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(106.1,97.89,106,98.69,105.9,99.01,105.8,99.18,105.7,98.45,105.6,98.13,105.4,98.29,105.4,99.1,105.5,99.26,105.6,98.85,105.7,98.05,105.9,98.53,106.1,99.34,106,100.14,105.8,100.3,105.8,100.22,105.7,99.9,105.5,99.58,105.3,99.9,105.2,100.78,105.2,100.78,105,100.46,105.1,100.06,105.1,100.28,105.2,100.78,104.9,101.58,104.8,102.06,104.5,102.02,104.5,101.68,104.4,101.32,104.4,101.81,104.2,102.3,104.1,102.12,103.9,102.1,103.8,101.75,103.9,101.5,104.2,102.16,104.1,103.47,103.8,104.05,103.6,104.09,103.7,103.55,103.5,102.77,103.4,102.89,103.1,103.6,103.1,103.76,103.1,103.92,103.2,103.35,103.3,103.32,103.5,104.2,103.6,105.44,103.5,105.81,103.3,106.25,103.2,105.94,103.1,105.82,103.2,105.96,103,106.49,103,106.32,103.1,105.88,103.4,105.07),dim=c(2,59),dimnames=list(c('Werkl','Infl'),1:59))
> y <- array(NA,dim=c(2,59),dimnames=list(c('Werkl','Infl'),1:59))
> 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
Werkl Infl M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 106.1 97.89 1 0 0 0 0 0 0 0 0 0 0 1
2 106.0 98.69 0 1 0 0 0 0 0 0 0 0 0 2
3 105.9 99.01 0 0 1 0 0 0 0 0 0 0 0 3
4 105.8 99.18 0 0 0 1 0 0 0 0 0 0 0 4
5 105.7 98.45 0 0 0 0 1 0 0 0 0 0 0 5
6 105.6 98.13 0 0 0 0 0 1 0 0 0 0 0 6
7 105.4 98.29 0 0 0 0 0 0 1 0 0 0 0 7
8 105.4 99.10 0 0 0 0 0 0 0 1 0 0 0 8
9 105.5 99.26 0 0 0 0 0 0 0 0 1 0 0 9
10 105.6 98.85 0 0 0 0 0 0 0 0 0 1 0 10
11 105.7 98.05 0 0 0 0 0 0 0 0 0 0 1 11
12 105.9 98.53 0 0 0 0 0 0 0 0 0 0 0 12
13 106.1 99.34 1 0 0 0 0 0 0 0 0 0 0 13
14 106.0 100.14 0 1 0 0 0 0 0 0 0 0 0 14
15 105.8 100.30 0 0 1 0 0 0 0 0 0 0 0 15
16 105.8 100.22 0 0 0 1 0 0 0 0 0 0 0 16
17 105.7 99.90 0 0 0 0 1 0 0 0 0 0 0 17
18 105.5 99.58 0 0 0 0 0 1 0 0 0 0 0 18
19 105.3 99.90 0 0 0 0 0 0 1 0 0 0 0 19
20 105.2 100.78 0 0 0 0 0 0 0 1 0 0 0 20
21 105.2 100.78 0 0 0 0 0 0 0 0 1 0 0 21
22 105.0 100.46 0 0 0 0 0 0 0 0 0 1 0 22
23 105.1 100.06 0 0 0 0 0 0 0 0 0 0 1 23
24 105.1 100.28 0 0 0 0 0 0 0 0 0 0 0 24
25 105.2 100.78 1 0 0 0 0 0 0 0 0 0 0 25
26 104.9 101.58 0 1 0 0 0 0 0 0 0 0 0 26
27 104.8 102.06 0 0 1 0 0 0 0 0 0 0 0 27
28 104.5 102.02 0 0 0 1 0 0 0 0 0 0 0 28
29 104.5 101.68 0 0 0 0 1 0 0 0 0 0 0 29
30 104.4 101.32 0 0 0 0 0 1 0 0 0 0 0 30
31 104.4 101.81 0 0 0 0 0 0 1 0 0 0 0 31
32 104.2 102.30 0 0 0 0 0 0 0 1 0 0 0 32
33 104.1 102.12 0 0 0 0 0 0 0 0 1 0 0 33
34 103.9 102.10 0 0 0 0 0 0 0 0 0 1 0 34
35 103.8 101.75 0 0 0 0 0 0 0 0 0 0 1 35
36 103.9 101.50 0 0 0 0 0 0 0 0 0 0 0 36
37 104.2 102.16 1 0 0 0 0 0 0 0 0 0 0 37
38 104.1 103.47 0 1 0 0 0 0 0 0 0 0 0 38
39 103.8 104.05 0 0 1 0 0 0 0 0 0 0 0 39
40 103.6 104.09 0 0 0 1 0 0 0 0 0 0 0 40
41 103.7 103.55 0 0 0 0 1 0 0 0 0 0 0 41
42 103.5 102.77 0 0 0 0 0 1 0 0 0 0 0 42
43 103.4 102.89 0 0 0 0 0 0 1 0 0 0 0 43
44 103.1 103.60 0 0 0 0 0 0 0 1 0 0 0 44
45 103.1 103.76 0 0 0 0 0 0 0 0 1 0 0 45
46 103.1 103.92 0 0 0 0 0 0 0 0 0 1 0 46
47 103.2 103.35 0 0 0 0 0 0 0 0 0 0 1 47
48 103.3 103.32 0 0 0 0 0 0 0 0 0 0 0 48
49 103.5 104.20 1 0 0 0 0 0 0 0 0 0 0 49
50 103.6 105.44 0 1 0 0 0 0 0 0 0 0 0 50
51 103.5 105.81 0 0 1 0 0 0 0 0 0 0 0 51
52 103.3 106.25 0 0 0 1 0 0 0 0 0 0 0 52
53 103.2 105.94 0 0 0 0 1 0 0 0 0 0 0 53
54 103.1 105.82 0 0 0 0 0 1 0 0 0 0 0 54
55 103.2 105.96 0 0 0 0 0 0 1 0 0 0 0 55
56 103.0 106.49 0 0 0 0 0 0 0 1 0 0 0 56
