R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(603.6
+ ,0
+ ,741.7
+ ,993.3
+ ,-820.8
+ ,-145.8
+ ,0
+ ,603.6
+ ,741.7
+ ,993.3
+ ,-35.1
+ ,0
+ ,-145.8
+ ,603.6
+ ,741.7
+ ,395.1
+ ,0
+ ,-35.1
+ ,-145.8
+ ,603.6
+ ,523.1
+ ,0
+ ,395.1
+ ,-35.1
+ ,-145.8
+ ,462.3
+ ,0
+ ,523.1
+ ,395.1
+ ,-35.1
+ ,183.4
+ ,0
+ ,462.3
+ ,523.1
+ ,395.1
+ ,791.5
+ ,0
+ ,183.4
+ ,462.3
+ ,523.1
+ ,344.8
+ ,0
+ ,791.5
+ ,183.4
+ ,462.3
+ ,-217.0
+ ,0
+ ,344.8
+ ,791.5
+ ,183.4
+ ,406.7
+ ,0
+ ,-217.0
+ ,344.8
+ ,791.5
+ ,228.6
+ ,0
+ ,406.7
+ ,-217.0
+ ,344.8
+ ,-580.1
+ ,0
+ ,228.6
+ ,406.7
+ ,-217.0
+ ,-1550.4
+ ,0
+ ,-580.1
+ ,228.6
+ ,406.7
+ ,-1447.5
+ ,0
+ ,-1550.4
+ ,-580.1
+ ,228.6
+ ,-40.1
+ ,0
+ ,-1447.5
+ ,-1550.4
+ ,-580.1
+ ,-1033.5
+ ,0
+ ,-40.1
+ ,-1447.5
+ ,-1550.4
+ ,-925.6
+ ,0
+ ,-1033.5
+ ,-40.1
+ ,-1447.5
+ ,-347.8
+ ,0
+ ,-925.6
+ ,-1033.5
+ ,-40.1
+ ,-447.7
+ ,0
+ ,-347.8
+ ,-925.6
+ ,-1033.5
+ ,-102.6
+ ,0
+ ,-447.7
+ ,-347.8
+ ,-925.6
+ ,-2062.2
+ ,0
+ ,-102.6
+ ,-447.7
+ ,-347.8
+ ,-929.7
+ ,1
+ ,-2062.2
+ ,-102.6
+ ,-447.7
+ ,-720.7
+ ,1
+ ,-929.7
+ ,-2062.2
+ ,-102.6
+ ,-1541.8
+ ,1
+ ,-720.7
+ ,-929.7
+ ,-2062.2
+ ,-1432.3
+ ,1
+ ,-1541.8
+ ,-720.7
+ ,-929.7
+ ,-1216.2
+ ,1
+ ,-1432.3
+ ,-1541.8
+ ,-720.7
+ ,-212.8
+ ,1
+ ,-1216.2
+ ,-1432.3
+ ,-1541.8
+ ,-378.2
+ ,1
+ ,-212.8
+ ,-1216.2
+ ,-1432.3
+ ,76.9
+ ,1
+ ,-378.2
+ ,-212.8
+ ,-1216.2
+ ,-101.3
+ ,1
+ ,76.9
+ ,-378.2
+ ,-212.8
+ ,220.4
+ ,1
+ ,-101.3
+ ,76.9
+ ,-378.2
+ ,495.6
+ ,1
+ ,220.4
+ ,-101.3
+ ,76.9
+ ,-1035.2
+ ,1
+ ,495.6
+ ,220.4
+ ,-101.3
+ ,61.8
+ ,1
+ ,-1035.2
+ ,495.6
+ ,220.4
+ ,-734.8
+ ,1
+ ,61.8
+ ,-1035.2
+ ,495.6
+ ,-6.9
+ ,1
+ ,-734.8
+ ,61.8
+ ,-1035.2
+ ,-1061.1
+ ,1
+ ,-6.9
+ ,-734.8
+ ,61.8
+ ,-854.6
+ ,1
+ ,-1061.1
+ ,-6.9
+ ,-734.8
+ ,-186.5
+ ,1
+ ,-854.6
+ ,-1061.1
+ ,-6.9
+ ,244.0
+ ,1
+ ,-186.5
+ ,-854.6
+ ,-1061.1
+ ,-992.6
+ ,1
+ ,244.0
+ ,-186.5
+ ,-854.6
+ ,-335.2
+ ,1
+ ,-992.6
+ ,244.0
+ ,-186.5
+ ,316.8
+ ,1
+ ,-335.2
+ ,-992.6
+ ,244.0
+ ,477.6
+ ,1
+ ,316.8
