R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS'
+ ,'O'
+ ,'H
')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('CM','D','PE','PC','PS','O','H
'),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 = '5'
> #'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
PS CM D PE PC O H\r t
1 24 24 14 11 12 26 10 1
2 25 25 11 7 8 23 14 2
3 30 17 6 17 8 25 18 3
4 19 18 12 10 8 23 15 4
5 22 18 8 12 9 19 18 5
6 22 16 10 12 7 29 11 6
7 25 20 10 11 4 25 17 7
8 23 16 11 11 11 21 19 8
9 17 18 16 12 7 22 7 9
10 21 17 11 13 7 25 12 10
11 19 23 13 14 12 24 13 11
12 19 30 12 16 10 18 15 12
13 15 23 8 11 10 22 14 13
14 16 18 12 10 8 15 14 14
15 23 15 11 11 8 22 16 15
16 27 12 4 15 4 28 16 16
17 22 21 9 9 9 20 12 17
18 14 15 8 11 8 12 12 18
19 22 20 8 17 7 24 13 19
20 23 31 14 17 11 20 16 20
21 23 27 15 11 9 21 9 21
22 19 21 9 14 13 21 11 22
23 18 31 14 10 8 23 12 23
24 20 19 11 11 8 28 11 24
25 23 16 8 15 9 24 14 25
26 25 20 9 15 6 24 18 26
27 19 21 9 13 9 24 11 27
28 24 22 9 16 9 23 14 28
29 22 17 9 13 6 23 17 29
30 26 25 16 18 16 24 12 30
31 29 26 11 18 5 18 14 31
32 32 25 8 12 7 25 14 32
33 25 17 9 17 9 21 15 33
34 29 32 16 9 6 26 11 34
35 28 33 11 9 6 22 15 35
36 17 13 16 12 5 22 14 36
37 28 32 12 18 12 22 11 37
38 29 25 12 12 7 23 12 38
39 26 29 14 18 10 30 17 39
40 25 22 9 14 9 23 15 40
41 14 18 10 15 8 17 9 41
42 25 17 9 16 5 23 16 42
43 26 20 10 10 8 23 13 43
44 20 15 12 11 8 25 15 44
45 18 20 14 14 10 24 11 45
46 32 33 14 9 6 24 10 46
47 25 29 10 12 8 23 16 47
48 25 23 14 17 7 21 13 48
49 23 26 16 5 4 24 9 49
50 21 18 9 12 8 24 14 50
51 20 20 10 12 8 28 16 51
52 15 11 6 6 4 16 15 52
53 30 28 8 24 20 20 14 53
54 24 26 13 12 8 29 13 54
55 26 22 10 12 8 27 14 55
56 24 17 8 14 6 22 16 56
57 22 12 7 7 4 28 15 57
58 14 14 15 13 8 16 16 58
59 24 17 9 12 9 25 15 59
60 24 21 10 13 6 24 13 60
61 24 19 12 14 7 28 11 61
62 24 18 13 8 9 24 16 62
63 19 10 10 11 5 23 17 63
64 31 29 11 9 5 30 10 64
65 22 31 8 11 8 24 17 65
66 27 19 9 13 8 21 11 66
67 19 9 13 10 6 25 14 67
68 25 20 11 11 8 25 15 68
69 20 28 8 12 7 22 16 69
70 21 19 9 9 7 23 15 70
71 27 30 9 15 9 26 16 71
72 23 29 15 18 11 23 15 72
73 25 26 9 15 6 25 14 73
74 20 23 10 12 8 21 17 74
75 22 21 12 14 9 24 12 75
76 23 19 12 10 8 29 12 76
77 25 28 11 13 6 22 9 77
78 25 23 14 13 10 27 12 78
79 17 18 6 11 8 26 17 79
80 19 21 12 13 8 22 11 80
81 25 20 8 16 10 24 16 81
82 19 23 14 8 5 27 9 82
83 20 21 11 16 7 24 15 83
84 26 21 10 11 5 24 17 84
85 23 15 14 9 8 29 17 85
86 27 28 12 16 14 22 12 86
87 17 19 10 12 7 21 15 87
88 17 26 14 14 8 24 18 88
89 17 16 11 9 5 23 13 89
90 22 22 10 15 6 20 15 90
91 21 19 9 11 10 27 16 91
92 32 31 10 21 12 26 17 92
93 21 31 16 14 9 25 15 93
94 21 29 13 18 12 21 13 94
95 18 19 9 12 7 21 12 95
96 18 22 10 13 8 19 11 96
97 23 23 10 15 10 21 15 97
98 19 15 7 12 6 21 15 98
99 20 20 9 19 10 16 15 99
100 21 18 8 15 10 22 18 100
101 20 23 14 11 10 29 16 101
102 17 25 14 11 5 15 12 102
103 18 21 8 10 7 17 16 103
104 19 24 9 13 10 15 15 104
105 22 25 14 15 11 21 15 105
106 15 17 14 12 6 21 15 106
107 14 13 8 12 7 19 17 107
108 18 28 8 16 12 24 15 108
109 24 21 8 9 11 20 13 109
110 35 25 7 18 11 17 16 110
111 29 9 6 8 11 23 13 111
112 21 16 8 13 5 24 13 112
113 20 17 11 9 6 19 15 113
114 22 25 14 15 9 24 13 114
115 13 20 11 8 4 13 16 115
116 26 29 11 7 4 22 14 116
117 17 14 11 12 7 16 15 117
118 25 22 14 14 11 19 11 118
119 20 15 8 6 6 25 15 119
120 19 19 20 8 7 25 14 120
121 21 20 11 17 8 23 14 121
122 22 15 8 10 4 24 17 122
123 24 20 11 11 8 26 15 123
124 21 18 10 14 9 26 14 124
