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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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(14544.5
+ ,94.6
+ ,-3.0
+ ,14097.8
+ ,15116.3
+ ,95.9
+ ,-3.7
+ ,14776.8
+ ,17413.2
+ ,104.7
+ ,-4.7
+ ,16833.3
+ ,16181.5
+ ,102.8
+ ,-6.4
+ ,15385.5
+ ,15607.4
+ ,98.1
+ ,-7.5
+ ,15172.6
+ ,17160.9
+ ,113.9
+ ,-7.8
+ ,16858.9
+ ,14915.8
+ ,80.9
+ ,-7.7
+ ,14143.5
+ ,13768
+ ,95.7
+ ,-6.6
+ ,14731.8
+ ,17487.5
+ ,113.2
+ ,-4.2
+ ,16471.6
+ ,16198.1
+ ,105.9
+ ,-2.0
+ ,15214
+ ,17535.2
+ ,108.8
+ ,-0.7
+ ,17637.4
+ ,16571.8
+ ,102.3
+ ,0.1
+ ,17972.4
+ ,16198.9
+ ,99
+ ,0.9
+ ,16896.2
+ ,16554.2
+ ,100.7
+ ,2.1
+ ,16698
+ ,19554.2
+ ,115.5
+ ,3.5
+ ,19691.6
+ ,15903.8
+ ,100.7
+ ,4.9
+ ,15930.7
+ ,18003.8
+ ,109.9
+ ,5.7
+ ,17444.6
+ ,18329.6
+ ,114.6
+ ,6.2
+ ,17699.4
+ ,16260.7
+ ,85.4
+ ,6.5
+ ,15189.8
+ ,14851.9
+ ,100.5
+ ,6.5
+ ,15672.7
+ ,18174.1
+ ,114.8
+ ,6.3
+ ,17180.8
+ ,18406.6
+ ,116.5
+ ,6.2
+ ,17664.9
+ ,18466.5
+ ,112.9
+ ,6.4
+ ,17862.9
+ ,16016.5
+ ,102
+ ,6.3
+ ,16162.3
+ ,17428.5
+ ,106
+ ,5.8
+ ,17463.6
+ ,17167.2
+ ,105.3
+ ,5.1
+ ,16772.1
+ ,19630
+ ,118.8
+ ,5.1
+ ,19106.9
+ ,17183.6
+ ,106.1
+ ,5.8
+ ,16721.3
+ ,18344.7
+ ,109.3
+ ,6.7
+ ,18161.3
+ ,19301.4
+ ,117.2
+ ,7.1
+ ,18509.9
+ ,18147.5
+ ,92.5
+ ,6.7
+ ,17802.7
+ ,16192.9
+ ,104.2
+ ,5.5
+ ,16409.9
+ ,18374.4
+ ,112.5
+ ,4.2
+ ,17967.7
+ ,20515.2
+ ,122.4
+ ,3.0
+ ,20286.6
+ ,18957.2
+ ,113.3
+ ,2.2
+ ,19537.3
+ ,16471.5
+ ,100
+ ,2.0
+ ,18021.9
+ ,18746.8
+ ,110.7
+ ,1.8
+ ,20194.3
+ ,19009.5
+ ,112.8
+ ,1.8
+ ,19049.6
+ ,19211.2
+ ,109.8
+ ,1.5
+ ,20244.7
+ ,20547.7
+ ,117.3
+ ,0.4
+ ,21473.3
+ ,19325.8
+ ,109.1
+ ,-0.9
+ ,19673.6
+ ,20605.5
+ ,115.9
+ ,-1.7
+ ,21053.2
+ ,20056.9
+ ,96
+ ,-2.6
+ ,20159.5
+ ,16141.4
+ ,99.8
+ ,-4.4
+ ,18203.6
+ ,20359.8
+ ,116.8
+ ,-8.3
+ ,21289.5
+ ,19711.6
+ ,115.7
+ ,-14.4
+ ,20432.3
+ ,15638.6
+ ,99.4
+ ,-21.3
+ ,17180.4
+ ,14384.5
+ ,94.3
