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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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(24
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+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('CM'
+ ,'PE'
+ ,'PC'
+ ,'O'
+ ,'D'
+ ,'PS'
+ ,'Time')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('CM','PE','PC','O','D','PS','Time'),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 = 'No 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
D CM PE PC O PS Time
1 14 24 11 12 26 24 237.588
2 11 25 7 8 23 25 164.083
3 6 17 17 8 25 30 278.261
4 12 18 10 8 23 19 220.360
5 8 18 12 9 19 22 253.967
6 10 16 12 7 29 22 422.310
7 10 20 11 4 25 25 136.921
8 11 16 11 11 21 23 143.495
9 16 18 12 7 22 17 189.785
10 11 17 13 7 25 21 219.529
11 13 23 14 12 24 19 217.761
12 12 30 16 10 18 19 221.754
13 8 23 11 10 22 15 159.854
14 12 18 10 8 15 16 209.464
15 11 15 11 8 22 23 174.283
16 4 12 15 4 28 27 154.550
17 9 21 9 9 20 22 153.024
18 8 15 11 8 12 14 162.490
19 8 20 17 7 24 22 154.462
20 14 31 17 11 20 23 249.671
21 15 27 11 9 21 23 259.473
22 16 34 18 11 20 21 155.337
23 9 21 14 13 21 19 151.289
24 14 31 10 8 23 18 276.614
25 11 19 11 8 28 20 188.214
26 8 16 15 9 24 23 181.098
27 9 20 15 6 24 25 240.898
28 9 21 13 9 24 19 244.551
29 9 22 16 9 23 24 250.238
30 9 17 13 6 23 22 183.129
31 10 24 9 6 29 25 310.331
32 16 25 18 16 24 26 281.942
33 11 26 18 5 18 29 230.343
34 8 25 12 7 25 32 161.563
35 9 17 17 9 21 25 392.527
36 16 32 9 6 26 29 1077.414
37 11 33 9 6 22 28 248.275
38 16 13 12 5 22 17 557.386
39 12 32 18 12 22 28 731.874
40 12 25 12 7 23 29 301.429
41 14 29 18 10 30 26 226.360
42 9 22 14 9 23 25 215.018
43 10 18 15 8 17 14 157.672
44 9 17 16 5 23 25 219.118
45 10 20 10 8 23 26 213.019
46 12 15 11 8 25 20 390.642
47 14 20 14 10 24 18 157.124
48 14 33 9 6 24 32 227.652
49 10 29 12 8 23 25 239.266
50 14 23 17 7 21 25 506.343
51 16 26 5 4 24 23 149.219
52 9 18 12 8 24 21 213.351
53 10 20 12 8 28 20 174.517
54 6 11 6 4 16 15 172.531
55 8 28 24 20 20 30 320.656
56 13 26 12 8 29 24 305.011
57 10 22 12 8 27 26 266.495
58 8 17 14 6 22 24 361.511
59 7 12 7 4 28 22 361.019
60 15 14 13 8 16 14 382.187
61 9 17 12 9 25 24 196.763
62 10 21 13 6 24 24 273.212
63 12 19 14 7 28 24 186.397
64 13 18 8 9 24 24 294.205
65 10 10 11 5 23 19 364.685
66 11 29 9 5 30 31 230.501
67 8 31 11 8 24 22 217.510
68 9 19 13 8 21 27 262.297
69 13 9 10 6 25 19 169.246
70 11 20 11 8 25 25 260.428
71 8 28 12 7 22 20 348.187
72 9 19 9 7 23 21 512.937
73 9 30 15 9 26 27 164.496
74 15 29 18 11 23 23 111.187
75 9 26 15 6 25 25 169.999
76 10 23 12 8 21 20 240.187
77 14 13 13 6 25 21 187.158
78 12 21 14 9 24 22 194.096
79 12 19 10 8 29 23 265.846
80 11 28 13 6 22 25 283.319
81 14 23 13 10 27 25 356.938
82 6 18 11 8 26 17 240.802
83 12 21 13 8 22 19 326.662
84 8 20 16 10 24 25 249.266
85 14 23 8 5 27 19 277.368
86 11 21 16 7 24 20 394.618
87 10 21 11 5 24 26 235.686
88 14 15 9 8 29 23 227.641
89 12 28 16 14 22 27 159.593
90 10 19 12 7 21 17 268.866
91 14 26 14 8 24 17 206.466
92 5 10 8 6 24 19 233.064
93 11 16 9 5 23 17 133.824
94 10 22 15 6 20 22 486.783
95 9 19 11 10 27 21 228.859
96 10 31 21 12 26 32 155.238
97 16 31 14 9 25 21 2042.451
98 13 29 18 12 21 21 205.218
99 9 19 12 7 21 18 373.648
100 10 22 13 8 19 18 229.151
101 10 23 15 10 21 23 199.156
102 7 15 12 6 21 19 234.410
103 9 20 19 10 16 20 56.519
104 8 18 15 10 22 21 289.239
105 14 23 11 10 29 20 199.227
106 14 25 11 5 15 17 274.513
107 8 21 10 7 17 18 174.499
108 9 24 13 10 15 19 217.714
109 14 25 15 11 21 22 239.717
