R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(25,0,0,23.6,0,0,22.3,0,0,21.8,0,0,20.8,0,0,19.7,0,0,18.3,0,0,17.4,0,0,17,0,0,18.1,0,0,23.9,0,0,25.6,0,0,25.3,0,0,23.6,0,0,21.9,0,0,21.4,0,0,20.6,0,0,20.5,0,0,20.2,0,0,20.6,0,0,19.7,0,0,19.3,0,0,22.8,0,0,23.5,0,0,23.8,0,0,22.6,0,0,22,0,0,21.7,0,0,20.7,0,0,20.2,0,0,19.1,0,0,19.5,0,0,18.7,0,0,18.6,0,0,22.2,0,0,23.2,0,0,23.5,0,1,21.3,0,1,20,0,1,18.7,0,1,18.9,0,1,18.3,0,1,18.4,0,1,19.9,0,1,19.2,0,1,18.5,0,1,20.9,1,1,20.5,1,1,19.4,1,1,18.1,1,1,17,1,1,17,1,1,17.3,1,1,16.7,1,1,15.5,1,1,15.3,1,1,13.7,1,1,14.1,1,1,17.3,1,1,18.1,1,1,18.1,1,1),dim=c(3,61),dimnames=list(c('Werklozen','Jobtonic','Samenwerking'),1:61))
> y <- array(NA,dim=c(3,61),dimnames=list(c('Werklozen','Jobtonic','Samenwerking'),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 = '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
Werklozen Jobtonic Samenwerking M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 25.0 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 23.6 0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 22.3 0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 21.8 0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 20.8 0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 19.7 0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 18.3 0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 17.4 0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 17.0 0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 18.1 0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 23.9 0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 25.6 0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 25.3 0 0 1 0 0 0 0 0 0 0 0 0 0 13
14 23.6 0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 21.9 0 0 0 0 1 0 0 0 0 0 0 0 0 15
16 21.4 0 0 0 0 0 1 0 0 0 0 0 0 0 16
17 20.6 0 0 0 0 0 0 1 0 0 0 0 0 0 17
18 20.5 0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 20.2 0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 20.6 0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 19.7 0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 19.3 0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 22.8 0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 23.5 0 0 0 0 0 0 0 0 0 0 0 0 0 24
25 23.8 0 0 1 0 0 0 0 0 0 0 0 0 0 25
26 22.6 0 0 0 1 0 0 0 0 0 0 0 0 0 26
27 22.0 0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 21.7 0 0 0 0 0 1 0 0 0 0 0 0 0 28
29 20.7 0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 20.2 0 0 0 0 0 0 0 1 0 0 0 0 0 30
31 19.1 0 0 0 0 0 0 0 0 1 0 0 0 0 31
32 19.5 0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 18.7 0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 18.6 0 0 0 0 0 0 0 0 0 0 0 1 0 34
35 22.2 0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 23.2 0 0 0 0 0 0 0 0 0 0 0 0 0 36
37 23.5 0 1 1 0 0 0 0 0 0 0 0 0 0 37
38 21.3 0 1 0 1 0 0 0 0 0 0 0 0 0 38
39 20.0 0 1 0 0 1 0 0 0 0 0 0 0 0 39
40 18.7 0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 18.9 0 1 0 0 0 0 1 0 0 0 0 0 0 41
42 18.3 0 1 0 0 0 0 0 1 0 0 0 0 0 42
43 18.4 0 1 0 0 0 0 0 0 1 0 0 0 0 43
44 19.9 0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 19.2 0 1 0 0 0 0 0 0 0 0 1 0 0 45
46 18.5 0 1 0 0 0 0 0 0 0 0 0 1 0 46
47 20.9 1 1 0 0 0 0 0 0 0 0 0 0 1 47
48 20.5 1 1 0 0 0 0 0 0 0 0 0 0 0 48
49 19.4 1 1 1 0 0 0 0 0 0 0 0 0 0 49
50 18.1 1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 17.0 1 1 0 0 1 0 0 0 0 0 0 0 0 51
52 17.0 1 1 0 0 0 1 0 0 0 0 0 0 0 52
53 17.3 1 1 0 0 0 0 1 0 0 0 0 0 0 53
54 16.7 1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 15.5 1 1 0 0 0 0 0 0 1 0 0 0 0 55
56 15.3 1 1 0 0 0 0 0 0 0 1 0 0 0 56
57 13.7 1 1 0 0 0 0 0 0 0 0 1 0 0 57
58 14.1 1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 17.3 1 1 0 0 0 0 0 0 0 0 0 0 1 59
60 18.1 1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 18.1 1 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Jobtonic Samenwerking M1 M2
24.39342 -3.15819 -0.67021 0.09841 -1.16110
M3 M4 M5 M6 M7
-2.34215 -2.84321 -3.28426 -3.84531 -4.60637
M8 M9 M10 M11 t
-4.34742 -5.20848 -5.12953 -0.77895 -0.01895
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.4944 -0.5114 0.1329 0.5271 2.0044
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.39342 0.57900 42.130 < 2e-16 ***
Jobtonic -3.15819 0.45733 -6.906 1.26e-08 ***
Samenwerking -0.67021 0.52212 -1.284 0.205694
M1 0.09841 0.61225 0.161 0.873004
M2 -1.16110 0.64423 -1.802 0.078054 .
