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(79.8,109.87,83.4,95.74,113.6,123.06,112.9,123.39,104,120.28,109.9,115.33,99,110.4,106.3,114.49,128.9,132.03,111.1,123.16,102.9,118.82,130,128.32,87,112.24,87.5,104.53,117.6,132.57,103.4,122.52,110.8,131.8,112.6,124.55,102.5,120.96,112.4,122.6,135.6,145.52,105.1,118.57,127.7,134.25,137,136.7,91,121.37,90.5,111.63,122.4,134.42,123.3,137.65,124.3,137.86,120,119.77,118.1,130.69,119,128.28,142.7,147.45,123.6,128.42,129.6,136.9,151.6,143.95,110.4,135.64,99.2,122.48,130.5,136.83,136.2,153.04,129.7,142.71,128,123.46,121.6,144.37,135.8,146.15,143.8,147.61,147.5,158.51,136.2,147.4,156.6,165.05,123.3,154.64,104.5,126.2,139.8,157.36,136.5,154.15,112.1,123.21,118.5,113.07,94.4,110.45,102.3,113.57,111.4,122.44,99.2,114.93,87.8,111.85,115.8,126.04),dim=c(2,60),dimnames=list(c('Investgoed','Uitvoer'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Investgoed','Uitvoer'),1:60))
> 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
Investgoed Uitvoer M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 79.8 109.87 1 0 0 0 0 0 0 0 0 0 0
2 83.4 95.74 0 1 0 0 0 0 0 0 0 0 0
3 113.6 123.06 0 0 1 0 0 0 0 0 0 0 0
4 112.9 123.39 0 0 0 1 0 0 0 0 0 0 0
5 104.0 120.28 0 0 0 0 1 0 0 0 0 0 0
6 109.9 115.33 0 0 0 0 0 1 0 0 0 0 0
7 99.0 110.40 0 0 0 0 0 0 1 0 0 0 0
8 106.3 114.49 0 0 0 0 0 0 0 1 0 0 0
9 128.9 132.03 0 0 0 0 0 0 0 0 1 0 0
10 111.1 123.16 0 0 0 0 0 0 0 0 0 1 0
11 102.9 118.82 0 0 0 0 0 0 0 0 0 0 1
12 130.0 128.32 0 0 0 0 0 0 0 0 0 0 0
13 87.0 112.24 1 0 0 0 0 0 0 0 0 0 0
14 87.5 104.53 0 1 0 0 0 0 0 0 0 0 0
15 117.6 132.57 0 0 1 0 0 0 0 0 0 0 0
16 103.4 122.52 0 0 0 1 0 0 0 0 0 0 0
17 110.8 131.80 0 0 0 0 1 0 0 0 0 0 0
18 112.6 124.55 0 0 0 0 0 1 0 0 0 0 0
19 102.5 120.96 0 0 0 0 0 0 1 0 0 0 0
20 112.4 122.60 0 0 0 0 0 0 0 1 0 0 0
21 135.6 145.52 0 0 0 0 0 0 0 0 1 0 0
22 105.1 118.57 0 0 0 0 0 0 0 0 0 1 0
23 127.7 134.25 0 0 0 0 0 0 0 0 0 0 1
24 137.0 136.70 0 0 0 0 0 0 0 0 0 0 0
25 91.0 121.37 1 0 0 0 0 0 0 0 0 0 0
26 90.5 111.63 0 1 0 0 0 0 0 0 0 0 0
27 122.4 134.42 0 0 1 0 0 0 0 0 0 0 0
28 123.3 137.65 0 0 0 1 0 0 0 0 0 0 0
29 124.3 137.86 0 0 0 0 1 0 0 0 0 0 0
30 120.0 119.77 0 0 0 0 0 1 0 0 0 0 0
31 118.1 130.69 0 0 0 0 0 0 1 0 0 0 0
32 119.0 128.28 0 0 0 0 0 0 0 1 0 0 0
33 142.7 147.45 0 0 0 0 0 0 0 0 1 0 0
34 123.6 128.42 0 0 0 0 0 0 0 0 0 1 0
35 129.6 136.90 0 0 0 0 0 0 0 0 0 0 1
36 151.6 143.95 0 0 0 0 0 0 0 0 0 0 0
37 110.4 135.64 1 0 0 0 0 0 0 0 0 0 0
38 99.2 122.48 0 1 0 0 0 0 0 0 0 0 0
39 130.5 136.83 0 0 1 0 0 0 0 0 0 0 0
40 136.2 153.04 0 0 0 1 0 0 0 0 0 0 0
41 129.7 142.71 0 0 0 0 1 0 0 0 0 0 0
42 128.0 123.46 0 0 0 0 0 1 0 0 0 0 0
43 121.6 144.37 0 0 0 0 0 0 1 0 0 0 0
44 135.8 146.15 0 0 0 0 0 0 0 1 0 0 0
45 143.8 147.61 0 0 0 0 0 0 0 0 1 0 0
46 147.5 158.51 0 0 0 0 0 0 0 0 0 1 0
47 136.2 147.40 0 0 0 0 0 0 0 0 0 0 1
48 156.6 165.05 0 0 0 0 0 0 0 0 0 0 0
49 123.3 154.64 1 0 0 0 0 0 0 0 0 0 0
50 104.5 126.20 0 1 0 0 0 0 0 0 0 0 0
51 139.8 157.36 0 0 1 0 0 0 0 0 0 0 0
52 136.5 154.15 0 0 0 1 0 0 0 0 0 0 0
53 112.1 123.21 0 0 0 0 1 0 0 0 0 0 0
54 118.5 113.07 0 0 0 0 0 1 0 0 0 0 0
55 94.4 110.45 0 0 0 0 0 0 1 0 0 0 0
56 102.3 113.57 0 0 0 0 0 0 0 1 0 0 0
57 111.4 122.44 0 0 0 0 0 0 0 0 1 0 0
58 99.2 114.93 0 0 0 0 0 0 0 0 0 1 0
59 87.8 111.85 0 0 0 0 0 0 0 0 0 0 1