110 14 17 12 6 21 15 241.529
111 8 13 12 7 19 14 155.561
112 8 28 16 12 24 18 204.107
113 8 21 9 11 20 24 745.970
114 7 25 18 11 17 35 241.772
115 6 9 8 11 23 29 110.267
116 8 16 13 5 24 21 186.580
117 6 19 17 8 14 25 227.906
118 11 17 9 6 19 20 197.518
119 14 25 15 9 24 22 254.094
120 11 20 8 4 13 13 173.942
121 11 29 7 4 22 26 294.420
122 11 14 12 7 16 17 211.924
123 14 22 14 11 19 25 262.479
124 8 15 6 6 25 20 193.495
125 20 19 8 7 25 19 165.972
126 11 20 17 8 23 21 237.352
127 8 15 10 4 24 22 205.814
128 11 20 11 8 26 24 227.526
129 10 18 14 9 26 21 250.439
130 14 33 11 8 25 26 470.849
131 11 22 13 11 18 24 176.469
132 9 16 12 8 21 16 298.691
133 9 17 11 5 26 23 193.922
134 8 16 9 4 23 18 212.422
135 10 21 12 8 23 16 203.284
136 13 26 20 10 22 26 240.560
137 13 18 12 6 20 19 445.327
138 12 18 13 9 13 21 248.984
139 8 17 12 9 24 21 174.440
140 13 22 12 13 15 22 165.024
141 14 30 9 9 14 23 249.681
142 12 30 15 10 22 29 238.312
143 14 24 24 20 10 21 250.437
144 15 21 7 5 24 21 174.750
145 13 21 17 11 22 23 4941.633
146 16 29 11 6 24 27 138.936
147 9 31 17 9 19 25 203.181
148 9 20 11 7 20 21 187.747
149 9 16 12 9 13 10 270.950
150 8 22 14 10 20 20 307.688
151 7 20 11 9 22 26 184.477
152 16 28 16 8 24 24 230.916
153 11 38 21 7 29 29 187.286
154 9 22 14 6 12 19 169.376
155 11 20 20 13 20 24 182.838
156 9 17 13 6 21 19 176.081
157 14 28 11 8 24 24 248.056
158 13 22 15 10 22 22 235.240
159 16 31 19 16 20 17 76.347
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM PE PC O PS
7.4732544 0.2467420 -0.1118291 0.1443446 0.1058029 -0.1917108
Time
0.0007842
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.5734 -1.8049 -0.3583 1.5862 8.5901
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.4732544 1.5740901 4.748 4.72e-06 ***
CM 0.2467420 0.0398349 6.194 5.23e-09 ***
PE -0.1118291 0.0736176 -1.519 0.130826
PC 0.1443446 0.0923782 1.563 0.120240
O 0.1058029 0.0563797 1.877 0.062488 .
PS -0.1917108 0.0564464 -3.396 0.000872 ***
Time 0.0007842 0.0004755 1.649 0.101178
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.468 on 152 degrees of freedom
Multiple R-squared: 0.2529, Adjusted R-squared: 0.2234
F-statistic: 8.576 on 6 and 152 DF, p-value: 4.931e-08
> 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,] 0.17319726 0.3463945 0.8268027
[2,] 0.27390789 0.5478158 0.7260921
[3,] 0.15819228 0.3163846 0.8418077
[4,] 0.85428979 0.2914204 0.1457102
[5,] 0.79527067 0.4094587 0.2047293
[6,] 0.72131203 0.5573759 0.2786880
[7,] 0.76623209 0.4675358 0.2337679
[8,] 0.74304904 0.5139019 0.2569510
[9,] 0.72145862 0.5570828 0.2785414
[10,] 0.65217379 0.6956524 0.3478262
[11,] 0.63932681 0.7213464 0.3606732
[12,] 0.62685310 0.7462938 0.3731469
[13,] 0.61405413 0.7718917 0.3859459
[14,] 0.59341097 0.8131781 0.4065890
[15,] 0.55819204 0.8836159 0.4418080
[16,] 0.48541165 0.9708233 0.5145884
[17,] 0.41869671 0.8373934 0.5813033
[18,] 0.35255282 0.7051056 0.6474472
[19,] 0.35001174 0.7000235 0.6499883
[20,] 0.30264354 0.6052871 0.6973565
[21,] 0.24744127 0.4948825 0.7525587
[22,] 0.23503121 0.4700624 0.7649688
[23,] 0.30140205 0.6028041 0.6985979
[24,] 0.26746510 0.5349302 0.7325349
[25,] 0.25202928 0.5040586 0.7479707
[26,] 0.20892643 0.4178529 0.7910736
[27,] 0.17877335 0.3575467 0.8212267
[28,] 0.15870508 0.3174102 0.8412949
[29,] 0.34067894 0.6813579 0.6593211
[30,] 0.37023400 0.7404680 0.6297660
[31,] 0.34494029 0.6898806 0.6550597
[32,] 0.32646274 0.6529255 0.6735373
[33,] 0.29064777 0.5812955 0.7093522
[34,] 0.25040907 0.5008181 0.7495909
[35,] 0.21194280 0.4238856 0.7880572
[36,] 0.17495092 0.3499018 0.8250491
