R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(56.6,0,56,0,54.8,0,52.7,0,50.9,0,50.6,0,52.1,0,53.3,0,53.9,0,54.3,0,54.2,0,54.2,0,53.5,0,51.4,0,50.5,0,50.3,0,49.8,0,50.7,0,52.8,0,55.3,0,57.3,0,57.5,0,56.8,0,56.4,0,56.3,0,56.4,0,57,0,57.9,0,58.9,0,58.8,0,56.5,1,51.9,1,47.4,1,44.9,1,43.9,1,43.4,1,42.9,1,42.6,1,42.2,1,41.2,1,40.2,1,39.3,1,38.5,1,38.3,1,37.9,1,37.6,1,37.3,1,36,1,34.5,1,33.5,1,32.9,1,32.9,1,32.8,1,31.9,1,30.5,1,29.2,1,28.7,1,28.4,1,28,1,27.4,1,26.9,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 56.6 0 1 0 0 0 0 0 0 0 0 0 0
2 56.0 0 0 1 0 0 0 0 0 0 0 0 0
3 54.8 0 0 0 1 0 0 0 0 0 0 0 0
4 52.7 0 0 0 0 1 0 0 0 0 0 0 0
5 50.9 0 0 0 0 0 1 0 0 0 0 0 0
6 50.6 0 0 0 0 0 0 1 0 0 0 0 0
7 52.1 0 0 0 0 0 0 0 1 0 0 0 0
8 53.3 0 0 0 0 0 0 0 0 1 0 0 0
9 53.9 0 0 0 0 0 0 0 0 0 1 0 0
10 54.3 0 0 0 0 0 0 0 0 0 0 1 0
11 54.2 0 0 0 0 0 0 0 0 0 0 0 1
12 54.2 0 0 0 0 0 0 0 0 0 0 0 0
13 53.5 0 1 0 0 0 0 0 0 0 0 0 0
14 51.4 0 0 1 0 0 0 0 0 0 0 0 0
15 50.5 0 0 0 1 0 0 0 0 0 0 0 0
16 50.3 0 0 0 0 1 0 0 0 0 0 0 0
17 49.8 0 0 0 0 0 1 0 0 0 0 0 0
18 50.7 0 0 0 0 0 0 1 0 0 0 0 0
19 52.8 0 0 0 0 0 0 0 1 0 0 0 0
20 55.3 0 0 0 0 0 0 0 0 1 0 0 0
21 57.3 0 0 0 0 0 0 0 0 0 1 0 0
22 57.5 0 0 0 0 0 0 0 0 0 0 1 0
23 56.8 0 0 0 0 0 0 0 0 0 0 0 1
24 56.4 0 0 0 0 0 0 0 0 0 0 0 0
25 56.3 0 1 0 0 0 0 0 0 0 0 0 0
26 56.4 0 0 1 0 0 0 0 0 0 0 0 0
27 57.0 0 0 0 1 0 0 0 0 0 0 0 0
28 57.9 0 0 0 0 1 0 0 0 0 0 0 0
29 58.9 0 0 0 0 0 1 0 0 0 0 0 0
30 58.8 0 0 0 0 0 0 1 0 0 0 0 0
31 56.5 1 0 0 0 0 0 0 1 0 0 0 0
32 51.9 1 0 0 0 0 0 0 0 1 0 0 0
33 47.4 1 0 0 0 0 0 0 0 0 1 0 0
34 44.9 1 0 0 0 0 0 0 0 0 0 1 0
35 43.9 1 0 0 0 0 0 0 0 0 0 0 1
36 43.4 1 0 0 0 0 0 0 0 0 0 0 0
37 42.9 1 1 0 0 0 0 0 0 0 0 0 0
38 42.6 1 0 1 0 0 0 0 0 0 0 0 0
39 42.2 1 0 0 1 0 0 0 0 0 0 0 0
40 41.2 1 0 0 0 1 0 0 0 0 0 0 0
41 40.2 1 0 0 0 0 1 0 0 0 0 0 0
42 39.3 1 0 0 0 0 0 1 0 0 0 0 0
43 38.5 1 0 0 0 0 0 0 1 0 0 0 0
44 38.3 1 0 0 0 0 0 0 0 1 0 0 0
45 37.9 1 0 0 0 0 0 0 0 0 1 0 0
46 37.6 1 0 0 0 0 0 0 0 0 0 1 0
47 37.3 1 0 0 0 0 0 0 0 0 0 0 1
48 36.0 1 0 0 0 0 0 0 0 0 0 0 0
49 34.5 1 1 0 0 0 0 0 0 0 0 0 0
50 33.5 1 0 1 0 0 0 0 0 0 0 0 0
51 32.9 1 0 0 1 0 0 0 0 0 0 0 0
52 32.9 1 0 0 0 1 0 0 0 0 0 0 0
53 32.8 1 0 0 0 0 1 0 0 0 0 0 0
54 31.9 1 0 0 0 0 0 1 0 0 0 0 0
55 30.5 1 0 0 0 0 0 0 1 0 0 0 0
56 29.2 1 0 0 0 0 0 0 0 1 0 0 0
57 28.7 1 0 0 0 0 0 0 0 0 1 0 0
58 28.4 1 0 0 0 0 0 0 0 0 0 1 0
59 28.0 1 0 0 0 0 0 0 0 0 0 0 1
60 27.4 1 0 0 0 0 0 0 0 0 0 0 0
61 26.9 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) X M1 M2 M3 M4
53.78898 -17.18163 -0.08150 1.06367 0.56367 0.08367
M5 M6 M7 M8 M9 M10
-0.39633 -0.65633 2.60000 2.12000 1.56000 1.06000
M11
0.56000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.6259 -3.5727 -0.2673 2.8925 17.2927
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 53.78898 2.87546 18.706 < 2e-16 ***
X -17.18163 1.58289 -10.855 1.61e-14 ***
M1 -0.08150 3.67831 -0.022 0.982
M2 1.06367 3.85134 0.276 0.784
M3 0.56367 3.85134 0.146 0.884
M4 0.08367 3.85134 0.022 0.983
M5 -0.39633 3.85134 -0.103 0.918
M6 -0.65633 3.85134 -0.170 0.865
M7 2.60000 3.83831 0.677 0.501
M8 2.12000 3.83831 0.552 0.583