+ ,-26.5
+ ,15816.8
+ ,13855.6
+ ,91
+ ,-29.2
+ ,15071.8
+ ,14308.3
+ ,93.2
+ ,-30.8
+ ,14521.1
+ ,15290.6
+ ,103.1
+ ,-30.9
+ ,15668.8
+ ,14423.8
+ ,94.1
+ ,-29.5
+ ,14346.9
+ ,13779.7
+ ,91.8
+ ,-27.1
+ ,13881
+ ,15686.3
+ ,102.7
+ ,-24.4
+ ,15465.9
+ ,14733.8
+ ,82.6
+ ,-21.9
+ ,14238.2
+ ,12522.5
+ ,89.1
+ ,-19.3
+ ,13557.7
+ ,16189.4
+ ,104.5
+ ,-17.0
+ ,16127.6
+ ,16059.1
+ ,105.1
+ ,-13.8
+ ,16793.9
+ ,16007.1
+ ,95.1
+ ,-9.9
+ ,16014
+ ,15806.8
+ ,88.7
+ ,-7.9
+ ,16867.9
+ ,15160
+ ,86.3
+ ,-7.2
+ ,16014.6
+ ,15692.1
+ ,91.8
+ ,-6.2
+ ,15878.6
+ ,18908.9
+ ,111.5
+ ,-4.5
+ ,18664.9
+ ,16969.9
+ ,99.7
+ ,-3.9
+ ,17962.5
+ ,16997.5
+ ,97.5
+ ,-5.0
+ ,17332.7
+ ,19858.9
+ ,111.7
+ ,-6.2
+ ,19542.1
+ ,17681.2
+ ,86.2
+ ,-6.1
+ ,17203.6
+ ,16006.9
+ ,95.4
+ ,-5.0
+ ,16579)
+ ,dim=c(4
+ ,68)
+ ,dimnames=list(c('uitvoer'
+ ,'productie'
+ ,'ondernemersvertrouwen'
+ ,'invoer')
+ ,1:68))
> y <- array(NA,dim=c(4,68),dimnames=list(c('uitvoer','productie','ondernemersvertrouwen','invoer'),1:68))
> 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 = '2'
> #'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
productie uitvoer ondernemersvertrouwen invoer M1 M2 M3 M4 M5 M6 M7 M8 M9
1 94.6 14544.5 -3.0 14097.8 1 0 0 0 0 0 0 0 0
2 95.9 15116.3 -3.7 14776.8 0 1 0 0 0 0 0 0 0
3 104.7 17413.2 -4.7 16833.3 0 0 1 0 0 0 0 0 0
4 102.8 16181.5 -6.4 15385.5 0 0 0 1 0 0 0 0 0
5 98.1 15607.4 -7.5 15172.6 0 0 0 0 1 0 0 0 0
6 113.9 17160.9 -7.8 16858.9 0 0 0 0 0 1 0 0 0
7 80.9 14915.8 -7.7 14143.5 0 0 0 0 0 0 1 0 0
8 95.7 13768.0 -6.6 14731.8 0 0 0 0 0 0 0 1 0
9 113.2 17487.5 -4.2 16471.6 0 0 0 0 0 0 0 0 1
10 105.9 16198.1 -2.0 15214.0 0 0 0 0 0 0 0 0 0
11 108.8 17535.2 -0.7 17637.4 0 0 0 0 0 0 0 0 0
12 102.3 16571.8 0.1 17972.4 0 0 0 0 0 0 0 0 0
13 99.0 16198.9 0.9 16896.2 1 0 0 0 0 0 0 0 0
14 100.7 16554.2 2.1 16698.0 0 1 0 0 0 0 0 0 0
15 115.5 19554.2 3.5 19691.6 0 0 1 0 0 0 0 0 0
16 100.7 15903.8 4.9 15930.7 0 0 0 1 0 0 0 0 0
