833209514
[122,] 0.224720698 0.449441396 0.775279302
[123,] 0.193771218 0.387542437 0.806228782
[124,] 0.237184745 0.474369490 0.762815255
[125,] 0.270649219 0.541298439 0.729350781
[126,] 0.469793162 0.939586325 0.530206838
[127,] 0.407878206 0.815756411 0.592121794
[128,] 0.438838160 0.877676320 0.561161840
[129,] 0.362480462 0.724960923 0.637519538
[130,] 0.367027339 0.734054677 0.632972661
[131,] 0.302660838 0.605321676 0.697339162
[132,] 0.467089143 0.934178286 0.532910857
[133,] 0.384050137 0.768100274 0.615949863
[134,] 0.304018466 0.608036932 0.695981534
[135,] 0.296198154 0.592396308 0.703801846
[136,] 0.213434399 0.426868798 0.786565601
[137,] 0.456287845 0.912575689 0.543712155
[138,] 0.347816400 0.695632800 0.652183600
[139,] 0.810730907 0.378538187 0.189269093
> postscript(file="/var/fisher/rcomp/tmp/1vfft1353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/229fb1353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/39xsx1353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4tjoy1353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5mpx61353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-1.415702924 -1.892870006 -0.670091540 2.324059451 3.191631702 -1.158707378
7 8 9 10 11 12
2.204108764 1.044364710 0.501462690 -1.617713341 -1.356757604 0.323481388
13 14 15 16 17 18
-1.555210302 1.927773865 -1.121165897 3.131816128 -3.614547838 -0.609982027
19 20 21 22 23 24
1.987097513 1.674126162 -1.184859485 -0.262974455 -4.613704762 0.087638878
25 26 27 28 29 30
-1.169639089 -1.058444428 -1.061164561 -1.169859418 -2.712917652 -1.498677021
31 32 33 34 35 36
-0.814191435 -2.081379670 6.880622070 3.396593179 -1.739895274 2.958742031
37 38 39 40 41 42
-2.231880623 -1.281080296 -0.379423104 -0.508267588 1.074773706 -0.440391314
43 44 45 46 47 48
0.207992360 0.723911418 0.469463097 8.865680585 -0.513046633 6.753963730
49 50 51 52 53 54
-0.096298328 1.472966983 2.035792396 -2.568743692 -0.872057972 -2.602581094
55 56 57 58 59 60
-1.591843520 -0.570603922 -1.678509598 0.217411623 -1.132316762 -2.863689596
61 62 63 64 65 66
-1.377675647 -0.833510155 -0.943777634 -0.003673701 10.441287971 0.602123877
67 68 69 70 71 72
-0.125961373 -1.311085056 -2.449720241 -1.616102301 -1.280596858 -0.940390249
73 74 75 76 77 78
-0.108606076 3.346396433 -1.153124290 -0.116457256 -0.128745610 -0.096661213
79 80 81 82 83 84
-1.059723269 -1.949460546 1.583937360 -1.625796182 -1.463387988 -1.217170950
85 86 87 88 89 90
1.052998019 -1.506602845 -0.085271858 6.389601061 1.846372166 2.089382162
91 92 93 94 95 96
-3.812920165 -0.178718333 0.284370498 -1.977002738 0.659226326 1.373718748
97 98 99 100 101 102
-0.717928409 -0.619216399 -1.877405627 2.078543182 -1.854960115 0.639427550
103 104 105 106 107 108
1.262356266 -1.995010036 -1.733087797 2.837308536 1.104711594 -0.504056333
109 110 111 112 113 114
-0.806185114 -1.672193619 -0.143013265 0.028831276 -2.763970953 -0.383258765
115 116 117 118 119 120
-1.277195927 -1.104836722 -0.733999017 1.965642795 0.359989111 0.116937866
121 122 123 124 125 126
2.377045145 1.066813053 -0.978200835 3.448538024 1.871410749 0.167649380
127 128 129 130 131 132
-2.605429174 0.462690869 -2.983763003 -1.462561623 -0.346918373 0.580623574
133 134 135 136 137 138
2.901424980 -1.920233684 -1.028388177 2.208781064 5.561903659 0.747589757
