R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(105.6
+ ,86.2
+ ,96.9
+ ,97.6
+ ,102.8
+ ,86.1
+ ,105.6
+ ,96.9
+ ,101.7
+ ,86.2
+ ,102.8
+ ,105.6
+ ,104.2
+ ,88.8
+ ,101.7
+ ,102.8
+ ,92.7
+ ,89.6
+ ,104.2
+ ,101.7
+ ,91.9
+ ,87.8
+ ,92.7
+ ,104.2
+ ,106.5
+ ,88.3
+ ,91.9
+ ,92.7
+ ,112.3
+ ,88.6
+ ,106.5
+ ,91.9
+ ,102.8
+ ,91
+ ,112.3
+ ,106.5
+ ,96.5
+ ,91.5
+ ,102.8
+ ,112.3
+ ,101
+ ,95.4
+ ,96.5
+ ,102.8
+ ,98.9
+ ,98.7
+ ,101
+ ,96.5
+ ,105.1
+ ,99.9
+ ,98.9
+ ,101
+ ,103
+ ,98.6
+ ,105.1
+ ,98.9
+ ,99
+ ,100.3
+ ,103
+ ,105.1
+ ,104.3
+ ,100.2
+ ,99
+ ,103
+ ,94.6
+ ,100.4
+ ,104.3
+ ,99
+ ,90.4
+ ,101.4
+ ,94.6
+ ,104.3
+ ,108.9
+ ,103
+ ,90.4
+ ,94.6
+ ,111.4
+ ,109.1
+ ,108.9
+ ,90.4
+ ,100.8
+ ,111.4
+ ,111.4
+ ,108.9
+ ,102.5
+ ,114.1
+ ,100.8
+ ,111.4
+ ,98.2
+ ,121.8
+ ,102.5
+ ,100.8
+ ,98.7
+ ,127.6
+ ,98.2
+ ,102.5
+ ,113.3
+ ,129.9
+ ,98.7
+ ,98.2
+ ,104.6
+ ,128
+ ,113.3
+ ,98.7
+ ,99.3
+ ,123.5
+ ,104.6
+ ,113.3
+ ,111.8
+ ,124
+ ,99.3
+ ,104.6
+ ,97.3
+ ,127.4
+ ,111.8
+ ,99.3
+ ,97.7
+ ,127.6
+ ,97.3
+ ,111.8
+ ,115.6
+ ,128.4
+ ,97.7
+ ,97.3
+ ,111.9
+ ,131.4
+ ,115.6
+ ,97.7
+ ,107
+ ,135.1
+ ,111.9
+ ,115.6
+ ,107.1
+ ,134
+ ,107
+ ,111.9
+ ,100.6
+ ,144.5
+ ,107.1
+ ,107
+ ,99.2
+ ,147.3
+ ,100.6
+ ,107.1
+ ,108.4
+ ,150.9
+ ,99.2
+ ,100.6
+ ,103
+ ,148.7
+ ,108.4
+ ,99.2
+ ,99.8
+ ,141.4
+ ,103
+ ,108.4
+ ,115
+ ,138.9
+ ,99.8
+ ,103
+ ,90.8
+ ,139.8
+ ,115
+ ,99.8
+ ,95.9
+ ,145.6
+ ,90.8
+ ,115
+ ,114.4
+ ,147.9
+ ,95.9
+ ,90.8
+ ,108.2
+ ,148.5
+ ,114.4
+ ,95.9
+ ,112.6
+ ,151.1
+ ,108.2
+ ,114.4
+ ,109.1
+ ,157.5
+ ,112.6
+ ,108.2
+ ,105
+ ,167.5
+ ,109.1
+ ,112.6
+ ,105
+ ,172.3
+ ,105
+ ,109.1
+ ,118.5
+ ,173.5
+ ,105
+ ,105
+ ,103.7
+ ,187.5
+ ,118.5
+ ,105
+ ,112.5
+ ,205.5
+ ,103.7
+ ,118.5
+ ,116.6
+ ,195.1
+ ,112.5
+ ,103.7
+ ,96.6
+ ,204.5
+ ,116.6
+ ,112.5
+ ,101.9
+ ,204.5
+ ,96.6
+ ,116.6
+ ,116.5
+ ,201.7
+ ,101.9
+ ,96.6
+ ,119.3
+ ,207
+ ,116.5
+ ,101.9
+ ,115.4
+ ,206.6
+ ,119.3
+ ,116.5
+ ,108.5
+ ,210.6
+ ,115.4
+ ,119.3
+ ,111.5
+ ,211.1
+ ,108.5
+ ,115.4
+ ,108.8
+ ,215
+ ,111.5
+ ,108.5
+ ,121.8
+ ,223.9
+ ,108.8
+ ,111.5
+ ,109.6
+ ,238.2
+ ,121.8
+ ,108.8
+ ,112.2
+ ,238.9
+ ,109.6
+ ,121.8
+ ,119.6
+ ,229.6
+ ,112.2
+ ,109.6
+ ,104.1
+ ,232.2
+ ,119.6
+ ,112.2
+ ,105.3
+ ,222.1
+ ,104.1
+ ,119.6
+ ,115
+ ,221.6
+ ,105.3
+ ,104.1
+ ,124.1
+ ,227.3
+ ,115
+ ,105.3
+ ,116.8
+ ,221
+ ,124.1
+ ,115
+ ,107.5
+ ,213.6
+ ,116.8
+ ,124.1
+ ,115.6
+ ,243.4
+ ,107.5
+ ,116.8)
+ ,dim=c(4
+ ,71)
+ ,dimnames=list(c('tot_indus'
+ ,'prijsindex'
+ ,'y(t-1)'
+ ,'y(t-2)')
+ ,1:71))