17 109.9 18003.8 5.7 17444.6 0 0 0 0 1 0 0 0 0
18 114.6 18329.6 6.2 17699.4 0 0 0 0 0 1 0 0 0
19 85.4 16260.7 6.5 15189.8 0 0 0 0 0 0 1 0 0
20 100.5 14851.9 6.5 15672.7 0 0 0 0 0 0 0 1 0
21 114.8 18174.1 6.3 17180.8 0 0 0 0 0 0 0 0 1
22 116.5 18406.6 6.2 17664.9 0 0 0 0 0 0 0 0 0
23 112.9 18466.5 6.4 17862.9 0 0 0 0 0 0 0 0 0
24 102.0 16016.5 6.3 16162.3 0 0 0 0 0 0 0 0 0
25 106.0 17428.5 5.8 17463.6 1 0 0 0 0 0 0 0 0
26 105.3 17167.2 5.1 16772.1 0 1 0 0 0 0 0 0 0
27 118.8 19630.0 5.1 19106.9 0 0 1 0 0 0 0 0 0
28 106.1 17183.6 5.8 16721.3 0 0 0 1 0 0 0 0 0
29 109.3 18344.7 6.7 18161.3 0 0 0 0 1 0 0 0 0
30 117.2 19301.4 7.1 18509.9 0 0 0 0 0 1 0 0 0
31 92.5 18147.5 6.7 17802.7 0 0 0 0 0 0 1 0 0
32 104.2 16192.9 5.5 16409.9 0 0 0 0 0 0 0 1 0
33 112.5 18374.4 4.2 17967.7 0 0 0 0 0 0 0 0 1
34 122.4 20515.2 3.0 20286.6 0 0 0 0 0 0 0 0 0
35 113.3 18957.2 2.2 19537.3 0 0 0 0 0 0 0 0 0
36 100.0 16471.5 2.0 18021.9 0 0 0 0 0 0 0 0 0
37 110.7 18746.8 1.8 20194.3 1 0 0 0 0 0 0 0 0
38 112.8 19009.5 1.8 19049.6 0 1 0 0 0 0 0 0 0
39 109.8 19211.2 1.5 20244.7 0 0 1 0 0 0 0 0 0
40 117.3 20547.7 0.4 21473.3 0 0 0 1 0 0 0 0 0
41 109.1 19325.8 -0.9 19673.6 0 0 0 0 1 0 0 0 0
42 115.9 20605.5 -1.7 21053.2 0 0 0 0 0 1 0 0 0
43 96.0 20056.9 -2.6 20159.5 0 0 0 0 0 0 1 0 0
44 99.8 16141.4 -4.4 18203.6 0 0 0 0 0 0 0 1 0
45 116.8 20359.8 -8.3 21289.5 0 0 0 0 0 0 0 0 1
46 115.7 19711.6 -14.4 20432.3 0 0 0 0 0 0 0 0 0
47 99.4 15638.6 -21.3 17180.4 0 0 0 0 0 0 0 0 0
48 94.3 14384.5 -26.5 15816.8 0 0 0 0 0 0 0 0 0
49 91.0 13855.6 -29.2 15071.8 1 0 0 0 0 0 0 0 0
50 93.2 14308.3 -30.8 14521.1 0 1 0 0 0 0 0 0 0
51 103.1 15290.6 -30.9 15668.8 0 0 1 0 0 0 0 0 0
52 94.1 14423.8 -29.5 14346.9 0 0 0 1 0 0 0 0 0
53 91.8 13779.7 -27.1 13881.0 0 0 0 0 1 0 0 0 0
54 102.7 15686.3 -24.4 15465.9 0 0 0 0 0 1 0 0 0
55 82.6 14733.8 -21.9 14238.2 0 0 0 0 0 0 1 0 0
56 89.1 12522.5 -19.3 13557.7 0 0 0 0 0 0 0 1 0
57 104.5 16189.4 -17.0 16127.6 0 0 0 0 0 0 0 0 1
