R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'license()' or 'licence()' for distribution details.
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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(95.26
+ ,96.8
+ ,94.76
+ ,119.93
+ ,101.21
+ ,108.01
+ ,117.96
+ ,114.1
+ ,95.26
+ ,94.76
+ ,119.93
+ ,101.21
+ ,115.86
+ ,110.3
+ ,117.96
+ ,95.26
+ ,94.76
+ ,119.93
+ ,111.44
+ ,103.9
+ ,115.86
+ ,117.96
+ ,95.26
+ ,94.76
+ ,108.16
+ ,101.6
+ ,111.44
+ ,115.86
+ ,117.96
+ ,95.26
+ ,108.77
+ ,94.6
+ ,108.16
+ ,111.44
+ ,115.86
+ ,117.96
+ ,109.45
+ ,95.9
+ ,108.77
+ ,108.16
+ ,111.44
+ ,115.86
+ ,124.83
+ ,104.7
+ ,109.45
+ ,108.77
+ ,108.16
+ ,111.44
+ ,115.31
+ ,102.8
+ ,124.83
+ ,109.45
+ ,108.77
+ ,108.16
+ ,109.49
+ ,98.1
+ ,115.31
+ ,124.83
+ ,109.45
+ ,108.77
+ ,124.24
+ ,113.9
+ ,109.49
+ ,115.31
+ ,124.83
+ ,109.45
+ ,92.85
+ ,80.9
+ ,124.24
+ ,109.49
+ ,115.31
+ ,124.83
+ ,98.42
+ ,95.7
+ ,92.85
+ ,124.24
+ ,109.49
+ ,115.31
+ ,120.88
+ ,113.2
+ ,98.42
+ ,92.85
+ ,124.24
+ ,109.49
+ ,111.72
+ ,105.9
+ ,120.88
+ ,98.42
+ ,92.85
+ ,124.24
+ ,116.1
+ ,108.8
+ ,111.72
+ ,120.88
+ ,98.42
+ ,92.85
+ ,109.37
+ ,102.3
+ ,116.1
+ ,111.72
+ ,120.88
+ ,98.42
+ ,111.65
+ ,99
+ ,109.37
+ ,116.1
+ ,111.72
+ ,120.88
+ ,114.29
+ ,100.7
+ ,111.65
+ ,109.37
+ ,116.1
+ ,111.72
+ ,133.68
+ ,115.5
+ ,114.29
+ ,111.65
+ ,109.37
+ ,116.1
+ ,114.27
+ ,100.7
+ ,133.68
+ ,114.29
+ ,111.65
+ ,109.37
+ ,126.49
+ ,109.9
+ ,114.27
+ ,133.68
+ ,114.29
+ ,111.65
+ ,131
+ ,114.6
+ ,126.49
+ ,114.27
+ ,133.68
+ ,114.29
+ ,104
+ ,85.4
+ ,131
+ ,126.49
+ ,114.27
+ ,133.68
+ ,108.88
+ ,100.5
+ ,104
+ ,131
+ ,126.49
+ ,114.27
+ ,128.48
+ ,114.8
+ ,108.88
+ ,104
+ ,131
+ ,126.49
+ ,132.44
+ ,116.5
+ ,128.48
+ ,108.88
+ ,104
+ ,131
+ ,128.04
+ ,112.9
+ ,132.44
+ ,128.48
+ ,108.88
+ ,104
+ ,116.35
+ ,102
+ ,128.04
+ ,132.44
+ ,128.48
+ ,108.88
+ ,120.93
+ ,106
+ ,116.35
+ ,128.04
+ ,132.44
+ ,128.48
+ ,118.59
+ ,105.3
+ ,120.93
+ ,116.35
+ ,128.04
+ ,132.44
+ ,133.1
+ ,118.8
+ ,118.59
+ ,120.93
+ ,116.35
+ ,128.04
+ ,121.05
+ ,106.1
+ ,133.1
+ ,118.59
+ ,120.93
+ ,116.35
+ ,127.62
+ ,109.3
+ ,121.05
+ ,133.1
+ ,118.59
+ ,120.93
+ ,135.44
+ ,117.2
+ ,127.62
+ ,121.05
+ ,133.1
+ ,118.59
+ ,114.88
+ ,92.5
+ ,135.44
+ ,127.62
+ ,121.05
+ ,133.1
+ ,114.34
+ ,104.2
+ ,114.88
+ ,135.44
+ ,127.62
+ ,121.05
+ ,128.85
+ ,112.5
+ ,114.34
+ ,114.88
+ ,135.44
+ ,127.62
+ ,138.9
+ ,122.4
+ ,128.85
+ ,114.34
+ ,114.88
+ ,135.44
+ ,129.44
+ ,113.3
+ ,138.9
+ ,128.85
+ ,114.34
+ ,114.88
+ ,114.96
+ ,100
+ ,129.44
+ ,138.9
+ ,128.85
+ ,114.34
+ ,127.98
+ ,110.7
+ ,114.96
