R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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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(1671,0,1385,0,1632,0,1313,0,1300,0,1431,0,1398,0,1198,0,1292,0,1434,0,1660,0,1837,0,1455,0,1315,0,1642,0,1069,0,1209,0,1586,0,1122,0,1063,0,1125,0,1414,0,1347,0,1403,0,1299,0,1547,0,1515,0,1247,0,1639,0,1296,0,1063,0,1282,0,1365,0,1268,0,1532,0,1455,0,1393,0,1515,0,1510,0,1225,0,1577,0,1417,0,1224,0,1693,0,1633,0,1639,0,1914,0,1586,0,1552,0,2081,0,1500,0,1437,0,1470,0,1849,0,1387,0,1592,0,1589,0,1798,0,1935,0,1887,0,2027,0,2080,0,1556,0,1682,0,1785,0,1869,0,1781,0,2082,0,2570,1,1862,0,1936,0,1504,0,1765,0,1607,0,1577,0,1493,0,1615,0,1700,0,1335,0,1523,0,1621,0,1539,0,1637,0,1523,0,1418,0,1819,0,1594,0,1359,0,1261,0,1722,0,1407,0,1380,0,1642,0,1681,0,1542,0,1704,0,1431,0),dim=c(2,97),dimnames=list(c('Gebouwen','Dummy'),1:97))
> y <- array(NA,dim=c(2,97),dimnames=list(c('Gebouwen','Dummy'),1:97))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Gebouwen Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1671 0 1 0 0 0 0 0 0 0 0 0 0
2 1385 0 0 1 0 0 0 0 0 0 0 0 0
3 1632 0 0 0 1 0 0 0 0 0 0 0 0
4 1313 0 0 0 0 1 0 0 0 0 0 0 0
5 1300 0 0 0 0 0 1 0 0 0 0 0 0
6 1431 0 0 0 0 0 0 1 0 0 0 0 0
7 1398 0 0 0 0 0 0 0 1 0 0 0 0
8 1198 0 0 0 0 0 0 0 0 1 0 0 0
9 1292 0 0 0 0 0 0 0 0 0 1 0 0
10 1434 0 0 0 0 0 0 0 0 0 0 1 0
11 1660 0 0 0 0 0 0 0 0 0 0 0 1
12 1837 0 0 0 0 0 0 0 0 0 0 0 0
13 1455 0 1 0 0 0 0 0 0 0 0 0 0
14 1315 0 0 1 0 0 0 0 0 0 0 0 0
15 1642 0 0 0 1 0 0 0 0 0 0 0 0
16 1069 0 0 0 0 1 0 0 0 0 0 0 0
17 1209 0 0 0 0 0 1 0 0 0 0 0 0
18 1586 0 0 0 0 0 0 1 0 0 0 0 0
19 1122 0 0 0 0 0 0 0 1 0 0 0 0
20 1063 0 0 0 0 0 0 0 0 1 0 0 0
21 1125 0 0 0 0 0 0 0 0 0 1 0 0
22 1414 0 0 0 0 0 0 0 0 0 0 1 0
23 1347 0 0 0 0 0 0 0 0 0 0 0 1
24 1403 0 0 0 0 0 0 0 0 0 0 0 0
25 1299 0 1 0 0 0 0 0 0 0 0 0 0
26 1547 0 0 1 0 0 0 0 0 0 0 0 0
27 1515 0 0 0 1 0 0 0 0 0 0 0 0
28 1247 0 0 0 0 1 0 0 0 0 0 0 0
29 1639 0 0 0 0 0 1 0 0 0 0 0 0
30 1296 0 0 0 0 0 0 1 0 0 0 0 0
31 1063 0 0 0 0 0 0 0 1 0 0 0 0
32 1282 0 0 0 0 0 0 0 0 1 0 0 0
33 1365 0 0 0 0 0 0 0 0 0 1 0 0
34 1268 0 0 0 0 0 0 0 0 0 0 1 0
35 1532 0 0 0 0 0 0 0 0 0 0 0 1
36 1455 0 0 0 0 0 0 0 0 0 0 0 0
37 1393 0 1 0 0 0 0 0 0 0 0 0 0
38 1515 0 0 1 0 0 0 0 0 0 0 0 0
39 1510 0 0 0 1 0 0 0 0 0 0 0 0
40 1225 0 0 0 0 1 0 0 0 0 0 0 0
41 1577 0 0 0 0 0 1 0 0 0 0 0 0
42 1417 0 0 0 0 0 0 1 0 0 0 0 0
43 1224 0 0 0 0 0 0 0 1 0 0 0 0
44 1693 0 0 0 0 0 0 0 0 1 0 0 0
45 1633 0 0 0 0 0 0 0 0 0 1 0 0
46 1639 0 0 0 0 0 0 0 0 0 0 1 0
47 1914 0 0 0 0 0 0 0 0 0 0 0 1