58 105.1 16059.1 -13.8 16793.9 0 0 0 0 0 0 0 0 0
59 95.1 16007.1 -9.9 16014.0 0 0 0 0 0 0 0 0 0
60 88.7 15806.8 -7.9 16867.9 0 0 0 0 0 0 0 0 0
61 86.3 15160.0 -7.2 16014.6 1 0 0 0 0 0 0 0 0
62 91.8 15692.1 -6.2 15878.6 0 1 0 0 0 0 0 0 0
63 111.5 18908.9 -4.5 18664.9 0 0 1 0 0 0 0 0 0
64 99.7 16969.9 -3.9 17962.5 0 0 0 1 0 0 0 0 0
65 97.5 16997.5 -5.0 17332.7 0 0 0 0 1 0 0 0 0
66 111.7 19858.9 -6.2 19542.1 0 0 0 0 0 1 0 0 0
67 86.2 17681.2 -6.1 17203.6 0 0 0 0 0 0 1 0 0
68 95.4 16006.9 -5.0 16579.0 0 0 0 0 0 0 0 1 0
M10 M11
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 1 0
11 0 1
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 0 0
18 0 0
19 0 0
20 0 0
21 0 0
22 1 0
23 0 1
24 0 0
25 0 0
26 0 0
27 0 0
28 0 0
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 1 0
35 0 1
36 0 0
37 0 0
38 0 0
39 0 0
40 0 0
41 0 0
42 0 0
43 0 0
44 0 0
45 0 0
46 1 0
47 0 1
48 0 0
49 0 0
50 0 0
51 0 0
52 0 0
53 0 0
54 0 0
55 0 0
56 0 0
57 0 0
58 1 0
59 0 1
60 0 0
61 0 0
62 0 0
63 0 0
64 0 0
65 0 0
66 0 0
67 0 0
68 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) uitvoer ondernemersvertrouwen
46.155245 0.005052 0.038367
invoer M1 M2
-0.001683 -0.811036 -0.973083
M3 M4 M5
2.905435 0.833636 -0.761321
M6 M7 M8
3.894943 -16.730729 2.802074
M9 M10 M11
4.807897 5.730732 2.131480
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.6060 -0.8208 0.4911 1.7876 5.8397
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.616e+01 6.365e+00 7.251 1.78e-09 ***
uitvoer 5.052e-03 1.218e-03 4.149 0.000122 ***
ondernemersvertrouwen 3.837e-02 6.459e-02 0.594 0.555026
invoer -1.683e-03 9.341e-04 -1.802 0.077217 .
M1 -8.110e-01 2.032e+00 -0.399 0.691359
M2 -9.731e-01 2.304e+00 -0.422 0.674491
M3 2.905e+00 2.666e+00 1.090 0.280703
M4 8.336e-01 2.319e+00 0.360 0.720626
M5 -7.613e-01 2.424e+00 -0.314 0.754717
M6 3.895e+00 2.885e+00 1.350 0.182668
M7 -1.673e+01 2.655e+00 -6.302 5.96e-08 ***
M8 2.802e+00 2.009e+00 1.395 0.168850
M9 4.808e+00 2.836e+00 1.696 0.095832 .
