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(100.0,100.0,95.3,100.6,90.7,114.2,88.4,91.5,86.0,94.7,86.0,110.6,95.3,71.3,95.3,104.1,88.4,112.3,86.0,110.2,81.4,112.9,83.7,95.1,95.3,103.1,88.4,101.9,86.0,100.4,83.7,106.9,76.7,100.7,79.1,114.3,86.0,73.3,86.0,105.9,79.1,113.9,76.7,112.1,69.8,117.5,69.8,97.5,76.7,112.3,69.8,106.9,67.4,120.9,65.1,92.7,58.1,110.9,60.5,116.5,65.1,77.1,62.8,113.1,55.8,115.9,51.2,123.5,48.8,123.6,48.8,101.5,53.5,121.0,48.8,112.2,46.5,126.0,44.2,101.8,39.5,117.9,41.9,122.2,48.8,82.7,46.5,120.5,41.9,120.3,39.5,134.2,37.2,128.2,37.2,100.5,41.9,126.0,39.5,122.9,39.5,106.1,34.9,130.4,34.9,121.3,34.9,126.1,41.9,88.7,41.9,118.7,39.5,129.3,39.5,136.2,41.9,123.0,46.5,103.5),dim=c(2,60),dimnames=list(c('Werkloosheid','Productie'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Werkloosheid','Productie'),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
Werkloosheid Productie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 100.0 100.0 1 0 0 0 0 0 0 0 0 0 0
2 95.3 100.6 0 1 0 0 0 0 0 0 0 0 0
3 90.7 114.2 0 0 1 0 0 0 0 0 0 0 0
4 88.4 91.5 0 0 0 1 0 0 0 0 0 0 0
5 86.0 94.7 0 0 0 0 1 0 0 0 0 0 0
6 86.0 110.6 0 0 0 0 0 1 0 0 0 0 0
7 95.3 71.3 0 0 0 0 0 0 1 0 0 0 0
8 95.3 104.1 0 0 0 0 0 0 0 1 0 0 0
9 88.4 112.3 0 0 0 0 0 0 0 0 1 0 0
10 86.0 110.2 0 0 0 0 0 0 0 0 0 1 0
11 81.4 112.9 0 0 0 0 0 0 0 0 0 0 1
12 83.7 95.1 0 0 0 0 0 0 0 0 0 0 0
13 95.3 103.1 1 0 0 0 0 0 0 0 0 0 0
14 88.4 101.9 0 1 0 0 0 0 0 0 0 0 0
15 86.0 100.4 0 0 1 0 0 0 0 0 0 0 0
16 83.7 106.9 0 0 0 1 0 0 0 0 0 0 0
17 76.7 100.7 0 0 0 0 1 0 0 0 0 0 0
18 79.1 114.3 0 0 0 0 0 1 0 0 0 0 0
19 86.0 73.3 0 0 0 0 0 0 1 0 0 0 0
20 86.0 105.9 0 0 0 0 0 0 0 1 0 0 0
21 79.1 113.9 0 0 0 0 0 0 0 0 1 0 0
22 76.7 112.1 0 0 0 0 0 0 0 0 0 1 0
23 69.8 117.5 0 0 0 0 0 0 0 0 0 0 1
24 69.8 97.5 0 0 0 0 0 0 0 0 0 0 0
25 76.7 112.3 1 0 0 0 0 0 0 0 0 0 0
26 69.8 106.9 0 1 0 0 0 0 0 0 0 0 0
27 67.4 120.9 0 0 1 0 0 0 0 0 0 0 0
28 65.1 92.7 0 0 0 1 0 0 0 0 0 0 0
29 58.1 110.9 0 0 0 0 1 0 0 0 0 0 0
30 60.5 116.5 0 0 0 0 0 1 0 0 0 0 0
31 65.1 77.1 0 0 0 0 0 0 1 0 0 0 0
32 62.8 113.1 0 0 0 0 0 0 0 1 0 0 0
33 55.8 115.9 0 0 0 0 0 0 0 0 1 0 0
34 51.2 123.5 0 0 0 0 0 0 0 0 0 1 0
35 48.8 123.6 0 0 0 0 0 0 0 0 0 0 1
36 48.8 101.5 0 0 0 0 0 0 0 0 0 0 0
37 53.5 121.0 1 0 0 0 0 0 0 0 0 0 0
38 48.8 112.2 0 1 0 0 0 0 0 0 0 0 0
39 46.5 126.0 0 0 1 0 0 0 0 0 0 0 0
40 44.2 101.8 0 0 0 1 0 0 0 0 0 0 0
41 39.5 117.9 0 0 0 0 1 0 0 0 0 0 0
42 41.9 122.2 0 0 0 0 0 1 0 0 0 0 0
43 48.8 82.7 0 0 0 0 0 0 1 0 0 0 0
44 46.5 120.5 0 0 0 0 0 0 0 1 0 0 0
45 41.9 120.3 0 0 0 0 0 0 0 0 1 0 0
46 39.5 134.2 0 0 0 0 0 0 0 0 0 1 0
47 37.2 128.2 0 0 0 0 0 0 0 0 0 0 1
48 37.2 100.5 0 0 0 0 0 0 0 0 0 0 0
49 41.9 126.0 1 0 0 0 0 0 0 0 0 0 0
50 39.5 122.9 0 1 0 0 0 0 0 0 0 0 0
51 39.5 106.1 0 0 1 0 0 0 0 0 0 0 0
52 34.9 130.4 0 0 0 1 0 0 0 0 0 0 0
53 34.9 121.3 0 0 0 0 1 0 0 0 0 0 0
54 34.9 126.1 0 0 0 0 0 1 0 0 0 0 0
55 41.9 88.7 0 0 0 0 0 0 1 0 0 0 0
56 41.9 118.7 0 0 0 0 0 0 0 1 0 0 0
57 39.5 129.3 0 0 0 0 0 0 0 0 1 0 0
58 39.5 136.2 0 0 0 0 0 0 0 0 0 1 0
59 41.9 123.0 0 0 0 0 0 0 0 0 0 0 1
60 46.5 103.5 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) Productie M1 M2 M3 M4
249.415 -1.929 41.093 29.066 35.640 15.785
M5 M6 M7 M8 M9 M10