48 1586 0 0 0 0 0 0 0 0 0 0 0 0
49 1552 0 1 0 0 0 0 0 0 0 0 0 0
50 2081 0 0 1 0 0 0 0 0 0 0 0 0
51 1500 0 0 0 1 0 0 0 0 0 0 0 0
52 1437 0 0 0 0 1 0 0 0 0 0 0 0
53 1470 0 0 0 0 0 1 0 0 0 0 0 0
54 1849 0 0 0 0 0 0 1 0 0 0 0 0
55 1387 0 0 0 0 0 0 0 1 0 0 0 0
56 1592 0 0 0 0 0 0 0 0 1 0 0 0
57 1589 0 0 0 0 0 0 0 0 0 1 0 0
58 1798 0 0 0 0 0 0 0 0 0 0 1 0
59 1935 0 0 0 0 0 0 0 0 0 0 0 1
60 1887 0 0 0 0 0 0 0 0 0 0 0 0
61 2027 0 1 0 0 0 0 0 0 0 0 0 0
62 2080 0 0 1 0 0 0 0 0 0 0 0 0
63 1556 0 0 0 1 0 0 0 0 0 0 0 0
64 1682 0 0 0 0 1 0 0 0 0 0 0 0
65 1785 0 0 0 0 0 1 0 0 0 0 0 0
66 1869 0 0 0 0 0 0 1 0 0 0 0 0
67 1781 0 0 0 0 0 0 0 1 0 0 0 0
68 2082 0 0 0 0 0 0 0 0 1 0 0 0
69 2570 1 0 0 0 0 0 0 0 0 1 0 0
70 1862 0 0 0 0 0 0 0 0 0 0 1 0
71 1936 0 0 0 0 0 0 0 0 0 0 0 1
72 1504 0 0 0 0 0 0 0 0 0 0 0 0
73 1765 0 1 0 0 0 0 0 0 0 0 0 0
74 1607 0 0 1 0 0 0 0 0 0 0 0 0
75 1577 0 0 0 1 0 0 0 0 0 0 0 0
76 1493 0 0 0 0 1 0 0 0 0 0 0 0
77 1615 0 0 0 0 0 1 0 0 0 0 0 0
78 1700 0 0 0 0 0 0 1 0 0 0 0 0
79 1335 0 0 0 0 0 0 0 1 0 0 0 0
80 1523 0 0 0 0 0 0 0 0 1 0 0 0
81 1621 0 0 0 0 0 0 0 0 0 1 0 0
82 1539 0 0 0 0 0 0 0 0 0 0 1 0
83 1637 0 0 0 0 0 0 0 0 0 0 0 1
84 1523 0 0 0 0 0 0 0 0 0 0 0 0
85 1418 0 1 0 0 0 0 0 0 0 0 0 0
86 1819 0 0 1 0 0 0 0 0 0 0 0 0
87 1594 0 0 0 1 0 0 0 0 0 0 0 0
88 1359 0 0 0 0 1 0 0 0 0 0 0 0
89 1261 0 0 0 0 0 1 0 0 0 0 0 0
90 1722 0 0 0 0 0 0 1 0 0 0 0 0
91 1407 0 0 0 0 0 0 0 1 0 0 0 0
92 1380 0 0 0 0 0 0 0 0 1 0 0 0
93 1642 0 0 0 0 0 0 0 0 0 1 0 0
94 1681 0 0 0 0 0 0 0 0 0 0 1 0
95 1542 0 0 0 0 0 0 0 0 0 0 0 1
96 1704 0 0 0 0 0 0 0 0 0 0 0 0
97 1431 0 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
1612.375 1103.286 -55.597 56.250 -46.625 -259.250
M5 M6 M7 M8 M9 M10
-130.375 -3.625 -272.750 -135.750 -145.661 -33.000
M11
75.500
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-413.6 -153.6 -12.0 133.0 605.4
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1612.375 77.896 20.699 < 2e-16 ***
Dummy 1103.286 235.535 4.684 1.07e-05 ***
M1 -55.597 107.058 -0.519 0.6049
M2 56.250 110.161 0.511 0.6110
M3 -46.625 110.161 -0.423 0.6732
M4 -259.250 110.161 -2.353 0.0209 *
M5 -130.375 110.161 -1.183 0.2400
M6 -3.625 110.161 -0.033 0.9738
M7 -272.750 110.161 -2.476 0.0153 *
M8 -135.750 110.161 -1.232 0.2213
M9 -145.661 114.028 -1.277 0.2050
M10 -33.000 110.161 -0.300 0.7653
M11 75.500 110.161 0.685 0.4950
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 220.3 on 84 degrees of freedom
Multiple R-squared: 0.3466, Adjusted R-squared: 0.2533
F-statistic: 3.714 on 12 and 84 DF, p-value: 0.0001671
