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
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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(100.5
+ ,98.60
+ ,96.33
+ ,106.29
+ ,96.90
+ ,96.33
+ ,101.09
+ ,95.10
+ ,95.05
+ ,104.53
+ ,97.00
+ ,96.84
+ ,122.74
+ ,112.70
+ ,96.92
+ ,109.84
+ ,102.90
+ ,97.44
+ ,101.99
+ ,97.40
+ ,97.78
+ ,125.12
+ ,111.40
+ ,97.69
+ ,103.5
+ ,87.40
+ ,96.67
+ ,102.8
+ ,96.80
+ ,98.29
+ ,118.72
+ ,114.10
+ ,98.20
+ ,119.01
+ ,110.30
+ ,98.71
+ ,118.61
+ ,103.90
+ ,98.54
+ ,120.43
+ ,101.60
+ ,98.20
+ ,111.83
+ ,94.60
+ ,100.80
+ ,116.79
+ ,95.90
+ ,101.33
+ ,131.71
+ ,104.70
+ ,101.88
+ ,120.57
+ ,102.80
+ ,101.85
+ ,117.83
+ ,98.10
+ ,102.04
+ ,130.8
+ ,113.90
+ ,102.22
+ ,107.46
+ ,80.90
+ ,102.63
+ ,112.09
+ ,95.70
+ ,102.65
+ ,129.47
+ ,113.20
+ ,102.54
+ ,119.72
+ ,105.90
+ ,102.37
+ ,134.81
+ ,108.80
+ ,102.68
+ ,135.8
+ ,102.30
+ ,102.76
+ ,129.27
+ ,99.00
+ ,102.82
+ ,126.94
+ ,100.70
+ ,103.31
+ ,153.45
+ ,115.50
+ ,103.23
+ ,121.86
+ ,100.70
+ ,103.60
+ ,133.47
+ ,109.90
+ ,103.95
+ ,135.34
+ ,114.60
+ ,103.93
+ ,117.1
+ ,85.40
+ ,104.25
+ ,120.65
+ ,100.50
+ ,104.38
+ ,132.49
+ ,114.80
+ ,104.36
+ ,137.6
+ ,116.50
+ ,104.32
+ ,138.69
+ ,112.90
+ ,104.58
+ ,125.53
+ ,102.00
+ ,104.68
+ ,133.09
+ ,106.00
+ ,104.92
+ ,129.08
+ ,105.30
+ ,105.46
+ ,145.94
+ ,118.80
+ ,105.23
+ ,129.07
+ ,106.10
+ ,105.58
+ ,139.69
+ ,109.30
+ ,105.34
+ ,142.09
+ ,117.20
+ ,105.28
+ ,137.29
+ ,92.50
+ ,105.70
+ ,127.03
+ ,104.20
+ ,105.67
+ ,137.25
+ ,112.50
+ ,105.71
+ ,156.87
+ ,122.40
+ ,106.19
+ ,150.89
+ ,113.30
+ ,106.93
+ ,139.14
+ ,100.00
+ ,107.44
+ ,158.3
+ ,110.70
+ ,107.85
+ ,149
+ ,112.80
+ ,108.71
+ ,158.36
+ ,109.80
+ ,109.32
+ ,168.06
+ ,117.30
+ ,109.49
+ ,153.38
+ ,109.10
+ ,110.20
+ ,173.86
+ ,115.90
+ ,110.62
+ ,162.47
+ ,96.00
+ ,111.22
+ ,145.17
+ ,99.80
+ ,110.88
+ ,168.89
+ ,116.80
+ ,111.15
+ ,166.64
+ ,115.70
+ ,111.29
+ ,140.07
+ ,99.40
+ ,111.09
+ ,128.84
+ ,94.30
+ ,111.24
+ ,123.41
+ ,91.00
+ ,111.45
+ ,120.3
+ ,93.20
+ ,111.75
+ ,129.67
+ ,103.10
+ ,111.07
+ ,118.1
+ ,94.10
+ ,111.17
+ ,113.91
+ ,91.80
+ ,110.96
+ ,131.09
+ ,102.70
+ ,110.50
+ ,119.15
+ ,82.60
+ ,110.48
+ ,122.3
+ ,89.10
+ ,110.66)
+ ,dim=c(3
+ ,70)
+ ,dimnames=list(c('Invoer'
+ ,'TIP'
+ ,'CONS')
+ ,1:70))
> y <- array(NA,dim=c(3,70),dimnames=list(c('Invoer','TIP','CONS'),1:70))
> 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
Invoer TIP CONS
1 100.50 98.6 96.33
2 106.29 96.9 96.33
3 101.09 95.1 95.05
4 104.53 97.0 96.84
5 122.74 112.7 96.92
6 109.84 102.9 97.44
7 101.99 97.4 97.78
8 125.12 111.4 97.69
9 103.50 87.4 96.67
10 102.80 96.8 98.29
11 118.72 114.1 98.20
12 119.01 110.3 98.71
13 118.61 103.9 98.54
14 120.43 101.6 98.20
15 111.83 94.6 100.80
16 116.79 95.9 101.33
17 131.71 104.7 101.88
18 120.57 102.8 101.85
19 117.83 98.1 102.04
20 130.80 113.9 102.22
21 107.46 80.9 102.63
22 112.09 95.7 102.65
23 129.47 113.2 102.54
24 119.72 105.9 102.37
25 134.81 108.8 102.68
26 135.80 102.3 102.76
27 129.27 99.0 102.82
28 126.94 100.7 103.31
29 153.45 115.5 103.23
30 121.86 100.7 103.60
31 133.47 109.9 103.95
32 135.34 114.6 103.93
33 117.10 85.4 104.25
34 120.65 100.5 104.38
35 132.49 114.8 104.36