125 26 33 14 11 8 25 15 125
126 24 22 11 13 11 18 13 126
127 16 16 9 12 8 21 15 127
128 23 17 9 11 5 26 16 128
129 18 16 8 9 4 23 12 129
130 16 21 10 12 8 23 14 130
131 26 26 13 20 10 22 12 131
132 19 18 13 12 6 20 14 132
133 21 18 12 13 9 13 14 133
134 21 17 8 12 9 24 15 134
135 22 22 13 12 13 15 13 135
136 23 30 14 9 9 14 15 136
137 29 30 12 15 10 22 16 137
138 21 24 14 24 20 10 10 138
139 21 21 15 7 5 24 8 139
140 23 21 13 17 11 22 15 140
141 27 29 16 11 6 24 14 141
142 25 31 9 17 9 19 13 142
143 21 20 9 11 7 20 15 143
144 10 16 9 12 9 13 13 144
145 20 22 8 14 10 20 14 145
146 26 20 7 11 9 22 19 146
147 24 28 16 16 8 24 17 147
148 29 38 11 21 7 29 16 148
149 19 22 9 14 6 12 16 149
150 24 20 11 20 13 20 14 150
151 19 17 9 13 6 21 12 151
152 24 28 14 11 8 24 13 152
153 22 22 13 15 10 22 14 153
154 17 31 16 19 16 20 15 154
155 24 24 14 11 12 26 10 155
156 25 25 11 7 8 23 14 156
157 30 17 6 17 8 25 18 157
158 19 18 12 10 8 23 15 158
159 22 18 8 12 9 19 18 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM D PE PC O
6.967229 0.338739 -0.370734 0.173455 0.048156 0.414415
`H\r` t
0.019527 -0.002547
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.8030 -2.0823 0.1592 2.1454 11.4303
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.967229 3.106557 2.243 0.02637 *
CM 0.338739 0.057193 5.923 2.06e-08 ***
D -0.370734 0.116406 -3.185 0.00176 **
PE 0.173455 0.101683 1.706 0.09009 .
PC 0.048156 0.128585 0.375 0.70855
O 0.414415 0.075193 5.511 1.50e-07 ***
`H\r` 0.019527 0.127692 0.153 0.87867
t -0.002547 0.006168 -0.413 0.68018
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.435 on 151 degrees of freedom
Multiple R-squared: 0.3777, Adjusted R-squared: 0.3488
F-statistic: 13.09 on 7 and 151 DF, p-value: 4.103e-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,] 0.42114635 0.84229270 0.5788536
[2,] 0.29539314 0.59078628 0.7046069
[3,] 0.22586032 0.45172064 0.7741397
[4,] 0.20543698 0.41087396 0.7945630
[5,] 0.34096278 0.68192556 0.6590372
[6,] 0.32048241 0.64096483 0.6795176
[7,] 0.48609786 0.97219572 0.5139021
[8,] 0.39474932 0.78949864 0.6052507
[9,] 0.31374779 0.62749559 0.6862522
[10,] 0.29878853 0.59757707 0.7012115
[11,] 0.39711396 0.79422792 0.6028860
[12,] 0.33110619 0.66221239 0.6688938
[13,] 0.36308831 0.72617661 0.6369117
[14,] 0.31728336 0.63456673 0.6827166
[15,] 0.26492314 0.52984627 0.7350769
[16,] 0.21098729 0.42197457 0.7890127
[17,] 0.18046326 0.36092653 0.8195367
[18,] 0.15673307 0.31346615 0.8432669
[19,] 0.11886035 0.23772070 0.8811396
[20,] 0.13572087 0.27144174 0.8642791
[21,] 0.30704327 0.61408653 0.6929567
[22,] 0.57958058 0.84083884 0.4204194
[23,] 0.54086279 0.91827443 0.4591372
[24,] 0.54666838 0.90666324 0.4533316
[25,] 0.49975639 0.99951278 0.5002436
[26,] 0.51699203 0.96601594 0.4830080
[27,] 0.48086557 0.96173114 0.5191344
[28,] 0.54313036 0.91373929 0.4568696
[29,] 0.59867096 0.80265808 0.4013290
[30,] 0.54545138 0.90909725 0.4545486
[31,] 0.60723677 0.78552646 0.3927632
[32,] 0.57265822 0.85468356 0.4273418
[33,] 0.56872347 0.86255306 0.4312765
[34,] 0.53847271 0.92305457 0.4615273
[35,] 0.53939818 0.92120363 0.4606018
[36,] 0.63953624 0.72092752 0.3604638
[37,] 0.62279966 0.75440068 0.3772003
[38,] 0.60564571 0.78870859 0.3943543
[39,] 0.56351164 0.87297672 0.4364884
[40,] 0.53094454 0.93811091 0.4690555
[41,] 0.60422941 0.79154118 0.3957706
[42,] 0.56694348 0.86611304 0.4330565
[43,] 0.60790667 0.78418666 0.3920933
[44,] 0.58226836 0.83546328 0.4177316
[45,] 0.53911091 0.92177818 0.4608891