[37,] 0.15019678 0.3003936 0.8498032
[38,] 0.15317620 0.3063524 0.8468238
[39,] 0.15625481 0.3125096 0.8437452
[40,] 0.15103374 0.3020675 0.8489663
[41,] 0.16703358 0.3340672 0.8329664
[42,] 0.24002082 0.4800416 0.7599792
[43,] 0.21461046 0.4292209 0.7853895
[44,] 0.18915031 0.3783006 0.8108497
[45,] 0.20996707 0.4199341 0.7900329
[46,] 0.23100134 0.4620027 0.7689987
[47,] 0.19535513 0.3907103 0.8046449
[48,] 0.16557226 0.3311445 0.8344277
[49,] 0.14441398 0.2888280 0.8555860
[50,] 0.14602700 0.2920540 0.8539730
[51,] 0.21955861 0.4391172 0.7804414
[52,] 0.18708490 0.3741698 0.8129151
[53,] 0.15643214 0.3128643 0.8435679
[54,] 0.14977888 0.2995578 0.8502211
[55,] 0.15452847 0.3090569 0.8454715
[56,] 0.13216418 0.2643284 0.8678358
[57,] 0.10875889 0.2175178 0.8912411
[58,] 0.23356166 0.4671233 0.7664383
[59,] 0.19832848 0.3966570 0.8016715
[60,] 0.28618308 0.5723662 0.7138169
[61,] 0.24938601 0.4987720 0.7506140
[62,] 0.39024545 0.7804909 0.6097545
[63,] 0.40027032 0.8005406 0.5997297
[64,] 0.41925001 0.8385000 0.5807500
[65,] 0.44403922 0.8880784 0.5559608
[66,] 0.42356859 0.8471372 0.5764314
[67,] 0.39617256 0.7923451 0.6038274
[68,] 0.56810129 0.8637974 0.4318987
[69,] 0.53340619 0.9331876 0.4665938
[70,] 0.49287779 0.9857556 0.5071222
[71,] 0.44947399 0.8989480 0.5505260
[72,] 0.44510605 0.8902121 0.5548940
[73,] 0.63194272 0.7361146 0.3680573
[74,] 0.58939130 0.8212174 0.4106087
[75,] 0.57034788 0.8593042 0.4296521
[76,] 0.54008563 0.9198287 0.4599144
[77,] 0.49550309 0.9910062 0.5044969
[78,] 0.44887398 0.8977480 0.5511260
[79,] 0.52724094 0.9455181 0.4727591
[80,] 0.48289548 0.9657910 0.5171045
[81,] 0.44330536 0.8866107 0.5566946
[82,] 0.40647530 0.8129506 0.5935247
[83,] 0.46782102 0.9356420 0.5321790
[84,] 0.42633859 0.8526772 0.5736614
[85,] 0.38423023 0.7684605 0.6157698
[86,] 0.37936292 0.7587258 0.6206371
[87,] 0.34477999 0.6895600 0.6552200
[88,] 0.32404814 0.6480963 0.6759519
[89,] 0.28309834 0.5661967 0.7169017
[90,] 0.26502056 0.5300411 0.7349794
[91,] 0.23701264 0.4740253 0.7629874
[92,] 0.20538210 0.4107642 0.7946179
[93,] 0.19850515 0.3970103 0.8014949
[94,] 0.16680237 0.3336047 0.8331976
[95,] 0.15909244 0.3181849 0.8409076
[96,] 0.13592742 0.2718548 0.8640726
[97,] 0.13202586 0.2640517 0.8679741
[98,] 0.14286087 0.2857217 0.8571391
[99,] 0.14669065 0.2933813 0.8533094
[100,] 0.13753191 0.2750638 0.8624681
[101,] 0.16096950 0.3219390 0.8390305
[102,] 0.14182883 0.2836577 0.8581712
[103,] 0.33552672 0.6710534 0.6644733
[104,] 0.41928090 0.8385618 0.5807191
[105,] 0.38270436 0.7654087 0.6172956
[106,] 0.36994742 0.7398948 0.6300526
[107,] 0.32753687 0.6550737 0.6724631
[108,] 0.31231459 0.6246292 0.6876854
[109,] 0.27346602 0.5469320 0.7265340
[110,] 0.25582328 0.5116466 0.7441767
[111,] 0.21412681 0.4282536 0.7858732
[112,] 0.19017978 0.3803596 0.8098202
[113,] 0.17876974 0.3575395 0.8212303
[114,] 0.19129114 0.3825823 0.8087089
[115,] 0.20184923 0.4036985 0.7981508
[116,] 0.76911857 0.4617629 0.2308814
[117,] 0.73329388 0.5334122 0.2667061
[118,] 0.68228874 0.6354225 0.3177113
[119,] 0.62321554 0.7535689 0.3767845
[120,] 0.56082039 0.8783592 0.4391796
[121,] 0.49928034 0.9985607 0.5007197
[122,] 0.44062174 0.8812435 0.5593783
[123,] 0.38475231 0.7695046 0.6152477
[124,] 0.32191167 0.6438233 0.6780883
[125,] 0.28062419 0.5612484 0.7193758
[126,] 0.24753177 0.4950635 0.7524682
[127,] 0.23345589 0.4669118 0.7665441
[128,] 0.26727588 0.5345518 0.7327241
[129,] 0.27950169 0.5590034 0.7204983
[130,] 0.28028154 0.5605631 0.7197185
[131,] 0.22141332 0.4428266 0.7785867
[132,] 0.16638652 0.3327730 0.8336135
