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(105.31
+ ,1576.23
+ ,29.29
+ ,105.63
+ ,1546.37
+ ,28.99
+ ,106.02
+ ,1545.05
+ ,28.91
+ ,105.85
+ ,1552.34
+ ,29.29
+ ,106.57
+ ,1594.3
+ ,30.96
+ ,106.48
+ ,1605.78
+ ,30.57
+ ,106.60
+ ,1673.21
+ ,30.59
+ ,106.75
+ ,1612.94
+ ,31.39
+ ,106.69
+ ,1566.34
+ ,31.28
+ ,106.69
+ ,1530.17
+ ,31.1
+ ,106.93
+ ,1582.54
+ ,31.7
+ ,107.21
+ ,1702.16
+ ,32.57
+ ,107.88
+ ,1701.93
+ ,32.49
+ ,108.84
+ ,1811.15
+ ,32.46
+ ,108.96
+ ,1924.2
+ ,32.3
+ ,109.52
+ ,2034.25
+ ,32.97
+ ,108.45
+ ,2011.13
+ ,32.9
+ ,108.67
+ ,2013.04
+ ,32.93
+ ,108.96
+ ,2151.67
+ ,33.72
+ ,108.76
+ ,1902.09
+ ,33.33
+ ,107.85
+ ,1944.01
+ ,33.44
+ ,108.78
+ ,1916.67
+ ,33.89
+ ,107.51
+ ,1967.31
+ ,34.34
+ ,108.83
+ ,2119.88
+ ,33.56
+ ,111.54
+ ,2216.38
+ ,32.67
+ ,111.74
+ ,2522.83
+ ,32.57
+ ,112.04
+ ,2647.64
+ ,33.23
+ ,111.74
+ ,2631.23
+ ,32.85
+ ,111.81
+ ,2693.41
+ ,32.61
+ ,111.86
+ ,3021.76
+ ,32.57
+ ,114.23
+ ,2953.67
+ ,32.98
+ ,114.80
+ ,2796.8
+ ,31.33
+ ,115.17
+ ,2672.05
+ ,29.8
+ ,115.11
+ ,2251.23
+ ,28.06
+ ,114.43
+ ,2046.08
+ ,25.47
+ ,114.66
+ ,2420.04
+ ,24.65
+ ,115.11
+ ,2608.89
+ ,23.94
+ ,117.74
+ ,2660.47
+ ,23.89
+ ,118.18
+ ,2493.98
+ ,23.54
+ ,118.56
+ ,2541.7
+ ,24.28
+ ,117.63
+ ,2554.6
+ ,25.51
+ ,117.71
+ ,2699.61
+ ,27.03
+ ,117.46
+ ,2805.48
+ ,27.09
+ ,117.37
+ ,2956.66
+ ,27.3
+ ,117.34
+ ,3149.51
+ ,27.11
+ ,117.09
+ ,3372.5
+ ,26.39
+ ,116.65
+ ,3379.33
+ ,27.54
+ ,116.71
+ ,3517.54
+ ,26.85
+ ,116.82
+ ,3527.34
+ ,26.82
+ ,117.33
+ ,3281.06
+ ,25.9
+ ,117.95
+ ,3089.65
+ ,24.96
+ ,123.53
+ ,3222.76
+ ,25.4
+ ,124.91
+ ,3165.76
+ ,24.38
+ ,125.99
+ ,3232.43
+ ,24.73
+ ,126.29
+ ,3229.54
+ ,25.43
+ ,125.68
+ ,3071.74
+ ,26.04
+ ,125.52
+ ,2850.17
+ ,25.59)
+ ,dim=c(3
+ ,57)
+ ,dimnames=list(c('PC&S'
+ ,'PCacao'
+ ,'PSuiker')
+ ,1:57))
> y <- array(NA,dim=c(3,57),dimnames=list(c('PC&S','PCacao','PSuiker'),1:57))
> 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
PC&S PCacao PSuiker M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 105.31 1576.23 29.29 1 0 0 0 0 0 0 0 0 0 0 1
2 105.63 1546.37 28.99 0 1 0 0 0 0 0 0 0 0 0 2
3 106.02 1545.05 28.91 0 0 1 0 0 0 0 0 0 0 0 3
4 105.85 1552.34 29.29 0 0 0 1 0 0 0 0 0 0 0 4
5 106.57 1594.30 30.96 0 0 0 0 1 0 0 0 0 0 0 5
6 106.48 1605.78 30.57 0 0 0 0 0 1 0 0 0 0 0 6
7 106.60 1673.21 30.59 0 0 0 0 0 0 1 0 0 0 0 7
8 106.75 1612.94 31.39 0 0 0 0 0 0 0 1 0 0 0 8
9 106.69 1566.34 31.28 0 0 0 0 0 0 0 0 1 0 0 9
10 106.69 1530.17 31.10 0 0 0 0 0 0 0 0 0 1 0 10
11 106.93 1582.54 31.70 0 0 0 0 0 0 0 0 0 0 1 11
12 107.21 1702.16 32.57 0 0 0 0 0 0 0 0 0 0 0 12
13 107.88 1701.93 32.49 1 0 0 0 0 0 0 0 0 0 0 13
14 108.84 1811.15 32.46 0 1 0 0 0 0 0 0 0 0 0 14
15 108.96 1924.20 32.30 0 0 1 0 0 0 0 0 0 0 0 15
16 109.52 2034.25 32.97 0 0 0 1 0 0 0 0 0 0 0 16
17 108.45 2011.13 32.90 0 0 0 0 1 0 0 0 0 0 0 17
18 108.67 2013.04 32.93 0 0 0 0 0 1 0 0 0 0 0 18
19 108.96 2151.67 33.72 0 0 0 0 0 0 1 0 0 0 0 19
