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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(151.7,105.2,121.3,105.2,133.0,105.6,119.6,105.6,122.2,106.2,117.4,106.3,106.7,106.4,87.5,106.9,81.0,107.2,110.3,107.3,87.0,107.3,55.7,107.4,146.0,107.55,137.5,107.87,138.5,108.37,135.6,108.38,107.3,107.92,99.0,108.03,91.4,108.14,68.4,108.3,82.6,108.64,98.4,108.66,71.3,109.04,47.6,109.03,130.8,109.03,113.6,109.54,125.7,109.75,113.6,109.83,97.1,109.65,104.4,109.82,91.8,109.95,75.1,110.12,89.2,110.15,110.2,110.2,78.4,109.99,68.4,110.14,122.8,110.14,129.7,110.81,159.1,110.97,139.0,110.99,102.2,109.73,113.6,109.81,81.5,110.02,77.4,110.18,87.6,110.21,101.2,110.25,87.2,110.36,64.9,110.51,133.1,110.64,118.0,110.95,135.9,111.18,125.7,111.19,108.0,111.69,128.3,111.7,84.7,111.83,86.4,111.77,92.2,111.73,95.8,112.01,92.3,111.86,54.3,112.04),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 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> 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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 151.7 105.20 1 0 0 0 0 0 0 0 0 0 0
2 121.3 105.20 0 1 0 0 0 0 0 0 0 0 0
3 133.0 105.60 0 0 1 0 0 0 0 0 0 0 0
4 119.6 105.60 0 0 0 1 0 0 0 0 0 0 0
5 122.2 106.20 0 0 0 0 1 0 0 0 0 0 0
6 117.4 106.30 0 0 0 0 0 1 0 0 0 0 0
7 106.7 106.40 0 0 0 0 0 0 1 0 0 0 0
8 87.5 106.90 0 0 0 0 0 0 0 1 0 0 0
9 81.0 107.20 0 0 0 0 0 0 0 0 1 0 0
10 110.3 107.30 0 0 0 0 0 0 0 0 0 1 0
11 87.0 107.30 0 0 0 0 0 0 0 0 0 0 1
12 55.7 107.40 0 0 0 0 0 0 0 0 0 0 0
13 146.0 107.55 1 0 0 0 0 0 0 0 0 0 0
14 137.5 107.87 0 1 0 0 0 0 0 0 0 0 0
15 138.5 108.37 0 0 1 0 0 0 0 0 0 0 0
16 135.6 108.38 0 0 0 1 0 0 0 0 0 0 0
17 107.3 107.92 0 0 0 0 1 0 0 0 0 0 0
18 99.0 108.03 0 0 0 0 0 1 0 0 0 0 0
19 91.4 108.14 0 0 0 0 0 0 1 0 0 0 0
20 68.4 108.30 0 0 0 0 0 0 0 1 0 0 0
21 82.6 108.64 0 0 0 0 0 0 0 0 1 0 0
22 98.4 108.66 0 0 0 0 0 0 0 0 0 1 0
23 71.3 109.04 0 0 0 0 0 0 0 0 0 0 1
24 47.6 109.03 0 0 0 0 0 0 0 0 0 0 0
25 130.8 109.03 1 0 0 0 0 0 0 0 0 0 0
26 113.6 109.54 0 1 0 0 0 0 0 0 0 0 0
27 125.7 109.75 0 0 1 0 0 0 0 0 0 0 0
28 113.6 109.83 0 0 0 1 0 0 0 0 0 0 0
29 97.1 109.65 0 0 0 0 1 0 0 0 0 0 0
30 104.4 109.82 0 0 0 0 0 1 0 0 0 0 0
31 91.8 109.95 0 0 0 0 0 0 1 0 0 0 0
32 75.1 110.12 0 0 0 0 0 0 0 1 0 0 0
33 89.2 110.15 0 0 0 0 0 0 0 0 1 0 0
34 110.2 110.20 0 0 0 0 0 0 0 0 0 1 0
35 78.4 109.99 0 0 0 0 0 0 0 0 0 0 1
36 68.4 110.14 0 0 0 0 0 0 0 0 0 0 0
37 122.8 110.14 1 0 0 0 0 0 0 0 0 0 0
38 129.7 110.81 0 1 0 0 0 0 0 0 0 0 0
39 159.1 110.97 0 0 1 0 0 0 0 0 0 0 0
40 139.0 110.99 0 0 0 1 0 0 0 0 0 0 0
41 102.2 109.73 0 0 0 0 1 0 0 0 0 0 0
42 113.6 109.81 0 0 0 0 0 1 0 0 0 0 0
43 81.5 110.02 0 0 0 0 0 0 1 0 0 0 0
44 77.4 110.18 0 0 0 0 0 0 0 1 0 0 0
45 87.6 110.21 0 0 0 0 0 0 0 0 1 0 0
46 101.2 110.25 0 0 0 0 0 0 0 0 0 1 0
47 87.2 110.36 0 0 0 0 0 0 0 0 0 0 1
48 64.9 110.51 0 0 0 0 0 0 0 0 0 0 0
49 133.1 110.64 1 0 0 0 0 0 0 0 0 0 0
50 118.0 110.95 0 1 0 0 0 0 0 0 0 0 0
51 135.9 111.18 0 0 1 0 0 0 0 0 0 0 0
52 125.7 111.19 0 0 0 1 0 0 0 0 0 0 0
53 108.0 111.69 0 0 0 0 1 0 0 0 0 0 0
54 128.3 111.70 0 0 0 0 0 1 0 0 0 0 0
55 84.7 111.83 0 0 0 0 0 0 1 0 0 0 0
56 86.4 111.77 0 0 0 0 0 0 0 1 0 0 0
57 92.2 111.73 0 0 0 0 0 0 0 0 1 0 0
58 95.8 112.01 0 0 0 0 0 0 0 0 0 1 0
59 92.3 111.86 0 0 0 0 0 0 0 0 0 0 1
