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(89.1,0,82.6,0,102.7,0,91.8,0,94.1,0,103.1,0,93.2,0,91,0,94.3,0,99.4,0,115.7,0,116.8,0,99.8,0,96,0,115.9,0,109.1,0,117.3,0,109.8,0,112.8,0,110.7,0,100,0,113.3,0,122.4,0,112.5,0,104.2,0,92.5,0,117.2,0,109.3,0,106.1,0,118.8,0,105.3,0,106,0,102,0,112.9,0,116.5,0,114.8,0,100.5,0,85.4,0,114.6,0,109.9,0,100.7,0,115.5,0,100.7,1,99,1,102.3,1,108.8,1,105.9,1,113.2,1,95.7,1,80.9,1,113.9,1,98.1,1,102.8,1,104.7,1,95.9,1,94.6,1,101.6,1,103.9,1,110.3,1,114.1,1),dim=c(2,60),dimnames=list(c('TotaleIndustrieleProductie','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TotaleIndustrieleProductie','X'),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 = '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
TotaleIndustrieleProductie X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 89.1 0 1 0 0 0 0 0 0 0 0 0 0 1
2 82.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 102.7 0 0 0 1 0 0 0 0 0 0 0 0 3
4 91.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 94.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 103.1 0 0 0 0 0 0 1 0 0 0 0 0 6
7 93.2 0 0 0 0 0 0 0 1 0 0 0 0 7
8 91.0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 94.3 0 0 0 0 0 0 0 0 0 1 0 0 9
10 99.4 0 0 0 0 0 0 0 0 0 0 1 0 10
11 115.7 0 0 0 0 0 0 0 0 0 0 0 1 11
12 116.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 99.8 0 1 0 0 0 0 0 0 0 0 0 0 13
14 96.0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 115.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 109.1 0 0 0 0 1 0 0 0 0 0 0 0 16
17 117.3 0 0 0 0 0 1 0 0 0 0 0 0 17
18 109.8 0 0 0 0 0 0 1 0 0 0 0 0 18
19 112.8 0 0 0 0 0 0 0 1 0 0 0 0 19
20 110.7 0 0 0 0 0 0 0 0 1 0 0 0 20
21 100.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 113.3 0 0 0 0 0 0 0 0 0 0 1 0 22
23 122.4 0 0 0 0 0 0 0 0 0 0 0 1 23
24 112.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 104.2 0 1 0 0 0 0 0 0 0 0 0 0 25
26 92.5 0 0 1 0 0 0 0 0 0 0 0 0 26
27 117.2 0 0 0 1 0 0 0 0 0 0 0 0 27
28 109.3 0 0 0 0 1 0 0 0 0 0 0 0 28
29 106.1 0 0 0 0 0 1 0 0 0 0 0 0 29
30 118.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 105.3 0 0 0 0 0 0 0 1 0 0 0 0 31
32 106.0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 102.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 112.9 0 0 0 0 0 0 0 0 0 0 1 0 34
35 116.5 0 0 0 0 0 0 0 0 0 0 0 1 35
36 114.8 0 0 0 0 0 0 0 0 0 0 0 0 36
37 100.5 0 1 0 0 0 0 0 0 0 0 0 0 37
38 85.4 0 0 1 0 0 0 0 0 0 0 0 0 38
39 114.6 0 0 0 1 0 0 0 0 0 0 0 0 39
40 109.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 100.7 0 0 0 0 0 1 0 0 0 0 0 0 41
42 115.5 0 0 0 0 0 0 1 0 0 0 0 0 42
43 100.7 1 0 0 0 0 0 0 1 0 0 0 0 43
44 99.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 102.3 1 0 0 0 0 0 0 0 0 1 0 0 45
46 108.8 1 0 0 0 0 0 0 0 0 0 1 0 46
47 105.9 1 0 0 0 0 0 0 0 0 0 0 1 47
48 113.2 1 0 0 0 0 0 0 0 0 0 0 0 48
49 95.7 1 1 0 0 0 0 0 0 0 0 0 0 49
50 80.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 113.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 98.1 1 0 0 0 1 0 0 0 0 0 0 0 52
53 102.8 1 0 0 0 0 1 0 0 0 0 0 0 53
54 104.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 95.9 1 0 0 0 0 0 0 1 0 0 0 0 55
56 94.6 1 0 0 0 0 0 0 0 1 0 0 0 56
