R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(25
+ ,11
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+ ,24)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'O'
+ ,'PS')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('CM','D','PE','PC','O','PS'),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
> 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
CM D PE PC O PS
1 25 11 7 8 23 25
2 17 6 17 8 25 30
3 18 8 12 9 19 22
4 16 10 12 7 29 22
5 20 10 11 4 25 25
6 16 11 11 11 21 23
7 18 16 12 7 22 17
8 17 11 13 7 25 21
9 30 12 16 10 18 19
10 23 8 11 10 22 15
11 18 12 10 8 15 16
12 21 9 9 9 20 22
13 31 14 17 11 20 23
14 27 15 11 9 21 23
15 21 9 14 13 21 19
16 16 8 15 9 24 23
17 20 9 15 6 24 25
18 17 9 13 6 23 22
19 25 16 18 16 24 26
20 26 11 18 5 18 29
21 25 8 12 7 25 32
22 17 9 17 9 21 25
23 32 12 18 12 22 28
24 22 9 14 9 23 25
25 17 9 16 5 23 25
26 20 14 14 10 24 18
27 29 10 12 8 23 25
28 23 14 17 7 21 25
29 20 10 12 8 28 20
30 11 6 6 4 16 15
31 26 13 12 8 29 24
32 22 10 12 8 27 26
33 14 15 13 8 16 14
34 19 12 14 7 28 24
35 20 11 11 8 25 25
36 28 8 12 7 22 20
37 19 9 9 7 23 21
38 30 9 15 9 26 27
39 29 15 18 11 23 23
40 26 9 15 6 25 25
41 23 10 12 8 21 20
42 21 12 14 9 24 22
43 28 11 13 6 22 25
44 23 14 13 10 27 25
45 18 6 11 8 26 17
46 20 8 16 10 24 25
47 21 10 11 5 24 26
48 28 12 16 14 22 27
49 10 5 8 6 24 19
50 22 10 15 6 20 22
51 31 10 21 12 26 32
52 29 13 18 12 21 21
53 22 10 13 8 19 18
54 23 10 15 10 21 23
55 20 9 19 10 16 20
56 18 8 15 10 22 21
57 25 14 11 5 15 17
58 21 8 10 7 17 18
59 24 9 13 10 15 19
60 25 14 15 11 21 22
61 13 8 12 7 19 14
62 28 8 16 12 24 18
63 25 7 18 11 17 35
64 9 6 8 11 23 29
65 16 8 13 5 24 21
66 19 6 17 8 14 25
67 29 11 7 4 22 26
68 14 11 12 7 16 17
69 22 14 14 11 19 25
70 15 8 6 6 25 20
71 15 8 10 4 24 22
72 20 11 11 8 26 24
73 18 10 14 9 26 21
74 33 14 11 8 25 26
75 22 11 13 11 18 24
76 16 9 12 8 21 16
77 16 8 9 4 23 18
78 18 13 12 6 20 19
79 18 12 13 9 13 21
80 22 13 12 13 15 22
81 30 14 9 9 14 23
82 30 12 15 10 22 29
83 24 14 24 20 10 21
84 21 13 17 11 22 23
85 29 16 11 6 24 27
86 31 9 17 9 19 25
87 20 9 11 7 20 21
88 16 9 12 9 13 10
89 22 8 14 10 20 20
90 20 7 11 9 22 26
91 28 16 16 8 24 24
92 38 11 21 7 29 29
93 22 9 14 6 12 19
94 20 11 20 13 20 24
95 17 9 13 6 21 19
96 22 13 15 10 22 22
97 31 16 19 16 20 17
98 24 14 11 12 26 24
99 18 12 10 8 23 19
100 23 13 14 12 24 19
101 15 11 11 8 22 23
102 12 4 15 4 28 27
103 15 8 11 8 12 14
104 20 8 17 7 24 22
105 34 16 18 11 20 21
106 31 14 10 8 23 18
107 19 11 11 8 28 20
108 21 9 13 9 24 19
109 22 9 16 9 23 24
110 24 10 9 6 29 25
111 32 16 9 6 26 29
112 33 11 9 6 22 28
113 13 16 12 5 22 17
114 25 12 12 7 23 29
115 29 14 18 10 30 26
116 18 10 15 8 17 14
117 20 10 10 8 23 26
118 15 12 11 8 25 20
119 33 14 9 6 24 32
120 26 16 5 4 24 23
121 18 9 12 8 24 21
122 28 8 24 20 20 30
123 17 8 14 6 22 24
124 12 7 7 4 28 22
125 17 9 12 9 25 24
126 21 10 13 6 24 24
127 18 13 8 9 24 24
128 10 10 11 5 23 19
129 29 11 9 5 30 31
130 31 8 11 8 24 22
131 19 9 13 8 21 27
132 9 13 10 6 25 19
133 13 14 13 6 25 21
134 19 12 10 8 29 23
135 21 12 13 8 22 19
136 23 14 8 5 27 19
137 21 11 16 7 24 20
138 15 14 9 8 29 23
139 19 10 12 7 21 17
140 26 14 14 8 24 17
141 16 11 9 5 23 17
142 19 9 11 10 27 21
143 31 16 14 9 25 21
144 19 9 12 7 21 18
145 15 7 12 6 21 19
146 23 14 11 10 29 20
147 17 14 12 6 21 15
148 21 8 9 11 20 24
149 17 11 9 6 19 20
150 25 14 15 9 24 22
151 20 11 8 4 13 13
152 19 20 8 7 25 19
153 20 11 17 8 23 21
154 17 9 11 5 26 23
155 21 10 12 8 23 16
156 26 13 20 10 22 26
157 17 8 12 9 24 21
158 21 15 7 5 24 21
159 28 14 11 8 24 24
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D PE PC O PS
-1.9716 0.8101 0.2513 0.1885 -0.1157 0.5661
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.7273 -2.4896 -0.3354 2.7482 12.5424
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.97156 3.05291 -0.646 0.5194
D 0.81012 0.13033 6.216 4.63e-09 ***
PE 0.25125 0.13276 1.893 0.0603 .