M9 1.56000 3.83831 0.406 0.686
M10 1.06000 3.83831 0.276 0.784
M11 0.56000 3.83831 0.146 0.885
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.069 on 48 degrees of freedom
Multiple R-squared: 0.7154, Adjusted R-squared: 0.6443
F-statistic: 10.06 on 12 and 48 DF, p-value: 1.919e-09
> 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,] 9.079147e-02 1.815829e-01 0.9092085
[2,] 3.242297e-02 6.484593e-02 0.9675770
[3,] 1.012050e-02 2.024100e-02 0.9898795
[4,] 3.178133e-03 6.356267e-03 0.9968219
[5,] 1.068430e-03 2.136861e-03 0.9989316
[6,] 5.092090e-04 1.018418e-03 0.9994908
[7,] 2.226142e-04 4.452284e-04 0.9997774
[8,] 8.049479e-05 1.609896e-04 0.9999195
[9,] 2.578105e-05 5.156210e-05 0.9999742
[10,] 6.888331e-06 1.377666e-05 0.9999931
[11,] 2.821655e-06 5.643310e-06 0.9999972
[12,] 2.517789e-06 5.035579e-06 0.9999975
[13,] 6.186122e-06 1.237224e-05 0.9999938
[14,] 2.971767e-05 5.943534e-05 0.9999703
[15,] 6.048083e-05 1.209617e-04 0.9999395
[16,] 1.126712e-04 2.253424e-04 0.9998873
[17,] 2.888042e-04 5.776084e-04 0.9997112
[18,] 9.768205e-04 1.953641e-03 0.9990232
[19,] 2.498306e-03 4.996611e-03 0.9975017
[20,] 4.497090e-03 8.994181e-03 0.9955029
[21,] 7.941696e-03 1.588339e-02 0.9920583
[22,] 1.694496e-02 3.388993e-02 0.9830550
[23,] 1.753881e-02 3.507762e-02 0.9824612
[24,] 1.834696e-02 3.669392e-02 0.9816530
[25,] 1.765549e-02 3.531099e-02 0.9823445
[26,] 1.559258e-02 3.118516e-02 0.9844074
[27,] 1.440667e-02 2.881335e-02 0.9855933
[28,] 2.182926e-02 4.365852e-02 0.9781707
[29,] 3.348491e-02 6.696982e-02 0.9665151
[30,] 4.649602e-02 9.299203e-02 0.9535040
> postscript(file="/var/www/html/rcomp/tmp/1kvt51258648079.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wzae1258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mpqx1258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41rdv1258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z2kx1258648079.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
2.89251701 1.14734694 0.44734694 -1.17265306 -2.49265306 -2.53265306
7 8 9 10 11 12
-4.28897959 -2.60897959 -1.44897959 -0.54897959 -0.14897959 0.41102041
13 14 15 16 17 18
-0.20748299 -3.45265306 -3.85265306 -3.57265306 -3.59265306 -2.43265306
19 20 21 22 23 24
-3.58897959 -0.60897959 1.95102041 2.65102041 2.45102041 2.61102041
25 26 27 28 29 30
2.59251701 1.54734694 2.64734694 4.02734694 5.50734694 5.66734694
31 32 33 34 35 36
17.29265306 13.17265306 9.23265306 7.23265306 6.73265306 6.79265306
37 38 39 40 41 42
6.37414966 4.92897959 5.02897959 4.50897959 3.98897959 3.34897959
43 44 45 46 47 48
-0.70734694 -0.42734694 -0.26734694 -0.06734694 0.13265306 -0.60734694
49 50 51 52 53 54
-2.02585034 -4.17102041 -4.27102041 -3.79102041 -3.41102041 -4.05102041
55 56 57 58 59 60
-8.70734694 -9.52734694 -9.46734694 -9.26734694 -9.16734694 -9.20734694
61
-9.62585034
> postscript(file="/var/www/html/rcomp/tmp/63hzn1258648079.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 2.89251701 NA
1 1.14734694 2.89251701
2 0.44734694 1.14734694
3 -1.17265306 0.44734694
4 -2.49265306 -1.17265306
5 -2.53265306 -2.49265306
6 -4.28897959 -2.53265306
7 -2.60897959 -4.28897959
