R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(280,1258,557,1199,831,1158,1081,1427,1318,934,1578,709,1859,1186,2141,986,2428,1033,2715,1257,3004,1105,3309,1179,269,1092,537,1092,813,1087,1068,2028,1411,2039,1675,2010,1958,754,2242,760,2524,715,2836,855,3143,971,3522,815,285,915,574,843,865,761,1147,1858,1516,2968,1789,4061,2087,3661,2372,3269,2669,2857,2966,2568,3270,2274,3652,1987,329,683,658,381,988,71,1303,1772,1603,3485,1929,5181,2235,4479,2544,3782,2872,3067,3198,2489,3544,1903,3903,1330,332,736,665,483,1001,242,1329,1334,1639,2423,1975,3523,2304,2986,2640,2462,2992,1908,3330,1575,3690,1237,4063,904),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 280 1258 1 0 0 0 0 0 0 0 0 0 0 1
2 557 1199 0 1 0 0 0 0 0 0 0 0 0 2
3 831 1158 0 0 1 0 0 0 0 0 0 0 0 3
4 1081 1427 0 0 0 1 0 0 0 0 0 0 0 4
5 1318 934 0 0 0 0 1 0 0 0 0 0 0 5
6 1578 709 0 0 0 0 0 1 0 0 0 0 0 6
7 1859 1186 0 0 0 0 0 0 1 0 0 0 0 7
8 2141 986 0 0 0 0 0 0 0 1 0 0 0 8
9 2428 1033 0 0 0 0 0 0 0 0 1 0 0 9
10 2715 1257 0 0 0 0 0 0 0 0 0 1 0 10
11 3004 1105 0 0 0 0 0 0 0 0 0 0 1 11
12 3309 1179 0 0 0 0 0 0 0 0 0 0 0 12
13 269 1092 1 0 0 0 0 0 0 0 0 0 0 13
14 537 1092 0 1 0 0 0 0 0 0 0 0 0 14
15 813 1087 0 0 1 0 0 0 0 0 0 0 0 15
16 1068 2028 0 0 0 1 0 0 0 0 0 0 0 16
17 1411 2039 0 0 0 0 1 0 0 0 0 0 0 17
18 1675 2010 0 0 0 0 0 1 0 0 0 0 0 18
19 1958 754 0 0 0 0 0 0 1 0 0 0 0 19
20 2242 760 0 0 0 0 0 0 0 1 0 0 0 20
21 2524 715 0 0 0 0 0 0 0 0 1 0 0 21
22 2836 855 0 0 0 0 0 0 0 0 0 1 0 22
23 3143 971 0 0 0 0 0 0 0 0 0 0 1 23
24 3522 815 0 0 0 0 0 0 0 0 0 0 0 24
25 285 915 1 0 0 0 0 0 0 0 0 0 0 25
26 574 843 0 1 0 0 0 0 0 0 0 0 0 26
27 865 761 0 0 1 0 0 0 0 0 0 0 0 27
28 1147 1858 0 0 0 1 0 0 0 0 0 0 0 28
29 1516 2968 0 0 0 0 1 0 0 0 0 0 0 29
30 1789 4061 0 0 0 0 0 1 0 0 0 0 0 30
31 2087 3661 0 0 0 0 0 0 1 0 0 0 0 31
32 2372 3269 0 0 0 0 0 0 0 1 0 0 0 32
33 2669 2857 0 0 0 0 0 0 0 0 1 0 0 33
34 2966 2568 0 0 0 0 0 0 0 0 0 1 0 34
35 3270 2274 0 0 0 0 0 0 0 0 0 0 1 35
36 3652 1987 0 0 0 0 0 0 0 0 0 0 0 36
37 329 683 1 0 0 0 0 0 0 0 0 0 0 37
38 658 381 0 1 0 0 0 0 0 0 0 0 0 38
39 988 71 0 0 1 0 0 0 0 0 0 0 0 39
40 1303 1772 0 0 0 1 0 0 0 0 0 0 0 40
41 1603 3485 0 0 0 0 1 0 0 0 0 0 0 41
42 1929 5181 0 0 0 0 0 1 0 0 0 0 0 42
43 2235 4479 0 0 0 0 0 0 1 0 0 0 0 43
44 2544 3782 0 0 0 0 0 0 0 1 0 0 0 44
45 2872 3067 0 0 0 0 0 0 0 0 1 0 0 45
46 3198 2489 0 0 0 0 0 0 0 0 0 1 0 46
47 3544 1903 0 0 0 0 0 0 0 0 0 0 1 47
48 3903 1330 0 0 0 0 0 0 0 0 0 0 0 48
49 332 736 1 0 0 0 0 0 0 0 0 0 0 49
50 665 483 0 1 0 0 0 0 0 0 0 0 0 50
51 1001 242 0 0 1 0 0 0 0 0 0 0 0 51
52 1329 1334 0 0 0 1 0 0 0 0 0 0 0 52
53 1639 2423 0 0 0 0 1 0 0 0 0 0 0 53
54 1975 3523 0 0 0 0 0 1 0 0 0 0 0 54
55 2304 2986 0 0 0 0 0 0 1 0 0 0 0 55
56 2640 2462 0 0 0 0 0 0 0 1 0 0 0 56
57 2992 1908 0 0 0 0 0 0 0 0 1 0 0 57
58 3330 1575 0 0 0 0 0 0 0 0 0 1 0 58
59 3690 1237 0 0 0 0 0 0 0 0 0 0 1 59
60 4063 904 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
3.354e+03 2.632e-02 -3.290e+03 -2.996e+03 -2.699e+03 -2.449e+03
M5 M6 M7 M8 M9 M10
-2.163e+03 -1.899e+03 -1.595e+03 -1.295e+03 -9.853e+02 -6.773e+02
M11 t
