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.
R is a collaborative project with many contributors.
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(108.5
+ ,98.71
+ ,115.5
+ ,116.6
+ ,112.3
+ ,108.5
+ ,112.3
+ ,98.54
+ ,120.1
+ ,115.5
+ ,116.6
+ ,112.3
+ ,116.6
+ ,98.2
+ ,132.9
+ ,120.1
+ ,115.5
+ ,116.6
+ ,115.5
+ ,96.92
+ ,128.1
+ ,132.9
+ ,120.1
+ ,115.5
+ ,120.1
+ ,99.06
+ ,129.3
+ ,128.1
+ ,132.9
+ ,120.1
+ ,132.9
+ ,99.65
+ ,132.5
+ ,129.3
+ ,128.1
+ ,132.9
+ ,128.1
+ ,99.82
+ ,131
+ ,132.5
+ ,129.3
+ ,128.1
+ ,129.3
+ ,99.99
+ ,124.9
+ ,131
+ ,132.5
+ ,129.3
+ ,132.5
+ ,100.33
+ ,120.8
+ ,124.9
+ ,131
+ ,132.5
+ ,131
+ ,99.31
+ ,122
+ ,120.8
+ ,124.9
+ ,131
+ ,124.9
+ ,101.1
+ ,122.1
+ ,122
+ ,120.8
+ ,124.9
+ ,120.8
+ ,101.1
+ ,127.4
+ ,122.1
+ ,122
+ ,120.8
+ ,122
+ ,100.93
+ ,135.2
+ ,127.4
+ ,122.1
+ ,122
+ ,122.1
+ ,100.85
+ ,137.3
+ ,135.2
+ ,127.4
+ ,122.1
+ ,127.4
+ ,100.93
+ ,135
+ ,137.3
+ ,135.2
+ ,127.4
+ ,135.2
+ ,99.6
+ ,136
+ ,135
+ ,137.3
+ ,135.2
+ ,137.3
+ ,101.88
+ ,138.4
+ ,136
+ ,135
+ ,137.3
+ ,135
+ ,101.81
+ ,134.7
+ ,138.4
+ ,136
+ ,135
+ ,136
+ ,102.38
+ ,138.4
+ ,134.7
+ ,138.4
+ ,136
+ ,138.4
+ ,102.74
+ ,133.9
+ ,138.4
+ ,134.7
+ ,138.4
+ ,134.7
+ ,102.82
+ ,133.6
+ ,133.9
+ ,138.4
+ ,134.7
+ ,138.4
+ ,101.72
+ ,141.2
+ ,133.6
+ ,133.9
+ ,138.4
+ ,133.9
+ ,103.47
+ ,151.8
+ ,141.2
+ ,133.6
+ ,133.9
+ ,133.6
+ ,102.98
+ ,155.4
+ ,151.8
+ ,141.2
+ ,133.6
+ ,141.2
+ ,102.68
+ ,156.6
+ ,155.4
+ ,151.8
+ ,141.2
+ ,151.8
+ ,102.9
+ ,161.6
+ ,156.6
+ ,155.4
+ ,151.8
+ ,155.4
+ ,103.03
+ ,160.7
+ ,161.6
+ ,156.6
+ ,155.4
+ ,156.6
+ ,101.29
+ ,156
+ ,160.7
+ ,161.6
+ ,156.6
+ ,161.6
+ ,103.69
+ ,159.5
+ ,156
+ ,160.7
+ ,161.6
+ ,160.7
+ ,103.68
+ ,168.7
+ ,159.5
+ ,156
+ ,160.7
+ ,156
+ ,104.2
+ ,169.9
+ ,168.7
+ ,159.5
+ ,156
+ ,159.5
+ ,104.08
+ ,169.9
+ ,169.9
+ ,168.7
+ ,159.5
+ ,168.7
+ ,104.16
+ ,185.9
+ ,169.9
+ ,169.9
+ ,168.7
+ ,169.9
+ ,103.05
+ ,190.8
+ ,185.9
+ ,169.9
+ ,169.9
+ ,169.9
+ ,104.66
+ ,195.8
+ ,190.8
+ ,185.9
+ ,169.9
+ ,185.9
+ ,104.46
+ ,211.9
+ ,195.8
+ ,190.8
+ ,185.9
+ ,190.8
+ ,104.95
+ ,227.1
+ ,211.9
+ ,195.8
+ ,190.8
+ ,195.8
+ ,105.85
+ ,251.3
+ ,227.1
+ ,211.9
+ ,195.8
+ ,211.9
+ ,106.23
+ ,256.7
+ ,251.3
+ ,227.1
+ ,211.9
+ ,227.1
+ ,104.86
+ ,251.9
+ ,256.7
+ ,251.3
+ ,227.1
+ ,251.3
+ ,107.44
+ ,251.2
+ ,251.9
+ ,256.7
+ ,251.3
+ ,256.7
+ ,108.23
+ ,270.3
+ ,251.2
+ ,251.9
+ ,256.7
+ ,251.9
+ ,108.45
+ ,267.2
+ ,270.3
+ ,251.2
+ ,251.9
+ ,251.2
+ ,109.39
+ ,243
+ ,267.2
+ ,270.3
+ ,251.2
+ ,270.3
