R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(1
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('Week'
+ ,'Consern'
+ ,'Doubts'
+ ,'PExpect'
+ ,'PCritisism'
+ ,'PStandards'
+ ,'Organisation')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('Week','Consern','Doubts','PExpect','PCritisism','PStandards','Organisation'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
PStandards Week Consern Doubts PExpect PCritisism Organisation t
1 24 1 24 14 11 12 26 1
2 25 1 25 11 7 8 23 2
3 30 1 17 6 17 8 25 3
4 19 1 18 12 10 8 23 4
5 22 1 18 8 12 9 19 5
6 22 1 16 10 12 7 29 6
7 25 1 20 10 11 4 25 7
8 23 1 16 11 11 11 21 8
9 17 1 18 16 12 7 22 9
10 21 2 17 11 13 7 25 10
11 19 2 23 13 14 12 24 11
12 19 2 30 12 16 10 18 12
13 15 2 23 8 11 10 22 13
14 16 2 18 12 10 8 15 14
15 23 2 15 11 11 8 22 15
16 27 2 12 4 15 4 28 16
17 22 2 21 9 9 9 20 17
18 14 2 15 8 11 8 12 18
19 22 2 20 8 17 7 24 19
20 23 3 31 14 17 11 20 20
21 23 3 27 15 11 9 21 21
22 21 3 34 16 18 11 20 22
23 19 3 21 9 14 13 21 23
24 18 3 31 14 10 8 23 24
25 20 3 19 11 11 8 28 25
26 23 3 16 8 15 9 24 26
27 25 3 20 9 15 6 24 27
28 19 3 21 9 13 9 24 28
29 24 3 22 9 16 9 23 29
30 22 3 17 9 13 6 23 30
31 25 3 24 10 9 6 29 31
32 26 3 25 16 18 16 24 32
33 29 3 26 11 18 5 18 33
34 32 3 25 8 12 7 25 34
35 25 3 17 9 17 9 21 35
36 29 3 32 16 9 6 26 36
37 28 3 33 11 9 6 22 37
38 17 3 13 16 12 5 22 38
39 28 3 32 12 18 12 22 39
40 29 3 25 12 12 7 23 40
41 26 3 29 14 18 10 30 41
42 25 3 22 9 14 9 23 42
43 14 3 18 10 15 8 17 43
44 25 3 17 9 16 5 23 44
45 26 3 20 10 10 8 23 45
46 20 3 15 12 11 8 25 46
47 18 3 20 14 14 10 24 47
48 32 3 33 14 9 6 24 48
49 25 3 29 10 12 8 23 49
50 25 3 23 14 17 7 21 50
51 23 3 26 16 5 4 24 51
52 21 3 18 9 12 8 24 52
53 20 3 20 10 12 8 28 53
54 15 3 11 6 6 4 16 54
55 30 3 28 8 24 20 20 55
56 24 3 26 13 12 8 29 56
57 26 3 22 10 12 8 27 57
58 24 3 17 8 14 6 22 58
59 22 3 12 7 7 4 28 59
60 14 3 14 15 13 8 16 60
61 24 3 17 9 12 9 25 61
62 24 3 21 10 13 6 24 62
63 24 3 19 12 14 7 28 63
64 24 3 18 13 8 9 24 64
65 19 3 10 10 11 5 23 65
66 31 3 29 11 9 5 30 66
67 22 3 31 8 11 8 24 67
68 27 3 19 9 13 8 21 68
69 19 3 9 13 10 6 25 69
70 25 3 20 11 11 8 25 70
71 20 3 28 8 12 7 22 71
72 21 3 19 9 9 7 23 72
73 27 3 30 9 15 9 26 73
74 23 3 29 15 18 11 23 74
75 25 3 26 9 15 6 25 75
76 20 3 23 10 12 8 21 76
77 21 3 13 14 13 6 25 77
78 22 3 21 12 14 9 24 78
79 23 3 19 12 10 8 29 79
80 25 3 28 11 13 6 22 80
81 25 3 23 14 13 10 27 81
82 17 3 18 6 11 8 26 82
83 19 3 21 12 13 8 22 83
84 25 3 20 8 16 10 24 84
85 19 4 23 14 8 5 27 85
86 20 4 21 11 16 7 24 86
87 26 4 21 10 11 5 24 87
88 23 4 15 14 9 8 29 88
89 27 4 28 12 16 14 22 89
90 17 4 19 10 12 7 21 90
91 17 4 26 14 14 8 24 91
92 19 4 10 5 8 6 24 92
93 17 4 16 11 9 5 23 93
94 22 4 22 10 15 6 20 94
95 21 4 19 9 11 10 27 95
96 32 4 31 10 21 12 26 96
97 21 4 31 16 14 9 25 97
98 21 4 29 13 18 12 21 98
99 18 4 19 9 12 7 21 99
100 18 4 22 10 13 8 19 100
101 23 4 23 10 15 10 21 101
102 19 4 15 7 12 6 21 102
103 20 4 20 9 19 10 16 103
104 21 4 18 8 15 10 22 104
105 20 4 23 14 11 10 29 105
106 17 4 25 14 11 5 15 106
107 18 4 21 8 10 7 17 107
108 19 4 24 9 13 10 15 108
109 22 4 25 14 15 11 21 109
110 15 4 17 14 12 6 21 110
111 14 4 13 8 12 7 19 111
112 18 4 28 8 16 12 24 112
113 24 4 21 8 9 11 20 113
114 35 4 25 7 18 11 17 114
115 29 4 9 6 8 11 23 115
116 21 4 16 8 13 5 24 116
117 25 4 19 6 17 8 14 117
118 20 4 17 11 9 6 19 118
119 22 4 25 14 15 9 24 119
120 13 4 20 11 8 4 13 120
121 26 4 29 11 7 4 22 121
122 17 4 14 11 12 7 16 122
123 25 4 22 14 14 11 19 123
