R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(91.2,0,99.2,0,108.2,0,101.5,0,106.9,0,104.4,0,77.9,0,60,0,99.5,0,95,0,105.6,0,102.5,0,93.3,0,97.3,0,127,0,111.7,0,96.4,0,133,0,72.2,0,95.8,0,124.1,0,127.6,0,110.7,0,104.6,0,112.7,0,115.3,0,139.4,0,119,0,97.4,0,154,0,81.5,0,88.8,0,127.7,1,105.1,1,114.9,1,106.4,1,104.5,1,121.6,1,141.4,1,99,1,126.7,1,134.1,1,81.3,1,88.6,1,132.7,1,132.9,1,134.4,1,103.7,1,119.7,1,115,1,132.9,1,108.5,1,113.9,1,142,1,97.7,1,92.2,1,128.8,1,134.9,1,128.2,1,114.8,1),dim=c(2,60),dimnames=list(c('Transportmiddelen','Conjunctuur'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Transportmiddelen','Conjunctuur'),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
Transportmiddelen Conjunctuur M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 91.2 0 1 0 0 0 0 0 0 0 0 0 0
2 99.2 0 0 1 0 0 0 0 0 0 0 0 0
3 108.2 0 0 0 1 0 0 0 0 0 0 0 0
4 101.5 0 0 0 0 1 0 0 0 0 0 0 0
5 106.9 0 0 0 0 0 1 0 0 0 0 0 0
6 104.4 0 0 0 0 0 0 1 0 0 0 0 0
7 77.9 0 0 0 0 0 0 0 1 0 0 0 0
8 60.0 0 0 0 0 0 0 0 0 1 0 0 0
9 99.5 0 0 0 0 0 0 0 0 0 1 0 0
10 95.0 0 0 0 0 0 0 0 0 0 0 1 0
11 105.6 0 0 0 0 0 0 0 0 0 0 0 1
12 102.5 0 0 0 0 0 0 0 0 0 0 0 0
13 93.3 0 1 0 0 0 0 0 0 0 0 0 0
14 97.3 0 0 1 0 0 0 0 0 0 0 0 0
15 127.0 0 0 0 1 0 0 0 0 0 0 0 0
16 111.7 0 0 0 0 1 0 0 0 0 0 0 0
17 96.4 0 0 0 0 0 1 0 0 0 0 0 0
18 133.0 0 0 0 0 0 0 1 0 0 0 0 0
19 72.2 0 0 0 0 0 0 0 1 0 0 0 0
20 95.8 0 0 0 0 0 0 0 0 1 0 0 0
21 124.1 0 0 0 0 0 0 0 0 0 1 0 0
22 127.6 0 0 0 0 0 0 0 0 0 0 1 0
23 110.7 0 0 0 0 0 0 0 0 0 0 0 1
24 104.6 0 0 0 0 0 0 0 0 0 0 0 0
25 112.7 0 1 0 0 0 0 0 0 0 0 0 0
26 115.3 0 0 1 0 0 0 0 0 0 0 0 0
27 139.4 0 0 0 1 0 0 0 0 0 0 0 0
28 119.0 0 0 0 0 1 0 0 0 0 0 0 0
29 97.4 0 0 0 0 0 1 0 0 0 0 0 0
30 154.0 0 0 0 0 0 0 1 0 0 0 0 0
31 81.5 0 0 0 0 0 0 0 1 0 0 0 0
32 88.8 0 0 0 0 0 0 0 0 1 0 0 0
33 127.7 1 0 0 0 0 0 0 0 0 1 0 0
34 105.1 1 0 0 0 0 0 0 0 0 0 1 0
35 114.9 1 0 0 0 0 0 0 0 0 0 0 1
36 106.4 1 0 0 0 0 0 0 0 0 0 0 0
37 104.5 1 1 0 0 0 0 0 0 0 0 0 0
38 121.6 1 0 1 0 0 0 0 0 0 0 0 0
39 141.4 1 0 0 1 0 0 0 0 0 0 0 0
40 99.0 1 0 0 0 1 0 0 0 0 0 0 0
41 126.7 1 0 0 0 0 1 0 0 0 0 0 0
42 134.1 1 0 0 0 0 0 1 0 0 0 0 0
43 81.3 1 0 0 0 0 0 0 1 0 0 0 0
44 88.6 1 0 0 0 0 0 0 0 1 0 0 0
45 132.7 1 0 0 0 0 0 0 0 0 1 0 0
46 132.9 1 0 0 0 0 0 0 0 0 0 1 0
47 134.4 1 0 0 0 0 0 0 0 0 0 0 1
48 103.7 1 0 0 0 0 0 0 0 0 0 0 0
49 119.7 1 1 0 0 0 0 0 0 0 0 0 0
50 115.0 1 0 1 0 0 0 0 0 0 0 0 0
51 132.9 1 0 0 1 0 0 0 0 0 0 0 0
52 108.5 1 0 0 0 1 0 0 0 0 0 0 0
53 113.9 1 0 0 0 0 1 0 0 0 0 0 0
54 142.0 1 0 0 0 0 0 1 0 0 0 0 0
55 97.7 1 0 0 0 0 0 0 1 0 0 0 0
56 92.2 1 0 0 0 0 0 0 0 1 0 0 0
57 128.8 1 0 0 0 0 0 0 0 0 1 0 0
58 134.9 1 0 0 0 0 0 0 0 0 0 1 0
59 128.2 1 0 0 0 0 0 0 0 0 0 0 1
60 114.8 1 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) Conjunctuur M1 M2 M3 M4
99.6558 11.2403 0.1281 5.5281 25.6281 3.7881
M5 M6 M7 M8 M9 M10
4.1081 29.3481 -22.0319 -19.0719 16.1600 12.7000
M11
12.3600
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-24.6039 -6.4328 0.5099 8.2261 24.9961
