R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(119.5,0,125,0,145,0,105.3,0,116.9,0,120.1,0,88.9,0,78.4,0,114.6,0,113.3,0,117,0,99.6,0,99.4,0,101.9,0,115.2,0,108.5,0,113.8,0,121,0,92.2,0,90.2,0,101.5,0,126.6,0,93.9,0,89.8,0,93.4,0,101.5,0,110.4,0,105.9,0,108.4,0,113.9,0,86.1,0,69.4,0,101.2,0,100.5,0,98,0,106.6,0,90.1,0,96.9,0,109.9,0,99,0,106.3,0,128.9,0,111.1,0,102.9,0,130,0,87,0,87.5,0,117.6,0,103.4,0,110.8,0,112.6,0,102.5,1,112.4,1,135.6,1,105.1,1,127.7,1,137,1,91,1,90.5,1,122.4,1,123.3,1,124.3,1,120,1,118.1,1,119,1,142.7,1,123.6,1,129.6,1,151.6,1,110.4,1,99.2,1,130.5,1,136.2,1,129.7,1,128,1,121.6,1),dim=c(2,76),dimnames=list(c('invest','dummyvar'),1:76))
> y <- array(NA,dim=c(2,76),dimnames=list(c('invest','dummyvar'),1:76))
> 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
invest dummyvar M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 119.5 0 1 0 0 0 0 0 0 0 0 0 0 1
2 125.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 145.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 105.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 116.9 0 0 0 0 0 1 0 0 0 0 0 0 5
6 120.1 0 0 0 0 0 0 1 0 0 0 0 0 6
7 88.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 78.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 114.6 0 0 0 0 0 0 0 0 0 1 0 0 9
10 113.3 0 0 0 0 0 0 0 0 0 0 1 0 10
11 117.0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 99.6 0 0 0 0 0 0 0 0 0 0 0 0 12
13 99.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 101.9 0 0 1 0 0 0 0 0 0 0 0 0 14
15 115.2 0 0 0 1 0 0 0 0 0 0 0 0 15
16 108.5 0 0 0 0 1 0 0 0 0 0 0 0 16
17 113.8 0 0 0 0 0 1 0 0 0 0 0 0 17
18 121.0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 92.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 90.2 0 0 0 0 0 0 0 0 1 0 0 0 20
21 101.5 0 0 0 0 0 0 0 0 0 1 0 0 21
22 126.6 0 0 0 0 0 0 0 0 0 0 1 0 22
23 93.9 0 0 0 0 0 0 0 0 0 0 0 1 23
24 89.8 0 0 0 0 0 0 0 0 0 0 0 0 24
25 93.4 0 1 0 0 0 0 0 0 0 0 0 0 25
26 101.5 0 0 1 0 0 0 0 0 0 0 0 0 26
27 110.4 0 0 0 1 0 0 0 0 0 0 0 0 27
28 105.9 0 0 0 0 1 0 0 0 0 0 0 0 28
29 108.4 0 0 0 0 0 1 0 0 0 0 0 0 29
30 113.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 86.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 69.4 0 0 0 0 0 0 0 0 1 0 0 0 32
33 101.2 0 0 0 0 0 0 0 0 0 1 0 0 33
34 100.5 0 0 0 0 0 0 0 0 0 0 1 0 34
35 98.0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 106.6 0 0 0 0 0 0 0 0 0 0 0 0 36
37 90.1 0 1 0 0 0 0 0 0 0 0 0 0 37
38 96.9 0 0 1 0 0 0 0 0 0 0 0 0 38
39 109.9 0 0 0 1 0 0 0 0 0 0 0 0 39
40 99.0 0 0 0 0 1 0 0 0 0 0 0 0 40
41 106.3 0 0 0 0 0 1 0 0 0 0 0 0 41
42 128.9 0 0 0 0 0 0 1 0 0 0 0 0 42
43 111.1 0 0 0 0 0 0 0 1 0 0 0 0 43
44 102.9 0 0 0 0 0 0 0 0 1 0 0 0 44
45 130.0 0 0 0 0 0 0 0 0 0 1 0 0 45
46 87.0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 87.5 0 0 0 0 0 0 0 0 0 0 0 1 47
48 117.6 0 0 0 0 0 0 0 0 0 0 0 0 48
49 103.4 0 1 0 0 0 0 0 0 0 0 0 0 49
50 110.8 0 0 1 0 0 0 0 0 0 0 0 0 50
51 112.6 0 0 0 1 0 0 0 0 0 0 0 0 51
52 102.5 1 0 0 0 1 0 0 0 0 0 0 0 52
53 112.4 1 0 0 0 0 1 0 0 0 0 0 0 53
54 135.6 1 0 0 0 0 0 1 0 0 0 0 0 54
55 105.1 1 0 0 0 0 0 0 1 0 0 0 0 55
56 127.7 1 0 0 0 0 0 0 0 1 0 0 0 56
57 137.0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 91.0 1 0 0 0 0 0 0 0 0 0 1 0 58
