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(114,1,113.8,1,113.6,1,113.7,1,114.2,1,114.8,0,115.2,1,115.3,1,114.9,1,115.1,0,116,0,116,0,116,0,115.9,1,115.6,1,116.6,1,116.9,0,117.9,1,117.9,1,117.7,0,117.4,1,117.3,0,119,1,119.1,0,119,0,118.5,0,117,1,117.5,1,118.2,1,118.2,1,118.3,0,118.2,1,117.9,1,117.8,0,118.6,0,118.9,0,120.8,1,121.8,1,121.3,0,121.9,1,122,1,121.9,0,122,1,122.2,0,123,1,123.1,0,124.9,1,125.4,0,124.7,0,124.4,1,124,0,125,1,125.1,0,125.4,0,125.7,1,126.4,1,125.7,1,125.4,0,126.4,1,126.2,0),dim=c(2,60),dimnames=list(c('CPItot','CPIlandbouw'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('CPItot','CPIlandbouw'),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'
> #'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
CPItot CPIlandbouw M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 114.0 1 1 0 0 0 0 0 0 0 0 0 0
2 113.8 1 0 1 0 0 0 0 0 0 0 0 0
3 113.6 1 0 0 1 0 0 0 0 0 0 0 0
4 113.7 1 0 0 0 1 0 0 0 0 0 0 0
5 114.2 1 0 0 0 0 1 0 0 0 0 0 0
6 114.8 0 0 0 0 0 0 1 0 0 0 0 0
7 115.2 1 0 0 0 0 0 0 1 0 0 0 0
8 115.3 1 0 0 0 0 0 0 0 1 0 0 0
9 114.9 1 0 0 0 0 0 0 0 0 1 0 0
10 115.1 0 0 0 0 0 0 0 0 0 0 1 0
11 116.0 0 0 0 0 0 0 0 0 0 0 0 1
12 116.0 0 0 0 0 0 0 0 0 0 0 0 0
13 116.0 0 1 0 0 0 0 0 0 0 0 0 0
14 115.9 1 0 1 0 0 0 0 0 0 0 0 0
15 115.6 1 0 0 1 0 0 0 0 0 0 0 0
16 116.6 1 0 0 0 1 0 0 0 0 0 0 0
17 116.9 0 0 0 0 0 1 0 0 0 0 0 0
18 117.9 1 0 0 0 0 0 1 0 0 0 0 0
19 117.9 1 0 0 0 0 0 0 1 0 0 0 0
20 117.7 0 0 0 0 0 0 0 0 1 0 0 0
21 117.4 1 0 0 0 0 0 0 0 0 1 0 0
22 117.3 0 0 0 0 0 0 0 0 0 0 1 0
23 119.0 1 0 0 0 0 0 0 0 0 0 0 1
24 119.1 0 0 0 0 0 0 0 0 0 0 0 0
25 119.0 0 1 0 0 0 0 0 0 0 0 0 0
26 118.5 0 0 1 0 0 0 0 0 0 0 0 0
27 117.0 1 0 0 1 0 0 0 0 0 0 0 0
28 117.5 1 0 0 0 1 0 0 0 0 0 0 0
29 118.2 1 0 0 0 0 1 0 0 0 0 0 0
30 118.2 1 0 0 0 0 0 1 0 0 0 0 0
31 118.3 0 0 0 0 0 0 0 1 0 0 0 0
32 118.2 1 0 0 0 0 0 0 0 1 0 0 0
33 117.9 1 0 0 0 0 0 0 0 0 1 0 0
34 117.8 0 0 0 0 0 0 0 0 0 0 1 0
35 118.6 0 0 0 0 0 0 0 0 0 0 0 1
36 118.9 0 0 0 0 0 0 0 0 0 0 0 0
37 120.8 1 1 0 0 0 0 0 0 0 0 0 0
38 121.8 1 0 1 0 0 0 0 0 0 0 0 0
39 121.3 0 0 0 1 0 0 0 0 0 0 0 0
40 121.9 1 0 0 0 1 0 0 0 0 0 0 0
41 122.0 1 0 0 0 0 1 0 0 0 0 0 0
42 121.9 0 0 0 0 0 0 1 0 0 0 0 0
43 122.0 1 0 0 0 0 0 0 1 0 0 0 0
44 122.2 0 0 0 0 0 0 0 0 1 0 0 0
45 123.0 1 0 0 0 0 0 0 0 0 1 0 0
46 123.1 0 0 0 0 0 0 0 0 0 0 1 0
47 124.9 1 0 0 0 0 0 0 0 0 0 0 1
48 125.4 0 0 0 0 0 0 0 0 0 0 0 0
49 124.7 0 1 0 0 0 0 0 0 0 0 0 0
50 124.4 1 0 1 0 0 0 0 0 0 0 0 0
51 124.0 0 0 0 1 0 0 0 0 0 0 0 0
52 125.0 1 0 0 0 1 0 0 0 0 0 0 0
53 125.1 0 0 0 0 0 1 0 0 0 0 0 0
54 125.4 0 0 0 0 0 0 1 0 0 0 0 0
55 125.7 1 0 0 0 0 0 0 1 0 0 0 0
56 126.4 1 0 0 0 0 0 0 0 1 0 0 0
57 125.7 1 0 0 0 0 0 0 0 0 1 0 0
58 125.4 0 0 0 0 0 0 0 0 0 0 1 0
59 126.4 1 0 0 0 0 0 0 0 0 0 0 1
60 126.2 0 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) CPIlandbouw M1 M2 M3 M4
121.1200 -1.0273 -1.8091 -1.4182 -2.2036 -1.1527
M5 M6 M7 M8 M9 M10
-1.2236 -1.0691 -0.4782 -0.5436 -0.3127 -1.3800
M11
0.4764
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.596 -2.906 -1.275 3.255 6.851