57 101.6 1 0 0 0 0 0 0 0 0 1 0 0 57
58 103.9 1 0 0 0 0 0 0 0 0 0 1 0 58
59 110.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 114.1 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
109.7127 -11.4567 -15.9155 -26.5497 -1.4238 -10.8980
M5 M6 M7 M8 M9 M10
-10.5922 -4.6663 -11.4292 -13.0033 -13.4775 -6.1117
M11 t
0.1342 0.2542
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.84133 -3.53633 -0.02133 3.13517 13.85867
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 109.71267 3.08616 35.550 < 2e-16 ***
X -11.45667 2.64636 -4.329 8.01e-05 ***
M1 -15.91550 3.58223 -4.443 5.54e-05 ***
M2 -26.54967 3.57612 -7.424 2.11e-09 ***
M3 -1.42383 3.57136 -0.399 0.691973
M4 -10.89800 3.56795 -3.054 0.003744 **
M5 -10.59217 3.56591 -2.970 0.004715 **
M6 -4.66633 3.56522 -1.309 0.197087
M7 -11.42917 3.56754 -3.204 0.002465 **
M8 -13.00333 3.56140 -3.651 0.000666 ***
M9 -13.47750 3.55662 -3.789 0.000437 ***
M10 -6.11167 3.55320 -1.720 0.092146 .
M11 0.13417 3.55115 0.038 0.970026
t 0.25417 0.06974 3.645 0.000679 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.614 on 46 degrees of freedom
Multiple R-squared: 0.7348, Adjusted R-squared: 0.6599
F-statistic: 9.804 on 13 and 46 DF, p-value: 2.423e-09
> 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,] 0.6540371 0.691925713 0.345962857
[2,] 0.7973493 0.405301419 0.202650709
[3,] 0.7938525 0.412294918 0.206147459
[4,] 0.7764617 0.447076553 0.223538277
[5,] 0.9062850 0.187429918 0.093714959
[6,] 0.8502981 0.299403897 0.149701948
[7,] 0.8938300 0.212339961 0.106169981
[8,] 0.9949094 0.010181254 0.005090627
[9,] 0.9943747 0.011250641 0.005625321
[10,] 0.9980879 0.003824185 0.001912092
[11,] 0.9969709 0.006058299 0.003029149
[12,] 0.9943671 0.011265883 0.005632942
[13,] 0.9951665 0.009667088 0.004833544
[14,] 0.9932278 0.013544308 0.006772154
[15,] 0.9911028 0.017794341 0.008897171
[16,] 0.9884886 0.023022888 0.011511444
[17,] 0.9881815 0.023637067 0.011818533
[18,] 0.9775436 0.044912825 0.022456412
[19,] 0.9775772 0.044845560 0.022422780
[20,] 0.9743172 0.051365559 0.025682780
[21,] 0.9569487 0.086102624 0.043051312
[22,] 0.9479117 0.104176624 0.052088312
[23,] 0.9326018 0.134796375 0.067398187
[24,] 0.9187793 0.162441478 0.081220739
[25,] 0.9722451 0.055509850 0.027754925
[26,] 0.9266593 0.146681317 0.073340659
[27,] 0.8619589 0.276082102 0.138041051
> postscript(file="/var/www/html/rcomp/tmp/12c9r1258725251.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l00w1258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30nyt1258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4obfd1258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/557le1258725251.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 7
-4.9513333 -1.0713333 -6.3513333 -8.0313333 -6.2913333 -3.4713333 -6.8626667
8 9 10 11 12 13 14
-7.7426667 -4.2226667 -6.7426667 3.0573333 4.0373333 2.6986667 9.2786667
15 16 17 18 19 20 21
3.7986667 6.2186667 13.8586667 0.1786667 9.6873333 8.9073333 -1.5726667
22 23 24 25 26 27 28
4.1073333 6.7073333 -3.3126667 4.0486667 2.7286667 2.0486667 3.3686667
29 30 31 32 33 34 35
-0.3913333 6.1286667 -0.8626667 1.1573333 -2.6226667 0.6573333 -2.2426667
36 37 38 39 40 41 42
