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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> x <- array(list(98.8,6.3,100.5,6.1,110.4,6.1,96.4,6.3,101.9,6.3,106.2,6,81,6.2,94.7,6.4,101,6.8,109.4,7.5,102.3,7.5,90.7,7.6,96.2,7.6,96.1,7.4,106,7.3,103.1,7.1,102,6.9,104.7,6.8,86,7.5,92.1,7.6,106.9,7.8,112.6,8,101.7,8.1,92,8.2,97.4,8.3,97,8.2,105.4,8,102.7,7.9,98.1,7.6,104.5,7.6,87.4,8.3,89.9,8.4,109.8,8.4,111.7,8.4,98.6,8.4,96.9,8.6,95.1,8.9,97,8.8,112.7,8.3,102.9,7.5,97.4,7.2,111.4,7.4,87.4,8.8,96.8,9.3,114.1,9.3,110.3,8.7,103.9,8.2,101.6,8.3,94.6,8.5,95.9,8.6,104.7,8.5,102.8,8.2,98.1,8.1,113.9,7.9,80.9,8.6,95.7,8.7,113.2,8.7,105.9,8.5,108.8,8.4,102.3,8.5,99,8.7,100.7,8.7,115.5,8.6,100.7,8.5,109.9,8.3,114.6,8,85.4,8.2,100.5,8.1,114.8,8.1,116.5,8,112.9,7.9,102,7.9,106,8,105.3,8,118.8,7.9,106.1,8,109.3,7.7,117.2,7.2,92.5,7.5,104.2,7.3,112.5,7,122.4,7,113.3,7,100,7.2,110.7,7.3,112.8,7.1,109.8,6.8,117.3,6.4,109.1,6.1,115.9,6.5,96,7.7,99.8,7.9,116.8,7.5,115.7,6.9,99.4,6.6,94.3,6.9,91,7.7),dim=c(2,97),dimnames=list(c('Y','X'),1:97))
> y <- array(NA,dim=c(2,97),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 98.8 6.3 1 0 0 0 0 0 0 0 0 0 0
2 100.5 6.1 0 1 0 0 0 0 0 0 0 0 0
3 110.4 6.1 0 0 1 0 0 0 0 0 0 0 0
4 96.4 6.3 0 0 0 1 0 0 0 0 0 0 0
5 101.9 6.3 0 0 0 0 1 0 0 0 0 0 0
6 106.2 6.0 0 0 0 0 0 1 0 0 0 0 0
7 81.0 6.2 0 0 0 0 0 0 1 0 0 0 0
8 94.7 6.4 0 0 0 0 0 0 0 1 0 0 0
9 101.0 6.8 0 0 0 0 0 0 0 0 1 0 0
10 109.4 7.5 0 0 0 0 0 0 0 0 0 1 0
11 102.3 7.5 0 0 0 0 0 0 0 0 0 0 1
12 90.7 7.6 0 0 0 0 0 0 0 0 0 0 0
13 96.2 7.6 1 0 0 0 0 0 0 0 0 0 0
14 96.1 7.4 0 1 0 0 0 0 0 0 0 0 0
15 106.0 7.3 0 0 1 0 0 0 0 0 0 0 0
16 103.1 7.1 0 0 0 1 0 0 0 0 0 0 0
17 102.0 6.9 0 0 0 0 1 0 0 0 0 0 0
18 104.7 6.8 0 0 0 0 0 1 0 0 0 0 0
19 86.0 7.5 0 0 0 0 0 0 1 0 0 0 0
20 92.1 7.6 0 0 0 0 0 0 0 1 0 0 0
21 106.9 7.8 0 0 0 0 0 0 0 0 1 0 0
22 112.6 8.0 0 0 0 0 0 0 0 0 0 1 0
23 101.7 8.1 0 0 0 0 0 0 0 0 0 0 1
24 92.0 8.2 0 0 0 0 0 0 0 0 0 0 0
25 97.4 8.3 1 0 0 0 0 0 0 0 0 0 0
26 97.0 8.2 0 1 0 0 0 0 0 0 0 0 0
27 105.4 8.0 0 0 1 0 0 0 0 0 0 0 0
28 102.7 7.9 0 0 0 1 0 0 0 0 0 0 0
29 98.1 7.6 0 0 0 0 1 0 0 0 0 0 0
30 104.5 7.6 0 0 0 0 0 1 0 0 0 0 0
31 87.4 8.3 0 0 0 0 0 0 1 0 0 0 0
32 89.9 8.4 0 0 0 0 0 0 0 1 0 0 0
33 109.8 8.4 0 0 0 0 0 0 0 0 1 0 0
34 111.7 8.4 0 0 0 0 0 0 0 0 0 1 0
35 98.6 8.4 0 0 0 0 0 0 0 0 0 0 1
36 96.9 8.6 0 0 0 0 0 0 0 0 0 0 0
37 95.1 8.9 1 0 0 0 0 0 0 0 0 0 0
38 97.0 8.8 0 1 0 0 0 0 0 0 0 0 0
39 112.7 8.3 0 0 1 0 0 0 0 0 0 0 0
40 102.9 7.5 0 0 0 1 0 0 0 0 0 0 0
41 97.4 7.2 0 0 0 0 1 0 0 0 0 0 0
42 111.4 7.4 0 0 0 0 0 1 0 0 0 0 0
43 87.4 8.8 0 0 0 0 0 0 1 0 0 0 0
44 96.8 9.3 0 0 0 0 0 0 0 1 0 0 0
45 114.1 9.3 0 0 0 0 0 0 0 0 1 0 0