36 137.60 116.5 104.32
37 138.69 112.9 104.58
38 125.53 102.0 104.68
39 133.09 106.0 104.92
40 129.08 105.3 105.46
41 145.94 118.8 105.23
42 129.07 106.1 105.58
43 139.69 109.3 105.34
44 142.09 117.2 105.28
45 137.29 92.5 105.70
46 127.03 104.2 105.67
47 137.25 112.5 105.71
48 156.87 122.4 106.19
49 150.89 113.3 106.93
50 139.14 100.0 107.44
51 158.30 110.7 107.85
52 149.00 112.8 108.71
53 158.36 109.8 109.32
54 168.06 117.3 109.49
55 153.38 109.1 110.20
56 173.86 115.9 110.62
57 162.47 96.0 111.22
58 145.17 99.8 110.88
59 168.89 116.8 111.15
60 166.64 115.7 111.29
61 140.07 99.4 111.09
62 128.84 94.3 111.24
63 123.41 91.0 111.45
64 120.30 93.2 111.75
65 129.67 103.1 111.07
66 118.10 94.1 111.17
67 113.91 91.8 110.96
68 131.09 102.7 110.50
69 119.15 82.6 110.48
70 122.30 89.1 110.66
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TIP CONS
-227.017 1.157 2.268
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16.9780 -5.4947 -0.5072 4.9712 26.1324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -227.0169 24.0408 -9.443 6.2e-14 ***
TIP 1.1571 0.1054 10.983 < 2e-16 ***
CONS 2.2682 0.2083 10.890 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.285 on 67 degrees of freedom
Multiple R-squared: 0.7872, Adjusted R-squared: 0.7809
F-statistic: 123.9 on 2 and 67 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,] 7.208858e-02 1.441772e-01 0.9279114
[2,] 2.486282e-02 4.972563e-02 0.9751372
[3,] 1.459896e-02 2.919792e-02 0.9854010
[4,] 8.062220e-02 1.612444e-01 0.9193778
[5,] 4.849666e-02 9.699332e-02 0.9515033
[6,] 3.061866e-02 6.123731e-02 0.9693813
[7,] 1.518673e-02 3.037346e-02 0.9848133
[8,] 1.382527e-02 2.765054e-02 0.9861747
[9,] 2.412133e-02 4.824266e-02 0.9758787
[10,] 1.257391e-02 2.514782e-02 0.9874261
[11,] 6.995215e-03 1.399043e-02 0.9930048
[12,] 7.420323e-03 1.484065e-02 0.9925797
[13,] 4.408894e-03 8.817788e-03 0.9955911
[14,] 2.262006e-03 4.524012e-03 0.9977380
[15,] 1.240585e-03 2.481170e-03 0.9987594
[16,] 6.842948e-04 1.368590e-03 0.9993157
[17,] 6.376427e-04 1.275285e-03 0.9993624
[18,] 3.629955e-04 7.259911e-04 0.9996370
[19,] 3.102603e-04 6.205207e-04 0.9996897
[20,] 3.093365e-04 6.186731e-04 0.9996907
[21,] 1.388847e-03 2.777695e-03 0.9986112
[22,] 1.540722e-03 3.081443e-03 0.9984593
[23,] 8.648288e-04 1.729658e-03 0.9991352
[24,] 5.568752e-03 1.113750e-02 0.9944312
[25,] 4.022568e-03 8.045137e-03 0.9959774
[26,] 2.417797e-03 4.835593e-03 0.9975822
[27,] 1.728018e-03 3.456037e-03 0.9982720
[28,] 1.815985e-03 3.631970e-03 0.9981840
[29,] 1.627950e-03 3.255899e-03 0.9983721
[30,] 1.847872e-03 3.695744e-03 0.9981521
[31,] 1.326459e-03 2.652918e-03 0.9986735
[32,] 7.425507e-04 1.485101e-03 0.9992574
[33,] 4.380647e-04 8.761294e-04 0.9995619
[34,] 2.248321e-04 4.496643e-04 0.9997752
[35,] 1.436724e-04 2.873448e-04 0.9998563
[36,] 8.848361e-05 1.769672e-04 0.9999115
[37,] 6.761189e-05 1.352238e-04 0.9999324
[38,] 3.632399e-05 7.264799e-05 0.9999637
[39,] 3.048393e-05 6.096785e-05 0.9999695
[40,] 5.405120e-04 1.081024e-03 0.9994595
[41,] 4.205324e-04 8.410649e-04 0.9995795
[42,] 3.755532e-04 7.511064e-04 0.9996244
[43,] 5.649318e-04 1.129864e-03 0.9994351
[44,] 5.095260e-04 1.019052e-03 0.9994905