+ ,-335.2
+ ,-992.6
+ ,-572.1
+ ,1
+ ,477.6
+ ,316.8
+ ,-335.2
+ ,1115.2
+ ,1
+ ,-572.1
+ ,477.6
+ ,316.8)
+ ,dim=c(5
+ ,47)
+ ,dimnames=list(c('Totaal'
+ ,'Dummy'
+ ,'vertraging1'
+ ,'vertraging2'
+ ,'vertraging3')
+ ,1:47))
> y <- array(NA,dim=c(5,47),dimnames=list(c('Totaal','Dummy','vertraging1','vertraging2','vertraging3'),1:47))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Totaal Dummy vertraging1 vertraging2 vertraging3 M1 M2 M3 M4 M5 M6 M7 M8 M9
1 603.6 0 741.7 993.3 -820.8 1 0 0 0 0 0 0 0 0
2 -145.8 0 603.6 741.7 993.3 0 1 0 0 0 0 0 0 0
3 -35.1 0 -145.8 603.6 741.7 0 0 1 0 0 0 0 0 0
4 395.1 0 -35.1 -145.8 603.6 0 0 0 1 0 0 0 0 0
5 523.1 0 395.1 -35.1 -145.8 0 0 0 0 1 0 0 0 0
6 462.3 0 523.1 395.1 -35.1 0 0 0 0 0 1 0 0 0
7 183.4 0 462.3 523.1 395.1 0 0 0 0 0 0 1 0 0
8 791.5 0 183.4 462.3 523.1 0 0 0 0 0 0 0 1 0
9 344.8 0 791.5 183.4 462.3 0 0 0 0 0 0 0 0 1
10 -217.0 0 344.8 791.5 183.4 0 0 0 0 0 0 0 0 0
11 406.7 0 -217.0 344.8 791.5 0 0 0 0 0 0 0 0 0
12 228.6 0 406.7 -217.0 344.8 0 0 0 0 0 0 0 0 0
13 -580.1 0 228.6 406.7 -217.0 1 0 0 0 0 0 0 0 0
14 -1550.4 0 -580.1 228.6 406.7 0 1 0 0 0 0 0 0 0
15 -1447.5 0 -1550.4 -580.1 228.6 0 0 1 0 0 0 0 0 0
16 -40.1 0 -1447.5 -1550.4 -580.1 0 0 0 1 0 0 0 0 0
17 -1033.5 0 -40.1 -1447.5 -1550.4 0 0 0 0 1 0 0 0 0
18 -925.6 0 -1033.5 -40.1 -1447.5 0 0 0 0 0 1 0 0 0
19 -347.8 0 -925.6 -1033.5 -40.1 0 0 0 0 0 0 1 0 0
20 -447.7 0 -347.8 -925.6 -1033.5 0 0 0 0 0 0 0 1 0
21 -102.6 0 -447.7 -347.8 -925.6 0 0 0 0 0 0 0 0 1
22 -2062.2 0 -102.6 -447.7 -347.8 0 0 0 0 0 0 0 0 0
23 -929.7 1 -2062.2 -102.6 -447.7 0 0 0 0 0 0 0 0 0
24 -720.7 1 -929.7 -2062.2 -102.6 0 0 0 0 0 0 0 0 0
25 -1541.8 1 -720.7 -929.7 -2062.2 1 0 0 0 0 0 0 0 0
26 -1432.3 1 -1541.8 -720.7 -929.7 0 1 0 0 0 0 0 0 0
27 -1216.2 1 -1432.3 -1541.8 -720.7 0 0 1 0 0 0 0 0 0
28 -212.8 1 -1216.2 -1432.3 -1541.8 0 0 0 1 0 0 0 0 0
29 -378.2 1 -212.8 -1216.2 -1432.3 0 0 0 0 1 0 0 0 0
30 76.9 1 -378.2 -212.8 -1216.2 0 0 0 0 0 1 0 0 0
31 -101.3 1 76.9 -378.2 -212.8 0 0 0 0 0 0 1 0 0