[46,] 0.50756800 0.98486401 0.4924320
[47,] 0.45876020 0.91752039 0.5412398
[48,] 0.42045978 0.84091956 0.5795402
[49,] 0.38357097 0.76714193 0.6164290
[50,] 0.34354100 0.68708199 0.6564590
[51,] 0.30121527 0.60243055 0.6987847
[52,] 0.31339810 0.62679619 0.6866019
[53,] 0.27841900 0.55683801 0.7215810
[54,] 0.28288593 0.56577186 0.7171141
[55,] 0.38910991 0.77821982 0.6108901
[56,] 0.48120581 0.96241161 0.5187942
[57,] 0.44729234 0.89458469 0.5527077
[58,] 0.43242749 0.86485498 0.5675725
[59,] 0.51969619 0.96060762 0.4803038
[60,] 0.47541147 0.95082294 0.5245885
[61,] 0.43865517 0.87731035 0.5613448
[62,] 0.40819896 0.81639792 0.5918010
[63,] 0.37741968 0.75483937 0.6225803
[64,] 0.35030819 0.70061638 0.6496918
[65,] 0.30959978 0.61919957 0.6904002
[66,] 0.27142942 0.54285885 0.7285706
[67,] 0.24696821 0.49393642 0.7530318
[68,] 0.22640482 0.45280963 0.7735952
[69,] 0.32818971 0.65637942 0.6718103
[70,] 0.29759262 0.59518524 0.7024074
[71,] 0.26730233 0.53460466 0.7326977
[72,] 0.25578740 0.51157480 0.7442126
[73,] 0.24358042 0.48716084 0.7564196
[74,] 0.26390682 0.52781365 0.7360932
[75,] 0.25497335 0.50994670 0.7450267
[76,] 0.25456287 0.50912575 0.7454371
[77,] 0.24649713 0.49299425 0.7535029
[78,] 0.32020205 0.64040410 0.6797979
[79,] 0.28835551 0.57671103 0.7116445
[80,] 0.25548408 0.51096816 0.7445159
[81,] 0.22981508 0.45963016 0.7701849
[82,] 0.24700768 0.49401536 0.7529923
[83,] 0.23574924 0.47149847 0.7642508
[84,] 0.21464250 0.42928500 0.7853575
[85,] 0.19250482 0.38500963 0.8074952
[86,] 0.17317031 0.34634061 0.8268297
[87,] 0.14693890 0.29387781 0.8530611
[88,] 0.12140656 0.24281312 0.8785934
[89,] 0.09880766 0.19761533 0.9011923
[90,] 0.07983179 0.15966358 0.9201682
[91,] 0.08099425 0.16198851 0.9190057
[92,] 0.06444167 0.12888333 0.9355583
[93,] 0.05472643 0.10945286 0.9452736
[94,] 0.04676997 0.09353994 0.9532300
[95,] 0.03687755 0.07375510 0.9631225
[96,] 0.03467012 0.06934025 0.9653299
[97,] 0.04438080 0.08876161 0.9556192
[98,] 0.23046910 0.46093819 0.7695309
[99,] 0.23940205 0.47880409 0.7605980
[100,] 0.64630741 0.70738517 0.3536926
[101,] 0.88418739 0.23162522 0.1158126
[102,] 0.85572828 0.28854344 0.1442717
[103,] 0.83080922 0.33838157 0.1691908
[104,] 0.80024094 0.39951813 0.1997591
[105,] 0.82852949 0.34294102 0.1714705
[106,] 0.80852642 0.38294715 0.1914736
[107,] 0.76989940 0.46020120 0.2301006
[108,] 0.81990289 0.36019422 0.1800971
[109,] 0.78067127 0.43865745 0.2193287
[110,] 0.74167292 0.51665416 0.2583271
[111,] 0.69728774 0.60542452 0.3027123
[112,] 0.64657485 0.70685029 0.3534251
[113,] 0.59646733 0.80706535 0.4035327
[114,] 0.54767947 0.90464105 0.4523205
[115,] 0.48988037 0.97976074 0.5101196
[116,] 0.48969098 0.97938195 0.5103090
[117,] 0.52249410 0.95501179 0.4775059
[118,] 0.45883941 0.91767881 0.5411606
[119,] 0.42137464 0.84274928 0.5786254
[120,] 0.67599573 0.64800854 0.3240043
[121,] 0.62536132 0.74927735 0.3746387
[122,] 0.58536215 0.82927570 0.4146378
[123,] 0.56707461 0.86585079 0.4329254
[124,] 0.63467145 0.73065711 0.3653286
[125,] 0.57969789 0.84060422 0.4203021
[126,] 0.55496030 0.89007939 0.4450397
[127,] 0.52795320 0.94409360 0.4720468
[128,] 0.75773023 0.48453954 0.2422698
[129,] 0.68388508 0.63222984 0.3161149
[130,] 0.60493691 0.79012617 0.3950631
[131,] 0.65930376 0.68139249 0.3406962
[132,] 0.66395457 0.67209085 0.3360454
[133,] 0.56343070 0.87313861 0.4365693
[134,] 0.67150406 0.65699188 0.3284959
[135,] 0.68008070 0.63983859 0.3199193
[136,] 0.87728616 0.24542768 0.1227138
[137,] 0.84055949 0.31888102 0.1594405
