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.
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(1593,0,1477.9,0,1733.7,0,1569.7,0,1843.7,0,1950.3,0,1657.5,0,1772.1,0,1568.3,0,1809.8,0,1646.7,0,1808.5,0,1763.9,0,1625.5,0,1538.8,0,1342.4,0,1645.1,0,1619.9,0,1338.1,0,1505.5,0,1529.1,0,1511.9,0,1656.7,0,1694.4,0,1662.3,0,1588.7,0,1483.3,0,1585.6,0,1658.9,0,1584.4,0,1470.6,0,1618.7,0,1407.6,0,1473.9,0,1515.3,0,1485.4,0,1496.1,0,1493.5,0,1298.4,0,1375.3,0,1507.9,0,1455.3,0,1363.3,0,1392.8,0,1348.8,0,1880.3,0,1669.2,0,1543.6,0,1701.2,0,1516.5,0,1466.8,0,1484.1,0,1577.2,0,1684.5,0,1414.7,0,1674.5,0,1598.7,0,1739.1,0,1674.6,0,1671.8,0,1802,0,1526.8,0,1580.9,0,1634.8,0,1610.3,0,1712,0,1678.8,0,1708.1,0,1680.6,0,2056,1,1624,1,2021.4,1,1861.1,1,1750.8,1,1767.5,1,1710.3,1,2151.5,1,2047.9,1,1915.4,1,1984.7,1,1896.5,1,2170.8,1,2139.9,1,2330.5,1,2121.8,1,2226.8,1,1857.9,1,2155.9,1,2341.7,1,2290.2,1,2006.5,1,2111.9,1,1731.3,1,1762.2,1,1863.2,1,1943.5,1,1975.2,1),dim=c(2,97),dimnames=list(c('M','D'),1:97))
> y <- array(NA,dim=c(2,97),dimnames=list(c('M','D'),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 = '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
M D M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1593.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 1477.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 1733.7 0 0 0 1 0 0 0 0 0 0 0 0 3
4 1569.7 0 0 0 0 1 0 0 0 0 0 0 0 4
5 1843.7 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1950.3 0 0 0 0 0 0 1 0 0 0 0 0 6
7 1657.5 0 0 0 0 0 0 0 1 0 0 0 0 7
8 1772.1 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1568.3 0 0 0 0 0 0 0 0 0 1 0 0 9
10 1809.8 0 0 0 0 0 0 0 0 0 0 1 0 10
11 1646.7 0 0 0 0 0 0 0 0 0 0 0 1 11
12 1808.5 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1763.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 1625.5 0 0 1 0 0 0 0 0 0 0 0 0 14
15 1538.8 0 0 0 1 0 0 0 0 0 0 0 0 15
16 1342.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 1645.1 0 0 0 0 0 1 0 0 0 0 0 0 17
18 1619.9 0 0 0 0 0 0 1 0 0 0 0 0 18
19 1338.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 1505.5 0 0 0 0 0 0 0 0 1 0 0 0 20
21 1529.1 0 0 0 0 0 0 0 0 0 1 0 0 21
22 1511.9 0 0 0 0 0 0 0 0 0 0 1 0 22
23 1656.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 1694.4 0 0 0 0 0 0 0 0 0 0 0 0 24
25 1662.3 0 1 0 0 0 0 0 0 0 0 0 0 25
26 1588.7 0 0 1 0 0 0 0 0 0 0 0 0 26
27 1483.3 0 0 0 1 0 0 0 0 0 0 0 0 27
28 1585.6 0 0 0 0 1 0 0 0 0 0 0 0 28
29 1658.9 0 0 0 0 0 1 0 0 0 0 0 0 29
30 1584.4 0 0 0 0 0 0 1 0 0 0 0 0 30
31 1470.6 0 0 0 0 0 0 0 1 0 0 0 0 31
32 1618.7 0 0 0 0 0 0 0 0 1 0 0 0 32
33 1407.6 0 0 0 0 0 0 0 0 0 1 0 0 33
34 1473.9 0 0 0 0 0 0 0 0 0 0 1 0 34
35 1515.3 0 0 0 0 0 0 0 0 0 0 0 1 35
36 1485.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 1496.1 0 1 0 0 0 0 0 0 0 0 0 0 37