> y <- array(NA,dim=c(4,71),dimnames=list(c('tot_indus','prijsindex','y(t-1)','y(t-2)'),1:71))
> 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 y(t-1) y(t-2)
1 105.6 86.2 96.9 97.6
2 102.8 86.1 105.6 96.9
3 101.7 86.2 102.8 105.6
4 104.2 88.8 101.7 102.8
5 92.7 89.6 104.2 101.7
6 91.9 87.8 92.7 104.2
7 106.5 88.3 91.9 92.7
8 112.3 88.6 106.5 91.9
9 102.8 91.0 112.3 106.5
10 96.5 91.5 102.8 112.3
11 101.0 95.4 96.5 102.8
12 98.9 98.7 101.0 96.5
13 105.1 99.9 98.9 101.0
14 103.0 98.6 105.1 98.9
15 99.0 100.3 103.0 105.1
16 104.3 100.2 99.0 103.0
17 94.6 100.4 104.3 99.0
18 90.4 101.4 94.6 104.3
19 108.9 103.0 90.4 94.6
20 111.4 109.1 108.9 90.4
21 100.8 111.4 111.4 108.9
22 102.5 114.1 100.8 111.4
23 98.2 121.8 102.5 100.8
24 98.7 127.6 98.2 102.5
25 113.3 129.9 98.7 98.2
26 104.6 128.0 113.3 98.7
27 99.3 123.5 104.6 113.3
28 111.8 124.0 99.3 104.6
29 97.3 127.4 111.8 99.3
30 97.7 127.6 97.3 111.8
31 115.6 128.4 97.7 97.3
32 111.9 131.4 115.6 97.7
33 107.0 135.1 111.9 115.6
34 107.1 134.0 107.0 111.9
35 100.6 144.5 107.1 107.0
36 99.2 147.3 100.6 107.1
37 108.4 150.9 99.2 100.6
38 103.0 148.7 108.4 99.2
39 99.8 141.4 103.0 108.4
40 115.0 138.9 99.8 103.0
41 90.8 139.8 115.0 99.8
42 95.9 145.6 90.8 115.0
43 114.4 147.9 95.9 90.8
44 108.2 148.5 114.4 95.9
45 112.6 151.1 108.2 114.4
46 109.1 157.5 112.6 108.2
47 105.0 167.5 109.1 112.6
48 105.0 172.3 105.0 109.1
49 118.5 173.5 105.0 105.0
50 103.7 187.5 118.5 105.0
51 112.5 205.5 103.7 118.5
52 116.6 195.1 112.5 103.7
53 96.6 204.5 116.6 112.5
54 101.9 204.5 96.6 116.6
55 116.5 201.7 101.9 96.6
56 119.3 207.0 116.5 101.9
57 115.4 206.6 119.3 116.5
58 108.5 210.6 115.4 119.3
59 111.5 211.1 108.5 115.4
60 108.8 215.0 111.5 108.5
61 121.8 223.9 108.8 111.5
62 109.6 238.2 121.8 108.8
63 112.2 238.9 109.6 121.8
64 119.6 229.6 112.2 109.6
65 104.1 232.2 119.6 112.2
66 105.3 222.1 104.1 119.6
67 115.0 221.6 105.3 104.1
68 124.1 227.3 115.0 105.3
69 116.8 221.0 124.1 115.0
70 107.5 213.6 116.8 124.1
71 115.6 243.4 107.5 116.8
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) prijsindex `y(t-1)` `y(t-2)`
134.9401 0.1284 -0.0277 -0.4252
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16.4696 -4.4760 0.9307 4.3613 9.8988
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 134.94015 14.89429 9.060 2.99e-13 ***
prijsindex 0.12837 0.02023 6.347 2.19e-08 ***
`y(t-1)` -0.02770 0.11089 -0.250 0.803504
`y(t-2)` -0.42517 0.11193 -3.798 0.000316 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.017 on 67 degrees of freedom
Multiple R-squared: 0.4326, Adjusted R-squared: 0.4072
F-statistic: 17.02 on 3 and 67 DF, p-value: 2.524e-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,] 0.60470967 0.7905807 0.3952903