> 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,] 0.22694281 0.453885615 0.773057193
[2,] 0.12649437 0.252988732 0.873505634
[3,] 0.08261309 0.165226182 0.917386909
[4,] 0.10399048 0.207980951 0.896009524
[5,] 0.08041604 0.160832085 0.919583957
[6,] 0.06800898 0.136017960 0.931991020
[7,] 0.03817459 0.076349185 0.961825408
[8,] 0.06870649 0.137412975 0.931293512
[9,] 0.16144522 0.322890434 0.838554783
[10,] 0.18995337 0.379906739 0.810046630
[11,] 0.18178125 0.363562508 0.818218746
[12,] 0.14013434 0.280268677 0.859865661
[13,] 0.10280180 0.205603600 0.897198200
[14,] 0.17251913 0.345038256 0.827480872
[15,] 0.19306877 0.386137549 0.806931226
[16,] 0.20305686 0.406113725 0.796943138
[17,] 0.20430914 0.408618283 0.795690859
[18,] 0.18863106 0.377262128 0.811368936
[19,] 0.21405408 0.428108163 0.785945919
[20,] 0.18430574 0.368611474 0.815694263
[21,] 0.16823973 0.336479464 0.831760268
[22,] 0.14758970 0.295179399 0.852410300
[23,] 0.15701818 0.314036369 0.842981816
[24,] 0.12311598 0.246231965 0.876884018
[25,] 0.10781802 0.215636031 0.892181985
[26,] 0.09913110 0.198262204 0.900868898
[27,] 0.10424989 0.208499785 0.895750107
[28,] 0.09228390 0.184567792 0.907716104
[29,] 0.23590520 0.471810397 0.764094801
[30,] 0.29241471 0.584829411 0.707585294
[31,] 0.29370840 0.587416802 0.706291599
[32,] 0.36957826 0.739156524 0.630421738
[33,] 0.31067656 0.621353128 0.689323436
[34,] 0.26665815 0.533316302 0.733341849
[35,] 0.52948386 0.941032282 0.470516141
[36,] 0.46926311 0.938526213 0.530736894
[37,] 0.43744656 0.874893115 0.562553442
[38,] 0.37688288 0.753765751 0.623117125
[39,] 0.41686733 0.833734667 0.583132666
[40,] 0.37920813 0.758416263 0.620791869
[41,] 0.35911417 0.718228349 0.640885826
[42,] 0.32361249 0.647224972 0.676387514
[43,] 0.32683503 0.653670051 0.673164974
[44,] 0.34200529 0.684010581 0.657994709
[45,] 0.38402824 0.768056478 0.615971761
[46,] 0.62949864 0.741002723 0.370501362
[47,] 0.74363862 0.512722754 0.256361377
[48,] 0.68013765 0.639724692 0.319862346
[49,] 0.71312021 0.573759586 0.286879793
[50,] 0.76451100 0.470977991 0.235488995
[51,] 0.74831788 0.503364250 0.251682125
[52,] 0.85548451 0.289030989 0.144515495
[53,] 0.99023124 0.019537514 0.009768757
[54,] 0.98235842 0.035283167 0.017641583
[55,] 0.98504329 0.029913425 0.014956712
[56,] 0.99367008 0.012659831 0.006329915
[57,] 0.98929570 0.021408593 0.010704296
[58,] 0.99684736 0.006305279 0.003152639
[59,] 0.99642736 0.007145274 0.003572637
[60,] 0.99144132 0.017117354 0.008558677
[61,] 0.98521592 0.029568154 0.014784077
[62,] 0.99766812 0.004663755 0.002331878
[63,] 0.99269386 0.014612281 0.007306141