38 1493.5 0 0 1 0 0 0 0 0 0 0 0 0 38
39 1298.4 0 0 0 1 0 0 0 0 0 0 0 0 39
40 1375.3 0 0 0 0 1 0 0 0 0 0 0 0 40
41 1507.9 0 0 0 0 0 1 0 0 0 0 0 0 41
42 1455.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 1363.3 0 0 0 0 0 0 0 1 0 0 0 0 43
44 1392.8 0 0 0 0 0 0 0 0 1 0 0 0 44
45 1348.8 0 0 0 0 0 0 0 0 0 1 0 0 45
46 1880.3 0 0 0 0 0 0 0 0 0 0 1 0 46
47 1669.2 0 0 0 0 0 0 0 0 0 0 0 1 47
48 1543.6 0 0 0 0 0 0 0 0 0 0 0 0 48
49 1701.2 0 1 0 0 0 0 0 0 0 0 0 0 49
50 1516.5 0 0 1 0 0 0 0 0 0 0 0 0 50
51 1466.8 0 0 0 1 0 0 0 0 0 0 0 0 51
52 1484.1 0 0 0 0 1 0 0 0 0 0 0 0 52
53 1577.2 0 0 0 0 0 1 0 0 0 0 0 0 53
54 1684.5 0 0 0 0 0 0 1 0 0 0 0 0 54
55 1414.7 0 0 0 0 0 0 0 1 0 0 0 0 55
56 1674.5 0 0 0 0 0 0 0 0 1 0 0 0 56
57 1598.7 0 0 0 0 0 0 0 0 0 1 0 0 57
58 1739.1 0 0 0 0 0 0 0 0 0 0 1 0 58
59 1674.6 0 0 0 0 0 0 0 0 0 0 0 1 59
60 1671.8 0 0 0 0 0 0 0 0 0 0 0 0 60
61 1802.0 0 1 0 0 0 0 0 0 0 0 0 0 61
62 1526.8 0 0 1 0 0 0 0 0 0 0 0 0 62
63 1580.9 0 0 0 1 0 0 0 0 0 0 0 0 63
64 1634.8 0 0 0 0 1 0 0 0 0 0 0 0 64
65 1610.3 0 0 0 0 0 1 0 0 0 0 0 0 65
66 1712.0 0 0 0 0 0 0 1 0 0 0 0 0 66
67 1678.8 0 0 0 0 0 0 0 1 0 0 0 0 67
68 1708.1 0 0 0 0 0 0 0 0 1 0 0 0 68
69 1680.6 0 0 0 0 0 0 0 0 0 1 0 0 69
70 2056.0 1 0 0 0 0 0 0 0 0 0 1 0 70
71 1624.0 1 0 0 0 0 0 0 0 0 0 0 1 71
72 2021.4 1 0 0 0 0 0 0 0 0 0 0 0 72
73 1861.1 1 1 0 0 0 0 0 0 0 0 0 0 73
74 1750.8 1 0 1 0 0 0 0 0 0 0 0 0 74
75 1767.5 1 0 0 1 0 0 0 0 0 0 0 0 75
76 1710.3 1 0 0 0 1 0 0 0 0 0 0 0 76
77 2151.5 1 0 0 0 0 1 0 0 0 0 0 0 77
78 2047.9 1 0 0 0 0 0 1 0 0 0 0 0 78
79 1915.4 1 0 0 0 0 0 0 1 0 0 0 0 79
80 1984.7 1 0 0 0 0 0 0 0 1 0 0 0 80
81 1896.5 1 0 0 0 0 0 0 0 0 1 0 0 81
82 2170.8 1 0 0 0 0 0 0 0 0 0 1 0 82
83 2139.9 1 0 0 0 0 0 0 0 0 0 0 1 83
84 2330.5 1 0 0 0 0 0 0 0 0 0 0 0 84
85 2121.8 1 1 0 0 0 0 0 0 0 0 0 0 85
86 2226.8 1 0 1 0 0 0 0 0 0 0 0 0 86
87 1857.9 1 0 0 1 0 0 0 0 0 0 0 0 87
88 2155.9 1 0 0 0 1 0 0 0 0 0 0 0 88
89 2341.7 1 0 0 0 0 1 0 0 0 0 0 0 89
90 2290.2 1 0 0 0 0 0 1 0 0 0 0 0 90
91 2006.5 1 0 0 0 0 0 0 1 0 0 0 0 91
92 2111.9 1 0 0 0 0 0 0 0 1 0 0 0 92
93 1731.3 1 0 0 0 0 0 0 0 0 1 0 0 93
94 1762.2 1 0 0 0 0 0 0 0 0 0 1 0 94
95 1863.2 1 0 0 0 0 0 0 0 0 0 0 1 95
96 1943.5 1 0 0 0 0 0 0 0 0 0 0 0 96
97 1975.2 1 1 0 0 0 0 0 0 0 0 0 0 97
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D M1 M2 M3 M4
1670.0006 404.1463 -21.2192 -112.7545 -172.4847 -155.9649
M5 M6 M7 M8 M9 M10
28.9799 30.1746 -157.1056 -41.5108 -167.2660 -12.2271
M11 t
-88.8573 -0.1698
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-349.235 -110.682 -4.357 94.883 280.008
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1670.0006 61.7186 27.058 < 2e-16 ***
D 404.1463 53.5655 7.545 5.2e-11 ***
M1 -21.2192 71.4226 -0.297 0.7671
M2 -112.7545 73.6270 -1.531 0.1295
