R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(122.7
+ ,119.4
+ ,104
+ ,126.6
+ ,126.2
+ ,129.8
+ ,113.1
+ ,128.5
+ ,125.9
+ ,131.9
+ ,117.3
+ ,127.1
+ ,133.2
+ ,129.8
+ ,120.5
+ ,135.9
+ ,131
+ ,131.6
+ ,119.9
+ ,133.2
+ ,133.8
+ ,134.3
+ ,120.5
+ ,126.4
+ ,142.4
+ ,136.7
+ ,124.7
+ ,146.2
+ ,134.2
+ ,134.7
+ ,118.9
+ ,137.1
+ ,124.1
+ ,138.1
+ ,120.2
+ ,123.9
+ ,118.7
+ ,132.4
+ ,116.5
+ ,118.2
+ ,114.3
+ ,125
+ ,110.9
+ ,114.2
+ ,107.8
+ ,117.7
+ ,108.6
+ ,106.9
+ ,104.4
+ ,112
+ ,108.6
+ ,103.1
+ ,98.1
+ ,106.3
+ ,104.9
+ ,96.2
+ ,97.1
+ ,100.5
+ ,102.1
+ ,95.9
+ ,95.3
+ ,95.6
+ ,98
+ ,94.8
+ ,94.3
+ ,89.5
+ ,93.1
+ ,94.9
+ ,95.6
+ ,87.7
+ ,96.4
+ ,96
+ ,105
+ ,88.2
+ ,103.7
+ ,106.4
+ ,98.3
+ ,88.7
+ ,93.5
+ ,99.9
+ ,93.1
+ ,91.4
+ ,87.9
+ ,94.2
+ ,96.6
+ ,95.7
+ ,91.8
+ ,97.6
+ ,93.1
+ ,96.8
+ ,89.3
+ ,93.6
+ ,94.9
+ ,93.8
+ ,85.1
+ ,96.8
+ ,90.8
+ ,91
+ ,82.6
+ ,92.4
+ ,84.6
+ ,86.8
+ ,80.4
+ ,85.3
+ ,88.4
+ ,91.5
+ ,80.6
+ ,89.6
+ ,80.4
+ ,89.3
+ ,72.3
+ ,81.3
+ ,84.6
+ ,97.9
+ ,69.4
+ ,86.7
+ ,73.2
+ ,95.7
+ ,65.1
+ ,73.3
+ ,64.6
+ ,86.9
+ ,62.6
+ ,63.4
+ ,60.5
+ ,82
+ ,59.2
+ ,59.3
+ ,56.4
+ ,83.2
+ ,58.2
+ ,54.2
+ ,58.5
+ ,85.7
+ ,60.1
+ ,56.3
+ ,56.7
+ ,77.8
+ ,63.1
+ ,54
+ ,69.6
+ ,79.4
+ ,69.4
+ ,69
+ ,89.1
+ ,83.4
+ ,79.2
+ ,91.5
+ ,121.3
+ ,102.8
+ ,96.7
+ ,127.3
+ ,137.2
+ ,108.7
+ ,105
+ ,145.5
+ ,157.5
+ ,120.3
+ ,113.2
+ ,168.7
+ ,155.4
+ ,121.9
+ ,112.3
+ ,166
+ ,146.2
+ ,112.7
+ ,112.9
+ ,155
+ ,131.5
+ ,113.1
+ ,113.2
+ ,136.4
+ ,125.8
+ ,115.7
+ ,112.6
+ ,129
+ ,116.7
+ ,113.5
+ ,106.9
+ ,118.8
+ ,111.2
+ ,103.1
+ ,101.3
+ ,113.7
+ ,107.9
+ ,95.5
+ ,92.7
+ ,111.7
+ ,110
+ ,88.5
+ ,96.2
+ ,114.2
+ ,100.9
+ ,86.2
+ ,98.5
+ ,102.4
+ ,94.8
+ ,83.8
+ ,96.2
+ ,95.3
+ ,88.5
+ ,76.4
+ ,97.3
+ ,87.7
+ ,92.4
+ ,76
+ ,103
+ ,91.5
+ ,87.2
+ ,75.7
+ ,102.6
+ ,85
+ ,84.4
+ ,71.5
+ ,108.1
+ ,80.7
+ ,84.4
+ ,69.7
+ ,107.7
+ ,80.9
+ ,79.2
+ ,72.1
+ ,101.6
+ ,75.4
+ ,75.8
+ ,72.6
+ ,98.3
+ ,71.7
+ ,71.4
+ ,70.2
+ ,96.6
+ ,66.6
+ ,78.7
+ ,69.4
+ ,96.8
+ ,75.8
+ ,75.3
+ ,68
+ ,94.5
+ ,72.1)
+ ,dim=c(4
+ ,60)
+ ,dimnames=list(c('Algemeen'
+ ,'Levensmiddelen'
+ ,'Industrie'
+ ,'Energie')
+ ,1:60))
> y <- array(NA,dim=c(4,60),dimnames=list(c('Algemeen','Levensmiddelen','Industrie','Energie'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = '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
> 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
Algemeen Levensmiddelen Industrie Energie t
1 122.7 119.4 104.0 126.6 1
2 126.2 129.8 113.1 128.5 2
3 125.9 131.9 117.3 127.1 3
4 133.2 129.8 120.5 135.9 4
5 131.0 131.6 119.9 133.2 5
6 133.8 134.3 120.5 126.4 6
7 142.4 136.7 124.7 146.2 7
8 134.2 134.7 118.9 137.1 8
9 124.1 138.1 120.2 123.9 9
10 118.7 132.4 116.5 118.2 10
11 114.3 125.0 110.9 114.2 11
12 107.8 117.7 108.6 106.9 12
13 104.4 112.0 108.6 103.1 13
14 98.1 106.3 104.9 96.2 14
15 97.1 100.5 102.1 95.9 15
16 95.3 95.6 98.0 94.8 16
17 94.3 89.5 93.1 94.9 17
18 95.6 87.7 96.4 96.0 18
19 105.0 88.2 103.7 106.4 19
20 98.3 88.7 93.5 99.9 20
