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(1322.4
+ ,0
+ ,1.0622
+ ,1089.2
+ ,0
+ ,1.0773
+ ,1147.3
+ ,0
+ ,1.0807
+ ,1196.4
+ ,0
+ ,1.0848
+ ,1190.2
+ ,0
+ ,1.1582
+ ,1146
+ ,0
+ ,1.1663
+ ,1139.8
+ ,0
+ ,1.1372
+ ,1045.6
+ ,0
+ ,1.1139
+ ,1050.9
+ ,0
+ ,1.1222
+ ,1117.3
+ ,0
+ ,1.1692
+ ,1120
+ ,0
+ ,1.1702
+ ,1052.1
+ ,0
+ ,1.2286
+ ,1065.8
+ ,0
+ ,1.2613
+ ,1092.5
+ ,0
+ ,1.2646
+ ,1422
+ ,0
+ ,1.2262
+ ,1367.5
+ ,0
+ ,1.1985
+ ,1136.3
+ ,0
+ ,1.2007
+ ,1293.7
+ ,0
+ ,1.2138
+ ,1154.8
+ ,0
+ ,1.2266
+ ,1206.7
+ ,0
+ ,1.2176
+ ,1199
+ ,0
+ ,1.2218
+ ,1265
+ ,0
+ ,1.249
+ ,1247.1
+ ,0
+ ,1.2991
+ ,1116.5
+ ,0
+ ,1.3408
+ ,1153.9
+ ,0
+ ,1.3119
+ ,1077.4
+ ,0
+ ,1.3014
+ ,1132.5
+ ,0
+ ,1.3201
+ ,1058.8
+ ,0
+ ,1.2938
+ ,1195.1
+ ,0
+ ,1.2694
+ ,1263.4
+ ,0
+ ,1.2165
+ ,1023.1
+ ,0
+ ,1.2037
+ ,1141
+ ,0
+ ,1.2292
+ ,1116.3
+ ,0
+ ,1.2256
+ ,1135.6
+ ,0
+ ,1.2015
+ ,1210.5
+ ,0
+ ,1.1786
+ ,1230
+ ,0
+ ,1.1856
+ ,1136.5
+ ,0
+ ,1.2103
+ ,1068.7
+ ,0
+ ,1.1938
+ ,1372.5
+ ,0
+ ,1.202
+ ,1049.9
+ ,0
+ ,1.2271
+ ,1302.2
+ ,0
+ ,1.277
+ ,1305.9
+ ,0
+ ,1.265
+ ,1173.5
+ ,0
+ ,1.2684
+ ,1277.4
+ ,0
+ ,1.2811
+ ,1238.6
+ ,0
+ ,1.2727
+ ,1508.6
+ ,0
+ ,1.2611
+ ,1423.4
+ ,0
+ ,1.2881
+ ,1375.1
+ ,0
+ ,1.3213
+ ,1344.1
+ ,0
+ ,1.2999
+ ,1287.5
+ ,0
+ ,1.3074
+ ,1446.9
+ ,0
+ ,1.3242
+ ,1451
+ ,0
+ ,1.3516
+ ,1604.4
+ ,0
+ ,1.3511
+ ,1501.5
+ ,0
+ ,1.3419
+ ,1522.8
+ ,0
+ ,1.3716
+ ,1328
+ ,0
+ ,1.3622
+ ,1420.5
+ ,0
+ ,1.3896
+ ,1648
+ ,0
+ ,1.4227
+ ,1631.1
+ ,0
+ ,1.4684
+ ,1396.6
+ ,0
+ ,1.457
+ ,1663.4
+ ,0
+ ,1.4718
+ ,1283
+ ,0
+ ,1.4748
+ ,1582.4
+ ,0
+ ,1.5527
+ ,1785.2
+ ,0
+ ,1.575
+ ,1853.6
+ ,0
+ ,1.5557
+ ,1994.1
+ ,0
+ ,1.5553
+ ,2042.8
+ ,0
+ ,1.577
+ ,1586.1
+ ,0
+ ,1.4975
+ ,1942.4
+ ,0
+ ,1.4369
+ ,1763.6
+ ,1
+ ,1.3322
+ ,1819.9
+ ,1
+ ,1.2732
+ ,1836
+ ,1
+ ,1.3449
+ ,1449.9
+ ,1
+ ,1.3239
+ ,1513.3
+ ,1
+ ,1.2785
+ ,1677.7
+ ,1
+ ,1.305
+ ,1494.4
+ ,1
+ ,1.319
+ ,1375.3
+ ,1
+ ,1.365
+ ,1577.7
+ ,1
+ ,1.4016
+ ,1537.7
+ ,1
+ ,1.4088
+ ,1356.6
+ ,1
+ ,1.4268
+ ,1469.6
+ ,1
+ ,1.4562)
+ ,dim=c(3
+ ,81)
+ ,dimnames=list(c('Import_Uit_USA'
+ ,'Dummy_Crisis'
+ ,'Wisselkoers_EUR/DOLLAR')
+ ,1:81))
> y <- array(NA,dim=c(3,81),dimnames=list(c('Import_Uit_USA','Dummy_Crisis','Wisselkoers_EUR/DOLLAR'),1:81))
> 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 = 'Do not include Seasonal 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
Import_Uit_USA Dummy_Crisis Wisselkoers_EUR/DOLLAR
1 1322.4 0 1.0622
2 1089.2 0 1.0773
3 1147.3 0 1.0807
4 1196.4 0 1.0848
5 1190.2 0 1.1582
6 1146.0 0 1.1663
7 1139.8 0 1.1372
8 1045.6 0 1.1139
9 1050.9 0 1.1222
10 1117.3 0 1.1692
11 1120.0 0 1.1702
12 1052.1 0 1.2286
13 1065.8 0 1.2613
14 1092.5 0 1.2646
15 1422.0 0 1.2262
16 1367.5 0 1.1985
17 1136.3 0 1.2007
18 1293.7 0 1.2138
19 1154.8 0 1.2266
20 1206.7 0 1.2176
21 1199.0 0 1.2218
22 1265.0 0 1.2490
23 1247.1 0 1.2991
24 1116.5 0 1.3408
25 1153.9 0 1.3119
26 1077.4 0 1.3014