59 90.5 1 0 0 0 0 0 0 0 0 0 0 1 59
60 122.4 1 0 0 0 0 0 0 0 0 0 0 0 60
61 123.3 1 1 0 0 0 0 0 0 0 0 0 0 61
62 124.3 1 0 1 0 0 0 0 0 0 0 0 0 62
63 120.0 1 0 0 1 0 0 0 0 0 0 0 0 63
64 118.1 1 0 0 0 1 0 0 0 0 0 0 0 64
65 119.0 1 0 0 0 0 1 0 0 0 0 0 0 65
66 142.7 1 0 0 0 0 0 1 0 0 0 0 0 66
67 123.6 1 0 0 0 0 0 0 1 0 0 0 0 67
68 129.6 1 0 0 0 0 0 0 0 1 0 0 0 68
69 151.6 1 0 0 0 0 0 0 0 0 1 0 0 69
70 110.4 1 0 0 0 0 0 0 0 0 0 1 0 70
71 99.2 1 0 0 0 0 0 0 0 0 0 0 1 71
72 130.5 1 0 0 0 0 0 0 0 0 0 0 0 72
73 136.2 1 1 0 0 0 0 0 0 0 0 0 0 73
74 129.7 1 0 1 0 0 0 0 0 0 0 0 0 74
75 128.0 1 0 0 1 0 0 0 0 0 0 0 0 75
76 121.6 1 0 0 0 1 0 0 0 0 0 0 0 76
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dummyvar M1 M2 M3 M4
107.82460 18.76280 -1.21791 2.39627 9.75331 -4.31291
M5 M6 M7 M8 M9 M10
1.21741 15.52207 -10.27328 -11.66862 11.35270 -6.42598
M11 t
-13.47132 -0.07132
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-25.0247 -7.3108 -0.6623 6.1328 27.6361
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 107.82460 6.17454 17.463 < 2e-16 ***
dummyvar 18.76280 5.31316 3.531 0.000786 ***
M1 -1.21791 6.95583 -0.175 0.861578
M2 2.39627 6.95082 0.345 0.731452
M3 9.75331 6.94768 1.404 0.165361
M4 -4.31291 6.98050 -0.618 0.538936
M5 1.21741 7.25095 0.168 0.867211
M6 15.52207 7.23932 2.144 0.035951 *
M7 -10.27328 7.22947 -1.421 0.160319
M8 -11.66862 7.22139 -1.616 0.111206
M9 11.35270 7.21510 1.573 0.120701
M10 -6.42598 7.21061 -0.891 0.376276
M11 -13.47132 7.20791 -1.869 0.066353 .
t -0.07132 0.11387 -0.626 0.533374
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.48 on 62 degrees of freedom
Multiple R-squared: 0.5094, Adjusted R-squared: 0.4065
F-statistic: 4.952 on 13 and 62 DF, p-value: 7.174e-06
> 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,] 0.6197935 0.7604129175 3.802065e-01
[2,] 0.5673344 0.8653312111 4.326656e-01
[3,] 0.5197923 0.9604153913 4.802077e-01
[4,] 0.5635516 0.8728968497 4.364484e-01
[5,] 0.4664977 0.9329953641 5.335023e-01
[6,] 0.7596002 0.4807995971 2.403998e-01
[7,] 0.8077076 0.3845847868 1.922924e-01
[8,] 0.7454947 0.5090105416 2.545053e-01
[9,] 0.6678735 0.6642529728 3.321265e-01
[10,] 0.5835998 0.8328003248 4.164002e-01
[11,] 0.5564302 0.8871395213 4.435698e-01
[12,] 0.6073911 0.7852178593 3.926089e-01
[13,] 0.5850005 0.8299989077 4.149995e-01
[14,] 0.5063050 0.9873900680 4.936950e-01
[15,] 0.4499231 0.8998462659 5.500769e-01
[16,] 0.6975824 0.6048352139 3.024176e-01
[17,] 0.8001040 0.3997919376 1.998960e-01
[18,] 0.9077483 0.1845033918 9.225170e-02
[19,] 0.9876667 0.0246665703 1.233329e-02
[20,] 0.9942861 0.0114278997 5.713950e-03
[21,] 0.9964713 0.0070574198 3.528710e-03
[22,] 0.9955192 0.0089616769 4.480838e-03
[23,] 0.9950929 0.0098141728 4.907086e-03
[24,] 0.9922280 0.0155440785 7.772039e-03
[25,] 0.9886113 0.0227773003 1.138865e-02
[26,] 0.9934517 0.0130966718 6.548336e-03
[27,] 0.9994097 0.0011806294 5.903147e-04
[28,] 0.9999313 0.0001374670 6.873351e-05
[29,] 0.9999427 0.0001146990 5.734950e-05
[30,] 0.9999072 0.0001855317 9.276585e-05
[31,] 0.9998895 0.0002209906 1.104953e-04
[32,] 0.9999270 0.0001459188 7.295940e-05
[33,] 0.9999668 0.0000664079 3.320395e-05
[34,] 0.9999182 0.0001635374 8.176871e-05
[35,] 0.9997368 0.0005264782 2.632391e-04
[36,] 0.9992833 0.0014333358 7.166679e-04