PC 0.18852 0.16826 1.120 0.2643
O -0.11572 0.10302 -1.123 0.2631
PS 0.56606 0.09581 5.908 2.17e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.478 on 153 degrees of freedom
Multiple R-squared: 0.4072, Adjusted R-squared: 0.3878
F-statistic: 21.02 on 5 and 153 DF, p-value: 5.863e-16
> 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,] 0.88674240 0.22651520 0.11325760
[2,] 0.84747659 0.30504683 0.15252341
[3,] 0.86900624 0.26198752 0.13099376
[4,] 0.79574823 0.40850354 0.20425177
[5,] 0.82472208 0.35055585 0.17527792
[6,] 0.76892216 0.46215568 0.23107784
[7,] 0.70304026 0.59391949 0.29695974
[8,] 0.65962384 0.68075232 0.34037616
[9,] 0.58082518 0.83834964 0.41917482
[10,] 0.49718291 0.99436582 0.50281709
[11,] 0.47987231 0.95974461 0.52012769
[12,] 0.40204297 0.80408594 0.59795703
[13,] 0.39584443 0.79168886 0.60415557
[14,] 0.41844812 0.83689625 0.58155188
[15,] 0.47365316 0.94730631 0.52634684
[16,] 0.40411470 0.80822941 0.59588530
[17,] 0.36394444 0.72788887 0.63605556
[18,] 0.30275734 0.60551468 0.69724266
[19,] 0.42356033 0.84712066 0.57643967
[20,] 0.37422826 0.74845651 0.62577174
[21,] 0.34494847 0.68989694 0.65505153
[22,] 0.31490556 0.62981112 0.68509444
[23,] 0.30547286 0.61094571 0.69452714
[24,] 0.25272001 0.50544002 0.74727999
[25,] 0.31287434 0.62574868 0.68712566
[26,] 0.27715796 0.55431592 0.72284204
[27,] 0.24257529 0.48515057 0.75742471
[28,] 0.50160099 0.99679802 0.49839901
[29,] 0.44453253 0.88906505 0.55546747
[30,] 0.53051024 0.93897952 0.46948976
[31,] 0.50305306 0.99389389 0.49694694
[32,] 0.52187093 0.95625813 0.47812907
[33,] 0.49451727 0.98903455 0.50548273
[34,] 0.44508813 0.89017626 0.55491187
[35,] 0.47407096 0.94814191 0.52592904
[36,] 0.43287848 0.86575695 0.56712152
[37,] 0.40690099 0.81380198 0.59309901
[38,] 0.37130862 0.74261723 0.62869138
[39,] 0.32326959 0.64653918 0.67673041
[40,] 0.27809439 0.55618877 0.72190561
[41,] 0.28088983 0.56177966 0.71911017
[42,] 0.24210683 0.48421366 0.75789317
[43,] 0.21184868 0.42369736 0.78815132
[44,] 0.20569185 0.41138370 0.79430815
[45,] 0.18396473 0.36792946 0.81603527
[46,] 0.15236058 0.30472116 0.84763942
[47,] 0.13128592 0.26257183 0.86871408
[48,] 0.11267202 0.22534403 0.88732798
[49,] 0.11190798 0.22381597 0.88809202
[50,] 0.10487126 0.20974251 0.89512874
[51,] 0.09707690 0.19415381 0.90292310
[52,] 0.07758583 0.15517166 0.92241417
[53,] 0.06527728 0.13055455 0.93472272
[54,] 0.13565847 0.27131695 0.86434153
[55,] 0.12860370 0.25720741 0.87139630
[56,] 0.33248187 0.66496374 0.66751813
[57,] 0.29927187 0.59854374 0.70072813
[58,] 0.27227739 0.54455478 0.72772261
[59,] 0.36757680 0.73515360 0.63242320
[60,] 0.38805009 0.77610017 0.61194991
[61,] 0.39373448 0.78746896 0.60626552
[62,] 0.35042166 0.70084332 0.64957834
[63,] 0.32043543 0.64087087 0.67956457
[64,] 0.28483845 0.56967690 0.71516155
[65,] 0.25724077 0.51448153 0.74275923
[66,] 0.33492245 0.66984491 0.66507755
[67,] 0.29957819 0.59915638 0.70042181
[68,] 0.26236691 0.52473383 0.73763309
[69,] 0.22725944 0.45451888 0.77274056
[70,] 0.21070312 0.42140624 0.78929688
[71,] 0.22112612 0.44225223 0.77887388
[72,] 0.19992041 0.39984081 0.80007959
[73,] 0.21243391 0.42486781 0.78756609
[74,] 0.19146549 0.38293098 0.80853451
[75,] 0.22484902 0.44969804 0.77515098
[76,] 0.22844979 0.45689958 0.77155021
[77,] 0.19666029 0.39332057 0.80333971
[78,] 0.25766626 0.51533251 0.74233374