-3.579e+02 8.410e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-177.27 -48.72 -12.53 44.04 180.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.354e+03 4.711e+01 71.201 < 2e-16 ***
X 2.632e-02 1.430e-02 1.841 0.072 .
M1 -3.290e+03 5.648e+01 -58.250 < 2e-16 ***
M2 -2.996e+03 5.650e+01 -53.023 < 2e-16 ***
M3 -2.699e+03 5.661e+01 -47.682 < 2e-16 ***
M4 -2.449e+03 5.687e+01 -43.054 < 2e-16 ***
M5 -2.163e+03 5.899e+01 -36.671 < 2e-16 ***
M6 -1.899e+03 6.277e+01 -30.250 < 2e-16 ***
M7 -1.595e+03 5.986e+01 -26.649 < 2e-16 ***
M8 -1.295e+03 5.815e+01 -22.269 < 2e-16 ***
M9 -9.853e+02 5.698e+01 -17.292 < 2e-16 ***
M10 -6.773e+02 5.653e+01 -11.981 9.60e-16 ***
M11 -3.579e+02 5.612e+01 -6.377 7.85e-08 ***
t 8.410e+00 7.253e-01 11.595 3.00e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 88.51 on 46 degrees of freedom
Multiple R-squared: 0.9947, Adjusted R-squared: 0.9932
F-statistic: 666.3 on 13 and 46 DF, p-value: < 2.2e-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,] 0.04324202 0.0864840312 0.9567579844
[2,] 0.01242042 0.0248408405 0.9875795797
[3,] 0.14104967 0.2820993363 0.8589503318
[4,] 0.14472199 0.2894439721 0.8552780140
[5,] 0.11245830 0.2249166086 0.8875416957
[6,] 0.10027652 0.2005530373 0.8997234813
[7,] 0.10533071 0.2106614243 0.8946692879
[8,] 0.22855698 0.4571139640 0.7714430180
[9,] 0.30752487 0.6150497484 0.6924751258
[10,] 0.29919352 0.5983870495 0.7008064752
[11,] 0.24244480 0.4848896068 0.7575551966
[12,] 0.16993106 0.3398621190 0.8300689405
[13,] 0.16239900 0.3247980005 0.8376009998
[14,] 0.11562003 0.2312400614 0.8843799693
[15,] 0.07808728 0.1561745657 0.9219127171
[16,] 0.04879899 0.0975979731 0.9512010135
[17,] 0.03528019 0.0705603879 0.9647198061
[18,] 0.03719753 0.0743950568 0.9628024716
[19,] 0.12827297 0.2565459390 0.8717270305
[20,] 0.78125699 0.4374860108 0.2187430054
[21,] 0.75041548 0.4991690367 0.2495845183
[22,] 0.71651554 0.5669689263 0.2834844632
[23,] 0.81308704 0.3738259133 0.1869129567
[24,] 0.97601091 0.0479781768 0.0239890884
[25,] 0.99963231 0.0007353878 0.0003676939
[26,] 0.99921039 0.0015792177 0.0007896089
[27,] 0.99913098 0.0017380389 0.0008690195
> postscript(file="/var/www/html/rcomp/tmp/17m0l1258723100.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2sbpd1258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lot41258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ce4n1258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59f0g1258723100.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 = 60
Frequency = 1
1 2 3 4 5 6
174.392122 150.133653 120.238220 104.006894 60.241868 53.501492
7 8 9 10 11 12
9.815489 -11.633064 -43.909560 -79.207170 -114.007895 -177.268206
13 14 15 16 17 18
66.838206 32.026672 3.183606 -25.736866 23.231249 15.331538
19 20 21 22 23 24
19.263527 -5.607593 -40.462360 -48.548827 -72.404152 -55.610142
25 26 27 28 29 30
-13.426156 -25.342424 -37.158609 -43.185490 2.853501 -25.580766
31 32 33 34 35 36
-29.181380 -42.575892 -52.770069 -64.563911 -80.626752 -57.384412
37 38 39 40 41 42
-64.242745 -30.104693 3.080797 14.154743 -24.679115 -15.986232
43 44 45 46 47 48
-3.637259 14.996784 43.778517 68.592060 102.215574 109.986331
49 50 51 52 53 54