32 220.4 1 -101.3 76.9 -378.2 0 0 0 0 0 0 0 1 0
33 495.6 1 220.4 -101.3 76.9 0 0 0 0 0 0 0 0 1
34 -1035.2 1 495.6 220.4 -101.3 0 0 0 0 0 0 0 0 0
35 61.8 1 -1035.2 495.6 220.4 0 0 0 0 0 0 0 0 0
36 -734.8 1 61.8 -1035.2 495.6 0 0 0 0 0 0 0 0 0
37 -6.9 1 -734.8 61.8 -1035.2 1 0 0 0 0 0 0 0 0
38 -1061.1 1 -6.9 -734.8 61.8 0 1 0 0 0 0 0 0 0
39 -854.6 1 -1061.1 -6.9 -734.8 0 0 1 0 0 0 0 0 0
40 -186.5 1 -854.6 -1061.1 -6.9 0 0 0 1 0 0 0 0 0
41 244.0 1 -186.5 -854.6 -1061.1 0 0 0 0 1 0 0 0 0
42 -992.6 1 244.0 -186.5 -854.6 0 0 0 0 0 1 0 0 0
43 -335.2 1 -992.6 244.0 -186.5 0 0 0 0 0 0 1 0 0
44 316.8 1 -335.2 -992.6 244.0 0 0 0 0 0 0 0 1 0
45 477.6 1 316.8 -335.2 -992.6 0 0 0 0 0 0 0 0 1
46 -572.1 1 477.6 316.8 -335.2 0 0 0 0 0 0 0 0 0
47 1115.2 1 -572.1 477.6 316.8 0 0 0 0 0 0 0 0 0
M10 M11 t
1 0 0 1
2 0 0 2
3 0 0 3
4 0 0 4
5 0 0 5
6 0 0 6
7 0 0 7
8 0 0 8
9 0 0 9
10 1 0 10
11 0 1 11
12 0 0 12
13 0 0 13
14 0 0 14
15 0 0 15
16 0 0 16
17 0 0 17
18 0 0 18
19 0 0 19
20 0 0 20
21 0 0 21
22 1 0 22
23 0 1 23
24 0 0 24
25 0 0 25
26 0 0 26
27 0 0 27
28 0 0 28
29 0 0 29
30 0 0 30
31 0 0 31
32 0 0 32
33 0 0 33
34 1 0 34
35 0 1 35
36 0 0 36
37 0 0 37
38 0 0 38
39 0 0 39
40 0 0 40
41 0 0 41
42 0 0 42
43 0 0 43
44 0 0 44
45 0 0 45
46 1 0 46
47 0 1 47
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy vertraging1 vertraging2 vertraging3 M1
-12.6451 275.1693 0.2952 0.4561 0.1499 -322.7354
M2 M3 M4 M5 M6 M7
-968.2277 -453.0026 722.8908 343.9559 -216.4691 -28.0228
M8 M9 M10 M11 t
394.0644 309.9072 -1185.5683 166.4654 -2.8081
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-779.56 -273.25 71.91 276.94 721.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -12.6451 344.3605 -0.037 0.9710
Dummy 275.1693 281.7920 0.976 0.3366
vertraging1 0.2952 0.1832 1.611 0.1176
vertraging2 0.4561 0.1738 2.624 0.0135 *
vertraging3 0.1499 0.1836 0.817 0.4206
M1 -322.7354 498.7720 -0.647 0.5225
M2 -968.2277 389.3493 -2.487 0.0187 *
M3 -453.0026 415.4905 -1.090 0.2843
M4 722.8908 367.3786 1.968 0.0584 .