8 -1.44897959 -2.60897959
9 -0.54897959 -1.44897959
10 -0.14897959 -0.54897959
11 0.41102041 -0.14897959
12 -0.20748299 0.41102041
13 -3.45265306 -0.20748299
14 -3.85265306 -3.45265306
15 -3.57265306 -3.85265306
16 -3.59265306 -3.57265306
17 -2.43265306 -3.59265306
18 -3.58897959 -2.43265306
19 -0.60897959 -3.58897959
20 1.95102041 -0.60897959
21 2.65102041 1.95102041
22 2.45102041 2.65102041
23 2.61102041 2.45102041
24 2.59251701 2.61102041
25 1.54734694 2.59251701
26 2.64734694 1.54734694
27 4.02734694 2.64734694
28 5.50734694 4.02734694
29 5.66734694 5.50734694
30 17.29265306 5.66734694
31 13.17265306 17.29265306
32 9.23265306 13.17265306
33 7.23265306 9.23265306
34 6.73265306 7.23265306
35 6.79265306 6.73265306
36 6.37414966 6.79265306
37 4.92897959 6.37414966
38 5.02897959 4.92897959
39 4.50897959 5.02897959
40 3.98897959 4.50897959
41 3.34897959 3.98897959
42 -0.70734694 3.34897959
43 -0.42734694 -0.70734694
44 -0.26734694 -0.42734694
45 -0.06734694 -0.26734694
46 0.13265306 -0.06734694
47 -0.60734694 0.13265306
48 -2.02585034 -0.60734694
49 -4.17102041 -2.02585034
50 -4.27102041 -4.17102041
51 -3.79102041 -4.27102041
52 -3.41102041 -3.79102041
53 -4.05102041 -3.41102041
54 -8.70734694 -4.05102041
55 -9.52734694 -8.70734694
56 -9.46734694 -9.52734694
57 -9.26734694 -9.46734694
58 -9.16734694 -9.26734694
59 -9.20734694 -9.16734694
60 -9.62585034 -9.20734694
61 NA -9.62585034
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.14734694 2.89251701
[2,] 0.44734694 1.14734694
[3,] -1.17265306 0.44734694
[4,] -2.49265306 -1.17265306
[5,] -2.53265306 -2.49265306
[6,] -4.28897959 -2.53265306
[7,] -2.60897959 -4.28897959
[8,] -1.44897959 -2.60897959
[9,] -0.54897959 -1.44897959
[10,] -0.14897959 -0.54897959
[11,] 0.41102041 -0.14897959
[12,] -0.20748299 0.41102041
[13,] -3.45265306 -0.20748299
[14,] -3.85265306 -3.45265306
[15,] -3.57265306 -3.85265306
[16,] -3.59265306 -3.57265306
[17,] -2.43265306 -3.59265306
[18,] -3.58897959 -2.43265306
[19,] -0.60897959 -3.58897959
[20,] 1.95102041 -0.60897959
[21,] 2.65102041 1.95102041
[22,] 2.45102041 2.65102041
[23,] 2.61102041 2.45102041
[24,] 2.59251701 2.61102041
[25,] 1.54734694 2.59251701
[26,] 2.64734694 1.54734694
[27,] 4.02734694 2.64734694
[28,] 5.50734694 4.02734694
[29,] 5.66734694 5.50734694
[30,] 17.29265306 5.66734694
[31,] 13.17265306 17.29265306
[32,] 9.23265306 13.17265306
[33,] 7.23265306 9.23265306
[34,] 6.73265306 7.23265306
[35,] 6.79265306 6.73265306
[36,] 6.37414966 6.79265306
[37,] 4.92897959 6.37414966
[38,] 5.02897959 4.92897959
[39,] 4.50897959 5.02897959
[40,] 3.98897959 4.50897959
[41,] 3.34897959 3.98897959
[42,] -0.70734694 3.34897959
[43,] -0.42734694 -0.70734694
[44,] -0.26734694 -0.42734694
[45,] -0.06734694 -0.26734694
[46,] 0.13265306 -0.06734694
[47,] -0.60734694 0.13265306
[48,] -2.02585034 -0.60734694
[49,] -4.17102041 -2.02585034
[50,] -4.27102041 -4.17102041
[51,] -3.79102041 -4.27102041
[52,] -3.41102041 -3.79102041
[53,] -4.05102041 -3.41102041
[54,] -8.70734694 -4.05102041
[55,] -9.52734694 -8.70734694
[56,] -9.46734694 -9.52734694
[57,] -9.26734694 -9.46734694