-4.0626667 -2.7013333 -7.4213333 -3.6013333 0.9186667 -8.8413333 -0.2213333
43 44 45 46 47 48 49
2.9440000 2.5640000 6.0840000 4.9640000 -4.4360000 2.7440000 0.9053333
50 51 52 53 54 55 56
-3.5146667 4.1053333 -2.4746667 1.6653333 -2.6146667 -4.9060000 -4.8860000
57 58 59 60
2.3340000 -2.9860000 -3.0860000 0.5940000
> postscript(file="/var/www/html/rcomp/tmp/67kym1258725251.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 -4.9513333 NA
1 -1.0713333 -4.9513333
2 -6.3513333 -1.0713333
3 -8.0313333 -6.3513333
4 -6.2913333 -8.0313333
5 -3.4713333 -6.2913333
6 -6.8626667 -3.4713333
7 -7.7426667 -6.8626667
8 -4.2226667 -7.7426667
9 -6.7426667 -4.2226667
10 3.0573333 -6.7426667
11 4.0373333 3.0573333
12 2.6986667 4.0373333
13 9.2786667 2.6986667
14 3.7986667 9.2786667
15 6.2186667 3.7986667
16 13.8586667 6.2186667
17 0.1786667 13.8586667
18 9.6873333 0.1786667
19 8.9073333 9.6873333
20 -1.5726667 8.9073333
21 4.1073333 -1.5726667
22 6.7073333 4.1073333
23 -3.3126667 6.7073333
24 4.0486667 -3.3126667
25 2.7286667 4.0486667
26 2.0486667 2.7286667
27 3.3686667 2.0486667
28 -0.3913333 3.3686667
29 6.1286667 -0.3913333
30 -0.8626667 6.1286667
31 1.1573333 -0.8626667
32 -2.6226667 1.1573333
33 0.6573333 -2.6226667
34 -2.2426667 0.6573333
35 -4.0626667 -2.2426667
36 -2.7013333 -4.0626667
37 -7.4213333 -2.7013333
38 -3.6013333 -7.4213333
39 0.9186667 -3.6013333
40 -8.8413333 0.9186667
41 -0.2213333 -8.8413333
42 2.9440000 -0.2213333
43 2.5640000 2.9440000
44 6.0840000 2.5640000
45 4.9640000 6.0840000
46 -4.4360000 4.9640000
47 2.7440000 -4.4360000
48 0.9053333 2.7440000
49 -3.5146667 0.9053333
50 4.1053333 -3.5146667
51 -2.4746667 4.1053333
52 1.6653333 -2.4746667
53 -2.6146667 1.6653333
54 -4.9060000 -2.6146667
55 -4.8860000 -4.9060000
56 2.3340000 -4.8860000
57 -2.9860000 2.3340000
58 -3.0860000 -2.9860000
59 0.5940000 -3.0860000
60 NA 0.5940000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.0713333 -4.9513333
[2,] -6.3513333 -1.0713333
[3,] -8.0313333 -6.3513333
[4,] -6.2913333 -8.0313333
[5,] -3.4713333 -6.2913333
[6,] -6.8626667 -3.4713333
[7,] -7.7426667 -6.8626667
[8,] -4.2226667 -7.7426667
[9,] -6.7426667 -4.2226667
[10,] 3.0573333 -6.7426667
[11,] 4.0373333 3.0573333
[12,] 2.6986667 4.0373333
[13,] 9.2786667 2.6986667
[14,] 3.7986667 9.2786667
[15,] 6.2186667 3.7986667
[16,] 13.8586667 6.2186667
[17,] 0.1786667 13.8586667
[18,] 9.6873333 0.1786667
[19,] 8.9073333 9.6873333
[20,] -1.5726667 8.9073333
[21,] 4.1073333 -1.5726667
[22,] 6.7073333 4.1073333
[23,] -3.3126667 6.7073333
[24,] 4.0486667 -3.3126667
[25,] 2.7286667 4.0486667
[26,] 2.0486667 2.7286667
[27,] 3.3686667 2.0486667
[28,] -0.3913333 3.3686667
[29,] 6.1286667 -0.3913333
[30,] -0.8626667 6.1286667
[31,] 1.1573333 -0.8626667
[32,] -2.6226667 1.1573333
[33,] 0.6573333 -2.6226667
[34,] -2.2426667 0.6573333
[35,] -4.0626667 -2.2426667
[36,] -2.7013333 -4.0626667
[37,] -7.4213333 -2.7013333
[38,] -3.6013333 -7.4213333
[39,] 0.9186667 -3.6013333
[40,] -8.8413333 0.9186667
[41,] -0.2213333 -8.8413333
[42,] 2.9440000 -0.2213333
[43,] 2.5640000 2.9440000
[44,] 6.0840000 2.5640000
[45,] 4.9640000 6.0840000
[46,] -4.4360000 4.9640000
[47,] 2.7440000 -4.4360000