M5 343.9559 433.5831 0.793 0.4338
M6 -216.4691 472.9480 -0.458 0.6505
M7 -28.0228 395.2554 -0.071 0.9439
M8 394.0644 386.5218 1.020 0.3161
M9 309.9072 420.3228 0.737 0.4667
M10 -1185.5683 438.5420 -2.703 0.0112 *
M11 166.4654 478.1065 0.348 0.7301
t -2.8081 10.5611 -0.266 0.7921
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 441.6 on 30 degrees of freedom
Multiple R-squared: 0.7502, Adjusted R-squared: 0.617
F-statistic: 5.631 on 16 and 30 DF, p-value: 2.443e-05
> 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,] 0.20613693 0.4122739 0.7938631
[2,] 0.28460193 0.5692039 0.7153981
[3,] 0.19471960 0.3894392 0.8052804
[4,] 0.15066522 0.3013304 0.8493348
[5,] 0.08550736 0.1710147 0.9144926
[6,] 0.12851650 0.2570330 0.8714835
[7,] 0.18006285 0.3601257 0.8199371
[8,] 0.14453585 0.2890717 0.8554642
> postscript(file="/var/www/html/rcomp/tmp/1mrrq1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mrrq1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3fiqb1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fiqb1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fiqb1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 47
Frequency = 1
1 2 3 4 5 6 7
392.87141 175.28854 95.52906 -317.55845 127.05025 378.89630 -190.57057
8 9 10 11 12 13 14
89.12874 -313.82735 519.02394 71.90950 202.13713 -428.64977 -524.18180
15 16 17 18 19 20 21
-251.70737 516.00040 -412.61840 -105.48917 496.84861 -93.18022 88.68750
22 23 24 25 26 27 28
-515.58830 -571.34492 314.53153 -465.40465 269.69389 284.18547 123.87393
29 30 31 32 33 34 35
-70.99769 506.15186 -67.06291 -294.78571 -14.56408 -248.32668 -222.34650
36 37 38 39 40 41 42
-516.66866 501.18301 79.19937 -128.00716 -322.31588 356.56584 -779.55899
43 44 45 46 47
-239.21513 298.83718 239.70394 244.89103 721.78192
> postscript(file="/var/www/html/rcomp/tmp/6897w1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 47
Frequency = 1
lag(myerror, k = 1) myerror
0 392.87141 NA
1 175.28854 392.87141
2 95.52906 175.28854
3 -317.55845 95.52906
4 127.05025 -317.55845
5 378.89630 127.05025
6 -190.57057 378.89630
7 89.12874 -190.57057
8 -313.82735 89.12874
9 519.02394 -313.82735
10 71.90950 519.02394
11 202.13713 71.90950
12 -428.64977 202.13713
13 -524.18180 -428.64977
14 -251.70737 -524.18180
15 516.00040 -251.70737
16 -412.61840 516.00040
17 -105.48917 -412.61840
18 496.84861 -105.48917
19 -93.18022 496.84861
20 88.68750 -93.18022
21 -515.58830 88.68750
22 -571.34492 -515.58830
23 314.53153 -571.34492
24 -465.40465 314.53153
25 269.69389 -465.40465
26 284.18547 269.69389
27 123.87393 284.18547
28 -70.99769 123.87393
29 506.15186 -70.99769
30 -67.06291 506.15186
31 -294.78571 -67.06291
32 -14.56408 -294.78571
33 -248.32668 -14.56408
34 -222.34650 -248.32668
35 -516.66866 -222.34650
36 501.18301 -516.66866
37 79.19937 501.18301
38 -128.00716 79.19937
39 -322.31588 -128.00716
40 356.56584 -322.31588
41 -779.55899 356.56584
42 -239.21513 -779.55899
43 298.83718 -239.21513
44 239.70394 298.83718
45 244.89103 239.70394
46 721.78192 244.89103
47 NA 721.78192
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 175.28854 392.87141
[2,] 95.52906 175.28854
[3,] -317.55845 95.52906
[4,] 127.05025 -317.55845
[5,] 378.89630 127.05025
[6,] -190.57057 378.89630
[7,] 89.12874 -190.57057
[8,] -313.82735 89.12874
[9,] 519.02394 -313.82735
[10,] 71.90950 519.02394
[11,] 202.13713 71.90950
[12,] -428.64977 202.13713
[13,] -524.18180 -428.64977
[14,] -251.70737 -524.18180
[15,] 516.00040 -251.70737
[16,] -412.61840 516.00040