139 140 141 142 143 144
-1.079613581 1.204851922 -0.504383864 0.417153391 -2.790325806 -2.312539734
145 146 147 148 149 150
2.555674085 -2.152333790 2.222235136 2.132849466 -1.884699282 2.670247893
151 152 153 154 155 156
-0.638623112 0.596460894 -1.543131502 0.003508317 -0.149843268 -1.683709307
157 158 159 160 161 162
-1.583286896 7.032062148 0.104841764 -1.145441531 -2.745593482 -3.054321689
> postscript(file="/var/fisher/rcomp/tmp/6v5zm1353253606.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 -1.415702924 NA
1 -1.892870006 -1.415702924
2 -0.670091540 -1.892870006
3 2.324059451 -0.670091540
4 3.191631702 2.324059451
5 -1.158707378 3.191631702
6 2.204108764 -1.158707378
7 1.044364710 2.204108764
8 0.501462690 1.044364710
9 -1.617713341 0.501462690
10 -1.356757604 -1.617713341
11 0.323481388 -1.356757604
12 -1.555210302 0.323481388
13 1.927773865 -1.555210302
14 -1.121165897 1.927773865
15 3.131816128 -1.121165897
16 -3.614547838 3.131816128
17 -0.609982027 -3.614547838
18 1.987097513 -0.609982027
19 1.674126162 1.987097513
20 -1.184859485 1.674126162
21 -0.262974455 -1.184859485
22 -4.613704762 -0.262974455
23 0.087638878 -4.613704762
24 -1.169639089 0.087638878
25 -1.058444428 -1.169639089
26 -1.061164561 -1.058444428
27 -1.169859418 -1.061164561
28 -2.712917652 -1.169859418
29 -1.498677021 -2.712917652
30 -0.814191435 -1.498677021
31 -2.081379670 -0.814191435
32 6.880622070 -2.081379670
33 3.396593179 6.880622070
34 -1.739895274 3.396593179
35 2.958742031 -1.739895274
36 -2.231880623 2.958742031
37 -1.281080296 -2.231880623
38 -0.379423104 -1.281080296
39 -0.508267588 -0.379423104
40 1.074773706 -0.508267588
41 -0.440391314 1.074773706
42 0.207992360 -0.440391314
43 0.723911418 0.207992360
44 0.469463097 0.723911418
45 8.865680585 0.469463097
46 -0.513046633 8.865680585
47 6.753963730 -0.513046633
48 -0.096298328 6.753963730
49 1.472966983 -0.096298328
50 2.035792396 1.472966983
51 -2.568743692 2.035792396
52 -0.872057972 -2.568743692
53 -2.602581094 -0.872057972
54 -1.591843520 -2.602581094
55 -0.570603922 -1.591843520
56 -1.678509598 -0.570603922
57 0.217411623 -1.678509598
58 -1.132316762 0.217411623
59 -2.863689596 -1.132316762
60 -1.377675647 -2.863689596
61 -0.833510155 -1.377675647
62 -0.943777634 -0.833510155
63 -0.003673701 -0.943777634
64 10.441287971 -0.003673701
65 0.602123877 10.441287971
66 -0.125961373 0.602123877
67 -1.311085056 -0.125961373
68 -2.449720241 -1.311085056
69 -1.616102301 -2.449720241
70 -1.280596858 -1.616102301
71 -0.940390249 -1.280596858
72 -0.108606076 -0.940390249
73 3.346396433 -0.108606076
74 -1.153124290 3.346396433
75 -0.116457256 -1.153124290
76 -0.128745610 -0.116457256
77 -0.096661213 -0.128745610
78 -1.059723269 -0.096661213
79 -1.949460546 -1.059723269
80 1.583937360 -1.949460546
81 -1.625796182 1.583937360
82 -1.463387988 -1.625796182
83 -1.217170950 -1.463387988
84 1.052998019 -1.217170950
85 -1.506602845 1.052998019
86 -0.085271858 -1.506602845
87 6.389601061 -0.085271858
88 1.846372166 6.389601061
89 2.089382162 1.846372166
90 -3.812920165 2.089382162
91 -0.178718333 -3.812920165
92 0.284370498 -0.178718333
93 -1.977002738 0.284370498
94 0.659226326 -1.977002738
95 1.373718748 0.659226326
96 -0.717928409 1.373718748
97 -0.619216399 -0.717928409
98 -1.877405627 -0.619216399