M3 -172.4847 73.5921 -2.344 0.0215 *
M4 -155.9649 73.5675 -2.120 0.0370 *
M5 28.9799 73.5530 0.394 0.6946
M6 30.1746 73.5487 0.410 0.6827
M7 -157.1056 73.5545 -2.136 0.0356 *
M8 -41.5108 73.5706 -0.564 0.5741
M9 -167.2660 73.5968 -2.273 0.0256 *
M10 -12.2271 73.4522 -0.166 0.8682
M11 -88.8573 73.4369 -1.210 0.2297
t -0.1698 0.8655 -0.196 0.8450
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 146.9 on 83 degrees of freedom
Multiple R-squared: 0.6794, Adjusted R-squared: 0.6292
F-statistic: 13.53 on 13 and 83 DF, p-value: 1.665e-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,] 0.746255579 0.50748884 0.2537444
[2,] 0.761073247 0.47785351 0.2389268
[3,] 0.731074087 0.53785183 0.2689259
[4,] 0.650292033 0.69941593 0.3497080
[5,] 0.572626793 0.85474641 0.4273732
[6,] 0.514284946 0.97143011 0.4857151
[7,] 0.485741517 0.97148303 0.5142585
[8,] 0.400483503 0.80096701 0.5995165
[9,] 0.402525045 0.80505009 0.5974750
[10,] 0.421140709 0.84228142 0.5788593
[11,] 0.351439156 0.70287831 0.6485608
[12,] 0.448009964 0.89601993 0.5519900
[13,] 0.375563956 0.75112791 0.6244360
[14,] 0.319236739 0.63847348 0.6807633
[15,] 0.269052519 0.53810504 0.7309475
[16,] 0.234204590 0.46840918 0.7657954
[17,] 0.184612569 0.36922514 0.8153874
[18,] 0.148291810 0.29658362 0.8517082
[19,] 0.112102962 0.22420592 0.8878970
[20,] 0.102472544 0.20494509 0.8975275
[21,] 0.072770848 0.14554170 0.9272292
[22,] 0.055740934 0.11148187 0.9442591
[23,] 0.047986222 0.09597244 0.9520138
[24,] 0.033383077 0.06676615 0.9666169
[25,] 0.023876248 0.04775250 0.9761238
[26,] 0.021083745 0.04216749 0.9789163
[27,] 0.014807687 0.02961537 0.9851923
[28,] 0.012951978 0.02590396 0.9870480
[29,] 0.008930547 0.01786109 0.9910695
[30,] 0.091199103 0.18239821 0.9088009
[31,] 0.107668068 0.21533614 0.8923319
[32,] 0.084209686 0.16841937 0.9157903
[33,] 0.098884749 0.19776950 0.9011153
[34,] 0.080441514 0.16088303 0.9195585
[35,] 0.061839533 0.12367907 0.9381605
[36,] 0.053204915 0.10640983 0.9467951
[37,] 0.045090433 0.09018087 0.9549096
[38,] 0.038621376 0.07724275 0.9613786
[39,] 0.034471238 0.06894248 0.9655288
[40,] 0.033616000 0.06723200 0.9663840
[41,] 0.036264796 0.07252959 0.9637352
[42,] 0.030919785 0.06183957 0.9690802
[43,] 0.027744103 0.05548821 0.9722559
[44,] 0.020711022 0.04142204 0.9792890
[45,] 0.026762271 0.05352454 0.9732377
[46,] 0.019446182 0.03889236 0.9805538
[47,] 0.015828939 0.03165788 0.9841711
[48,] 0.014603741 0.02920748 0.9853963
[49,] 0.017443707 0.03488741 0.9825563
[50,] 0.014842682 0.02968536 0.9851573
[51,] 0.015019917 0.03003983 0.9849801
[52,] 0.012370366 0.02474073 0.9876296
[53,] 0.009789035 0.01957807 0.9902110
[54,] 0.007776930 0.01555386 0.9922231
[55,] 0.013821903 0.02764381 0.9861781
[56,] 0.009319185 0.01863837 0.9906808