60 115.8 126.04 0 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) Uitvoer M1 M2 M3 M4
2.2637 0.9709 -27.0260 -18.0960 -10.3481 -13.9322
M5 M6 M7 M8 M9 M10
-13.4373 -0.2288 -14.9263 -8.4825 -4.7472 -9.9348
M11
-11.4880
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.56980 -2.51719 0.05023 3.01567 9.57663
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.26371 7.55208 0.300 0.765692
Uitvoer 0.97089 0.05159 18.819 < 2e-16 ***
M1 -27.02600 3.19016 -8.472 5.08e-11 ***
M2 -18.09605 3.43227 -5.272 3.33e-06 ***
M3 -10.34810 3.12022 -3.316 0.001764 **
M4 -13.93220 3.11743 -4.469 4.93e-05 ***
M5 -13.43733 3.14915 -4.267 9.52e-05 ***
M6 -0.22878 3.29516 -0.069 0.944942
M7 -14.92633 3.23202 -4.618 3.02e-05 ***
M8 -8.48247 3.21054 -2.642 0.011156 *
M9 -4.74717 3.11638 -1.523 0.134385
M10 -9.93477 3.16996 -3.134 0.002968 **
M11 -11.48799 3.15980 -3.636 0.000686 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.927 on 47 degrees of freedom
Multiple R-squared: 0.9392, Adjusted R-squared: 0.9237
F-statistic: 60.5 on 12 and 47 DF, p-value: < 2.2e-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,] 0.42567928 0.85135855 0.5743207
[2,] 0.27820514 0.55641028 0.7217949
[3,] 0.25070113 0.50140227 0.7492989
[4,] 0.15430755 0.30861510 0.8456925
[5,] 0.09200354 0.18400709 0.9079965
[6,] 0.04977211 0.09954423 0.9502279
[7,] 0.03388401 0.06776802 0.9661160
[8,] 0.28202798 0.56405597 0.7179720
[9,] 0.20729248 0.41458497 0.7927075
[10,] 0.13932146 0.27864292 0.8606785
[11,] 0.10440660 0.20881320 0.8955934
[12,] 0.06723099 0.13446198 0.9327690
[13,] 0.05050266 0.10100532 0.9494973
[14,] 0.05574844 0.11149688 0.9442516
[15,] 0.08156843 0.16313686 0.9184316
[16,] 0.07232234 0.14464468 0.9276777
[17,] 0.04469219 0.08938437 0.9553078
[18,] 0.02828042 0.05656084 0.9717196
[19,] 0.04895141 0.09790281 0.9510486
[20,] 0.06250161 0.12500321 0.9374984
[21,] 0.34787540 0.69575080 0.6521246
[22,] 0.34054649 0.68109299 0.6594535
[23,] 0.29879690 0.59759380 0.7012031
[24,] 0.57415960 0.85168079 0.4258404
[25,] 0.45878931 0.91757862 0.5412107
[26,] 0.38508529 0.77017059 0.6149147
[27,] 0.35321108 0.70642216 0.6467889
[28,] 0.63417086 0.73165829 0.3658291
[29,] 0.54002514 0.91994973 0.4599749
> postscript(file="/var/www/html/rcomp/tmp/1b9dm1258713120.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2z7ao1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32vsl1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49axs1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53vh11258713120.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 = 60
Frequency = 1
1 2 3 4 5 6
-2.10943075 6.27929878 2.20663481 4.77034013 -1.60506337 -4.10770267
7 8 9 10 11 12
4.47633014 1.36153258 3.19681396 -0.80379197 -3.23690585 3.15164884
13 14 15 16 17 18
2.78955935 1.84517346 -3.02653150 -3.88498535 -5.98971908 -10.35931080
19 20 21 22 23 24
-2.27627093 -0.41238737 -3.20049555 -2.34740571 6.58225755 2.01558852
25 26 27 28 29 30
-2.07466866 -2.04814734 -0.02267847 1.32544513 1.62668599 1.68154460
31 32 33 34 35 36
3.87696691 0.67295600 2.02568627 6.58932530 5.90939838 9.57663418
37 38 39 40 41 42
3.47072743 -3.88230658 5.73747602 -0.71655586 2.31786826 6.09895957
43 44 45 46 47 48
-5.90481175 0.12314718 2.97034383 1.27523759 2.31505072 -5.90915015
49 50 51 52 53 54