M10 5.731e+00 2.742e+00 2.090 0.041403 *
M11 2.131e+00 2.368e+00 0.900 0.372146
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.258 on 53 degrees of freedom
Multiple R-squared: 0.9152, Adjusted R-squared: 0.8928
F-statistic: 40.84 on 14 and 53 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,] 2.042412e-01 0.4084824747 0.7957588
[2,] 9.159027e-02 0.1831805366 0.9084097
[3,] 4.486051e-02 0.0897210124 0.9551395
[4,] 1.772139e-02 0.0354427862 0.9822786
[5,] 7.181356e-03 0.0143627112 0.9928186
[6,] 2.486085e-03 0.0049721707 0.9975139
[7,] 1.461204e-03 0.0029224072 0.9985388
[8,] 5.071770e-04 0.0010143539 0.9994928
[9,] 1.888184e-04 0.0003776368 0.9998112
[10,] 3.599070e-04 0.0007198139 0.9996401
[11,] 1.399965e-04 0.0002799930 0.9998600
[12,] 6.603967e-05 0.0001320793 0.9999340
[13,] 3.460448e-04 0.0006920897 0.9996540
[14,] 1.784334e-04 0.0003568668 0.9998216
[15,] 2.366416e-04 0.0004732832 0.9997634
[16,] 3.238912e-04 0.0006477823 0.9996761
[17,] 2.537530e-04 0.0005075060 0.9997462
[18,] 2.701657e-04 0.0005403314 0.9997298
[19,] 4.572556e-04 0.0009145112 0.9995427
[20,] 2.218002e-03 0.0044360032 0.9977820
[21,] 4.816144e-02 0.0963228881 0.9518386
[22,] 5.069352e-02 0.1013870303 0.9493065
[23,] 1.876737e-01 0.3753473597 0.8123263
[24,] 4.992554e-01 0.9985107514 0.5007446
[25,] 7.060321e-01 0.5879358943 0.2939679
[26,] 6.394711e-01 0.7210577075 0.3605289
[27,] 5.536135e-01 0.8927729707 0.4463865
[28,] 4.590316e-01 0.9180631907 0.5409684
[29,] 3.851887e-01 0.7703773707 0.6148113
[30,] 3.190909e-01 0.6381818769 0.6809091
[31,] 4.788765e-01 0.9577530403 0.5211235
[32,] 6.373747e-01 0.7252506971 0.3626253
[33,] 8.020953e-01 0.3958093180 0.1979047
> postscript(file="/var/www/html/rcomp/tmp/16bga1292686676.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26bga1292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36bga1292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4h2fv1292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5h2fv1292686676.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 = 68
Frequency = 1
1 2 3 4 5 6
-0.369598058 -0.626156618 -3.807378302 0.214443368 -0.306670104 5.839667364
7 8 9 10 11 12
0.231687714 2.245279760 1.786745286 -2.124031658 1.650458131 2.681914391
13 14 15 16 17 18
0.234296462 -0.078189176 0.674263644 0.001536947 2.705962954 1.513639541
19 20 21 22 23 24
-0.845657373 2.651174702 0.709347612 1.130797745 1.153103900 1.902024988
25 26 27 28 29 30
1.790018216 1.434816226 2.545664084 0.232876041 1.552016053 0.534374564
31 32 33 34 35 36
1.113923877 0.856356938 -1.197229521 0.915192997 2.054152569 0.899023804
37 38 39 40 41 42
4.580887484 3.588855526 -2.285196433 2.645590891 -0.766681550 -2.734337700
43 44 45 46 47 48
-0.707303683 0.115911496 -0.855097116 -0.812477459 1.852367642 3.123054755
49 50 51 52 53 54
2.155322988 1.364828700 4.360022574 -0.468481118 1.203827108 0.380641501
55 56 57 58 59 60
3.555308531 0.447802610 -0.443766261 0.890518375 -6.710082242 -8.606017937
61 62 63 64 65 66
-8.390927092 -5.684154658 -1.487375566 -2.625966129 -4.388454461 -5.533985270
67 68
-3.347959066 -6.316525506
> postscript(file="/var/www/html/rcomp/tmp/6h2fv1292686676.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 = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.369598058 NA
1 -0.626156618 -0.369598058
2 -3.807378302 -0.626156618
3 0.214443368 -3.807378302
4 -0.306670104 0.214443368
5 5.839667364 -0.306670104
6 0.231687714 5.839667364
7 2.245279760 0.231687714
8 1.786745286 2.245279760
9 -2.124031658 1.786745286
10 1.650458131 -2.124031658
11 2.681914391 1.650458131