21 93.1 91.4 87.9 94.2 21
22 96.6 95.7 91.8 97.6 22
23 93.1 96.8 89.3 93.6 23
24 94.9 93.8 85.1 96.8 24
25 90.8 91.0 82.6 92.4 25
26 84.6 86.8 80.4 85.3 26
27 88.4 91.5 80.6 89.6 27
28 80.4 89.3 72.3 81.3 28
29 84.6 97.9 69.4 86.7 29
30 73.2 95.7 65.1 73.3 30
31 64.6 86.9 62.6 63.4 31
32 60.5 82.0 59.2 59.3 32
33 56.4 83.2 58.2 54.2 33
34 58.5 85.7 60.1 56.3 34
35 56.7 77.8 63.1 54.0 35
36 69.6 79.4 69.4 69.0 36
37 89.1 83.4 79.2 91.5 37
38 121.3 102.8 96.7 127.3 38
39 137.2 108.7 105.0 145.5 39
40 157.5 120.3 113.2 168.7 40
41 155.4 121.9 112.3 166.0 41
42 146.2 112.7 112.9 155.0 42
43 131.5 113.1 113.2 136.4 43
44 125.8 115.7 112.6 129.0 44
45 116.7 113.5 106.9 118.8 45
46 111.2 103.1 101.3 113.7 46
47 107.9 95.5 92.7 111.7 47
48 110.0 88.5 96.2 114.2 48
49 100.9 86.2 98.5 102.4 49
50 94.8 83.8 96.2 95.3 50
51 88.5 76.4 97.3 87.7 51
52 92.4 76.0 103.0 91.5 52
53 87.2 75.7 102.6 85.0 53
54 84.4 71.5 108.1 80.7 54
55 84.4 69.7 107.7 80.9 55
56 79.2 72.1 101.6 75.4 56
57 75.8 72.6 98.3 71.7 57
58 71.4 70.2 96.6 66.6 58
59 78.7 69.4 96.8 75.8 59
60 75.3 68.0 94.5 72.1 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Levensmiddelen Industrie Energie t
-1.806498 0.075918 0.165414 0.778152 0.002403
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.8952 -0.2740 0.0165 0.0761 7.1055
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.806498 1.423790 -1.269 0.210
Levensmiddelen 0.075918 0.017412 4.360 5.75e-05 ***
Industrie 0.165414 0.011731 14.100 < 2e-16 ***
Energie 0.778152 0.010905 71.358 < 2e-16 ***
t 0.002403 0.013029 0.184 0.854
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.009 on 55 degrees of freedom
Multiple R-squared: 0.9985, Adjusted R-squared: 0.9984
F-statistic: 9053 on 4 and 55 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,] 1.0000000 5.329675e-60 2.664837e-60
[2,] 1.0000000 1.052000e-64 5.259998e-65
[3,] 1.0000000 4.747500e-64 2.373750e-64
[4,] 1.0000000 4.280483e-62 2.140241e-62
[5,] 1.0000000 1.892434e-60 9.462171e-61
[6,] 1.0000000 7.196820e-59 3.598410e-59
[7,] 1.0000000 5.041763e-57 2.520881e-57
[8,] 1.0000000 2.398999e-55 1.199499e-55
[9,] 1.0000000 3.602881e-54 1.801441e-54
[10,] 1.0000000 5.096010e-53 2.548005e-53
[11,] 1.0000000 2.360867e-51 1.180434e-51
[12,] 1.0000000 1.317743e-49 6.588714e-50
[13,] 1.0000000 3.806279e-48 1.903140e-48
[14,] 1.0000000 1.224321e-46 6.121607e-47
[15,] 1.0000000 5.054014e-45 2.527007e-45
[16,] 1.0000000 1.510850e-43 7.554252e-44
[17,] 1.0000000 2.430057e-42 1.215028e-42
[18,] 1.0000000 9.678119e-41 4.839060e-41
[19,] 1.0000000 2.669879e-39 1.334939e-39
[20,] 1.0000000 1.227909e-38 6.139546e-39
[21,] 1.0000000 6.884896e-38 3.442448e-38
[22,] 1.0000000 1.670663e-36 8.353317e-37
[23,] 1.0000000 1.063689e-35 5.318445e-36
[24,] 1.0000000 1.856466e-34 9.282328e-35
[25,] 1.0000000 8.331570e-33 4.165785e-33
[26,] 1.0000000 3.773739e-31 1.886869e-31
[27,] 1.0000000 1.568327e-29 7.841636e-30
[28,] 1.0000000 4.559239e-28 2.279620e-28
[29,] 1.0000000 1.646991e-26 8.234954e-27