[133,] 0.12238706 0.2447741 0.8776129
[134,] 0.15557418 0.3111484 0.8444258
[135,] 0.14402687 0.2880537 0.8559731
[136,] 0.13573397 0.2714679 0.8642660
[137,] 0.19193026 0.3838605 0.8080697
[138,] 0.15508580 0.3101716 0.8449142
[139,] 0.09292566 0.1858513 0.9070743
[140,] 0.05636212 0.1127242 0.9436379
> postscript(file="/var/www/html/rcomp/tmp/14lyj1290548313.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24lyj1290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xvg41290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xvg41290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xvg41290548313.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
1.76678835 -0.78312869 -2.03349367 1.08515447 -1.86354313 -0.27141534
7 8 9 10 11 12
0.28497090 1.29616103 5.19951585 0.98419583 0.61761798 -0.96554248
13 14 15 16 17 18
-4.93900614 1.36498984 1.84598959 -3.24158897 -1.96589842 -1.81213058
19 20 21 22 23 24
-1.96017367 1.28854461 2.77973734 2.35070415 -2.66370595 -0.35831682
25 26 27 28 29 30
-0.36185266 -1.31473072 -0.53213907 -2.58870245 -1.44006014 -0.43959773
31 32 33 34 35 36
-1.77354602 4.28571603 1.87717878 -1.94729264 0.19721048 2.73521421
37 38 39 40 41 42
-1.62980613 6.43363763 -0.62191414 1.57949611 1.57358667 -1.44438760
43 44 45 46 47 48
-0.63027607 0.58714350 -0.06059705 1.78377726 2.50237454 1.94160408
49 50 51 52 53 54
-2.26991047 3.91619204 3.84628164 -1.40807209 -1.48602416 -3.04630480
55 56 57 58 59 60
-3.20121904 0.59222938 -0.79557077 -0.97843354 -2.25669118 5.06276903
61 62 63 64 65 66
-0.82333674 -0.21959104 1.88624728 2.51199258 1.49077158 -0.75584571
67 68 69 70 71 72
-5.53909738 -0.11369569 4.42299976 0.61073681 -4.81699215 -1.97509287
73 74 75 76 77 78
-3.20086122 2.68504987 -2.06279382 -1.53712917 5.14089399 1.13782642
79 80 81 82 83 84
0.93476777 -0.55139425 2.51818988 -5.51987747 0.70285543 -2.00425066
85 86 87 88 89 90
1.59290245 0.10950044 0.11394528 3.83986732 0.10978526 -1.00343953
91 92 93 94 95 96
1.08020661 -3.99164437 0.58428410 -0.37036989 -2.38490251 -1.24384823
97 98 99 100 101 102
0.92339063 0.29514917 -1.89390007 -1.34171939 -0.88302172 -2.46168472
103 104 105 106 107 108
-0.62973980 -2.20918056 1.24805092 2.32335760 -3.03165562 -2.50000121
109 110 111 112 113 114
2.25563056 3.27240543 -1.79765924 -5.57343676 -3.33616155 -1.49504204
115 116 117 118 119 120
-1.34741579 -1.34873114 -2.28221096 1.14159176 2.21563653 -0.11044371
121 122 123 124 125 126
-0.99741634 1.80393921 3.65291518 -2.33207394 8.59014492 0.74457047
127 128 129 130 131 132
-1.11650463 0.33902531 -0.56944910 0.42978744 0.52262826 -1.62265817
133 134 135 136 137 138
-0.65307314 -2.14129803 -1.99315435 2.37275786 2.73848859 2.69530012
139 140 141 142 143 144
-2.27516025 2.06507231 1.56415236 0.40353982 3.17344700 3.75586173
145 146 147 148 149 150
-1.13513549 4.26324835 -2.89708372 -1.42575006 -2.04908970 -3.30255046
151 152 153 154 155 156
-2.96492670 4.13318240 -2.16699232 -0.96199395 1.29362627 -0.79759722
157 158 159
1.56059552 2.03790907 1.77613791
> postscript(file="/var/www/html/rcomp/tmp/6p4fp1290548313.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 1.76678835 NA
1 -0.78312869 1.76678835
2 -2.03349367 -0.78312869
3 1.08515447 -2.03349367
4 -1.86354313 1.08515447
5 -0.27141534 -1.86354313
6 0.28497090 -0.27141534
7 1.29616103 0.28497090
8 5.19951585 1.29616103
9 0.98419583 5.19951585
10 0.61761798 0.98419583
11 -0.96554248 0.61761798
12 -4.93900614 -0.96554248
13 1.36498984 -4.93900614
14 1.84598959 1.36498984
15 -3.24158897 1.84598959
16 -1.96589842 -3.24158897