[58,] -9.16734694 -9.26734694
[59,] -9.20734694 -9.16734694
[60,] -9.62585034 -9.20734694
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.14734694 2.89251701
2 0.44734694 1.14734694
3 -1.17265306 0.44734694
4 -2.49265306 -1.17265306
5 -2.53265306 -2.49265306
6 -4.28897959 -2.53265306
7 -2.60897959 -4.28897959
8 -1.44897959 -2.60897959
9 -0.54897959 -1.44897959
10 -0.14897959 -0.54897959
11 0.41102041 -0.14897959
12 -0.20748299 0.41102041
13 -3.45265306 -0.20748299
14 -3.85265306 -3.45265306
15 -3.57265306 -3.85265306
16 -3.59265306 -3.57265306
17 -2.43265306 -3.59265306
18 -3.58897959 -2.43265306
19 -0.60897959 -3.58897959
20 1.95102041 -0.60897959
21 2.65102041 1.95102041
22 2.45102041 2.65102041
23 2.61102041 2.45102041
24 2.59251701 2.61102041
25 1.54734694 2.59251701
26 2.64734694 1.54734694
27 4.02734694 2.64734694
28 5.50734694 4.02734694
29 5.66734694 5.50734694
30 17.29265306 5.66734694
31 13.17265306 17.29265306
32 9.23265306 13.17265306
33 7.23265306 9.23265306
34 6.73265306 7.23265306
35 6.79265306 6.73265306
36 6.37414966 6.79265306
37 4.92897959 6.37414966
38 5.02897959 4.92897959
39 4.50897959 5.02897959
40 3.98897959 4.50897959
41 3.34897959 3.98897959
42 -0.70734694 3.34897959
43 -0.42734694 -0.70734694
44 -0.26734694 -0.42734694
45 -0.06734694 -0.26734694
46 0.13265306 -0.06734694
47 -0.60734694 0.13265306
48 -2.02585034 -0.60734694
49 -4.17102041 -2.02585034
50 -4.27102041 -4.17102041
51 -3.79102041 -4.27102041
52 -3.41102041 -3.79102041
53 -4.05102041 -3.41102041
54 -8.70734694 -4.05102041
55 -9.52734694 -8.70734694
56 -9.46734694 -9.52734694
57 -9.26734694 -9.46734694
58 -9.16734694 -9.26734694
59 -9.20734694 -9.16734694
60 -9.62585034 -9.20734694
> 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/7b8cc1258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8uvg41258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9lqu21258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104woa1258648079.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11afvz1258648079.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/1294rr1258648079.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/13beq71258648079.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/148gdv1258648079.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/155cq21258648079.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/16w0by1258648079.tab")
+ }
>
> system("convert tmp/1kvt51258648079.ps tmp/1kvt51258648079.png")
> system("convert tmp/2wzae1258648079.ps tmp/2wzae1258648079.png")
> system("convert tmp/3mpqx1258648079.ps tmp/3mpqx1258648079.png")
> system("convert tmp/41rdv1258648079.ps tmp/41rdv1258648079.png")
> system("convert tmp/5z2kx1258648079.ps tmp/5z2kx1258648079.png")
> system("convert tmp/63hzn1258648079.ps tmp/63hzn1258648079.png")
> system("convert tmp/7b8cc1258648079.ps tmp/7b8cc1258648079.png")
> system("convert tmp/8uvg41258648079.ps tmp/8uvg41258648079.png")
> system("convert tmp/9lqu21258648079.ps tmp/9lqu21258648079.png")
> system("convert tmp/104woa1258648079.ps tmp/104woa1258648079.png")
>
>
> proc.time()
user system elapsed
2.390 1.555 2.819