+ ,110.15
+ ,229.9
+ ,243
+ ,267.2
+ ,270.3
+ ,267.2
+ ,109.13
+ ,187.2
+ ,229.9
+ ,243
+ ,267.2
+ ,243
+ ,110.28
+ ,178.2
+ ,187.2
+ ,229.9
+ ,243
+ ,229.9
+ ,110.17
+ ,175.2
+ ,178.2
+ ,187.2
+ ,229.9
+ ,187.2
+ ,109.99
+ ,192.4
+ ,175.2
+ ,178.2
+ ,187.2
+ ,178.2
+ ,109.26
+ ,187
+ ,192.4
+ ,175.2
+ ,178.2
+ ,175.2
+ ,109.11
+ ,184
+ ,187
+ ,192.4
+ ,175.2
+ ,192.4
+ ,107.06
+ ,194.1
+ ,184
+ ,187
+ ,192.4
+ ,187
+ ,109.53
+ ,212.7
+ ,194.1
+ ,184
+ ,187
+ ,184
+ ,108.92
+ ,217.5
+ ,212.7
+ ,194.1
+ ,184
+ ,194.1
+ ,109.24
+ ,200.5
+ ,217.5
+ ,212.7
+ ,194.1
+ ,212.7
+ ,109.12
+ ,205.9
+ ,200.5
+ ,217.5
+ ,212.7
+ ,217.5
+ ,109
+ ,196.5
+ ,205.9
+ ,200.5
+ ,217.5
+ ,200.5
+ ,107.23
+ ,206.3
+ ,196.5
+ ,205.9
+ ,200.5)
+ ,dim=c(6
+ ,58)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:58))
> y <- array(NA,dim=c(6,58),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:58))
> 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
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 108.5 98.71 115.5 116.6 112.3 108.5 1 0 0 0 0 0 0 0 0 0 0 1
2 112.3 98.54 120.1 115.5 116.6 112.3 0 1 0 0 0 0 0 0 0 0 0 2
3 116.6 98.20 132.9 120.1 115.5 116.6 0 0 1 0 0 0 0 0 0 0 0 3
4 115.5 96.92 128.1 132.9 120.1 115.5 0 0 0 1 0 0 0 0 0 0 0 4
5 120.1 99.06 129.3 128.1 132.9 120.1 0 0 0 0 1 0 0 0 0 0 0 5
6 132.9 99.65 132.5 129.3 128.1 132.9 0 0 0 0 0 1 0 0 0 0 0 6
7 128.1 99.82 131.0 132.5 129.3 128.1 0 0 0 0 0 0 1 0 0 0 0 7
8 129.3 99.99 124.9 131.0 132.5 129.3 0 0 0 0 0 0 0 1 0 0 0 8
9 132.5 100.33 120.8 124.9 131.0 132.5 0 0 0 0 0 0 0 0 1 0 0 9
10 131.0 99.31 122.0 120.8 124.9 131.0 0 0 0 0 0 0 0 0 0 1 0 10
11 124.9 101.10 122.1 122.0 120.8 124.9 0 0 0 0 0 0 0 0 0 0 1 11
12 120.8 101.10 127.4 122.1 122.0 120.8 0 0 0 0 0 0 0 0 0 0 0 12
13 122.0 100.93 135.2 127.4 122.1 122.0 1 0 0 0 0 0 0 0 0 0 0 13
14 122.1 100.85 137.3 135.2 127.4 122.1 0 1 0 0 0 0 0 0 0 0 0 14
15 127.4 100.93 135.0 137.3 135.2 127.4 0 0 1 0 0 0 0 0 0 0 0 15
16 135.2 99.60 136.0 135.0 137.3 135.2 0 0 0 1 0 0 0 0 0 0 0 16
17 137.3 101.88 138.4 136.0 135.0 137.3 0 0 0 0 1 0 0 0 0 0 0 17
18 135.0 101.81 134.7 138.4 136.0 135.0 0 0 0 0 0 1 0 0 0 0 0 18
19 136.0 102.38 138.4 134.7 138.4 136.0 0 0 0 0 0 0 1 0 0 0 0 19
20 138.4 102.74 133.9 138.4 134.7 138.4 0 0 0 0 0 0 0 1 0 0 0 20
21 134.7 102.82 133.6 133.9 138.4 134.7 0 0 0 0 0 0 0 0 1 0 0 21
22 138.4 101.72 141.2 133.6 133.9 138.4 0 0 0 0 0 0 0 0 0 1 0 22
23 133.9 103.47 151.8 141.2 133.6 133.9 0 0 0 0 0 0 0 0 0 0 1 23
24 133.6 102.98 155.4 151.8 141.2 133.6 0 0 0 0 0 0 0 0 0 0 0 24