17 -1.81213058 -1.96589842
18 -1.96017367 -1.81213058
19 1.28854461 -1.96017367
20 2.77973734 1.28854461
21 2.35070415 2.77973734
22 -2.66370595 2.35070415
23 -0.35831682 -2.66370595
24 -0.36185266 -0.35831682
25 -1.31473072 -0.36185266
26 -0.53213907 -1.31473072
27 -2.58870245 -0.53213907
28 -1.44006014 -2.58870245
29 -0.43959773 -1.44006014
30 -1.77354602 -0.43959773
31 4.28571603 -1.77354602
32 1.87717878 4.28571603
33 -1.94729264 1.87717878
34 0.19721048 -1.94729264
35 2.73521421 0.19721048
36 -1.62980613 2.73521421
37 6.43363763 -1.62980613
38 -0.62191414 6.43363763
39 1.57949611 -0.62191414
40 1.57358667 1.57949611
41 -1.44438760 1.57358667
42 -0.63027607 -1.44438760
43 0.58714350 -0.63027607
44 -0.06059705 0.58714350
45 1.78377726 -0.06059705
46 2.50237454 1.78377726
47 1.94160408 2.50237454
48 -2.26991047 1.94160408
49 3.91619204 -2.26991047
50 3.84628164 3.91619204
51 -1.40807209 3.84628164
52 -1.48602416 -1.40807209
53 -3.04630480 -1.48602416
54 -3.20121904 -3.04630480
55 0.59222938 -3.20121904
56 -0.79557077 0.59222938
57 -0.97843354 -0.79557077
58 -2.25669118 -0.97843354
59 5.06276903 -2.25669118
60 -0.82333674 5.06276903
61 -0.21959104 -0.82333674
62 1.88624728 -0.21959104
63 2.51199258 1.88624728
64 1.49077158 2.51199258
65 -0.75584571 1.49077158
66 -5.53909738 -0.75584571
67 -0.11369569 -5.53909738
68 4.42299976 -0.11369569
69 0.61073681 4.42299976
70 -4.81699215 0.61073681
71 -1.97509287 -4.81699215
72 -3.20086122 -1.97509287
73 2.68504987 -3.20086122
74 -2.06279382 2.68504987
75 -1.53712917 -2.06279382
76 5.14089399 -1.53712917
77 1.13782642 5.14089399
78 0.93476777 1.13782642
79 -0.55139425 0.93476777
80 2.51818988 -0.55139425
81 -5.51987747 2.51818988
82 0.70285543 -5.51987747
83 -2.00425066 0.70285543
84 1.59290245 -2.00425066
85 0.10950044 1.59290245
86 0.11394528 0.10950044
87 3.83986732 0.11394528
88 0.10978526 3.83986732
89 -1.00343953 0.10978526
90 1.08020661 -1.00343953
91 -3.99164437 1.08020661
92 0.58428410 -3.99164437
93 -0.37036989 0.58428410
94 -2.38490251 -0.37036989
95 -1.24384823 -2.38490251
96 0.92339063 -1.24384823
97 0.29514917 0.92339063
98 -1.89390007 0.29514917
99 -1.34171939 -1.89390007
100 -0.88302172 -1.34171939
101 -2.46168472 -0.88302172
102 -0.62973980 -2.46168472
103 -2.20918056 -0.62973980
104 1.24805092 -2.20918056
105 2.32335760 1.24805092
106 -3.03165562 2.32335760
107 -2.50000121 -3.03165562
108 2.25563056 -2.50000121
109 3.27240543 2.25563056
110 -1.79765924 3.27240543
111 -5.57343676 -1.79765924
112 -3.33616155 -5.57343676
113 -1.49504204 -3.33616155
114 -1.34741579 -1.49504204
115 -1.34873114 -1.34741579
116 -2.28221096 -1.34873114
117 1.14159176 -2.28221096
118 2.21563653 1.14159176
119 -0.11044371 2.21563653
120 -0.99741634 -0.11044371
121 1.80393921 -0.99741634
122 3.65291518 1.80393921
123 -2.33207394 3.65291518
124 8.59014492 -2.33207394
125 0.74457047 8.59014492
126 -1.11650463 0.74457047
127 0.33902531 -1.11650463
128 -0.56944910 0.33902531
129 0.42978744 -0.56944910
130 0.52262826 0.42978744
131 -1.62265817 0.52262826
132 -0.65307314 -1.62265817
133 -2.14129803 -0.65307314
134 -1.99315435 -2.14129803
135 2.37275786 -1.99315435
136 2.73848859 2.37275786
137 2.69530012 2.73848859
138 -2.27516025 2.69530012
139 2.06507231 -2.27516025
140 1.56415236 2.06507231
141 0.40353982 1.56415236
142 3.17344700 0.40353982
143 3.75586173 3.17344700
144 -1.13513549 3.75586173
145 4.26324835 -1.13513549
146 -2.89708372 4.26324835
147 -1.42575006 -2.89708372
148 -2.04908970 -1.42575006
149 -3.30255046 -2.04908970
150 -2.96492670 -3.30255046
151 4.13318240 -2.96492670