12 0.234296462 2.681914391
13 -0.078189176 0.234296462
14 0.674263644 -0.078189176
15 0.001536947 0.674263644
16 2.705962954 0.001536947
17 1.513639541 2.705962954
18 -0.845657373 1.513639541
19 2.651174702 -0.845657373
20 0.709347612 2.651174702
21 1.130797745 0.709347612
22 1.153103900 1.130797745
23 1.902024988 1.153103900
24 1.790018216 1.902024988
25 1.434816226 1.790018216
26 2.545664084 1.434816226
27 0.232876041 2.545664084
28 1.552016053 0.232876041
29 0.534374564 1.552016053
30 1.113923877 0.534374564
31 0.856356938 1.113923877
32 -1.197229521 0.856356938
33 0.915192997 -1.197229521
34 2.054152569 0.915192997
35 0.899023804 2.054152569
36 4.580887484 0.899023804
37 3.588855526 4.580887484
38 -2.285196433 3.588855526
39 2.645590891 -2.285196433
40 -0.766681550 2.645590891
41 -2.734337700 -0.766681550
42 -0.707303683 -2.734337700
43 0.115911496 -0.707303683
44 -0.855097116 0.115911496
45 -0.812477459 -0.855097116
46 1.852367642 -0.812477459
47 3.123054755 1.852367642
48 2.155322988 3.123054755
49 1.364828700 2.155322988
50 4.360022574 1.364828700
51 -0.468481118 4.360022574
52 1.203827108 -0.468481118
53 0.380641501 1.203827108
54 3.555308531 0.380641501
55 0.447802610 3.555308531
56 -0.443766261 0.447802610
57 0.890518375 -0.443766261
58 -6.710082242 0.890518375
59 -8.606017937 -6.710082242
60 -8.390927092 -8.606017937
61 -5.684154658 -8.390927092
62 -1.487375566 -5.684154658
63 -2.625966129 -1.487375566
64 -4.388454461 -2.625966129
65 -5.533985270 -4.388454461
66 -3.347959066 -5.533985270
67 -6.316525506 -3.347959066
68 NA -6.316525506
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.626156618 -0.369598058
[2,] -3.807378302 -0.626156618
[3,] 0.214443368 -3.807378302
[4,] -0.306670104 0.214443368
[5,] 5.839667364 -0.306670104
[6,] 0.231687714 5.839667364
[7,] 2.245279760 0.231687714
[8,] 1.786745286 2.245279760
[9,] -2.124031658 1.786745286
[10,] 1.650458131 -2.124031658
[11,] 2.681914391 1.650458131
[12,] 0.234296462 2.681914391
[13,] -0.078189176 0.234296462
[14,] 0.674263644 -0.078189176
[15,] 0.001536947 0.674263644
[16,] 2.705962954 0.001536947
[17,] 1.513639541 2.705962954
[18,] -0.845657373 1.513639541
[19,] 2.651174702 -0.845657373
[20,] 0.709347612 2.651174702
[21,] 1.130797745 0.709347612
[22,] 1.153103900 1.130797745
[23,] 1.902024988 1.153103900
[24,] 1.790018216 1.902024988
[25,] 1.434816226 1.790018216
[26,] 2.545664084 1.434816226
[27,] 0.232876041 2.545664084
[28,] 1.552016053 0.232876041
[29,] 0.534374564 1.552016053
[30,] 1.113923877 0.534374564
[31,] 0.856356938 1.113923877
[32,] -1.197229521 0.856356938
[33,] 0.915192997 -1.197229521
[34,] 2.054152569 0.915192997
[35,] 0.899023804 2.054152569
[36,] 4.580887484 0.899023804
[37,] 3.588855526 4.580887484
[38,] -2.285196433 3.588855526
[39,] 2.645590891 -2.285196433
[40,] -0.766681550 2.645590891
[41,] -2.734337700 -0.766681550
[42,] -0.707303683 -2.734337700
[43,] 0.115911496 -0.707303683
[44,] -0.855097116 0.115911496
[45,] -0.812477459 -0.855097116
[46,] 1.852367642 -0.812477459
[47,] 3.123054755 1.852367642
[48,] 2.155322988 3.123054755
[49,] 1.364828700 2.155322988
[50,] 4.360022574 1.364828700
[51,] -0.468481118 4.360022574
[52,] 1.203827108 -0.468481118
[53,] 0.380641501 1.203827108
[54,] 3.555308531 0.380641501
[55,] 0.447802610 3.555308531
[56,] -0.443766261 0.447802610
[57,] 0.890518375 -0.443766261
[58,] -6.710082242 0.890518375
[59,] -8.606017937 -6.710082242
[60,] -8.390927092 -8.606017937
[61,] -5.684154658 -8.390927092
[62,] -1.487375566 -5.684154658
[63,] -2.625966129 -1.487375566
[64,] -4.388454461 -2.625966129