[64,] 0.98090793 0.038184136 0.019092068
[65,] 0.96742785 0.065144304 0.032572152
[66,] 0.90686165 0.186276706 0.093138353
> postscript(file="/var/www/html/rcomp/tmp/17ulb1227463757.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25nb11227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jbip1227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4aiiw1227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yy7a1227463757.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 = 97
Frequency = 1
1 2 3 4 5
1.142222e+02 -2.836250e+02 6.625000e+01 -4.012500e+01 -1.820000e+02
6 7 8 9 10
-1.777500e+02 5.837500e+01 -2.786250e+02 -1.747143e+02 -1.453750e+02
11 12 13 14 15
-2.787500e+01 2.246250e+02 -1.017778e+02 -3.536250e+02 7.625000e+01
16 17 18 19 20
-2.841250e+02 -2.730000e+02 -2.275000e+01 -2.176250e+02 -4.136250e+02
21 22 23 24 25
-3.417143e+02 -1.653750e+02 -3.408750e+02 -2.093750e+02 -2.577778e+02
26 27 28 29 30
-1.216250e+02 -5.075000e+01 -1.061250e+02 1.570000e+02 -3.127500e+02
31 32 33 34 35
-2.766250e+02 -1.946250e+02 -1.017143e+02 -3.113750e+02 -1.558750e+02
36 37 38 39 40
-1.573750e+02 -1.637778e+02 -1.536250e+02 -5.575000e+01 -1.281250e+02
41 42 43 44 45
9.500000e+01 -1.917500e+02 -1.156250e+02 2.163750e+02 1.662857e+02
46 47 48 49 50
5.962500e+01 2.261250e+02 -2.637500e+01 -4.777778e+00 4.123750e+02
51 52 53 54 55
-6.575000e+01 8.387500e+01 -1.200000e+01 2.402500e+02 4.737500e+01
56 57 58 59 60
1.153750e+02 1.222857e+02 2.186250e+02 2.471250e+02 2.746250e+02
61 62 63 64 65
4.702222e+02 4.113750e+02 -9.750000e+00 3.288750e+02 3.030000e+02
66 67 68 69 70
2.602500e+02 4.413750e+02 6.053750e+02 9.219292e-13 2.826250e+02
71 72 73 74 75
2.481250e+02 -1.083750e+02 2.082222e+02 -6.162500e+01 1.125000e+01
76 77 78 79 80
1.398750e+02 1.330000e+02 9.125000e+01 -4.625000e+00 4.637500e+01
81 82 83 84 85
1.542857e+02 -4.037500e+01 -5.087500e+01 -8.937500e+01 -1.387778e+02
86 87 88 89 90
1.503750e+02 2.825000e+01 5.875000e+00 -2.210000e+02 1.132500e+02
91 92 93 94 95
6.737500e+01 -9.662500e+01 1.752857e+02 1.016250e+02 -1.458750e+02
96 97
9.162500e+01 -1.257778e+02
> postscript(file="/var/www/html/rcomp/tmp/6m7vl1227463757.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 = 97
Frequency = 1
lag(myerror, k = 1) myerror
0 1.142222e+02 NA
1 -2.836250e+02 1.142222e+02
2 6.625000e+01 -2.836250e+02
3 -4.012500e+01 6.625000e+01
4 -1.820000e+02 -4.012500e+01
5 -1.777500e+02 -1.820000e+02
6 5.837500e+01 -1.777500e+02
7 -2.786250e+02 5.837500e+01
8 -1.747143e+02 -2.786250e+02
9 -1.453750e+02 -1.747143e+02
10 -2.787500e+01 -1.453750e+02