[45,] 2.914355e-04 5.828710e-04 0.9997086
[46,] 4.540597e-04 9.081193e-04 0.9995459
[47,] 4.213666e-04 8.427333e-04 0.9995786
[48,] 3.285051e-04 6.570102e-04 0.9996715
[49,] 3.106779e-04 6.213558e-04 0.9996893
[50,] 1.499642e-04 2.999285e-04 0.9998500
[51,] 2.675339e-04 5.350679e-04 0.9997325
[52,] 1.159884e-01 2.319769e-01 0.8840116
[53,] 1.300680e-01 2.601361e-01 0.8699320
[54,] 1.337931e-01 2.675862e-01 0.8662069
[55,] 4.240902e-01 8.481803e-01 0.5759098
[56,] 7.572738e-01 4.854524e-01 0.2427262
[57,] 8.163086e-01 3.673829e-01 0.1836914
[58,] 8.209130e-01 3.581740e-01 0.1790870
[59,] 8.453856e-01 3.092288e-01 0.1546144
> postscript(file="/var/www/html/rcomp/tmp/1flzt1261150401.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2z8e01261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cj791261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gkt51261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5bk7k1261150401.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 = 70
Frequency = 1
1 2 3 4 5 6
-5.07216753 2.68491173 2.47103488 -0.34759570 -0.48560956 -3.22545535
7 8 9 10 11 12
-5.48257331 1.65209105 10.11621556 -5.13510692 -9.02889054 -5.49868672
13 14 15 16 17 18
1.89238711 7.14492756 0.74726982 3.00087127 6.49081729 -2.38263549
19 20 21 22 23 24
-0.11520343 -5.83575075 8.07875506 -4.46177015 -7.08161028 -7.99914155
25 26 27 28 29 30
3.03210161 11.36182868 8.51418291 3.10567135 12.67196906 -2.63211511
31 32 33 34 35 36
-2.46136544 -5.98439646 8.83724927 -5.37991278 -10.04115610 -6.80750620
37 38 39 40 41 42
-2.14166617 -2.91603971 -0.52883650 -4.95370645 -3.19293728 -6.16157831
43 44 45 46 47 48
1.30005926 -5.30498002 17.52286828 -6.20721858 -5.68192297 1.39398310
49 50 51 52 53 54
4.26515308 6.74785865 12.59685639 -1.08374214 10.36395440 11.00006458
55 56 57 58 59 60
4.19788654 15.85691324 26.13237414 5.20657136 8.64335687 7.34862078
61 62 63 64 65 66
0.09308541 -5.57591118 -7.66379134 -13.99989207 -14.54284016 -15.92571405
67 68 69 70
-16.97804340 -11.36710735 -0.00392327 -4.78338993
> postscript(file="/var/www/html/rcomp/tmp/6b2cm1261150401.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 = 70
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.07216753 NA
1 2.68491173 -5.07216753
2 2.47103488 2.68491173
3 -0.34759570 2.47103488
4 -0.48560956 -0.34759570
5 -3.22545535 -0.48560956
6 -5.48257331 -3.22545535
7 1.65209105 -5.48257331
8 10.11621556 1.65209105
9 -5.13510692 10.11621556
10 -9.02889054 -5.13510692
11 -5.49868672 -9.02889054
12 1.89238711 -5.49868672
13 7.14492756 1.89238711
14 0.74726982 7.14492756
15 3.00087127 0.74726982
16 6.49081729 3.00087127
17 -2.38263549 6.49081729
18 -0.11520343 -2.38263549
19 -5.83575075 -0.11520343
20 8.07875506 -5.83575075
21 -4.46177015 8.07875506
22 -7.08161028 -4.46177015
23 -7.99914155 -7.08161028
24 3.03210161 -7.99914155
25 11.36182868 3.03210161
26 8.51418291 11.36182868
27 3.10567135 8.51418291
28 12.67196906 3.10567135
29 -2.63211511 12.67196906
30 -2.46136544 -2.63211511
31 -5.98439646 -2.46136544
32 8.83724927 -5.98439646
33 -5.37991278 8.83724927
34 -10.04115610 -5.37991278
35 -6.80750620 -10.04115610
36 -2.14166617 -6.80750620
37 -2.91603971 -2.14166617
38 -0.52883650 -2.91603971