20 108.76 1902.09 33.33 0 0 0 0 0 0 0 1 0 0 0 20
21 107.85 1944.01 33.44 0 0 0 0 0 0 0 0 1 0 0 21
22 108.78 1916.67 33.89 0 0 0 0 0 0 0 0 0 1 0 22
23 107.51 1967.31 34.34 0 0 0 0 0 0 0 0 0 0 1 23
24 108.83 2119.88 33.56 0 0 0 0 0 0 0 0 0 0 0 24
25 111.54 2216.38 32.67 1 0 0 0 0 0 0 0 0 0 0 25
26 111.74 2522.83 32.57 0 1 0 0 0 0 0 0 0 0 0 26
27 112.04 2647.64 33.23 0 0 1 0 0 0 0 0 0 0 0 27
28 111.74 2631.23 32.85 0 0 0 1 0 0 0 0 0 0 0 28
29 111.81 2693.41 32.61 0 0 0 0 1 0 0 0 0 0 0 29
30 111.86 3021.76 32.57 0 0 0 0 0 1 0 0 0 0 0 30
31 114.23 2953.67 32.98 0 0 0 0 0 0 1 0 0 0 0 31
32 114.80 2796.80 31.33 0 0 0 0 0 0 0 1 0 0 0 32
33 115.17 2672.05 29.80 0 0 0 0 0 0 0 0 1 0 0 33
34 115.11 2251.23 28.06 0 0 0 0 0 0 0 0 0 1 0 34
35 114.43 2046.08 25.47 0 0 0 0 0 0 0 0 0 0 1 35
36 114.66 2420.04 24.65 0 0 0 0 0 0 0 0 0 0 0 36
37 115.11 2608.89 23.94 1 0 0 0 0 0 0 0 0 0 0 37
38 117.74 2660.47 23.89 0 1 0 0 0 0 0 0 0 0 0 38
39 118.18 2493.98 23.54 0 0 1 0 0 0 0 0 0 0 0 39
40 118.56 2541.70 24.28 0 0 0 1 0 0 0 0 0 0 0 40
41 117.63 2554.60 25.51 0 0 0 0 1 0 0 0 0 0 0 41
42 117.71 2699.61 27.03 0 0 0 0 0 1 0 0 0 0 0 42
43 117.46 2805.48 27.09 0 0 0 0 0 0 1 0 0 0 0 43
44 117.37 2956.66 27.30 0 0 0 0 0 0 0 1 0 0 0 44
45 117.34 3149.51 27.11 0 0 0 0 0 0 0 0 1 0 0 45
46 117.09 3372.50 26.39 0 0 0 0 0 0 0 0 0 1 0 46
47 116.65 3379.33 27.54 0 0 0 0 0 0 0 0 0 0 1 47
48 116.71 3517.54 26.85 0 0 0 0 0 0 0 0 0 0 0 48
49 116.82 3527.34 26.82 1 0 0 0 0 0 0 0 0 0 0 49
50 117.33 3281.06 25.90 0 1 0 0 0 0 0 0 0 0 0 50
51 117.95 3089.65 24.96 0 0 1 0 0 0 0 0 0 0 0 51
52 123.53 3222.76 25.40 0 0 0 1 0 0 0 0 0 0 0 52
53 124.91 3165.76 24.38 0 0 0 0 1 0 0 0 0 0 0 53
54 125.99 3232.43 24.73 0 0 0 0 0 1 0 0 0 0 0 54
55 126.29 3229.54 25.43 0 0 0 0 0 0 1 0 0 0 0 55
56 125.68 3071.74 26.04 0 0 0 0 0 0 0 1 0 0 0 56
57 125.52 2850.17 25.59 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PCacao PSuiker M1 M2 M3
113.738844 -0.002073 -0.273358 1.012109 1.565134 1.467599
M4 M5 M6 M7 M8 M9
2.521886 2.283366 2.487496 2.887973 2.259124 1.543265
M10 M11 t
0.578953 -0.408249 0.373660
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.0859 -0.8122 -0.2118 0.9666 3.0463
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.137e+02 3.049e+00 37.307 < 2e-16 ***
PCacao -2.073e-03 9.879e-04 -2.098 0.04194 *
PSuiker -2.734e-01 9.366e-02 -2.919 0.00563 **
M1 1.012e+00 1.041e+00 0.972 0.33670
M2 1.565e+00 1.043e+00 1.501 0.14082
M3 1.468e+00 1.038e+00 1.414 0.16476
M4 2.522e+00 1.034e+00 2.439 0.01903 *
M5 2.283e+00 1.031e+00 2.215 0.03221 *
M6 2.487e+00 1.033e+00 2.409 0.02047 *
M7 2.888e+00 1.034e+00 2.793 0.00783 **
M8 2.259e+00 1.035e+00 2.183 0.03470 *
M9 1.543e+00 1.039e+00 1.485 0.14505
M10 5.790e-01 1.092e+00 0.530 0.59865
M11 -4.082e-01 1.099e+00 -0.371 0.71214
t 3.737e-01 4.379e-02 8.533 1.02e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.536 on 42 degrees of freedom
Multiple R-squared: 0.9503, Adjusted R-squared: 0.9338