+ ,129.44
+ ,138.9
+ ,128.85
+ ,127.03
+ ,112.8
+ ,127.98
+ ,114.96
+ ,129.44
+ ,138.9
+ ,128.75
+ ,109.8
+ ,127.03
+ ,127.98
+ ,114.96
+ ,129.44
+ ,137.91
+ ,117.3
+ ,128.75
+ ,127.03
+ ,127.98
+ ,114.96
+ ,128.37
+ ,109.1
+ ,137.91
+ ,128.75
+ ,127.03
+ ,127.98
+ ,135.9
+ ,115.9
+ ,128.37
+ ,137.91
+ ,128.75
+ ,127.03
+ ,122.19
+ ,96
+ ,135.9
+ ,128.37
+ ,137.91
+ ,128.75
+ ,113.08
+ ,99.8
+ ,122.19
+ ,135.9
+ ,128.37
+ ,137.91
+ ,136.2
+ ,116.8
+ ,113.08
+ ,122.19
+ ,135.9
+ ,128.37
+ ,138
+ ,115.7
+ ,136.2
+ ,113.08
+ ,122.19
+ ,135.9
+ ,115.24
+ ,99.4
+ ,138
+ ,136.2
+ ,113.08
+ ,122.19
+ ,110.95
+ ,94.3
+ ,115.24
+ ,138
+ ,136.2
+ ,113.08
+ ,99.23
+ ,91
+ ,110.95
+ ,115.24
+ ,138
+ ,136.2
+ ,102.39
+ ,93.2
+ ,99.23
+ ,110.95
+ ,115.24
+ ,138
+ ,112.67
+ ,103.1
+ ,102.39
+ ,99.23
+ ,110.95
+ ,115.24)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:56))
> 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
1 95.26 96.8 94.76 119.93 101.21 108.01 1 0 0 0 0 0 0 0 0 0 0
2 117.96 114.1 95.26 94.76 119.93 101.21 0 1 0 0 0 0 0 0 0 0 0
3 115.86 110.3 117.96 95.26 94.76 119.93 0 0 1 0 0 0 0 0 0 0 0
4 111.44 103.9 115.86 117.96 95.26 94.76 0 0 0 1 0 0 0 0 0 0 0
5 108.16 101.6 111.44 115.86 117.96 95.26 0 0 0 0 1 0 0 0 0 0 0
6 108.77 94.6 108.16 111.44 115.86 117.96 0 0 0 0 0 1 0 0 0 0 0
7 109.45 95.9 108.77 108.16 111.44 115.86 0 0 0 0 0 0 1 0 0 0 0
8 124.83 104.7 109.45 108.77 108.16 111.44 0 0 0 0 0 0 0 1 0 0 0
9 115.31 102.8 124.83 109.45 108.77 108.16 0 0 0 0 0 0 0 0 1 0 0
10 109.49 98.1 115.31 124.83 109.45 108.77 0 0 0 0 0 0 0 0 0 1 0
11 124.24 113.9 109.49 115.31 124.83 109.45 0 0 0 0 0 0 0 0 0 0 1
12 92.85 80.9 124.24 109.49 115.31 124.83 0 0 0 0 0 0 0 0 0 0 0
13 98.42 95.7 92.85 124.24 109.49 115.31 1 0 0 0 0 0 0 0 0 0 0
14 120.88 113.2 98.42 92.85 124.24 109.49 0 1 0 0 0 0 0 0 0 0 0
15 111.72 105.9 120.88 98.42 92.85 124.24 0 0 1 0 0 0 0 0 0 0 0
16 116.10 108.8 111.72 120.88 98.42 92.85 0 0 0 1 0 0 0 0 0 0 0
17 109.37 102.3 116.10 111.72 120.88 98.42 0 0 0 0 1 0 0 0 0 0 0
18 111.65 99.0 109.37 116.10 111.72 120.88 0 0 0 0 0 1 0 0 0 0 0
19 114.29 100.7 111.65 109.37 116.10 111.72 0 0 0 0 0 0 1 0 0 0 0
20 133.68 115.5 114.29 111.65 109.37 116.10 0 0 0 0 0 0 0 1 0 0 0
21 114.27 100.7 133.68 114.29 111.65 109.37 0 0 0 0 0 0 0 0 1 0 0
22 126.49 109.9 114.27 133.68 114.29 111.65 0 0 0 0 0 0 0 0 0 1 0
23 131.00 114.6 126.49 114.27 133.68 114.29 0 0 0 0 0 0 0 0 0 0 1
24 104.00 85.4 131.00 126.49 114.27 133.68 0 0 0 0 0 0 0 0 0 0 0
25 108.88 100.5 104.00 131.00 126.49 114.27 1 0 0 0 0 0 0 0 0 0 0