46 110.3 8.7 0 0 0 0 0 0 0 0 0 1 0
47 103.9 8.2 0 0 0 0 0 0 0 0 0 0 1
48 101.6 8.3 0 0 0 0 0 0 0 0 0 0 0
49 94.6 8.5 1 0 0 0 0 0 0 0 0 0 0
50 95.9 8.6 0 1 0 0 0 0 0 0 0 0 0
51 104.7 8.5 0 0 1 0 0 0 0 0 0 0 0
52 102.8 8.2 0 0 0 1 0 0 0 0 0 0 0
53 98.1 8.1 0 0 0 0 1 0 0 0 0 0 0
54 113.9 7.9 0 0 0 0 0 1 0 0 0 0 0
55 80.9 8.6 0 0 0 0 0 0 1 0 0 0 0
56 95.7 8.7 0 0 0 0 0 0 0 1 0 0 0
57 113.2 8.7 0 0 0 0 0 0 0 0 1 0 0
58 105.9 8.5 0 0 0 0 0 0 0 0 0 1 0
59 108.8 8.4 0 0 0 0 0 0 0 0 0 0 1
60 102.3 8.5 0 0 0 0 0 0 0 0 0 0 0
61 99.0 8.7 1 0 0 0 0 0 0 0 0 0 0
62 100.7 8.7 0 1 0 0 0 0 0 0 0 0 0
63 115.5 8.6 0 0 1 0 0 0 0 0 0 0 0
64 100.7 8.5 0 0 0 1 0 0 0 0 0 0 0
65 109.9 8.3 0 0 0 0 1 0 0 0 0 0 0
66 114.6 8.0 0 0 0 0 0 1 0 0 0 0 0
67 85.4 8.2 0 0 0 0 0 0 1 0 0 0 0
68 100.5 8.1 0 0 0 0 0 0 0 1 0 0 0
69 114.8 8.1 0 0 0 0 0 0 0 0 1 0 0
70 116.5 8.0 0 0 0 0 0 0 0 0 0 1 0
71 112.9 7.9 0 0 0 0 0 0 0 0 0 0 1
72 102.0 7.9 0 0 0 0 0 0 0 0 0 0 0
73 106.0 8.0 1 0 0 0 0 0 0 0 0 0 0
74 105.3 8.0 0 1 0 0 0 0 0 0 0 0 0
75 118.8 7.9 0 0 1 0 0 0 0 0 0 0 0
76 106.1 8.0 0 0 0 1 0 0 0 0 0 0 0
77 109.3 7.7 0 0 0 0 1 0 0 0 0 0 0
78 117.2 7.2 0 0 0 0 0 1 0 0 0 0 0
79 92.5 7.5 0 0 0 0 0 0 1 0 0 0 0
80 104.2 7.3 0 0 0 0 0 0 0 1 0 0 0
81 112.5 7.0 0 0 0 0 0 0 0 0 1 0 0
82 122.4 7.0 0 0 0 0 0 0 0 0 0 1 0
83 113.3 7.0 0 0 0 0 0 0 0 0 0 0 1
84 100.0 7.2 0 0 0 0 0 0 0 0 0 0 0
85 110.7 7.3 1 0 0 0 0 0 0 0 0 0 0
86 112.8 7.1 0 1 0 0 0 0 0 0 0 0 0
87 109.8 6.8 0 0 1 0 0 0 0 0 0 0 0
88 117.3 6.4 0 0 0 1 0 0 0 0 0 0 0
89 109.1 6.1 0 0 0 0 1 0 0 0 0 0 0
90 115.9 6.5 0 0 0 0 0 1 0 0 0 0 0
91 96.0 7.7 0 0 0 0 0 0 1 0 0 0 0
92 99.8 7.9 0 0 0 0 0 0 0 1 0 0 0
93 116.8 7.5 0 0 0 0 0 0 0 0 1 0 0
94 115.7 6.9 0 0 0 0 0 0 0 0 0 1 0
95 99.4 6.6 0 0 0 0 0 0 0 0 0 0 1
96 94.3 6.9 0 0 0 0 0 0 0 0 0 0 0
97 91.0 7.7 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) X M1 M2 M3 M4
100.8087 -0.4220 1.2899 3.1717 12.8478 6.3509
M5 M6 M7 M8 M9 M10
5.4863 13.2691 -10.4211 -0.7361 13.6836 15.5770
M11
7.5795
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.6228 -3.9117 -0.9063 3.8455 12.8411
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.8087 6.1135 16.490 < 2e-16 ***
X -0.4220 0.7352 -0.574 0.567509
M1 1.2899 2.6231 0.492 0.624172
M2 3.1717 2.6992 1.175 0.243303
M3 12.8478 2.7036 4.752 8.23e-06 ***
M4 6.3509 2.7161 2.338 0.021750 *
M5 5.4863 2.7379 2.004 0.048314 *
M6 13.2691 2.7512 4.823 6.24e-06 ***
M7 -10.4211 2.6993 -3.861 0.000221 ***
M8 -0.7361 2.6995 -0.273 0.785760
M9 13.6836 2.6993 5.069 2.34e-06 ***
M10 15.5770 2.6992 5.771 1.28e-07 ***
M11 7.5795 2.7010 2.806 0.006229 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.398 on 84 degrees of freedom
Multiple R-squared: 0.6776, Adjusted R-squared: 0.6315
