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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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(97.6,82.9,96.9,83.8,105.6,86.2,102.8,86.1,101.7,86.2,104.2,88.8,92.7,89.6,91.9,87.8,106.5,88.3,112.3,88.6,102.8,91,96.5,91.5,101,95.4,98.9,98.7,105.1,99.9,103,98.6,99,100.3,104.3,100.2,94.6,100.4,90.4,101.4,108.9,103,111.4,109.1,100.8,111.4,102.5,114.1,98.2,121.8,98.7,127.6,113.3,129.9,104.6,128,99.3,123.5,111.8,124,97.3,127.4,97.7,127.6,115.6,128.4,111.9,131.4,107,135.1,107.1,134,100.6,144.5,99.2,147.3,108.4,150.9,103,148.7,99.8,141.4,115,138.9,90.8,139.8,95.9,145.6,114.4,147.9,108.2,148.5,112.6,151.1,109.1,157.5,105,167.5,105,172.3,118.5,173.5,103.7,187.5,112.5,205.5,116.6,195.1,96.6,204.5,101.9,204.5,116.5,201.7,119.3,207,115.4,206.6,108.5,210.6,111.5,211.1,108.8,215,121.8,223.9,109.6,238.2,112.2,238.9,119.6,229.6,104.1,232.2,105.3,222.1,115,221.6,124.1,227.3,116.8,221,107.5,213.6,115.6,243.4),dim=c(2,73),dimnames=list(c('tot_indus','prijsindex'),1:73))
> y <- array(NA,dim=c(2,73),dimnames=list(c('tot_indus','prijsindex'),1:73))
> 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 = 'Do not include Seasonal 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
tot_indus prijsindex
1 97.6 82.9
2 96.9 83.8
3 105.6 86.2
4 102.8 86.1
5 101.7 86.2
6 104.2 88.8
7 92.7 89.6
8 91.9 87.8
9 106.5 88.3
10 112.3 88.6
11 102.8 91.0
12 96.5 91.5
13 101.0 95.4
14 98.9 98.7
15 105.1 99.9
16 103.0 98.6
17 99.0 100.3
18 104.3 100.2
19 94.6 100.4
20 90.4 101.4
21 108.9 103.0
22 111.4 109.1
23 100.8 111.4
24 102.5 114.1
25 98.2 121.8
26 98.7 127.6
27 113.3 129.9
28 104.6 128.0
29 99.3 123.5
30 111.8 124.0
31 97.3 127.4
32 97.7 127.6
33 115.6 128.4
34 111.9 131.4
35 107.0 135.1
36 107.1 134.0
37 100.6 144.5
38 99.2 147.3
39 108.4 150.9
40 103.0 148.7
41 99.8 141.4
42 115.0 138.9
43 90.8 139.8
44 95.9 145.6
45 114.4 147.9
46 108.2 148.5
47 112.6 151.1
48 109.1 157.5
49 105.0 167.5
50 105.0 172.3
51 118.5 173.5
52 103.7 187.5
53 112.5 205.5
54 116.6 195.1
55 96.6 204.5
56 101.9 204.5
57 116.5 201.7
58 119.3 207.0
59 115.4 206.6
60 108.5 210.6
61 111.5 211.1
62 108.8 215.0
63 121.8 223.9
64 109.6 238.2
65 112.2 238.9
66 119.6 229.6
67 104.1 232.2
68 105.3 222.1
69 115.0 221.6
70 124.1 227.3
71 116.8 221.0
72 107.5 213.6
73 115.6 243.4
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) prijsindex
92.75007 0.08947
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.4587 -4.5002 0.3971 5.1372 11.6225
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 92.75007 2.37132 39.113 < 2e-16 ***
prijsindex 0.08947 0.01511 5.922 1.03e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.466 on 71 degrees of freedom
Multiple R-squared: 0.3306, Adjusted R-squared: 0.3212