25 141.2 102.68 156.6 155.4 151.8 141.2 1 0 0 0 0 0 0 0 0 0 0 25
26 151.8 102.90 161.6 156.6 155.4 151.8 0 1 0 0 0 0 0 0 0 0 0 26
27 155.4 103.03 160.7 161.6 156.6 155.4 0 0 1 0 0 0 0 0 0 0 0 27
28 156.6 101.29 156.0 160.7 161.6 156.6 0 0 0 1 0 0 0 0 0 0 0 28
29 161.6 103.69 159.5 156.0 160.7 161.6 0 0 0 0 1 0 0 0 0 0 0 29
30 160.7 103.68 168.7 159.5 156.0 160.7 0 0 0 0 0 1 0 0 0 0 0 30
31 156.0 104.20 169.9 168.7 159.5 156.0 0 0 0 0 0 0 1 0 0 0 0 31
32 159.5 104.08 169.9 169.9 168.7 159.5 0 0 0 0 0 0 0 1 0 0 0 32
33 168.7 104.16 185.9 169.9 169.9 168.7 0 0 0 0 0 0 0 0 1 0 0 33
34 169.9 103.05 190.8 185.9 169.9 169.9 0 0 0 0 0 0 0 0 0 1 0 34
35 169.9 104.66 195.8 190.8 185.9 169.9 0 0 0 0 0 0 0 0 0 0 1 35
36 185.9 104.46 211.9 195.8 190.8 185.9 0 0 0 0 0 0 0 0 0 0 0 36
37 190.8 104.95 227.1 211.9 195.8 190.8 1 0 0 0 0 0 0 0 0 0 0 37
38 195.8 105.85 251.3 227.1 211.9 195.8 0 1 0 0 0 0 0 0 0 0 0 38
39 211.9 106.23 256.7 251.3 227.1 211.9 0 0 1 0 0 0 0 0 0 0 0 39
40 227.1 104.86 251.9 256.7 251.3 227.1 0 0 0 1 0 0 0 0 0 0 0 40
41 251.3 107.44 251.2 251.9 256.7 251.3 0 0 0 0 1 0 0 0 0 0 0 41
42 256.7 108.23 270.3 251.2 251.9 256.7 0 0 0 0 0 1 0 0 0 0 0 42
43 251.9 108.45 267.2 270.3 251.2 251.9 0 0 0 0 0 0 1 0 0 0 0 43
44 251.2 109.39 243.0 267.2 270.3 251.2 0 0 0 0 0 0 0 1 0 0 0 44
45 270.3 110.15 229.9 243.0 267.2 270.3 0 0 0 0 0 0 0 0 1 0 0 45
46 267.2 109.13 187.2 229.9 243.0 267.2 0 0 0 0 0 0 0 0 0 1 0 46
47 243.0 110.28 178.2 187.2 229.9 243.0 0 0 0 0 0 0 0 0 0 0 1 47
48 229.9 110.17 175.2 178.2 187.2 229.9 0 0 0 0 0 0 0 0 0 0 0 48
49 187.2 109.99 192.4 175.2 178.2 187.2 1 0 0 0 0 0 0 0 0 0 0 49
50 178.2 109.26 187.0 192.4 175.2 178.2 0 1 0 0 0 0 0 0 0 0 0 50
51 175.2 109.11 184.0 187.0 192.4 175.2 0 0 1 0 0 0 0 0 0 0 0 51
52 192.4 107.06 194.1 184.0 187.0 192.4 0 0 0 1 0 0 0 0 0 0 0 52
53 187.0 109.53 212.7 194.1 184.0 187.0 0 0 0 0 1 0 0 0 0 0 0 53
54 184.0 108.92 217.5 212.7 194.1 184.0 0 0 0 0 0 1 0 0 0 0 0 54
55 194.1 109.24 200.5 217.5 212.7 194.1 0 0 0 0 0 0 1 0 0 0 0 55
56 212.7 109.12 205.9 200.5 217.5 212.7 0 0 0 0 0 0 0 1 0 0 0 56
57 217.5 109.00 196.5 205.9 200.5 217.5 0 0 0 0 0 0 0 0 1 0 0 57
58 200.5 107.23 206.3 196.5 205.9 200.5 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
2.164e-14 -5.164e-16 1.591e-16 -7.928e-17 -4.191e-16 1.000e+00
M1 M2 M3 M4 M5 M6
-4.894e-16 -6.665e-16 5.340e-16 2.157e-16 2.350e-16 6.200e-15
M7 M8 M9 M10 M11 t
8.969e-16 1.857e-16 7.648e-16 -2.310e-16 -7.899e-17 6.329e-18