[30,] 1.0000000 5.331881e-25 2.665940e-25
[31,] 1.0000000 1.148755e-24 5.743774e-25
[32,] 1.0000000 3.327343e-23 1.663672e-23
[33,] 1.0000000 7.297366e-22 3.648683e-22
[34,] 1.0000000 6.960795e-21 3.480397e-21
[35,] 1.0000000 3.191699e-19 1.595850e-19
[36,] 1.0000000 1.119550e-19 5.597752e-20
[37,] 1.0000000 6.228038e-18 3.114019e-18
[38,] 1.0000000 3.624775e-16 1.812387e-16
[39,] 1.0000000 1.004017e-14 5.020087e-15
[40,] 1.0000000 4.746630e-13 2.373315e-13
[41,] 1.0000000 1.462205e-11 7.311025e-12
[42,] 1.0000000 6.664096e-10 3.332048e-10
[43,] 1.0000000 3.171647e-08 1.585823e-08
[44,] 0.9999994 1.243645e-06 6.218227e-07
[45,] 0.9999822 3.550011e-05 1.775005e-05
> postscript(file="/var/wessaorg/rcomp/tmp/19p5a1322130961.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2abla1322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3omow1322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4v95w1322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5wfz71322130961.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 = 60
Frequency = 1
1 2 3 4 5 6
-0.277613848 -0.553323084 -0.620480955 -0.540514829 -0.679312553 7.105488191
7 8 9 10 11 12
-0.581260442 -0.591244824 -0.895206092 -0.817377906 -0.619059706 -0.506299247
13 14 15 16 17 18
-0.518991246 -0.407381064 -0.272852686 -0.169091179 0.024321554 0.056738591
19 20 21 22 23 24
0.116076799 0.120924181 0.075325270 -0.044356652 -0.104127681 0.125878289
25 26 27 28 29 30
0.073449337 0.078691295 0.140337575 0.136550676 -0.041067699 -0.137937355
31 32 33 34 35 36
0.044977708 0.067404365 0.007887244 -0.032716435 0.058142173 0.119886647
37 38 39 40 41 42
0.184340486 0.156546946 0.070929238 0.078360247 0.104370403 0.060836034
43 44 45 46 47 48
-0.247937258 -0.290156811 -0.345532107 -0.163492144 0.089948572 0.194645684
49 50 51 52 53 54
0.068592398 0.053722802 0.045112786 0.073240951 0.017765190 -0.029505777
55 56 57 58 59 60
0.015279781 -0.080466939 -0.095801232 -0.066222592 0.100031246 0.063527682
> postscript(file="/var/wessaorg/rcomp/tmp/6goer1322130961.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.277613848 NA
1 -0.553323084 -0.277613848
2 -0.620480955 -0.553323084
3 -0.540514829 -0.620480955
4 -0.679312553 -0.540514829
5 7.105488191 -0.679312553
6 -0.581260442 7.105488191
7 -0.591244824 -0.581260442
8 -0.895206092 -0.591244824
9 -0.817377906 -0.895206092
10 -0.619059706 -0.817377906
11 -0.506299247 -0.619059706
12 -0.518991246 -0.506299247
13 -0.407381064 -0.518991246
14 -0.272852686 -0.407381064
15 -0.169091179 -0.272852686
16 0.024321554 -0.169091179
17 0.056738591 0.024321554
18 0.116076799 0.056738591
19 0.120924181 0.116076799
20 0.075325270 0.120924181
21 -0.044356652 0.075325270
22 -0.104127681 -0.044356652
23 0.125878289 -0.104127681
24 0.073449337 0.125878289
25 0.078691295 0.073449337
26 0.140337575 0.078691295
27 0.136550676 0.140337575
28 -0.041067699 0.136550676
29 -0.137937355 -0.041067699
30 0.044977708 -0.137937355
31 0.067404365 0.044977708
32 0.007887244 0.067404365
33 -0.032716435 0.007887244