F-statistic: 35.07 on 1 and 71 DF, p-value: 1.035e-07
> 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,] 0.03196391 0.06392782 0.9680361
[2,] 0.01971607 0.03943215 0.9802839
[3,] 0.28272730 0.56545459 0.7172727
[4,] 0.35474157 0.70948315 0.6452584
[5,] 0.38267611 0.76535222 0.6173239
[6,] 0.59617212 0.80765575 0.4038279
[7,] 0.49232164 0.98464328 0.5076784
[8,] 0.46732984 0.93465968 0.5326702
[9,] 0.37180057 0.74360115 0.6281994
[10,] 0.29454833 0.58909666 0.7054517
[11,] 0.24447691 0.48895381 0.7555231
[12,] 0.18034004 0.36068008 0.8196600
[13,] 0.13855812 0.27711625 0.8614419
[14,] 0.10225074 0.20450149 0.8977493
[15,] 0.11414809 0.22829619 0.8858519
[16,] 0.19505131 0.39010262 0.8049487
[17,] 0.24345895 0.48691790 0.7565411
[18,] 0.31118947 0.62237894 0.6888105
[19,] 0.25509001 0.51018003 0.7449100
[20,] 0.19847179 0.39694358 0.8015282
[21,] 0.17811591 0.35623182 0.8218841
[22,] 0.15017946 0.30035892 0.8498205
[23,] 0.21883385 0.43766770 0.7811661
[24,] 0.16924790 0.33849580 0.8307521
[25,] 0.14620219 0.29240438 0.8537978
[26,] 0.16891260 0.33782521 0.8310874
[27,] 0.17492132 0.34984264 0.8250787
[28,] 0.17129858 0.34259717 0.8287014
[29,] 0.28066606 0.56133212 0.7193339
[30,] 0.29278443 0.58556886 0.7072156
[31,] 0.24158832 0.48317665 0.7584117
[32,] 0.19745837 0.39491674 0.8025416
[33,] 0.18299313 0.36598627 0.8170069
[34,] 0.18376066 0.36752131 0.8162393
[35,] 0.14660274 0.29320549 0.8533973
[36,] 0.11661512 0.23323025 0.8833849
[37,] 0.10571902 0.21143805 0.8942810
[38,] 0.15369538 0.30739076 0.8463046
[39,] 0.35090315 0.70180630 0.6490968
[40,] 0.46599477 0.93198953 0.5340052
[41,] 0.48868392 0.97736784 0.5113161
[42,] 0.42356031 0.84712061 0.5764397
[43,] 0.40501788 0.81003577 0.5949821
[44,] 0.34301798 0.68603596 0.6569820
[45,] 0.29122985 0.58245970 0.7087701
[46,] 0.25253327 0.50506653 0.7474667
[47,] 0.32731595 0.65463191 0.6726840
[48,] 0.30657686 0.61315373 0.6934231
[49,] 0.24496054 0.48992109 0.7550395
[50,] 0.24327930 0.48655860 0.7567207
[51,] 0.51037914 0.97924172 0.4896209
[52,] 0.63944691 0.72110619 0.3605531
[53,] 0.59072704 0.81854591 0.4092730
[54,] 0.60877613 0.78244773 0.3912239
[55,] 0.55444191 0.89111618 0.4455581
[56,] 0.48103660 0.96207321 0.5189634
[57,] 0.38647945 0.77295889 0.6135206
[58,] 0.32593338 0.65186676 0.6740666
[59,] 0.38437577 0.76875154 0.6156242
[60,] 0.32358392 0.64716784 0.6764161
[61,] 0.24000086 0.48000172 0.7599991
[62,] 0.21090040 0.42180081 0.7890996
[63,] 0.34114442 0.68228884 0.6588556
[64,] 0.43925604 0.87851209 0.5607440
> postscript(file="/var/www/html/rcomp/tmp/15ei81258643624.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jjvm1258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a4nw1258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jgdz1258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5s0ra1258643624.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 = 73
