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(127.96,0,127.47,0,126.47,0,125.75,0,125.42,0,125.14,0,125.15,0,125.51,0,125.63,0,126.22,0,126.88,0,127.96,0,128.74,0,129.6,0,131.2,0,132.72,0,134.67,0,135.94,0,136.39,0,136.74,0,137.2,0,137.36,0,138.63,0,141.07,0,143.32,0,147.91,0,152.56,0,151.61,0,156.56,0,157.45,0,158.13,0,159.18,0,159.47,0,159.79,0,161.65,0,162.77,0,163.48,0,166.16,0,163.86,0,162.12,0,149.08,0,145.32,0,141.21,0,134.68,0,133.65,0,139.17,0,138.61,0,144.96,1,157.99,1,167.18,1,174.48,1,182.77,1,190.00,1,189.70,1,188.90,1,198.28,1,201.18,1,204.14,1,221.02,1,221.12,1,220.68,1),dim=c(2,61),dimnames=list(c('Gasindex','dumivariable'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Gasindex','dumivariable'),1:61))
> 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'
> #'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
Gasindex dumivariable M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 127.96 0 1 0 0 0 0 0 0 0 0 0 0
2 127.47 0 0 1 0 0 0 0 0 0 0 0 0
3 126.47 0 0 0 1 0 0 0 0 0 0 0 0
4 125.75 0 0 0 0 1 0 0 0 0 0 0 0
5 125.42 0 0 0 0 0 1 0 0 0 0 0 0
6 125.14 0 0 0 0 0 0 1 0 0 0 0 0
7 125.15 0 0 0 0 0 0 0 1 0 0 0 0
8 125.51 0 0 0 0 0 0 0 0 1 0 0 0
9 125.63 0 0 0 0 0 0 0 0 0 1 0 0
10 126.22 0 0 0 0 0 0 0 0 0 0 1 0
11 126.88 0 0 0 0 0 0 0 0 0 0 0 1
12 127.96 0 0 0 0 0 0 0 0 0 0 0 0
13 128.74 0 1 0 0 0 0 0 0 0 0 0 0
14 129.60 0 0 1 0 0 0 0 0 0 0 0 0
15 131.20 0 0 0 1 0 0 0 0 0 0 0 0
16 132.72 0 0 0 0 1 0 0 0 0 0 0 0
17 134.67 0 0 0 0 0 1 0 0 0 0 0 0
18 135.94 0 0 0 0 0 0 1 0 0 0 0 0
19 136.39 0 0 0 0 0 0 0 1 0 0 0 0
20 136.74 0 0 0 0 0 0 0 0 1 0 0 0
21 137.20 0 0 0 0 0 0 0 0 0 1 0 0
22 137.36 0 0 0 0 0 0 0 0 0 0 1 0
23 138.63 0 0 0 0 0 0 0 0 0 0 0 1
24 141.07 0 0 0 0 0 0 0 0 0 0 0 0
25 143.32 0 1 0 0 0 0 0 0 0 0 0 0
26 147.91 0 0 1 0 0 0 0 0 0 0 0 0
27 152.56 0 0 0 1 0 0 0 0 0 0 0 0
28 151.61 0 0 0 0 1 0 0 0 0 0 0 0
29 156.56 0 0 0 0 0 1 0 0 0 0 0 0
30 157.45 0 0 0 0 0 0 1 0 0 0 0 0
31 158.13 0 0 0 0 0 0 0 1 0 0 0 0
32 159.18 0 0 0 0 0 0 0 0 1 0 0 0
33 159.47 0 0 0 0 0 0 0 0 0 1 0 0
34 159.79 0 0 0 0 0 0 0 0 0 0 1 0
35 161.65 0 0 0 0 0 0 0 0 0 0 0 1
36 162.77 0 0 0 0 0 0 0 0 0 0 0 0
37 163.48 0 1 0 0 0 0 0 0 0 0 0 0
38 166.16 0 0 1 0 0 0 0 0 0 0 0 0
39 163.86 0 0 0 1 0 0 0 0 0 0 0 0
40 162.12 0 0 0 0 1 0 0 0 0 0 0 0
41 149.08 0 0 0 0 0 1 0 0 0 0 0 0
42 145.32 0 0 0 0 0 0 1 0 0 0 0 0
43 141.21 0 0 0 0 0 0 0 1 0 0 0 0
44 134.68 0 0 0 0 0 0 0 0 1 0 0 0
45 133.65 0 0 0 0 0 0 0 0 0 1 0 0
46 139.17 0 0 0 0 0 0 0 0 0 0 1 0
47 138.61 0 0 0 0 0 0 0 0 0 0 0 1
48 144.96 1 0 0 0 0 0 0 0 0 0 0 0
49 157.99 1 1 0 0 0 0 0 0 0 0 0 0
50 167.18 1 0 1 0 0 0 0 0 0 0 0 0
51 174.48 1 0 0 1 0 0 0 0 0 0 0 0
52 182.77 1 0 0 0 1 0 0 0 0 0 0 0
53 190.00 1 0 0 0 0 1 0 0 0 0 0 0
54 189.70 1 0 0 0 0 0 1 0 0 0 0 0
55 188.90 1 0 0 0 0 0 0 1 0 0 0 0
56 198.28 1 0 0 0 0 0 0 0 1 0 0 0
57 201.18 1 0 0 0 0 0 0 0 0 1 0 0
58 204.14 1 0 0 0 0 0 0 0 0 0 1 0
59 221.02 1 0 0 0 0 0 0 0 0 0 0 1
60 221.12 1 0 0 0 0 0 0 0 0 0 0 0
61 220.68 1 1 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) dumivariable M1 M2 M3
139.98071 48.98823 0.71822 -2.11435 -0.06435
M4 M5 M6 M7 M8
1.21565 1.36765 0.93165 0.17765 1.09965
M9 M10 M11
1.64765 3.55765 7.57965