26 128.48 114.8 108.88 104.00 131.00 126.49 0 1 0 0 0 0 0 0 0 0 0
27 132.44 116.5 128.48 108.88 104.00 131.00 0 0 1 0 0 0 0 0 0 0 0
28 128.04 112.9 132.44 128.48 108.88 104.00 0 0 0 1 0 0 0 0 0 0 0
29 116.35 102.0 128.04 132.44 128.48 108.88 0 0 0 0 1 0 0 0 0 0 0
30 120.93 106.0 116.35 128.04 132.44 128.48 0 0 0 0 0 1 0 0 0 0 0
31 118.59 105.3 120.93 116.35 128.04 132.44 0 0 0 0 0 0 1 0 0 0 0
32 133.10 118.8 118.59 120.93 116.35 128.04 0 0 0 0 0 0 0 1 0 0 0
33 121.05 106.1 133.10 118.59 120.93 116.35 0 0 0 0 0 0 0 0 1 0 0
34 127.62 109.3 121.05 133.10 118.59 120.93 0 0 0 0 0 0 0 0 0 1 0
35 135.44 117.2 127.62 121.05 133.10 118.59 0 0 0 0 0 0 0 0 0 0 1
36 114.88 92.5 135.44 127.62 121.05 133.10 0 0 0 0 0 0 0 0 0 0 0
37 114.34 104.2 114.88 135.44 127.62 121.05 1 0 0 0 0 0 0 0 0 0 0
38 128.85 112.5 114.34 114.88 135.44 127.62 0 1 0 0 0 0 0 0 0 0 0
39 138.90 122.4 128.85 114.34 114.88 135.44 0 0 1 0 0 0 0 0 0 0 0
40 129.44 113.3 138.90 128.85 114.34 114.88 0 0 0 1 0 0 0 0 0 0 0
41 114.96 100.0 129.44 138.90 128.85 114.34 0 0 0 0 1 0 0 0 0 0 0
42 127.98 110.7 114.96 129.44 138.90 128.85 0 0 0 0 0 1 0 0 0 0 0
43 127.03 112.8 127.98 114.96 129.44 138.90 0 0 0 0 0 0 1 0 0 0 0
44 128.75 109.8 127.03 127.98 114.96 129.44 0 0 0 0 0 0 0 1 0 0 0
45 137.91 117.3 128.75 127.03 127.98 114.96 0 0 0 0 0 0 0 0 1 0 0
46 128.37 109.1 137.91 128.75 127.03 127.98 0 0 0 0 0 0 0 0 0 1 0
47 135.90 115.9 128.37 137.91 128.75 127.03 0 0 0 0 0 0 0 0 0 0 1
48 122.19 96.0 135.90 128.37 137.91 128.75 0 0 0 0 0 0 0 0 0 0 0
49 113.08 99.8 122.19 135.90 128.37 137.91 1 0 0 0 0 0 0 0 0 0 0
50 136.20 116.8 113.08 122.19 135.90 128.37 0 1 0 0 0 0 0 0 0 0 0
51 138.00 115.7 136.20 113.08 122.19 135.90 0 0 1 0 0 0 0 0 0 0 0
52 115.24 99.4 138.00 136.20 113.08 122.19 0 0 0 1 0 0 0 0 0 0 0
53 110.95 94.3 115.24 138.00 136.20 113.08 0 0 0 0 1 0 0 0 0 0 0
54 99.23 91.0 110.95 115.24 138.00 136.20 0 0 0 0 0 1 0 0 0 0 0
55 102.39 93.2 99.23 110.95 115.24 138.00 0 0 0 0 0 0 1 0 0 0 0
56 112.67 103.1 102.39 99.23 110.95 115.24 0 0 0 0 0 0 0 1 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-40.08887 0.96554 0.21200 0.31498 0.15741 -0.17912
M1 M2 M3 M4 M5 M6
-10.63113 1.03583 3.24682 -8.72618 -11.38376 -2.30380
M7 M8 M9 M10 M11 t
0.54262 3.56311 -4.05011 -4.49687 -4.03470 0.01180
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.06872 -1.40714 -0.02654 1.24060 5.64215
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -40.08887 12.06175 -3.324 0.001975 **
X 0.96554 0.08652 11.160 1.48e-13 ***
Y1 0.21200 0.07587 2.794 0.008105 **
Y2 0.31498 0.06896 4.567 5.08e-05 ***
Y3 0.15741 0.09088 1.732 0.091377 .