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.356e-15 -6.590e-16 -3.132e-17 6.650e-16 2.071e-14
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.164e-14 8.686e-14 2.490e-01 0.8046
X -5.164e-16 8.995e-16 -5.740e-01 0.5691
Y1 1.591e-16 6.271e-17 2.537e+00 0.0152 *
Y2 -7.928e-17 9.198e-17 -8.620e-01 0.3939
Y3 -4.191e-16 9.511e-17 -4.407e+00 7.67e-05 ***
Y4 1.000e+00 7.201e-17 1.389e+16 < 2e-16 ***
M1 -4.894e-16 2.670e-15 -1.830e-01 0.8555
M2 -6.665e-16 2.705e-15 -2.460e-01 0.8067
M3 5.340e-16 2.757e-15 1.940e-01 0.8474
M4 2.157e-16 3.354e-15 6.400e-02 0.9491
M5 2.350e-16 2.678e-15 8.800e-02 0.9305
M6 6.200e-15 2.638e-15 2.350e+00 0.0238 *
M7 8.969e-16 2.764e-15 3.250e-01 0.7472
M8 1.857e-16 2.843e-15 6.500e-02 0.9482
M9 7.648e-16 2.707e-15 2.830e-01 0.7790
M10 -2.310e-16 3.103e-15 -7.400e-02 0.9410
M11 -7.899e-17 2.861e-15 -2.800e-02 0.9781
t 6.329e-18 1.695e-16 3.700e-02 0.9704
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.877e-15 on 40 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 4.524e+32 on 17 and 40 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,] 3.549222e-03 7.098444e-03 9.964508e-01
[2,] 6.727446e-05 1.345489e-04 9.999327e-01
[3,] 3.582081e-02 7.164163e-02 9.641792e-01
[4,] 9.038895e-01 1.922210e-01 9.611052e-02
[5,] 8.312190e-04 1.662438e-03 9.991688e-01
[6,] 9.854102e-01 2.917951e-02 1.458975e-02
[7,] 1.449311e-07 2.898622e-07 9.999999e-01
[8,] 9.999866e-01 2.675064e-05 1.337532e-05
[9,] 4.183705e-03 8.367410e-03 9.958163e-01
[10,] 9.881849e-01 2.363017e-02 1.181508e-02
[11,] 4.556925e-01 9.113851e-01 5.443075e-01
[12,] 1.375385e-07 2.750770e-07 9.999999e-01
[13,] 2.001861e-04 4.003721e-04 9.997998e-01
[14,] 9.999899e-01 2.013565e-05 1.006783e-05
[15,] 7.040510e-01 5.918980e-01 2.959490e-01
[16,] 6.154823e-14 1.230965e-13 1.000000e+00
[17,] 2.474344e-04 4.948688e-04 9.997526e-01
> postscript(file="/var/www/html/rcomp/tmp/1466r1258725464.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l2n21258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32lgz1258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4b76a1258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53y681258725464.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 = 58
Frequency = 1
1 2 3 4 5
-3.298397e-15 -3.311881e-15 -8.770209e-16 7.661830e-16 -6.903672e-16
6 7 8 9 10
2.070677e-14 -1.328108e-15 -5.378453e-16 -1.191667e-15 1.322773e-15
11 12 13 14 15
-2.125310e-16 -3.789682e-16 1.518222e-16 1.636712e-16 -1.289410e-15
16 17 18 19 20
-6.370880e-16 -6.663576e-16 -6.355886e-15 -8.267010e-16 5.890122e-17