M3 -2.34215 0.64136 -3.652 0.000664 ***
M4 -2.84321 0.63888 -4.450 5.41e-05 ***
M5 -3.28426 0.63679 -5.158 5.17e-06 ***
M6 -3.84531 0.63511 -6.055 2.40e-07 ***
M7 -4.60637 0.63382 -7.268 3.62e-09 ***
M8 -4.34742 0.63295 -6.869 1.44e-08 ***
M9 -5.20848 0.63248 -8.235 1.33e-10 ***
M10 -5.12953 0.63242 -8.111 2.03e-10 ***
M11 -0.77895 0.62709 -1.242 0.220473
t -0.01895 0.01608 -1.179 0.244658
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9912 on 46 degrees of freedom
Multiple R-squared: 0.895, Adjusted R-squared: 0.8631
F-statistic: 28.01 on 14 and 46 DF, p-value: < 2.2e-16
> 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,] 0.1814945 0.36298905 0.81850548
[2,] 0.4956403 0.99128054 0.50435973
[3,] 0.8657469 0.26850615 0.13425307
[4,] 0.8982875 0.20342503 0.10171252
[5,] 0.8561431 0.28771387 0.14385694
[6,] 0.9073026 0.18539488 0.09269744
[7,] 0.9681610 0.06367796 0.03183898
[8,] 0.9732888 0.05342231 0.02671116
[9,] 0.9627104 0.07457916 0.03728958
[10,] 0.9442814 0.11143724 0.05571862
[11,] 0.9356161 0.12876772 0.06438386
[12,] 0.8960238 0.20795247 0.10397624
[13,] 0.8426134 0.31477329 0.15738664
[14,] 0.7757658 0.44846843 0.22423422
[15,] 0.7135104 0.57297912 0.28648956
[16,] 0.6300186 0.73996274 0.36998137
[17,] 0.5486808 0.90263834 0.45131917
[18,] 0.4984431 0.99688630 0.50155685
[19,] 0.4346548 0.86930968 0.56534516
[20,] 0.3323165 0.66463304 0.66768348
[21,] 0.2413041 0.48260830 0.75869585
[22,] 0.1619174 0.32383474 0.83808263
[23,] 0.1647737 0.32954749 0.83522625
[24,] 0.1999740 0.39994809 0.80002596
[25,] 0.4079924 0.81598489 0.59200755
[26,] 0.5349732 0.93005364 0.46502682
> postscript(file="/var/www/html/rcomp/tmp/1wvce1229443873.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2g3vx1229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38agb1229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4osb01229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/576zc1229443873.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
0.52711063 0.40556769 0.30556769 0.32556769 -0.21443231 -0.73443231
7 8 9 10 11 12
-1.35443231 -2.49443231 -2.01443231 -0.97443231 0.49393013 1.43393013
13 14 15 16 17 18
1.05446507 0.63292213 0.13292213 0.15292213 -0.18707787 0.29292213
19 20 21 22 23 24
0.77292213 0.93292213 0.91292213 0.45292213 -0.37871543 -0.43871543
25 26 27 28 29 30
-0.21818049 -0.13972344 0.46027656 0.68027656 0.14027656 0.22027656
31 32 33 34 35 36
-0.09972344 0.06027656 0.14027656 -0.01972344 -0.75136099 -0.51136099
37 38 39 40 41 42
0.37938865 -0.54215429 -0.64215429 -1.42215429 -0.76215429 -0.78215429
43 44 45 46 47 48
0.09784571 1.35784571 1.53784571 0.77784571 2.00439592 0.84439592
49 50 51 52 53 54
-0.33506914 -0.35661208 -0.25661208 0.26338792 1.02338792 1.00338792
55 56 57 58 59 60
0.58338792 0.14338792 -0.57661208 -0.23661208 -1.36824964 -1.32824964
61
-1.40771470
> postscript(file="/var/www/html/rcomp/tmp/692c81229443873.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 0.52711063 NA
1 0.40556769 0.52711063
2 0.30556769 0.40556769
3 0.32556769 0.30556769
4 -0.21443231 0.32556769
5 -0.73443231 -0.21443231