Y4 -0.17912 0.09768 -1.834 0.074529 .
M1 -10.63113 2.61716 -4.062 0.000235 ***
M2 1.03583 3.50009 0.296 0.768884
M3 3.24682 3.86879 0.839 0.406588
M4 -8.72618 3.68731 -2.367 0.023155 *
M5 -11.38376 2.66567 -4.271 0.000126 ***
M6 -2.30380 2.50076 -0.921 0.362736
M7 0.54262 2.58302 0.210 0.834734
M8 3.56311 3.19415 1.116 0.271637
M9 -4.05011 2.83430 -1.429 0.161184
M10 -4.49687 2.60726 -1.725 0.092699 .
M11 -4.03470 2.83204 -1.425 0.162417
t 0.01180 0.05348 0.221 0.826536
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.495 on 38 degrees of freedom
Multiple R-squared: 0.9673, Adjusted R-squared: 0.9527
F-statistic: 66.18 on 17 and 38 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,] 0.0886574 0.1773148 0.9113426
[2,] 0.4730560 0.9461120 0.5269440
[3,] 0.4080290 0.8160579 0.5919710
[4,] 0.3138280 0.6276559 0.6861720
[5,] 0.3373429 0.6746858 0.6626571
[6,] 0.3629823 0.7259646 0.6370177
[7,] 0.4306656 0.8613311 0.5693344
[8,] 0.3200595 0.6401190 0.6799405
[9,] 0.2711753 0.5423506 0.7288247
[10,] 0.7116316 0.5767368 0.2883684
[11,] 0.7654772 0.4690456 0.2345228
[12,] 0.8295407 0.3409185 0.1704593
[13,] 0.7122160 0.5755680 0.2877840
[14,] 0.5894221 0.8211559 0.4105779
[15,] 0.5177774 0.9644453 0.4822226
> postscript(file="/var/www/html/rcomp/tmp/1hxes1258660744.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2g0xn1258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3s0s01258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4r8301258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yqec1258660744.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 = 56
Frequency = 1
1 2 3 4 5
-1.9445355426 -3.9698968307 -2.2782190991 0.1503640797 -0.1483286908
6 7 8 9 10
4.6129066251 2.4028924923 5.6421522224 1.3998445145 -2.2710909680
11 12 13 14 15
-1.3173367519 -1.9313893064 1.1875238358 0.4138584897 -2.8531613419
16 17 18 19 20
-0.9440128289 0.6666601394 2.5533239881 -0.0001016305 2.6337606507
21 22 23 24 25
-1.3914269206 0.3808591878 0.8226512407 -0.3067129749 -0.4839918079
26 27 28 29 30
2.5788748603 2.0403963477 0.4599430353 -0.5855925923 -2.2078762332
31 32 33 34 35
-2.6171358239 -4.0687221135 -1.4089022458 1.6793001168 1.0970484437
36 37 38 39 40
1.1080058044 -1.4065574280 -0.0529784221 -0.0534527234 0.9354562585
41 42 43 44 45
-1.5979243937 -0.9344274322 -1.6802947449 -3.4107830412 1.4004846519
46 47 48 49 50
0.2109316634 -0.6023629325 1.1300964769 2.6475609427 1.0301419028
51 52 53 54 55
3.1444368167 -0.6017505446 1.6651855374 -4.0239269479 1.8946397070
56
-0.7964077184
> postscript(file="/var/www/html/rcomp/tmp/6s7gy1258660744.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.9445355426 NA
1 -3.9698968307 -1.9445355426
2 -2.2782190991 -3.9698968307
3 0.1503640797 -2.2782190991
4 -0.1483286908 0.1503640797
5 4.6129066251 -0.1483286908
6 2.4028924923 4.6129066251
7 5.6421522224 2.4028924923
8 1.3998445145 5.6421522224
9 -2.2710909680 1.3998445145
10 -1.3173367519 -2.2710909680
11 -1.9313893064 -1.3173367519
12 1.1875238358 -1.9313893064
13 0.4138584897 1.1875238358
14 -2.8531613419 0.4138584897
15 -0.9440128289 -2.8531613419