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 99.6558 5.3784 18.529 < 2e-16 ***
Conjunctuur 11.2403 2.9880 3.762 0.000467 ***
M1 0.1281 7.1961 0.018 0.985878
M2 5.5281 7.1961 0.768 0.446208
M3 25.6281 7.1961 3.561 0.000858 ***
M4 3.7881 7.1961 0.526 0.601081
M5 4.1081 7.1961 0.571 0.570804
M6 29.3481 7.1961 4.078 0.000174 ***
M7 -22.0319 7.1961 -3.062 0.003634 **
M8 -19.0719 7.1961 -2.650 0.010922 *
M9 16.1600 7.1712 2.253 0.028936 *
M10 12.7000 7.1712 1.771 0.083050 .
M11 12.3600 7.1712 1.724 0.091361 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.34 on 47 degrees of freedom
Multiple R-squared: 0.7176, Adjusted R-squared: 0.6455
F-statistic: 9.954 on 12 and 47 DF, p-value: 2.775e-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,] 0.5610891 0.87782179 0.438910893
[2,] 0.5146958 0.97060833 0.485304164
[3,] 0.8323253 0.33534947 0.167674734
[4,] 0.7778205 0.44435910 0.222179548
[5,] 0.9487018 0.10259637 0.051298185
[6,] 0.9593921 0.08121579 0.040607895
[7,] 0.9791880 0.04162402 0.020812012
[8,] 0.9724637 0.05507265 0.027536325
[9,] 0.9524570 0.09508594 0.047542972
[10,] 0.9509828 0.09803442 0.049017209
[11,] 0.9405709 0.11885824 0.059429122
[12,] 0.9380475 0.12390499 0.061952495
[13,] 0.9413751 0.11724982 0.058624911
[14,] 0.9553283 0.08934348 0.044671742
[15,] 0.9849383 0.03012341 0.015061705
[16,] 0.9741787 0.05164255 0.025821273
[17,] 0.9578054 0.08438912 0.042194558
[18,] 0.9298371 0.14032581 0.070162905
[19,] 0.9878352 0.02432958 0.012164790
[20,] 0.9921495 0.01570107 0.007850536
[21,] 0.9838999 0.03220029 0.016100143
[22,] 0.9855682 0.02886354 0.014431770
[23,] 0.9759461 0.04810775 0.024053873
[24,] 0.9631477 0.07370469 0.036852346
[25,] 0.9536079 0.09278416 0.046392078
[26,] 0.9555245 0.08895101 0.044475507
[27,] 0.9246355 0.15072907 0.075364535
[28,] 0.9628616 0.07427688 0.037138439
[29,] 0.9029971 0.19400574 0.097002872
> postscript(file="/var/www/html/rcomp/tmp/1tk9p1229093694.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24h7c1229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3oc791229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49om51229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5on4s1229093694.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
-8.5838889 -5.9838889 -17.0838889 -1.9438889 3.1361111 -24.6038889
7 8 9 10 11 12
0.2761111 -20.5838889 -16.3158333 -17.3558333 -6.4158333 2.8441667
13 14 15 16 17 18
-6.4838889 -7.8838889 1.7161111 8.2561111 -7.3638889 3.9961111
19 20 21 22 23 24
-5.4238889 15.2161111 8.2841667 15.2441667 -1.3158333 4.9441667
25 26 27 28 29 30
12.9161111 10.1161111 14.1161111 15.5561111 -6.3638889 24.9961111
31 32 33 34 35 36
3.8761111 8.2161111 0.6438889 -18.4961111 -8.3561111 -4.4961111
37 38 39 40 41 42
-6.5241667 5.1758333 4.8758333 -15.6841667 11.6958333 -6.1441667
43 44 45 46 47 48
-7.5641667 -3.2241667 5.6438889 9.3038889 11.1438889 -7.1961111
49 50 51 52 53 54
8.6758333 -1.4241667 -3.6241667 -6.1841667 -1.1041667 1.7558333
55 56 57 58 59 60
8.8358333 0.3758333 1.7438889 11.3038889 4.9438889 3.9038889