[48,] 0.9053333 2.7440000
[49,] -3.5146667 0.9053333
[50,] 4.1053333 -3.5146667
[51,] -2.4746667 4.1053333
[52,] 1.6653333 -2.4746667
[53,] -2.6146667 1.6653333
[54,] -4.9060000 -2.6146667
[55,] -4.8860000 -4.9060000
[56,] 2.3340000 -4.8860000
[57,] -2.9860000 2.3340000
[58,] -3.0860000 -2.9860000
[59,] 0.5940000 -3.0860000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.0713333 -4.9513333
2 -6.3513333 -1.0713333
3 -8.0313333 -6.3513333
4 -6.2913333 -8.0313333
5 -3.4713333 -6.2913333
6 -6.8626667 -3.4713333
7 -7.7426667 -6.8626667
8 -4.2226667 -7.7426667
9 -6.7426667 -4.2226667
10 3.0573333 -6.7426667
11 4.0373333 3.0573333
12 2.6986667 4.0373333
13 9.2786667 2.6986667
14 3.7986667 9.2786667
15 6.2186667 3.7986667
16 13.8586667 6.2186667
17 0.1786667 13.8586667
18 9.6873333 0.1786667
19 8.9073333 9.6873333
20 -1.5726667 8.9073333
21 4.1073333 -1.5726667
22 6.7073333 4.1073333
23 -3.3126667 6.7073333
24 4.0486667 -3.3126667
25 2.7286667 4.0486667
26 2.0486667 2.7286667
27 3.3686667 2.0486667
28 -0.3913333 3.3686667
29 6.1286667 -0.3913333
30 -0.8626667 6.1286667
31 1.1573333 -0.8626667
32 -2.6226667 1.1573333
33 0.6573333 -2.6226667
34 -2.2426667 0.6573333
35 -4.0626667 -2.2426667
36 -2.7013333 -4.0626667
37 -7.4213333 -2.7013333
38 -3.6013333 -7.4213333
39 0.9186667 -3.6013333
40 -8.8413333 0.9186667
41 -0.2213333 -8.8413333
42 2.9440000 -0.2213333
43 2.5640000 2.9440000
44 6.0840000 2.5640000
45 4.9640000 6.0840000
46 -4.4360000 4.9640000
47 2.7440000 -4.4360000
48 0.9053333 2.7440000
49 -3.5146667 0.9053333
50 4.1053333 -3.5146667
51 -2.4746667 4.1053333
52 1.6653333 -2.4746667
53 -2.6146667 1.6653333
54 -4.9060000 -2.6146667
55 -4.8860000 -4.9060000
56 2.3340000 -4.8860000
57 -2.9860000 2.3340000
58 -3.0860000 -2.9860000
59 0.5940000 -3.0860000
> 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/7ztke1258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mzsn1258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9thwv1258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cgz21258725251.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tgyo1258725251.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/12i2sh1258725251.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/13w5z01258725251.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/14i7t21258725251.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/152pzx1258725251.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/16alk11258725251.tab")
+ }
>
> system("convert tmp/12c9r1258725251.ps tmp/12c9r1258725251.png")
> system("convert tmp/2l00w1258725251.ps tmp/2l00w1258725251.png")
> system("convert tmp/30nyt1258725251.ps tmp/30nyt1258725251.png")
> system("convert tmp/4obfd1258725251.ps tmp/4obfd1258725251.png")
> system("convert tmp/557le1258725251.ps tmp/557le1258725251.png")
> system("convert tmp/67kym1258725251.ps tmp/67kym1258725251.png")
> system("convert tmp/7ztke1258725251.ps tmp/7ztke1258725251.png")
> system("convert tmp/8mzsn1258725251.ps tmp/8mzsn1258725251.png")
> system("convert tmp/9thwv1258725251.ps tmp/9thwv1258725251.png")
> system("convert tmp/10cgz21258725251.ps tmp/10cgz21258725251.png")
>
>
> proc.time()
user system elapsed
2.402 1.550 2.757