[79,] 0.22262738 0.44525477 0.77737262
[80,] 0.19459344 0.38918687 0.80540656
[81,] 0.17759149 0.35518299 0.82240851
[82,] 0.14877898 0.29755796 0.85122102
[83,] 0.12344004 0.24688008 0.87655996
[84,] 0.30358648 0.60717296 0.69641352
[85,] 0.27441353 0.54882706 0.72558647
[86,] 0.30502017 0.61004034 0.69497983
[87,] 0.26760484 0.53520968 0.73239516
[88,] 0.24057690 0.48115380 0.75942310
[89,] 0.23497986 0.46995972 0.76502014
[90,] 0.20423834 0.40847668 0.79576166
[91,] 0.17811832 0.35623663 0.82188168
[92,] 0.14851627 0.29703253 0.85148373
[93,] 0.18997312 0.37994624 0.81002688
[94,] 0.19751458 0.39502917 0.80248542
[95,] 0.16735604 0.33471208 0.83264396
[96,] 0.14007051 0.28014103 0.85992949
[97,] 0.17217352 0.34434704 0.82782648
[98,] 0.32657329 0.65314658 0.67342671
[99,] 0.28512556 0.57025111 0.71487444
[100,] 0.26381743 0.52763485 0.73618257
[101,] 0.22422023 0.44844046 0.77577977
[102,] 0.21242735 0.42485470 0.78757265
[103,] 0.20155882 0.40311764 0.79844118
[104,] 0.31888570 0.63777139 0.68111430
[105,] 0.44324875 0.88649750 0.55675125
[106,] 0.39341895 0.78683791 0.60658105
[107,] 0.37022930 0.74045859 0.62977070
[108,] 0.32197451 0.64394901 0.67802549
[109,] 0.28715543 0.57431087 0.71284457
[110,] 0.29527429 0.59054858 0.70472571
[111,] 0.31569896 0.63139792 0.68430104
[112,] 0.31196258 0.62392516 0.68803742
[113,] 0.26610284 0.53220568 0.73389716
[114,] 0.24536551 0.49073103 0.75463449
[115,] 0.21667749 0.43335497 0.78332251
[116,] 0.18941644 0.37883288 0.81058356
[117,] 0.18129013 0.36258025 0.81870987
[118,] 0.14580262 0.29160525 0.85419738
[119,] 0.15092756 0.30185511 0.84907244
[120,] 0.21149653 0.42299306 0.78850347
[121,] 0.34882543 0.69765086 0.65117457
[122,] 0.78600776 0.42798447 0.21399224
[123,] 0.74294757 0.51410485 0.25705243
[124,] 0.91883229 0.16233543 0.08116771
[125,] 0.97555929 0.04888143 0.02444071
[126,] 0.96301159 0.07397682 0.03698841
[127,] 0.94515687 0.10968626 0.05484313
[128,] 0.96210337 0.07579325 0.03789663
[129,] 0.94178516 0.11642968 0.05821484
[130,] 0.96298062 0.07403875 0.03701938
[131,] 0.94145451 0.11709098 0.05854549
[132,] 0.93914615 0.12170769 0.06085385
[133,] 0.90588992 0.18822016 0.09411008
[134,] 0.85977552 0.28044896 0.14022448
[135,] 0.93750746 0.12498508 0.06249254
[136,] 0.89858814 0.20282371 0.10141186
[137,] 0.85515631 0.28968739 0.14484369
[138,] 0.80560010 0.38879981 0.19439990
[139,] 0.74749607 0.50500785 0.25250393
[140,] 0.63520967 0.72958066 0.36479033
[141,] 0.64155719 0.71688561 0.35844281
[142,] 0.50979089 0.98041822 0.49020911
> postscript(file="/var/www/rcomp/tmp/1x5vj1292918923.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2x5vj1292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/38edm1292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/48edm1292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/58edm1292918923.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
3.30317248 -5.75762753 -1.47592131 -3.56194532 -0.90620471 -6.36670620
7 8 9 10 11 12
-4.40239954 -3.52013359 7.67252311 7.89642341 -1.09187945 1.58343372
13 14 15 16 17 18
4.57967991 1.76983335 1.38700368 -4.21715322 -1.59385095 -2.50886805
19 20 21 22 23 24
-5.46973622 -0.73791368 0.93478062 -6.00907079 4.16126995 -0.02387250
25 26 27 28 29 30
-4.77230448 -2.18484145 6.85702899 -3.68265576 1.26594676 -0.79025918
31 32 33 34 35 36