27 1132.5 0 1.3201
28 1058.8 0 1.2938
29 1195.1 0 1.2694
30 1263.4 0 1.2165
31 1023.1 0 1.2037
32 1141.0 0 1.2292
33 1116.3 0 1.2256
34 1135.6 0 1.2015
35 1210.5 0 1.1786
36 1230.0 0 1.1856
37 1136.5 0 1.2103
38 1068.7 0 1.1938
39 1372.5 0 1.2020
40 1049.9 0 1.2271
41 1302.2 0 1.2770
42 1305.9 0 1.2650
43 1173.5 0 1.2684
44 1277.4 0 1.2811
45 1238.6 0 1.2727
46 1508.6 0 1.2611
47 1423.4 0 1.2881
48 1375.1 0 1.3213
49 1344.1 0 1.2999
50 1287.5 0 1.3074
51 1446.9 0 1.3242
52 1451.0 0 1.3516
53 1604.4 0 1.3511
54 1501.5 0 1.3419
55 1522.8 0 1.3716
56 1328.0 0 1.3622
57 1420.5 0 1.3896
58 1648.0 0 1.4227
59 1631.1 0 1.4684
60 1396.6 0 1.4570
61 1663.4 0 1.4718
62 1283.0 0 1.4748
63 1582.4 0 1.5527
64 1785.2 0 1.5750
65 1853.6 0 1.5557
66 1994.1 0 1.5553
67 2042.8 0 1.5770
68 1586.1 0 1.4975
69 1942.4 0 1.4369
70 1763.6 1 1.3322
71 1819.9 1 1.2732
72 1836.0 1 1.3449
73 1449.9 1 1.3239
74 1513.3 1 1.2785
75 1677.7 1 1.3050
76 1494.4 1 1.3190
77 1375.3 1 1.3650
78 1577.7 1 1.4016
79 1537.7 1 1.4088
80 1356.6 1 1.4268
81 1469.6 1 1.4562
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy_Crisis `Wisselkoers_EUR/DOLLAR`
-490.8 165.7 1402.7
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-319.66 -94.67 -23.62 89.68 417.68
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -490.77 198.76 -2.469 0.01573 *
Dummy_Crisis 165.71 51.91 3.192 0.00203 **
`Wisselkoers_EUR/DOLLAR` 1402.67 153.97 9.110 6.6e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 162.5 on 78 degrees of freedom
Multiple R-squared: 0.5839, Adjusted R-squared: 0.5732
F-statistic: 54.73 on 2 and 78 DF, p-value: 1.408e-15
> 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,] 0.2268884105 0.4537768210 0.7731116
[2,] 0.1112930833 0.2225861665 0.8887069
[3,] 0.1069428751 0.2138857502 0.8930571
[4,] 0.0797472953 0.1594945906 0.9202527
[5,] 0.0391471669 0.0782943337 0.9608528
[6,] 0.0180755066 0.0361510132 0.9819245
[7,] 0.0084223645 0.0168447290 0.9915776
[8,] 0.0039007584 0.0078015168 0.9960992
[9,] 0.0018763988 0.0037527976 0.9981236
[10,] 0.0716835568 0.1433671136 0.9283164
[11,] 0.1297437195 0.2594874389 0.8702563
[12,] 0.0878139179 0.1756278358 0.9121861
[13,] 0.0816738412 0.1633476824 0.9183262
[14,] 0.0536876661 0.1073753322 0.9463123
[15,] 0.0349558106 0.0699116213 0.9650442
[16,] 0.0217785883 0.0435571765 0.9782214
[17,] 0.0154998347 0.0309996694 0.9845002
[18,] 0.0097821838 0.0195643676 0.9902178
[19,] 0.0091133063 0.0182266126 0.9908867
[20,] 0.0062956777 0.0125913554 0.9937043
[21,] 0.0063495461 0.0126990923 0.9936505
[22,] 0.0049699116 0.0099398231 0.9950301
[23,] 0.0057157474 0.0114314947 0.9942843
[24,] 0.0036419102 0.0072838203 0.9963581
[25,] 0.0027607652 0.0055215304 0.9972392
[26,] 0.0033953355 0.0067906710 0.9966047
[27,] 0.0021193307 0.0042386614 0.9978807
[28,] 0.0014224981 0.0028449963 0.9985775
[29,] 0.0008432628 0.0016865256 0.9991567
[30,] 0.0004900009 0.0009800017 0.9995100
[31,] 0.0003003242 0.0006006484 0.9996997