99 2.078543182 -1.877405627
100 -1.854960115 2.078543182
101 0.639427550 -1.854960115
102 1.262356266 0.639427550
103 -1.995010036 1.262356266
104 -1.733087797 -1.995010036
105 2.837308536 -1.733087797
106 1.104711594 2.837308536
107 -0.504056333 1.104711594
108 -0.806185114 -0.504056333
109 -1.672193619 -0.806185114
110 -0.143013265 -1.672193619
111 0.028831276 -0.143013265
112 -2.763970953 0.028831276
113 -0.383258765 -2.763970953
114 -1.277195927 -0.383258765
115 -1.104836722 -1.277195927
116 -0.733999017 -1.104836722
117 1.965642795 -0.733999017
118 0.359989111 1.965642795
119 0.116937866 0.359989111
120 2.377045145 0.116937866
121 1.066813053 2.377045145
122 -0.978200835 1.066813053
123 3.448538024 -0.978200835
124 1.871410749 3.448538024
125 0.167649380 1.871410749
126 -2.605429174 0.167649380
127 0.462690869 -2.605429174
128 -2.983763003 0.462690869
129 -1.462561623 -2.983763003
130 -0.346918373 -1.462561623
131 0.580623574 -0.346918373
132 2.901424980 0.580623574
133 -1.920233684 2.901424980
134 -1.028388177 -1.920233684
135 2.208781064 -1.028388177
136 5.561903659 2.208781064
137 0.747589757 5.561903659
138 -1.079613581 0.747589757
139 1.204851922 -1.079613581
140 -0.504383864 1.204851922
141 0.417153391 -0.504383864
142 -2.790325806 0.417153391
143 -2.312539734 -2.790325806
144 2.555674085 -2.312539734
145 -2.152333790 2.555674085
146 2.222235136 -2.152333790
147 2.132849466 2.222235136
148 -1.884699282 2.132849466
149 2.670247893 -1.884699282
150 -0.638623112 2.670247893
151 0.596460894 -0.638623112
152 -1.543131502 0.596460894
153 0.003508317 -1.543131502
154 -0.149843268 0.003508317
155 -1.683709307 -0.149843268
156 -1.583286896 -1.683709307
157 7.032062148 -1.583286896
158 0.104841764 7.032062148
159 -1.145441531 0.104841764
160 -2.745593482 -1.145441531
161 -3.054321689 -2.745593482
162 NA -3.054321689
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.892870006 -1.415702924
[2,] -0.670091540 -1.892870006
[3,] 2.324059451 -0.670091540
[4,] 3.191631702 2.324059451
[5,] -1.158707378 3.191631702
[6,] 2.204108764 -1.158707378
[7,] 1.044364710 2.204108764
[8,] 0.501462690 1.044364710
[9,] -1.617713341 0.501462690
[10,] -1.356757604 -1.617713341
[11,] 0.323481388 -1.356757604
[12,] -1.555210302 0.323481388
[13,] 1.927773865 -1.555210302
[14,] -1.121165897 1.927773865
[15,] 3.131816128 -1.121165897
[16,] -3.614547838 3.131816128
[17,] -0.609982027 -3.614547838
[18,] 1.987097513 -0.609982027
[19,] 1.674126162 1.987097513
[20,] -1.184859485 1.674126162
[21,] -0.262974455 -1.184859485
[22,] -4.613704762 -0.262974455
[23,] 0.087638878 -4.613704762
[24,] -1.169639089 0.087638878
[25,] -1.058444428 -1.169639089
[26,] -1.061164561 -1.058444428
[27,] -1.169859418 -1.061164561
[28,] -2.712917652 -1.169859418
[29,] -1.498677021 -2.712917652
[30,] -0.814191435 -1.498677021
[31,] -2.081379670 -0.814191435
[32,] 6.880622070 -2.081379670
[33,] 3.396593179 6.880622070
[34,] -1.739895274 3.396593179
[35,] 2.958742031 -1.739895274
[36,] -2.231880623 2.958742031
[37,] -1.281080296 -2.231880623
[38,] -0.379423104 -1.281080296
[39,] -0.508267588 -0.379423104
[40,] 1.074773706 -0.508267588
[41,] -0.440391314 1.074773706
[42,] 0.207992360 -0.440391314
[43,] 0.723911418 0.207992360
[44,] 0.469463097 0.723911418
[45,] 8.865680585 0.469463097