11 2.246250e+02 -2.787500e+01
12 -1.017778e+02 2.246250e+02
13 -3.536250e+02 -1.017778e+02
14 7.625000e+01 -3.536250e+02
15 -2.841250e+02 7.625000e+01
16 -2.730000e+02 -2.841250e+02
17 -2.275000e+01 -2.730000e+02
18 -2.176250e+02 -2.275000e+01
19 -4.136250e+02 -2.176250e+02
20 -3.417143e+02 -4.136250e+02
21 -1.653750e+02 -3.417143e+02
22 -3.408750e+02 -1.653750e+02
23 -2.093750e+02 -3.408750e+02
24 -2.577778e+02 -2.093750e+02
25 -1.216250e+02 -2.577778e+02
26 -5.075000e+01 -1.216250e+02
27 -1.061250e+02 -5.075000e+01
28 1.570000e+02 -1.061250e+02
29 -3.127500e+02 1.570000e+02
30 -2.766250e+02 -3.127500e+02
31 -1.946250e+02 -2.766250e+02
32 -1.017143e+02 -1.946250e+02
33 -3.113750e+02 -1.017143e+02
34 -1.558750e+02 -3.113750e+02
35 -1.573750e+02 -1.558750e+02
36 -1.637778e+02 -1.573750e+02
37 -1.536250e+02 -1.637778e+02
38 -5.575000e+01 -1.536250e+02
39 -1.281250e+02 -5.575000e+01
40 9.500000e+01 -1.281250e+02
41 -1.917500e+02 9.500000e+01
42 -1.156250e+02 -1.917500e+02
43 2.163750e+02 -1.156250e+02
44 1.662857e+02 2.163750e+02
45 5.962500e+01 1.662857e+02
46 2.261250e+02 5.962500e+01
47 -2.637500e+01 2.261250e+02
48 -4.777778e+00 -2.637500e+01
49 4.123750e+02 -4.777778e+00
50 -6.575000e+01 4.123750e+02
51 8.387500e+01 -6.575000e+01
52 -1.200000e+01 8.387500e+01
53 2.402500e+02 -1.200000e+01
54 4.737500e+01 2.402500e+02
55 1.153750e+02 4.737500e+01
56 1.222857e+02 1.153750e+02
57 2.186250e+02 1.222857e+02
58 2.471250e+02 2.186250e+02
59 2.746250e+02 2.471250e+02
60 4.702222e+02 2.746250e+02
61 4.113750e+02 4.702222e+02
62 -9.750000e+00 4.113750e+02
63 3.288750e+02 -9.750000e+00
64 3.030000e+02 3.288750e+02
65 2.602500e+02 3.030000e+02
66 4.413750e+02 2.602500e+02
67 6.053750e+02 4.413750e+02
68 9.219292e-13 6.053750e+02
69 2.826250e+02 9.219292e-13
70 2.481250e+02 2.826250e+02
71 -1.083750e+02 2.481250e+02
72 2.082222e+02 -1.083750e+02
73 -6.162500e+01 2.082222e+02
74 1.125000e+01 -6.162500e+01
75 1.398750e+02 1.125000e+01
76 1.330000e+02 1.398750e+02
77 9.125000e+01 1.330000e+02
78 -4.625000e+00 9.125000e+01
79 4.637500e+01 -4.625000e+00
80 1.542857e+02 4.637500e+01
81 -4.037500e+01 1.542857e+02
82 -5.087500e+01 -4.037500e+01
83 -8.937500e+01 -5.087500e+01
84 -1.387778e+02 -8.937500e+01
85 1.503750e+02 -1.387778e+02
86 2.825000e+01 1.503750e+02
87 5.875000e+00 2.825000e+01
88 -2.210000e+02 5.875000e+00
89 1.132500e+02 -2.210000e+02
90 6.737500e+01 1.132500e+02
91 -9.662500e+01 6.737500e+01
92 1.752857e+02 -9.662500e+01
93 1.016250e+02 1.752857e+02
94 -1.458750e+02 1.016250e+02
95 9.162500e+01 -1.458750e+02
96 -1.257778e+02 9.162500e+01
