R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(26
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+ ,17)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('Yt'
+ ,'X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('Yt','X1','X2','X3','X4','X5'),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 = '1'
> #'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
Yt X1 X2 X3 X4 X5
1 26 24 14 11 12 24
2 23 25 11 7 8 25
3 25 17 6 17 8 30
4 23 18 12 10 8 19
5 19 18 8 12 9 22
6 29 16 10 12 7 22
7 25 20 10 11 4 25
8 21 16 11 11 11 23
9 22 18 16 12 7 17
10 25 17 11 13 7 21
11 24 23 13 14 12 19
12 18 30 12 16 10 19
13 22 23 8 11 10 15
14 15 18 12 10 8 16
15 22 15 11 11 8 23
16 28 12 4 15 4 27
17 20 21 9 9 9 22
18 12 15 8 11 8 14
19 24 20 8 17 7 22
20 20 31 14 17 11 23
21 21 27 15 11 9 23
22 20 34 16 18 11 21
23 21 21 9 14 13 19
24 23 31 14 10 8 18
25 28 19 11 11 8 20
26 24 16 8 15 9 23
27 24 20 9 15 6 25
28 24 21 9 13 9 19
29 23 22 9 16 9 24
30 23 17 9 13 6 22
31 29 24 10 9 6 25
32 24 25 16 18 16 26
33 18 26 11 18 5 29
34 25 25 8 12 7 32
35 21 17 9 17 9 25
36 26 32 16 9 6 29
37 22 33 11 9 6 28
38 22 13 16 12 5 17
39 22 32 12 18 12 28
40 23 25 12 12 7 29
41 30 29 14 18 10 26
42 23 22 9 14 9 25
43 17 18 10 15 8 14
44 23 17 9 16 5 25
45 23 20 10 10 8 26
46 25 15 12 11 8 20
47 24 20 14 14 10 18
48 24 33 14 9 6 32
49 23 29 10 12 8 25
50 21 23 14 17 7 25
51 24 26 16 5 4 23
52 24 18 9 12 8 21
53 28 20 10 12 8 20
54 16 11 6 6 4 15
55 20 28 8 24 20 30
56 29 26 13 12 8 24
57 27 22 10 12 8 26
58 22 17 8 14 6 24
59 28 12 7 7 4 22
60 16 14 15 13 8 14
61 25 17 9 12 9 24
62 24 21 10 13 6 24
63 28 19 12 14 7 24
64 24 18 13 8 9 24
65 23 10 10 11 5 19
66 30 29 11 9 5 31
67 24 31 8 11 8 22
68 21 19 9 13 8 27
69 25 9 13 10 6 19
70 25 20 11 11 8 25
71 22 28 8 12 7 20
72 23 19 9 9 7 21
73 26 30 9 15 9 27
74 23 29 15 18 11 23
75 25 26 9 15 6 25
76 21 23 10 12 8 20
77 25 13 14 13 6 21
78 24 21 12 14 9 22
79 29 19 12 10 8 23
80 22 28 11 13 6 25
81 27 23 14 13 10 25
82 26 18 6 11 8 17
83 22 21 12 13 8 19
84 24 20 8 16 10 25
85 27 23 14 8 5 19
86 24 21 11 16 7 20
87 24 21 10 11 5 26
88 29 15 14 9 8 23
89 22 28 12 16 14 27
90 21 19 10 12 7 17
91 24 26 14 14 8 17
92 24 10 5 8 6 19
93 23 16 11 9 5 17
94 20 22 10 15 6 22
95 27 19 9 11 10 21
96 26 31 10 21 12 32
97 25 31 16 14 9 21
98 21 29 13 18 12 21
99 21 19 9 12 7 18
100 19 22 10 13 8 18
101 21 23 10 15 10 23
102 21 15 7 12 6 19
103 16 20 9 19 10 20
104 22 18 8 15 10 21
105 29 23 14 11 10 20
106 15 25 14 11 5 17
107 17 21 8 10 7 18
108 15 24 9 13 10 19
109 21 25 14 15 11 22
110 21 17 14 12 6 15
111 19 13 8 12 7 14
112 24 28 8 16 12 18
113 20 21 8 9 11 24
114 17 25 7 18 11 35
115 23 9 6 8 11 29
116 24 16 8 13 5 21
117 14 19 6 17 8 25
118 19 17 11 9 6 20
119 24 25 14 15 9 22
120 13 20 11 8 4 13
121 22 29 11 7 4 26
122 16 14 11 12 7 17
123 19 22 14 14 11 25
124 25 15 8 6 6 20
125 25 19 20 8 7 19
126 23 20 11 17 8 21
127 24 15 8 10 4 22
128 26 20 11 11 8 24
129 26 18 10 14 9 21
130 25 33 14 11 8 26
131 18 22 11 13 11 24
132 21 16 9 12 8 16
133 26 17 9 11 5 23
134 23 16 8 9 4 18
135 23 21 10 12 8 16
136 22 26 13 20 10 26
137 20 18 13 12 6 19
138 13 18 12 13 9 21
139 24 17 8 12 9 21
140 15 22 13 12 13 22
141 14 30 14 9 9 23
142 22 30 12 15 10 29
143 10 24 14 24 20 21
144 24 21 15 7 5 21
145 22 21 13 17 11 23
146 24 29 16 11 6 27
147 19 31 9 17 9 25
148 20 20 9 11 7 21
149 13 16 9 12 9 10
150 20 22 8 14 10 20
151 22 20 7 11 9 26
152 24 28 16 16 8 24
153 29 38 11 21 7 29
154 12 22 9 14 6 19
155 20 20 11 20 13 24
156 21 17 9 13 6 19
157 24 28 14 11 8 24
158 22 22 13 15 10 22
159 20 31 16 19 16 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 X2 X3 X4 X5
16.13438 -0.07068 0.21817 -0.14895 -0.25516 0.42276
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.1549 -1.7377 0.2698 2.2317 7.1717
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.13438 2.00161 8.061 2.02e-13 ***
X1 -0.07068 0.06292 -1.123 0.2631
X2 0.21817 0.11262 1.937 0.0546 .