-163.561428 -126.713209 -89.344015 -49.239281 -61.647504 -27.266033
55 56 57 58 59 60
3.739623 44.819764 93.363472 123.727848 164.823225 180.276429
> postscript(file="/var/www/html/rcomp/tmp/6pfuf1258723100.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 174.392122 NA
1 150.133653 174.392122
2 120.238220 150.133653
3 104.006894 120.238220
4 60.241868 104.006894
5 53.501492 60.241868
6 9.815489 53.501492
7 -11.633064 9.815489
8 -43.909560 -11.633064
9 -79.207170 -43.909560
10 -114.007895 -79.207170
11 -177.268206 -114.007895
12 66.838206 -177.268206
13 32.026672 66.838206
14 3.183606 32.026672
15 -25.736866 3.183606
16 23.231249 -25.736866
17 15.331538 23.231249
18 19.263527 15.331538
19 -5.607593 19.263527
20 -40.462360 -5.607593
21 -48.548827 -40.462360
22 -72.404152 -48.548827
23 -55.610142 -72.404152
24 -13.426156 -55.610142
25 -25.342424 -13.426156
26 -37.158609 -25.342424
27 -43.185490 -37.158609
28 2.853501 -43.185490
29 -25.580766 2.853501
30 -29.181380 -25.580766
31 -42.575892 -29.181380
32 -52.770069 -42.575892
33 -64.563911 -52.770069
34 -80.626752 -64.563911
35 -57.384412 -80.626752
36 -64.242745 -57.384412
37 -30.104693 -64.242745
38 3.080797 -30.104693
39 14.154743 3.080797
40 -24.679115 14.154743
41 -15.986232 -24.679115
42 -3.637259 -15.986232
43 14.996784 -3.637259
44 43.778517 14.996784
45 68.592060 43.778517
46 102.215574 68.592060
47 109.986331 102.215574
48 -163.561428 109.986331
49 -126.713209 -163.561428
50 -89.344015 -126.713209
51 -49.239281 -89.344015
52 -61.647504 -49.239281
53 -27.266033 -61.647504
54 3.739623 -27.266033
55 44.819764 3.739623
56 93.363472 44.819764
57 123.727848 93.363472
58 164.823225 123.727848
59 180.276429 164.823225
60 NA 180.276429
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 150.133653 174.392122
[2,] 120.238220 150.133653
[3,] 104.006894 120.238220
[4,] 60.241868 104.006894
[5,] 53.501492 60.241868
[6,] 9.815489 53.501492
[7,] -11.633064 9.815489
[8,] -43.909560 -11.633064
[9,] -79.207170 -43.909560
[10,] -114.007895 -79.207170
[11,] -177.268206 -114.007895
[12,] 66.838206 -177.268206
[13,] 32.026672 66.838206
[14,] 3.183606 32.026672
[15,] -25.736866 3.183606
[16,] 23.231249 -25.736866
[17,] 15.331538 23.231249
[18,] 19.263527 15.331538
[19,] -5.607593 19.263527
[20,] -40.462360 -5.607593
[21,] -48.548827 -40.462360
[22,] -72.404152 -48.548827
[23,] -55.610142 -72.404152
[24,] -13.426156 -55.610142
[25,] -25.342424 -13.426156
[26,] -37.158609 -25.342424
[27,] -43.185490 -37.158609
[28,] 2.853501 -43.185490
[29,] -25.580766 2.853501
[30,] -29.181380 -25.580766
[31,] -42.575892 -29.181380
[32,] -52.770069 -42.575892
[33,] -64.563911 -52.770069
[34,] -80.626752 -64.563911
[35,] -57.384412 -80.626752
[36,] -64.242745 -57.384412
[37,] -30.104693 -64.242745
[38,] 3.080797 -30.104693
[39,] 14.154743 3.080797
[40,] -24.679115 14.154743
[41,] -15.986232 -24.679115
[42,] -3.637259 -15.986232
[43,] 14.996784 -3.637259
[44,] 43.778517 14.996784
[45,] 68.592060 43.778517
[46,] 102.215574 68.592060
[47,] 109.986331 102.215574
[48,] -163.561428 109.986331
[49,] -126.713209 -163.561428
[50,] -89.344015 -126.713209