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44.01 -11.96 -4.34 12.64 32.15
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 139.98071 8.29111 16.883 < 2e-16 ***
dumivariable 48.98823 5.50705 8.896 1.01e-11 ***
M1 0.71822 10.82897 0.066 0.947
M2 -2.11435 11.35752 -0.186 0.853
M3 -0.06435 11.35752 -0.006 0.996
M4 1.21565 11.35752 0.107 0.915
M5 1.36765 11.35752 0.120 0.905
M6 0.93165 11.35752 0.082 0.935
M7 0.17765 11.35752 0.016 0.988
M8 1.09965 11.35752 0.097 0.923
M9 1.64765 11.35752 0.145 0.885
M10 3.55765 11.35752 0.313 0.755
M11 7.57965 11.35752 0.667 0.508
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 17.87 on 48 degrees of freedom
Multiple R-squared: 0.6292, Adjusted R-squared: 0.5365
F-statistic: 6.787 on 12 and 48 DF, p-value: 6.044e-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,] 0.0103125236 0.020625047 0.9896875
[2,] 0.0071748149 0.014349630 0.9928252
[3,] 0.0054882541 0.010976508 0.9945117
[4,] 0.0039110109 0.007822022 0.9960890
[5,] 0.0026214083 0.005242817 0.9973786
[6,] 0.0017797317 0.003559463 0.9982203
[7,] 0.0011558295 0.002311659 0.9988442
[8,] 0.0008551027 0.001710205 0.9991449
[9,] 0.0006314159 0.001262832 0.9993686
[10,] 0.0007397983 0.001479597 0.9992602
[11,] 0.0012835691 0.002567138 0.9987164
[12,] 0.0028810496 0.005762099 0.9971190
[13,] 0.0040574829 0.008114966 0.9959425
[14,] 0.0069353083 0.013870617 0.9930647
[15,] 0.0100274723 0.020054945 0.9899725
[16,] 0.0132140225 0.026428045 0.9867860
[17,] 0.0161576769 0.032315354 0.9838423
[18,] 0.0179928656 0.035985731 0.9820071
[19,] 0.0181107969 0.036221594 0.9818892
[20,] 0.0173250818 0.034650164 0.9826749
[21,] 0.0181459658 0.036291932 0.9818540
[22,] 0.0193979803 0.038795961 0.9806020
[23,] 0.0342220102 0.068444020 0.9657780
[24,] 0.0463614485 0.092722897 0.9536386
[25,] 0.0527061263 0.105412253 0.9472939
[26,] 0.0346824346 0.069364869 0.9653176
[27,] 0.0208472493 0.041694499 0.9791528
[28,] 0.0115739208 0.023147842 0.9884261
[29,] 0.0049423913 0.009884783 0.9950576
[30,] 0.0017767130 0.003553426 0.9982233
> postscript(file="/var/www/html/freestat/rcomp/tmp/1i5k31229945909.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2egfm1229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3aafy1229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4b9bg1229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5v3c61229945909.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 = 61
Frequency = 1
1 2 3 4 5 6
-12.7389241 -10.3963544 -13.4463544 -15.4463544 -15.9283544 -15.7723544
7 8 9 10 11 12
-15.0083544 -15.5703544 -15.9983544 -17.3183544 -20.6803544 -12.0207089
13 14 15 16 17 18
-11.9589241 -8.2663544 -8.7163544 -8.4763544 -6.6783544 -4.9723544
19 20 21 22 23 24
-3.7683544 -4.3403544 -4.4283544 -6.1783544 -8.9303544 1.0892911
25 26 27 28 29 30
2.6210759 10.0436456 12.6436456 10.4136456 15.2116456 16.5376456
31 32 33 34 35 36
17.9716456 18.0996456 17.8416456 16.2516456 14.0896456 22.7892911
37 38 39 40 41 42
22.7810759 28.2936456 23.9436456 20.9236456 7.7316456 4.4076456
43 44 45 46 47 48
1.0516456 -6.4003544 -7.9783544 -4.3683544 -8.9503544 -44.0089367
49 50 51 52 53 54
-31.6971519 -19.6745823 -14.4245823 -7.4145823 -0.3365823 -0.2005823
55 56 57 58 59 60
-0.2465823 8.2114177 10.5634177 11.6134177 24.4714177 32.1510633