[17,] -105.48917 -412.61840
[18,] 496.84861 -105.48917
[19,] -93.18022 496.84861
[20,] 88.68750 -93.18022
[21,] -515.58830 88.68750
[22,] -571.34492 -515.58830
[23,] 314.53153 -571.34492
[24,] -465.40465 314.53153
[25,] 269.69389 -465.40465
[26,] 284.18547 269.69389
[27,] 123.87393 284.18547
[28,] -70.99769 123.87393
[29,] 506.15186 -70.99769
[30,] -67.06291 506.15186
[31,] -294.78571 -67.06291
[32,] -14.56408 -294.78571
[33,] -248.32668 -14.56408
[34,] -222.34650 -248.32668
[35,] -516.66866 -222.34650
[36,] 501.18301 -516.66866
[37,] 79.19937 501.18301
[38,] -128.00716 79.19937
[39,] -322.31588 -128.00716
[40,] 356.56584 -322.31588
[41,] -779.55899 356.56584
[42,] -239.21513 -779.55899
[43,] 298.83718 -239.21513
[44,] 239.70394 298.83718
[45,] 244.89103 239.70394
[46,] 721.78192 244.89103
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 175.28854 392.87141
2 95.52906 175.28854
3 -317.55845 95.52906
4 127.05025 -317.55845
5 378.89630 127.05025
6 -190.57057 378.89630
7 89.12874 -190.57057
8 -313.82735 89.12874
9 519.02394 -313.82735
10 71.90950 519.02394
11 202.13713 71.90950
12 -428.64977 202.13713
13 -524.18180 -428.64977
14 -251.70737 -524.18180
15 516.00040 -251.70737
16 -412.61840 516.00040
17 -105.48917 -412.61840
18 496.84861 -105.48917
19 -93.18022 496.84861
20 88.68750 -93.18022
21 -515.58830 88.68750
22 -571.34492 -515.58830
23 314.53153 -571.34492
24 -465.40465 314.53153
25 269.69389 -465.40465
26 284.18547 269.69389
27 123.87393 284.18547
28 -70.99769 123.87393
29 506.15186 -70.99769
30 -67.06291 506.15186
31 -294.78571 -67.06291
32 -14.56408 -294.78571
33 -248.32668 -14.56408
34 -222.34650 -248.32668
35 -516.66866 -222.34650
36 501.18301 -516.66866
37 79.19937 501.18301
38 -128.00716 79.19937
39 -322.31588 -128.00716
40 356.56584 -322.31588
41 -779.55899 356.56584
42 -239.21513 -779.55899
43 298.83718 -239.21513
44 239.70394 298.83718
45 244.89103 239.70394
46 721.78192 244.89103
> 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/70ioy1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80ioy1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90ioy1291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bso11291329278.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11esm71291329278.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/120blv1291329278.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/13w20m1291329278.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/14z3za1291329278.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/153mxy1291329278.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/1664e31291329278.tab")
+ }
>
> try(system("convert tmp/1mrrq1291329278.ps tmp/1mrrq1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mrrq1291329278.ps tmp/2mrrq1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fiqb1291329278.ps tmp/3fiqb1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fiqb1291329278.ps tmp/4fiqb1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fiqb1291329278.ps tmp/5fiqb1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/6897w1291329278.ps tmp/6897w1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/70ioy1291329278.ps tmp/70ioy1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/80ioy1291329278.ps tmp/80ioy1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/90ioy1291329278.ps tmp/90ioy1291329278.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bso11291329278.ps tmp/10bso11291329278.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.264 1.591 5.120