39 -4.95370645 -0.52883650
40 -3.19293728 -4.95370645
41 -6.16157831 -3.19293728
42 1.30005926 -6.16157831
43 -5.30498002 1.30005926
44 17.52286828 -5.30498002
45 -6.20721858 17.52286828
46 -5.68192297 -6.20721858
47 1.39398310 -5.68192297
48 4.26515308 1.39398310
49 6.74785865 4.26515308
50 12.59685639 6.74785865
51 -1.08374214 12.59685639
52 10.36395440 -1.08374214
53 11.00006458 10.36395440
54 4.19788654 11.00006458
55 15.85691324 4.19788654
56 26.13237414 15.85691324
57 5.20657136 26.13237414
58 8.64335687 5.20657136
59 7.34862078 8.64335687
60 0.09308541 7.34862078
61 -5.57591118 0.09308541
62 -7.66379134 -5.57591118
63 -13.99989207 -7.66379134
64 -14.54284016 -13.99989207
65 -15.92571405 -14.54284016
66 -16.97804340 -15.92571405
67 -11.36710735 -16.97804340
68 -0.00392327 -11.36710735
69 -4.78338993 -0.00392327
70 NA -4.78338993
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.68491173 -5.07216753
[2,] 2.47103488 2.68491173
[3,] -0.34759570 2.47103488
[4,] -0.48560956 -0.34759570
[5,] -3.22545535 -0.48560956
[6,] -5.48257331 -3.22545535
[7,] 1.65209105 -5.48257331
[8,] 10.11621556 1.65209105
[9,] -5.13510692 10.11621556
[10,] -9.02889054 -5.13510692
[11,] -5.49868672 -9.02889054
[12,] 1.89238711 -5.49868672
[13,] 7.14492756 1.89238711
[14,] 0.74726982 7.14492756
[15,] 3.00087127 0.74726982
[16,] 6.49081729 3.00087127
[17,] -2.38263549 6.49081729
[18,] -0.11520343 -2.38263549
[19,] -5.83575075 -0.11520343
[20,] 8.07875506 -5.83575075
[21,] -4.46177015 8.07875506
[22,] -7.08161028 -4.46177015
[23,] -7.99914155 -7.08161028
[24,] 3.03210161 -7.99914155
[25,] 11.36182868 3.03210161
[26,] 8.51418291 11.36182868
[27,] 3.10567135 8.51418291
[28,] 12.67196906 3.10567135
[29,] -2.63211511 12.67196906
[30,] -2.46136544 -2.63211511
[31,] -5.98439646 -2.46136544
[32,] 8.83724927 -5.98439646
[33,] -5.37991278 8.83724927
[34,] -10.04115610 -5.37991278
[35,] -6.80750620 -10.04115610
[36,] -2.14166617 -6.80750620
[37,] -2.91603971 -2.14166617
[38,] -0.52883650 -2.91603971
[39,] -4.95370645 -0.52883650
[40,] -3.19293728 -4.95370645
[41,] -6.16157831 -3.19293728
[42,] 1.30005926 -6.16157831
[43,] -5.30498002 1.30005926
[44,] 17.52286828 -5.30498002
[45,] -6.20721858 17.52286828
[46,] -5.68192297 -6.20721858
[47,] 1.39398310 -5.68192297
[48,] 4.26515308 1.39398310
[49,] 6.74785865 4.26515308
[50,] 12.59685639 6.74785865
[51,] -1.08374214 12.59685639
[52,] 10.36395440 -1.08374214
[53,] 11.00006458 10.36395440
[54,] 4.19788654 11.00006458
[55,] 15.85691324 4.19788654
[56,] 26.13237414 15.85691324
[57,] 5.20657136 26.13237414
[58,] 8.64335687 5.20657136
[59,] 7.34862078 8.64335687
[60,] 0.09308541 7.34862078
[61,] -5.57591118 0.09308541
[62,] -7.66379134 -5.57591118
[63,] -13.99989207 -7.66379134
[64,] -14.54284016 -13.99989207
[65,] -15.92571405 -14.54284016
[66,] -16.97804340 -15.92571405
[67,] -11.36710735 -16.97804340
[68,] -0.00392327 -11.36710735
[69,] -4.78338993 -0.00392327
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.68491173 -5.07216753
2 2.47103488 2.68491173
3 -0.34759570 2.47103488
4 -0.48560956 -0.34759570
5 -3.22545535 -0.48560956
6 -5.48257331 -3.22545535
7 1.65209105 -5.48257331
8 10.11621556 1.65209105
9 -5.13510692 10.11621556