[57,] 0.006153965 0.01230793 0.9938460
[58,] 0.017989702 0.03597940 0.9820103
[59,] 0.010899628 0.02179926 0.9891004
[60,] 0.050741232 0.10148246 0.9492588
[61,] 0.076786016 0.15357203 0.9232140
[62,] 0.177573365 0.35514673 0.8224266
[63,] 0.238649859 0.47729972 0.7613501
[64,] 0.632314390 0.73537122 0.3676856
> postscript(file="/var/www/html/rcomp/tmp/1gz3f1227192763.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bms91227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3v6bj1227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46qrn1227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5oyz21227192763.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 6
-55.611621 -79.006541 236.693459 56.343459 145.568459 251.143459
7 8 9 10 11 12
145.793459 144.968459 67.093459 153.724245 67.424245 140.536745
13 14 15 16 17 18
117.325698 70.630777 43.830777 -168.919223 -50.994223 -77.219223
19 20 21 22 23 24
-171.569223 -119.594223 29.930777 -142.138437 79.461563 28.474063
25 26 27 28 29 30
17.763016 35.868095 -9.631905 76.318095 -35.156905 -110.681905
31 32 33 34 35 36
-37.031905 -4.356905 -89.531905 -178.101118 -59.901118 -178.488618
37 38 39 40 41 42
-146.399666 -57.294586 -192.494586 -131.944586 -184.119586 -237.744586
43 44 45 46 47 48
-142.294586 -228.219586 -146.294586 230.336200 96.036200 -118.251300
49 50 51 52 53 54
60.737653 -32.257268 -22.057268 -21.107268 -112.782268 -6.507268
55 56 57 58 59 60
-88.857268 55.517732 105.642732 91.173519 103.473519 11.986019
61 62 63 64 65 66
163.574971 -19.919949 94.080051 131.630051 -77.644949 23.030051
67 68 69 70 71 72
177.280051 91.155051 189.580051 5.964545 -349.235455 -40.522955
73 74 75 76 77 78
-179.434002 -198.028923 -121.428923 -194.978923 61.446077 -43.178923
79 80 81 82 83 84
11.771077 -34.353923 3.371077 122.801864 168.701864 270.614364
85 86 87 88 89 90
83.303316 280.008395 -28.991605 252.658395 253.683395 201.158395
91 92 93 94 95 96
104.908395 94.883395 -159.791605 -283.760818 -105.960818 -114.348318
97
-61.259365
> postscript(file="/var/www/html/rcomp/tmp/6zlwv1227192763.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 -55.611621 NA
1 -79.006541 -55.611621
2 236.693459 -79.006541
3 56.343459 236.693459
4 145.568459 56.343459
5 251.143459 145.568459
6 145.793459 251.143459
7 144.968459 145.793459
8 67.093459 144.968459
9 153.724245 67.093459
10 67.424245 153.724245
11 140.536745 67.424245
12 117.325698 140.536745
13 70.630777 117.325698
14 43.830777 70.630777
15 -168.919223 43.830777
16 -50.994223 -168.919223
17 -77.219223 -50.994223
18 -171.569223 -77.219223
19 -119.594223 -171.569223
20 29.930777 -119.594223
21 -142.138437 29.930777
22 79.461563 -142.138437
23 28.474063 79.461563
24 17.763016 28.474063
25 35.868095 17.763016
26 -9.631905 35.868095