57 103.0 106.32 0 0 0 0 0 0 0 0 1 0 0 57
58 103.1 105.88 0 0 0 0 0 0 0 0 0 1 0 58
59 103.4 105.07 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Infl M1 M2 M3 M4
91.49138 0.15327 0.07394 -0.09756 -0.23587 -0.33187
M5 M6 M7 M8 M9 M10
-0.22297 -0.22449 -0.26195 -0.44655 -0.36539 -0.29358
M11 t
-0.02352 -0.08024
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.45441 -0.22190 -0.02567 0.15852 0.56389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.49138 12.23189 7.480 1.99e-09 ***
Infl 0.15327 0.12668 1.210 0.233
M1 0.07394 0.21532 0.343 0.733
M2 -0.09756 0.27668 -0.353 0.726
M3 -0.23587 0.29849 -0.790 0.434
M4 -0.33187 0.29472 -1.126 0.266
M5 -0.22297 0.24383 -0.914 0.365
M6 -0.22449 0.21125 -1.063 0.294
M7 -0.26195 0.21630 -1.211 0.232
M8 -0.44655 0.25270 -1.767 0.084 .
M9 -0.36539 0.24115 -1.515 0.137
M10 -0.29358 0.21844 -1.344 0.186
M11 -0.02352 0.19663 -0.120 0.905
t -0.08024 0.01855 -4.327 8.32e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2926 on 45 degrees of freedom
Multiple R-squared: 0.9402, Adjusted R-squared: 0.923
F-statistic: 54.44 on 13 and 45 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,] 0.0047582694 0.0095165387 0.995241731
[2,] 0.0018938585 0.0037877171 0.998106141
[3,] 0.0004261113 0.0008522227 0.999573889
[4,] 0.0003125930 0.0006251861 0.999687407
[5,] 0.0013573243 0.0027146485 0.998642676
[6,] 0.0303172594 0.0606345187 0.969682741
[7,] 0.0177926154 0.0355852308 0.982207385
[8,] 0.0585337692 0.1170675385 0.941466231
[9,] 0.2871854526 0.5743709052 0.712814547
[10,] 0.5406677243 0.9186645513 0.459332276
[11,] 0.5644975457 0.8710049087 0.435502454
[12,] 0.6914249601 0.6171500798 0.308575040
[13,] 0.6774482178 0.6451035644 0.322551782
[14,] 0.6751011361 0.6497977279 0.324898864
[15,] 0.6603805552 0.6792388897 0.339619445
[16,] 0.7421458605 0.5157082791 0.257854140
[17,] 0.8873186665 0.2253626670 0.112681334
[18,] 0.9069343114 0.1861313772 0.093065689
[19,] 0.8745288971 0.2509422059 0.125471103
[20,] 0.9257428919 0.1485142162 0.074257108
[21,] 0.9635572144 0.0728855711 0.036442786
[22,] 0.9672718211 0.0654563578 0.032728179
[23,] 0.9559871609 0.0880256781 0.044012839
[24,] 0.9353366056 0.1293267888 0.064663394
[25,] 0.9910214550 0.0179570900 0.008978545
[26,] 0.9935780917 0.0128438165 0.006421908
> postscript(file="/var/www/html/rcomp/tmp/114zy1258706860.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/24uvl1258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qj3e1258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/422561258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5o6wp1258706860.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.38840443 -0.35928366 -0.28978109 -0.23959019 -0.25636883 -0.22556486
7 8 9 10 11 12
-0.33238388 -0.19169555 -0.11713792 0.05412860 0.08692779 0.27007746
13 14 15 16 17 18
0.35223501 0.38135578 0.37538111 0.56388882 0.48427061 0.41507458
19 20 21 22 23 24
0.28373280 0.31369242 0.31277282 0.17024528 0.14173758 0.16473672
25 26 27 28 29 30
0.19440712 0.02352790 0.06850771 -0.04911528 -0.02566814 0.01126652
31 32 33 34 35 36
0.05386931 0.04360315 -0.02972835 -0.21823605 -0.45440712 -0.25937237
37 38 39 40 41 42
-0.05422473 -0.10327025 -0.27361716 -0.30350152 -0.14940094 -0.14809404
43 44 45 46 47 48