[37,] 0.9981161 0.0037677092 1.883855e-03
[38,] 0.9955147 0.0089706199 4.485310e-03
[39,] 0.9947710 0.0104580930 5.229047e-03
[40,] 0.9951051 0.0097898806 4.894940e-03
[41,] 0.9886010 0.0227979610 1.139898e-02
[42,] 0.9967724 0.0064552650 3.227633e-03
[43,] 0.9841195 0.0317609769 1.588049e-02
> postscript(file="/var/www/html/rcomp/tmp/1mfw41227816023.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2oy8g1227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32k311227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4amd21227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55d0b1227816023.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 = 76
Frequency = 1
1 2 3 4 5 6
12.96462976 14.92177262 27.63605833 2.07360070 8.21460066 -2.81873267
7 8 9 10 11 12
-8.15206600 -17.18539934 -3.93539934 12.61460066 23.43126733 -7.36873267
13 14 15 16 17 18
-6.27950430 -7.32236144 -1.30807572 6.12946664 5.97046661 -1.06286673
19 20 21 22 23 24
-3.99620006 -4.52953339 -16.17953339 26.77046661 1.18713327 -16.31286673
25 26 27 28 29 30
-11.42363836 -6.86649550 -5.25220978 4.38533258 1.42633255 -7.30700079
31 32 33 34 35 36
-9.24033412 -24.47366745 -15.62366745 1.52633255 6.14299921 1.34299921
37 38 39 40 41 42
-13.86777241 -10.61062956 -4.89634384 -1.65880148 0.18219849 8.54886515
43 44 45 46 47 48
16.61553182 9.88219849 14.03219849 -11.11780151 -3.50113485 13.19886515
49 50 51 52 53 54
0.28809353 4.14523638 -1.34047790 -16.06573209 -11.62473212 -2.65806545
55 56 57 58 59 60
-7.29139879 16.77526788 3.12526788 -25.02473212 -18.40806545 0.09193455
61 62 63 64 65 66
2.28116292 -0.26169422 -11.84740851 0.39013385 -4.16886618 5.29780049
67 68 69 70 71 72
12.06446715 19.53113382 18.58113382 -4.76886618 -8.85219951 9.04780049
73 74 75 76
16.03702886 5.99417172 -2.99154257 4.74599979
> postscript(file="/var/www/html/rcomp/tmp/6xgzp1227816023.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 = 76
Frequency = 1
lag(myerror, k = 1) myerror
0 12.96462976 NA
1 14.92177262 12.96462976
2 27.63605833 14.92177262
3 2.07360070 27.63605833
4 8.21460066 2.07360070
5 -2.81873267 8.21460066
6 -8.15206600 -2.81873267
7 -17.18539934 -8.15206600
8 -3.93539934 -17.18539934
9 12.61460066 -3.93539934
10 23.43126733 12.61460066
11 -7.36873267 23.43126733
12 -6.27950430 -7.36873267
13 -7.32236144 -6.27950430
14 -1.30807572 -7.32236144
15 6.12946664 -1.30807572
16 5.97046661 6.12946664
17 -1.06286673 5.97046661
18 -3.99620006 -1.06286673
19 -4.52953339 -3.99620006
20 -16.17953339 -4.52953339
21 26.77046661 -16.17953339
22 1.18713327 26.77046661
23 -16.31286673 1.18713327
24 -11.42363836 -16.31286673
25 -6.86649550 -11.42363836
26 -5.25220978 -6.86649550
27 4.38533258 -5.25220978
28 1.42633255 4.38533258
29 -7.30700079 1.42633255
30 -9.24033412 -7.30700079
31 -24.47366745 -9.24033412
32 -15.62366745 -24.47366745
33 1.52633255 -15.62366745
34 6.14299921 1.52633255
35 1.34299921 6.14299921
36 -13.86777241 1.34299921
37 -10.61062956 -13.86777241
38 -4.89634384 -10.61062956
39 -1.65880148 -4.89634384
40 0.18219849 -1.65880148
41 8.54886515 0.18219849
42 16.61553182 8.54886515
43 9.88219849 16.61553182
44 14.03219849 9.88219849
45 -11.11780151 14.03219849
46 -3.50113485 -11.11780151
47 13.19886515 -3.50113485