X3 -0.14895 0.10427 -1.429 0.1552
X4 -0.25516 0.13041 -1.957 0.0522 .
X5 0.42276 0.07562 5.591 1.01e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.499 on 153 degrees of freedom
Multiple R-squared: 0.2224, Adjusted R-squared: 0.197
F-statistic: 8.75 on 5 and 153 DF, p-value: 2.548e-07
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.725321323 0.549357354 0.2746787
[2,] 0.598646411 0.802707178 0.4013536
[3,] 0.489326082 0.978652163 0.5106739
[4,] 0.444855893 0.889711786 0.5551441
[5,] 0.452954235 0.905908470 0.5470458
[6,] 0.693424169 0.613151662 0.3065758
[7,] 0.624897425 0.750205151 0.3751026
[8,] 0.572942458 0.854115085 0.4270575
[9,] 0.506926763 0.986146474 0.4930732
[10,] 0.637026245 0.725947511 0.3629738
[11,] 0.562011259 0.875977481 0.4379887
[12,] 0.567128160 0.865743681 0.4328718
[13,] 0.519310435 0.961379131 0.4806896
[14,] 0.450175129 0.900350257 0.5498249
[15,] 0.391153994 0.782307989 0.6088460
[16,] 0.391798133 0.783596265 0.6082019
[17,] 0.535610016 0.928779968 0.4643900
[18,] 0.473009182 0.946018364 0.5269908
[19,] 0.409442267 0.818884533 0.5905577
[20,] 0.402731633 0.805463266 0.5972684
[21,] 0.341805979 0.683611957 0.6581940
[22,] 0.285095564 0.570191127 0.7149044
[23,] 0.309301997 0.618603994 0.6906980
[24,] 0.260709370 0.521418739 0.7392906
[25,] 0.485169644 0.970339289 0.5148304
[26,] 0.438774416 0.877548832 0.5612256
[27,] 0.403999253 0.807998506 0.5960007
[28,] 0.348330711 0.696661422 0.6516693
[29,] 0.325699881 0.651399761 0.6743001
[30,] 0.276376932 0.552753864 0.7236231
[31,] 0.231547277 0.463094554 0.7684527
[32,] 0.208251520 0.416503041 0.7917485
[33,] 0.359776282 0.719552564 0.6402237
[34,] 0.309654831 0.619309663 0.6903452
[35,] 0.277892036 0.555784072 0.7221080
[36,] 0.236006680 0.472013360 0.7639933
[37,] 0.203401765 0.406803530 0.7965982
[38,] 0.182515928 0.365031857 0.8174841
[39,] 0.170211098 0.340422195 0.8297889
[40,] 0.157288664 0.314577328 0.8427113
[41,] 0.128207897 0.256415793 0.8717921
[42,] 0.118759202 0.237518403 0.8812408
[43,] 0.097944029 0.195888058 0.9020560
[44,] 0.082903652 0.165807304 0.9170963
[45,] 0.138931976 0.277863951 0.8610680
[46,] 0.170471402 0.340942804 0.8295286
[47,] 0.162698873 0.325397747 0.8373011
[48,] 0.218070248 0.436140496 0.7819298
[49,] 0.208985549 0.417971097 0.7910145
[50,] 0.179227057 0.358454114 0.8207729
[51,] 0.189699480 0.379398960 0.8103005
[52,] 0.218014963 0.436029926 0.7819850
[53,] 0.191967615 0.383935230 0.8080324
[54,] 0.161100317 0.322200635 0.8388997
[55,] 0.175850700 0.351701400 0.8241493
[56,] 0.148204637 0.296409275 0.8517954
[57,] 0.122207971 0.244415942 0.8777920
[58,] 0.118450250 0.236900501 0.8815497
[59,] 0.108257660 0.216515321 0.8917423
[60,] 0.108501929 0.217003858 0.8914981
[61,] 0.092112173 0.184224345 0.9078878
[62,] 0.075137236 0.150274473 0.9248628
[63,] 0.061093221 0.122186442 0.9389068
[64,] 0.048267854 0.096535709 0.9517321
[65,] 0.045358816 0.090717632 0.9546412
[66,] 0.036381888 0.072763775 0.9636181
[67,] 0.030303698 0.060607396 0.9696963
[68,] 0.023281259 0.046562519 0.9767187
[69,] 0.018281921 0.036563842 0.9817181
[70,] 0.014639713 0.029279425 0.9853603
[71,] 0.021949337 0.043898674 0.9780507
[72,] 0.017522162 0.035044323 0.9824778
[73,] 0.017144765 0.034289530 0.9828552
[74,] 0.030841012 0.061682025 0.9691590
[75,] 0.023814650 0.047629299 0.9761854
[76,] 0.019906217 0.039812434 0.9800938
[77,] 0.021894103 0.043788207 0.9781059
[78,] 0.019472449 0.038944898 0.9805276
[79,] 0.014840397 0.029680795 0.9851596
[80,] 0.019433503 0.038867007 0.9805665