21 22 23 24 25
-4.199945e-16 -5.088215e-16 8.210877e-17 2.319369e-16 7.147671e-16
26 27 28 29 30
5.977735e-16 -5.107185e-16 -3.245215e-16 -1.271274e-16 -5.380048e-15
31 32 33 34 35
-6.649721e-16 -3.193224e-16 -5.784941e-16 3.382878e-16 -4.465881e-16
36 37 38 39 40
7.881017e-16 1.137866e-15 1.120559e-15 6.815183e-16 -1.215501e-16
41 42 43 44 45
8.684294e-16 -5.072836e-15 4.790320e-16 4.287687e-16 5.220210e-16
46 47 48 49 50
-1.982339e-15 5.770104e-16 -6.410704e-16 1.293942e-15 1.429876e-15
51 52 53 54 55
1.995631e-15 3.169766e-16 6.154228e-16 -3.898002e-15 2.340749e-15
56 57 58
3.694978e-16 1.668135e-15 8.301000e-16
> postscript(file="/var/www/html/rcomp/tmp/6phg81258725464.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -3.298397e-15 NA
1 -3.311881e-15 -3.298397e-15
2 -8.770209e-16 -3.311881e-15
3 7.661830e-16 -8.770209e-16
4 -6.903672e-16 7.661830e-16
5 2.070677e-14 -6.903672e-16
6 -1.328108e-15 2.070677e-14
7 -5.378453e-16 -1.328108e-15
8 -1.191667e-15 -5.378453e-16
9 1.322773e-15 -1.191667e-15
10 -2.125310e-16 1.322773e-15
11 -3.789682e-16 -2.125310e-16
12 1.518222e-16 -3.789682e-16
13 1.636712e-16 1.518222e-16
14 -1.289410e-15 1.636712e-16
15 -6.370880e-16 -1.289410e-15
16 -6.663576e-16 -6.370880e-16
17 -6.355886e-15 -6.663576e-16
18 -8.267010e-16 -6.355886e-15
19 5.890122e-17 -8.267010e-16
20 -4.199945e-16 5.890122e-17
21 -5.088215e-16 -4.199945e-16
22 8.210877e-17 -5.088215e-16
23 2.319369e-16 8.210877e-17
24 7.147671e-16 2.319369e-16
25 5.977735e-16 7.147671e-16
26 -5.107185e-16 5.977735e-16
27 -3.245215e-16 -5.107185e-16
28 -1.271274e-16 -3.245215e-16
29 -5.380048e-15 -1.271274e-16
30 -6.649721e-16 -5.380048e-15
31 -3.193224e-16 -6.649721e-16
32 -5.784941e-16 -3.193224e-16
33 3.382878e-16 -5.784941e-16
34 -4.465881e-16 3.382878e-16
35 7.881017e-16 -4.465881e-16
36 1.137866e-15 7.881017e-16
37 1.120559e-15 1.137866e-15
38 6.815183e-16 1.120559e-15
39 -1.215501e-16 6.815183e-16
40 8.684294e-16 -1.215501e-16
41 -5.072836e-15 8.684294e-16
42 4.790320e-16 -5.072836e-15
43 4.287687e-16 4.790320e-16
44 5.220210e-16 4.287687e-16
45 -1.982339e-15 5.220210e-16
46 5.770104e-16 -1.982339e-15
47 -6.410704e-16 5.770104e-16
48 1.293942e-15 -6.410704e-16
49 1.429876e-15 1.293942e-15
50 1.995631e-15 1.429876e-15
51 3.169766e-16 1.995631e-15
52 6.154228e-16 3.169766e-16
53 -3.898002e-15 6.154228e-16
54 2.340749e-15 -3.898002e-15
55 3.694978e-16 2.340749e-15
56 1.668135e-15 3.694978e-16
57 8.301000e-16 1.668135e-15
58 NA 8.301000e-16
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.311881e-15 -3.298397e-15