[32,] 0.0001729691 0.0003459381 0.9998270
[33,] 0.0001475146 0.0002950291 0.9998525
[34,] 0.0003505915 0.0007011830 0.9996494
[35,] 0.0004229860 0.0008459720 0.9995770
[36,] 0.0004060029 0.0008120058 0.9995940
[37,] 0.0003682879 0.0007365757 0.9996317
[38,] 0.0002768563 0.0005537125 0.9997231
[39,] 0.0002238644 0.0004477287 0.9997761
[40,] 0.0001690917 0.0003381834 0.9998309
[41,] 0.0011661009 0.0023322017 0.9988339
[42,] 0.0016806169 0.0033612338 0.9983194
[43,] 0.0015692286 0.0031384573 0.9984308
[44,] 0.0012700450 0.0025400901 0.9987300
[45,] 0.0010466335 0.0020932671 0.9989534
[46,] 0.0012018437 0.0024036873 0.9987982
[47,] 0.0012121396 0.0024242792 0.9987879
[48,] 0.0027432506 0.0054865011 0.9972567
[49,] 0.0026036244 0.0052072489 0.9973964
[50,] 0.0022570759 0.0045141518 0.9977429
[51,] 0.0023732862 0.0047465723 0.9976267
[52,] 0.0024194727 0.0048389455 0.9975805
[53,] 0.0025749553 0.0051499107 0.9974250
[54,] 0.0019615586 0.0039231172 0.9980384
[55,] 0.0040694691 0.0081389382 0.9959305
[56,] 0.0034443391 0.0068886781 0.9965557
[57,] 0.0806899307 0.1613798614 0.9193101
[58,] 0.1117996046 0.2235992092 0.8882004
[59,] 0.0935843376 0.1871686753 0.9064157
[60,] 0.0825537867 0.1651075735 0.9174462
[61,] 0.1164998993 0.2329997987 0.8835001
[62,] 0.3395910709 0.6791821418 0.6604089
[63,] 0.3682367988 0.7364735976 0.6317632
[64,] 0.3649228513 0.7298457025 0.6350771
[65,] 0.3667104630 0.7334209259 0.6332895
[66,] 0.4063256409 0.8126512818 0.5936744
[67,] 0.7931435350 0.4137129300 0.2068565
[68,] 0.7416846108 0.5166307784 0.2583154
[69,] 0.6340251692 0.7319496615 0.3659748
[70,] 0.6453105039 0.7093789922 0.3546895
> postscript(file="/var/www/html/rcomp/tmp/18u4s1261046180.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2q1v01261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ne8h1261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4pavg1261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/505uv1261046180.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 = 81
Frequency = 1
1 2 3 4 5 6
323.2612099 68.8809489 122.2118836 165.5609518 56.4052465 0.8436496
7 8 9 10 11 12
35.4612385 -26.0566371 -32.3987673 -31.9240827 -30.6267490 -180.4424600
13 14 15 16 17 18
-212.6096475 -190.5384463 192.8239391 177.1777952 -57.1080707 81.9170010
19 20 21 22 23 24
-74.9371274 -10.4131309 -24.0043293 3.8431478 -84.3304331 -273.4216172
25 26 27 28 29 30
-195.4845616 -257.2565656 -228.3864251 -265.1963018 -94.6712445 47.8298020
31 32 33 34 35 36
-174.5160695 -92.3840598 -112.0344612 -58.9302037 48.0908542 57.7721902
37 38 39 40 41 42
-70.3736670 -115.0296733 177.2684632 -180.5384606 1.7684918 22.3004872
43 44 45 46 47 48
-114.8685782 -28.7824400 -55.8000432 230.4708857 107.3988960 12.5303753
49 50 51 52 53 54
11.5474338 -55.5725633 80.2626431 45.9295869 200.0309200 110.0354499
55 56 57 58 59 60
89.6762612 -91.9386757 -37.8717320 143.2000140 62.1981648 -156.3114396
61 62 63 64 65 66