16 0.6666601394 -0.9440128289
17 2.5533239881 0.6666601394
18 -0.0001016305 2.5533239881
19 2.6337606507 -0.0001016305
20 -1.3914269206 2.6337606507
21 0.3808591878 -1.3914269206
22 0.8226512407 0.3808591878
23 -0.3067129749 0.8226512407
24 -0.4839918079 -0.3067129749
25 2.5788748603 -0.4839918079
26 2.0403963477 2.5788748603
27 0.4599430353 2.0403963477
28 -0.5855925923 0.4599430353
29 -2.2078762332 -0.5855925923
30 -2.6171358239 -2.2078762332
31 -4.0687221135 -2.6171358239
32 -1.4089022458 -4.0687221135
33 1.6793001168 -1.4089022458
34 1.0970484437 1.6793001168
35 1.1080058044 1.0970484437
36 -1.4065574280 1.1080058044
37 -0.0529784221 -1.4065574280
38 -0.0534527234 -0.0529784221
39 0.9354562585 -0.0534527234
40 -1.5979243937 0.9354562585
41 -0.9344274322 -1.5979243937
42 -1.6802947449 -0.9344274322
43 -3.4107830412 -1.6802947449
44 1.4004846519 -3.4107830412
45 0.2109316634 1.4004846519
46 -0.6023629325 0.2109316634
47 1.1300964769 -0.6023629325
48 2.6475609427 1.1300964769
49 1.0301419028 2.6475609427
50 3.1444368167 1.0301419028
51 -0.6017505446 3.1444368167
52 1.6651855374 -0.6017505446
53 -4.0239269479 1.6651855374
54 1.8946397070 -4.0239269479
55 -0.7964077184 1.8946397070
56 NA -0.7964077184
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.9698968307 -1.9445355426
[2,] -2.2782190991 -3.9698968307
[3,] 0.1503640797 -2.2782190991
[4,] -0.1483286908 0.1503640797
[5,] 4.6129066251 -0.1483286908
[6,] 2.4028924923 4.6129066251
[7,] 5.6421522224 2.4028924923
[8,] 1.3998445145 5.6421522224
[9,] -2.2710909680 1.3998445145
[10,] -1.3173367519 -2.2710909680
[11,] -1.9313893064 -1.3173367519
[12,] 1.1875238358 -1.9313893064
[13,] 0.4138584897 1.1875238358
[14,] -2.8531613419 0.4138584897
[15,] -0.9440128289 -2.8531613419
[16,] 0.6666601394 -0.9440128289
[17,] 2.5533239881 0.6666601394
[18,] -0.0001016305 2.5533239881
[19,] 2.6337606507 -0.0001016305
[20,] -1.3914269206 2.6337606507
[21,] 0.3808591878 -1.3914269206
[22,] 0.8226512407 0.3808591878
[23,] -0.3067129749 0.8226512407
[24,] -0.4839918079 -0.3067129749
[25,] 2.5788748603 -0.4839918079
[26,] 2.0403963477 2.5788748603
[27,] 0.4599430353 2.0403963477
[28,] -0.5855925923 0.4599430353
[29,] -2.2078762332 -0.5855925923
[30,] -2.6171358239 -2.2078762332
[31,] -4.0687221135 -2.6171358239
[32,] -1.4089022458 -4.0687221135
[33,] 1.6793001168 -1.4089022458
[34,] 1.0970484437 1.6793001168
[35,] 1.1080058044 1.0970484437
[36,] -1.4065574280 1.1080058044
[37,] -0.0529784221 -1.4065574280
[38,] -0.0534527234 -0.0529784221
[39,] 0.9354562585 -0.0534527234
[40,] -1.5979243937 0.9354562585
[41,] -0.9344274322 -1.5979243937
[42,] -1.6802947449 -0.9344274322
[43,] -3.4107830412 -1.6802947449
[44,] 1.4004846519 -3.4107830412
[45,] 0.2109316634 1.4004846519
[46,] -0.6023629325 0.2109316634
[47,] 1.1300964769 -0.6023629325
[48,] 2.6475609427 1.1300964769
[49,] 1.0301419028 2.6475609427
[50,] 3.1444368167 1.0301419028
[51,] -0.6017505446 3.1444368167
[52,] 1.6651855374 -0.6017505446
[53,] -4.0239269479 1.6651855374
[54,] 1.8946397070 -4.0239269479
[55,] -0.7964077184 1.8946397070