27 76.318095 -9.631905
28 -35.156905 76.318095
29 -110.681905 -35.156905
30 -37.031905 -110.681905
31 -4.356905 -37.031905
32 -89.531905 -4.356905
33 -178.101118 -89.531905
34 -59.901118 -178.101118
35 -178.488618 -59.901118
36 -146.399666 -178.488618
37 -57.294586 -146.399666
38 -192.494586 -57.294586
39 -131.944586 -192.494586
40 -184.119586 -131.944586
41 -237.744586 -184.119586
42 -142.294586 -237.744586
43 -228.219586 -142.294586
44 -146.294586 -228.219586
45 230.336200 -146.294586
46 96.036200 230.336200
47 -118.251300 96.036200
48 60.737653 -118.251300
49 -32.257268 60.737653
50 -22.057268 -32.257268
51 -21.107268 -22.057268
52 -112.782268 -21.107268
53 -6.507268 -112.782268
54 -88.857268 -6.507268
55 55.517732 -88.857268
56 105.642732 55.517732
57 91.173519 105.642732
58 103.473519 91.173519
59 11.986019 103.473519
60 163.574971 11.986019
61 -19.919949 163.574971
62 94.080051 -19.919949
63 131.630051 94.080051
64 -77.644949 131.630051
65 23.030051 -77.644949
66 177.280051 23.030051
67 91.155051 177.280051
68 189.580051 91.155051
69 5.964545 189.580051
70 -349.235455 5.964545
71 -40.522955 -349.235455
72 -179.434002 -40.522955
73 -198.028923 -179.434002
74 -121.428923 -198.028923
75 -194.978923 -121.428923
76 61.446077 -194.978923
77 -43.178923 61.446077
78 11.771077 -43.178923
79 -34.353923 11.771077
80 3.371077 -34.353923
81 122.801864 3.371077
82 168.701864 122.801864
83 270.614364 168.701864
84 83.303316 270.614364
85 280.008395 83.303316
86 -28.991605 280.008395
87 252.658395 -28.991605
88 253.683395 252.658395
89 201.158395 253.683395
90 104.908395 201.158395
91 94.883395 104.908395
92 -159.791605 94.883395
93 -283.760818 -159.791605
94 -105.960818 -283.760818
95 -114.348318 -105.960818
96 -61.259365 -114.348318
97 NA -61.259365
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -79.006541 -55.611621
[2,] 236.693459 -79.006541
[3,] 56.343459 236.693459
[4,] 145.568459 56.343459
[5,] 251.143459 145.568459
[6,] 145.793459 251.143459
[7,] 144.968459 145.793459
[8,] 67.093459 144.968459
[9,] 153.724245 67.093459
[10,] 67.424245 153.724245
[11,] 140.536745 67.424245
[12,] 117.325698 140.536745
[13,] 70.630777 117.325698
[14,] 43.830777 70.630777
[15,] -168.919223 43.830777
[16,] -50.994223 -168.919223
[17,] -77.219223 -50.994223
[18,] -171.569223 -77.219223
[19,] -119.594223 -171.569223
[20,] 29.930777 -119.594223
[21,] -142.138437 29.930777
[22,] 79.461563 -142.138437
[23,] 28.474063 79.461563
[24,] 17.763016 28.474063
[25,] 35.868095 17.763016
[26,] -9.631905 35.868095
[27,] 76.318095 -9.631905
[28,] -35.156905 76.318095
[29,] -110.681905 -35.156905
[30,] -37.031905 -110.681905
[31,] -4.356905 -37.031905
[32,] -89.531905 -4.356905
[33,] -178.101118 -89.531905
[34,] -59.901118 -178.101118
[35,] -178.488618 -59.901118