-0.14878237 -0.29276732 -0.31820968 -0.33430549 -0.33675777 -0.17544181
49 50 51 52 53 54
-0.10401296 0.05767023 0.11950943 0.02831818 -0.05283270 -0.05268218
55 56 57 58 59
0.14356414 0.12716730 0.15230313 0.32816766 0.56249952
> postscript(file="/var/www/html/rcomp/tmp/6jw251258706860.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.38840443 NA
1 -0.35928366 -0.38840443
2 -0.28978109 -0.35928366
3 -0.23959019 -0.28978109
4 -0.25636883 -0.23959019
5 -0.22556486 -0.25636883
6 -0.33238388 -0.22556486
7 -0.19169555 -0.33238388
8 -0.11713792 -0.19169555
9 0.05412860 -0.11713792
10 0.08692779 0.05412860
11 0.27007746 0.08692779
12 0.35223501 0.27007746
13 0.38135578 0.35223501
14 0.37538111 0.38135578
15 0.56388882 0.37538111
16 0.48427061 0.56388882
17 0.41507458 0.48427061
18 0.28373280 0.41507458
19 0.31369242 0.28373280
20 0.31277282 0.31369242
21 0.17024528 0.31277282
22 0.14173758 0.17024528
23 0.16473672 0.14173758
24 0.19440712 0.16473672
25 0.02352790 0.19440712
26 0.06850771 0.02352790
27 -0.04911528 0.06850771
28 -0.02566814 -0.04911528
29 0.01126652 -0.02566814
30 0.05386931 0.01126652
31 0.04360315 0.05386931
32 -0.02972835 0.04360315
33 -0.21823605 -0.02972835
34 -0.45440712 -0.21823605
35 -0.25937237 -0.45440712
36 -0.05422473 -0.25937237
37 -0.10327025 -0.05422473
38 -0.27361716 -0.10327025
39 -0.30350152 -0.27361716
40 -0.14940094 -0.30350152
41 -0.14809404 -0.14940094
42 -0.14878237 -0.14809404
43 -0.29276732 -0.14878237
44 -0.31820968 -0.29276732
45 -0.33430549 -0.31820968
46 -0.33675777 -0.33430549
47 -0.17544181 -0.33675777
48 -0.10401296 -0.17544181
49 0.05767023 -0.10401296
50 0.11950943 0.05767023
51 0.02831818 0.11950943
52 -0.05283270 0.02831818
53 -0.05268218 -0.05283270
54 0.14356414 -0.05268218
55 0.12716730 0.14356414
56 0.15230313 0.12716730
57 0.32816766 0.15230313
58 0.56249952 0.32816766
59 NA 0.56249952
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.35928366 -0.38840443
[2,] -0.28978109 -0.35928366
[3,] -0.23959019 -0.28978109
[4,] -0.25636883 -0.23959019
[5,] -0.22556486 -0.25636883
[6,] -0.33238388 -0.22556486
[7,] -0.19169555 -0.33238388
[8,] -0.11713792 -0.19169555
[9,] 0.05412860 -0.11713792
[10,] 0.08692779 0.05412860
[11,] 0.27007746 0.08692779
[12,] 0.35223501 0.27007746
[13,] 0.38135578 0.35223501
[14,] 0.37538111 0.38135578
[15,] 0.56388882 0.37538111
[16,] 0.48427061 0.56388882
[17,] 0.41507458 0.48427061
[18,] 0.28373280 0.41507458
[19,] 0.31369242 0.28373280
[20,] 0.31277282 0.31369242
[21,] 0.17024528 0.31277282
[22,] 0.14173758 0.17024528
[23,] 0.16473672 0.14173758
[24,] 0.19440712 0.16473672
[25,] 0.02352790 0.19440712
[26,] 0.06850771 0.02352790
[27,] -0.04911528 0.06850771
[28,] -0.02566814 -0.04911528
[29,] 0.01126652 -0.02566814
[30,] 0.05386931 0.01126652
[31,] 0.04360315 0.05386931
[32,] -0.02972835 0.04360315
[33,] -0.21823605 -0.02972835
[34,] -0.45440712 -0.21823605
[35,] -0.25937237 -0.45440712
[36,] -0.05422473 -0.25937237
[37,] -0.10327025 -0.05422473
[38,] -0.27361716 -0.10327025
[39,] -0.30350152 -0.27361716
[40,] -0.14940094 -0.30350152
[41,] -0.14809404 -0.14940094
[42,] -0.14878237 -0.14809404
[43,] -0.29276732 -0.14878237
[44,] -0.31820968 -0.29276732
[45,] -0.33430549 -0.31820968
[46,] -0.33675777 -0.33430549
[47,] -0.17544181 -0.33675777