152 -2.16699232 4.13318240
153 -0.96199395 -2.16699232
154 1.29362627 -0.96199395
155 -0.79759722 1.29362627
156 1.56059552 -0.79759722
157 2.03790907 1.56059552
158 1.77613791 2.03790907
159 NA 1.77613791
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.78312869 1.76678835
[2,] -2.03349367 -0.78312869
[3,] 1.08515447 -2.03349367
[4,] -1.86354313 1.08515447
[5,] -0.27141534 -1.86354313
[6,] 0.28497090 -0.27141534
[7,] 1.29616103 0.28497090
[8,] 5.19951585 1.29616103
[9,] 0.98419583 5.19951585
[10,] 0.61761798 0.98419583
[11,] -0.96554248 0.61761798
[12,] -4.93900614 -0.96554248
[13,] 1.36498984 -4.93900614
[14,] 1.84598959 1.36498984
[15,] -3.24158897 1.84598959
[16,] -1.96589842 -3.24158897
[17,] -1.81213058 -1.96589842
[18,] -1.96017367 -1.81213058
[19,] 1.28854461 -1.96017367
[20,] 2.77973734 1.28854461
[21,] 2.35070415 2.77973734
[22,] -2.66370595 2.35070415
[23,] -0.35831682 -2.66370595
[24,] -0.36185266 -0.35831682
[25,] -1.31473072 -0.36185266
[26,] -0.53213907 -1.31473072
[27,] -2.58870245 -0.53213907
[28,] -1.44006014 -2.58870245
[29,] -0.43959773 -1.44006014
[30,] -1.77354602 -0.43959773
[31,] 4.28571603 -1.77354602
[32,] 1.87717878 4.28571603
[33,] -1.94729264 1.87717878
[34,] 0.19721048 -1.94729264
[35,] 2.73521421 0.19721048
[36,] -1.62980613 2.73521421
[37,] 6.43363763 -1.62980613
[38,] -0.62191414 6.43363763
[39,] 1.57949611 -0.62191414
[40,] 1.57358667 1.57949611
[41,] -1.44438760 1.57358667
[42,] -0.63027607 -1.44438760
[43,] 0.58714350 -0.63027607
[44,] -0.06059705 0.58714350
[45,] 1.78377726 -0.06059705
[46,] 2.50237454 1.78377726
[47,] 1.94160408 2.50237454
[48,] -2.26991047 1.94160408
[49,] 3.91619204 -2.26991047
[50,] 3.84628164 3.91619204
[51,] -1.40807209 3.84628164
[52,] -1.48602416 -1.40807209
[53,] -3.04630480 -1.48602416
[54,] -3.20121904 -3.04630480
[55,] 0.59222938 -3.20121904
[56,] -0.79557077 0.59222938
[57,] -0.97843354 -0.79557077
[58,] -2.25669118 -0.97843354
[59,] 5.06276903 -2.25669118
[60,] -0.82333674 5.06276903
[61,] -0.21959104 -0.82333674
[62,] 1.88624728 -0.21959104
[63,] 2.51199258 1.88624728
[64,] 1.49077158 2.51199258
[65,] -0.75584571 1.49077158
[66,] -5.53909738 -0.75584571
[67,] -0.11369569 -5.53909738
[68,] 4.42299976 -0.11369569
[69,] 0.61073681 4.42299976
[70,] -4.81699215 0.61073681
[71,] -1.97509287 -4.81699215
[72,] -3.20086122 -1.97509287
[73,] 2.68504987 -3.20086122
[74,] -2.06279382 2.68504987
[75,] -1.53712917 -2.06279382
[76,] 5.14089399 -1.53712917
[77,] 1.13782642 5.14089399
[78,] 0.93476777 1.13782642
[79,] -0.55139425 0.93476777
[80,] 2.51818988 -0.55139425
[81,] -5.51987747 2.51818988
[82,] 0.70285543 -5.51987747
[83,] -2.00425066 0.70285543
[84,] 1.59290245 -2.00425066
[85,] 0.10950044 1.59290245
[86,] 0.11394528 0.10950044
[87,] 3.83986732 0.11394528
[88,] 0.10978526 3.83986732
[89,] -1.00343953 0.10978526
[90,] 1.08020661 -1.00343953
[91,] -3.99164437 1.08020661
[92,] 0.58428410 -3.99164437
[93,] -0.37036989 0.58428410
[94,] -2.38490251 -0.37036989
[95,] -1.24384823 -2.38490251
[96,] 0.92339063 -1.24384823
[97,] 0.29514917 0.92339063
[98,] -1.89390007 0.29514917
[99,] -1.34171939 -1.89390007
[100,] -0.88302172 -1.34171939
[101,] -2.46168472 -0.88302172
[102,] -0.62973980 -2.46168472
[103,] -2.20918056 -0.62973980
[104,] 1.24805092 -2.20918056
[105,] 2.32335760 1.24805092
[106,] -3.03165562 2.32335760
[107,] -2.50000121 -3.03165562
[108,] 2.25563056 -2.50000121
[109,] 3.27240543 2.25563056
[110,] -1.79765924 3.27240543
[111,] -5.57343676 -1.79765924
[112,] -3.33616155 -5.57343676
[113,] -1.49504204 -3.33616155
[114,] -1.34741579 -1.49504204