-2.07618737 -2.19401832 -4.89490087 -1.49424405 3.65022819 6.68650930
55 56 57 58 59 60
-0.17221437 -1.74524839 -4.99234851 -4.71336519 -11.56980079 -8.83472139
> postscript(file="/var/www/html/rcomp/tmp/6povh1258713120.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.10943075 NA
1 6.27929878 -2.10943075
2 2.20663481 6.27929878
3 4.77034013 2.20663481
4 -1.60506337 4.77034013
5 -4.10770267 -1.60506337
6 4.47633014 -4.10770267
7 1.36153258 4.47633014
8 3.19681396 1.36153258
9 -0.80379197 3.19681396
10 -3.23690585 -0.80379197
11 3.15164884 -3.23690585
12 2.78955935 3.15164884
13 1.84517346 2.78955935
14 -3.02653150 1.84517346
15 -3.88498535 -3.02653150
16 -5.98971908 -3.88498535
17 -10.35931080 -5.98971908
18 -2.27627093 -10.35931080
19 -0.41238737 -2.27627093
20 -3.20049555 -0.41238737
21 -2.34740571 -3.20049555
22 6.58225755 -2.34740571
23 2.01558852 6.58225755
24 -2.07466866 2.01558852
25 -2.04814734 -2.07466866
26 -0.02267847 -2.04814734
27 1.32544513 -0.02267847
28 1.62668599 1.32544513
29 1.68154460 1.62668599
30 3.87696691 1.68154460
31 0.67295600 3.87696691
32 2.02568627 0.67295600
33 6.58932530 2.02568627
34 5.90939838 6.58932530
35 9.57663418 5.90939838
36 3.47072743 9.57663418
37 -3.88230658 3.47072743
38 5.73747602 -3.88230658
39 -0.71655586 5.73747602
40 2.31786826 -0.71655586
41 6.09895957 2.31786826
42 -5.90481175 6.09895957
43 0.12314718 -5.90481175
44 2.97034383 0.12314718
45 1.27523759 2.97034383
46 2.31505072 1.27523759
47 -5.90915015 2.31505072
48 -2.07618737 -5.90915015
49 -2.19401832 -2.07618737
50 -4.89490087 -2.19401832
51 -1.49424405 -4.89490087
52 3.65022819 -1.49424405
53 6.68650930 3.65022819
54 -0.17221437 6.68650930
55 -1.74524839 -0.17221437
56 -4.99234851 -1.74524839
57 -4.71336519 -4.99234851
58 -11.56980079 -4.71336519
59 -8.83472139 -11.56980079
60 NA -8.83472139
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.27929878 -2.10943075
[2,] 2.20663481 6.27929878
[3,] 4.77034013 2.20663481
[4,] -1.60506337 4.77034013
[5,] -4.10770267 -1.60506337
[6,] 4.47633014 -4.10770267
[7,] 1.36153258 4.47633014
[8,] 3.19681396 1.36153258
[9,] -0.80379197 3.19681396
[10,] -3.23690585 -0.80379197
[11,] 3.15164884 -3.23690585
[12,] 2.78955935 3.15164884
[13,] 1.84517346 2.78955935
[14,] -3.02653150 1.84517346
[15,] -3.88498535 -3.02653150
[16,] -5.98971908 -3.88498535
[17,] -10.35931080 -5.98971908
[18,] -2.27627093 -10.35931080
[19,] -0.41238737 -2.27627093
[20,] -3.20049555 -0.41238737
[21,] -2.34740571 -3.20049555
[22,] 6.58225755 -2.34740571
[23,] 2.01558852 6.58225755
[24,] -2.07466866 2.01558852
[25,] -2.04814734 -2.07466866
[26,] -0.02267847 -2.04814734
[27,] 1.32544513 -0.02267847
[28,] 1.62668599 1.32544513
[29,] 1.68154460 1.62668599
[30,] 3.87696691 1.68154460
[31,] 0.67295600 3.87696691
[32,] 2.02568627 0.67295600
[33,] 6.58932530 2.02568627
[34,] 5.90939838 6.58932530
[35,] 9.57663418 5.90939838
[36,] 3.47072743 9.57663418
[37,] -3.88230658 3.47072743
[38,] 5.73747602 -3.88230658
[39,] -0.71655586 5.73747602
[40,] 2.31786826 -0.71655586
[41,] 6.09895957 2.31786826
[42,] -5.90481175 6.09895957
[43,] 0.12314718 -5.90481175
[44,] 2.97034383 0.12314718
[45,] 1.27523759 2.97034383
[46,] 2.31505072 1.27523759
[47,] -5.90915015 2.31505072
[48,] -2.07618737 -5.90915015
[49,] -2.19401832 -2.07618737