89.7290994 -294.8788995 -104.7466031 66.7739388 162.2453981 303.3064646
67 68 69 70 71 72
321.5686062 -23.6194241 417.6821528 220.0285921 359.0859029 274.6147303
73 74 75 76 77 78
-82.0292777 45.0517716 172.2811151 -30.6562129 -214.2788621 -63.2164481
79 80 81
-113.3156454 -319.6636385 -247.9020273
> postscript(file="/var/www/html/rcomp/tmp/6jb8a1261046180.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 = 81
Frequency = 1
lag(myerror, k = 1) myerror
0 323.2612099 NA
1 68.8809489 323.2612099
2 122.2118836 68.8809489
3 165.5609518 122.2118836
4 56.4052465 165.5609518
5 0.8436496 56.4052465
6 35.4612385 0.8436496
7 -26.0566371 35.4612385
8 -32.3987673 -26.0566371
9 -31.9240827 -32.3987673
10 -30.6267490 -31.9240827
11 -180.4424600 -30.6267490
12 -212.6096475 -180.4424600
13 -190.5384463 -212.6096475
14 192.8239391 -190.5384463
15 177.1777952 192.8239391
16 -57.1080707 177.1777952
17 81.9170010 -57.1080707
18 -74.9371274 81.9170010
19 -10.4131309 -74.9371274
20 -24.0043293 -10.4131309
21 3.8431478 -24.0043293
22 -84.3304331 3.8431478
23 -273.4216172 -84.3304331
24 -195.4845616 -273.4216172
25 -257.2565656 -195.4845616
26 -228.3864251 -257.2565656
27 -265.1963018 -228.3864251
28 -94.6712445 -265.1963018
29 47.8298020 -94.6712445
30 -174.5160695 47.8298020
31 -92.3840598 -174.5160695
32 -112.0344612 -92.3840598
33 -58.9302037 -112.0344612
34 48.0908542 -58.9302037
35 57.7721902 48.0908542
36 -70.3736670 57.7721902
37 -115.0296733 -70.3736670
38 177.2684632 -115.0296733
39 -180.5384606 177.2684632
40 1.7684918 -180.5384606
41 22.3004872 1.7684918
42 -114.8685782 22.3004872
43 -28.7824400 -114.8685782
44 -55.8000432 -28.7824400
45 230.4708857 -55.8000432
46 107.3988960 230.4708857
47 12.5303753 107.3988960
48 11.5474338 12.5303753
49 -55.5725633 11.5474338
50 80.2626431 -55.5725633
51 45.9295869 80.2626431
52 200.0309200 45.9295869
53 110.0354499 200.0309200
54 89.6762612 110.0354499
55 -91.9386757 89.6762612
56 -37.8717320 -91.9386757
57 143.2000140 -37.8717320
58 62.1981648 143.2000140
59 -156.3114396 62.1981648
60 89.7290994 -156.3114396
61 -294.8788995 89.7290994
62 -104.7466031 -294.8788995
63 66.7739388 -104.7466031
64 162.2453981 66.7739388
65 303.3064646 162.2453981
66 321.5686062 303.3064646
67 -23.6194241 321.5686062
68 417.6821528 -23.6194241
69 220.0285921 417.6821528
70 359.0859029 220.0285921
71 274.6147303 359.0859029
72 -82.0292777 274.6147303
73 45.0517716 -82.0292777
74 172.2811151 45.0517716
75 -30.6562129 172.2811151
76 -214.2788621 -30.6562129
77 -63.2164481 -214.2788621
78 -113.3156454 -63.2164481
79 -319.6636385 -113.3156454
80 -247.9020273 -319.6636385
81 NA -247.9020273
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 68.8809489 323.2612099
[2,] 122.2118836 68.8809489
[3,] 165.5609518 122.2118836
[4,] 56.4052465 165.5609518
[5,] 0.8436496 56.4052465
[6,] 35.4612385 0.8436496
[7,] -26.0566371 35.4612385
[8,] -32.3987673 -26.0566371
[9,] -31.9240827 -32.3987673
[10,] -30.6267490 -31.9240827