[138,] 0.75673502 0.48652997 0.2432650
> postscript(file="/var/www/html/rcomp/tmp/1j36l1291898320.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tdno1291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3tdno1291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4tdno1291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tdno1291898320.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 = 159
Frequency = 1
1 2 3 4 5 6
0.63991709 2.24310468 5.46041215 -1.54978348 1.17384433 -1.31581625
7 8 9 10 11 12
2.19019715 3.19995196 -1.78223634 -0.80895276 -4.11671798 -4.65923843
13 14 15 16 17 18
-8.53931285 -1.18946157 3.34515469 2.78109594 0.78203767 -2.53714547
19 20 21 22 23 24
-2.21337593 -1.30609906 1.28145022 -3.66001084 -6.10494572 -3.37573391
25 26 27 28 29 30
0.38793581 1.47262078 -4.52444206 -0.02516288 0.27733161 2.49948908
31 32 33 34 35 36
6.28677997 6.55937501 3.31711868 4.37185057 2.76154389 -0.06013802
37 38 39 40 41 42
1.70419137 5.92547805 -1.86919948 1.33278774 -5.46062167 2.85776530
43 44 45 46 47 48
4.16966984 -0.43395799 -3.50779251 7.17056895 -0.27428289 3.31192755
49 50 51 52 53 54
2.10050977 -1.28660565 -4.28751725 -1.49339166 2.96124601 -1.55594401
55 56 57 58 59 60
1.49866239 2.23586295 0.40490204 -1.58405380 1.59296174 1.03576752
61 62 63 64 65 66
0.61704275 3.83350702 0.50091394 4.02084053 -4.90786416 5.54378360
67 68 69 70 71 72
1.31709236 2.56274825 -5.15839905 -0.61098985 -0.73438555 -1.52259954
73 74 75 76 77 78
-0.77639819 -2.36376624 -0.48295032 -0.13302628 0.98556903 1.47073410
79 80 81 82 83 84
-7.03889041 -2.40024601 0.91496563 -3.35244404 -3.14248343 3.41386173
85 86 87 88 89 90
2.06214507 2.41503825 -3.88848507 -6.47106779 -2.66954051 0.04514635
91 92 93 94 95 96
-2.72606037 3.14638073 -3.81455031 -3.38829778 -3.18026010 -3.19644979
97 98 99 100 101 102
0.11720008 -1.56955337 -0.85396412 -1.39592504 -4.03070528 -1.58493716
103 104 105 106 107 108
-2.28163011 -1.74104076 -0.10512025 -3.63151654 -4.75679401 -8.80295472
109 110 111 112 113 114
2.52981982 11.43025002 9.78852590 -0.83138734 1.62330795 -1.19007501
115 116 117 118 119 120
-3.65108391 2.78558186 0.32444062 5.02460455 -0.76225847 0.95859921
121 122 123 124 125 126
-1.49461430 1.02323814 1.28843609 -1.95126519 0.41653815 3.48159831
127 128 129 130 131 132
-4.18926400 0.70086331 -2.61216624 -6.31388784 2.07668455 0.15918300
133 134 135 136 137 138
4.37398114 -1.17230717 3.56638068 3.31809750 4.15544467 1.97938760
139 140 141 142 143 144
1.27719294 1.20693451 4.08397320 -0.27968697 0.13256230 -6.83973987
145 146 147 148 149 150
-2.55585991 3.39548780 0.41583116 -2.69434932 0.29395484 2.06136092
151 152 153 154 155 156
-1.48543027 0.63248053 0.31590250 -6.79145106 1.03220570 2.63539328
157 158 159
5.85270076 -1.15749487 1.56613294
> postscript(file="/var/www/html/rcomp/tmp/6hjgl1291898320.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 0.63991709 NA
1 2.24310468 0.63991709
2 5.46041215 2.24310468
3 -1.54978348 5.46041215
4 1.17384433 -1.54978348
5 -1.31581625 1.17384433
6 2.19019715 -1.31581625
7 3.19995196 2.19019715
8 -1.78223634 3.19995196
9 -0.80895276 -1.78223634
10 -4.11671798 -0.80895276
11 -4.65923843 -4.11671798
12 -8.53931285 -4.65923843
13 -1.18946157 -8.53931285
14 3.34515469 -1.18946157
15 2.78109594 3.34515469
16 0.78203767 2.78109594
17 -2.53714547 0.78203767
18 -2.21337593 -2.53714547
19 -1.30609906 -2.21337593
20 1.28145022 -1.30609906
21 -3.66001084 1.28145022
22 -6.10494572 -3.66001084
23 -3.37573391 -6.10494572