2.68703270 -0.24616086 -7.02816544 -3.93254992 -2.47040444 10.38040215
37 38 39 40 41 42
0.87369237 6.93990050 1.86545801 4.52186780 3.45591557 -1.64033287
43 44 45 46 47 48
5.05696974 -2.54888532 4.22445539 -1.78905525 -0.77650707 0.85280436
49 50 51 52 53 54
-3.19818879 0.83134401 2.22637389 4.19788038 3.10535412 0.62692272
55 56 57 58 59 60
-1.44836644 -1.50497988 4.03610951 4.43644527 4.50950124 -0.23602934
61 62 63 64 65 66
-1.57036519 9.79636083 -3.14063607 -11.72727421 -1.82844117 -2.20020963
67 68 69 70 71 72
7.37546414 -5.04608941 -4.91440766 -0.57640121 -2.45222637 -1.78862089
73 74 75 76 77 78
-2.22258154 7.53315720 -1.78243444 -0.46970066 0.94756774 -3.14707429
79 80 81 82 83 84
-5.09592061 -2.74349408 5.27243388 2.72600355 -5.90082233 -4.37875809
85 86 87 88 89 90
1.60816222 7.75949173 1.02402894 1.81241949 3.08090105 -0.33164622
91 92 93 94 95 96
0.67305104 11.40419406 2.66516664 -5.68680977 -1.04211109 -2.12166727
97 98 99 100 101 102
4.91071853 -0.97306965 -1.86432396 0.68218064 -6.68543104 -5.83544542
103 104 105 106 107 108
-0.31766159 0.22344200 6.84030690 10.08149182 -0.29292415 2.73948872
109 110 111 112 113 114
0.03968511 3.68213985 4.20997728 9.36378971 -9.02536193 -0.83896049
115 116 117 118 119 120
1.97593809 0.63566868 -2.20652862 -5.45020489 4.90059439 2.75698069
121 122 123 124 125 126
-0.95286850 -0.97747114 -3.19784550 -3.42546609 -3.72386300 -0.33540345
127 128 129 130 131 132
-5.07506540 -7.92977211 4.77986400 12.54244481 -3.94766720 -11.06597338
133 134 135 136 137 138
-9.76198833 -2.43427076 0.26619649 3.04636559 0.17645168 -7.80326620
139 140 141 142 143 144
1.34262882 4.75826097 -1.10525980 0.26850372 5.80095262 1.58668852
145 146 147 148 149 150
-1.17060845 1.01538343 -2.57722082 0.88439100 -2.45484801 0.48816450
151 152 153 154 155 156
4.44158444 -6.42285660 -1.94510428 -2.03675063 3.95161231 -1.64219453
157 158 159
-1.33126279 -0.99179118 3.54956808
> postscript(file="/var/www/rcomp/tmp/6i5c61292918923.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 3.30317248 NA
1 -5.75762753 3.30317248
2 -1.47592131 -5.75762753
3 -3.56194532 -1.47592131
4 -0.90620471 -3.56194532
5 -6.36670620 -0.90620471
6 -4.40239954 -6.36670620
7 -3.52013359 -4.40239954
8 7.67252311 -3.52013359
9 7.89642341 7.67252311
10 -1.09187945 7.89642341
11 1.58343372 -1.09187945
12 4.57967991 1.58343372
13 1.76983335 4.57967991
14 1.38700368 1.76983335
15 -4.21715322 1.38700368
16 -1.59385095 -4.21715322
17 -2.50886805 -1.59385095
18 -5.46973622 -2.50886805
19 -0.73791368 -5.46973622
20 0.93478062 -0.73791368
21 -6.00907079 0.93478062
22 4.16126995 -6.00907079
23 -0.02387250 4.16126995
24 -4.77230448 -0.02387250
25 -2.18484145 -4.77230448
26 6.85702899 -2.18484145
27 -3.68265576 6.85702899
28 1.26594676 -3.68265576
29 -0.79025918 1.26594676
30 2.68703270 -0.79025918
31 -0.24616086 2.68703270
32 -7.02816544 -0.24616086
33 -3.93254992 -7.02816544
34 -2.47040444 -3.93254992
35 10.38040215 -2.47040444
36 0.87369237 10.38040215
37 6.93990050 0.87369237
38 1.86545801 6.93990050
39 4.52186780 1.86545801
40 3.45591557 4.52186780
41 -1.64033287 3.45591557
42 5.05696974 -1.64033287
43 -2.54888532 5.05696974
44 4.22445539 -2.54888532
45 -1.78905525 4.22445539
46 -0.77650707 -1.78905525
47 0.85280436 -0.77650707
48 -3.19818879 0.85280436
49 0.83134401 -3.19818879
50 2.22637389 0.83134401
51 4.19788038 2.22637389
52 3.10535412 4.19788038