[46,] -0.513046633 8.865680585
[47,] 6.753963730 -0.513046633
[48,] -0.096298328 6.753963730
[49,] 1.472966983 -0.096298328
[50,] 2.035792396 1.472966983
[51,] -2.568743692 2.035792396
[52,] -0.872057972 -2.568743692
[53,] -2.602581094 -0.872057972
[54,] -1.591843520 -2.602581094
[55,] -0.570603922 -1.591843520
[56,] -1.678509598 -0.570603922
[57,] 0.217411623 -1.678509598
[58,] -1.132316762 0.217411623
[59,] -2.863689596 -1.132316762
[60,] -1.377675647 -2.863689596
[61,] -0.833510155 -1.377675647
[62,] -0.943777634 -0.833510155
[63,] -0.003673701 -0.943777634
[64,] 10.441287971 -0.003673701
[65,] 0.602123877 10.441287971
[66,] -0.125961373 0.602123877
[67,] -1.311085056 -0.125961373
[68,] -2.449720241 -1.311085056
[69,] -1.616102301 -2.449720241
[70,] -1.280596858 -1.616102301
[71,] -0.940390249 -1.280596858
[72,] -0.108606076 -0.940390249
[73,] 3.346396433 -0.108606076
[74,] -1.153124290 3.346396433
[75,] -0.116457256 -1.153124290
[76,] -0.128745610 -0.116457256
[77,] -0.096661213 -0.128745610
[78,] -1.059723269 -0.096661213
[79,] -1.949460546 -1.059723269
[80,] 1.583937360 -1.949460546
[81,] -1.625796182 1.583937360
[82,] -1.463387988 -1.625796182
[83,] -1.217170950 -1.463387988
[84,] 1.052998019 -1.217170950
[85,] -1.506602845 1.052998019
[86,] -0.085271858 -1.506602845
[87,] 6.389601061 -0.085271858
[88,] 1.846372166 6.389601061
[89,] 2.089382162 1.846372166
[90,] -3.812920165 2.089382162
[91,] -0.178718333 -3.812920165
[92,] 0.284370498 -0.178718333
[93,] -1.977002738 0.284370498
[94,] 0.659226326 -1.977002738
[95,] 1.373718748 0.659226326
[96,] -0.717928409 1.373718748
[97,] -0.619216399 -0.717928409
[98,] -1.877405627 -0.619216399
[99,] 2.078543182 -1.877405627
[100,] -1.854960115 2.078543182
[101,] 0.639427550 -1.854960115
[102,] 1.262356266 0.639427550
[103,] -1.995010036 1.262356266
[104,] -1.733087797 -1.995010036
[105,] 2.837308536 -1.733087797
[106,] 1.104711594 2.837308536
[107,] -0.504056333 1.104711594
[108,] -0.806185114 -0.504056333
[109,] -1.672193619 -0.806185114
[110,] -0.143013265 -1.672193619
[111,] 0.028831276 -0.143013265
[112,] -2.763970953 0.028831276
[113,] -0.383258765 -2.763970953
[114,] -1.277195927 -0.383258765
[115,] -1.104836722 -1.277195927
[116,] -0.733999017 -1.104836722
[117,] 1.965642795 -0.733999017
[118,] 0.359989111 1.965642795
[119,] 0.116937866 0.359989111
[120,] 2.377045145 0.116937866
[121,] 1.066813053 2.377045145
[122,] -0.978200835 1.066813053
[123,] 3.448538024 -0.978200835
[124,] 1.871410749 3.448538024
[125,] 0.167649380 1.871410749
[126,] -2.605429174 0.167649380
[127,] 0.462690869 -2.605429174
[128,] -2.983763003 0.462690869
[129,] -1.462561623 -2.983763003
[130,] -0.346918373 -1.462561623
[131,] 0.580623574 -0.346918373
[132,] 2.901424980 0.580623574
[133,] -1.920233684 2.901424980
[134,] -1.028388177 -1.920233684
[135,] 2.208781064 -1.028388177
[136,] 5.561903659 2.208781064
[137,] 0.747589757 5.561903659
[138,] -1.079613581 0.747589757
[139,] 1.204851922 -1.079613581
[140,] -0.504383864 1.204851922
[141,] 0.417153391 -0.504383864
[142,] -2.790325806 0.417153391
[143,] -2.312539734 -2.790325806
[144,] 2.555674085 -2.312539734
[145,] -2.152333790 2.555674085
[146,] 2.222235136 -2.152333790
[147,] 2.132849466 2.222235136
[148,] -1.884699282 2.132849466
[149,] 2.670247893 -1.884699282