124 20 4 15 8 6 6 25 124
125 19 4 19 20 8 7 25 125
126 21 4 20 11 17 8 23 126
127 22 4 15 8 10 4 24 127
128 24 4 20 11 11 8 26 128
129 21 4 18 10 14 9 26 129
130 26 4 33 14 11 8 25 130
131 24 4 22 11 13 11 18 131
132 16 4 16 9 12 8 21 132
133 23 4 17 9 11 5 26 133
134 18 4 16 8 9 4 23 134
135 16 4 21 10 12 8 23 135
136 26 4 26 13 20 10 22 136
137 19 4 18 13 12 6 20 137
138 21 4 18 12 13 9 13 138
139 21 4 17 8 12 9 24 139
140 22 4 22 13 12 13 15 140
141 23 4 30 14 9 9 14 141
142 29 4 30 12 15 10 22 142
143 21 4 24 14 24 20 10 143
144 21 4 21 15 7 5 24 144
145 23 4 21 13 17 11 22 145
146 27 4 29 16 11 6 24 146
147 25 4 31 9 17 9 19 147
148 21 4 20 9 11 7 20 148
149 10 4 16 9 12 9 13 149
150 20 4 22 8 14 10 20 150
151 26 4 20 7 11 9 22 151
152 24 4 28 16 16 8 24 152
153 29 4 38 11 21 7 29 153
154 19 4 22 9 14 6 12 154
155 24 4 20 11 20 13 20 155
156 19 4 17 9 13 6 21 156
157 24 4 28 14 11 8 24 157
158 22 4 22 13 15 10 22 158
159 17 4 31 16 19 16 20 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Week Consern Doubts PExpect
9.080428 -0.597428 0.334374 -0.360544 0.194459
PCritisism Organisation t
0.011904 0.390931 0.005182
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.61777 -2.29244 0.08834 1.95562 11.60598
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.080428 2.675944 3.393 0.000882 ***
Week -0.597428 0.654515 -0.913 0.362813
Consern 0.334374 0.055969 5.974 1.59e-08 ***
Doubts -0.360544 0.107471 -3.355 0.001004 **
PExpect 0.194459 0.101631 1.913 0.057591 .
PCritisism 0.011904 0.129376 0.092 0.926809
Organisation 0.390931 0.074053 5.279 4.44e-07 ***
t 0.005182 0.011773 0.440 0.660454
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.417 on 151 degrees of freedom
Multiple R-squared: 0.3723, Adjusted R-squared: 0.3432
F-statistic: 12.8 on 7 and 151 DF, p-value: 7.56e-13
> 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,] 0.45330165 0.90660330 0.5466983
[2,] 0.34881705 0.69763410 0.6511829
[3,] 0.46166484 0.92332968 0.5383352
[4,] 0.37325524 0.74651047 0.6267448
[5,] 0.63376822 0.73246357 0.3662318
[6,] 0.56620013 0.86759975 0.4337999
[7,] 0.57108617 0.85782765 0.4289138
[8,] 0.54660448 0.90679104 0.4533955
[9,] 0.45895664 0.91791328 0.5410434
[10,] 0.57201578 0.85596843 0.4279842
[11,] 0.61299974 0.77400052 0.3870003
[12,] 0.54749610 0.90500780 0.4525039
[13,] 0.48337906 0.96675813 0.5166209
[14,] 0.47582933 0.95165867 0.5241707
[15,] 0.41634772 0.83269545 0.5836523
[16,] 0.37295994 0.74591987 0.6270401
[17,] 0.35710779 0.71421559 0.6428922
[18,] 0.32435457 0.64870915 0.6756454
[19,] 0.30342022 0.60684044 0.6965798
[20,] 0.25489160 0.50978321 0.7451084
[21,] 0.22288349 0.44576698 0.7771165
[22,] 0.25742955 0.51485909 0.7425705
[23,] 0.35991714 0.71983428 0.6400829
[24,] 0.48077451 0.96154903 0.5192255
[25,] 0.43066462 0.86132925 0.5693354
[26,] 0.39686884 0.79373768 0.6031312
[27,] 0.34442535 0.68885070 0.6555747
[28,] 0.33772745 0.67545489 0.6622726
[29,] 0.30708750 0.61417501 0.6929125
[30,] 0.30467396 0.60934792 0.6953260
[31,] 0.38461790 0.76923580 0.6153821
[32,] 0.35063652 0.70127304 0.6493635
[33,] 0.58615821 0.82768357 0.4138418
[34,] 0.54891171 0.90217658 0.4510883
[35,] 0.52436379 0.95127242 0.4756362
[36,] 0.49311985 0.98623969 0.5068802
[37,] 0.53872957 0.92254086 0.4612704
[38,] 0.59090870 0.81818259 0.4090913
[39,] 0.58366782 0.83266436 0.4163322
[40,] 0.55233409 0.89533182 0.4476659
[41,] 0.51565376 0.96869247 0.4843462
[42,] 0.50361991 0.99276017 0.4963801
[43,] 0.58704910 0.82590181 0.4129509
