R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3.7
+ ,91.1
+ ,88
+ ,109.9
+ ,96.8
+ ,96.2
+ ,3.7
+ ,106.4
+ ,91.1
+ ,88
+ ,109.9
+ ,96.8
+ ,4.1
+ ,68.6
+ ,106.4
+ ,91.1
+ ,88
+ ,109.9
+ ,4.1
+ ,100.1
+ ,68.6
+ ,106.4
+ ,91.1
+ ,88
+ ,3.8
+ ,108
+ ,100.1
+ ,68.6
+ ,106.4
+ ,91.1
+ ,3.7
+ ,106
+ ,108
+ ,100.1
+ ,68.6
+ ,106.4
+ ,3.5
+ ,108.6
+ ,106
+ ,108
+ ,100.1
+ ,68.6
+ ,3.6
+ ,91.5
+ ,108.6
+ ,106
+ ,108
+ ,100.1
+ ,4.1
+ ,99.2
+ ,91.5
+ ,108.6
+ ,106
+ ,108
+ ,3.8
+ ,98
+ ,99.2
+ ,91.5
+ ,108.6
+ ,106
+ ,3.7
+ ,96.6
+ ,98
+ ,99.2
+ ,91.5
+ ,108.6
+ ,3.6
+ ,102.8
+ ,96.6
+ ,98
+ ,99.2
+ ,91.5
+ ,3.3
+ ,96.9
+ ,102.8
+ ,96.6
+ ,98
+ ,99.2
+ ,3.4
+ ,110
+ ,96.9
+ ,102.8
+ ,96.6
+ ,98
+ ,3.7
+ ,70.5
+ ,110
+ ,96.9
+ ,102.8
+ ,96.6
+ ,3.7
+ ,101.9
+ ,70.5
+ ,110
+ ,96.9
+ ,102.8
+ ,3.4
+ ,109.6
+ ,101.9
+ ,70.5
+ ,110
+ ,96.9
+ ,3.3
+ ,107.8
+ ,109.6
+ ,101.9
+ ,70.5
+ ,110
+ ,3
+ ,113
+ ,107.8
+ ,109.6
+ ,101.9
+ ,70.5
+ ,3
+ ,93.8
+ ,113
+ ,107.8
+ ,109.6
+ ,101.9
+ ,3.3
+ ,108
+ ,93.8
+ ,113
+ ,107.8
+ ,109.6
+ ,3
+ ,102.8
+ ,108
+ ,93.8
+ ,113
+ ,107.8
+ ,2.9
+ ,116.3
+ ,102.8
+ ,108
+ ,93.8
+ ,113
+ ,2.8
+ ,89.2
+ ,116.3
+ ,102.8
+ ,108
+ ,93.8
+ ,2.5
+ ,106.7
+ ,89.2
+ ,116.3
+ ,102.8
+ ,108
+ ,2.6
+ ,112.1
+ ,106.7
+ ,89.2
+ ,116.3
+ ,102.8
+ ,2.8
+ ,74.2
+ ,112.1
+ ,106.7
+ ,89.2
+ ,116.3
+ ,2.7
+ ,108.8
+ ,74.2
+ ,112.1
+ ,106.7
+ ,89.2
+ ,2.4
+ ,111.5
+ ,108.8
+ ,74.2
+ ,112.1
+ ,106.7
+ ,2.2
+ ,118.8
+ ,111.5
+ ,108.8
+ ,74.2
+ ,112.1
+ ,2.1
+ ,118.9
+ ,118.8
+ ,111.5
+ ,108.8
+ ,74.2
+ ,2.1
+ ,97.6
+ ,118.9
+ ,118.8
+ ,111.5
+ ,108.8
+ ,2.3
+ ,116.4
+ ,97.6
+ ,118.9
+ ,118.8
+ ,111.5
+ ,2.1
+ ,107.9
+ ,116.4
+ ,97.6
+ ,118.9
+ ,118.8
+ ,2
+ ,121.2
+ ,107.9
+ ,116.4
+ ,97.6
+ ,118.9
+ ,1.9
+ ,97.9
+ ,121.2
+ ,107.9
+ ,116.4
+ ,97.6
+ ,1.7
+ ,113.4
+ ,97.9
+ ,121.2
+ ,107.9
+ ,116.4
+ ,1.8
+ ,117.6
+ ,113.4
+ ,97.9
+ ,121.2
+ ,107.9
+ ,2.1
+ ,79.6
+ ,117.6
+ ,113.4
+ ,97.9
+ ,121.2
+ ,2
+ ,115.9
+ ,79.6
+ ,117.6
+ ,113.4
+ ,97.9
+ ,1.8
+ ,115.7
+ ,115.9
+ ,79.6
+ ,117.6
+ ,113.4
+ ,1.7
+ ,129.1
+ ,115.7
+ ,115.9
+ ,79.6
+ ,117.6
+ ,1.6