F-statistic: 14.71 on 12 and 84 DF, p-value: 5.248e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.2937304445 0.5874608890 0.7062696
[2,] 0.1582499673 0.3164999347 0.8417500
[3,] 0.0839358007 0.1678716014 0.9160642
[4,] 0.1017454780 0.2034909560 0.8982545
[5,] 0.0615388342 0.1230776684 0.9384612
[6,] 0.0769674979 0.1539349958 0.9230325
[7,] 0.0510950549 0.1021901098 0.9489049
[8,] 0.0279842889 0.0559685778 0.9720157
[9,] 0.0162221938 0.0324443877 0.9837778
[10,] 0.0080679250 0.0161358499 0.9919321
[11,] 0.0043917814 0.0087835628 0.9956082
[12,] 0.0029951566 0.0059903133 0.9970048
[13,] 0.0021138849 0.0042277697 0.9978861
[14,] 0.0018627438 0.0037254877 0.9981373
[15,] 0.0012210327 0.0024420655 0.9987790
[16,] 0.0011136414 0.0022272829 0.9988864
[17,] 0.0011125542 0.0022251084 0.9988874
[18,] 0.0018857905 0.0037715811 0.9981142
[19,] 0.0009651816 0.0019303633 0.9990348
[20,] 0.0008657965 0.0017315930 0.9991342
[21,] 0.0010944641 0.0021889282 0.9989055
[22,] 0.0007030533 0.0014061065 0.9992969
[23,] 0.0004044311 0.0008088622 0.9995956
[24,] 0.0005287020 0.0010574041 0.9994713
[25,] 0.0003446191 0.0006892381 0.9996554
[26,] 0.0004569547 0.0009139095 0.9995430
[27,] 0.0007762261 0.0015524523 0.9992238
[28,] 0.0004649760 0.0009299521 0.9995350
[29,] 0.0003504551 0.0007009103 0.9996495
[30,] 0.0007108435 0.0014216870 0.9992892
[31,] 0.0004087469 0.0008174939 0.9995913
[32,] 0.0002732768 0.0005465537 0.9997267
[33,] 0.0007429243 0.0014858486 0.9992571
[34,] 0.0005966886 0.0011933773 0.9994033
[35,] 0.0006175428 0.0012350856 0.9993825
[36,] 0.0008683826 0.0017367651 0.9991316
[37,] 0.0005423919 0.0010847837 0.9994576
[38,] 0.0007145580 0.0014291160 0.9992854
[39,] 0.0009133667 0.0018267334 0.9990866
[40,] 0.0015925732 0.0031851465 0.9984074
[41,] 0.0011109970 0.0022219940 0.9988890
[42,] 0.0009293385 0.0018586770 0.9990707
[43,] 0.0023401579 0.0046803157 0.9976598
[44,] 0.0028050930 0.0056101861 0.9971949
[45,] 0.0038620227 0.0077240453 0.9961380
[46,] 0.0024748957 0.0049497913 0.9975251
[47,] 0.0023799447 0.0047598894 0.9976201
[48,] 0.0026059013 0.0052118026 0.9973941
[49,] 0.0039282513 0.0078565025 0.9960717
[50,] 0.0058895912 0.0117791825 0.9941104
[51,] 0.0047047349 0.0094094698 0.9952953
[52,] 0.0056885513 0.0113771027 0.9943114
[53,] 0.0051116862 0.0102233724 0.9948883
[54,] 0.0040120566 0.0080241132 0.9959879
[55,] 0.0036485068 0.0072970137 0.9963515
[56,] 0.0068648101 0.0137296201 0.9931352
[57,] 0.0065049417 0.0130098835 0.9934951
[58,] 0.0087653945 0.0175307890 0.9912346
[59,] 0.0090742055 0.0181484110 0.9909258
[60,] 0.0189444008 0.0378888016 0.9810556
[61,] 0.0219144817 0.0438289635 0.9780855