[44,] 0.56108826 0.87782347 0.4389117
[45,] 0.55129423 0.89741155 0.4487058
[46,] 0.54365151 0.91269697 0.4563485
[47,] 0.49834747 0.99669494 0.5016525
[48,] 0.46030393 0.92060786 0.5396961
[49,] 0.41398546 0.82797093 0.5860145
[50,] 0.38278598 0.76557195 0.6172140
[51,] 0.34094697 0.68189394 0.6590530
[52,] 0.30768722 0.61537445 0.6923128
[53,] 0.27197597 0.54395195 0.7280240
[54,] 0.27991934 0.55983868 0.7200807
[55,] 0.24232475 0.48464950 0.7576752
[56,] 0.24539776 0.49079552 0.7546022
[57,] 0.35926486 0.71852972 0.6407351
[58,] 0.40718354 0.81436707 0.5928165
[59,] 0.36691763 0.73383526 0.6330824
[60,] 0.34414506 0.68829013 0.6558549
[61,] 0.45314485 0.90628970 0.5468552
[62,] 0.41163524 0.82327049 0.5883648
[63,] 0.37916092 0.75832185 0.6208391
[64,] 0.35238792 0.70477583 0.6476121
[65,] 0.32558674 0.65117348 0.6744133
[66,] 0.30448038 0.60896076 0.6955196
[67,] 0.28068029 0.56136059 0.7193197
[68,] 0.24476107 0.48952213 0.7552389
[69,] 0.21196834 0.42393668 0.7880317
[70,] 0.18914306 0.37828612 0.8108569
[71,] 0.17693713 0.35387426 0.8230629
[72,] 0.25992579 0.51985157 0.7400742
[73,] 0.24201535 0.48403070 0.7579847
[74,] 0.20874581 0.41749163 0.7912542
[75,] 0.20200379 0.40400758 0.7979962
[76,] 0.18914500 0.37828999 0.8108550
[77,] 0.20920128 0.41840255 0.7907987
[78,] 0.20533782 0.41067564 0.7946622
[79,] 0.20258045 0.40516089 0.7974196
[80,] 0.19610323 0.39220646 0.8038968
[81,] 0.25153149 0.50306298 0.7484685
[82,] 0.21509071 0.43018141 0.7849093
[83,] 0.19090190 0.38180380 0.8090981
[84,] 0.16325889 0.32651777 0.8367411
[85,] 0.14276905 0.28553810 0.8572310
[86,] 0.15885119 0.31770237 0.8411488
[87,] 0.14896138 0.29792277 0.8510386
[88,] 0.13516402 0.27032804 0.8648360
[89,] 0.12148884 0.24297769 0.8785112
[90,] 0.11067860 0.22135720 0.8893214
[91,] 0.09090319 0.18180637 0.9090968
[92,] 0.07323077 0.14646154 0.9267692
[93,] 0.05784519 0.11569039 0.9421548
[94,] 0.04559982 0.09119965 0.9544002
[95,] 0.04644959 0.09289917 0.9535504
[96,] 0.03724611 0.07449223 0.9627539
[97,] 0.03269758 0.06539516 0.9673024
[98,] 0.02995151 0.05990301 0.9700485
[99,] 0.02361177 0.04722353 0.9763882
[100,] 0.02442501 0.04885002 0.9755750
[101,] 0.03421246 0.06842493 0.9657875
[102,] 0.23607037 0.47214075 0.7639296
[103,] 0.24848741 0.49697482 0.7515126
[104,] 0.60636201 0.78727597 0.3936380
[105,] 0.86553830 0.26892341 0.1344617
[106,] 0.83411693 0.33176614 0.1658831
[107,] 0.86345346 0.27309309 0.1365465
[108,] 0.83485900 0.33028200 0.1651410
[109,] 0.81330173 0.37339654 0.1866983
[110,] 0.86941998 0.26116004 0.1305800
[111,] 0.84182792 0.31634416 0.1581721
[112,] 0.80709230 0.38581539 0.1929077
[113,] 0.81453846 0.37092308 0.1854615
[114,] 0.77084361 0.45831279 0.2291564
[115,] 0.73590590 0.52818821 0.2640941
[116,] 0.69707050 0.60585899 0.3029295
[117,] 0.64811922 0.70376155 0.3518808
[118,] 0.59516218 0.80967564 0.4048378
[119,] 0.54029120 0.91941761 0.4597088
[120,] 0.48873148 0.97746296 0.5112685
[121,] 0.45919023 0.91838046 0.5408098
[122,] 0.48356870 0.96713741 0.5164313
[123,] 0.41849513 0.83699026 0.5815049
[124,] 0.38964377 0.77928753 0.6103562
[125,] 0.69155262 0.61689476 0.3084474
[126,] 0.62206281 0.75587439 0.3779372
[127,] 0.60792781 0.78414438 0.3920722
[128,] 0.55128858 0.89742284 0.4487114
[129,] 0.57508346 0.84983309 0.4249165
[130,] 0.49747881 0.99495762 0.5025212
[131,] 0.43440512 0.86881023 0.5655949
[132,] 0.39023690 0.78047381 0.6097631
[133,] 0.40853510 0.81707021 0.5914649
[134,] 0.34887169 0.69774337 0.6511283
[135,] 0.24978847 0.49957694 0.7502115