61
30.9928481
> postscript(file="/var/www/html/freestat/rcomp/tmp/6ftsh1229945909.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -12.7389241 NA
1 -10.3963544 -12.7389241
2 -13.4463544 -10.3963544
3 -15.4463544 -13.4463544
4 -15.9283544 -15.4463544
5 -15.7723544 -15.9283544
6 -15.0083544 -15.7723544
7 -15.5703544 -15.0083544
8 -15.9983544 -15.5703544
9 -17.3183544 -15.9983544
10 -20.6803544 -17.3183544
11 -12.0207089 -20.6803544
12 -11.9589241 -12.0207089
13 -8.2663544 -11.9589241
14 -8.7163544 -8.2663544
15 -8.4763544 -8.7163544
16 -6.6783544 -8.4763544
17 -4.9723544 -6.6783544
18 -3.7683544 -4.9723544
19 -4.3403544 -3.7683544
20 -4.4283544 -4.3403544
21 -6.1783544 -4.4283544
22 -8.9303544 -6.1783544
23 1.0892911 -8.9303544
24 2.6210759 1.0892911
25 10.0436456 2.6210759
26 12.6436456 10.0436456
27 10.4136456 12.6436456
28 15.2116456 10.4136456
29 16.5376456 15.2116456
30 17.9716456 16.5376456
31 18.0996456 17.9716456
32 17.8416456 18.0996456
33 16.2516456 17.8416456
34 14.0896456 16.2516456
35 22.7892911 14.0896456
36 22.7810759 22.7892911
37 28.2936456 22.7810759
38 23.9436456 28.2936456
39 20.9236456 23.9436456
40 7.7316456 20.9236456
41 4.4076456 7.7316456
42 1.0516456 4.4076456
43 -6.4003544 1.0516456
44 -7.9783544 -6.4003544
45 -4.3683544 -7.9783544
46 -8.9503544 -4.3683544
47 -44.0089367 -8.9503544
48 -31.6971519 -44.0089367
49 -19.6745823 -31.6971519
50 -14.4245823 -19.6745823
51 -7.4145823 -14.4245823
52 -0.3365823 -7.4145823
53 -0.2005823 -0.3365823
54 -0.2465823 -0.2005823
55 8.2114177 -0.2465823
56 10.5634177 8.2114177
57 11.6134177 10.5634177
58 24.4714177 11.6134177
59 32.1510633 24.4714177
60 30.9928481 32.1510633
61 NA 30.9928481
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -10.3963544 -12.7389241
[2,] -13.4463544 -10.3963544
[3,] -15.4463544 -13.4463544
[4,] -15.9283544 -15.4463544
[5,] -15.7723544 -15.9283544
[6,] -15.0083544 -15.7723544
[7,] -15.5703544 -15.0083544
[8,] -15.9983544 -15.5703544
[9,] -17.3183544 -15.9983544
[10,] -20.6803544 -17.3183544
[11,] -12.0207089 -20.6803544
[12,] -11.9589241 -12.0207089
[13,] -8.2663544 -11.9589241
[14,] -8.7163544 -8.2663544
[15,] -8.4763544 -8.7163544
[16,] -6.6783544 -8.4763544
[17,] -4.9723544 -6.6783544
[18,] -3.7683544 -4.9723544
[19,] -4.3403544 -3.7683544
[20,] -4.4283544 -4.3403544
[21,] -6.1783544 -4.4283544
[22,] -8.9303544 -6.1783544
[23,] 1.0892911 -8.9303544
[24,] 2.6210759 1.0892911
[25,] 10.0436456 2.6210759
[26,] 12.6436456 10.0436456
[27,] 10.4136456 12.6436456
[28,] 15.2116456 10.4136456
[29,] 16.5376456 15.2116456
[30,] 17.9716456 16.5376456
[31,] 18.0996456 17.9716456
[32,] 17.8416456 18.0996456
[33,] 16.2516456 17.8416456
[34,] 14.0896456 16.2516456
[35,] 22.7892911 14.0896456
[36,] 22.7810759 22.7892911
[37,] 28.2936456 22.7810759
[38,] 23.9436456 28.2936456
[39,] 20.9236456 23.9436456
[40,] 7.7316456 20.9236456
[41,] 4.4076456 7.7316456
[42,] 1.0516456 4.4076456
[43,] -6.4003544 1.0516456
[44,] -7.9783544 -6.4003544
[45,] -4.3683544 -7.9783544
[46,] -8.9503544 -4.3683544
[47,] -44.0089367 -8.9503544
[48,] -31.6971519 -44.0089367
[49,] -19.6745823 -31.6971519
[50,] -14.4245823 -19.6745823
[51,] -7.4145823 -14.4245823
[52,] -0.3365823 -7.4145823