[2,] 0.51361558 0.9727688 0.4863844
[3,] 0.46644792 0.9328958 0.5335521
[4,] 0.38164580 0.7632916 0.6183542
[5,] 0.27229781 0.5445956 0.7277022
[6,] 0.24425682 0.4885136 0.7557432
[7,] 0.23200471 0.4640094 0.7679953
[8,] 0.15911830 0.3182366 0.8408817
[9,] 0.10350458 0.2070092 0.8964954
[10,] 0.08698755 0.1739751 0.9130125
[11,] 0.14861607 0.2972321 0.8513839
[12,] 0.17950725 0.3590145 0.8204927
[13,] 0.20093115 0.4018623 0.7990688
[14,] 0.15780370 0.3156074 0.8421963
[15,] 0.12162429 0.2432486 0.8783757
[16,] 0.12515886 0.2503177 0.8748411
[17,] 0.12108569 0.2421714 0.8789143
[18,] 0.09712675 0.1942535 0.9028732
[19,] 0.13986933 0.2797387 0.8601307
[20,] 0.10667736 0.2133547 0.8933226
[21,] 0.07878996 0.1575799 0.9212100
[22,] 0.12772596 0.2554519 0.8722740
[23,] 0.17642768 0.3528554 0.8235723
[24,] 0.13893875 0.2778775 0.8610612
[25,] 0.18483274 0.3696655 0.8151673
[26,] 0.16694423 0.3338885 0.8330558
[27,] 0.19213863 0.3842773 0.8078614
[28,] 0.18869602 0.3773920 0.8113040
[29,] 0.16835635 0.3367127 0.8316437
[30,] 0.16242193 0.3248439 0.8375781
[31,] 0.12200803 0.2440161 0.8779920
[32,] 0.11341331 0.2268266 0.8865867
[33,] 0.09162483 0.1832497 0.9083752
[34,] 0.14121016 0.2824203 0.8587898
[35,] 0.46909999 0.9382000 0.5309000
[36,] 0.48286704 0.9657341 0.5171330
[37,] 0.41655044 0.8331009 0.5834496
[38,] 0.36242344 0.7248469 0.6375766
[39,] 0.45856273 0.9171255 0.5414373
[40,] 0.40384964 0.8076993 0.5961504
[41,] 0.33185231 0.6637046 0.6681477
[42,] 0.27889829 0.5577966 0.7211017
[43,] 0.35605461 0.7121092 0.6439454
[44,] 0.36938149 0.7387630 0.6306185
[45,] 0.34626790 0.6925358 0.6537321
[46,] 0.30157893 0.6031579 0.6984211
[47,] 0.63157940 0.7368412 0.3684206
[48,] 0.65154549 0.6969090 0.3484545
[49,] 0.58648077 0.8270385 0.4135192
[50,] 0.51159405 0.9768119 0.4884059
[51,] 0.49441294 0.9888259 0.5055871
[52,] 0.39440413 0.7888083 0.6055959
[53,] 0.29855358 0.5971072 0.7014464
[54,] 0.29791504 0.5958301 0.7020850
[55,] 0.31784850 0.6356970 0.6821515
[56,] 0.33459887 0.6691977 0.6654011
[57,] 0.23161033 0.4632207 0.7683897
[58,] 0.15885962 0.3177192 0.8411404
> postscript(file="/var/www/html/rcomp/tmp/17u6x1258645764.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2chh21258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3421p1258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4dswn1258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58fin1258645764.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 = 71
Frequency = 1
1 2 3 4 5 6
3.7744448 0.9306667 3.4392051 4.3844995 -7.6166283 -7.4412061