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 121.1200 1.9379 62.500 <2e-16 ***
CPIlandbouw -1.0273 1.4608 -0.703 0.485
M1 -1.8091 2.8022 -0.646 0.522
M2 -1.4182 2.9794 -0.476 0.636
M3 -2.2036 2.8774 -0.766 0.448
M4 -1.1527 3.1056 -0.371 0.712
M5 -1.2236 2.8774 -0.425 0.673
M6 -1.0691 2.8022 -0.382 0.705
M7 -0.4782 2.9794 -0.160 0.873
M8 -0.5436 2.8774 -0.189 0.851
M9 -0.3127 3.1056 -0.101 0.920
M10 -1.3800 2.7406 -0.504 0.617
M11 0.4764 2.8774 0.166 0.869
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.333 on 47 degrees of freedom
Multiple R-squared: 0.05127, Adjusted R-squared: -0.191
F-statistic: 0.2117 on 12 and 47 DF, p-value: 0.9972
> 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,] 0.10054759 0.20109518 0.89945241
[2,] 0.03806639 0.07613279 0.96193361
[3,] 0.06662917 0.13325834 0.93337083
[4,] 0.04667890 0.09335781 0.95332110
[5,] 0.02610950 0.05221901 0.97389050
[6,] 0.01862833 0.03725665 0.98137167
[7,] 0.01256225 0.02512450 0.98743775
[8,] 0.01312558 0.02625117 0.98687442
[9,] 0.01157291 0.02314582 0.98842709
[10,] 0.01460418 0.02920835 0.98539582
[11,] 0.01332685 0.02665369 0.98667315
[12,] 0.01160612 0.02321223 0.98839388
[13,] 0.01149596 0.02299192 0.98850404
[14,] 0.01393515 0.02787029 0.98606485
[15,] 0.01646945 0.03293891 0.98353055
[16,] 0.01223073 0.02446146 0.98776927
[17,] 0.01728432 0.03456864 0.98271568
[18,] 0.02527955 0.05055911 0.97472045
[19,] 0.04246348 0.08492695 0.95753652
[20,] 0.08670742 0.17341484 0.91329258
[21,] 0.25909145 0.51818290 0.74090855
[22,] 0.38974064 0.77948127 0.61025936
[23,] 0.49842358 0.99684716 0.50157642
[24,] 0.55902359 0.88195282 0.44097641
[25,] 0.62588113 0.74823774 0.37411887
[26,] 0.81173581 0.37652838 0.18826419
[27,] 0.84675431 0.30649138 0.15324569
[28,] 0.90780795 0.18438409 0.09219205
[29,] 0.80944025 0.38111949 0.19055975
> postscript(file="/var/www/html/rcomp/tmp/1hx9a1258801160.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2c3pk1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3974z1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fphy1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vw711258801160.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 7
-4.2836364 -4.8745455 -4.2890909 -5.2400000 -4.6690909 -5.2509091 -4.4145455
8 9 10 11 12 13 14
-4.2490909 -4.8800000 -4.6400000 -5.5963636 -5.1200000 -3.3109091 -2.7745455
15 16 17 18 19 20 21
-2.2890909 -2.3400000 -2.9963636 -1.1236364 -1.7145455 -2.8763636 -2.3800000
22 23 24 25 26 27 28
-2.4400000 -1.5690909 -2.0200000 -0.3109091 -1.2018182 -0.8890909 -1.4400000
29 30 31 32 33 34 35
-0.6690909 -0.8236364 -2.3418182 -1.3490909 -1.8800000 -1.9400000 -2.9963636
36 37 38 39 40 41 42
-2.2200000 2.5163636 3.1254545 2.3836364 2.9600000 3.1309091 1.8490909
43 44 45 46 47 48 49
2.3854545 1.6236364 3.2200000 3.3600000 4.3309091 4.2800000 5.3890909
50 51 52 53 54 55 56
5.7254545 5.0836364 6.0600000 5.2036364 5.3490909 6.0854545 6.8509091
57 58 59 60
5.9200000 5.6600000 5.8309091 5.0800000