[11,] -180.4424600 -30.6267490
[12,] -212.6096475 -180.4424600
[13,] -190.5384463 -212.6096475
[14,] 192.8239391 -190.5384463
[15,] 177.1777952 192.8239391
[16,] -57.1080707 177.1777952
[17,] 81.9170010 -57.1080707
[18,] -74.9371274 81.9170010
[19,] -10.4131309 -74.9371274
[20,] -24.0043293 -10.4131309
[21,] 3.8431478 -24.0043293
[22,] -84.3304331 3.8431478
[23,] -273.4216172 -84.3304331
[24,] -195.4845616 -273.4216172
[25,] -257.2565656 -195.4845616
[26,] -228.3864251 -257.2565656
[27,] -265.1963018 -228.3864251
[28,] -94.6712445 -265.1963018
[29,] 47.8298020 -94.6712445
[30,] -174.5160695 47.8298020
[31,] -92.3840598 -174.5160695
[32,] -112.0344612 -92.3840598
[33,] -58.9302037 -112.0344612
[34,] 48.0908542 -58.9302037
[35,] 57.7721902 48.0908542
[36,] -70.3736670 57.7721902
[37,] -115.0296733 -70.3736670
[38,] 177.2684632 -115.0296733
[39,] -180.5384606 177.2684632
[40,] 1.7684918 -180.5384606
[41,] 22.3004872 1.7684918
[42,] -114.8685782 22.3004872
[43,] -28.7824400 -114.8685782
[44,] -55.8000432 -28.7824400
[45,] 230.4708857 -55.8000432
[46,] 107.3988960 230.4708857
[47,] 12.5303753 107.3988960
[48,] 11.5474338 12.5303753
[49,] -55.5725633 11.5474338
[50,] 80.2626431 -55.5725633
[51,] 45.9295869 80.2626431
[52,] 200.0309200 45.9295869
[53,] 110.0354499 200.0309200
[54,] 89.6762612 110.0354499
[55,] -91.9386757 89.6762612
[56,] -37.8717320 -91.9386757
[57,] 143.2000140 -37.8717320
[58,] 62.1981648 143.2000140
[59,] -156.3114396 62.1981648
[60,] 89.7290994 -156.3114396
[61,] -294.8788995 89.7290994
[62,] -104.7466031 -294.8788995
[63,] 66.7739388 -104.7466031
[64,] 162.2453981 66.7739388
[65,] 303.3064646 162.2453981
[66,] 321.5686062 303.3064646
[67,] -23.6194241 321.5686062
[68,] 417.6821528 -23.6194241
[69,] 220.0285921 417.6821528
[70,] 359.0859029 220.0285921
[71,] 274.6147303 359.0859029
[72,] -82.0292777 274.6147303
[73,] 45.0517716 -82.0292777
[74,] 172.2811151 45.0517716
[75,] -30.6562129 172.2811151
[76,] -214.2788621 -30.6562129
[77,] -63.2164481 -214.2788621
[78,] -113.3156454 -63.2164481
[79,] -319.6636385 -113.3156454
[80,] -247.9020273 -319.6636385
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 68.8809489 323.2612099
2 122.2118836 68.8809489
3 165.5609518 122.2118836
4 56.4052465 165.5609518
5 0.8436496 56.4052465
6 35.4612385 0.8436496
7 -26.0566371 35.4612385
8 -32.3987673 -26.0566371
9 -31.9240827 -32.3987673
10 -30.6267490 -31.9240827
11 -180.4424600 -30.6267490
12 -212.6096475 -180.4424600
13 -190.5384463 -212.6096475
14 192.8239391 -190.5384463
15 177.1777952 192.8239391
16 -57.1080707 177.1777952
17 81.9170010 -57.1080707
18 -74.9371274 81.9170010
19 -10.4131309 -74.9371274
20 -24.0043293 -10.4131309
21 3.8431478 -24.0043293
22 -84.3304331 3.8431478
23 -273.4216172 -84.3304331
24 -195.4845616 -273.4216172
25 -257.2565656 -195.4845616
26 -228.3864251 -257.2565656
27 -265.1963018 -228.3864251
28 -94.6712445 -265.1963018
29 47.8298020 -94.6712445