[2,] -8.770209e-16 -3.311881e-15
[3,] 7.661830e-16 -8.770209e-16
[4,] -6.903672e-16 7.661830e-16
[5,] 2.070677e-14 -6.903672e-16
[6,] -1.328108e-15 2.070677e-14
[7,] -5.378453e-16 -1.328108e-15
[8,] -1.191667e-15 -5.378453e-16
[9,] 1.322773e-15 -1.191667e-15
[10,] -2.125310e-16 1.322773e-15
[11,] -3.789682e-16 -2.125310e-16
[12,] 1.518222e-16 -3.789682e-16
[13,] 1.636712e-16 1.518222e-16
[14,] -1.289410e-15 1.636712e-16
[15,] -6.370880e-16 -1.289410e-15
[16,] -6.663576e-16 -6.370880e-16
[17,] -6.355886e-15 -6.663576e-16
[18,] -8.267010e-16 -6.355886e-15
[19,] 5.890122e-17 -8.267010e-16
[20,] -4.199945e-16 5.890122e-17
[21,] -5.088215e-16 -4.199945e-16
[22,] 8.210877e-17 -5.088215e-16
[23,] 2.319369e-16 8.210877e-17
[24,] 7.147671e-16 2.319369e-16
[25,] 5.977735e-16 7.147671e-16
[26,] -5.107185e-16 5.977735e-16
[27,] -3.245215e-16 -5.107185e-16
[28,] -1.271274e-16 -3.245215e-16
[29,] -5.380048e-15 -1.271274e-16
[30,] -6.649721e-16 -5.380048e-15
[31,] -3.193224e-16 -6.649721e-16
[32,] -5.784941e-16 -3.193224e-16
[33,] 3.382878e-16 -5.784941e-16
[34,] -4.465881e-16 3.382878e-16
[35,] 7.881017e-16 -4.465881e-16
[36,] 1.137866e-15 7.881017e-16
[37,] 1.120559e-15 1.137866e-15
[38,] 6.815183e-16 1.120559e-15
[39,] -1.215501e-16 6.815183e-16
[40,] 8.684294e-16 -1.215501e-16
[41,] -5.072836e-15 8.684294e-16
[42,] 4.790320e-16 -5.072836e-15
[43,] 4.287687e-16 4.790320e-16
[44,] 5.220210e-16 4.287687e-16
[45,] -1.982339e-15 5.220210e-16
[46,] 5.770104e-16 -1.982339e-15
[47,] -6.410704e-16 5.770104e-16
[48,] 1.293942e-15 -6.410704e-16
[49,] 1.429876e-15 1.293942e-15
[50,] 1.995631e-15 1.429876e-15
[51,] 3.169766e-16 1.995631e-15
[52,] 6.154228e-16 3.169766e-16
[53,] -3.898002e-15 6.154228e-16
[54,] 2.340749e-15 -3.898002e-15
[55,] 3.694978e-16 2.340749e-15
[56,] 1.668135e-15 3.694978e-16
[57,] 8.301000e-16 1.668135e-15
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.311881e-15 -3.298397e-15
2 -8.770209e-16 -3.311881e-15
3 7.661830e-16 -8.770209e-16
4 -6.903672e-16 7.661830e-16
5 2.070677e-14 -6.903672e-16
6 -1.328108e-15 2.070677e-14
7 -5.378453e-16 -1.328108e-15
8 -1.191667e-15 -5.378453e-16
9 1.322773e-15 -1.191667e-15
10 -2.125310e-16 1.322773e-15
11 -3.789682e-16 -2.125310e-16
12 1.518222e-16 -3.789682e-16
13 1.636712e-16 1.518222e-16
14 -1.289410e-15 1.636712e-16
15 -6.370880e-16 -1.289410e-15
16 -6.663576e-16 -6.370880e-16
17 -6.355886e-15 -6.663576e-16
18 -8.267010e-16 -6.355886e-15
19 5.890122e-17 -8.267010e-16
20 -4.199945e-16 5.890122e-17