> postscript(file="/var/www/html/rcomp/tmp/636s01258801160.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 -4.2836364 NA
1 -4.8745455 -4.2836364
2 -4.2890909 -4.8745455
3 -5.2400000 -4.2890909
4 -4.6690909 -5.2400000
5 -5.2509091 -4.6690909
6 -4.4145455 -5.2509091
7 -4.2490909 -4.4145455
8 -4.8800000 -4.2490909
9 -4.6400000 -4.8800000
10 -5.5963636 -4.6400000
11 -5.1200000 -5.5963636
12 -3.3109091 -5.1200000
13 -2.7745455 -3.3109091
14 -2.2890909 -2.7745455
15 -2.3400000 -2.2890909
16 -2.9963636 -2.3400000
17 -1.1236364 -2.9963636
18 -1.7145455 -1.1236364
19 -2.8763636 -1.7145455
20 -2.3800000 -2.8763636
21 -2.4400000 -2.3800000
22 -1.5690909 -2.4400000
23 -2.0200000 -1.5690909
24 -0.3109091 -2.0200000
25 -1.2018182 -0.3109091
26 -0.8890909 -1.2018182
27 -1.4400000 -0.8890909
28 -0.6690909 -1.4400000
29 -0.8236364 -0.6690909
30 -2.3418182 -0.8236364
31 -1.3490909 -2.3418182
32 -1.8800000 -1.3490909
33 -1.9400000 -1.8800000
34 -2.9963636 -1.9400000
35 -2.2200000 -2.9963636
36 2.5163636 -2.2200000
37 3.1254545 2.5163636
38 2.3836364 3.1254545
39 2.9600000 2.3836364
40 3.1309091 2.9600000
41 1.8490909 3.1309091
42 2.3854545 1.8490909
43 1.6236364 2.3854545
44 3.2200000 1.6236364
45 3.3600000 3.2200000
46 4.3309091 3.3600000
47 4.2800000 4.3309091
48 5.3890909 4.2800000
49 5.7254545 5.3890909
50 5.0836364 5.7254545
51 6.0600000 5.0836364
52 5.2036364 6.0600000
53 5.3490909 5.2036364
54 6.0854545 5.3490909
55 6.8509091 6.0854545
56 5.9200000 6.8509091
57 5.6600000 5.9200000
58 5.8309091 5.6600000
59 5.0800000 5.8309091
60 NA 5.0800000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.8745455 -4.2836364
[2,] -4.2890909 -4.8745455
[3,] -5.2400000 -4.2890909
[4,] -4.6690909 -5.2400000
[5,] -5.2509091 -4.6690909
[6,] -4.4145455 -5.2509091
[7,] -4.2490909 -4.4145455
[8,] -4.8800000 -4.2490909
[9,] -4.6400000 -4.8800000
[10,] -5.5963636 -4.6400000
[11,] -5.1200000 -5.5963636
[12,] -3.3109091 -5.1200000
[13,] -2.7745455 -3.3109091
[14,] -2.2890909 -2.7745455
[15,] -2.3400000 -2.2890909
[16,] -2.9963636 -2.3400000
[17,] -1.1236364 -2.9963636
[18,] -1.7145455 -1.1236364
[19,] -2.8763636 -1.7145455
[20,] -2.3800000 -2.8763636
[21,] -2.4400000 -2.3800000
[22,] -1.5690909 -2.4400000
[23,] -2.0200000 -1.5690909
[24,] -0.3109091 -2.0200000
[25,] -1.2018182 -0.3109091
[26,] -0.8890909 -1.2018182
[27,] -1.4400000 -0.8890909
[28,] -0.6690909 -1.4400000
[29,] -0.8236364 -0.6690909
[30,] -2.3418182 -0.8236364
[31,] -1.3490909 -2.3418182
[32,] -1.8800000 -1.3490909
[33,] -1.9400000 -1.8800000
[34,] -2.9963636 -1.9400000
[35,] -2.2200000 -2.9963636
[36,] 2.5163636 -2.2200000
[37,] 3.1254545 2.5163636
[38,] 2.3836364 3.1254545
[39,] 2.9600000 2.3836364
[40,] 3.1309091 2.9600000
[41,] 1.8490909 3.1309091
[42,] 2.3854545 1.8490909
[43,] 1.6236364 2.3854545
[44,] 3.2200000 1.6236364
[45,] 3.3600000 3.2200000
[46,] 4.3309091 3.3600000
[47,] 4.2800000 4.3309091
[48,] 5.3890909 4.2800000
[49,] 5.7254545 5.3890909
[50,] 5.0836364 5.7254545
[51,] 6.0600000 5.0836364
[52,] 5.2036364 6.0600000
[53,] 5.3490909 5.2036364