[81,] 0.015331480 0.030662959 0.9846685
[82,] 0.011500087 0.023000173 0.9884999
[83,] 0.011620489 0.023240978 0.9883795
[84,] 0.010188723 0.020377446 0.9898113
[85,] 0.007838045 0.015676091 0.9921620
[86,] 0.006501947 0.013003893 0.9934981
[87,] 0.011982282 0.023964564 0.9880177
[88,] 0.010896219 0.021792438 0.9891038
[89,] 0.010409658 0.020819316 0.9895903
[90,] 0.007997341 0.015994681 0.9920027
[91,] 0.005925454 0.011850908 0.9940745
[92,] 0.004556769 0.009113538 0.9954432
[93,] 0.003383170 0.006766340 0.9966168
[94,] 0.002419195 0.004838389 0.9975808
[95,] 0.002601225 0.005202451 0.9973988
[96,] 0.002048888 0.004097777 0.9979511
[97,] 0.008725193 0.017450387 0.9912748
[98,] 0.019600015 0.039200029 0.9804000
[99,] 0.019535725 0.039071449 0.9804643
[100,] 0.024699494 0.049398988 0.9753005
[101,] 0.019175533 0.038351066 0.9808245
[102,] 0.014101682 0.028203365 0.9858983
[103,] 0.010282752 0.020565504 0.9897172
[104,] 0.025191723 0.050383445 0.9748083
[105,] 0.022986166 0.045972332 0.9770138
[106,] 0.063646832 0.127293665 0.9363532
[107,] 0.054629944 0.109259887 0.9453701
[108,] 0.044624498 0.089248995 0.9553755
[109,] 0.129387033 0.258774066 0.8706130
[110,] 0.124835817 0.249671633 0.8751642
[111,] 0.111484017 0.222968035 0.8885160
[112,] 0.192312215 0.384624429 0.8076878
[113,] 0.184371448 0.368742895 0.8156286
[114,] 0.219367209 0.438734418 0.7806328
[115,] 0.213118892 0.426237784 0.7868811
[116,] 0.212845029 0.425690059 0.7871550
[117,] 0.194886459 0.389772918 0.8051135
[118,] 0.160685225 0.321370449 0.8393148
[119,] 0.127496804 0.254993608 0.8725032
[120,] 0.132029072 0.264058144 0.8679709
[121,] 0.187366426 0.374732852 0.8126336
[122,] 0.163448625 0.326897249 0.8365514
[123,] 0.144802100 0.289604201 0.8551979
[124,] 0.127110096 0.254220192 0.8728899
[125,] 0.117873103 0.235746206 0.8821269
[126,] 0.097915894 0.195831787 0.9020841
[127,] 0.132789384 0.265578767 0.8672106
[128,] 0.100909490 0.201818979 0.8990905
[129,] 0.075558355 0.151116711 0.9244416
[130,] 0.185211620 0.370423239 0.8147884
[131,] 0.258473502 0.516947005 0.7415265
[132,] 0.239168503 0.478337007 0.7608315
[133,] 0.483356134 0.966712267 0.5166439
[134,] 0.435364617 0.870729234 0.5646354
[135,] 0.747117314 0.505765373 0.2528827
[136,] 0.696970796 0.606058408 0.3030292
[137,] 0.597987981 0.804024037 0.4020120
[138,] 0.535180865 0.929638269 0.4648191
[139,] 0.558078277 0.883843445 0.4419217
[140,] 0.430789275 0.861578551 0.5692107
[141,] 0.337911227 0.675822454 0.6620888
[142,] 0.241861645 0.483723289 0.7581384
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ypdq1290506928.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/29gcb1290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/39gcb1290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/49gcb1290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5kque1290506928.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
3.061660080 -1.252360362 0.648853683 1.018139502 -2.824368247 6.087610317
7 8 9 10 11 12
0.187603441 -1.681627639 0.033704154 2.511822594 3.769803424 -1.729710422
13 14 15 16 17 18
3.594516129 -5.713589619 -1.517787759 3.681541238 -2.277375757 -7.058454122
19 20 21 22 23 24
2.551427048 -1.382295076 -2.287214675 -0.612148470 1.756308587 1.923330862
25 26 27 28 29 30
6.033185192 2.058384154 0.511914038 3.586708985 0.990460101 0.270251432
31 32 33 34 35 36
4.682721650 1.913794741 -6.999709164 -1.067532288 -1.636720982 -0.751944044
37 38 39 40 41 42
-3.167643235 -0.829996503 -0.584942025 -2.671956073 7.101874941 0.269796210
43 44 45 46 47 48