Frequency = 1
1 2 3 4 5 6
-2.5675375 -3.3480648 5.1371956 2.3461430 1.2371956 3.5045610
7 8 9 10 11 12
-8.0670189 -8.7059642 5.8492984 11.6224559 1.9077163 -4.4370211
13 14 15 16 17 18
-0.2859730 -2.6812399 3.4113903 1.4277075 -2.7243997 2.5845478
19 20 21 22 23 24
-7.1333472 -11.4228220 6.9340183 8.8882217 -1.9175704 -0.4591525
25 26 27 28 29 30
-5.4481087 -5.4670628 8.9271451 0.3971473 -4.5002160 7.9550466
31 32 33 34 35 36
-6.8491678 -6.4670628 11.3613573 7.3929328 2.1618759 2.3602982
37 38 39 40 41 42
-5.0791876 -6.7297171 2.1481734 -3.0549819 -5.6018156 9.8218715
43 44 45 46 47 48
-14.4586559 -9.8776099 8.4165979 2.1629130 6.3302785 2.2576395
49 50 51 52 53 54
-2.7371089 -3.1665881 10.2260420 -5.8266057 1.3628472 6.3933855
55 56 57 58 59 60
-14.4476780 -9.1476780 5.7028516 8.0286349 4.1644249 -3.0934745
61 62 63 64 65 66
-0.1382119 -3.1871638 9.0165101 -4.4629801 -1.9256125 6.3065035
67 68 69 70 71 72
-9.4261310 -7.3224352 2.4223023 11.0122957 4.2759872 -4.3618990
73
1.0717507
> postscript(file="/var/www/html/rcomp/tmp/68tvd1258643624.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 = 73
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.5675375 NA
1 -3.3480648 -2.5675375
2 5.1371956 -3.3480648
3 2.3461430 5.1371956
4 1.2371956 2.3461430
5 3.5045610 1.2371956
6 -8.0670189 3.5045610
7 -8.7059642 -8.0670189
8 5.8492984 -8.7059642
9 11.6224559 5.8492984
10 1.9077163 11.6224559
11 -4.4370211 1.9077163
12 -0.2859730 -4.4370211
13 -2.6812399 -0.2859730
14 3.4113903 -2.6812399
15 1.4277075 3.4113903
16 -2.7243997 1.4277075
17 2.5845478 -2.7243997
18 -7.1333472 2.5845478
19 -11.4228220 -7.1333472
20 6.9340183 -11.4228220
21 8.8882217 6.9340183
22 -1.9175704 8.8882217
23 -0.4591525 -1.9175704
24 -5.4481087 -0.4591525
25 -5.4670628 -5.4481087
26 8.9271451 -5.4670628
27 0.3971473 8.9271451
28 -4.5002160 0.3971473
29 7.9550466 -4.5002160
30 -6.8491678 7.9550466
31 -6.4670628 -6.8491678
32 11.3613573 -6.4670628
33 7.3929328 11.3613573
34 2.1618759 7.3929328
35 2.3602982 2.1618759
36 -5.0791876 2.3602982
37 -6.7297171 -5.0791876
38 2.1481734 -6.7297171
39 -3.0549819 2.1481734
40 -5.6018156 -3.0549819
41 9.8218715 -5.6018156
42 -14.4586559 9.8218715
43 -9.8776099 -14.4586559
44 8.4165979 -9.8776099
45 2.1629130 8.4165979
46 6.3302785 2.1629130
47 2.2576395 6.3302785
48 -2.7371089 2.2576395
49 -3.1665881 -2.7371089
50 10.2260420 -3.1665881
51 -5.8266057 10.2260420
52 1.3628472 -5.8266057
53 6.3933855 1.3628472
54 -14.4476780 6.3933855
55 -9.1476780 -14.4476780
56 5.7028516 -9.1476780
57 8.0286349 5.7028516
58 4.1644249 8.0286349
59 -3.0934745 4.1644249
60 -0.1382119 -3.0934745