20.132 38.628 -30.299 34.075 39.860 46.954
M11
39.950
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-40.8368 -5.3745 -0.7124 7.4410 25.9920
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 249.4152 21.0715 11.837 1.06e-15 ***
Productie -1.9295 0.2029 -9.508 1.59e-12 ***
M1 41.0932 8.8034 4.668 2.56e-05 ***
M2 29.0656 8.6160 3.373 0.001495 **
M3 35.6398 8.8682 4.019 0.000210 ***
M4 15.7846 8.4697 1.864 0.068624 .
M5 20.1315 8.6250 2.334 0.023917 *
M6 38.6281 9.1929 4.202 0.000117 ***
M7 -30.2992 9.4260 -3.214 0.002364 **
M8 34.0746 8.8022 3.871 0.000333 ***
M9 39.8599 9.2261 4.320 8.01e-05 ***
M10 46.9544 9.6780 4.852 1.39e-05 ***
M11 39.9495 9.4648 4.221 0.000110 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.29 on 47 degrees of freedom
Multiple R-squared: 0.6785, Adjusted R-squared: 0.5964
F-statistic: 8.266 on 12 and 47 DF, p-value: 4.502e-08
> 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,] 0.05844334 0.116886681 0.941556660
[2,] 0.04219800 0.084395993 0.957802003
[3,] 0.02551279 0.051025577 0.974487211
[4,] 0.02351446 0.047028924 0.976485538
[5,] 0.02254237 0.045084742 0.977457629
[6,] 0.02558780 0.051175597 0.974412202
[7,] 0.02164482 0.043289645 0.978355177
[8,] 0.02650451 0.053009017 0.973495492
[9,] 0.05157543 0.103150859 0.948424571
[10,] 0.10816889 0.216337772 0.891831114
[11,] 0.23153868 0.463077356 0.768461322
[12,] 0.42499255 0.849985095 0.575007452
[13,] 0.75073978 0.498520446 0.249260223
[14,] 0.79223238 0.415535248 0.207767624
[15,] 0.90479974 0.190400525 0.095200263
[16,] 0.96273313 0.074533734 0.037266867
[17,] 0.98822414 0.023551729 0.011775865
[18,] 0.99623259 0.007534828 0.003767414
[19,] 0.99604456 0.007910884 0.003955442
[20,] 0.99648789 0.007024230 0.003512115
[21,] 0.99654011 0.006919782 0.003459891
[22,] 0.99700069 0.005998620 0.002999310
[23,] 0.99666918 0.006661631 0.003330816
[24,] 0.99856172 0.002876559 0.001438280
[25,] 0.99721746 0.005565080 0.002782540
[26,] 0.99206594 0.015868115 0.007934057
[27,] 0.98516529 0.029669415 0.014834707
[28,] 0.97131458 0.057370847 0.028685423
[29,] 0.93246647 0.135067062 0.067533531
> postscript(file="/var/www/html/rcomp/tmp/16osa1261305346.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22mgh1261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3y7h81261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44hft1261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5sckt1261305346.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
2.4400409 10.9252836 25.9920491 -0.2520081 -0.8245682 11.3575882
7 8 9 10 11 12
13.7561779 12.6695146 15.8059172 2.2595300 9.8740011 17.7787328
13 14 15 16 17 18
3.7214410 6.5336127 -5.3348288 24.7620439 1.4523352 11.5966786
19 20 21 22 23 24
8.3151457 6.8425856 9.5930915 -3.3744506 7.1496270 8.5094941
25 26 27 28 29 30
2.8726929 -2.4189678 15.6195912 -21.2366274 2.5330710 -2.7584568
31 32 33 34 35 36
-5.2528155 -2.4651303 -9.8479407 -6.8783342 -2.0805212 -4.7725703
37 38 39 40 41 42
-3.5407972 -13.1927031 4.5599591 -24.5783240 -2.5605417 -10.3603986
43 44 45 46 47 48
-10.7477057 -4.4869494 -15.2582116 2.0671435 -4.8048953 -18.3020542
49 50 51 52 53 54
-5.4933777 -1.8472254 -40.8367705 21.3049156 -0.6002964 -9.8354114
55 56 57 58 59 60