48 0.28809353 13.19886515
49 4.14523638 0.28809353
50 -1.34047790 4.14523638
51 -16.06573209 -1.34047790
52 -11.62473212 -16.06573209
53 -2.65806545 -11.62473212
54 -7.29139879 -2.65806545
55 16.77526788 -7.29139879
56 3.12526788 16.77526788
57 -25.02473212 3.12526788
58 -18.40806545 -25.02473212
59 0.09193455 -18.40806545
60 2.28116292 0.09193455
61 -0.26169422 2.28116292
62 -11.84740851 -0.26169422
63 0.39013385 -11.84740851
64 -4.16886618 0.39013385
65 5.29780049 -4.16886618
66 12.06446715 5.29780049
67 19.53113382 12.06446715
68 18.58113382 19.53113382
69 -4.76886618 18.58113382
70 -8.85219951 -4.76886618
71 9.04780049 -8.85219951
72 16.03702886 9.04780049
73 5.99417172 16.03702886
74 -2.99154257 5.99417172
75 4.74599979 -2.99154257
76 NA 4.74599979
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 14.92177262 12.96462976
[2,] 27.63605833 14.92177262
[3,] 2.07360070 27.63605833
[4,] 8.21460066 2.07360070
[5,] -2.81873267 8.21460066
[6,] -8.15206600 -2.81873267
[7,] -17.18539934 -8.15206600
[8,] -3.93539934 -17.18539934
[9,] 12.61460066 -3.93539934
[10,] 23.43126733 12.61460066
[11,] -7.36873267 23.43126733
[12,] -6.27950430 -7.36873267
[13,] -7.32236144 -6.27950430
[14,] -1.30807572 -7.32236144
[15,] 6.12946664 -1.30807572
[16,] 5.97046661 6.12946664
[17,] -1.06286673 5.97046661
[18,] -3.99620006 -1.06286673
[19,] -4.52953339 -3.99620006
[20,] -16.17953339 -4.52953339
[21,] 26.77046661 -16.17953339
[22,] 1.18713327 26.77046661
[23,] -16.31286673 1.18713327
[24,] -11.42363836 -16.31286673
[25,] -6.86649550 -11.42363836
[26,] -5.25220978 -6.86649550
[27,] 4.38533258 -5.25220978
[28,] 1.42633255 4.38533258
[29,] -7.30700079 1.42633255
[30,] -9.24033412 -7.30700079
[31,] -24.47366745 -9.24033412
[32,] -15.62366745 -24.47366745
[33,] 1.52633255 -15.62366745
[34,] 6.14299921 1.52633255
[35,] 1.34299921 6.14299921
[36,] -13.86777241 1.34299921
[37,] -10.61062956 -13.86777241
[38,] -4.89634384 -10.61062956
[39,] -1.65880148 -4.89634384
[40,] 0.18219849 -1.65880148
[41,] 8.54886515 0.18219849
[42,] 16.61553182 8.54886515
[43,] 9.88219849 16.61553182
[44,] 14.03219849 9.88219849
[45,] -11.11780151 14.03219849
[46,] -3.50113485 -11.11780151
[47,] 13.19886515 -3.50113485
[48,] 0.28809353 13.19886515
[49,] 4.14523638 0.28809353
[50,] -1.34047790 4.14523638
[51,] -16.06573209 -1.34047790
[52,] -11.62473212 -16.06573209
[53,] -2.65806545 -11.62473212
[54,] -7.29139879 -2.65806545
[55,] 16.77526788 -7.29139879
[56,] 3.12526788 16.77526788
[57,] -25.02473212 3.12526788
[58,] -18.40806545 -25.02473212
[59,] 0.09193455 -18.40806545
[60,] 2.28116292 0.09193455
[61,] -0.26169422 2.28116292
[62,] -11.84740851 -0.26169422
[63,] 0.39013385 -11.84740851
[64,] -4.16886618 0.39013385
[65,] 5.29780049 -4.16886618
[66,] 12.06446715 5.29780049
[67,] 19.53113382 12.06446715
[68,] 18.58113382 19.53113382
[69,] -4.76886618 18.58113382
[70,] -8.85219951 -4.76886618
[71,] 9.04780049 -8.85219951
[72,] 16.03702886 9.04780049
[73,] 5.99417172 16.03702886
[74,] -2.99154257 5.99417172
[75,] 4.74599979 -2.99154257
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 14.92177262 12.96462976
2 27.63605833 14.92177262
3 2.07360070 27.63605833
4 8.21460066 2.07360070
5 -2.81873267 8.21460066
6 -8.15206600 -2.81873267
7 -17.18539934 -8.15206600