[62,] 0.0151777286 0.0303554571 0.9848223
[63,] 0.0101026945 0.0202053891 0.9898973
[64,] 0.0069229096 0.0138458191 0.9930771
[65,] 0.0054555467 0.0109110935 0.9945445
[66,] 0.0023641170 0.0047282340 0.9976359
> postscript(file="/var/www/html/rcomp/tmp/1jmyl1258648930.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/222y81258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37q9q1258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ttok1258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ecyj1258648930.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
-0.6401115 -0.9062513 -0.6824036 -8.1011090 -1.7364369 -5.3458342
7 8 9 10 11 12
-6.7712778 -2.6718540 -10.6227845 -3.8207450 -2.9232715 -6.9015960
13 14 15 16 17 18
-2.6915290 -4.7576688 -4.5760198 -1.0635198 -1.3832450 -6.5082450
19 20 21 22 23 24
-1.2226953 -4.7654701 -4.3007980 -0.4097517 -3.2700795 -5.3484040
25 26 27 28 29 30
-1.1961384 -3.5200795 -4.8806292 -1.1259306 -4.9878544 -6.3706557
31 32 33 34 35 36
0.5148939 -6.6278809 -1.1476061 -1.1409571 -6.2434836 -0.2796094
37 38 39 40 41 42
-3.2429465 -3.2668876 2.5459668 -1.0947252 -5.8566490 0.4449470
43 44 45 46 47 48
0.7258872 0.6519070 3.5321818 -2.4143611 -1.0278809 4.2937946
49 50 51 52 53 54
-3.9117411 -4.4512849 -5.3696359 -0.8993346 -4.7768611 3.1559402
55 56 57 58 59 60
-5.8585101 -0.7012849 2.3789899 -6.8987584 3.9565164 5.0781919
61 62 63 64 65 66
0.5726562 0.3909137 5.4725627 -2.8727386 7.1075362 3.8981389
67 68 69 70 71 72
-1.5273047 3.8455231 3.7257980 3.4902483 7.8455231 4.5250000
73 74 75 76 77 78
7.2772656 4.6955231 8.4771721 2.3162681 6.2543443 6.1605497
79 80 81 82 83 84
5.2773047 7.2079339 0.9616128 8.9682618 7.8657353 2.2296094
85 86 87 88 89 90
11.6818750 11.8157353 -0.9870131 12.8410896 5.3791658 4.5651591
91 92 93 94 95 96
8.8617020 3.0611258 5.4726061 2.2260631 -6.2030593 -3.5969865
97
-7.8493303
> postscript(file="/var/www/html/rcomp/tmp/6988r1258648930.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 -0.6401115 NA
1 -0.9062513 -0.6401115
2 -0.6824036 -0.9062513
3 -8.1011090 -0.6824036
4 -1.7364369 -8.1011090
5 -5.3458342 -1.7364369
6 -6.7712778 -5.3458342
7 -2.6718540 -6.7712778
8 -10.6227845 -2.6718540
9 -3.8207450 -10.6227845
10 -2.9232715 -3.8207450
11 -6.9015960 -2.9232715
12 -2.6915290 -6.9015960
13 -4.7576688 -2.6915290
14 -4.5760198 -4.7576688
15 -1.0635198 -4.5760198
16 -1.3832450 -1.0635198
17 -6.5082450 -1.3832450
18 -1.2226953 -6.5082450
19 -4.7654701 -1.2226953
20 -4.3007980 -4.7654701
21 -0.4097517 -4.3007980
22 -3.2700795 -0.4097517
23 -5.3484040 -3.2700795
24 -1.1961384 -5.3484040
25 -3.5200795 -1.1961384
26 -4.8806292 -3.5200795
27 -1.1259306 -4.8806292
28 -4.9878544 -1.1259306