F-statistic: 57.38 on 14 and 42 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,] 4.276366e-04 8.552732e-04 0.9995724
[2,] 6.418144e-04 1.283629e-03 0.9993582
[3,] 1.064231e-04 2.128463e-04 0.9998936
[4,] 1.402185e-04 2.804370e-04 0.9998598
[5,] 2.980931e-05 5.961862e-05 0.9999702
[6,] 3.608241e-04 7.216483e-04 0.9996392
[7,] 1.885700e-04 3.771401e-04 0.9998114
[8,] 2.518789e-03 5.037577e-03 0.9974812
[9,] 1.370012e-03 2.740023e-03 0.9986300
[10,] 9.930196e-04 1.986039e-03 0.9990070
[11,] 3.401117e-04 6.802235e-04 0.9996599
[12,] 1.084338e-04 2.168676e-04 0.9998916
[13,] 3.552161e-05 7.104323e-05 0.9999645
[14,] 1.410120e-04 2.820240e-04 0.9998590
[15,] 5.417602e-04 1.083520e-03 0.9994582
[16,] 6.704310e-03 1.340862e-02 0.9932957
[17,] 7.692261e-02 1.538452e-01 0.9230774
[18,] 5.260627e-02 1.052125e-01 0.9473937
[19,] 4.526826e-02 9.053651e-02 0.9547317
[20,] 1.376257e-01 2.752514e-01 0.8623743
[21,] 9.603333e-02 1.920667e-01 0.9039667
[22,] 8.792140e-01 2.415720e-01 0.1207860
> postscript(file="/var/www/html/rcomp/tmp/1zbds1292931581.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zbds1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zbds1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4a2ud1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a2ud1292931581.ps",horizontal=F,onefile=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 = 57
Frequency = 1
1 2 3 4 5 6
1.459213428 0.708627334 0.797897390 -0.681063715 0.447277865 -0.303326835
7 8 9 10 11 12
-0.812229099 -0.313281354 -0.157742929 0.308731721 1.434839361 1.418697255
13 14 15 16 17 18
0.680582999 0.932085610 0.966550775 0.509862200 -0.762335244 -1.107966164
19 20 21 22 23 24
-1.088801060 -1.657546884 -2.108387348 -0.521394647 -0.949876584 -0.308760989
25 26 27 28 29 30
0.972204850 0.853386615 1.316381055 -0.549456838 -0.551317061 -0.409445055
31 32 33 34 35 36
1.157359214 1.206349904 1.241732579 0.424474629 -0.775211149 -0.776137327
37 38 39 40 41 42
-1.514545946 0.282014529 0.005117076 -0.741632768 -1.443803576 -1.225516705
43 44 45 46 47 48
-2.013807033 -1.477851273 -0.817854544 -0.211811703 0.290248371 -0.333798938
49 50 51 52 53 54
-1.597455331 -2.776114088 -3.085946295 1.462291120 2.310178016 3.046254758
55 56 57
2.757477978 2.242329607 1.842252242
> postscript(file="/var/www/html/rcomp/tmp/6a2ud1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 1.459213428 NA
1 0.708627334 1.459213428
2 0.797897390 0.708627334
3 -0.681063715 0.797897390
4 0.447277865 -0.681063715
5 -0.303326835 0.447277865
6 -0.812229099 -0.303326835
7 -0.313281354 -0.812229099
8 -0.157742929 -0.313281354
9 0.308731721 -0.157742929
10 1.434839361 0.308731721
11 1.418697255 1.434839361
12 0.680582999 1.418697255
13 0.932085610 0.680582999
14 0.966550775 0.932085610
15 0.509862200 0.966550775
16 -0.762335244 0.509862200
17 -1.107966164 -0.762335244
18 -1.088801060 -1.107966164
19 -1.657546884 -1.088801060
20 -2.108387348 -1.657546884
21 -0.521394647 -2.108387348
22 -0.949876584 -0.521394647