> postscript(file="/var/www/html/rcomp/tmp/627ao1229093694.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 -8.5838889 NA
1 -5.9838889 -8.5838889
2 -17.0838889 -5.9838889
3 -1.9438889 -17.0838889
4 3.1361111 -1.9438889
5 -24.6038889 3.1361111
6 0.2761111 -24.6038889
7 -20.5838889 0.2761111
8 -16.3158333 -20.5838889
9 -17.3558333 -16.3158333
10 -6.4158333 -17.3558333
11 2.8441667 -6.4158333
12 -6.4838889 2.8441667
13 -7.8838889 -6.4838889
14 1.7161111 -7.8838889
15 8.2561111 1.7161111
16 -7.3638889 8.2561111
17 3.9961111 -7.3638889
18 -5.4238889 3.9961111
19 15.2161111 -5.4238889
20 8.2841667 15.2161111
21 15.2441667 8.2841667
22 -1.3158333 15.2441667
23 4.9441667 -1.3158333
24 12.9161111 4.9441667
25 10.1161111 12.9161111
26 14.1161111 10.1161111
27 15.5561111 14.1161111
28 -6.3638889 15.5561111
29 24.9961111 -6.3638889
30 3.8761111 24.9961111
31 8.2161111 3.8761111
32 0.6438889 8.2161111
33 -18.4961111 0.6438889
34 -8.3561111 -18.4961111
35 -4.4961111 -8.3561111
36 -6.5241667 -4.4961111
37 5.1758333 -6.5241667
38 4.8758333 5.1758333
39 -15.6841667 4.8758333
40 11.6958333 -15.6841667
41 -6.1441667 11.6958333
42 -7.5641667 -6.1441667
43 -3.2241667 -7.5641667
44 5.6438889 -3.2241667
45 9.3038889 5.6438889
46 11.1438889 9.3038889
47 -7.1961111 11.1438889
48 8.6758333 -7.1961111
49 -1.4241667 8.6758333
50 -3.6241667 -1.4241667
51 -6.1841667 -3.6241667
52 -1.1041667 -6.1841667
53 1.7558333 -1.1041667
54 8.8358333 1.7558333
55 0.3758333 8.8358333
56 1.7438889 0.3758333
57 11.3038889 1.7438889
58 4.9438889 11.3038889
59 3.9038889 4.9438889
60 NA 3.9038889
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.9838889 -8.5838889
[2,] -17.0838889 -5.9838889
[3,] -1.9438889 -17.0838889
[4,] 3.1361111 -1.9438889
[5,] -24.6038889 3.1361111
[6,] 0.2761111 -24.6038889
[7,] -20.5838889 0.2761111
[8,] -16.3158333 -20.5838889
[9,] -17.3558333 -16.3158333
[10,] -6.4158333 -17.3558333
[11,] 2.8441667 -6.4158333
[12,] -6.4838889 2.8441667
[13,] -7.8838889 -6.4838889
[14,] 1.7161111 -7.8838889
[15,] 8.2561111 1.7161111
[16,] -7.3638889 8.2561111
[17,] 3.9961111 -7.3638889
[18,] -5.4238889 3.9961111
[19,] 15.2161111 -5.4238889
[20,] 8.2841667 15.2161111
[21,] 15.2441667 8.2841667
[22,] -1.3158333 15.2441667
[23,] 4.9441667 -1.3158333
[24,] 12.9161111 4.9441667
[25,] 10.1161111 12.9161111
[26,] 14.1161111 10.1161111
[27,] 15.5561111 14.1161111
[28,] -6.3638889 15.5561111
[29,] 24.9961111 -6.3638889
[30,] 3.8761111 24.9961111
[31,] 8.2161111 3.8761111
[32,] 0.6438889 8.2161111
[33,] -18.4961111 0.6438889
[34,] -8.3561111 -18.4961111
[35,] -4.4961111 -8.3561111
[36,] -6.5241667 -4.4961111
[37,] 5.1758333 -6.5241667
[38,] 4.8758333 5.1758333
[39,] -15.6841667 4.8758333
[40,] 11.6958333 -15.6841667
[41,] -6.1441667 11.6958333
[42,] -7.5641667 -6.1441667
[43,] -3.2241667 -7.5641667
[44,] 5.6438889 -3.2241667
[45,] 9.3038889 5.6438889
[46,] 11.1438889 9.3038889
[47,] -7.1961111 11.1438889
[48,] 8.6758333 -7.1961111
[49,] -1.4241667 8.6758333
[50,] -3.6241667 -1.4241667
[51,] -6.1841667 -3.6241667
[52,] -1.1041667 -6.1841667
[53,] 1.7558333 -1.1041667
[54,] 8.8358333 1.7558333