+ ,123.3
+ ,129.1
+ ,115.7
+ ,115.9
+ ,79.6
+ ,1.6
+ ,96.7
+ ,123.3
+ ,129.1
+ ,115.7
+ ,115.9
+ ,1.8
+ ,121.2
+ ,96.7
+ ,123.3
+ ,129.1
+ ,115.7
+ ,1.7
+ ,118.2
+ ,121.2
+ ,96.7
+ ,123.3
+ ,129.1
+ ,1.7
+ ,102.1
+ ,118.2
+ ,121.2
+ ,96.7
+ ,123.3
+ ,1.5
+ ,125.4
+ ,102.1
+ ,118.2
+ ,121.2
+ ,96.7
+ ,1.5
+ ,116.7
+ ,125.4
+ ,102.1
+ ,118.2
+ ,121.2
+ ,1.5
+ ,121.3
+ ,116.7
+ ,125.4
+ ,102.1
+ ,118.2
+ ,1.8
+ ,85.3
+ ,121.3
+ ,116.7
+ ,125.4
+ ,102.1
+ ,1.8
+ ,114.2
+ ,85.3
+ ,121.3
+ ,116.7
+ ,125.4
+ ,1.7
+ ,124.4
+ ,114.2
+ ,85.3
+ ,121.3
+ ,116.7
+ ,1.7
+ ,131
+ ,124.4
+ ,114.2
+ ,85.3
+ ,121.3
+ ,1.8
+ ,118.3
+ ,131
+ ,124.4
+ ,114.2
+ ,85.3
+ ,2
+ ,99.6
+ ,118.3
+ ,131
+ ,124.4
+ ,114.2)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('unempl'
+ ,'proman'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)'
+ ,'Y(t-4)')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('unempl','proman','Y(t-1)','Y(t-2)','Y(t-3)','Y(t-4)'),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
unempl proman Y(t-1) Y(t-2) Y(t-3) Y(t-4) M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3.7 91.1 88.0 109.9 96.8 96.2 1 0 0 0 0 0 0 0 0 0 0
2 3.7 106.4 91.1 88.0 109.9 96.8 0 1 0 0 0 0 0 0 0 0 0
3 4.1 68.6 106.4 91.1 88.0 109.9 0 0 1 0 0 0 0 0 0 0 0
4 4.1 100.1 68.6 106.4 91.1 88.0 0 0 0 1 0 0 0 0 0 0 0
5 3.8 108.0 100.1 68.6 106.4 91.1 0 0 0 0 1 0 0 0 0 0 0
6 3.7 106.0 108.0 100.1 68.6 106.4 0 0 0 0 0 1 0 0 0 0 0
7 3.5 108.6 106.0 108.0 100.1 68.6 0 0 0 0 0 0 1 0 0 0 0
8 3.6 91.5 108.6 106.0 108.0 100.1 0 0 0 0 0 0 0 1 0 0 0
9 4.1 99.2 91.5 108.6 106.0 108.0 0 0 0 0 0 0 0 0 1 0 0
10 3.8 98.0 99.2 91.5 108.6 106.0 0 0 0 0 0 0 0 0 0 1 0
11 3.7 96.6 98.0 99.2 91.5 108.6 0 0 0 0 0 0 0 0 0 0 1
12 3.6 102.8 96.6 98.0 99.2 91.5 0 0 0 0 0 0 0 0 0 0 0
13 3.3 96.9 102.8 96.6 98.0 99.2 1 0 0 0 0 0 0 0 0 0 0
14 3.4 110.0 96.9 102.8 96.6 98.0 0 1 0 0 0 0 0 0 0 0 0
15 3.7 70.5 110.0 96.9 102.8 96.6 0 0 1 0 0 0 0 0 0 0 0
16 3.7 101.9 70.5 110.0 96.9 102.8 0 0 0 1 0 0 0 0 0 0 0
17 3.4 109.6 101.9 70.5 110.0 96.9 0 0 0 0 1 0 0 0 0 0 0
18 3.3 107.8 109.6 101.9 70.5 110.0 0 0 0 0 0 1 0 0 0 0 0