-1.686961039 -0.806320584 -1.363460848 2.532309455 3.252037096 -3.513192073
49 50 51 52 53 54
-0.006717296 -2.813859274 -1.745592323 2.125053512 6.470987842 -5.092979285
55 56 57 58 59 60
0.094549715 5.549492111 3.075797119 -1.208135355 3.949177314 -4.358438372
61 62 63 64 65 66
2.041268649 0.489265918 4.315682550 -0.356564360 0.272551555 3.026222280
67 68 69 70 71 72
2.690298485 -3.191859263 1.653563107 0.990075911 1.217577783 0.493707099
73 74 75 76 77 78
3.138639899 1.407133688 1.935967145 -0.316985605 1.319437990 1.812870573
79 80 81 82 83 84
5.397787181 -1.656936083 3.355811465 6.321648884 0.677026452 1.899687306
85 86 87 88 89 90
3.871778226 2.664142022 -0.909316467 4.529784312 -0.232471000 0.413421669
91 92 93 94 95 96
3.588524094 2.171721023 1.026037985 -2.296637713 5.557098633 2.536562347
97 98 99 100 101 102
3.069688046 0.944156474 0.208838375 -1.393193738 -0.628073019 -0.315434857
103 104 105 106 107 108
-3.757841165 1.300410643 7.171689333 -6.694496422 -3.729543854 -4.946102927
109 110 111 112 113 114
-0.681498154 -0.010271646 -0.306013227 5.934713235 -2.394392942 -8.203262571
115 116 117 118 119 120
-2.068890088 1.585345006 -8.096010482 -3.916395843 1.808178777 -7.404347105
121 122 123 124 125 126
-3.413061386 -5.158129586 -4.310749053 2.149913723 0.790357225 1.574824544
127 128 129 130 131 132
0.389890598 2.412832871 4.459948311 0.831579685 -4.382424560 1.097487275
133 134 135 136 137 138
2.294426007 1.002640489 3.232691198 -0.594071015 -2.412450301 -9.125352486
139 140 141 142 143 144
2.527713195 -6.611888371 -9.154921387 -2.106233485 -7.692381716 -0.482213861
145 146 147 148 149 150
0.129123412 -1.820549747 -2.647263733 -2.137710451 -4.110809432 -0.143083791
151 152 153 154 155 156
-1.304824850 0.632136011 5.805580515 -8.959146634 -0.970778269 -0.461477688
157 158 159
0.323716015 0.069487424 2.291613951
> postscript(file="/var/www/html/freestat/rcomp/tmp/6kque1290506928.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 3.061660080 NA
1 -1.252360362 3.061660080
2 0.648853683 -1.252360362
3 1.018139502 0.648853683
4 -2.824368247 1.018139502
5 6.087610317 -2.824368247
6 0.187603441 6.087610317
7 -1.681627639 0.187603441
8 0.033704154 -1.681627639
9 2.511822594 0.033704154
10 3.769803424 2.511822594
11 -1.729710422 3.769803424
12 3.594516129 -1.729710422
13 -5.713589619 3.594516129
14 -1.517787759 -5.713589619
15 3.681541238 -1.517787759
16 -2.277375757 3.681541238
17 -7.058454122 -2.277375757
18 2.551427048 -7.058454122
19 -1.382295076 2.551427048
20 -2.287214675 -1.382295076
21 -0.612148470 -2.287214675
22 1.756308587 -0.612148470
23 1.923330862 1.756308587
24 6.033185192 1.923330862
25 2.058384154 6.033185192
26 0.511914038 2.058384154
27 3.586708985 0.511914038
28 0.990460101 3.586708985
29 0.270251432 0.990460101
30 4.682721650 0.270251432
31 1.913794741 4.682721650
32 -6.999709164 1.913794741
33 -1.067532288 -6.999709164
34 -1.636720982 -1.067532288
35 -0.751944044 -1.636720982
36 -3.167643235 -0.751944044
37 -0.829996503 -3.167643235
38 -0.584942025 -0.829996503
39 -2.671956073 -0.584942025
40 7.101874941 -2.671956073
41 0.269796210 7.101874941
42 -1.686961039 0.269796210
43 -0.806320584 -1.686961039
44 -1.363460848 -0.806320584
45 2.532309455 -1.363460848
46 3.252037096 2.532309455
47 -3.513192073 3.252037096
48 -0.006717296 -3.513192073
49 -2.813859274 -0.006717296
50 -1.745592323 -2.813859274
51 2.125053512 -1.745592323
52 6.470987842 2.125053512
53 -5.092979285 6.470987842
54 0.094549715 -5.092979285
55 5.549492111 0.094549715