24 0.38793581 -3.37573391
25 1.47262078 0.38793581
26 -4.52444206 1.47262078
27 -0.02516288 -4.52444206
28 0.27733161 -0.02516288
29 2.49948908 0.27733161
30 6.28677997 2.49948908
31 6.55937501 6.28677997
32 3.31711868 6.55937501
33 4.37185057 3.31711868
34 2.76154389 4.37185057
35 -0.06013802 2.76154389
36 1.70419137 -0.06013802
37 5.92547805 1.70419137
38 -1.86919948 5.92547805
39 1.33278774 -1.86919948
40 -5.46062167 1.33278774
41 2.85776530 -5.46062167
42 4.16966984 2.85776530
43 -0.43395799 4.16966984
44 -3.50779251 -0.43395799
45 7.17056895 -3.50779251
46 -0.27428289 7.17056895
47 3.31192755 -0.27428289
48 2.10050977 3.31192755
49 -1.28660565 2.10050977
50 -4.28751725 -1.28660565
51 -1.49339166 -4.28751725
52 2.96124601 -1.49339166
53 -1.55594401 2.96124601
54 1.49866239 -1.55594401
55 2.23586295 1.49866239
56 0.40490204 2.23586295
57 -1.58405380 0.40490204
58 1.59296174 -1.58405380
59 1.03576752 1.59296174
60 0.61704275 1.03576752
61 3.83350702 0.61704275
62 0.50091394 3.83350702
63 4.02084053 0.50091394
64 -4.90786416 4.02084053
65 5.54378360 -4.90786416
66 1.31709236 5.54378360
67 2.56274825 1.31709236
68 -5.15839905 2.56274825
69 -0.61098985 -5.15839905
70 -0.73438555 -0.61098985
71 -1.52259954 -0.73438555
72 -0.77639819 -1.52259954
73 -2.36376624 -0.77639819
74 -0.48295032 -2.36376624
75 -0.13302628 -0.48295032
76 0.98556903 -0.13302628
77 1.47073410 0.98556903
78 -7.03889041 1.47073410
79 -2.40024601 -7.03889041
80 0.91496563 -2.40024601
81 -3.35244404 0.91496563
82 -3.14248343 -3.35244404
83 3.41386173 -3.14248343
84 2.06214507 3.41386173
85 2.41503825 2.06214507
86 -3.88848507 2.41503825
87 -6.47106779 -3.88848507
88 -2.66954051 -6.47106779
89 0.04514635 -2.66954051
90 -2.72606037 0.04514635
91 3.14638073 -2.72606037
92 -3.81455031 3.14638073
93 -3.38829778 -3.81455031
94 -3.18026010 -3.38829778
95 -3.19644979 -3.18026010
96 0.11720008 -3.19644979
97 -1.56955337 0.11720008
98 -0.85396412 -1.56955337
99 -1.39592504 -0.85396412
100 -4.03070528 -1.39592504
101 -1.58493716 -4.03070528
102 -2.28163011 -1.58493716
103 -1.74104076 -2.28163011
104 -0.10512025 -1.74104076
105 -3.63151654 -0.10512025
106 -4.75679401 -3.63151654
107 -8.80295472 -4.75679401
108 2.52981982 -8.80295472
109 11.43025002 2.52981982
110 9.78852590 11.43025002
111 -0.83138734 9.78852590
112 1.62330795 -0.83138734
113 -1.19007501 1.62330795
114 -3.65108391 -1.19007501
115 2.78558186 -3.65108391
116 0.32444062 2.78558186
117 5.02460455 0.32444062
118 -0.76225847 5.02460455
119 0.95859921 -0.76225847
120 -1.49461430 0.95859921
121 1.02323814 -1.49461430
122 1.28843609 1.02323814
123 -1.95126519 1.28843609
124 0.41653815 -1.95126519
125 3.48159831 0.41653815
126 -4.18926400 3.48159831
127 0.70086331 -4.18926400
128 -2.61216624 0.70086331
129 -6.31388784 -2.61216624
130 2.07668455 -6.31388784
131 0.15918300 2.07668455
132 4.37398114 0.15918300
133 -1.17230717 4.37398114
134 3.56638068 -1.17230717
135 3.31809750 3.56638068
136 4.15544467 3.31809750
137 1.97938760 4.15544467
138 1.27719294 1.97938760
139 1.20693451 1.27719294
140 4.08397320 1.20693451
141 -0.27968697 4.08397320
142 0.13256230 -0.27968697
143 -6.83973987 0.13256230
144 -2.55585991 -6.83973987
145 3.39548780 -2.55585991
146 0.41583116 3.39548780
147 -2.69434932 0.41583116
148 0.29395484 -2.69434932
149 2.06136092 0.29395484
150 -1.48543027 2.06136092
151 0.63248053 -1.48543027
152 0.31590250 0.63248053
153 -6.79145106 0.31590250
154 1.03220570 -6.79145106
155 2.63539328 1.03220570
156 5.85270076 2.63539328
157 -1.15749487 5.85270076