[36,] -146.399666 -178.488618
[37,] -57.294586 -146.399666
[38,] -192.494586 -57.294586
[39,] -131.944586 -192.494586
[40,] -184.119586 -131.944586
[41,] -237.744586 -184.119586
[42,] -142.294586 -237.744586
[43,] -228.219586 -142.294586
[44,] -146.294586 -228.219586
[45,] 230.336200 -146.294586
[46,] 96.036200 230.336200
[47,] -118.251300 96.036200
[48,] 60.737653 -118.251300
[49,] -32.257268 60.737653
[50,] -22.057268 -32.257268
[51,] -21.107268 -22.057268
[52,] -112.782268 -21.107268
[53,] -6.507268 -112.782268
[54,] -88.857268 -6.507268
[55,] 55.517732 -88.857268
[56,] 105.642732 55.517732
[57,] 91.173519 105.642732
[58,] 103.473519 91.173519
[59,] 11.986019 103.473519
[60,] 163.574971 11.986019
[61,] -19.919949 163.574971
[62,] 94.080051 -19.919949
[63,] 131.630051 94.080051
[64,] -77.644949 131.630051
[65,] 23.030051 -77.644949
[66,] 177.280051 23.030051
[67,] 91.155051 177.280051
[68,] 189.580051 91.155051
[69,] 5.964545 189.580051
[70,] -349.235455 5.964545
[71,] -40.522955 -349.235455
[72,] -179.434002 -40.522955
[73,] -198.028923 -179.434002
[74,] -121.428923 -198.028923
[75,] -194.978923 -121.428923
[76,] 61.446077 -194.978923
[77,] -43.178923 61.446077
[78,] 11.771077 -43.178923
[79,] -34.353923 11.771077
[80,] 3.371077 -34.353923
[81,] 122.801864 3.371077
[82,] 168.701864 122.801864
[83,] 270.614364 168.701864
[84,] 83.303316 270.614364
[85,] 280.008395 83.303316
[86,] -28.991605 280.008395
[87,] 252.658395 -28.991605
[88,] 253.683395 252.658395
[89,] 201.158395 253.683395
[90,] 104.908395 201.158395
[91,] 94.883395 104.908395
[92,] -159.791605 94.883395
[93,] -283.760818 -159.791605
[94,] -105.960818 -283.760818
[95,] -114.348318 -105.960818
[96,] -61.259365 -114.348318
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -79.006541 -55.611621
2 236.693459 -79.006541
3 56.343459 236.693459
4 145.568459 56.343459
5 251.143459 145.568459
6 145.793459 251.143459
7 144.968459 145.793459
8 67.093459 144.968459
9 153.724245 67.093459
10 67.424245 153.724245
11 140.536745 67.424245
12 117.325698 140.536745
13 70.630777 117.325698
14 43.830777 70.630777
15 -168.919223 43.830777
16 -50.994223 -168.919223
17 -77.219223 -50.994223
18 -171.569223 -77.219223
19 -119.594223 -171.569223
20 29.930777 -119.594223
21 -142.138437 29.930777
22 79.461563 -142.138437
23 28.474063 79.461563
24 17.763016 28.474063
25 35.868095 17.763016
26 -9.631905 35.868095
27 76.318095 -9.631905
28 -35.156905 76.318095
29 -110.681905 -35.156905
30 -37.031905 -110.681905
31 -4.356905 -37.031905
32 -89.531905 -4.356905
33 -178.101118 -89.531905
34 -59.901118 -178.101118
35 -178.488618 -59.901118
36 -146.399666 -178.488618
37 -57.294586 -146.399666
38 -192.494586 -57.294586
39 -131.944586 -192.494586
40 -184.119586 -131.944586
41 -237.744586 -184.119586