30 -174.5160695 47.8298020
31 -92.3840598 -174.5160695
32 -112.0344612 -92.3840598
33 -58.9302037 -112.0344612
34 48.0908542 -58.9302037
35 57.7721902 48.0908542
36 -70.3736670 57.7721902
37 -115.0296733 -70.3736670
38 177.2684632 -115.0296733
39 -180.5384606 177.2684632
40 1.7684918 -180.5384606
41 22.3004872 1.7684918
42 -114.8685782 22.3004872
43 -28.7824400 -114.8685782
44 -55.8000432 -28.7824400
45 230.4708857 -55.8000432
46 107.3988960 230.4708857
47 12.5303753 107.3988960
48 11.5474338 12.5303753
49 -55.5725633 11.5474338
50 80.2626431 -55.5725633
51 45.9295869 80.2626431
52 200.0309200 45.9295869
53 110.0354499 200.0309200
54 89.6762612 110.0354499
55 -91.9386757 89.6762612
56 -37.8717320 -91.9386757
57 143.2000140 -37.8717320
58 62.1981648 143.2000140
59 -156.3114396 62.1981648
60 89.7290994 -156.3114396
61 -294.8788995 89.7290994
62 -104.7466031 -294.8788995
63 66.7739388 -104.7466031
64 162.2453981 66.7739388
65 303.3064646 162.2453981
66 321.5686062 303.3064646
67 -23.6194241 321.5686062
68 417.6821528 -23.6194241
69 220.0285921 417.6821528
70 359.0859029 220.0285921
71 274.6147303 359.0859029
72 -82.0292777 274.6147303
73 45.0517716 -82.0292777
74 172.2811151 45.0517716
75 -30.6562129 172.2811151
76 -214.2788621 -30.6562129
77 -63.2164481 -214.2788621
78 -113.3156454 -63.2164481
79 -319.6636385 -113.3156454
80 -247.9020273 -319.6636385
> 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/717061261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88ror1261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nqxi1261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xmqf1261046180.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1117o81261046180.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/12rl261261046180.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/13p2791261046180.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/14xr6g1261046180.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/15la3m1261046180.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/16dygg1261046180.tab")
+ }
>
> try(system("convert tmp/18u4s1261046180.ps tmp/18u4s1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/2q1v01261046180.ps tmp/2q1v01261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ne8h1261046180.ps tmp/3ne8h1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pavg1261046180.ps tmp/4pavg1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/505uv1261046180.ps tmp/505uv1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jb8a1261046180.ps tmp/6jb8a1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/717061261046180.ps tmp/717061261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/88ror1261046180.ps tmp/88ror1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nqxi1261046180.ps tmp/9nqxi1261046180.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xmqf1261046180.ps tmp/10xmqf1261046180.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.687 1.598 4.331