158 1.56613294 -1.15749487
159 NA 1.56613294
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.24310468 0.63991709
[2,] 5.46041215 2.24310468
[3,] -1.54978348 5.46041215
[4,] 1.17384433 -1.54978348
[5,] -1.31581625 1.17384433
[6,] 2.19019715 -1.31581625
[7,] 3.19995196 2.19019715
[8,] -1.78223634 3.19995196
[9,] -0.80895276 -1.78223634
[10,] -4.11671798 -0.80895276
[11,] -4.65923843 -4.11671798
[12,] -8.53931285 -4.65923843
[13,] -1.18946157 -8.53931285
[14,] 3.34515469 -1.18946157
[15,] 2.78109594 3.34515469
[16,] 0.78203767 2.78109594
[17,] -2.53714547 0.78203767
[18,] -2.21337593 -2.53714547
[19,] -1.30609906 -2.21337593
[20,] 1.28145022 -1.30609906
[21,] -3.66001084 1.28145022
[22,] -6.10494572 -3.66001084
[23,] -3.37573391 -6.10494572
[24,] 0.38793581 -3.37573391
[25,] 1.47262078 0.38793581
[26,] -4.52444206 1.47262078
[27,] -0.02516288 -4.52444206
[28,] 0.27733161 -0.02516288
[29,] 2.49948908 0.27733161
[30,] 6.28677997 2.49948908
[31,] 6.55937501 6.28677997
[32,] 3.31711868 6.55937501
[33,] 4.37185057 3.31711868
[34,] 2.76154389 4.37185057
[35,] -0.06013802 2.76154389
[36,] 1.70419137 -0.06013802
[37,] 5.92547805 1.70419137
[38,] -1.86919948 5.92547805
[39,] 1.33278774 -1.86919948
[40,] -5.46062167 1.33278774
[41,] 2.85776530 -5.46062167
[42,] 4.16966984 2.85776530
[43,] -0.43395799 4.16966984
[44,] -3.50779251 -0.43395799
[45,] 7.17056895 -3.50779251
[46,] -0.27428289 7.17056895
[47,] 3.31192755 -0.27428289
[48,] 2.10050977 3.31192755
[49,] -1.28660565 2.10050977
[50,] -4.28751725 -1.28660565
[51,] -1.49339166 -4.28751725
[52,] 2.96124601 -1.49339166
[53,] -1.55594401 2.96124601
[54,] 1.49866239 -1.55594401
[55,] 2.23586295 1.49866239
[56,] 0.40490204 2.23586295
[57,] -1.58405380 0.40490204
[58,] 1.59296174 -1.58405380
[59,] 1.03576752 1.59296174
[60,] 0.61704275 1.03576752
[61,] 3.83350702 0.61704275
[62,] 0.50091394 3.83350702
[63,] 4.02084053 0.50091394
[64,] -4.90786416 4.02084053
[65,] 5.54378360 -4.90786416
[66,] 1.31709236 5.54378360
[67,] 2.56274825 1.31709236
[68,] -5.15839905 2.56274825
[69,] -0.61098985 -5.15839905
[70,] -0.73438555 -0.61098985
[71,] -1.52259954 -0.73438555
[72,] -0.77639819 -1.52259954
[73,] -2.36376624 -0.77639819
[74,] -0.48295032 -2.36376624
[75,] -0.13302628 -0.48295032
[76,] 0.98556903 -0.13302628
[77,] 1.47073410 0.98556903
[78,] -7.03889041 1.47073410
[79,] -2.40024601 -7.03889041
[80,] 0.91496563 -2.40024601
[81,] -3.35244404 0.91496563
[82,] -3.14248343 -3.35244404
[83,] 3.41386173 -3.14248343
[84,] 2.06214507 3.41386173
[85,] 2.41503825 2.06214507
[86,] -3.88848507 2.41503825
[87,] -6.47106779 -3.88848507
[88,] -2.66954051 -6.47106779
[89,] 0.04514635 -2.66954051
[90,] -2.72606037 0.04514635
[91,] 3.14638073 -2.72606037
[92,] -3.81455031 3.14638073
[93,] -3.38829778 -3.81455031
[94,] -3.18026010 -3.38829778
[95,] -3.19644979 -3.18026010
[96,] 0.11720008 -3.19644979
[97,] -1.56955337 0.11720008
[98,] -0.85396412 -1.56955337
[99,] -1.39592504 -0.85396412
[100,] -4.03070528 -1.39592504
[101,] -1.58493716 -4.03070528
[102,] -2.28163011 -1.58493716
[103,] -1.74104076 -2.28163011
[104,] -0.10512025 -1.74104076
[105,] -3.63151654 -0.10512025
[106,] -4.75679401 -3.63151654
[107,] -8.80295472 -4.75679401
[108,] 2.52981982 -8.80295472
[109,] 11.43025002 2.52981982
[110,] 9.78852590 11.43025002
[111,] -0.83138734 9.78852590
[112,] 1.62330795 -0.83138734
[113,] -1.19007501 1.62330795
[114,] -3.65108391 -1.19007501
[115,] 2.78558186 -3.65108391
[116,] 0.32444062 2.78558186
[117,] 5.02460455 0.32444062