[115,] -1.34873114 -1.34741579
[116,] -2.28221096 -1.34873114
[117,] 1.14159176 -2.28221096
[118,] 2.21563653 1.14159176
[119,] -0.11044371 2.21563653
[120,] -0.99741634 -0.11044371
[121,] 1.80393921 -0.99741634
[122,] 3.65291518 1.80393921
[123,] -2.33207394 3.65291518
[124,] 8.59014492 -2.33207394
[125,] 0.74457047 8.59014492
[126,] -1.11650463 0.74457047
[127,] 0.33902531 -1.11650463
[128,] -0.56944910 0.33902531
[129,] 0.42978744 -0.56944910
[130,] 0.52262826 0.42978744
[131,] -1.62265817 0.52262826
[132,] -0.65307314 -1.62265817
[133,] -2.14129803 -0.65307314
[134,] -1.99315435 -2.14129803
[135,] 2.37275786 -1.99315435
[136,] 2.73848859 2.37275786
[137,] 2.69530012 2.73848859
[138,] -2.27516025 2.69530012
[139,] 2.06507231 -2.27516025
[140,] 1.56415236 2.06507231
[141,] 0.40353982 1.56415236
[142,] 3.17344700 0.40353982
[143,] 3.75586173 3.17344700
[144,] -1.13513549 3.75586173
[145,] 4.26324835 -1.13513549
[146,] -2.89708372 4.26324835
[147,] -1.42575006 -2.89708372
[148,] -2.04908970 -1.42575006
[149,] -3.30255046 -2.04908970
[150,] -2.96492670 -3.30255046
[151,] 4.13318240 -2.96492670
[152,] -2.16699232 4.13318240
[153,] -0.96199395 -2.16699232
[154,] 1.29362627 -0.96199395
[155,] -0.79759722 1.29362627
[156,] 1.56059552 -0.79759722
[157,] 2.03790907 1.56059552
[158,] 1.77613791 2.03790907
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.78312869 1.76678835
2 -2.03349367 -0.78312869
3 1.08515447 -2.03349367
4 -1.86354313 1.08515447
5 -0.27141534 -1.86354313
6 0.28497090 -0.27141534
7 1.29616103 0.28497090
8 5.19951585 1.29616103
9 0.98419583 5.19951585
10 0.61761798 0.98419583
11 -0.96554248 0.61761798
12 -4.93900614 -0.96554248
13 1.36498984 -4.93900614
14 1.84598959 1.36498984
15 -3.24158897 1.84598959
16 -1.96589842 -3.24158897
17 -1.81213058 -1.96589842
18 -1.96017367 -1.81213058
19 1.28854461 -1.96017367
20 2.77973734 1.28854461
21 2.35070415 2.77973734
22 -2.66370595 2.35070415
23 -0.35831682 -2.66370595
24 -0.36185266 -0.35831682
25 -1.31473072 -0.36185266
26 -0.53213907 -1.31473072
27 -2.58870245 -0.53213907
28 -1.44006014 -2.58870245
29 -0.43959773 -1.44006014
30 -1.77354602 -0.43959773
31 4.28571603 -1.77354602
32 1.87717878 4.28571603
33 -1.94729264 1.87717878
34 0.19721048 -1.94729264
35 2.73521421 0.19721048
36 -1.62980613 2.73521421
37 6.43363763 -1.62980613
38 -0.62191414 6.43363763
39 1.57949611 -0.62191414
40 1.57358667 1.57949611
41 -1.44438760 1.57358667
42 -0.63027607 -1.44438760
43 0.58714350 -0.63027607
44 -0.06059705 0.58714350
45 1.78377726 -0.06059705
46 2.50237454 1.78377726
47 1.94160408 2.50237454
48 -2.26991047 1.94160408
49 3.91619204 -2.26991047
50 3.84628164 3.91619204
51 -1.40807209 3.84628164
52 -1.48602416 -1.40807209
53 -3.04630480 -1.48602416
54 -3.20121904 -3.04630480
55 0.59222938 -3.20121904
56 -0.79557077 0.59222938
57 -0.97843354 -0.79557077
58 -2.25669118 -0.97843354
59 5.06276903 -2.25669118
60 -0.82333674 5.06276903
61 -0.21959104 -0.82333674
62 1.88624728 -0.21959104
63 2.51199258 1.88624728
64 1.49077158 2.51199258
65 -0.75584571 1.49077158
66 -5.53909738 -0.75584571
67 -0.11369569 -5.53909738
68 4.42299976 -0.11369569
69 0.61073681 4.42299976
70 -4.81699215 0.61073681
71 -1.97509287 -4.81699215
72 -3.20086122 -1.97509287
73 2.68504987 -3.20086122
74 -2.06279382 2.68504987
75 -1.53712917 -2.06279382
76 5.14089399 -1.53712917
77 1.13782642 5.14089399
78 0.93476777 1.13782642
79 -0.55139425 0.93476777
80 2.51818988 -0.55139425
81 -5.51987747 2.51818988
82 0.70285543 -5.51987747
83 -2.00425066 0.70285543
84 1.59290245 -2.00425066
85 0.10950044 1.59290245