34 0.058142173 -0.032716435
35 0.119886647 0.058142173
36 0.184340486 0.119886647
37 0.156546946 0.184340486
38 0.070929238 0.156546946
39 0.078360247 0.070929238
40 0.104370403 0.078360247
41 0.060836034 0.104370403
42 -0.247937258 0.060836034
43 -0.290156811 -0.247937258
44 -0.345532107 -0.290156811
45 -0.163492144 -0.345532107
46 0.089948572 -0.163492144
47 0.194645684 0.089948572
48 0.068592398 0.194645684
49 0.053722802 0.068592398
50 0.045112786 0.053722802
51 0.073240951 0.045112786
52 0.017765190 0.073240951
53 -0.029505777 0.017765190
54 0.015279781 -0.029505777
55 -0.080466939 0.015279781
56 -0.095801232 -0.080466939
57 -0.066222592 -0.095801232
58 0.100031246 -0.066222592
59 0.063527682 0.100031246
60 NA 0.063527682
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.553323084 -0.277613848
[2,] -0.620480955 -0.553323084
[3,] -0.540514829 -0.620480955
[4,] -0.679312553 -0.540514829
[5,] 7.105488191 -0.679312553
[6,] -0.581260442 7.105488191
[7,] -0.591244824 -0.581260442
[8,] -0.895206092 -0.591244824
[9,] -0.817377906 -0.895206092
[10,] -0.619059706 -0.817377906
[11,] -0.506299247 -0.619059706
[12,] -0.518991246 -0.506299247
[13,] -0.407381064 -0.518991246
[14,] -0.272852686 -0.407381064
[15,] -0.169091179 -0.272852686
[16,] 0.024321554 -0.169091179
[17,] 0.056738591 0.024321554
[18,] 0.116076799 0.056738591
[19,] 0.120924181 0.116076799
[20,] 0.075325270 0.120924181
[21,] -0.044356652 0.075325270
[22,] -0.104127681 -0.044356652
[23,] 0.125878289 -0.104127681
[24,] 0.073449337 0.125878289
[25,] 0.078691295 0.073449337
[26,] 0.140337575 0.078691295
[27,] 0.136550676 0.140337575
[28,] -0.041067699 0.136550676
[29,] -0.137937355 -0.041067699
[30,] 0.044977708 -0.137937355
[31,] 0.067404365 0.044977708
[32,] 0.007887244 0.067404365
[33,] -0.032716435 0.007887244
[34,] 0.058142173 -0.032716435
[35,] 0.119886647 0.058142173
[36,] 0.184340486 0.119886647
[37,] 0.156546946 0.184340486
[38,] 0.070929238 0.156546946
[39,] 0.078360247 0.070929238
[40,] 0.104370403 0.078360247
[41,] 0.060836034 0.104370403
[42,] -0.247937258 0.060836034
[43,] -0.290156811 -0.247937258
[44,] -0.345532107 -0.290156811
[45,] -0.163492144 -0.345532107
[46,] 0.089948572 -0.163492144
[47,] 0.194645684 0.089948572
[48,] 0.068592398 0.194645684
[49,] 0.053722802 0.068592398
[50,] 0.045112786 0.053722802
[51,] 0.073240951 0.045112786
[52,] 0.017765190 0.073240951
[53,] -0.029505777 0.017765190
[54,] 0.015279781 -0.029505777
[55,] -0.080466939 0.015279781
[56,] -0.095801232 -0.080466939
[57,] -0.066222592 -0.095801232
[58,] 0.100031246 -0.066222592
[59,] 0.063527682 0.100031246
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.553323084 -0.277613848
2 -0.620480955 -0.553323084
3 -0.540514829 -0.620480955
4 -0.679312553 -0.540514829
5 7.105488191 -0.679312553
6 -0.581260442 7.105488191
7 -0.591244824 -0.581260442
8 -0.895206092 -0.591244824
9 -0.817377906 -0.895206092
10 -0.619059706 -0.817377906
11 -0.506299247 -0.619059706
12 -0.518991246 -0.506299247