[136,] 0.21246613 0.42493227 0.7875339
[137,] 0.17216047 0.34432094 0.8278395
[138,] 0.09517859 0.19035718 0.9048214
> postscript(file="/var/www/html/rcomp/tmp/1x3gb1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2x3gb1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3x3gb1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48cfw1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58cfw1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
0.08833632 1.66539787 4.80603197 -2.22718207 0.48836346 -2.01248853
7 8 9 10 11 12
1.43873192 2.61198567 -2.79699820 -1.04035071 -4.19373777 -4.91960252
13 14 15 16 17 18
-8.61777115 -1.55411948 3.15229726 2.55062125 0.57347957 -3.03556511
19 20 21 22 23 24
-2.55864586 -0.96514078 1.52735042 -3.45199772 -3.27102783 -5.76173379
25 26 27 28 29 30
-2.98517629 0.70511630 1.75869548 -4.22765439 0.24034331 0.52612228
31 32 33 34 35 36
-0.02688460 2.88230323 6.21656400 6.87055192 3.46852620 4.60827269
37 38 39 40 41 42
3.02972337 -0.05673355 1.89271785 6.06349990 -1.49707902 1.56189936
43 44 45 46 47 48
-5.58220902 2.88210413 4.36538762 -0.22315881 -3.39537775 7.27249296
49 50 51 52 53 54
-0.05362367 3.21108432 2.12029918 -1.14253069 -4.01964227 -1.55208327
55 56 57 58 59 60
3.22500275 -1.35073104 1.68181430 2.21696171 0.56254119 -1.75023323
61 62 63 64 65 66
1.74237184 0.99242312 0.60698763 4.00339705 0.44674587 4.10140136
67 68 69 70 71 72
-4.73320282 5.41852248 1.24271667 2.62006570 -5.15150007 -0.59432571
73 74 75 76 77 78
-0.64098002 -1.58291676 -0.88720335 -2.40542406 1.64093274 -0.59956920
79 80 81 82 83 84
-0.10091860 0.70094084 1.44698613 -6.96701817 -2.63725137 0.86071578
85 86 87 88 89 90
-2.94449424 -2.76924874 3.86113096 2.70291708 2.93366509 -3.53113993
91 92 93 94 95 96
-6.00838117 -0.71790988 -2.35769567 0.26446892 -2.10443475 3.66096928
97 98 99 100 101 102
-3.39308939 -3.06097933 -2.93831949 -3.01057996 0.45527416 -1.32555237
103 104 105 106 107 108
-0.73569148 -1.00042050 -3.47289148 -1.61425938 -2.05642073 -1.54140847
109 110 111 112 113 114
0.17534356 -3.51194703 -4.57293763 -8.38574484 2.88653666 11.60597541
115 116 117 118 119 120
10.18923789 -0.72727624 4.63909661 1.73020833 -1.02545964 -3.71942039
121 122 123 124 125 126
2.94210853 0.29011525 5.08224329 -0.47597652 1.10704954 -1.45757666
127 128 129 130 131 132
1.14538089 1.52602160 -1.76623845 0.64135977 3.54454850 -4.11809940
133 134 135 136 137 138
0.81785986 -2.63987447 -6.22683344 1.98919477 0.04415954 4.18478185
139 140 141 142 143 144
-1.03398808 3.56244571 3.26474284 4.23236134 1.77651149 1.14632847
145 146 147 148 149 150
1.18590263 4.03177552 -0.41377304 0.05879198 -7.09064170 -2.59995422
151 152 153 154 155 156
3.51648750 0.33895370 -2.72773676 -0.08506854 1.92204767 -1.74748595
157 158 159
0.56425397 0.18498901 -6.81532693
> postscript(file="/var/www/html/rcomp/tmp/68cfw1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 0.08833632 NA
1 1.66539787 0.08833632
2 4.80603197 1.66539787
3 -2.22718207 4.80603197
4 0.48836346 -2.22718207
5 -2.01248853 0.48836346
6 1.43873192 -2.01248853
7 2.61198567 1.43873192
8 -2.79699820 2.61198567
9 -1.04035071 -2.79699820
10 -4.19373777 -1.04035071
11 -4.91960252 -4.19373777
12 -8.61777115 -4.91960252
13 -1.55411948 -8.61777115
14 3.15229726 -1.55411948
15 2.55062125 3.15229726
16 0.57347957 2.55062125
17 -3.03556511 0.57347957
18 -2.55864586 -3.03556511
19 -0.96514078 -2.55864586
20 1.52735042 -0.96514078
21 -3.45199772 1.52735042
22 -3.27102783 -3.45199772
23 -5.76173379 -3.27102783