86 0.11394528 0.10950044
87 3.83986732 0.11394528
88 0.10978526 3.83986732
89 -1.00343953 0.10978526
90 1.08020661 -1.00343953
91 -3.99164437 1.08020661
92 0.58428410 -3.99164437
93 -0.37036989 0.58428410
94 -2.38490251 -0.37036989
95 -1.24384823 -2.38490251
96 0.92339063 -1.24384823
97 0.29514917 0.92339063
98 -1.89390007 0.29514917
99 -1.34171939 -1.89390007
100 -0.88302172 -1.34171939
101 -2.46168472 -0.88302172
102 -0.62973980 -2.46168472
103 -2.20918056 -0.62973980
104 1.24805092 -2.20918056
105 2.32335760 1.24805092
106 -3.03165562 2.32335760
107 -2.50000121 -3.03165562
108 2.25563056 -2.50000121
109 3.27240543 2.25563056
110 -1.79765924 3.27240543
111 -5.57343676 -1.79765924
112 -3.33616155 -5.57343676
113 -1.49504204 -3.33616155
114 -1.34741579 -1.49504204
115 -1.34873114 -1.34741579
116 -2.28221096 -1.34873114
117 1.14159176 -2.28221096
118 2.21563653 1.14159176
119 -0.11044371 2.21563653
120 -0.99741634 -0.11044371
121 1.80393921 -0.99741634
122 3.65291518 1.80393921
123 -2.33207394 3.65291518
124 8.59014492 -2.33207394
125 0.74457047 8.59014492
126 -1.11650463 0.74457047
127 0.33902531 -1.11650463
128 -0.56944910 0.33902531
129 0.42978744 -0.56944910
130 0.52262826 0.42978744
131 -1.62265817 0.52262826
132 -0.65307314 -1.62265817
133 -2.14129803 -0.65307314
134 -1.99315435 -2.14129803
135 2.37275786 -1.99315435
136 2.73848859 2.37275786
137 2.69530012 2.73848859
138 -2.27516025 2.69530012
139 2.06507231 -2.27516025
140 1.56415236 2.06507231
141 0.40353982 1.56415236
142 3.17344700 0.40353982
143 3.75586173 3.17344700
144 -1.13513549 3.75586173
145 4.26324835 -1.13513549
146 -2.89708372 4.26324835
147 -1.42575006 -2.89708372
148 -2.04908970 -1.42575006
149 -3.30255046 -2.04908970
150 -2.96492670 -3.30255046
151 4.13318240 -2.96492670
152 -2.16699232 4.13318240
153 -0.96199395 -2.16699232
154 1.29362627 -0.96199395
155 -0.79759722 1.29362627
156 1.56059552 -0.79759722
157 2.03790907 1.56059552
158 1.77613791 2.03790907
> 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/7p4fp1290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ivwa1290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ivwa1290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10tmvd1290548313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11enci1290548313.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/12m8zd1290548313.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/13dx8f1290548313.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/14zg6l1290548313.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/152y591290548313.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/166hmf1290548313.tab")
+ }
>
> try(system("convert tmp/14lyj1290548313.ps tmp/14lyj1290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/24lyj1290548313.ps tmp/24lyj1290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xvg41290548313.ps tmp/3xvg41290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xvg41290548313.ps tmp/4xvg41290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/5xvg41290548313.ps tmp/5xvg41290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/6p4fp1290548313.ps tmp/6p4fp1290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/7p4fp1290548313.ps tmp/7p4fp1290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ivwa1290548313.ps tmp/8ivwa1290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ivwa1290548313.ps tmp/9ivwa1290548313.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tmvd1290548313.ps tmp/10tmvd1290548313.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.199 1.786 10.582