56 3.075797119 5.549492111
57 -1.208135355 3.075797119
58 3.949177314 -1.208135355
59 -4.358438372 3.949177314
60 2.041268649 -4.358438372
61 0.489265918 2.041268649
62 4.315682550 0.489265918
63 -0.356564360 4.315682550
64 0.272551555 -0.356564360
65 3.026222280 0.272551555
66 2.690298485 3.026222280
67 -3.191859263 2.690298485
68 1.653563107 -3.191859263
69 0.990075911 1.653563107
70 1.217577783 0.990075911
71 0.493707099 1.217577783
72 3.138639899 0.493707099
73 1.407133688 3.138639899
74 1.935967145 1.407133688
75 -0.316985605 1.935967145
76 1.319437990 -0.316985605
77 1.812870573 1.319437990
78 5.397787181 1.812870573
79 -1.656936083 5.397787181
80 3.355811465 -1.656936083
81 6.321648884 3.355811465
82 0.677026452 6.321648884
83 1.899687306 0.677026452
84 3.871778226 1.899687306
85 2.664142022 3.871778226
86 -0.909316467 2.664142022
87 4.529784312 -0.909316467
88 -0.232471000 4.529784312
89 0.413421669 -0.232471000
90 3.588524094 0.413421669
91 2.171721023 3.588524094
92 1.026037985 2.171721023
93 -2.296637713 1.026037985
94 5.557098633 -2.296637713
95 2.536562347 5.557098633
96 3.069688046 2.536562347
97 0.944156474 3.069688046
98 0.208838375 0.944156474
99 -1.393193738 0.208838375
100 -0.628073019 -1.393193738
101 -0.315434857 -0.628073019
102 -3.757841165 -0.315434857
103 1.300410643 -3.757841165
104 7.171689333 1.300410643
105 -6.694496422 7.171689333
106 -3.729543854 -6.694496422
107 -4.946102927 -3.729543854
108 -0.681498154 -4.946102927
109 -0.010271646 -0.681498154
110 -0.306013227 -0.010271646
111 5.934713235 -0.306013227
112 -2.394392942 5.934713235
113 -8.203262571 -2.394392942
114 -2.068890088 -8.203262571
115 1.585345006 -2.068890088
116 -8.096010482 1.585345006
117 -3.916395843 -8.096010482
118 1.808178777 -3.916395843
119 -7.404347105 1.808178777
120 -3.413061386 -7.404347105
121 -5.158129586 -3.413061386
122 -4.310749053 -5.158129586
123 2.149913723 -4.310749053
124 0.790357225 2.149913723
125 1.574824544 0.790357225
126 0.389890598 1.574824544
127 2.412832871 0.389890598
128 4.459948311 2.412832871
129 0.831579685 4.459948311
130 -4.382424560 0.831579685
131 1.097487275 -4.382424560
132 2.294426007 1.097487275
133 1.002640489 2.294426007
134 3.232691198 1.002640489
135 -0.594071015 3.232691198
136 -2.412450301 -0.594071015
137 -9.125352486 -2.412450301
138 2.527713195 -9.125352486
139 -6.611888371 2.527713195
140 -9.154921387 -6.611888371
141 -2.106233485 -9.154921387
142 -7.692381716 -2.106233485
143 -0.482213861 -7.692381716
144 0.129123412 -0.482213861
145 -1.820549747 0.129123412
146 -2.647263733 -1.820549747
147 -2.137710451 -2.647263733
148 -4.110809432 -2.137710451
149 -0.143083791 -4.110809432
150 -1.304824850 -0.143083791
151 0.632136011 -1.304824850
152 5.805580515 0.632136011
153 -8.959146634 5.805580515
154 -0.970778269 -8.959146634
155 -0.461477688 -0.970778269
156 0.323716015 -0.461477688
157 0.069487424 0.323716015
158 2.291613951 0.069487424
159 NA 2.291613951
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.252360362 3.061660080
[2,] 0.648853683 -1.252360362
[3,] 1.018139502 0.648853683
[4,] -2.824368247 1.018139502
[5,] 6.087610317 -2.824368247
[6,] 0.187603441 6.087610317
[7,] -1.681627639 0.187603441
[8,] 0.033704154 -1.681627639
[9,] 2.511822594 0.033704154
[10,] 3.769803424 2.511822594
[11,] -1.729710422 3.769803424
[12,] 3.594516129 -1.729710422
[13,] -5.713589619 3.594516129
[14,] -1.517787759 -5.713589619
[15,] 3.681541238 -1.517787759
[16,] -2.277375757 3.681541238
[17,] -7.058454122 -2.277375757
[18,] 2.551427048 -7.058454122