60 54.3 112.04 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
96.9593 -0.3531 78.2367 65.5046 80.0305 68.2990
M5 M6 M7 M8 M9 M10
48.9025 54.1157 32.8437 20.6494 28.2560 44.9506
M11
25.0197
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13.9291 -6.0134 -0.8792 6.5683 21.2942
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 96.9593 74.9085 1.294 0.201861
X -0.3531 0.6810 -0.519 0.606516
M1 78.2367 6.1222 12.779 < 2e-16 ***
M2 65.5046 6.0911 10.754 2.91e-14 ***
M3 80.0305 6.0728 13.179 < 2e-16 ***
M4 68.2990 6.0716 11.249 6.27e-15 ***
M5 48.9025 6.0802 8.043 2.20e-10 ***
M6 54.1157 6.0749 8.908 1.17e-11 ***
M7 32.8437 6.0684 5.412 2.06e-06 ***
M8 20.6494 6.0619 3.406 0.001357 **
M9 28.2560 6.0588 4.664 2.60e-05 ***
M10 44.9506 6.0574 7.421 1.89e-09 ***
M11 25.0197 6.0571 4.131 0.000147 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.576 on 47 degrees of freedom
Multiple R-squared: 0.8889, Adjusted R-squared: 0.8606
F-statistic: 31.35 on 12 and 47 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)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.4536650 0.90733009 0.54633496
[2,] 0.6967345 0.60653098 0.30326549
[3,] 0.8258687 0.34826256 0.17413128
[4,] 0.8656790 0.26864190 0.13432095
[5,] 0.8872254 0.22554928 0.11277464
[6,] 0.8233846 0.35323083 0.17661541
[7,] 0.7861059 0.42778824 0.21389412
[8,] 0.7754500 0.44910006 0.22455003
[9,] 0.7189500 0.56210000 0.28105000
[10,] 0.7126169 0.57476616 0.28738308
[11,] 0.6548704 0.69025922 0.34512961
[12,] 0.6674448 0.66511043 0.33255522
[13,] 0.6827054 0.63458928 0.31729464
[14,] 0.6389096 0.72218084 0.36109042
[15,] 0.6639101 0.67217973 0.33608987
[16,] 0.6205979 0.75880412 0.37940206
[17,] 0.5522745 0.89545104 0.44772552
[18,] 0.5293132 0.94137363 0.47068681
[19,] 0.5773037 0.84539265 0.42269633
[20,] 0.5390039 0.92199230 0.46099615
[21,] 0.6399876 0.72002471 0.36001236
[22,] 0.6288745 0.74225091 0.37112545
[23,] 0.6396236 0.72075282 0.36037641
[24,] 0.9373300 0.12534005 0.06267002
[25,] 0.9631414 0.07371719 0.03685859
[26,] 0.9245775 0.15084495 0.07542248
[27,] 0.9378450 0.12431007 0.06215503
[28,] 0.8754607 0.24907866 0.12453933
[29,] 0.8457194 0.30856126 0.15428063
> postscript(file="/var/www/rcomp/tmp/1qalk1321900913.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2km0e1321900913.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3k39k1321900913.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4shr11321900913.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5jumq1321900913.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 60
Frequency = 1
1 2 3 4 5 6
13.6505179 -4.0173059 -6.7019955 -8.3704700 13.8378894 3.8600081
7 8 9 10 11 12
14.4672963 7.6381711 -6.3625073 6.2781989 2.9090182 -3.3359253
13 14 15 16 17 18
8.7803135 13.1254831 -0.2238960 8.6111605 -0.4547708 -13.9291212
19 20 21 22 23 24
-0.2183019 -10.9674826 -4.2540369 -5.1415790 -12.1765800 -10.8603650
25 26 27 28 29 30
-5.8970919 -10.1848324 -12.5366118 -12.8768380 -10.0439000 -7.8970641
31 32 33 34 35 36
0.8208173 -3.6248324 2.8791509 7.2022019 -4.7411307 10.3315810
37 38 39 40 41 42
-13.5051459 6.3636103 21.2941757 12.9327633 -4.9156517 1.2994048
43 44 45 46 47 48
-9.4544654 -1.3036461 1.3003372 -1.7801429 4.1895179 6.9622297
49 50 51 52 53 54