53 0.62692272 3.10535412
54 -1.44836644 0.62692272
55 -1.50497988 -1.44836644
56 4.03610951 -1.50497988
57 4.43644527 4.03610951
58 4.50950124 4.43644527
59 -0.23602934 4.50950124
60 -1.57036519 -0.23602934
61 9.79636083 -1.57036519
62 -3.14063607 9.79636083
63 -11.72727421 -3.14063607
64 -1.82844117 -11.72727421
65 -2.20020963 -1.82844117
66 7.37546414 -2.20020963
67 -5.04608941 7.37546414
68 -4.91440766 -5.04608941
69 -0.57640121 -4.91440766
70 -2.45222637 -0.57640121
71 -1.78862089 -2.45222637
72 -2.22258154 -1.78862089
73 7.53315720 -2.22258154
74 -1.78243444 7.53315720
75 -0.46970066 -1.78243444
76 0.94756774 -0.46970066
77 -3.14707429 0.94756774
78 -5.09592061 -3.14707429
79 -2.74349408 -5.09592061
80 5.27243388 -2.74349408
81 2.72600355 5.27243388
82 -5.90082233 2.72600355
83 -4.37875809 -5.90082233
84 1.60816222 -4.37875809
85 7.75949173 1.60816222
86 1.02402894 7.75949173
87 1.81241949 1.02402894
88 3.08090105 1.81241949
89 -0.33164622 3.08090105
90 0.67305104 -0.33164622
91 11.40419406 0.67305104
92 2.66516664 11.40419406
93 -5.68680977 2.66516664
94 -1.04211109 -5.68680977
95 -2.12166727 -1.04211109
96 4.91071853 -2.12166727
97 -0.97306965 4.91071853
98 -1.86432396 -0.97306965
99 0.68218064 -1.86432396
100 -6.68543104 0.68218064
101 -5.83544542 -6.68543104
102 -0.31766159 -5.83544542
103 0.22344200 -0.31766159
104 6.84030690 0.22344200
105 10.08149182 6.84030690
106 -0.29292415 10.08149182
107 2.73948872 -0.29292415
108 0.03968511 2.73948872
109 3.68213985 0.03968511
110 4.20997728 3.68213985
111 9.36378971 4.20997728
112 -9.02536193 9.36378971
113 -0.83896049 -9.02536193
114 1.97593809 -0.83896049
115 0.63566868 1.97593809
116 -2.20652862 0.63566868
117 -5.45020489 -2.20652862
118 4.90059439 -5.45020489
119 2.75698069 4.90059439
120 -0.95286850 2.75698069
121 -0.97747114 -0.95286850
122 -3.19784550 -0.97747114
123 -3.42546609 -3.19784550
124 -3.72386300 -3.42546609
125 -0.33540345 -3.72386300
126 -5.07506540 -0.33540345
127 -7.92977211 -5.07506540
128 4.77986400 -7.92977211
129 12.54244481 4.77986400
130 -3.94766720 12.54244481
131 -11.06597338 -3.94766720
132 -9.76198833 -11.06597338
133 -2.43427076 -9.76198833
134 0.26619649 -2.43427076
135 3.04636559 0.26619649
136 0.17645168 3.04636559
137 -7.80326620 0.17645168
138 1.34262882 -7.80326620
139 4.75826097 1.34262882
140 -1.10525980 4.75826097
141 0.26850372 -1.10525980
142 5.80095262 0.26850372
143 1.58668852 5.80095262
144 -1.17060845 1.58668852
145 1.01538343 -1.17060845
146 -2.57722082 1.01538343
147 0.88439100 -2.57722082
148 -2.45484801 0.88439100
149 0.48816450 -2.45484801
150 4.44158444 0.48816450
151 -6.42285660 4.44158444
152 -1.94510428 -6.42285660
153 -2.03675063 -1.94510428
154 3.95161231 -2.03675063
155 -1.64219453 3.95161231
156 -1.33126279 -1.64219453
157 -0.99179118 -1.33126279
158 3.54956808 -0.99179118
159 NA 3.54956808
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.75762753 3.30317248
[2,] -1.47592131 -5.75762753
[3,] -3.56194532 -1.47592131
[4,] -0.90620471 -3.56194532
[5,] -6.36670620 -0.90620471
[6,] -4.40239954 -6.36670620
[7,] -3.52013359 -4.40239954
[8,] 7.67252311 -3.52013359
[9,] 7.89642341 7.67252311
[10,] -1.09187945 7.89642341
[11,] 1.58343372 -1.09187945
[12,] 4.57967991 1.58343372
[13,] 1.76983335 4.57967991
[14,] 1.38700368 1.76983335
[15,] -4.21715322 1.38700368
[16,] -1.59385095 -4.21715322