[51,] -49.239281 -89.344015
[52,] -61.647504 -49.239281
[53,] -27.266033 -61.647504
[54,] 3.739623 -27.266033
[55,] 44.819764 3.739623
[56,] 93.363472 44.819764
[57,] 123.727848 93.363472
[58,] 164.823225 123.727848
[59,] 180.276429 164.823225
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 150.133653 174.392122
2 120.238220 150.133653
3 104.006894 120.238220
4 60.241868 104.006894
5 53.501492 60.241868
6 9.815489 53.501492
7 -11.633064 9.815489
8 -43.909560 -11.633064
9 -79.207170 -43.909560
10 -114.007895 -79.207170
11 -177.268206 -114.007895
12 66.838206 -177.268206
13 32.026672 66.838206
14 3.183606 32.026672
15 -25.736866 3.183606
16 23.231249 -25.736866
17 15.331538 23.231249
18 19.263527 15.331538
19 -5.607593 19.263527
20 -40.462360 -5.607593
21 -48.548827 -40.462360
22 -72.404152 -48.548827
23 -55.610142 -72.404152
24 -13.426156 -55.610142
25 -25.342424 -13.426156
26 -37.158609 -25.342424
27 -43.185490 -37.158609
28 2.853501 -43.185490
29 -25.580766 2.853501
30 -29.181380 -25.580766
31 -42.575892 -29.181380
32 -52.770069 -42.575892
33 -64.563911 -52.770069
34 -80.626752 -64.563911
35 -57.384412 -80.626752
36 -64.242745 -57.384412
37 -30.104693 -64.242745
38 3.080797 -30.104693
39 14.154743 3.080797
40 -24.679115 14.154743
41 -15.986232 -24.679115
42 -3.637259 -15.986232
43 14.996784 -3.637259
44 43.778517 14.996784
45 68.592060 43.778517
46 102.215574 68.592060
47 109.986331 102.215574
48 -163.561428 109.986331
49 -126.713209 -163.561428
50 -89.344015 -126.713209
51 -49.239281 -89.344015
52 -61.647504 -49.239281
53 -27.266033 -61.647504
54 3.739623 -27.266033
55 44.819764 3.739623
56 93.363472 44.819764
57 123.727848 93.363472
58 164.823225 123.727848
59 180.276429 164.823225
> 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/7366w1258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wzka1258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9drhs1258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100nwy1258723100.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11afaa1258723100.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/1298hc1258723100.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/13efh31258723100.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/141zx51258723100.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/150xio1258723100.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/16nkxt1258723100.tab")
+ }
>
> system("convert tmp/17m0l1258723100.ps tmp/17m0l1258723100.png")
> system("convert tmp/2sbpd1258723100.ps tmp/2sbpd1258723100.png")
> system("convert tmp/3lot41258723100.ps tmp/3lot41258723100.png")
> system("convert tmp/4ce4n1258723100.ps tmp/4ce4n1258723100.png")
> system("convert tmp/59f0g1258723100.ps tmp/59f0g1258723100.png")
> system("convert tmp/6pfuf1258723100.ps tmp/6pfuf1258723100.png")
> system("convert tmp/7366w1258723100.ps tmp/7366w1258723100.png")
> system("convert tmp/8wzka1258723100.ps tmp/8wzka1258723100.png")
> system("convert tmp/9drhs1258723100.ps tmp/9drhs1258723100.png")
> system("convert tmp/100nwy1258723100.ps tmp/100nwy1258723100.png")
>
>
> proc.time()
user system elapsed
2.452 1.592 5.266