10 -9.02889054 -5.13510692
11 -5.49868672 -9.02889054
12 1.89238711 -5.49868672
13 7.14492756 1.89238711
14 0.74726982 7.14492756
15 3.00087127 0.74726982
16 6.49081729 3.00087127
17 -2.38263549 6.49081729
18 -0.11520343 -2.38263549
19 -5.83575075 -0.11520343
20 8.07875506 -5.83575075
21 -4.46177015 8.07875506
22 -7.08161028 -4.46177015
23 -7.99914155 -7.08161028
24 3.03210161 -7.99914155
25 11.36182868 3.03210161
26 8.51418291 11.36182868
27 3.10567135 8.51418291
28 12.67196906 3.10567135
29 -2.63211511 12.67196906
30 -2.46136544 -2.63211511
31 -5.98439646 -2.46136544
32 8.83724927 -5.98439646
33 -5.37991278 8.83724927
34 -10.04115610 -5.37991278
35 -6.80750620 -10.04115610
36 -2.14166617 -6.80750620
37 -2.91603971 -2.14166617
38 -0.52883650 -2.91603971
39 -4.95370645 -0.52883650
40 -3.19293728 -4.95370645
41 -6.16157831 -3.19293728
42 1.30005926 -6.16157831
43 -5.30498002 1.30005926
44 17.52286828 -5.30498002
45 -6.20721858 17.52286828
46 -5.68192297 -6.20721858
47 1.39398310 -5.68192297
48 4.26515308 1.39398310
49 6.74785865 4.26515308
50 12.59685639 6.74785865
51 -1.08374214 12.59685639
52 10.36395440 -1.08374214
53 11.00006458 10.36395440
54 4.19788654 11.00006458
55 15.85691324 4.19788654
56 26.13237414 15.85691324
57 5.20657136 26.13237414
58 8.64335687 5.20657136
59 7.34862078 8.64335687
60 0.09308541 7.34862078
61 -5.57591118 0.09308541
62 -7.66379134 -5.57591118
63 -13.99989207 -7.66379134
64 -14.54284016 -13.99989207
65 -15.92571405 -14.54284016
66 -16.97804340 -15.92571405
67 -11.36710735 -16.97804340
68 -0.00392327 -11.36710735
69 -4.78338993 -0.00392327
> 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/7vbw01261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ktir1261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9d2nz1261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10cwdj1261150401.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118u2t1261150401.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/12ivnr1261150401.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/1339sb1261150401.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/143h3i1261150401.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/15nh491261150401.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/16l1hl1261150401.tab")
+ }
>
> try(system("convert tmp/1flzt1261150401.ps tmp/1flzt1261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/2z8e01261150401.ps tmp/2z8e01261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cj791261150401.ps tmp/3cj791261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gkt51261150401.ps tmp/4gkt51261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/5bk7k1261150401.ps tmp/5bk7k1261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b2cm1261150401.ps tmp/6b2cm1261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vbw01261150401.ps tmp/7vbw01261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ktir1261150401.ps tmp/8ktir1261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d2nz1261150401.ps tmp/9d2nz1261150401.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cwdj1261150401.ps tmp/10cwdj1261150401.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.601 1.581 6.687