29 -6.3706557 -4.9878544
30 0.5148939 -6.3706557
31 -6.6278809 0.5148939
32 -1.1476061 -6.6278809
33 -1.1409571 -1.1476061
34 -6.2434836 -1.1409571
35 -0.2796094 -6.2434836
36 -3.2429465 -0.2796094
37 -3.2668876 -3.2429465
38 2.5459668 -3.2668876
39 -1.0947252 2.5459668
40 -5.8566490 -1.0947252
41 0.4449470 -5.8566490
42 0.7258872 0.4449470
43 0.6519070 0.7258872
44 3.5321818 0.6519070
45 -2.4143611 3.5321818
46 -1.0278809 -2.4143611
47 4.2937946 -1.0278809
48 -3.9117411 4.2937946
49 -4.4512849 -3.9117411
50 -5.3696359 -4.4512849
51 -0.8993346 -5.3696359
52 -4.7768611 -0.8993346
53 3.1559402 -4.7768611
54 -5.8585101 3.1559402
55 -0.7012849 -5.8585101
56 2.3789899 -0.7012849
57 -6.8987584 2.3789899
58 3.9565164 -6.8987584
59 5.0781919 3.9565164
60 0.5726562 5.0781919
61 0.3909137 0.5726562
62 5.4725627 0.3909137
63 -2.8727386 5.4725627
64 7.1075362 -2.8727386
65 3.8981389 7.1075362
66 -1.5273047 3.8981389
67 3.8455231 -1.5273047
68 3.7257980 3.8455231
69 3.4902483 3.7257980
70 7.8455231 3.4902483
71 4.5250000 7.8455231
72 7.2772656 4.5250000
73 4.6955231 7.2772656
74 8.4771721 4.6955231
75 2.3162681 8.4771721
76 6.2543443 2.3162681
77 6.1605497 6.2543443
78 5.2773047 6.1605497
79 7.2079339 5.2773047
80 0.9616128 7.2079339
81 8.9682618 0.9616128
82 7.8657353 8.9682618
83 2.2296094 7.8657353
84 11.6818750 2.2296094
85 11.8157353 11.6818750
86 -0.9870131 11.8157353
87 12.8410896 -0.9870131
88 5.3791658 12.8410896
89 4.5651591 5.3791658
90 8.8617020 4.5651591
91 3.0611258 8.8617020
92 5.4726061 3.0611258
93 2.2260631 5.4726061
94 -6.2030593 2.2260631
95 -3.5969865 -6.2030593
96 -7.8493303 -3.5969865
97 NA -7.8493303
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.9062513 -0.6401115
[2,] -0.6824036 -0.9062513
[3,] -8.1011090 -0.6824036
[4,] -1.7364369 -8.1011090
[5,] -5.3458342 -1.7364369
[6,] -6.7712778 -5.3458342
[7,] -2.6718540 -6.7712778
[8,] -10.6227845 -2.6718540
[9,] -3.8207450 -10.6227845
[10,] -2.9232715 -3.8207450
[11,] -6.9015960 -2.9232715
[12,] -2.6915290 -6.9015960
[13,] -4.7576688 -2.6915290
[14,] -4.5760198 -4.7576688
[15,] -1.0635198 -4.5760198
[16,] -1.3832450 -1.0635198
[17,] -6.5082450 -1.3832450
[18,] -1.2226953 -6.5082450
[19,] -4.7654701 -1.2226953
[20,] -4.3007980 -4.7654701
[21,] -0.4097517 -4.3007980
[22,] -3.2700795 -0.4097517
[23,] -5.3484040 -3.2700795
[24,] -1.1961384 -5.3484040
[25,] -3.5200795 -1.1961384
[26,] -4.8806292 -3.5200795
[27,] -1.1259306 -4.8806292
[28,] -4.9878544 -1.1259306
[29,] -6.3706557 -4.9878544
[30,] 0.5148939 -6.3706557
[31,] -6.6278809 0.5148939
[32,] -1.1476061 -6.6278809
[33,] -1.1409571 -1.1476061
[34,] -6.2434836 -1.1409571
[35,] -0.2796094 -6.2434836
[36,] -3.2429465 -0.2796094
[37,] -3.2668876 -3.2429465