[65,] -5.533985270 -4.388454461
[66,] -3.347959066 -5.533985270
[67,] -6.316525506 -3.347959066
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.626156618 -0.369598058
2 -3.807378302 -0.626156618
3 0.214443368 -3.807378302
4 -0.306670104 0.214443368
5 5.839667364 -0.306670104
6 0.231687714 5.839667364
7 2.245279760 0.231687714
8 1.786745286 2.245279760
9 -2.124031658 1.786745286
10 1.650458131 -2.124031658
11 2.681914391 1.650458131
12 0.234296462 2.681914391
13 -0.078189176 0.234296462
14 0.674263644 -0.078189176
15 0.001536947 0.674263644
16 2.705962954 0.001536947
17 1.513639541 2.705962954
18 -0.845657373 1.513639541
19 2.651174702 -0.845657373
20 0.709347612 2.651174702
21 1.130797745 0.709347612
22 1.153103900 1.130797745
23 1.902024988 1.153103900
24 1.790018216 1.902024988
25 1.434816226 1.790018216
26 2.545664084 1.434816226
27 0.232876041 2.545664084
28 1.552016053 0.232876041
29 0.534374564 1.552016053
30 1.113923877 0.534374564
31 0.856356938 1.113923877
32 -1.197229521 0.856356938
33 0.915192997 -1.197229521
34 2.054152569 0.915192997
35 0.899023804 2.054152569
36 4.580887484 0.899023804
37 3.588855526 4.580887484
38 -2.285196433 3.588855526
39 2.645590891 -2.285196433
40 -0.766681550 2.645590891
41 -2.734337700 -0.766681550
42 -0.707303683 -2.734337700
43 0.115911496 -0.707303683
44 -0.855097116 0.115911496
45 -0.812477459 -0.855097116
46 1.852367642 -0.812477459
47 3.123054755 1.852367642
48 2.155322988 3.123054755
49 1.364828700 2.155322988
50 4.360022574 1.364828700
51 -0.468481118 4.360022574
52 1.203827108 -0.468481118
53 0.380641501 1.203827108
54 3.555308531 0.380641501
55 0.447802610 3.555308531
56 -0.443766261 0.447802610
57 0.890518375 -0.443766261
58 -6.710082242 0.890518375
59 -8.606017937 -6.710082242
60 -8.390927092 -8.606017937
61 -5.684154658 -8.390927092
62 -1.487375566 -5.684154658
63 -2.625966129 -1.487375566
64 -4.388454461 -2.625966129
65 -5.533985270 -4.388454461
66 -3.347959066 -5.533985270
67 -6.316525506 -3.347959066
> 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/79bwf1292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89bwf1292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9k3d11292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10k3d11292686676.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n3u61292686676.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/12r4sc1292686676.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/13x4po1292686676.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/148wp91292686676.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/15cwnf1292686676.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/167ol51292686676.tab")
+ }
>
> try(system("convert tmp/16bga1292686676.ps tmp/16bga1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/26bga1292686676.ps tmp/26bga1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/36bga1292686676.ps tmp/36bga1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h2fv1292686676.ps tmp/4h2fv1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/5h2fv1292686676.ps tmp/5h2fv1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h2fv1292686676.ps tmp/6h2fv1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/79bwf1292686676.ps tmp/79bwf1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/89bwf1292686676.ps tmp/89bwf1292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k3d11292686676.ps tmp/9k3d11292686676.png",intern=TRUE))
character(0)
> try(system("convert tmp/10k3d11292686676.ps tmp/10k3d11292686676.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.663 1.688 6.107