[150,] -0.638623112 2.670247893
[151,] 0.596460894 -0.638623112
[152,] -1.543131502 0.596460894
[153,] 0.003508317 -1.543131502
[154,] -0.149843268 0.003508317
[155,] -1.683709307 -0.149843268
[156,] -1.583286896 -1.683709307
[157,] 7.032062148 -1.583286896
[158,] 0.104841764 7.032062148
[159,] -1.145441531 0.104841764
[160,] -2.745593482 -1.145441531
[161,] -3.054321689 -2.745593482
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.892870006 -1.415702924
2 -0.670091540 -1.892870006
3 2.324059451 -0.670091540
4 3.191631702 2.324059451
5 -1.158707378 3.191631702
6 2.204108764 -1.158707378
7 1.044364710 2.204108764
8 0.501462690 1.044364710
9 -1.617713341 0.501462690
10 -1.356757604 -1.617713341
11 0.323481388 -1.356757604
12 -1.555210302 0.323481388
13 1.927773865 -1.555210302
14 -1.121165897 1.927773865
15 3.131816128 -1.121165897
16 -3.614547838 3.131816128
17 -0.609982027 -3.614547838
18 1.987097513 -0.609982027
19 1.674126162 1.987097513
20 -1.184859485 1.674126162
21 -0.262974455 -1.184859485
22 -4.613704762 -0.262974455
23 0.087638878 -4.613704762
24 -1.169639089 0.087638878
25 -1.058444428 -1.169639089
26 -1.061164561 -1.058444428
27 -1.169859418 -1.061164561
28 -2.712917652 -1.169859418
29 -1.498677021 -2.712917652
30 -0.814191435 -1.498677021
31 -2.081379670 -0.814191435
32 6.880622070 -2.081379670
33 3.396593179 6.880622070
34 -1.739895274 3.396593179
35 2.958742031 -1.739895274
36 -2.231880623 2.958742031
37 -1.281080296 -2.231880623
38 -0.379423104 -1.281080296
39 -0.508267588 -0.379423104
40 1.074773706 -0.508267588
41 -0.440391314 1.074773706
42 0.207992360 -0.440391314
43 0.723911418 0.207992360
44 0.469463097 0.723911418
45 8.865680585 0.469463097
46 -0.513046633 8.865680585
47 6.753963730 -0.513046633
48 -0.096298328 6.753963730
49 1.472966983 -0.096298328
50 2.035792396 1.472966983
51 -2.568743692 2.035792396
52 -0.872057972 -2.568743692
53 -2.602581094 -0.872057972
54 -1.591843520 -2.602581094
55 -0.570603922 -1.591843520
56 -1.678509598 -0.570603922
57 0.217411623 -1.678509598
58 -1.132316762 0.217411623
59 -2.863689596 -1.132316762
60 -1.377675647 -2.863689596
61 -0.833510155 -1.377675647
62 -0.943777634 -0.833510155
63 -0.003673701 -0.943777634
64 10.441287971 -0.003673701
65 0.602123877 10.441287971
66 -0.125961373 0.602123877
67 -1.311085056 -0.125961373
68 -2.449720241 -1.311085056
69 -1.616102301 -2.449720241
70 -1.280596858 -1.616102301
71 -0.940390249 -1.280596858
72 -0.108606076 -0.940390249
73 3.346396433 -0.108606076
74 -1.153124290 3.346396433
75 -0.116457256 -1.153124290
76 -0.128745610 -0.116457256
77 -0.096661213 -0.128745610
78 -1.059723269 -0.096661213
79 -1.949460546 -1.059723269
80 1.583937360 -1.949460546
81 -1.625796182 1.583937360
82 -1.463387988 -1.625796182
83 -1.217170950 -1.463387988
84 1.052998019 -1.217170950
85 -1.506602845 1.052998019
86 -0.085271858 -1.506602845
87 6.389601061 -0.085271858
88 1.846372166 6.389601061
89 2.089382162 1.846372166
90 -3.812920165 2.089382162
91 -0.178718333 -3.812920165
92 0.284370498 -0.178718333
93 -1.977002738 0.284370498
94 0.659226326 -1.977002738
95 1.373718748 0.659226326
96 -0.717928409 1.373718748
97 -0.619216399 -0.717928409
98 -1.877405627 -0.619216399
99 2.078543182 -1.877405627
100 -1.854960115 2.078543182
101 0.639427550 -1.854960115
102 1.262356266 0.639427550