[38,] 2.5459668 -3.2668876
[39,] -1.0947252 2.5459668
[40,] -5.8566490 -1.0947252
[41,] 0.4449470 -5.8566490
[42,] 0.7258872 0.4449470
[43,] 0.6519070 0.7258872
[44,] 3.5321818 0.6519070
[45,] -2.4143611 3.5321818
[46,] -1.0278809 -2.4143611
[47,] 4.2937946 -1.0278809
[48,] -3.9117411 4.2937946
[49,] -4.4512849 -3.9117411
[50,] -5.3696359 -4.4512849
[51,] -0.8993346 -5.3696359
[52,] -4.7768611 -0.8993346
[53,] 3.1559402 -4.7768611
[54,] -5.8585101 3.1559402
[55,] -0.7012849 -5.8585101
[56,] 2.3789899 -0.7012849
[57,] -6.8987584 2.3789899
[58,] 3.9565164 -6.8987584
[59,] 5.0781919 3.9565164
[60,] 0.5726562 5.0781919
[61,] 0.3909137 0.5726562
[62,] 5.4725627 0.3909137
[63,] -2.8727386 5.4725627
[64,] 7.1075362 -2.8727386
[65,] 3.8981389 7.1075362
[66,] -1.5273047 3.8981389
[67,] 3.8455231 -1.5273047
[68,] 3.7257980 3.8455231
[69,] 3.4902483 3.7257980
[70,] 7.8455231 3.4902483
[71,] 4.5250000 7.8455231
[72,] 7.2772656 4.5250000
[73,] 4.6955231 7.2772656
[74,] 8.4771721 4.6955231
[75,] 2.3162681 8.4771721
[76,] 6.2543443 2.3162681
[77,] 6.1605497 6.2543443
[78,] 5.2773047 6.1605497
[79,] 7.2079339 5.2773047
[80,] 0.9616128 7.2079339
[81,] 8.9682618 0.9616128
[82,] 7.8657353 8.9682618
[83,] 2.2296094 7.8657353
[84,] 11.6818750 2.2296094
[85,] 11.8157353 11.6818750
[86,] -0.9870131 11.8157353
[87,] 12.8410896 -0.9870131
[88,] 5.3791658 12.8410896
[89,] 4.5651591 5.3791658
[90,] 8.8617020 4.5651591
[91,] 3.0611258 8.8617020
[92,] 5.4726061 3.0611258
[93,] 2.2260631 5.4726061
[94,] -6.2030593 2.2260631
[95,] -3.5969865 -6.2030593
[96,] -7.8493303 -3.5969865
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.9062513 -0.6401115
2 -0.6824036 -0.9062513
3 -8.1011090 -0.6824036
4 -1.7364369 -8.1011090
5 -5.3458342 -1.7364369
6 -6.7712778 -5.3458342
7 -2.6718540 -6.7712778
8 -10.6227845 -2.6718540
9 -3.8207450 -10.6227845
10 -2.9232715 -3.8207450
11 -6.9015960 -2.9232715
12 -2.6915290 -6.9015960
13 -4.7576688 -2.6915290
14 -4.5760198 -4.7576688
15 -1.0635198 -4.5760198
16 -1.3832450 -1.0635198
17 -6.5082450 -1.3832450
18 -1.2226953 -6.5082450
19 -4.7654701 -1.2226953
20 -4.3007980 -4.7654701
21 -0.4097517 -4.3007980
22 -3.2700795 -0.4097517
23 -5.3484040 -3.2700795
24 -1.1961384 -5.3484040
25 -3.5200795 -1.1961384
26 -4.8806292 -3.5200795
27 -1.1259306 -4.8806292
28 -4.9878544 -1.1259306
29 -6.3706557 -4.9878544
30 0.5148939 -6.3706557
31 -6.6278809 0.5148939
32 -1.1476061 -6.6278809
33 -1.1409571 -1.1476061
34 -6.2434836 -1.1409571
35 -0.2796094 -6.2434836
36 -3.2429465 -0.2796094
37 -3.2668876 -3.2429465
38 2.5459668 -3.2668876
39 -1.0947252 2.5459668
40 -5.8566490 -1.0947252
41 0.4449470 -5.8566490
42 0.7258872 0.4449470
43 0.6519070 0.7258872