[50,] -4.89490087 -2.19401832
[51,] -1.49424405 -4.89490087
[52,] 3.65022819 -1.49424405
[53,] 6.68650930 3.65022819
[54,] -0.17221437 6.68650930
[55,] -1.74524839 -0.17221437
[56,] -4.99234851 -1.74524839
[57,] -4.71336519 -4.99234851
[58,] -11.56980079 -4.71336519
[59,] -8.83472139 -11.56980079
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.27929878 -2.10943075
2 2.20663481 6.27929878
3 4.77034013 2.20663481
4 -1.60506337 4.77034013
5 -4.10770267 -1.60506337
6 4.47633014 -4.10770267
7 1.36153258 4.47633014
8 3.19681396 1.36153258
9 -0.80379197 3.19681396
10 -3.23690585 -0.80379197
11 3.15164884 -3.23690585
12 2.78955935 3.15164884
13 1.84517346 2.78955935
14 -3.02653150 1.84517346
15 -3.88498535 -3.02653150
16 -5.98971908 -3.88498535
17 -10.35931080 -5.98971908
18 -2.27627093 -10.35931080
19 -0.41238737 -2.27627093
20 -3.20049555 -0.41238737
21 -2.34740571 -3.20049555
22 6.58225755 -2.34740571
23 2.01558852 6.58225755
24 -2.07466866 2.01558852
25 -2.04814734 -2.07466866
26 -0.02267847 -2.04814734
27 1.32544513 -0.02267847
28 1.62668599 1.32544513
29 1.68154460 1.62668599
30 3.87696691 1.68154460
31 0.67295600 3.87696691
32 2.02568627 0.67295600
33 6.58932530 2.02568627
34 5.90939838 6.58932530
35 9.57663418 5.90939838
36 3.47072743 9.57663418
37 -3.88230658 3.47072743
38 5.73747602 -3.88230658
39 -0.71655586 5.73747602
40 2.31786826 -0.71655586
41 6.09895957 2.31786826
42 -5.90481175 6.09895957
43 0.12314718 -5.90481175
44 2.97034383 0.12314718
45 1.27523759 2.97034383
46 2.31505072 1.27523759
47 -5.90915015 2.31505072
48 -2.07618737 -5.90915015
49 -2.19401832 -2.07618737
50 -4.89490087 -2.19401832
51 -1.49424405 -4.89490087
52 3.65022819 -1.49424405
53 6.68650930 3.65022819
54 -0.17221437 6.68650930
55 -1.74524839 -0.17221437
56 -4.99234851 -1.74524839
57 -4.71336519 -4.99234851
58 -11.56980079 -4.71336519
59 -8.83472139 -11.56980079
> 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/7qqzi1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ccgq1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93e1c1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rxqy1258713120.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119q1h1258713120.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/12dcv11258713120.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/13gce31258713120.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/14lbh31258713120.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/15kqff1258713120.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/16i4nf1258713120.tab")
+ }
>
> system("convert tmp/1b9dm1258713120.ps tmp/1b9dm1258713120.png")
> system("convert tmp/2z7ao1258713120.ps tmp/2z7ao1258713120.png")
> system("convert tmp/32vsl1258713120.ps tmp/32vsl1258713120.png")
> system("convert tmp/49axs1258713120.ps tmp/49axs1258713120.png")
> system("convert tmp/53vh11258713120.ps tmp/53vh11258713120.png")
> system("convert tmp/6povh1258713120.ps tmp/6povh1258713120.png")
> system("convert tmp/7qqzi1258713120.ps tmp/7qqzi1258713120.png")
> system("convert tmp/8ccgq1258713120.ps tmp/8ccgq1258713120.png")
> system("convert tmp/93e1c1258713120.ps tmp/93e1c1258713120.png")
> system("convert tmp/10rxqy1258713120.ps tmp/10rxqy1258713120.png")
>
>
> proc.time()
user system elapsed
2.382 1.551 2.820