24 -2.98517629 -5.76173379
25 0.70511630 -2.98517629
26 1.75869548 0.70511630
27 -4.22765439 1.75869548
28 0.24034331 -4.22765439
29 0.52612228 0.24034331
30 -0.02688460 0.52612228
31 2.88230323 -0.02688460
32 6.21656400 2.88230323
33 6.87055192 6.21656400
34 3.46852620 6.87055192
35 4.60827269 3.46852620
36 3.02972337 4.60827269
37 -0.05673355 3.02972337
38 1.89271785 -0.05673355
39 6.06349990 1.89271785
40 -1.49707902 6.06349990
41 1.56189936 -1.49707902
42 -5.58220902 1.56189936
43 2.88210413 -5.58220902
44 4.36538762 2.88210413
45 -0.22315881 4.36538762
46 -3.39537775 -0.22315881
47 7.27249296 -3.39537775
48 -0.05362367 7.27249296
49 3.21108432 -0.05362367
50 2.12029918 3.21108432
51 -1.14253069 2.12029918
52 -4.01964227 -1.14253069
53 -1.55208327 -4.01964227
54 3.22500275 -1.55208327
55 -1.35073104 3.22500275
56 1.68181430 -1.35073104
57 2.21696171 1.68181430
58 0.56254119 2.21696171
59 -1.75023323 0.56254119
60 1.74237184 -1.75023323
61 0.99242312 1.74237184
62 0.60698763 0.99242312
63 4.00339705 0.60698763
64 0.44674587 4.00339705
65 4.10140136 0.44674587
66 -4.73320282 4.10140136
67 5.41852248 -4.73320282
68 1.24271667 5.41852248
69 2.62006570 1.24271667
70 -5.15150007 2.62006570
71 -0.59432571 -5.15150007
72 -0.64098002 -0.59432571
73 -1.58291676 -0.64098002
74 -0.88720335 -1.58291676
75 -2.40542406 -0.88720335
76 1.64093274 -2.40542406
77 -0.59956920 1.64093274
78 -0.10091860 -0.59956920
79 0.70094084 -0.10091860
80 1.44698613 0.70094084
81 -6.96701817 1.44698613
82 -2.63725137 -6.96701817
83 0.86071578 -2.63725137
84 -2.94449424 0.86071578
85 -2.76924874 -2.94449424
86 3.86113096 -2.76924874
87 2.70291708 3.86113096
88 2.93366509 2.70291708
89 -3.53113993 2.93366509
90 -6.00838117 -3.53113993
91 -0.71790988 -6.00838117
92 -2.35769567 -0.71790988
93 0.26446892 -2.35769567
94 -2.10443475 0.26446892
95 3.66096928 -2.10443475
96 -3.39308939 3.66096928
97 -3.06097933 -3.39308939
98 -2.93831949 -3.06097933
99 -3.01057996 -2.93831949
100 0.45527416 -3.01057996
101 -1.32555237 0.45527416
102 -0.73569148 -1.32555237
103 -1.00042050 -0.73569148
104 -3.47289148 -1.00042050
105 -1.61425938 -3.47289148
106 -2.05642073 -1.61425938
107 -1.54140847 -2.05642073
108 0.17534356 -1.54140847
109 -3.51194703 0.17534356
110 -4.57293763 -3.51194703
111 -8.38574484 -4.57293763
112 2.88653666 -8.38574484
113 11.60597541 2.88653666
114 10.18923789 11.60597541
115 -0.72727624 10.18923789
116 4.63909661 -0.72727624
117 1.73020833 4.63909661
118 -1.02545964 1.73020833
119 -3.71942039 -1.02545964
120 2.94210853 -3.71942039
121 0.29011525 2.94210853
122 5.08224329 0.29011525
123 -0.47597652 5.08224329
124 1.10704954 -0.47597652
125 -1.45757666 1.10704954
126 1.14538089 -1.45757666
127 1.52602160 1.14538089
128 -1.76623845 1.52602160
129 0.64135977 -1.76623845
130 3.54454850 0.64135977
131 -4.11809940 3.54454850
132 0.81785986 -4.11809940
133 -2.63987447 0.81785986
134 -6.22683344 -2.63987447
135 1.98919477 -6.22683344
136 0.04415954 1.98919477
137 4.18478185 0.04415954
138 -1.03398808 4.18478185
139 3.56244571 -1.03398808
140 3.26474284 3.56244571
141 4.23236134 3.26474284
142 1.77651149 4.23236134
143 1.14632847 1.77651149
144 1.18590263 1.14632847
145 4.03177552 1.18590263
146 -0.41377304 4.03177552
147 0.05879198 -0.41377304
148 -7.09064170 0.05879198
149 -2.59995422 -7.09064170
150 3.51648750 -2.59995422
151 0.33895370 3.51648750
152 -2.72773676 0.33895370
153 -0.08506854 -2.72773676
154 1.92204767 -0.08506854
155 -1.74748595 1.92204767
156 0.56425397 -1.74748595