[19,] -1.382295076 2.551427048
[20,] -2.287214675 -1.382295076
[21,] -0.612148470 -2.287214675
[22,] 1.756308587 -0.612148470
[23,] 1.923330862 1.756308587
[24,] 6.033185192 1.923330862
[25,] 2.058384154 6.033185192
[26,] 0.511914038 2.058384154
[27,] 3.586708985 0.511914038
[28,] 0.990460101 3.586708985
[29,] 0.270251432 0.990460101
[30,] 4.682721650 0.270251432
[31,] 1.913794741 4.682721650
[32,] -6.999709164 1.913794741
[33,] -1.067532288 -6.999709164
[34,] -1.636720982 -1.067532288
[35,] -0.751944044 -1.636720982
[36,] -3.167643235 -0.751944044
[37,] -0.829996503 -3.167643235
[38,] -0.584942025 -0.829996503
[39,] -2.671956073 -0.584942025
[40,] 7.101874941 -2.671956073
[41,] 0.269796210 7.101874941
[42,] -1.686961039 0.269796210
[43,] -0.806320584 -1.686961039
[44,] -1.363460848 -0.806320584
[45,] 2.532309455 -1.363460848
[46,] 3.252037096 2.532309455
[47,] -3.513192073 3.252037096
[48,] -0.006717296 -3.513192073
[49,] -2.813859274 -0.006717296
[50,] -1.745592323 -2.813859274
[51,] 2.125053512 -1.745592323
[52,] 6.470987842 2.125053512
[53,] -5.092979285 6.470987842
[54,] 0.094549715 -5.092979285
[55,] 5.549492111 0.094549715
[56,] 3.075797119 5.549492111
[57,] -1.208135355 3.075797119
[58,] 3.949177314 -1.208135355
[59,] -4.358438372 3.949177314
[60,] 2.041268649 -4.358438372
[61,] 0.489265918 2.041268649
[62,] 4.315682550 0.489265918
[63,] -0.356564360 4.315682550
[64,] 0.272551555 -0.356564360
[65,] 3.026222280 0.272551555
[66,] 2.690298485 3.026222280
[67,] -3.191859263 2.690298485
[68,] 1.653563107 -3.191859263
[69,] 0.990075911 1.653563107
[70,] 1.217577783 0.990075911
[71,] 0.493707099 1.217577783
[72,] 3.138639899 0.493707099
[73,] 1.407133688 3.138639899
[74,] 1.935967145 1.407133688
[75,] -0.316985605 1.935967145
[76,] 1.319437990 -0.316985605
[77,] 1.812870573 1.319437990
[78,] 5.397787181 1.812870573
[79,] -1.656936083 5.397787181
[80,] 3.355811465 -1.656936083
[81,] 6.321648884 3.355811465
[82,] 0.677026452 6.321648884
[83,] 1.899687306 0.677026452
[84,] 3.871778226 1.899687306
[85,] 2.664142022 3.871778226
[86,] -0.909316467 2.664142022
[87,] 4.529784312 -0.909316467
[88,] -0.232471000 4.529784312
[89,] 0.413421669 -0.232471000
[90,] 3.588524094 0.413421669
[91,] 2.171721023 3.588524094
[92,] 1.026037985 2.171721023
[93,] -2.296637713 1.026037985
[94,] 5.557098633 -2.296637713
[95,] 2.536562347 5.557098633
[96,] 3.069688046 2.536562347
[97,] 0.944156474 3.069688046
[98,] 0.208838375 0.944156474
[99,] -1.393193738 0.208838375
[100,] -0.628073019 -1.393193738
[101,] -0.315434857 -0.628073019
[102,] -3.757841165 -0.315434857
[103,] 1.300410643 -3.757841165
[104,] 7.171689333 1.300410643
[105,] -6.694496422 7.171689333
[106,] -3.729543854 -6.694496422
[107,] -4.946102927 -3.729543854
[108,] -0.681498154 -4.946102927
[109,] -0.010271646 -0.681498154
[110,] -0.306013227 -0.010271646
[111,] 5.934713235 -0.306013227
[112,] -2.394392942 5.934713235
[113,] -8.203262571 -2.394392942
[114,] -2.068890088 -8.203262571
[115,] 1.585345006 -2.068890088
[116,] -8.096010482 1.585345006
[117,] -3.916395843 -8.096010482
[118,] 1.808178777 -3.916395843
[119,] -7.404347105 1.808178777
[120,] -3.413061386 -7.404347105
[121,] -5.158129586 -3.413061386
[122,] -4.310749053 -5.158129586
[123,] 2.149913723 -4.310749053
[124,] 0.790357225 2.149913723
[125,] 1.574824544 0.790357225
[126,] 0.389890598 1.574824544
[127,] 2.412832871 0.389890598
[128,] 4.459948311 2.412832871
[129,] 0.831579685 4.459948311
[130,] -4.382424560 0.831579685
[131,] 1.097487275 -4.382424560
[132,] 2.294426007 1.097487275