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.9698968307 -1.9445355426
2 -2.2782190991 -3.9698968307
3 0.1503640797 -2.2782190991
4 -0.1483286908 0.1503640797
5 4.6129066251 -0.1483286908
6 2.4028924923 4.6129066251
7 5.6421522224 2.4028924923
8 1.3998445145 5.6421522224
9 -2.2710909680 1.3998445145
10 -1.3173367519 -2.2710909680
11 -1.9313893064 -1.3173367519
12 1.1875238358 -1.9313893064
13 0.4138584897 1.1875238358
14 -2.8531613419 0.4138584897
15 -0.9440128289 -2.8531613419
16 0.6666601394 -0.9440128289
17 2.5533239881 0.6666601394
18 -0.0001016305 2.5533239881
19 2.6337606507 -0.0001016305
20 -1.3914269206 2.6337606507
21 0.3808591878 -1.3914269206
22 0.8226512407 0.3808591878
23 -0.3067129749 0.8226512407
24 -0.4839918079 -0.3067129749
25 2.5788748603 -0.4839918079
26 2.0403963477 2.5788748603
27 0.4599430353 2.0403963477
28 -0.5855925923 0.4599430353
29 -2.2078762332 -0.5855925923
30 -2.6171358239 -2.2078762332
31 -4.0687221135 -2.6171358239
32 -1.4089022458 -4.0687221135
33 1.6793001168 -1.4089022458
34 1.0970484437 1.6793001168
35 1.1080058044 1.0970484437
36 -1.4065574280 1.1080058044
37 -0.0529784221 -1.4065574280
38 -0.0534527234 -0.0529784221
39 0.9354562585 -0.0534527234
40 -1.5979243937 0.9354562585
41 -0.9344274322 -1.5979243937
42 -1.6802947449 -0.9344274322
43 -3.4107830412 -1.6802947449
44 1.4004846519 -3.4107830412
45 0.2109316634 1.4004846519
46 -0.6023629325 0.2109316634
47 1.1300964769 -0.6023629325
48 2.6475609427 1.1300964769
49 1.0301419028 2.6475609427
50 3.1444368167 1.0301419028
51 -0.6017505446 3.1444368167
52 1.6651855374 -0.6017505446
53 -4.0239269479 1.6651855374
54 1.8946397070 -4.0239269479
55 -0.7964077184 1.8946397070
> 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/7hre21258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8q1wb1258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9scf41258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107bfq1258660744.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z3l31258660744.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/12zp671258660744.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/130yvl1258660744.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/143ggm1258660744.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/153uqu1258660744.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/16vj7o1258660744.tab")
+ }
>
> system("convert tmp/1hxes1258660744.ps tmp/1hxes1258660744.png")
> system("convert tmp/2g0xn1258660744.ps tmp/2g0xn1258660744.png")
> system("convert tmp/3s0s01258660744.ps tmp/3s0s01258660744.png")
> system("convert tmp/4r8301258660744.ps tmp/4r8301258660744.png")
> system("convert tmp/5yqec1258660744.ps tmp/5yqec1258660744.png")
> system("convert tmp/6s7gy1258660744.ps tmp/6s7gy1258660744.png")
> system("convert tmp/7hre21258660744.ps tmp/7hre21258660744.png")
> system("convert tmp/8q1wb1258660744.ps tmp/8q1wb1258660744.png")
> system("convert tmp/9scf41258660744.ps tmp/9scf41258660744.png")
> system("convert tmp/107bfq1258660744.ps tmp/107bfq1258660744.png")
>
>
> proc.time()
user system elapsed
2.351 1.563 2.729