[17,] -2.50886805 -1.59385095
[18,] -5.46973622 -2.50886805
[19,] -0.73791368 -5.46973622
[20,] 0.93478062 -0.73791368
[21,] -6.00907079 0.93478062
[22,] 4.16126995 -6.00907079
[23,] -0.02387250 4.16126995
[24,] -4.77230448 -0.02387250
[25,] -2.18484145 -4.77230448
[26,] 6.85702899 -2.18484145
[27,] -3.68265576 6.85702899
[28,] 1.26594676 -3.68265576
[29,] -0.79025918 1.26594676
[30,] 2.68703270 -0.79025918
[31,] -0.24616086 2.68703270
[32,] -7.02816544 -0.24616086
[33,] -3.93254992 -7.02816544
[34,] -2.47040444 -3.93254992
[35,] 10.38040215 -2.47040444
[36,] 0.87369237 10.38040215
[37,] 6.93990050 0.87369237
[38,] 1.86545801 6.93990050
[39,] 4.52186780 1.86545801
[40,] 3.45591557 4.52186780
[41,] -1.64033287 3.45591557
[42,] 5.05696974 -1.64033287
[43,] -2.54888532 5.05696974
[44,] 4.22445539 -2.54888532
[45,] -1.78905525 4.22445539
[46,] -0.77650707 -1.78905525
[47,] 0.85280436 -0.77650707
[48,] -3.19818879 0.85280436
[49,] 0.83134401 -3.19818879
[50,] 2.22637389 0.83134401
[51,] 4.19788038 2.22637389
[52,] 3.10535412 4.19788038
[53,] 0.62692272 3.10535412
[54,] -1.44836644 0.62692272
[55,] -1.50497988 -1.44836644
[56,] 4.03610951 -1.50497988
[57,] 4.43644527 4.03610951
[58,] 4.50950124 4.43644527
[59,] -0.23602934 4.50950124
[60,] -1.57036519 -0.23602934
[61,] 9.79636083 -1.57036519
[62,] -3.14063607 9.79636083
[63,] -11.72727421 -3.14063607
[64,] -1.82844117 -11.72727421
[65,] -2.20020963 -1.82844117
[66,] 7.37546414 -2.20020963
[67,] -5.04608941 7.37546414
[68,] -4.91440766 -5.04608941
[69,] -0.57640121 -4.91440766
[70,] -2.45222637 -0.57640121
[71,] -1.78862089 -2.45222637
[72,] -2.22258154 -1.78862089
[73,] 7.53315720 -2.22258154
[74,] -1.78243444 7.53315720
[75,] -0.46970066 -1.78243444
[76,] 0.94756774 -0.46970066
[77,] -3.14707429 0.94756774
[78,] -5.09592061 -3.14707429
[79,] -2.74349408 -5.09592061
[80,] 5.27243388 -2.74349408
[81,] 2.72600355 5.27243388
[82,] -5.90082233 2.72600355
[83,] -4.37875809 -5.90082233
[84,] 1.60816222 -4.37875809
[85,] 7.75949173 1.60816222
[86,] 1.02402894 7.75949173
[87,] 1.81241949 1.02402894
[88,] 3.08090105 1.81241949
[89,] -0.33164622 3.08090105
[90,] 0.67305104 -0.33164622
[91,] 11.40419406 0.67305104
[92,] 2.66516664 11.40419406
[93,] -5.68680977 2.66516664
[94,] -1.04211109 -5.68680977
[95,] -2.12166727 -1.04211109
[96,] 4.91071853 -2.12166727
[97,] -0.97306965 4.91071853
[98,] -1.86432396 -0.97306965
[99,] 0.68218064 -1.86432396
[100,] -6.68543104 0.68218064
[101,] -5.83544542 -6.68543104
[102,] -0.31766159 -5.83544542
[103,] 0.22344200 -0.31766159
[104,] 6.84030690 0.22344200
[105,] 10.08149182 6.84030690
[106,] -0.29292415 10.08149182
[107,] 2.73948872 -0.29292415
[108,] 0.03968511 2.73948872
[109,] 3.68213985 0.03968511
[110,] 4.20997728 3.68213985
[111,] 9.36378971 4.20997728
[112,] -9.02536193 9.36378971
[113,] -0.83896049 -9.02536193
[114,] 1.97593809 -0.83896049
[115,] 0.63566868 1.97593809
[116,] -2.20652862 0.63566868
[117,] -5.45020489 -2.20652862
[118,] 4.90059439 -5.45020489
[119,] 2.75698069 4.90059439
[120,] -0.95286850 2.75698069
[121,] -0.97747114 -0.95286850
[122,] -3.19784550 -0.97747114
[123,] -3.42546609 -3.19784550
[124,] -3.72386300 -3.42546609
[125,] -0.33540345 -3.72386300
[126,] -5.07506540 -0.33540345
[127,] -7.92977211 -5.07506540
[128,] 4.77986400 -7.92977211
[129,] 12.54244481 4.77986400
[130,] -3.94766720 12.54244481
[131,] -11.06597338 -3.94766720