61 -3.1871638 -0.1382119
62 9.0165101 -3.1871638
63 -4.4629801 9.0165101
64 -1.9256125 -4.4629801
65 6.3065035 -1.9256125
66 -9.4261310 6.3065035
67 -7.3224352 -9.4261310
68 2.4223023 -7.3224352
69 11.0122957 2.4223023
70 4.2759872 11.0122957
71 -4.3618990 4.2759872
72 1.0717507 -4.3618990
73 NA 1.0717507
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.3480648 -2.5675375
[2,] 5.1371956 -3.3480648
[3,] 2.3461430 5.1371956
[4,] 1.2371956 2.3461430
[5,] 3.5045610 1.2371956
[6,] -8.0670189 3.5045610
[7,] -8.7059642 -8.0670189
[8,] 5.8492984 -8.7059642
[9,] 11.6224559 5.8492984
[10,] 1.9077163 11.6224559
[11,] -4.4370211 1.9077163
[12,] -0.2859730 -4.4370211
[13,] -2.6812399 -0.2859730
[14,] 3.4113903 -2.6812399
[15,] 1.4277075 3.4113903
[16,] -2.7243997 1.4277075
[17,] 2.5845478 -2.7243997
[18,] -7.1333472 2.5845478
[19,] -11.4228220 -7.1333472
[20,] 6.9340183 -11.4228220
[21,] 8.8882217 6.9340183
[22,] -1.9175704 8.8882217
[23,] -0.4591525 -1.9175704
[24,] -5.4481087 -0.4591525
[25,] -5.4670628 -5.4481087
[26,] 8.9271451 -5.4670628
[27,] 0.3971473 8.9271451
[28,] -4.5002160 0.3971473
[29,] 7.9550466 -4.5002160
[30,] -6.8491678 7.9550466
[31,] -6.4670628 -6.8491678
[32,] 11.3613573 -6.4670628
[33,] 7.3929328 11.3613573
[34,] 2.1618759 7.3929328
[35,] 2.3602982 2.1618759
[36,] -5.0791876 2.3602982
[37,] -6.7297171 -5.0791876
[38,] 2.1481734 -6.7297171
[39,] -3.0549819 2.1481734
[40,] -5.6018156 -3.0549819
[41,] 9.8218715 -5.6018156
[42,] -14.4586559 9.8218715
[43,] -9.8776099 -14.4586559
[44,] 8.4165979 -9.8776099
[45,] 2.1629130 8.4165979
[46,] 6.3302785 2.1629130
[47,] 2.2576395 6.3302785
[48,] -2.7371089 2.2576395
[49,] -3.1665881 -2.7371089
[50,] 10.2260420 -3.1665881
[51,] -5.8266057 10.2260420
[52,] 1.3628472 -5.8266057
[53,] 6.3933855 1.3628472
[54,] -14.4476780 6.3933855
[55,] -9.1476780 -14.4476780
[56,] 5.7028516 -9.1476780
[57,] 8.0286349 5.7028516
[58,] 4.1644249 8.0286349
[59,] -3.0934745 4.1644249
[60,] -0.1382119 -3.0934745
[61,] -3.1871638 -0.1382119
[62,] 9.0165101 -3.1871638
[63,] -4.4629801 9.0165101
[64,] -1.9256125 -4.4629801
[65,] 6.3065035 -1.9256125
[66,] -9.4261310 6.3065035
[67,] -7.3224352 -9.4261310
[68,] 2.4223023 -7.3224352
[69,] 11.0122957 2.4223023
[70,] 4.2759872 11.0122957
[71,] -4.3618990 4.2759872
[72,] 1.0717507 -4.3618990
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.3480648 -2.5675375
2 5.1371956 -3.3480648
3 2.3461430 5.1371956
4 1.2371956 2.3461430
5 3.5045610 1.2371956
6 -8.0670189 3.5045610
7 -8.7059642 -8.0670189
8 5.8492984 -8.7059642
9 11.6224559 5.8492984
10 1.9077163 11.6224559
11 -4.4370211 1.9077163
12 -0.2859730 -4.4370211
13 -2.6812399 -0.2859730
14 3.4113903 -2.6812399
15 1.4277075 3.4113903
16 -2.7243997 1.4277075
17 2.5845478 -2.7243997