-3.0285936 -5.2869551 -1.8316724 -0.2966158 1.5764331 16.6667723
55 56 57 58 59 60
-5.6153463 8.2577900 6.4370560 -6.5586789 9.8191747 -3.0975204
> postscript(file="/var/www/rcomp/tmp/6hp5t1321900913.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 13.6505179 NA
1 -4.0173059 13.6505179
2 -6.7019955 -4.0173059
3 -8.3704700 -6.7019955
4 13.8378894 -8.3704700
5 3.8600081 13.8378894
6 14.4672963 3.8600081
7 7.6381711 14.4672963
8 -6.3625073 7.6381711
9 6.2781989 -6.3625073
10 2.9090182 6.2781989
11 -3.3359253 2.9090182
12 8.7803135 -3.3359253
13 13.1254831 8.7803135
14 -0.2238960 13.1254831
15 8.6111605 -0.2238960
16 -0.4547708 8.6111605
17 -13.9291212 -0.4547708
18 -0.2183019 -13.9291212
19 -10.9674826 -0.2183019
20 -4.2540369 -10.9674826
21 -5.1415790 -4.2540369
22 -12.1765800 -5.1415790
23 -10.8603650 -12.1765800
24 -5.8970919 -10.8603650
25 -10.1848324 -5.8970919
26 -12.5366118 -10.1848324
27 -12.8768380 -12.5366118
28 -10.0439000 -12.8768380
29 -7.8970641 -10.0439000
30 0.8208173 -7.8970641
31 -3.6248324 0.8208173
32 2.8791509 -3.6248324
33 7.2022019 2.8791509
34 -4.7411307 7.2022019
35 10.3315810 -4.7411307
36 -13.5051459 10.3315810
37 6.3636103 -13.5051459
38 21.2941757 6.3636103
39 12.9327633 21.2941757
40 -4.9156517 12.9327633
41 1.2994048 -4.9156517
42 -9.4544654 1.2994048
43 -1.3036461 -9.4544654
44 1.3003372 -1.3036461
45 -1.7801429 1.3003372
46 4.1895179 -1.7801429
47 6.9622297 4.1895179
48 -3.0285936 6.9622297
49 -5.2869551 -3.0285936
50 -1.8316724 -5.2869551
51 -0.2966158 -1.8316724
52 1.5764331 -0.2966158
53 16.6667723 1.5764331
54 -5.6153463 16.6667723
55 8.2577900 -5.6153463
56 6.4370560 8.2577900
57 -6.5586789 6.4370560
58 9.8191747 -6.5586789
59 -3.0975204 9.8191747
60 NA -3.0975204
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.0173059 13.6505179
[2,] -6.7019955 -4.0173059
[3,] -8.3704700 -6.7019955
[4,] 13.8378894 -8.3704700
[5,] 3.8600081 13.8378894
[6,] 14.4672963 3.8600081
[7,] 7.6381711 14.4672963
[8,] -6.3625073 7.6381711
[9,] 6.2781989 -6.3625073
[10,] 2.9090182 6.2781989
[11,] -3.3359253 2.9090182
[12,] 8.7803135 -3.3359253
[13,] 13.1254831 8.7803135
[14,] -0.2238960 13.1254831
[15,] 8.6111605 -0.2238960
[16,] -0.4547708 8.6111605
[17,] -13.9291212 -0.4547708
[18,] -0.2183019 -13.9291212
[19,] -10.9674826 -0.2183019
[20,] -4.2540369 -10.9674826
[21,] -5.1415790 -4.2540369
[22,] -12.1765800 -5.1415790
[23,] -10.8603650 -12.1765800
[24,] -5.8970919 -10.8603650
[25,] -10.1848324 -5.8970919
[26,] -12.5366118 -10.1848324
[27,] -12.8768380 -12.5366118
[28,] -10.0439000 -12.8768380
[29,] -7.8970641 -10.0439000
[30,] 0.8208173 -7.8970641
[31,] -3.6248324 0.8208173
[32,] 2.8791509 -3.6248324
[33,] 7.2022019 2.8791509
[34,] -4.7411307 7.2022019
[35,] 10.3315810 -4.7411307
[36,] -13.5051459 10.3315810
[37,] 6.3636103 -13.5051459
[38,] 21.2941757 6.3636103
[39,] 12.9327633 21.2941757
[40,] -4.9156517 12.9327633
[41,] 1.2994048 -4.9156517
[42,] -9.4544654 1.2994048
[43,] -1.3036461 -9.4544654
[44,] 1.3003372 -1.3036461
[45,] -1.7801429 1.3003372
[46,] 4.1895179 -1.7801429
[47,] 6.9622297 4.1895179
[48,] -3.0285936 6.9622297
[49,] -5.2869551 -3.0285936
[50,] -1.8316724 -5.2869551
[51,] -0.2966158 -1.8316724
[52,] 1.5764331 -0.2966158
[53,] 16.6667723 1.5764331
[54,] -5.6153463 16.6667723