[48,] -0.10401296 -0.17544181
[49,] 0.05767023 -0.10401296
[50,] 0.11950943 0.05767023
[51,] 0.02831818 0.11950943
[52,] -0.05283270 0.02831818
[53,] -0.05268218 -0.05283270
[54,] 0.14356414 -0.05268218
[55,] 0.12716730 0.14356414
[56,] 0.15230313 0.12716730
[57,] 0.32816766 0.15230313
[58,] 0.56249952 0.32816766
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.35928366 -0.38840443
2 -0.28978109 -0.35928366
3 -0.23959019 -0.28978109
4 -0.25636883 -0.23959019
5 -0.22556486 -0.25636883
6 -0.33238388 -0.22556486
7 -0.19169555 -0.33238388
8 -0.11713792 -0.19169555
9 0.05412860 -0.11713792
10 0.08692779 0.05412860
11 0.27007746 0.08692779
12 0.35223501 0.27007746
13 0.38135578 0.35223501
14 0.37538111 0.38135578
15 0.56388882 0.37538111
16 0.48427061 0.56388882
17 0.41507458 0.48427061
18 0.28373280 0.41507458
19 0.31369242 0.28373280
20 0.31277282 0.31369242
21 0.17024528 0.31277282
22 0.14173758 0.17024528
23 0.16473672 0.14173758
24 0.19440712 0.16473672
25 0.02352790 0.19440712
26 0.06850771 0.02352790
27 -0.04911528 0.06850771
28 -0.02566814 -0.04911528
29 0.01126652 -0.02566814
30 0.05386931 0.01126652
31 0.04360315 0.05386931
32 -0.02972835 0.04360315
33 -0.21823605 -0.02972835
34 -0.45440712 -0.21823605
35 -0.25937237 -0.45440712
36 -0.05422473 -0.25937237
37 -0.10327025 -0.05422473
38 -0.27361716 -0.10327025
39 -0.30350152 -0.27361716
40 -0.14940094 -0.30350152
41 -0.14809404 -0.14940094
42 -0.14878237 -0.14809404
43 -0.29276732 -0.14878237
44 -0.31820968 -0.29276732
45 -0.33430549 -0.31820968
46 -0.33675777 -0.33430549
47 -0.17544181 -0.33675777
48 -0.10401296 -0.17544181
49 0.05767023 -0.10401296
50 0.11950943 0.05767023
51 0.02831818 0.11950943
52 -0.05283270 0.02831818
53 -0.05268218 -0.05283270
54 0.14356414 -0.05268218
55 0.12716730 0.14356414
56 0.15230313 0.12716730
57 0.32816766 0.15230313
58 0.56249952 0.32816766
> 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/7quk51258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pgbl1258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9a7381258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10obzo1258706860.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11d1gp1258706860.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/12esrk1258706860.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/13dgf71258706860.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/14plgn1258706860.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/15ibt31258706860.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/16vdwh1258706860.tab")
+ }
>
> system("convert tmp/114zy1258706860.ps tmp/114zy1258706860.png")
> system("convert tmp/24uvl1258706860.ps tmp/24uvl1258706860.png")
> system("convert tmp/3qj3e1258706860.ps tmp/3qj3e1258706860.png")
> system("convert tmp/422561258706860.ps tmp/422561258706860.png")
> system("convert tmp/5o6wp1258706860.ps tmp/5o6wp1258706860.png")
> system("convert tmp/6jw251258706860.ps tmp/6jw251258706860.png")
> system("convert tmp/7quk51258706860.ps tmp/7quk51258706860.png")
> system("convert tmp/8pgbl1258706860.ps tmp/8pgbl1258706860.png")
> system("convert tmp/9a7381258706860.ps tmp/9a7381258706860.png")
> system("convert tmp/10obzo1258706860.ps tmp/10obzo1258706860.png")
>
>
> proc.time()
user system elapsed
2.367 1.531 2.949