157 0.18498901 0.56425397
158 -6.81532693 0.18498901
159 NA -6.81532693
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.66539787 0.08833632
[2,] 4.80603197 1.66539787
[3,] -2.22718207 4.80603197
[4,] 0.48836346 -2.22718207
[5,] -2.01248853 0.48836346
[6,] 1.43873192 -2.01248853
[7,] 2.61198567 1.43873192
[8,] -2.79699820 2.61198567
[9,] -1.04035071 -2.79699820
[10,] -4.19373777 -1.04035071
[11,] -4.91960252 -4.19373777
[12,] -8.61777115 -4.91960252
[13,] -1.55411948 -8.61777115
[14,] 3.15229726 -1.55411948
[15,] 2.55062125 3.15229726
[16,] 0.57347957 2.55062125
[17,] -3.03556511 0.57347957
[18,] -2.55864586 -3.03556511
[19,] -0.96514078 -2.55864586
[20,] 1.52735042 -0.96514078
[21,] -3.45199772 1.52735042
[22,] -3.27102783 -3.45199772
[23,] -5.76173379 -3.27102783
[24,] -2.98517629 -5.76173379
[25,] 0.70511630 -2.98517629
[26,] 1.75869548 0.70511630
[27,] -4.22765439 1.75869548
[28,] 0.24034331 -4.22765439
[29,] 0.52612228 0.24034331
[30,] -0.02688460 0.52612228
[31,] 2.88230323 -0.02688460
[32,] 6.21656400 2.88230323
[33,] 6.87055192 6.21656400
[34,] 3.46852620 6.87055192
[35,] 4.60827269 3.46852620
[36,] 3.02972337 4.60827269
[37,] -0.05673355 3.02972337
[38,] 1.89271785 -0.05673355
[39,] 6.06349990 1.89271785
[40,] -1.49707902 6.06349990
[41,] 1.56189936 -1.49707902
[42,] -5.58220902 1.56189936
[43,] 2.88210413 -5.58220902
[44,] 4.36538762 2.88210413
[45,] -0.22315881 4.36538762
[46,] -3.39537775 -0.22315881
[47,] 7.27249296 -3.39537775
[48,] -0.05362367 7.27249296
[49,] 3.21108432 -0.05362367
[50,] 2.12029918 3.21108432
[51,] -1.14253069 2.12029918
[52,] -4.01964227 -1.14253069
[53,] -1.55208327 -4.01964227
[54,] 3.22500275 -1.55208327
[55,] -1.35073104 3.22500275
[56,] 1.68181430 -1.35073104
[57,] 2.21696171 1.68181430
[58,] 0.56254119 2.21696171
[59,] -1.75023323 0.56254119
[60,] 1.74237184 -1.75023323
[61,] 0.99242312 1.74237184
[62,] 0.60698763 0.99242312
[63,] 4.00339705 0.60698763
[64,] 0.44674587 4.00339705
[65,] 4.10140136 0.44674587
[66,] -4.73320282 4.10140136
[67,] 5.41852248 -4.73320282
[68,] 1.24271667 5.41852248
[69,] 2.62006570 1.24271667
[70,] -5.15150007 2.62006570
[71,] -0.59432571 -5.15150007
[72,] -0.64098002 -0.59432571
[73,] -1.58291676 -0.64098002
[74,] -0.88720335 -1.58291676
[75,] -2.40542406 -0.88720335
[76,] 1.64093274 -2.40542406
[77,] -0.59956920 1.64093274
[78,] -0.10091860 -0.59956920
[79,] 0.70094084 -0.10091860
[80,] 1.44698613 0.70094084
[81,] -6.96701817 1.44698613
[82,] -2.63725137 -6.96701817
[83,] 0.86071578 -2.63725137
[84,] -2.94449424 0.86071578
[85,] -2.76924874 -2.94449424
[86,] 3.86113096 -2.76924874
[87,] 2.70291708 3.86113096
[88,] 2.93366509 2.70291708
[89,] -3.53113993 2.93366509
[90,] -6.00838117 -3.53113993
[91,] -0.71790988 -6.00838117
[92,] -2.35769567 -0.71790988
[93,] 0.26446892 -2.35769567
[94,] -2.10443475 0.26446892
[95,] 3.66096928 -2.10443475
[96,] -3.39308939 3.66096928
[97,] -3.06097933 -3.39308939
[98,] -2.93831949 -3.06097933
[99,] -3.01057996 -2.93831949
[100,] 0.45527416 -3.01057996
[101,] -1.32555237 0.45527416
[102,] -0.73569148 -1.32555237
[103,] -1.00042050 -0.73569148
[104,] -3.47289148 -1.00042050
[105,] -1.61425938 -3.47289148
[106,] -2.05642073 -1.61425938
[107,] -1.54140847 -2.05642073
[108,] 0.17534356 -1.54140847
[109,] -3.51194703 0.17534356
[110,] -4.57293763 -3.51194703
[111,] -8.38574484 -4.57293763
[112,] 2.88653666 -8.38574484
[113,] 11.60597541 2.88653666
[114,] 10.18923789 11.60597541
[115,] -0.72727624 10.18923789
[116,] 4.63909661 -0.72727624