[118,] -0.76225847 5.02460455
[119,] 0.95859921 -0.76225847
[120,] -1.49461430 0.95859921
[121,] 1.02323814 -1.49461430
[122,] 1.28843609 1.02323814
[123,] -1.95126519 1.28843609
[124,] 0.41653815 -1.95126519
[125,] 3.48159831 0.41653815
[126,] -4.18926400 3.48159831
[127,] 0.70086331 -4.18926400
[128,] -2.61216624 0.70086331
[129,] -6.31388784 -2.61216624
[130,] 2.07668455 -6.31388784
[131,] 0.15918300 2.07668455
[132,] 4.37398114 0.15918300
[133,] -1.17230717 4.37398114
[134,] 3.56638068 -1.17230717
[135,] 3.31809750 3.56638068
[136,] 4.15544467 3.31809750
[137,] 1.97938760 4.15544467
[138,] 1.27719294 1.97938760
[139,] 1.20693451 1.27719294
[140,] 4.08397320 1.20693451
[141,] -0.27968697 4.08397320
[142,] 0.13256230 -0.27968697
[143,] -6.83973987 0.13256230
[144,] -2.55585991 -6.83973987
[145,] 3.39548780 -2.55585991
[146,] 0.41583116 3.39548780
[147,] -2.69434932 0.41583116
[148,] 0.29395484 -2.69434932
[149,] 2.06136092 0.29395484
[150,] -1.48543027 2.06136092
[151,] 0.63248053 -1.48543027
[152,] 0.31590250 0.63248053
[153,] -6.79145106 0.31590250
[154,] 1.03220570 -6.79145106
[155,] 2.63539328 1.03220570
[156,] 5.85270076 2.63539328
[157,] -1.15749487 5.85270076
[158,] 1.56613294 -1.15749487
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.24310468 0.63991709
2 5.46041215 2.24310468
3 -1.54978348 5.46041215
4 1.17384433 -1.54978348
5 -1.31581625 1.17384433
6 2.19019715 -1.31581625
7 3.19995196 2.19019715
8 -1.78223634 3.19995196
9 -0.80895276 -1.78223634
10 -4.11671798 -0.80895276
11 -4.65923843 -4.11671798
12 -8.53931285 -4.65923843
13 -1.18946157 -8.53931285
14 3.34515469 -1.18946157
15 2.78109594 3.34515469
16 0.78203767 2.78109594
17 -2.53714547 0.78203767
18 -2.21337593 -2.53714547
19 -1.30609906 -2.21337593
20 1.28145022 -1.30609906
21 -3.66001084 1.28145022
22 -6.10494572 -3.66001084
23 -3.37573391 -6.10494572
24 0.38793581 -3.37573391
25 1.47262078 0.38793581
26 -4.52444206 1.47262078
27 -0.02516288 -4.52444206
28 0.27733161 -0.02516288
29 2.49948908 0.27733161
30 6.28677997 2.49948908
31 6.55937501 6.28677997
32 3.31711868 6.55937501
33 4.37185057 3.31711868
34 2.76154389 4.37185057
35 -0.06013802 2.76154389
36 1.70419137 -0.06013802
37 5.92547805 1.70419137
38 -1.86919948 5.92547805
39 1.33278774 -1.86919948
40 -5.46062167 1.33278774
41 2.85776530 -5.46062167
42 4.16966984 2.85776530
43 -0.43395799 4.16966984
44 -3.50779251 -0.43395799
45 7.17056895 -3.50779251
46 -0.27428289 7.17056895
47 3.31192755 -0.27428289
48 2.10050977 3.31192755
49 -1.28660565 2.10050977
50 -4.28751725 -1.28660565
51 -1.49339166 -4.28751725
52 2.96124601 -1.49339166
53 -1.55594401 2.96124601
54 1.49866239 -1.55594401
55 2.23586295 1.49866239
56 0.40490204 2.23586295
57 -1.58405380 0.40490204
58 1.59296174 -1.58405380
59 1.03576752 1.59296174
60 0.61704275 1.03576752
61 3.83350702 0.61704275
62 0.50091394 3.83350702
63 4.02084053 0.50091394
64 -4.90786416 4.02084053
65 5.54378360 -4.90786416
66 1.31709236 5.54378360
67 2.56274825 1.31709236
68 -5.15839905 2.56274825
69 -0.61098985 -5.15839905
70 -0.73438555 -0.61098985
71 -1.52259954 -0.73438555
72 -0.77639819 -1.52259954
73 -2.36376624 -0.77639819
74 -0.48295032 -2.36376624
75 -0.13302628 -0.48295032
76 0.98556903 -0.13302628
77 1.47073410 0.98556903
78 -7.03889041 1.47073410
79 -2.40024601 -7.03889041
80 0.91496563 -2.40024601
81 -3.35244404 0.91496563
82 -3.14248343 -3.35244404
83 3.41386173 -3.14248343
84 2.06214507 3.41386173
85 2.41503825 2.06214507
86 -3.88848507 2.41503825
87 -6.47106779 -3.88848507