18 -7.1333472 2.5845478
19 -11.4228220 -7.1333472
20 6.9340183 -11.4228220
21 8.8882217 6.9340183
22 -1.9175704 8.8882217
23 -0.4591525 -1.9175704
24 -5.4481087 -0.4591525
25 -5.4670628 -5.4481087
26 8.9271451 -5.4670628
27 0.3971473 8.9271451
28 -4.5002160 0.3971473
29 7.9550466 -4.5002160
30 -6.8491678 7.9550466
31 -6.4670628 -6.8491678
32 11.3613573 -6.4670628
33 7.3929328 11.3613573
34 2.1618759 7.3929328
35 2.3602982 2.1618759
36 -5.0791876 2.3602982
37 -6.7297171 -5.0791876
38 2.1481734 -6.7297171
39 -3.0549819 2.1481734
40 -5.6018156 -3.0549819
41 9.8218715 -5.6018156
42 -14.4586559 9.8218715
43 -9.8776099 -14.4586559
44 8.4165979 -9.8776099
45 2.1629130 8.4165979
46 6.3302785 2.1629130
47 2.2576395 6.3302785
48 -2.7371089 2.2576395
49 -3.1665881 -2.7371089
50 10.2260420 -3.1665881
51 -5.8266057 10.2260420
52 1.3628472 -5.8266057
53 6.3933855 1.3628472
54 -14.4476780 6.3933855
55 -9.1476780 -14.4476780
56 5.7028516 -9.1476780
57 8.0286349 5.7028516
58 4.1644249 8.0286349
59 -3.0934745 4.1644249
60 -0.1382119 -3.0934745
61 -3.1871638 -0.1382119
62 9.0165101 -3.1871638
63 -4.4629801 9.0165101
64 -1.9256125 -4.4629801
65 6.3065035 -1.9256125
66 -9.4261310 6.3065035
67 -7.3224352 -9.4261310
68 2.4223023 -7.3224352
69 11.0122957 2.4223023
70 4.2759872 11.0122957
71 -4.3618990 4.2759872
72 1.0717507 -4.3618990
> 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/7gmdr1258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zaxt1258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95ery1258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yjg71258643624.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gsb51258643624.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/12dfzg1258643624.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/13r06q1258643624.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/14d2y61258643624.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/15aw0f1258643624.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/16ekz51258643624.tab")
+ }
>
> system("convert tmp/15ei81258643624.ps tmp/15ei81258643624.png")
> system("convert tmp/2jjvm1258643624.ps tmp/2jjvm1258643624.png")
> system("convert tmp/3a4nw1258643624.ps tmp/3a4nw1258643624.png")
> system("convert tmp/4jgdz1258643624.ps tmp/4jgdz1258643624.png")
> system("convert tmp/5s0ra1258643624.ps tmp/5s0ra1258643624.png")
> system("convert tmp/68tvd1258643624.ps tmp/68tvd1258643624.png")
> system("convert tmp/7gmdr1258643624.ps tmp/7gmdr1258643624.png")
> system("convert tmp/8zaxt1258643624.ps tmp/8zaxt1258643624.png")
> system("convert tmp/95ery1258643624.ps tmp/95ery1258643624.png")
> system("convert tmp/10yjg71258643624.ps tmp/10yjg71258643624.png")
>
>
> proc.time()
user system elapsed
2.652 1.628 6.083