7 8 9 10 11 12
2.1830440 8.0088369 4.5688224 0.4074337 0.1931881 -4.8843313
13 14 15 16 17 18
3.0166923 0.3624779 -1.2779040 3.0312812 -8.2482388 -10.5919372
19 20 21 22 23 24
3.4622157 3.9059145 0.9454684 3.0681408 -6.6799966 -6.3208970
25 26 27 28 29 30
6.1694833 -1.6695870 -0.4254915 8.1645664 -8.6790154 -3.3917897
31 32 33 34 35 36
8.2516935 4.8324904 6.9654748 5.4978378 -4.4306245 -6.3276116
37 38 39 40 41 42
-0.3921131 -5.8500720 -4.3510099 8.7853869 -16.4696206 -6.3220415
43 44 45 46 47 48
1.7349725 -1.8612360 9.8988056 3.0630747 -0.5468869 -2.7647336
49 50 51 52 53 54
8.8380400 -7.3852233 4.4338088 3.8202158 -13.5314648 -7.0423104
55 56 57 58 59 60
-0.4393565 4.3380779 6.7744056 0.4433401 1.5298700 -4.5213245
61 62 63 64 65 66
8.5368542 -6.2867184 1.4126160 4.8914948 -9.6318574 -4.4184293
67 68 69 70 71
-1.2110649 7.9361060 5.8210445 1.1377951 2.0509356
> postscript(file="/var/www/html/rcomp/tmp/6erdy1258645764.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 = 71
Frequency = 1
lag(myerror, k = 1) myerror
0 3.7744448 NA
1 0.9306667 3.7744448
2 3.4392051 0.9306667
3 4.3844995 3.4392051
4 -7.6166283 4.3844995
5 -7.4412061 -7.6166283
6 2.1830440 -7.4412061
7 8.0088369 2.1830440
8 4.5688224 8.0088369
9 0.4074337 4.5688224
10 0.1931881 0.4074337
11 -4.8843313 0.1931881
12 3.0166923 -4.8843313
13 0.3624779 3.0166923
14 -1.2779040 0.3624779
15 3.0312812 -1.2779040
16 -8.2482388 3.0312812
17 -10.5919372 -8.2482388
18 3.4622157 -10.5919372
19 3.9059145 3.4622157
20 0.9454684 3.9059145
21 3.0681408 0.9454684
22 -6.6799966 3.0681408
23 -6.3208970 -6.6799966
24 6.1694833 -6.3208970
25 -1.6695870 6.1694833
26 -0.4254915 -1.6695870
27 8.1645664 -0.4254915
28 -8.6790154 8.1645664
29 -3.3917897 -8.6790154
30 8.2516935 -3.3917897
31 4.8324904 8.2516935
32 6.9654748 4.8324904
33 5.4978378 6.9654748
34 -4.4306245 5.4978378
35 -6.3276116 -4.4306245
36 -0.3921131 -6.3276116
37 -5.8500720 -0.3921131
38 -4.3510099 -5.8500720
39 8.7853869 -4.3510099
40 -16.4696206 8.7853869
41 -6.3220415 -16.4696206
42 1.7349725 -6.3220415
43 -1.8612360 1.7349725
44 9.8988056 -1.8612360
45 3.0630747 9.8988056
46 -0.5468869 3.0630747
47 -2.7647336 -0.5468869
48 8.8380400 -2.7647336
49 -7.3852233 8.8380400
50 4.4338088 -7.3852233
51 3.8202158 4.4338088
52 -13.5314648 3.8202158
53 -7.0423104 -13.5314648
54 -0.4393565 -7.0423104
55 4.3380779 -0.4393565
56 6.7744056 4.3380779
57 0.4433401 6.7744056
58 1.5298700 0.4433401
59 -4.5213245 1.5298700
60 8.5368542 -4.5213245
61 -6.2867184 8.5368542
62 1.4126160 -6.2867184
63 4.8914948 1.4126160
64 -9.6318574 4.8914948