97 NA -1.257778e+02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.836250e+02 1.142222e+02
[2,] 6.625000e+01 -2.836250e+02
[3,] -4.012500e+01 6.625000e+01
[4,] -1.820000e+02 -4.012500e+01
[5,] -1.777500e+02 -1.820000e+02
[6,] 5.837500e+01 -1.777500e+02
[7,] -2.786250e+02 5.837500e+01
[8,] -1.747143e+02 -2.786250e+02
[9,] -1.453750e+02 -1.747143e+02
[10,] -2.787500e+01 -1.453750e+02
[11,] 2.246250e+02 -2.787500e+01
[12,] -1.017778e+02 2.246250e+02
[13,] -3.536250e+02 -1.017778e+02
[14,] 7.625000e+01 -3.536250e+02
[15,] -2.841250e+02 7.625000e+01
[16,] -2.730000e+02 -2.841250e+02
[17,] -2.275000e+01 -2.730000e+02
[18,] -2.176250e+02 -2.275000e+01
[19,] -4.136250e+02 -2.176250e+02
[20,] -3.417143e+02 -4.136250e+02
[21,] -1.653750e+02 -3.417143e+02
[22,] -3.408750e+02 -1.653750e+02
[23,] -2.093750e+02 -3.408750e+02
[24,] -2.577778e+02 -2.093750e+02
[25,] -1.216250e+02 -2.577778e+02
[26,] -5.075000e+01 -1.216250e+02
[27,] -1.061250e+02 -5.075000e+01
[28,] 1.570000e+02 -1.061250e+02
[29,] -3.127500e+02 1.570000e+02
[30,] -2.766250e+02 -3.127500e+02
[31,] -1.946250e+02 -2.766250e+02
[32,] -1.017143e+02 -1.946250e+02
[33,] -3.113750e+02 -1.017143e+02
[34,] -1.558750e+02 -3.113750e+02
[35,] -1.573750e+02 -1.558750e+02
[36,] -1.637778e+02 -1.573750e+02
[37,] -1.536250e+02 -1.637778e+02
[38,] -5.575000e+01 -1.536250e+02
[39,] -1.281250e+02 -5.575000e+01
[40,] 9.500000e+01 -1.281250e+02
[41,] -1.917500e+02 9.500000e+01
[42,] -1.156250e+02 -1.917500e+02
[43,] 2.163750e+02 -1.156250e+02
[44,] 1.662857e+02 2.163750e+02
[45,] 5.962500e+01 1.662857e+02
[46,] 2.261250e+02 5.962500e+01
[47,] -2.637500e+01 2.261250e+02
[48,] -4.777778e+00 -2.637500e+01
[49,] 4.123750e+02 -4.777778e+00
[50,] -6.575000e+01 4.123750e+02
[51,] 8.387500e+01 -6.575000e+01
[52,] -1.200000e+01 8.387500e+01
[53,] 2.402500e+02 -1.200000e+01
[54,] 4.737500e+01 2.402500e+02
[55,] 1.153750e+02 4.737500e+01
[56,] 1.222857e+02 1.153750e+02
[57,] 2.186250e+02 1.222857e+02
[58,] 2.471250e+02 2.186250e+02
[59,] 2.746250e+02 2.471250e+02
[60,] 4.702222e+02 2.746250e+02
[61,] 4.113750e+02 4.702222e+02
[62,] -9.750000e+00 4.113750e+02
[63,] 3.288750e+02 -9.750000e+00
[64,] 3.030000e+02 3.288750e+02
[65,] 2.602500e+02 3.030000e+02
[66,] 4.413750e+02 2.602500e+02
[67,] 6.053750e+02 4.413750e+02
[68,] 9.219292e-13 6.053750e+02
[69,] 2.826250e+02 9.219292e-13
[70,] 2.481250e+02 2.826250e+02
[71,] -1.083750e+02 2.481250e+02
[72,] 2.082222e+02 -1.083750e+02
[73,] -6.162500e+01 2.082222e+02
[74,] 1.125000e+01 -6.162500e+01
[75,] 1.398750e+02 1.125000e+01
[76,] 1.330000e+02 1.398750e+02