[54,] 6.0854545 5.3490909
[55,] 6.8509091 6.0854545
[56,] 5.9200000 6.8509091
[57,] 5.6600000 5.9200000
[58,] 5.8309091 5.6600000
[59,] 5.0800000 5.8309091
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.8745455 -4.2836364
2 -4.2890909 -4.8745455
3 -5.2400000 -4.2890909
4 -4.6690909 -5.2400000
5 -5.2509091 -4.6690909
6 -4.4145455 -5.2509091
7 -4.2490909 -4.4145455
8 -4.8800000 -4.2490909
9 -4.6400000 -4.8800000
10 -5.5963636 -4.6400000
11 -5.1200000 -5.5963636
12 -3.3109091 -5.1200000
13 -2.7745455 -3.3109091
14 -2.2890909 -2.7745455
15 -2.3400000 -2.2890909
16 -2.9963636 -2.3400000
17 -1.1236364 -2.9963636
18 -1.7145455 -1.1236364
19 -2.8763636 -1.7145455
20 -2.3800000 -2.8763636
21 -2.4400000 -2.3800000
22 -1.5690909 -2.4400000
23 -2.0200000 -1.5690909
24 -0.3109091 -2.0200000
25 -1.2018182 -0.3109091
26 -0.8890909 -1.2018182
27 -1.4400000 -0.8890909
28 -0.6690909 -1.4400000
29 -0.8236364 -0.6690909
30 -2.3418182 -0.8236364
31 -1.3490909 -2.3418182
32 -1.8800000 -1.3490909
33 -1.9400000 -1.8800000
34 -2.9963636 -1.9400000
35 -2.2200000 -2.9963636
36 2.5163636 -2.2200000
37 3.1254545 2.5163636
38 2.3836364 3.1254545
39 2.9600000 2.3836364
40 3.1309091 2.9600000
41 1.8490909 3.1309091
42 2.3854545 1.8490909
43 1.6236364 2.3854545
44 3.2200000 1.6236364
45 3.3600000 3.2200000
46 4.3309091 3.3600000
47 4.2800000 4.3309091
48 5.3890909 4.2800000
49 5.7254545 5.3890909
50 5.0836364 5.7254545
51 6.0600000 5.0836364
52 5.2036364 6.0600000
53 5.3490909 5.2036364
54 6.0854545 5.3490909
55 6.8509091 6.0854545
56 5.9200000 6.8509091
57 5.6600000 5.9200000
58 5.8309091 5.6600000
59 5.0800000 5.8309091
> 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/7h5fm1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8cpqg1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wcwi1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10a4fl1258801160.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tgrz1258801160.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/12zdi61258801160.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/139ln81258801161.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/14o1rm1258801161.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/15ad1z1258801161.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/16kq7q1258801161.tab")
+ }
>
> system("convert tmp/1hx9a1258801160.ps tmp/1hx9a1258801160.png")
> system("convert tmp/2c3pk1258801160.ps tmp/2c3pk1258801160.png")
> system("convert tmp/3974z1258801160.ps tmp/3974z1258801160.png")
> system("convert tmp/4fphy1258801160.ps tmp/4fphy1258801160.png")
> system("convert tmp/5vw711258801160.ps tmp/5vw711258801160.png")
> system("convert tmp/636s01258801160.ps tmp/636s01258801160.png")
> system("convert tmp/7h5fm1258801160.ps tmp/7h5fm1258801160.png")
> system("convert tmp/8cpqg1258801160.ps tmp/8cpqg1258801160.png")
> system("convert tmp/9wcwi1258801160.ps tmp/9wcwi1258801160.png")
> system("convert tmp/10a4fl1258801160.ps tmp/10a4fl1258801160.png")
>
>
> proc.time()
user system elapsed
2.392 1.555 2.942