[133,] 1.002640489 2.294426007
[134,] 3.232691198 1.002640489
[135,] -0.594071015 3.232691198
[136,] -2.412450301 -0.594071015
[137,] -9.125352486 -2.412450301
[138,] 2.527713195 -9.125352486
[139,] -6.611888371 2.527713195
[140,] -9.154921387 -6.611888371
[141,] -2.106233485 -9.154921387
[142,] -7.692381716 -2.106233485
[143,] -0.482213861 -7.692381716
[144,] 0.129123412 -0.482213861
[145,] -1.820549747 0.129123412
[146,] -2.647263733 -1.820549747
[147,] -2.137710451 -2.647263733
[148,] -4.110809432 -2.137710451
[149,] -0.143083791 -4.110809432
[150,] -1.304824850 -0.143083791
[151,] 0.632136011 -1.304824850
[152,] 5.805580515 0.632136011
[153,] -8.959146634 5.805580515
[154,] -0.970778269 -8.959146634
[155,] -0.461477688 -0.970778269
[156,] 0.323716015 -0.461477688
[157,] 0.069487424 0.323716015
[158,] 2.291613951 0.069487424
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.252360362 3.061660080
2 0.648853683 -1.252360362
3 1.018139502 0.648853683
4 -2.824368247 1.018139502
5 6.087610317 -2.824368247
6 0.187603441 6.087610317
7 -1.681627639 0.187603441
8 0.033704154 -1.681627639
9 2.511822594 0.033704154
10 3.769803424 2.511822594
11 -1.729710422 3.769803424
12 3.594516129 -1.729710422
13 -5.713589619 3.594516129
14 -1.517787759 -5.713589619
15 3.681541238 -1.517787759
16 -2.277375757 3.681541238
17 -7.058454122 -2.277375757
18 2.551427048 -7.058454122
19 -1.382295076 2.551427048
20 -2.287214675 -1.382295076
21 -0.612148470 -2.287214675
22 1.756308587 -0.612148470
23 1.923330862 1.756308587
24 6.033185192 1.923330862
25 2.058384154 6.033185192
26 0.511914038 2.058384154
27 3.586708985 0.511914038
28 0.990460101 3.586708985
29 0.270251432 0.990460101
30 4.682721650 0.270251432
31 1.913794741 4.682721650
32 -6.999709164 1.913794741
33 -1.067532288 -6.999709164
34 -1.636720982 -1.067532288
35 -0.751944044 -1.636720982
36 -3.167643235 -0.751944044
37 -0.829996503 -3.167643235
38 -0.584942025 -0.829996503
39 -2.671956073 -0.584942025
40 7.101874941 -2.671956073
41 0.269796210 7.101874941
42 -1.686961039 0.269796210
43 -0.806320584 -1.686961039
44 -1.363460848 -0.806320584
45 2.532309455 -1.363460848
46 3.252037096 2.532309455
47 -3.513192073 3.252037096
48 -0.006717296 -3.513192073
49 -2.813859274 -0.006717296
50 -1.745592323 -2.813859274
51 2.125053512 -1.745592323
52 6.470987842 2.125053512
53 -5.092979285 6.470987842
54 0.094549715 -5.092979285
55 5.549492111 0.094549715
56 3.075797119 5.549492111
57 -1.208135355 3.075797119
58 3.949177314 -1.208135355
59 -4.358438372 3.949177314
60 2.041268649 -4.358438372
61 0.489265918 2.041268649
62 4.315682550 0.489265918
63 -0.356564360 4.315682550
64 0.272551555 -0.356564360
65 3.026222280 0.272551555
66 2.690298485 3.026222280
67 -3.191859263 2.690298485
68 1.653563107 -3.191859263
69 0.990075911 1.653563107
70 1.217577783 0.990075911
71 0.493707099 1.217577783
72 3.138639899 0.493707099
73 1.407133688 3.138639899
74 1.935967145 1.407133688
75 -0.316985605 1.935967145
76 1.319437990 -0.316985605
77 1.812870573 1.319437990
78 5.397787181 1.812870573
79 -1.656936083 5.397787181
80 3.355811465 -1.656936083
81 6.321648884 3.355811465
82 0.677026452 6.321648884
83 1.899687306 0.677026452
84 3.871778226 1.899687306
85 2.664142022 3.871778226
86 -0.909316467 2.664142022
87 4.529784312 -0.909316467
88 -0.232471000 4.529784312
89 0.413421669 -0.232471000
90 3.588524094 0.413421669
91 2.171721023 3.588524094
92 1.026037985 2.171721023
93 -2.296637713 1.026037985
94 5.557098633 -2.296637713
95 2.536562347 5.557098633