88 -2.66954051 -6.47106779
89 0.04514635 -2.66954051
90 -2.72606037 0.04514635
91 3.14638073 -2.72606037
92 -3.81455031 3.14638073
93 -3.38829778 -3.81455031
94 -3.18026010 -3.38829778
95 -3.19644979 -3.18026010
96 0.11720008 -3.19644979
97 -1.56955337 0.11720008
98 -0.85396412 -1.56955337
99 -1.39592504 -0.85396412
100 -4.03070528 -1.39592504
101 -1.58493716 -4.03070528
102 -2.28163011 -1.58493716
103 -1.74104076 -2.28163011
104 -0.10512025 -1.74104076
105 -3.63151654 -0.10512025
106 -4.75679401 -3.63151654
107 -8.80295472 -4.75679401
108 2.52981982 -8.80295472
109 11.43025002 2.52981982
110 9.78852590 11.43025002
111 -0.83138734 9.78852590
112 1.62330795 -0.83138734
113 -1.19007501 1.62330795
114 -3.65108391 -1.19007501
115 2.78558186 -3.65108391
116 0.32444062 2.78558186
117 5.02460455 0.32444062
118 -0.76225847 5.02460455
119 0.95859921 -0.76225847
120 -1.49461430 0.95859921
121 1.02323814 -1.49461430
122 1.28843609 1.02323814
123 -1.95126519 1.28843609
124 0.41653815 -1.95126519
125 3.48159831 0.41653815
126 -4.18926400 3.48159831
127 0.70086331 -4.18926400
128 -2.61216624 0.70086331
129 -6.31388784 -2.61216624
130 2.07668455 -6.31388784
131 0.15918300 2.07668455
132 4.37398114 0.15918300
133 -1.17230717 4.37398114
134 3.56638068 -1.17230717
135 3.31809750 3.56638068
136 4.15544467 3.31809750
137 1.97938760 4.15544467
138 1.27719294 1.97938760
139 1.20693451 1.27719294
140 4.08397320 1.20693451
141 -0.27968697 4.08397320
142 0.13256230 -0.27968697
143 -6.83973987 0.13256230
144 -2.55585991 -6.83973987
145 3.39548780 -2.55585991
146 0.41583116 3.39548780
147 -2.69434932 0.41583116
148 0.29395484 -2.69434932
149 2.06136092 0.29395484
150 -1.48543027 2.06136092
151 0.63248053 -1.48543027
152 0.31590250 0.63248053
153 -6.79145106 0.31590250
154 1.03220570 -6.79145106
155 2.63539328 1.03220570
156 5.85270076 2.63539328
157 -1.15749487 5.85270076
158 1.56613294 -1.15749487
> 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/7saf61291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8saf61291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/921w91291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1021w91291898320.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11o2ce1291898320.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/12r2bl1291898320.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/13g3qw1291898320.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/148c7h1291898320.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/15uvon1291898320.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/16qn4e1291898320.tab")
+ }
>
> try(system("convert tmp/1j36l1291898320.ps tmp/1j36l1291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tdno1291898320.ps tmp/2tdno1291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tdno1291898320.ps tmp/3tdno1291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/4tdno1291898320.ps tmp/4tdno1291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tdno1291898320.ps tmp/5tdno1291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hjgl1291898320.ps tmp/6hjgl1291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/7saf61291898320.ps tmp/7saf61291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/8saf61291898320.ps tmp/8saf61291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/921w91291898320.ps tmp/921w91291898320.png",intern=TRUE))
character(0)
> try(system("convert tmp/1021w91291898320.ps tmp/1021w91291898320.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.165 1.826 9.652