6 -1.35443231 -0.73443231
7 -2.49443231 -1.35443231
8 -2.01443231 -2.49443231
9 -0.97443231 -2.01443231
10 0.49393013 -0.97443231
11 1.43393013 0.49393013
12 1.05446507 1.43393013
13 0.63292213 1.05446507
14 0.13292213 0.63292213
15 0.15292213 0.13292213
16 -0.18707787 0.15292213
17 0.29292213 -0.18707787
18 0.77292213 0.29292213
19 0.93292213 0.77292213
20 0.91292213 0.93292213
21 0.45292213 0.91292213
22 -0.37871543 0.45292213
23 -0.43871543 -0.37871543
24 -0.21818049 -0.43871543
25 -0.13972344 -0.21818049
26 0.46027656 -0.13972344
27 0.68027656 0.46027656
28 0.14027656 0.68027656
29 0.22027656 0.14027656
30 -0.09972344 0.22027656
31 0.06027656 -0.09972344
32 0.14027656 0.06027656
33 -0.01972344 0.14027656
34 -0.75136099 -0.01972344
35 -0.51136099 -0.75136099
36 0.37938865 -0.51136099
37 -0.54215429 0.37938865
38 -0.64215429 -0.54215429
39 -1.42215429 -0.64215429
40 -0.76215429 -1.42215429
41 -0.78215429 -0.76215429
42 0.09784571 -0.78215429
43 1.35784571 0.09784571
44 1.53784571 1.35784571
45 0.77784571 1.53784571
46 2.00439592 0.77784571
47 0.84439592 2.00439592
48 -0.33506914 0.84439592
49 -0.35661208 -0.33506914
50 -0.25661208 -0.35661208
51 0.26338792 -0.25661208
52 1.02338792 0.26338792
53 1.00338792 1.02338792
54 0.58338792 1.00338792
55 0.14338792 0.58338792
56 -0.57661208 0.14338792
57 -0.23661208 -0.57661208
58 -1.36824964 -0.23661208
59 -1.32824964 -1.36824964
60 -1.40771470 -1.32824964
61 NA -1.40771470
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.40556769 0.52711063
[2,] 0.30556769 0.40556769
[3,] 0.32556769 0.30556769
[4,] -0.21443231 0.32556769
[5,] -0.73443231 -0.21443231
[6,] -1.35443231 -0.73443231
[7,] -2.49443231 -1.35443231
[8,] -2.01443231 -2.49443231
[9,] -0.97443231 -2.01443231
[10,] 0.49393013 -0.97443231
[11,] 1.43393013 0.49393013
[12,] 1.05446507 1.43393013
[13,] 0.63292213 1.05446507
[14,] 0.13292213 0.63292213
[15,] 0.15292213 0.13292213
[16,] -0.18707787 0.15292213
[17,] 0.29292213 -0.18707787
[18,] 0.77292213 0.29292213
[19,] 0.93292213 0.77292213
[20,] 0.91292213 0.93292213
[21,] 0.45292213 0.91292213
[22,] -0.37871543 0.45292213
[23,] -0.43871543 -0.37871543
[24,] -0.21818049 -0.43871543
[25,] -0.13972344 -0.21818049
[26,] 0.46027656 -0.13972344
[27,] 0.68027656 0.46027656
[28,] 0.14027656 0.68027656
[29,] 0.22027656 0.14027656
[30,] -0.09972344 0.22027656
[31,] 0.06027656 -0.09972344
[32,] 0.14027656 0.06027656
[33,] -0.01972344 0.14027656
[34,] -0.75136099 -0.01972344
[35,] -0.51136099 -0.75136099
[36,] 0.37938865 -0.51136099
[37,] -0.54215429 0.37938865
[38,] -0.64215429 -0.54215429
[39,] -1.42215429 -0.64215429
[40,] -0.76215429 -1.42215429
[41,] -0.78215429 -0.76215429
[42,] 0.09784571 -0.78215429
[43,] 1.35784571 0.09784571
[44,] 1.53784571 1.35784571
[45,] 0.77784571 1.53784571
[46,] 2.00439592 0.77784571
[47,] 0.84439592 2.00439592
[48,] -0.33506914 0.84439592
[49,] -0.35661208 -0.33506914
[50,] -0.25661208 -0.35661208
[51,] 0.26338792 -0.25661208
[52,] 1.02338792 0.26338792
[53,] 1.00338792 1.02338792
[54,] 0.58338792 1.00338792
[55,] 0.14338792 0.58338792
[56,] -0.57661208 0.14338792
[57,] -0.23661208 -0.57661208