103 -1.995010036 1.262356266
104 -1.733087797 -1.995010036
105 2.837308536 -1.733087797
106 1.104711594 2.837308536
107 -0.504056333 1.104711594
108 -0.806185114 -0.504056333
109 -1.672193619 -0.806185114
110 -0.143013265 -1.672193619
111 0.028831276 -0.143013265
112 -2.763970953 0.028831276
113 -0.383258765 -2.763970953
114 -1.277195927 -0.383258765
115 -1.104836722 -1.277195927
116 -0.733999017 -1.104836722
117 1.965642795 -0.733999017
118 0.359989111 1.965642795
119 0.116937866 0.359989111
120 2.377045145 0.116937866
121 1.066813053 2.377045145
122 -0.978200835 1.066813053
123 3.448538024 -0.978200835
124 1.871410749 3.448538024
125 0.167649380 1.871410749
126 -2.605429174 0.167649380
127 0.462690869 -2.605429174
128 -2.983763003 0.462690869
129 -1.462561623 -2.983763003
130 -0.346918373 -1.462561623
131 0.580623574 -0.346918373
132 2.901424980 0.580623574
133 -1.920233684 2.901424980
134 -1.028388177 -1.920233684
135 2.208781064 -1.028388177
136 5.561903659 2.208781064
137 0.747589757 5.561903659
138 -1.079613581 0.747589757
139 1.204851922 -1.079613581
140 -0.504383864 1.204851922
141 0.417153391 -0.504383864
142 -2.790325806 0.417153391
143 -2.312539734 -2.790325806
144 2.555674085 -2.312539734
145 -2.152333790 2.555674085
146 2.222235136 -2.152333790
147 2.132849466 2.222235136
148 -1.884699282 2.132849466
149 2.670247893 -1.884699282
150 -0.638623112 2.670247893
151 0.596460894 -0.638623112
152 -1.543131502 0.596460894
153 0.003508317 -1.543131502
154 -0.149843268 0.003508317
155 -1.683709307 -0.149843268
156 -1.583286896 -1.683709307
157 7.032062148 -1.583286896
158 0.104841764 7.032062148
159 -1.145441531 0.104841764
160 -2.745593482 -1.145441531
161 -3.054321689 -2.745593482
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7020v1353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8j8i51353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9q96k1353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10vij91353253606.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11ohsk1353253606.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12b6ld1353253607.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/131rw11353253607.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14uxrg1353253607.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/151bc21353253607.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16nh3z1353253607.tab")
+ }
>
> try(system("convert tmp/1vfft1353253606.ps tmp/1vfft1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/229fb1353253606.ps tmp/229fb1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/39xsx1353253606.ps tmp/39xsx1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tjoy1353253606.ps tmp/4tjoy1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mpx61353253606.ps tmp/5mpx61353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v5zm1353253606.ps tmp/6v5zm1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/7020v1353253606.ps tmp/7020v1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/8j8i51353253606.ps tmp/8j8i51353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/9q96k1353253606.ps tmp/9q96k1353253606.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vij91353253606.ps tmp/10vij91353253606.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.128 1.338 9.460