[55,] 8.2577900 -5.6153463
[56,] 6.4370560 8.2577900
[57,] -6.5586789 6.4370560
[58,] 9.8191747 -6.5586789
[59,] -3.0975204 9.8191747
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.0173059 13.6505179
2 -6.7019955 -4.0173059
3 -8.3704700 -6.7019955
4 13.8378894 -8.3704700
5 3.8600081 13.8378894
6 14.4672963 3.8600081
7 7.6381711 14.4672963
8 -6.3625073 7.6381711
9 6.2781989 -6.3625073
10 2.9090182 6.2781989
11 -3.3359253 2.9090182
12 8.7803135 -3.3359253
13 13.1254831 8.7803135
14 -0.2238960 13.1254831
15 8.6111605 -0.2238960
16 -0.4547708 8.6111605
17 -13.9291212 -0.4547708
18 -0.2183019 -13.9291212
19 -10.9674826 -0.2183019
20 -4.2540369 -10.9674826
21 -5.1415790 -4.2540369
22 -12.1765800 -5.1415790
23 -10.8603650 -12.1765800
24 -5.8970919 -10.8603650
25 -10.1848324 -5.8970919
26 -12.5366118 -10.1848324
27 -12.8768380 -12.5366118
28 -10.0439000 -12.8768380
29 -7.8970641 -10.0439000
30 0.8208173 -7.8970641
31 -3.6248324 0.8208173
32 2.8791509 -3.6248324
33 7.2022019 2.8791509
34 -4.7411307 7.2022019
35 10.3315810 -4.7411307
36 -13.5051459 10.3315810
37 6.3636103 -13.5051459
38 21.2941757 6.3636103
39 12.9327633 21.2941757
40 -4.9156517 12.9327633
41 1.2994048 -4.9156517
42 -9.4544654 1.2994048
43 -1.3036461 -9.4544654
44 1.3003372 -1.3036461
45 -1.7801429 1.3003372
46 4.1895179 -1.7801429
47 6.9622297 4.1895179
48 -3.0285936 6.9622297
49 -5.2869551 -3.0285936
50 -1.8316724 -5.2869551
51 -0.2966158 -1.8316724
52 1.5764331 -0.2966158
53 16.6667723 1.5764331
54 -5.6153463 16.6667723
55 8.2577900 -5.6153463
56 6.4370560 8.2577900
57 -6.5586789 6.4370560
58 9.8191747 -6.5586789
59 -3.0975204 9.8191747
> 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/75de01321900913.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/8hby71321900913.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/9mjs71321900913.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/105tlq1321900913.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11wuap1321900913.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12c1qv1321900913.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13vcld1321900913.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/142q781321900913.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/154l8m1321900913.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/16x6uk1321900913.tab")
+ }
>
> try(system("convert tmp/1qalk1321900913.ps tmp/1qalk1321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/2km0e1321900913.ps tmp/2km0e1321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k39k1321900913.ps tmp/3k39k1321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/4shr11321900913.ps tmp/4shr11321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jumq1321900913.ps tmp/5jumq1321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hp5t1321900913.ps tmp/6hp5t1321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/75de01321900913.ps tmp/75de01321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hby71321900913.ps tmp/8hby71321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mjs71321900913.ps tmp/9mjs71321900913.png",intern=TRUE))
character(0)
> try(system("convert tmp/105tlq1321900913.ps tmp/105tlq1321900913.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.120 0.390 4.492