[132,] -9.76198833 -11.06597338
[133,] -2.43427076 -9.76198833
[134,] 0.26619649 -2.43427076
[135,] 3.04636559 0.26619649
[136,] 0.17645168 3.04636559
[137,] -7.80326620 0.17645168
[138,] 1.34262882 -7.80326620
[139,] 4.75826097 1.34262882
[140,] -1.10525980 4.75826097
[141,] 0.26850372 -1.10525980
[142,] 5.80095262 0.26850372
[143,] 1.58668852 5.80095262
[144,] -1.17060845 1.58668852
[145,] 1.01538343 -1.17060845
[146,] -2.57722082 1.01538343
[147,] 0.88439100 -2.57722082
[148,] -2.45484801 0.88439100
[149,] 0.48816450 -2.45484801
[150,] 4.44158444 0.48816450
[151,] -6.42285660 4.44158444
[152,] -1.94510428 -6.42285660
[153,] -2.03675063 -1.94510428
[154,] 3.95161231 -2.03675063
[155,] -1.64219453 3.95161231
[156,] -1.33126279 -1.64219453
[157,] -0.99179118 -1.33126279
[158,] 3.54956808 -0.99179118
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.75762753 3.30317248
2 -1.47592131 -5.75762753
3 -3.56194532 -1.47592131
4 -0.90620471 -3.56194532
5 -6.36670620 -0.90620471
6 -4.40239954 -6.36670620
7 -3.52013359 -4.40239954
8 7.67252311 -3.52013359
9 7.89642341 7.67252311
10 -1.09187945 7.89642341
11 1.58343372 -1.09187945
12 4.57967991 1.58343372
13 1.76983335 4.57967991
14 1.38700368 1.76983335
15 -4.21715322 1.38700368
16 -1.59385095 -4.21715322
17 -2.50886805 -1.59385095
18 -5.46973622 -2.50886805
19 -0.73791368 -5.46973622
20 0.93478062 -0.73791368
21 -6.00907079 0.93478062
22 4.16126995 -6.00907079
23 -0.02387250 4.16126995
24 -4.77230448 -0.02387250
25 -2.18484145 -4.77230448
26 6.85702899 -2.18484145
27 -3.68265576 6.85702899
28 1.26594676 -3.68265576
29 -0.79025918 1.26594676
30 2.68703270 -0.79025918
31 -0.24616086 2.68703270
32 -7.02816544 -0.24616086
33 -3.93254992 -7.02816544
34 -2.47040444 -3.93254992
35 10.38040215 -2.47040444
36 0.87369237 10.38040215
37 6.93990050 0.87369237
38 1.86545801 6.93990050
39 4.52186780 1.86545801
40 3.45591557 4.52186780
41 -1.64033287 3.45591557
42 5.05696974 -1.64033287
43 -2.54888532 5.05696974
44 4.22445539 -2.54888532
45 -1.78905525 4.22445539
46 -0.77650707 -1.78905525
47 0.85280436 -0.77650707
48 -3.19818879 0.85280436
49 0.83134401 -3.19818879
50 2.22637389 0.83134401
51 4.19788038 2.22637389
52 3.10535412 4.19788038
53 0.62692272 3.10535412
54 -1.44836644 0.62692272
55 -1.50497988 -1.44836644
56 4.03610951 -1.50497988
57 4.43644527 4.03610951
58 4.50950124 4.43644527
59 -0.23602934 4.50950124
60 -1.57036519 -0.23602934
61 9.79636083 -1.57036519
62 -3.14063607 9.79636083
63 -11.72727421 -3.14063607
64 -1.82844117 -11.72727421
65 -2.20020963 -1.82844117
66 7.37546414 -2.20020963
67 -5.04608941 7.37546414
68 -4.91440766 -5.04608941
69 -0.57640121 -4.91440766
70 -2.45222637 -0.57640121
71 -1.78862089 -2.45222637
72 -2.22258154 -1.78862089
73 7.53315720 -2.22258154
74 -1.78243444 7.53315720
75 -0.46970066 -1.78243444
76 0.94756774 -0.46970066
77 -3.14707429 0.94756774
78 -5.09592061 -3.14707429
79 -2.74349408 -5.09592061
80 5.27243388 -2.74349408
81 2.72600355 5.27243388
82 -5.90082233 2.72600355
83 -4.37875809 -5.90082233
84 1.60816222 -4.37875809
85 7.75949173 1.60816222
86 1.02402894 7.75949173
87 1.81241949 1.02402894
88 3.08090105 1.81241949
89 -0.33164622 3.08090105
90 0.67305104 -0.33164622
91 11.40419406 0.67305104
92 2.66516664 11.40419406
93 -5.68680977 2.66516664
94 -1.04211109 -5.68680977
95 -2.12166727 -1.04211109
96 4.91071853 -2.12166727