-6.0708023 -12.5600205 -0.2928565 5.9261113 -10.1382116 -3.2136025
> postscript(file="/var/www/html/rcomp/tmp/6t5061261305346.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 2.4400409 NA
1 10.9252836 2.4400409
2 25.9920491 10.9252836
3 -0.2520081 25.9920491
4 -0.8245682 -0.2520081
5 11.3575882 -0.8245682
6 13.7561779 11.3575882
7 12.6695146 13.7561779
8 15.8059172 12.6695146
9 2.2595300 15.8059172
10 9.8740011 2.2595300
11 17.7787328 9.8740011
12 3.7214410 17.7787328
13 6.5336127 3.7214410
14 -5.3348288 6.5336127
15 24.7620439 -5.3348288
16 1.4523352 24.7620439
17 11.5966786 1.4523352
18 8.3151457 11.5966786
19 6.8425856 8.3151457
20 9.5930915 6.8425856
21 -3.3744506 9.5930915
22 7.1496270 -3.3744506
23 8.5094941 7.1496270
24 2.8726929 8.5094941
25 -2.4189678 2.8726929
26 15.6195912 -2.4189678
27 -21.2366274 15.6195912
28 2.5330710 -21.2366274
29 -2.7584568 2.5330710
30 -5.2528155 -2.7584568
31 -2.4651303 -5.2528155
32 -9.8479407 -2.4651303
33 -6.8783342 -9.8479407
34 -2.0805212 -6.8783342
35 -4.7725703 -2.0805212
36 -3.5407972 -4.7725703
37 -13.1927031 -3.5407972
38 4.5599591 -13.1927031
39 -24.5783240 4.5599591
40 -2.5605417 -24.5783240
41 -10.3603986 -2.5605417
42 -10.7477057 -10.3603986
43 -4.4869494 -10.7477057
44 -15.2582116 -4.4869494
45 2.0671435 -15.2582116
46 -4.8048953 2.0671435
47 -18.3020542 -4.8048953
48 -5.4933777 -18.3020542
49 -1.8472254 -5.4933777
50 -40.8367705 -1.8472254
51 21.3049156 -40.8367705
52 -0.6002964 21.3049156
53 -9.8354114 -0.6002964
54 -6.0708023 -9.8354114
55 -12.5600205 -6.0708023
56 -0.2928565 -12.5600205
57 5.9261113 -0.2928565
58 -10.1382116 5.9261113
59 -3.2136025 -10.1382116
60 NA -3.2136025
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 10.9252836 2.4400409
[2,] 25.9920491 10.9252836
[3,] -0.2520081 25.9920491
[4,] -0.8245682 -0.2520081
[5,] 11.3575882 -0.8245682
[6,] 13.7561779 11.3575882
[7,] 12.6695146 13.7561779
[8,] 15.8059172 12.6695146
[9,] 2.2595300 15.8059172
[10,] 9.8740011 2.2595300
[11,] 17.7787328 9.8740011
[12,] 3.7214410 17.7787328
[13,] 6.5336127 3.7214410
[14,] -5.3348288 6.5336127
[15,] 24.7620439 -5.3348288
[16,] 1.4523352 24.7620439
[17,] 11.5966786 1.4523352
[18,] 8.3151457 11.5966786
[19,] 6.8425856 8.3151457
[20,] 9.5930915 6.8425856
[21,] -3.3744506 9.5930915
[22,] 7.1496270 -3.3744506
[23,] 8.5094941 7.1496270
[24,] 2.8726929 8.5094941
[25,] -2.4189678 2.8726929
[26,] 15.6195912 -2.4189678
[27,] -21.2366274 15.6195912
[28,] 2.5330710 -21.2366274
[29,] -2.7584568 2.5330710
[30,] -5.2528155 -2.7584568
[31,] -2.4651303 -5.2528155
[32,] -9.8479407 -2.4651303
[33,] -6.8783342 -9.8479407
[34,] -2.0805212 -6.8783342
[35,] -4.7725703 -2.0805212
[36,] -3.5407972 -4.7725703
[37,] -13.1927031 -3.5407972
[38,] 4.5599591 -13.1927031
[39,] -24.5783240 4.5599591
[40,] -2.5605417 -24.5783240
[41,] -10.3603986 -2.5605417
[42,] -10.7477057 -10.3603986
[43,] -4.4869494 -10.7477057
[44,] -15.2582116 -4.4869494
[45,] 2.0671435 -15.2582116
[46,] -4.8048953 2.0671435
[47,] -18.3020542 -4.8048953
[48,] -5.4933777 -18.3020542
[49,] -1.8472254 -5.4933777
[50,] -40.8367705 -1.8472254
[51,] 21.3049156 -40.8367705
[52,] -0.6002964 21.3049156
[53,] -9.8354114 -0.6002964
[54,] -6.0708023 -9.8354114