[53,] -0.2005823 -0.3365823
[54,] -0.2465823 -0.2005823
[55,] 8.2114177 -0.2465823
[56,] 10.5634177 8.2114177
[57,] 11.6134177 10.5634177
[58,] 24.4714177 11.6134177
[59,] 32.1510633 24.4714177
[60,] 30.9928481 32.1510633
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -10.3963544 -12.7389241
2 -13.4463544 -10.3963544
3 -15.4463544 -13.4463544
4 -15.9283544 -15.4463544
5 -15.7723544 -15.9283544
6 -15.0083544 -15.7723544
7 -15.5703544 -15.0083544
8 -15.9983544 -15.5703544
9 -17.3183544 -15.9983544
10 -20.6803544 -17.3183544
11 -12.0207089 -20.6803544
12 -11.9589241 -12.0207089
13 -8.2663544 -11.9589241
14 -8.7163544 -8.2663544
15 -8.4763544 -8.7163544
16 -6.6783544 -8.4763544
17 -4.9723544 -6.6783544
18 -3.7683544 -4.9723544
19 -4.3403544 -3.7683544
20 -4.4283544 -4.3403544
21 -6.1783544 -4.4283544
22 -8.9303544 -6.1783544
23 1.0892911 -8.9303544
24 2.6210759 1.0892911
25 10.0436456 2.6210759
26 12.6436456 10.0436456
27 10.4136456 12.6436456
28 15.2116456 10.4136456
29 16.5376456 15.2116456
30 17.9716456 16.5376456
31 18.0996456 17.9716456
32 17.8416456 18.0996456
33 16.2516456 17.8416456
34 14.0896456 16.2516456
35 22.7892911 14.0896456
36 22.7810759 22.7892911
37 28.2936456 22.7810759
38 23.9436456 28.2936456
39 20.9236456 23.9436456
40 7.7316456 20.9236456
41 4.4076456 7.7316456
42 1.0516456 4.4076456
43 -6.4003544 1.0516456
44 -7.9783544 -6.4003544
45 -4.3683544 -7.9783544
46 -8.9503544 -4.3683544
47 -44.0089367 -8.9503544
48 -31.6971519 -44.0089367
49 -19.6745823 -31.6971519
50 -14.4245823 -19.6745823
51 -7.4145823 -14.4245823
52 -0.3365823 -7.4145823
53 -0.2005823 -0.3365823
54 -0.2465823 -0.2005823
55 8.2114177 -0.2465823
56 10.5634177 8.2114177
57 11.6134177 10.5634177
58 24.4714177 11.6134177
59 32.1510633 24.4714177
60 30.9928481 32.1510633
> 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/7jdxg1229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8h3qo1229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/96o0w1229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/100f791229945909.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111ai81229945909.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/12g8sj1229945909.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/1388011229945909.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/14i69k1229945909.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/15ugj21229945909.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/16iu3g1229945909.tab")
+ }
>
> system("convert tmp/1i5k31229945909.ps tmp/1i5k31229945909.png")
> system("convert tmp/2egfm1229945909.ps tmp/2egfm1229945909.png")
> system("convert tmp/3aafy1229945909.ps tmp/3aafy1229945909.png")
> system("convert tmp/4b9bg1229945909.ps tmp/4b9bg1229945909.png")
> system("convert tmp/5v3c61229945909.ps tmp/5v3c61229945909.png")
> system("convert tmp/6ftsh1229945909.ps tmp/6ftsh1229945909.png")
> system("convert tmp/7jdxg1229945909.ps tmp/7jdxg1229945909.png")
> system("convert tmp/8h3qo1229945909.ps tmp/8h3qo1229945909.png")
> system("convert tmp/96o0w1229945909.ps tmp/96o0w1229945909.png")
> system("convert tmp/100f791229945909.ps tmp/100f791229945909.png")
>
>
> proc.time()
user system elapsed
3.660 2.456 4.723