65 -4.4184293 -9.6318574
66 -1.2110649 -4.4184293
67 7.9361060 -1.2110649
68 5.8210445 7.9361060
69 1.1377951 5.8210445
70 2.0509356 1.1377951
71 NA 2.0509356
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.9306667 3.7744448
[2,] 3.4392051 0.9306667
[3,] 4.3844995 3.4392051
[4,] -7.6166283 4.3844995
[5,] -7.4412061 -7.6166283
[6,] 2.1830440 -7.4412061
[7,] 8.0088369 2.1830440
[8,] 4.5688224 8.0088369
[9,] 0.4074337 4.5688224
[10,] 0.1931881 0.4074337
[11,] -4.8843313 0.1931881
[12,] 3.0166923 -4.8843313
[13,] 0.3624779 3.0166923
[14,] -1.2779040 0.3624779
[15,] 3.0312812 -1.2779040
[16,] -8.2482388 3.0312812
[17,] -10.5919372 -8.2482388
[18,] 3.4622157 -10.5919372
[19,] 3.9059145 3.4622157
[20,] 0.9454684 3.9059145
[21,] 3.0681408 0.9454684
[22,] -6.6799966 3.0681408
[23,] -6.3208970 -6.6799966
[24,] 6.1694833 -6.3208970
[25,] -1.6695870 6.1694833
[26,] -0.4254915 -1.6695870
[27,] 8.1645664 -0.4254915
[28,] -8.6790154 8.1645664
[29,] -3.3917897 -8.6790154
[30,] 8.2516935 -3.3917897
[31,] 4.8324904 8.2516935
[32,] 6.9654748 4.8324904
[33,] 5.4978378 6.9654748
[34,] -4.4306245 5.4978378
[35,] -6.3276116 -4.4306245
[36,] -0.3921131 -6.3276116
[37,] -5.8500720 -0.3921131
[38,] -4.3510099 -5.8500720
[39,] 8.7853869 -4.3510099
[40,] -16.4696206 8.7853869
[41,] -6.3220415 -16.4696206
[42,] 1.7349725 -6.3220415
[43,] -1.8612360 1.7349725
[44,] 9.8988056 -1.8612360
[45,] 3.0630747 9.8988056
[46,] -0.5468869 3.0630747
[47,] -2.7647336 -0.5468869
[48,] 8.8380400 -2.7647336
[49,] -7.3852233 8.8380400
[50,] 4.4338088 -7.3852233
[51,] 3.8202158 4.4338088
[52,] -13.5314648 3.8202158
[53,] -7.0423104 -13.5314648
[54,] -0.4393565 -7.0423104
[55,] 4.3380779 -0.4393565
[56,] 6.7744056 4.3380779
[57,] 0.4433401 6.7744056
[58,] 1.5298700 0.4433401
[59,] -4.5213245 1.5298700
[60,] 8.5368542 -4.5213245
[61,] -6.2867184 8.5368542
[62,] 1.4126160 -6.2867184
[63,] 4.8914948 1.4126160
[64,] -9.6318574 4.8914948
[65,] -4.4184293 -9.6318574
[66,] -1.2110649 -4.4184293
[67,] 7.9361060 -1.2110649
[68,] 5.8210445 7.9361060
[69,] 1.1377951 5.8210445
[70,] 2.0509356 1.1377951
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.9306667 3.7744448
2 3.4392051 0.9306667
3 4.3844995 3.4392051
4 -7.6166283 4.3844995
5 -7.4412061 -7.6166283
6 2.1830440 -7.4412061
7 8.0088369 2.1830440
8 4.5688224 8.0088369
9 0.4074337 4.5688224
10 0.1931881 0.4074337
11 -4.8843313 0.1931881
12 3.0166923 -4.8843313
13 0.3624779 3.0166923
14 -1.2779040 0.3624779
15 3.0312812 -1.2779040
16 -8.2482388 3.0312812
17 -10.5919372 -8.2482388
18 3.4622157 -10.5919372
19 3.9059145 3.4622157
20 0.9454684 3.9059145
21 3.0681408 0.9454684