97 -0.97306965 4.91071853
98 -1.86432396 -0.97306965
99 0.68218064 -1.86432396
100 -6.68543104 0.68218064
101 -5.83544542 -6.68543104
102 -0.31766159 -5.83544542
103 0.22344200 -0.31766159
104 6.84030690 0.22344200
105 10.08149182 6.84030690
106 -0.29292415 10.08149182
107 2.73948872 -0.29292415
108 0.03968511 2.73948872
109 3.68213985 0.03968511
110 4.20997728 3.68213985
111 9.36378971 4.20997728
112 -9.02536193 9.36378971
113 -0.83896049 -9.02536193
114 1.97593809 -0.83896049
115 0.63566868 1.97593809
116 -2.20652862 0.63566868
117 -5.45020489 -2.20652862
118 4.90059439 -5.45020489
119 2.75698069 4.90059439
120 -0.95286850 2.75698069
121 -0.97747114 -0.95286850
122 -3.19784550 -0.97747114
123 -3.42546609 -3.19784550
124 -3.72386300 -3.42546609
125 -0.33540345 -3.72386300
126 -5.07506540 -0.33540345
127 -7.92977211 -5.07506540
128 4.77986400 -7.92977211
129 12.54244481 4.77986400
130 -3.94766720 12.54244481
131 -11.06597338 -3.94766720
132 -9.76198833 -11.06597338
133 -2.43427076 -9.76198833
134 0.26619649 -2.43427076
135 3.04636559 0.26619649
136 0.17645168 3.04636559
137 -7.80326620 0.17645168
138 1.34262882 -7.80326620
139 4.75826097 1.34262882
140 -1.10525980 4.75826097
141 0.26850372 -1.10525980
142 5.80095262 0.26850372
143 1.58668852 5.80095262
144 -1.17060845 1.58668852
145 1.01538343 -1.17060845
146 -2.57722082 1.01538343
147 0.88439100 -2.57722082
148 -2.45484801 0.88439100
149 0.48816450 -2.45484801
150 4.44158444 0.48816450
151 -6.42285660 4.44158444
152 -1.94510428 -6.42285660
153 -2.03675063 -1.94510428
154 3.95161231 -2.03675063
155 -1.64219453 3.95161231
156 -1.33126279 -1.64219453
157 -0.99179118 -1.33126279
158 3.54956808 -0.99179118
> 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/rcomp/tmp/7twt91292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8twt91292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9twt91292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/1046su1292918923.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/1176r01292918923.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/rcomp/tmp/12s6761292918923.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/rcomp/tmp/137gnx1292918923.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/rcomp/tmp/14sh431292918923.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/rcomp/tmp/15vz2r1292918923.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/rcomp/tmp/16z0jf1292918923.tab")
+ }
>
> try(system("convert tmp/1x5vj1292918923.ps tmp/1x5vj1292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/2x5vj1292918923.ps tmp/2x5vj1292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/38edm1292918923.ps tmp/38edm1292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/48edm1292918923.ps tmp/48edm1292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/58edm1292918923.ps tmp/58edm1292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/6i5c61292918923.ps tmp/6i5c61292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/7twt91292918923.ps tmp/7twt91292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/8twt91292918923.ps tmp/8twt91292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/9twt91292918923.ps tmp/9twt91292918923.png",intern=TRUE))
character(0)
> try(system("convert tmp/1046su1292918923.ps tmp/1046su1292918923.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.600 0.860 5.433