21 -5.088215e-16 -4.199945e-16
22 8.210877e-17 -5.088215e-16
23 2.319369e-16 8.210877e-17
24 7.147671e-16 2.319369e-16
25 5.977735e-16 7.147671e-16
26 -5.107185e-16 5.977735e-16
27 -3.245215e-16 -5.107185e-16
28 -1.271274e-16 -3.245215e-16
29 -5.380048e-15 -1.271274e-16
30 -6.649721e-16 -5.380048e-15
31 -3.193224e-16 -6.649721e-16
32 -5.784941e-16 -3.193224e-16
33 3.382878e-16 -5.784941e-16
34 -4.465881e-16 3.382878e-16
35 7.881017e-16 -4.465881e-16
36 1.137866e-15 7.881017e-16
37 1.120559e-15 1.137866e-15
38 6.815183e-16 1.120559e-15
39 -1.215501e-16 6.815183e-16
40 8.684294e-16 -1.215501e-16
41 -5.072836e-15 8.684294e-16
42 4.790320e-16 -5.072836e-15
43 4.287687e-16 4.790320e-16
44 5.220210e-16 4.287687e-16
45 -1.982339e-15 5.220210e-16
46 5.770104e-16 -1.982339e-15
47 -6.410704e-16 5.770104e-16
48 1.293942e-15 -6.410704e-16
49 1.429876e-15 1.293942e-15
50 1.995631e-15 1.429876e-15
51 3.169766e-16 1.995631e-15
52 6.154228e-16 3.169766e-16
53 -3.898002e-15 6.154228e-16
54 2.340749e-15 -3.898002e-15
55 3.694978e-16 2.340749e-15
56 1.668135e-15 3.694978e-16
57 8.301000e-16 1.668135e-15
> 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/7fm3r1258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8c0kr1258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uixg1258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ipti1258725464.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11galj1258725464.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/12xhcg1258725464.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/13w0xz1258725464.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/14a2mc1258725464.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/15033n1258725464.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/16fftx1258725464.tab")
+ }
>
> system("convert tmp/1466r1258725464.ps tmp/1466r1258725464.png")
> system("convert tmp/2l2n21258725464.ps tmp/2l2n21258725464.png")
> system("convert tmp/32lgz1258725464.ps tmp/32lgz1258725464.png")
> system("convert tmp/4b76a1258725464.ps tmp/4b76a1258725464.png")
> system("convert tmp/53y681258725464.ps tmp/53y681258725464.png")
> system("convert tmp/6phg81258725464.ps tmp/6phg81258725464.png")
> system("convert tmp/7fm3r1258725464.ps tmp/7fm3r1258725464.png")
> system("convert tmp/8c0kr1258725464.ps tmp/8c0kr1258725464.png")
> system("convert tmp/9uixg1258725464.ps tmp/9uixg1258725464.png")
> system("convert tmp/10ipti1258725464.ps tmp/10ipti1258725464.png")
>
>
> proc.time()
user system elapsed
2.375 1.572 2.743