[58,] -1.36824964 -0.23661208
[59,] -1.32824964 -1.36824964
[60,] -1.40771470 -1.32824964
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.40556769 0.52711063
2 0.30556769 0.40556769
3 0.32556769 0.30556769
4 -0.21443231 0.32556769
5 -0.73443231 -0.21443231
6 -1.35443231 -0.73443231
7 -2.49443231 -1.35443231
8 -2.01443231 -2.49443231
9 -0.97443231 -2.01443231
10 0.49393013 -0.97443231
11 1.43393013 0.49393013
12 1.05446507 1.43393013
13 0.63292213 1.05446507
14 0.13292213 0.63292213
15 0.15292213 0.13292213
16 -0.18707787 0.15292213
17 0.29292213 -0.18707787
18 0.77292213 0.29292213
19 0.93292213 0.77292213
20 0.91292213 0.93292213
21 0.45292213 0.91292213
22 -0.37871543 0.45292213
23 -0.43871543 -0.37871543
24 -0.21818049 -0.43871543
25 -0.13972344 -0.21818049
26 0.46027656 -0.13972344
27 0.68027656 0.46027656
28 0.14027656 0.68027656
29 0.22027656 0.14027656
30 -0.09972344 0.22027656
31 0.06027656 -0.09972344
32 0.14027656 0.06027656
33 -0.01972344 0.14027656
34 -0.75136099 -0.01972344
35 -0.51136099 -0.75136099
36 0.37938865 -0.51136099
37 -0.54215429 0.37938865
38 -0.64215429 -0.54215429
39 -1.42215429 -0.64215429
40 -0.76215429 -1.42215429
41 -0.78215429 -0.76215429
42 0.09784571 -0.78215429
43 1.35784571 0.09784571
44 1.53784571 1.35784571
45 0.77784571 1.53784571
46 2.00439592 0.77784571
47 0.84439592 2.00439592
48 -0.33506914 0.84439592
49 -0.35661208 -0.33506914
50 -0.25661208 -0.35661208
51 0.26338792 -0.25661208
52 1.02338792 0.26338792
53 1.00338792 1.02338792
54 0.58338792 1.00338792
55 0.14338792 0.58338792
56 -0.57661208 0.14338792
57 -0.23661208 -0.57661208
58 -1.36824964 -0.23661208
59 -1.32824964 -1.36824964
60 -1.40771470 -1.32824964
> 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/7v3iv1229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8apk91229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vrkv1229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/103l6b1229443873.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11av1b1229443873.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/1268bf1229443874.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/13i8ou1229443874.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/14abff1229443874.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/154u561229443874.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/165qnf1229443874.tab")
+ }
>
> system("convert tmp/1wvce1229443873.ps tmp/1wvce1229443873.png")
> system("convert tmp/2g3vx1229443873.ps tmp/2g3vx1229443873.png")
> system("convert tmp/38agb1229443873.ps tmp/38agb1229443873.png")
> system("convert tmp/4osb01229443873.ps tmp/4osb01229443873.png")
> system("convert tmp/576zc1229443873.ps tmp/576zc1229443873.png")
> system("convert tmp/692c81229443873.ps tmp/692c81229443873.png")
> system("convert tmp/7v3iv1229443873.ps tmp/7v3iv1229443873.png")
> system("convert tmp/8apk91229443873.ps tmp/8apk91229443873.png")
> system("convert tmp/9vrkv1229443873.ps tmp/9vrkv1229443873.png")
> system("convert tmp/103l6b1229443873.ps tmp/103l6b1229443873.png")
>
>
> proc.time()
user system elapsed
2.354 1.573 2.895