23 -0.308760989 -0.949876584
24 0.972204850 -0.308760989
25 0.853386615 0.972204850
26 1.316381055 0.853386615
27 -0.549456838 1.316381055
28 -0.551317061 -0.549456838
29 -0.409445055 -0.551317061
30 1.157359214 -0.409445055
31 1.206349904 1.157359214
32 1.241732579 1.206349904
33 0.424474629 1.241732579
34 -0.775211149 0.424474629
35 -0.776137327 -0.775211149
36 -1.514545946 -0.776137327
37 0.282014529 -1.514545946
38 0.005117076 0.282014529
39 -0.741632768 0.005117076
40 -1.443803576 -0.741632768
41 -1.225516705 -1.443803576
42 -2.013807033 -1.225516705
43 -1.477851273 -2.013807033
44 -0.817854544 -1.477851273
45 -0.211811703 -0.817854544
46 0.290248371 -0.211811703
47 -0.333798938 0.290248371
48 -1.597455331 -0.333798938
49 -2.776114088 -1.597455331
50 -3.085946295 -2.776114088
51 1.462291120 -3.085946295
52 2.310178016 1.462291120
53 3.046254758 2.310178016
54 2.757477978 3.046254758
55 2.242329607 2.757477978
56 1.842252242 2.242329607
57 NA 1.842252242
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.708627334 1.459213428
[2,] 0.797897390 0.708627334
[3,] -0.681063715 0.797897390
[4,] 0.447277865 -0.681063715
[5,] -0.303326835 0.447277865
[6,] -0.812229099 -0.303326835
[7,] -0.313281354 -0.812229099
[8,] -0.157742929 -0.313281354
[9,] 0.308731721 -0.157742929
[10,] 1.434839361 0.308731721
[11,] 1.418697255 1.434839361
[12,] 0.680582999 1.418697255
[13,] 0.932085610 0.680582999
[14,] 0.966550775 0.932085610
[15,] 0.509862200 0.966550775
[16,] -0.762335244 0.509862200
[17,] -1.107966164 -0.762335244
[18,] -1.088801060 -1.107966164
[19,] -1.657546884 -1.088801060
[20,] -2.108387348 -1.657546884
[21,] -0.521394647 -2.108387348
[22,] -0.949876584 -0.521394647
[23,] -0.308760989 -0.949876584
[24,] 0.972204850 -0.308760989
[25,] 0.853386615 0.972204850
[26,] 1.316381055 0.853386615
[27,] -0.549456838 1.316381055
[28,] -0.551317061 -0.549456838
[29,] -0.409445055 -0.551317061
[30,] 1.157359214 -0.409445055
[31,] 1.206349904 1.157359214
[32,] 1.241732579 1.206349904
[33,] 0.424474629 1.241732579
[34,] -0.775211149 0.424474629
[35,] -0.776137327 -0.775211149
[36,] -1.514545946 -0.776137327
[37,] 0.282014529 -1.514545946
[38,] 0.005117076 0.282014529
[39,] -0.741632768 0.005117076
[40,] -1.443803576 -0.741632768
[41,] -1.225516705 -1.443803576
[42,] -2.013807033 -1.225516705
[43,] -1.477851273 -2.013807033
[44,] -0.817854544 -1.477851273
[45,] -0.211811703 -0.817854544
[46,] 0.290248371 -0.211811703
[47,] -0.333798938 0.290248371
[48,] -1.597455331 -0.333798938
[49,] -2.776114088 -1.597455331
[50,] -3.085946295 -2.776114088
[51,] 1.462291120 -3.085946295
[52,] 2.310178016 1.462291120
[53,] 3.046254758 2.310178016
[54,] 2.757477978 3.046254758
[55,] 2.242329607 2.757477978
[56,] 1.842252242 2.242329607
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.708627334 1.459213428
2 0.797897390 0.708627334
3 -0.681063715 0.797897390
4 0.447277865 -0.681063715
5 -0.303326835 0.447277865
6 -0.812229099 -0.303326835