22 -6.6799966 3.0681408
23 -6.3208970 -6.6799966
24 6.1694833 -6.3208970
25 -1.6695870 6.1694833
26 -0.4254915 -1.6695870
27 8.1645664 -0.4254915
28 -8.6790154 8.1645664
29 -3.3917897 -8.6790154
30 8.2516935 -3.3917897
31 4.8324904 8.2516935
32 6.9654748 4.8324904
33 5.4978378 6.9654748
34 -4.4306245 5.4978378
35 -6.3276116 -4.4306245
36 -0.3921131 -6.3276116
37 -5.8500720 -0.3921131
38 -4.3510099 -5.8500720
39 8.7853869 -4.3510099
40 -16.4696206 8.7853869
41 -6.3220415 -16.4696206
42 1.7349725 -6.3220415
43 -1.8612360 1.7349725
44 9.8988056 -1.8612360
45 3.0630747 9.8988056
46 -0.5468869 3.0630747
47 -2.7647336 -0.5468869
48 8.8380400 -2.7647336
49 -7.3852233 8.8380400
50 4.4338088 -7.3852233
51 3.8202158 4.4338088
52 -13.5314648 3.8202158
53 -7.0423104 -13.5314648
54 -0.4393565 -7.0423104
55 4.3380779 -0.4393565
56 6.7744056 4.3380779
57 0.4433401 6.7744056
58 1.5298700 0.4433401
59 -4.5213245 1.5298700
60 8.5368542 -4.5213245
61 -6.2867184 8.5368542
62 1.4126160 -6.2867184
63 4.8914948 1.4126160
64 -9.6318574 4.8914948
65 -4.4184293 -9.6318574
66 -1.2110649 -4.4184293
67 7.9361060 -1.2110649
68 5.8210445 7.9361060
69 1.1377951 5.8210445
70 2.0509356 1.1377951
> 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/79ghk1258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8kby91258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kgd01258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ywlg1258645764.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1181zi1258645764.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/1200np1258645764.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/132kyc1258645764.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/146hm01258645764.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/15sgzy1258645764.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/16u67w1258645764.tab")
+ }
>
> system("convert tmp/17u6x1258645764.ps tmp/17u6x1258645764.png")
> system("convert tmp/2chh21258645764.ps tmp/2chh21258645764.png")
> system("convert tmp/3421p1258645764.ps tmp/3421p1258645764.png")
> system("convert tmp/4dswn1258645764.ps tmp/4dswn1258645764.png")
> system("convert tmp/58fin1258645764.ps tmp/58fin1258645764.png")
> system("convert tmp/6erdy1258645764.ps tmp/6erdy1258645764.png")
> system("convert tmp/79ghk1258645764.ps tmp/79ghk1258645764.png")
> system("convert tmp/8kby91258645764.ps tmp/8kby91258645764.png")
> system("convert tmp/9kgd01258645764.ps tmp/9kgd01258645764.png")
> system("convert tmp/10ywlg1258645764.ps tmp/10ywlg1258645764.png")
>
>
> proc.time()
user system elapsed
2.583 1.586 3.369