13 -0.407381064 -0.518991246
14 -0.272852686 -0.407381064
15 -0.169091179 -0.272852686
16 0.024321554 -0.169091179
17 0.056738591 0.024321554
18 0.116076799 0.056738591
19 0.120924181 0.116076799
20 0.075325270 0.120924181
21 -0.044356652 0.075325270
22 -0.104127681 -0.044356652
23 0.125878289 -0.104127681
24 0.073449337 0.125878289
25 0.078691295 0.073449337
26 0.140337575 0.078691295
27 0.136550676 0.140337575
28 -0.041067699 0.136550676
29 -0.137937355 -0.041067699
30 0.044977708 -0.137937355
31 0.067404365 0.044977708
32 0.007887244 0.067404365
33 -0.032716435 0.007887244
34 0.058142173 -0.032716435
35 0.119886647 0.058142173
36 0.184340486 0.119886647
37 0.156546946 0.184340486
38 0.070929238 0.156546946
39 0.078360247 0.070929238
40 0.104370403 0.078360247
41 0.060836034 0.104370403
42 -0.247937258 0.060836034
43 -0.290156811 -0.247937258
44 -0.345532107 -0.290156811
45 -0.163492144 -0.345532107
46 0.089948572 -0.163492144
47 0.194645684 0.089948572
48 0.068592398 0.194645684
49 0.053722802 0.068592398
50 0.045112786 0.053722802
51 0.073240951 0.045112786
52 0.017765190 0.073240951
53 -0.029505777 0.017765190
54 0.015279781 -0.029505777
55 -0.080466939 0.015279781
56 -0.095801232 -0.080466939
57 -0.066222592 -0.095801232
58 0.100031246 -0.066222592
59 0.063527682 0.100031246
> 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/wessaorg/rcomp/tmp/7ppe61322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ygu81322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/950ig1322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10b3q01322130961.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11qewl1322130961.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/wessaorg/rcomp/tmp/12o1ik1322130961.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/wessaorg/rcomp/tmp/13hhsy1322130961.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/wessaorg/rcomp/tmp/1466pv1322130961.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/wessaorg/rcomp/tmp/15w42u1322130961.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/wessaorg/rcomp/tmp/16kx2q1322130961.tab")
+ }
>
> try(system("convert tmp/19p5a1322130961.ps tmp/19p5a1322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/2abla1322130961.ps tmp/2abla1322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/3omow1322130961.ps tmp/3omow1322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/4v95w1322130961.ps tmp/4v95w1322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wfz71322130961.ps tmp/5wfz71322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/6goer1322130961.ps tmp/6goer1322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ppe61322130961.ps tmp/7ppe61322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ygu81322130961.ps tmp/8ygu81322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/950ig1322130961.ps tmp/950ig1322130961.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b3q01322130961.ps tmp/10b3q01322130961.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.152 0.545 3.741