[117,] 1.73020833 4.63909661
[118,] -1.02545964 1.73020833
[119,] -3.71942039 -1.02545964
[120,] 2.94210853 -3.71942039
[121,] 0.29011525 2.94210853
[122,] 5.08224329 0.29011525
[123,] -0.47597652 5.08224329
[124,] 1.10704954 -0.47597652
[125,] -1.45757666 1.10704954
[126,] 1.14538089 -1.45757666
[127,] 1.52602160 1.14538089
[128,] -1.76623845 1.52602160
[129,] 0.64135977 -1.76623845
[130,] 3.54454850 0.64135977
[131,] -4.11809940 3.54454850
[132,] 0.81785986 -4.11809940
[133,] -2.63987447 0.81785986
[134,] -6.22683344 -2.63987447
[135,] 1.98919477 -6.22683344
[136,] 0.04415954 1.98919477
[137,] 4.18478185 0.04415954
[138,] -1.03398808 4.18478185
[139,] 3.56244571 -1.03398808
[140,] 3.26474284 3.56244571
[141,] 4.23236134 3.26474284
[142,] 1.77651149 4.23236134
[143,] 1.14632847 1.77651149
[144,] 1.18590263 1.14632847
[145,] 4.03177552 1.18590263
[146,] -0.41377304 4.03177552
[147,] 0.05879198 -0.41377304
[148,] -7.09064170 0.05879198
[149,] -2.59995422 -7.09064170
[150,] 3.51648750 -2.59995422
[151,] 0.33895370 3.51648750
[152,] -2.72773676 0.33895370
[153,] -0.08506854 -2.72773676
[154,] 1.92204767 -0.08506854
[155,] -1.74748595 1.92204767
[156,] 0.56425397 -1.74748595
[157,] 0.18498901 0.56425397
[158,] -6.81532693 0.18498901
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.66539787 0.08833632
2 4.80603197 1.66539787
3 -2.22718207 4.80603197
4 0.48836346 -2.22718207
5 -2.01248853 0.48836346
6 1.43873192 -2.01248853
7 2.61198567 1.43873192
8 -2.79699820 2.61198567
9 -1.04035071 -2.79699820
10 -4.19373777 -1.04035071
11 -4.91960252 -4.19373777
12 -8.61777115 -4.91960252
13 -1.55411948 -8.61777115
14 3.15229726 -1.55411948
15 2.55062125 3.15229726
16 0.57347957 2.55062125
17 -3.03556511 0.57347957
18 -2.55864586 -3.03556511
19 -0.96514078 -2.55864586
20 1.52735042 -0.96514078
21 -3.45199772 1.52735042
22 -3.27102783 -3.45199772
23 -5.76173379 -3.27102783
24 -2.98517629 -5.76173379
25 0.70511630 -2.98517629
26 1.75869548 0.70511630
27 -4.22765439 1.75869548
28 0.24034331 -4.22765439
29 0.52612228 0.24034331
30 -0.02688460 0.52612228
31 2.88230323 -0.02688460
32 6.21656400 2.88230323
33 6.87055192 6.21656400
34 3.46852620 6.87055192
35 4.60827269 3.46852620
36 3.02972337 4.60827269
37 -0.05673355 3.02972337
38 1.89271785 -0.05673355
39 6.06349990 1.89271785
40 -1.49707902 6.06349990
41 1.56189936 -1.49707902
42 -5.58220902 1.56189936
43 2.88210413 -5.58220902
44 4.36538762 2.88210413
45 -0.22315881 4.36538762
46 -3.39537775 -0.22315881
47 7.27249296 -3.39537775
48 -0.05362367 7.27249296
49 3.21108432 -0.05362367
50 2.12029918 3.21108432
51 -1.14253069 2.12029918
52 -4.01964227 -1.14253069
53 -1.55208327 -4.01964227
54 3.22500275 -1.55208327
55 -1.35073104 3.22500275
56 1.68181430 -1.35073104
57 2.21696171 1.68181430
58 0.56254119 2.21696171
59 -1.75023323 0.56254119
60 1.74237184 -1.75023323
61 0.99242312 1.74237184
62 0.60698763 0.99242312
63 4.00339705 0.60698763
64 0.44674587 4.00339705
65 4.10140136 0.44674587
66 -4.73320282 4.10140136
67 5.41852248 -4.73320282
68 1.24271667 5.41852248
69 2.62006570 1.24271667
70 -5.15150007 2.62006570
71 -0.59432571 -5.15150007
72 -0.64098002 -0.59432571
73 -1.58291676 -0.64098002
74 -0.88720335 -1.58291676
75 -2.40542406 -0.88720335
76 1.64093274 -2.40542406
77 -0.59956920 1.64093274
78 -0.10091860 -0.59956920
79 0.70094084 -0.10091860
80 1.44698613 0.70094084
81 -6.96701817 1.44698613
82 -2.63725137 -6.96701817
83 0.86071578 -2.63725137
84 -2.94449424 0.86071578
85 -2.76924874 -2.94449424
86 3.86113096 -2.76924874