[77,] 9.125000e+01 1.330000e+02
[78,] -4.625000e+00 9.125000e+01
[79,] 4.637500e+01 -4.625000e+00
[80,] 1.542857e+02 4.637500e+01
[81,] -4.037500e+01 1.542857e+02
[82,] -5.087500e+01 -4.037500e+01
[83,] -8.937500e+01 -5.087500e+01
[84,] -1.387778e+02 -8.937500e+01
[85,] 1.503750e+02 -1.387778e+02
[86,] 2.825000e+01 1.503750e+02
[87,] 5.875000e+00 2.825000e+01
[88,] -2.210000e+02 5.875000e+00
[89,] 1.132500e+02 -2.210000e+02
[90,] 6.737500e+01 1.132500e+02
[91,] -9.662500e+01 6.737500e+01
[92,] 1.752857e+02 -9.662500e+01
[93,] 1.016250e+02 1.752857e+02
[94,] -1.458750e+02 1.016250e+02
[95,] 9.162500e+01 -1.458750e+02
[96,] -1.257778e+02 9.162500e+01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.836250e+02 1.142222e+02
2 6.625000e+01 -2.836250e+02
3 -4.012500e+01 6.625000e+01
4 -1.820000e+02 -4.012500e+01
5 -1.777500e+02 -1.820000e+02
6 5.837500e+01 -1.777500e+02
7 -2.786250e+02 5.837500e+01
8 -1.747143e+02 -2.786250e+02
9 -1.453750e+02 -1.747143e+02
10 -2.787500e+01 -1.453750e+02
11 2.246250e+02 -2.787500e+01
12 -1.017778e+02 2.246250e+02
13 -3.536250e+02 -1.017778e+02
14 7.625000e+01 -3.536250e+02
15 -2.841250e+02 7.625000e+01
16 -2.730000e+02 -2.841250e+02
17 -2.275000e+01 -2.730000e+02
18 -2.176250e+02 -2.275000e+01
19 -4.136250e+02 -2.176250e+02
20 -3.417143e+02 -4.136250e+02
21 -1.653750e+02 -3.417143e+02
22 -3.408750e+02 -1.653750e+02
23 -2.093750e+02 -3.408750e+02
24 -2.577778e+02 -2.093750e+02
25 -1.216250e+02 -2.577778e+02
26 -5.075000e+01 -1.216250e+02
27 -1.061250e+02 -5.075000e+01
28 1.570000e+02 -1.061250e+02
29 -3.127500e+02 1.570000e+02
30 -2.766250e+02 -3.127500e+02
31 -1.946250e+02 -2.766250e+02
32 -1.017143e+02 -1.946250e+02
33 -3.113750e+02 -1.017143e+02
34 -1.558750e+02 -3.113750e+02
35 -1.573750e+02 -1.558750e+02
36 -1.637778e+02 -1.573750e+02
37 -1.536250e+02 -1.637778e+02
38 -5.575000e+01 -1.536250e+02
39 -1.281250e+02 -5.575000e+01
40 9.500000e+01 -1.281250e+02
41 -1.917500e+02 9.500000e+01
42 -1.156250e+02 -1.917500e+02
43 2.163750e+02 -1.156250e+02
44 1.662857e+02 2.163750e+02
45 5.962500e+01 1.662857e+02
46 2.261250e+02 5.962500e+01
47 -2.637500e+01 2.261250e+02
48 -4.777778e+00 -2.637500e+01
49 4.123750e+02 -4.777778e+00
50 -6.575000e+01 4.123750e+02
51 8.387500e+01 -6.575000e+01
52 -1.200000e+01 8.387500e+01
53 2.402500e+02 -1.200000e+01
54 4.737500e+01 2.402500e+02
55 1.153750e+02 4.737500e+01
56 1.222857e+02 1.153750e+02
57 2.186250e+02 1.222857e+02
58 2.471250e+02 2.186250e+02
59 2.746250e+02 2.471250e+02
60 4.702222e+02 2.746250e+02
61 4.113750e+02 4.702222e+02
62 -9.750000e+00 4.113750e+02