42 -142.294586 -237.744586
43 -228.219586 -142.294586
44 -146.294586 -228.219586
45 230.336200 -146.294586
46 96.036200 230.336200
47 -118.251300 96.036200
48 60.737653 -118.251300
49 -32.257268 60.737653
50 -22.057268 -32.257268
51 -21.107268 -22.057268
52 -112.782268 -21.107268
53 -6.507268 -112.782268
54 -88.857268 -6.507268
55 55.517732 -88.857268
56 105.642732 55.517732
57 91.173519 105.642732
58 103.473519 91.173519
59 11.986019 103.473519
60 163.574971 11.986019
61 -19.919949 163.574971
62 94.080051 -19.919949
63 131.630051 94.080051
64 -77.644949 131.630051
65 23.030051 -77.644949
66 177.280051 23.030051
67 91.155051 177.280051
68 189.580051 91.155051
69 5.964545 189.580051
70 -349.235455 5.964545
71 -40.522955 -349.235455
72 -179.434002 -40.522955
73 -198.028923 -179.434002
74 -121.428923 -198.028923
75 -194.978923 -121.428923
76 61.446077 -194.978923
77 -43.178923 61.446077
78 11.771077 -43.178923
79 -34.353923 11.771077
80 3.371077 -34.353923
81 122.801864 3.371077
82 168.701864 122.801864
83 270.614364 168.701864
84 83.303316 270.614364
85 280.008395 83.303316
86 -28.991605 280.008395
87 252.658395 -28.991605
88 253.683395 252.658395
89 201.158395 253.683395
90 104.908395 201.158395
91 94.883395 104.908395
92 -159.791605 94.883395
93 -283.760818 -159.791605
94 -105.960818 -283.760818
95 -114.348318 -105.960818
96 -61.259365 -114.348318
> 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/7nugi1227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ugyb1227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tdep1227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10y0f11227192763.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wcfc1227192763.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/12r2wz1227192763.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/13b0fo1227192763.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/14uwdm1227192763.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/15f9zt1227192763.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/166uqi1227192763.tab")
+ }
>
> system("convert tmp/1gz3f1227192763.ps tmp/1gz3f1227192763.png")
> system("convert tmp/2bms91227192763.ps tmp/2bms91227192763.png")
> system("convert tmp/3v6bj1227192763.ps tmp/3v6bj1227192763.png")
> system("convert tmp/46qrn1227192763.ps tmp/46qrn1227192763.png")
> system("convert tmp/5oyz21227192763.ps tmp/5oyz21227192763.png")
> system("convert tmp/6zlwv1227192763.ps tmp/6zlwv1227192763.png")
> system("convert tmp/7nugi1227192763.ps tmp/7nugi1227192763.png")
> system("convert tmp/8ugyb1227192763.ps tmp/8ugyb1227192763.png")
> system("convert tmp/9tdep1227192763.ps tmp/9tdep1227192763.png")
> system("convert tmp/10y0f11227192763.ps tmp/10y0f11227192763.png")
>
>
> proc.time()
user system elapsed
2.876 1.637 4.251