44 3.5321818 0.6519070
45 -2.4143611 3.5321818
46 -1.0278809 -2.4143611
47 4.2937946 -1.0278809
48 -3.9117411 4.2937946
49 -4.4512849 -3.9117411
50 -5.3696359 -4.4512849
51 -0.8993346 -5.3696359
52 -4.7768611 -0.8993346
53 3.1559402 -4.7768611
54 -5.8585101 3.1559402
55 -0.7012849 -5.8585101
56 2.3789899 -0.7012849
57 -6.8987584 2.3789899
58 3.9565164 -6.8987584
59 5.0781919 3.9565164
60 0.5726562 5.0781919
61 0.3909137 0.5726562
62 5.4725627 0.3909137
63 -2.8727386 5.4725627
64 7.1075362 -2.8727386
65 3.8981389 7.1075362
66 -1.5273047 3.8981389
67 3.8455231 -1.5273047
68 3.7257980 3.8455231
69 3.4902483 3.7257980
70 7.8455231 3.4902483
71 4.5250000 7.8455231
72 7.2772656 4.5250000
73 4.6955231 7.2772656
74 8.4771721 4.6955231
75 2.3162681 8.4771721
76 6.2543443 2.3162681
77 6.1605497 6.2543443
78 5.2773047 6.1605497
79 7.2079339 5.2773047
80 0.9616128 7.2079339
81 8.9682618 0.9616128
82 7.8657353 8.9682618
83 2.2296094 7.8657353
84 11.6818750 2.2296094
85 11.8157353 11.6818750
86 -0.9870131 11.8157353
87 12.8410896 -0.9870131
88 5.3791658 12.8410896
89 4.5651591 5.3791658
90 8.8617020 4.5651591
91 3.0611258 8.8617020
92 5.4726061 3.0611258
93 2.2260631 5.4726061
94 -6.2030593 2.2260631
95 -3.5969865 -6.2030593
96 -7.8493303 -3.5969865
> 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/7phvj1258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tcjt1258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9g2o81258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10b7sv1258648930.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sx6i1258648930.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/1212y21258648930.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/13pkub1258648930.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/145z081258648930.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/157p331258648930.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/169llz1258648930.tab")
+ }
>
> system("convert tmp/1jmyl1258648930.ps tmp/1jmyl1258648930.png")
> system("convert tmp/222y81258648930.ps tmp/222y81258648930.png")
> system("convert tmp/37q9q1258648930.ps tmp/37q9q1258648930.png")
> system("convert tmp/4ttok1258648930.ps tmp/4ttok1258648930.png")
> system("convert tmp/5ecyj1258648930.ps tmp/5ecyj1258648930.png")
> system("convert tmp/6988r1258648930.ps tmp/6988r1258648930.png")
> system("convert tmp/7phvj1258648930.ps tmp/7phvj1258648930.png")
> system("convert tmp/8tcjt1258648930.ps tmp/8tcjt1258648930.png")
> system("convert tmp/9g2o81258648930.ps tmp/9g2o81258648930.png")
> system("convert tmp/10b7sv1258648930.ps tmp/10b7sv1258648930.png")
>
>
> proc.time()
user system elapsed
2.960 1.612 3.339