7 -0.313281354 -0.812229099
8 -0.157742929 -0.313281354
9 0.308731721 -0.157742929
10 1.434839361 0.308731721
11 1.418697255 1.434839361
12 0.680582999 1.418697255
13 0.932085610 0.680582999
14 0.966550775 0.932085610
15 0.509862200 0.966550775
16 -0.762335244 0.509862200
17 -1.107966164 -0.762335244
18 -1.088801060 -1.107966164
19 -1.657546884 -1.088801060
20 -2.108387348 -1.657546884
21 -0.521394647 -2.108387348
22 -0.949876584 -0.521394647
23 -0.308760989 -0.949876584
24 0.972204850 -0.308760989
25 0.853386615 0.972204850
26 1.316381055 0.853386615
27 -0.549456838 1.316381055
28 -0.551317061 -0.549456838
29 -0.409445055 -0.551317061
30 1.157359214 -0.409445055
31 1.206349904 1.157359214
32 1.241732579 1.206349904
33 0.424474629 1.241732579
34 -0.775211149 0.424474629
35 -0.776137327 -0.775211149
36 -1.514545946 -0.776137327
37 0.282014529 -1.514545946
38 0.005117076 0.282014529
39 -0.741632768 0.005117076
40 -1.443803576 -0.741632768
41 -1.225516705 -1.443803576
42 -2.013807033 -1.225516705
43 -1.477851273 -2.013807033
44 -0.817854544 -1.477851273
45 -0.211811703 -0.817854544
46 0.290248371 -0.211811703
47 -0.333798938 0.290248371
48 -1.597455331 -0.333798938
49 -2.776114088 -1.597455331
50 -3.085946295 -2.776114088
51 1.462291120 -3.085946295
52 2.310178016 1.462291120
53 3.046254758 2.310178016
54 2.757477978 3.046254758
55 2.242329607 2.757477978
56 1.842252242 2.242329607
> 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/7lccg1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v3bj1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9v3bj1292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10oca41292931581.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sd9s1292931581.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/12k4qd1292931581.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/13r5571292931581.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/14u5lu1292931581.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/15ne3x1292931581.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/1616io1292931581.tab")
+ }
> try(system("convert tmp/1zbds1292931581.ps tmp/1zbds1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zbds1292931581.ps tmp/2zbds1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zbds1292931581.ps tmp/3zbds1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a2ud1292931581.ps tmp/4a2ud1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/5a2ud1292931581.ps tmp/5a2ud1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/6a2ud1292931581.ps tmp/6a2ud1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lccg1292931581.ps tmp/7lccg1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/8v3bj1292931581.ps tmp/8v3bj1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v3bj1292931581.ps tmp/9v3bj1292931581.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oca41292931581.ps tmp/10oca41292931581.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.426 1.625 5.492