[55,] 0.3758333 8.8358333
[56,] 1.7438889 0.3758333
[57,] 11.3038889 1.7438889
[58,] 4.9438889 11.3038889
[59,] 3.9038889 4.9438889
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.9838889 -8.5838889
2 -17.0838889 -5.9838889
3 -1.9438889 -17.0838889
4 3.1361111 -1.9438889
5 -24.6038889 3.1361111
6 0.2761111 -24.6038889
7 -20.5838889 0.2761111
8 -16.3158333 -20.5838889
9 -17.3558333 -16.3158333
10 -6.4158333 -17.3558333
11 2.8441667 -6.4158333
12 -6.4838889 2.8441667
13 -7.8838889 -6.4838889
14 1.7161111 -7.8838889
15 8.2561111 1.7161111
16 -7.3638889 8.2561111
17 3.9961111 -7.3638889
18 -5.4238889 3.9961111
19 15.2161111 -5.4238889
20 8.2841667 15.2161111
21 15.2441667 8.2841667
22 -1.3158333 15.2441667
23 4.9441667 -1.3158333
24 12.9161111 4.9441667
25 10.1161111 12.9161111
26 14.1161111 10.1161111
27 15.5561111 14.1161111
28 -6.3638889 15.5561111
29 24.9961111 -6.3638889
30 3.8761111 24.9961111
31 8.2161111 3.8761111
32 0.6438889 8.2161111
33 -18.4961111 0.6438889
34 -8.3561111 -18.4961111
35 -4.4961111 -8.3561111
36 -6.5241667 -4.4961111
37 5.1758333 -6.5241667
38 4.8758333 5.1758333
39 -15.6841667 4.8758333
40 11.6958333 -15.6841667
41 -6.1441667 11.6958333
42 -7.5641667 -6.1441667
43 -3.2241667 -7.5641667
44 5.6438889 -3.2241667
45 9.3038889 5.6438889
46 11.1438889 9.3038889
47 -7.1961111 11.1438889
48 8.6758333 -7.1961111
49 -1.4241667 8.6758333
50 -3.6241667 -1.4241667
51 -6.1841667 -3.6241667
52 -1.1041667 -6.1841667
53 1.7558333 -1.1041667
54 8.8358333 1.7558333
55 0.3758333 8.8358333
56 1.7438889 0.3758333
57 11.3038889 1.7438889
58 4.9438889 11.3038889
59 3.9038889 4.9438889
> 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/7obr81229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8opfe1229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9cdak1229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fv7i1229093694.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11c88e1229093694.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/12y3vc1229093694.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/137q9c1229093694.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/14hjb91229093694.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/150ya31229093694.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/16u0dz1229093694.tab")
+ }
>
> system("convert tmp/1tk9p1229093694.ps tmp/1tk9p1229093694.png")
> system("convert tmp/24h7c1229093694.ps tmp/24h7c1229093694.png")
> system("convert tmp/3oc791229093694.ps tmp/3oc791229093694.png")
> system("convert tmp/49om51229093694.ps tmp/49om51229093694.png")
> system("convert tmp/5on4s1229093694.ps tmp/5on4s1229093694.png")
> system("convert tmp/627ao1229093694.ps tmp/627ao1229093694.png")
> system("convert tmp/7obr81229093694.ps tmp/7obr81229093694.png")
> system("convert tmp/8opfe1229093694.ps tmp/8opfe1229093694.png")
> system("convert tmp/9cdak1229093694.ps tmp/9cdak1229093694.png")
> system("convert tmp/10fv7i1229093694.ps tmp/10fv7i1229093694.png")
>
>
> proc.time()
user system elapsed
4.940 2.722 5.357