8 -3.93539934 -17.18539934
9 12.61460066 -3.93539934
10 23.43126733 12.61460066
11 -7.36873267 23.43126733
12 -6.27950430 -7.36873267
13 -7.32236144 -6.27950430
14 -1.30807572 -7.32236144
15 6.12946664 -1.30807572
16 5.97046661 6.12946664
17 -1.06286673 5.97046661
18 -3.99620006 -1.06286673
19 -4.52953339 -3.99620006
20 -16.17953339 -4.52953339
21 26.77046661 -16.17953339
22 1.18713327 26.77046661
23 -16.31286673 1.18713327
24 -11.42363836 -16.31286673
25 -6.86649550 -11.42363836
26 -5.25220978 -6.86649550
27 4.38533258 -5.25220978
28 1.42633255 4.38533258
29 -7.30700079 1.42633255
30 -9.24033412 -7.30700079
31 -24.47366745 -9.24033412
32 -15.62366745 -24.47366745
33 1.52633255 -15.62366745
34 6.14299921 1.52633255
35 1.34299921 6.14299921
36 -13.86777241 1.34299921
37 -10.61062956 -13.86777241
38 -4.89634384 -10.61062956
39 -1.65880148 -4.89634384
40 0.18219849 -1.65880148
41 8.54886515 0.18219849
42 16.61553182 8.54886515
43 9.88219849 16.61553182
44 14.03219849 9.88219849
45 -11.11780151 14.03219849
46 -3.50113485 -11.11780151
47 13.19886515 -3.50113485
48 0.28809353 13.19886515
49 4.14523638 0.28809353
50 -1.34047790 4.14523638
51 -16.06573209 -1.34047790
52 -11.62473212 -16.06573209
53 -2.65806545 -11.62473212
54 -7.29139879 -2.65806545
55 16.77526788 -7.29139879
56 3.12526788 16.77526788
57 -25.02473212 3.12526788
58 -18.40806545 -25.02473212
59 0.09193455 -18.40806545
60 2.28116292 0.09193455
61 -0.26169422 2.28116292
62 -11.84740851 -0.26169422
63 0.39013385 -11.84740851
64 -4.16886618 0.39013385
65 5.29780049 -4.16886618
66 12.06446715 5.29780049
67 19.53113382 12.06446715
68 18.58113382 19.53113382
69 -4.76886618 18.58113382
70 -8.85219951 -4.76886618
71 9.04780049 -8.85219951
72 16.03702886 9.04780049
73 5.99417172 16.03702886
74 -2.99154257 5.99417172
75 4.74599979 -2.99154257
> 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/7x0mk1227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8fsjn1227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99hwj1227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/109k9i1227816023.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1162ht1227816023.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/12ztch1227816023.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/134gpj1227816023.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/14wn351227816023.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/15py1u1227816024.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/16jk2w1227816024.tab")
+ }
>
> system("convert tmp/1mfw41227816023.ps tmp/1mfw41227816023.png")
> system("convert tmp/2oy8g1227816023.ps tmp/2oy8g1227816023.png")
> system("convert tmp/32k311227816023.ps tmp/32k311227816023.png")
> system("convert tmp/4amd21227816023.ps tmp/4amd21227816023.png")
> system("convert tmp/55d0b1227816023.ps tmp/55d0b1227816023.png")
> system("convert tmp/6xgzp1227816023.ps tmp/6xgzp1227816023.png")
> system("convert tmp/7x0mk1227816023.ps tmp/7x0mk1227816023.png")
> system("convert tmp/8fsjn1227816023.ps tmp/8fsjn1227816023.png")
> system("convert tmp/99hwj1227816023.ps tmp/99hwj1227816023.png")
> system("convert tmp/109k9i1227816023.ps tmp/109k9i1227816023.png")
>
>
> proc.time()
user system elapsed
2.899 1.748 13.210