63 3.288750e+02 -9.750000e+00
64 3.030000e+02 3.288750e+02
65 2.602500e+02 3.030000e+02
66 4.413750e+02 2.602500e+02
67 6.053750e+02 4.413750e+02
68 9.219292e-13 6.053750e+02
69 2.826250e+02 9.219292e-13
70 2.481250e+02 2.826250e+02
71 -1.083750e+02 2.481250e+02
72 2.082222e+02 -1.083750e+02
73 -6.162500e+01 2.082222e+02
74 1.125000e+01 -6.162500e+01
75 1.398750e+02 1.125000e+01
76 1.330000e+02 1.398750e+02
77 9.125000e+01 1.330000e+02
78 -4.625000e+00 9.125000e+01
79 4.637500e+01 -4.625000e+00
80 1.542857e+02 4.637500e+01
81 -4.037500e+01 1.542857e+02
82 -5.087500e+01 -4.037500e+01
83 -8.937500e+01 -5.087500e+01
84 -1.387778e+02 -8.937500e+01
85 1.503750e+02 -1.387778e+02
86 2.825000e+01 1.503750e+02
87 5.875000e+00 2.825000e+01
88 -2.210000e+02 5.875000e+00
89 1.132500e+02 -2.210000e+02
90 6.737500e+01 1.132500e+02
91 -9.662500e+01 6.737500e+01
92 1.752857e+02 -9.662500e+01
93 1.016250e+02 1.752857e+02
94 -1.458750e+02 1.016250e+02
95 9.162500e+01 -1.458750e+02
96 -1.257778e+02 9.162500e+01
> 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/7npu61227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8443e1227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zvx91227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning message:
In dropInf(r.w/(s * sqrt(1 - hii))) :
Not plotting observations with leverage one:
69
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10e4hi1227463757.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zugy1227463757.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/126noj1227463758.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/13167u1227463758.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/1405ql1227463758.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/15isv51227463758.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/166dbz1227463758.tab")
+ }
>
> system("convert tmp/17ulb1227463757.ps tmp/17ulb1227463757.png")
> system("convert tmp/25nb11227463757.ps tmp/25nb11227463757.png")
> system("convert tmp/3jbip1227463757.ps tmp/3jbip1227463757.png")
> system("convert tmp/4aiiw1227463757.ps tmp/4aiiw1227463757.png")
> system("convert tmp/5yy7a1227463757.ps tmp/5yy7a1227463757.png")
> system("convert tmp/6m7vl1227463757.ps tmp/6m7vl1227463757.png")
> system("convert tmp/7npu61227463757.ps tmp/7npu61227463757.png")
> system("convert tmp/8443e1227463757.ps tmp/8443e1227463757.png")
> system("convert tmp/9zvx91227463757.ps tmp/9zvx91227463757.png")
> system("convert tmp/10e4hi1227463757.ps tmp/10e4hi1227463757.png")
>
>
> proc.time()
user system elapsed
2.939 1.604 3.345