87 2.70291708 3.86113096
88 2.93366509 2.70291708
89 -3.53113993 2.93366509
90 -6.00838117 -3.53113993
91 -0.71790988 -6.00838117
92 -2.35769567 -0.71790988
93 0.26446892 -2.35769567
94 -2.10443475 0.26446892
95 3.66096928 -2.10443475
96 -3.39308939 3.66096928
97 -3.06097933 -3.39308939
98 -2.93831949 -3.06097933
99 -3.01057996 -2.93831949
100 0.45527416 -3.01057996
101 -1.32555237 0.45527416
102 -0.73569148 -1.32555237
103 -1.00042050 -0.73569148
104 -3.47289148 -1.00042050
105 -1.61425938 -3.47289148
106 -2.05642073 -1.61425938
107 -1.54140847 -2.05642073
108 0.17534356 -1.54140847
109 -3.51194703 0.17534356
110 -4.57293763 -3.51194703
111 -8.38574484 -4.57293763
112 2.88653666 -8.38574484
113 11.60597541 2.88653666
114 10.18923789 11.60597541
115 -0.72727624 10.18923789
116 4.63909661 -0.72727624
117 1.73020833 4.63909661
118 -1.02545964 1.73020833
119 -3.71942039 -1.02545964
120 2.94210853 -3.71942039
121 0.29011525 2.94210853
122 5.08224329 0.29011525
123 -0.47597652 5.08224329
124 1.10704954 -0.47597652
125 -1.45757666 1.10704954
126 1.14538089 -1.45757666
127 1.52602160 1.14538089
128 -1.76623845 1.52602160
129 0.64135977 -1.76623845
130 3.54454850 0.64135977
131 -4.11809940 3.54454850
132 0.81785986 -4.11809940
133 -2.63987447 0.81785986
134 -6.22683344 -2.63987447
135 1.98919477 -6.22683344
136 0.04415954 1.98919477
137 4.18478185 0.04415954
138 -1.03398808 4.18478185
139 3.56244571 -1.03398808
140 3.26474284 3.56244571
141 4.23236134 3.26474284
142 1.77651149 4.23236134
143 1.14632847 1.77651149
144 1.18590263 1.14632847
145 4.03177552 1.18590263
146 -0.41377304 4.03177552
147 0.05879198 -0.41377304
148 -7.09064170 0.05879198
149 -2.59995422 -7.09064170
150 3.51648750 -2.59995422
151 0.33895370 3.51648750
152 -2.72773676 0.33895370
153 -0.08506854 -2.72773676
154 1.92204767 -0.08506854
155 -1.74748595 1.92204767
156 0.56425397 -1.74748595
157 0.18498901 0.56425397
158 -6.81532693 0.18498901
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7jmez1290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cdw21290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cdw21290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cdw21290528071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/1185ca1290528071.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/120ebd1290528071.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13px8p1290528071.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14067s1290528071.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15lp6y1290528071.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16zhlp1290528071.tab")
+ }
>
> try(system("convert tmp/1x3gb1290528071.ps tmp/1x3gb1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x3gb1290528071.ps tmp/2x3gb1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/3x3gb1290528071.ps tmp/3x3gb1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/48cfw1290528071.ps tmp/48cfw1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/58cfw1290528071.ps tmp/58cfw1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/68cfw1290528071.ps tmp/68cfw1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jmez1290528071.ps tmp/7jmez1290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cdw21290528071.ps tmp/8cdw21290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cdw21290528071.ps tmp/9cdw21290528071.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cdw21290528071.ps tmp/10cdw21290528071.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.110 1.678 8.869