96 3.069688046 2.536562347
97 0.944156474 3.069688046
98 0.208838375 0.944156474
99 -1.393193738 0.208838375
100 -0.628073019 -1.393193738
101 -0.315434857 -0.628073019
102 -3.757841165 -0.315434857
103 1.300410643 -3.757841165
104 7.171689333 1.300410643
105 -6.694496422 7.171689333
106 -3.729543854 -6.694496422
107 -4.946102927 -3.729543854
108 -0.681498154 -4.946102927
109 -0.010271646 -0.681498154
110 -0.306013227 -0.010271646
111 5.934713235 -0.306013227
112 -2.394392942 5.934713235
113 -8.203262571 -2.394392942
114 -2.068890088 -8.203262571
115 1.585345006 -2.068890088
116 -8.096010482 1.585345006
117 -3.916395843 -8.096010482
118 1.808178777 -3.916395843
119 -7.404347105 1.808178777
120 -3.413061386 -7.404347105
121 -5.158129586 -3.413061386
122 -4.310749053 -5.158129586
123 2.149913723 -4.310749053
124 0.790357225 2.149913723
125 1.574824544 0.790357225
126 0.389890598 1.574824544
127 2.412832871 0.389890598
128 4.459948311 2.412832871
129 0.831579685 4.459948311
130 -4.382424560 0.831579685
131 1.097487275 -4.382424560
132 2.294426007 1.097487275
133 1.002640489 2.294426007
134 3.232691198 1.002640489
135 -0.594071015 3.232691198
136 -2.412450301 -0.594071015
137 -9.125352486 -2.412450301
138 2.527713195 -9.125352486
139 -6.611888371 2.527713195
140 -9.154921387 -6.611888371
141 -2.106233485 -9.154921387
142 -7.692381716 -2.106233485
143 -0.482213861 -7.692381716
144 0.129123412 -0.482213861
145 -1.820549747 0.129123412
146 -2.647263733 -1.820549747
147 -2.137710451 -2.647263733
148 -4.110809432 -2.137710451
149 -0.143083791 -4.110809432
150 -1.304824850 -0.143083791
151 0.632136011 -1.304824850
152 5.805580515 0.632136011
153 -8.959146634 5.805580515
154 -0.970778269 -8.959146634
155 -0.461477688 -0.970778269
156 0.323716015 -0.461477688
157 0.069487424 0.323716015
158 2.291613951 0.069487424
> 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/freestat/rcomp/tmp/7czby1290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8czby1290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9nqs11290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10nqs11290506928.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/118qq71290506928.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/freestat/rcomp/tmp/12ur7v1290506928.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/freestat/rcomp/tmp/138j541290506928.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/freestat/rcomp/tmp/14b13a1290506928.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/freestat/rcomp/tmp/15xkky1290506928.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/freestat/rcomp/tmp/160k0l1290506928.tab")
+ }
>
> try(system("convert tmp/1ypdq1290506928.ps tmp/1ypdq1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/29gcb1290506928.ps tmp/29gcb1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/39gcb1290506928.ps tmp/39gcb1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/49gcb1290506928.ps tmp/49gcb1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kque1290506928.ps tmp/5kque1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kque1290506928.ps tmp/6kque1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/7czby1290506928.ps tmp/7czby1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/8czby1290506928.ps tmp/8czby1290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nqs11290506928.ps tmp/9nqs11290506928.png",intern=TRUE))
character(0)
> try(system("convert tmp/10nqs11290506928.ps tmp/10nqs11290506928.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.837 2.699 11.878