[55,] -12.5600205 -6.0708023
[56,] -0.2928565 -12.5600205
[57,] 5.9261113 -0.2928565
[58,] -10.1382116 5.9261113
[59,] -3.2136025 -10.1382116
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 10.9252836 2.4400409
2 25.9920491 10.9252836
3 -0.2520081 25.9920491
4 -0.8245682 -0.2520081
5 11.3575882 -0.8245682
6 13.7561779 11.3575882
7 12.6695146 13.7561779
8 15.8059172 12.6695146
9 2.2595300 15.8059172
10 9.8740011 2.2595300
11 17.7787328 9.8740011
12 3.7214410 17.7787328
13 6.5336127 3.7214410
14 -5.3348288 6.5336127
15 24.7620439 -5.3348288
16 1.4523352 24.7620439
17 11.5966786 1.4523352
18 8.3151457 11.5966786
19 6.8425856 8.3151457
20 9.5930915 6.8425856
21 -3.3744506 9.5930915
22 7.1496270 -3.3744506
23 8.5094941 7.1496270
24 2.8726929 8.5094941
25 -2.4189678 2.8726929
26 15.6195912 -2.4189678
27 -21.2366274 15.6195912
28 2.5330710 -21.2366274
29 -2.7584568 2.5330710
30 -5.2528155 -2.7584568
31 -2.4651303 -5.2528155
32 -9.8479407 -2.4651303
33 -6.8783342 -9.8479407
34 -2.0805212 -6.8783342
35 -4.7725703 -2.0805212
36 -3.5407972 -4.7725703
37 -13.1927031 -3.5407972
38 4.5599591 -13.1927031
39 -24.5783240 4.5599591
40 -2.5605417 -24.5783240
41 -10.3603986 -2.5605417
42 -10.7477057 -10.3603986
43 -4.4869494 -10.7477057
44 -15.2582116 -4.4869494
45 2.0671435 -15.2582116
46 -4.8048953 2.0671435
47 -18.3020542 -4.8048953
48 -5.4933777 -18.3020542
49 -1.8472254 -5.4933777
50 -40.8367705 -1.8472254
51 21.3049156 -40.8367705
52 -0.6002964 21.3049156
53 -9.8354114 -0.6002964
54 -6.0708023 -9.8354114
55 -12.5600205 -6.0708023
56 -0.2928565 -12.5600205
57 5.9261113 -0.2928565
58 -10.1382116 5.9261113
59 -3.2136025 -10.1382116
> 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/7xgkr1261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8oorm1261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95eay1261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fuws1261305346.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lpk41261305347.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/12azjf1261305347.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/1300nj1261305347.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/14w4o01261305347.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/15jybu1261305347.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/16yzbw1261305347.tab")
+ }
>
> try(system("convert tmp/16osa1261305346.ps tmp/16osa1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/22mgh1261305346.ps tmp/22mgh1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y7h81261305346.ps tmp/3y7h81261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/44hft1261305346.ps tmp/44hft1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sckt1261305346.ps tmp/5sckt1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/6t5061261305346.ps tmp/6t5061261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xgkr1261305346.ps tmp/7xgkr1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/8oorm1261305346.ps tmp/8oorm1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/95eay1261305346.ps tmp/95eay1261305346.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fuws1261305346.ps tmp/10fuws1261305346.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.394 1.555 3.334