19 3.0 113.0 107.8 109.6 101.9 70.5 0 0 0 0 0 0 1 0 0 0 0
20 3.0 93.8 113.0 107.8 109.6 101.9 0 0 0 0 0 0 0 1 0 0 0
21 3.3 108.0 93.8 113.0 107.8 109.6 0 0 0 0 0 0 0 0 1 0 0
22 3.0 102.8 108.0 93.8 113.0 107.8 0 0 0 0 0 0 0 0 0 1 0
23 2.9 116.3 102.8 108.0 93.8 113.0 0 0 0 0 0 0 0 0 0 0 1
24 2.8 89.2 116.3 102.8 108.0 93.8 0 0 0 0 0 0 0 0 0 0 0
25 2.5 106.7 89.2 116.3 102.8 108.0 1 0 0 0 0 0 0 0 0 0 0
26 2.6 112.1 106.7 89.2 116.3 102.8 0 1 0 0 0 0 0 0 0 0 0
27 2.8 74.2 112.1 106.7 89.2 116.3 0 0 1 0 0 0 0 0 0 0 0
28 2.7 108.8 74.2 112.1 106.7 89.2 0 0 0 1 0 0 0 0 0 0 0
29 2.4 111.5 108.8 74.2 112.1 106.7 0 0 0 0 1 0 0 0 0 0 0
30 2.2 118.8 111.5 108.8 74.2 112.1 0 0 0 0 0 1 0 0 0 0 0
31 2.1 118.9 118.8 111.5 108.8 74.2 0 0 0 0 0 0 1 0 0 0 0
32 2.1 97.6 118.9 118.8 111.5 108.8 0 0 0 0 0 0 0 1 0 0 0
33 2.3 116.4 97.6 118.9 118.8 111.5 0 0 0 0 0 0 0 0 1 0 0
34 2.1 107.9 116.4 97.6 118.9 118.8 0 0 0 0 0 0 0 0 0 1 0
35 2.0 121.2 107.9 116.4 97.6 118.9 0 0 0 0 0 0 0 0 0 0 1
36 1.9 97.9 121.2 107.9 116.4 97.6 0 0 0 0 0 0 0 0 0 0 0
37 1.7 113.4 97.9 121.2 107.9 116.4 1 0 0 0 0 0 0 0 0 0 0
38 1.8 117.6 113.4 97.9 121.2 107.9 0 1 0 0 0 0 0 0 0 0 0
39 2.1 79.6 117.6 113.4 97.9 121.2 0 0 1 0 0 0 0 0 0 0 0
40 2.0 115.9 79.6 117.6 113.4 97.9 0 0 0 1 0 0 0 0 0 0 0
41 1.8 115.7 115.9 79.6 117.6 113.4 0 0 0 0 1 0 0 0 0 0 0
42 1.7 129.1 115.7 115.9 79.6 117.6 0 0 0 0 0 1 0 0 0 0 0
43 1.6 123.3 129.1 115.7 115.9 79.6 0 0 0 0 0 0 1 0 0 0 0
44 1.6 96.7 123.3 129.1 115.7 115.9 0 0 0 0 0 0 0 1 0 0 0
45 1.8 121.2 96.7 123.3 129.1 115.7 0 0 0 0 0 0 0 0 1 0 0
46 1.7 118.2 121.2 96.7 123.3 129.1 0 0 0 0 0 0 0 0 0 1 0
47 1.7 102.1 118.2 121.2 96.7 123.3 0 0 0 0 0 0 0 0 0 0 1
48 1.5 125.4 102.1 118.2 121.2 96.7 0 0 0 0 0 0 0 0 0 0 0
49 1.5 116.7 125.4 102.1 118.2 121.2 1 0 0 0 0 0 0 0 0 0 0
50 1.5 121.3 116.7 125.4 102.1 118.2 0 1 0 0 0 0 0 0 0 0 0
51 1.8 85.3 121.3 116.7 125.4 102.1 0 0 1 0 0 0 0 0 0 0 0
52 1.8 114.2 85.3 121.3 116.7 125.4 0 0 0 1 0 0 0 0 0 0 0
53 1.7 124.4 114.2 85.3 121.3 116.7 0 0 0 0 1 0 0 0 0 0 0
54 1.7 131.0 124.4 114.2 85.3 121.3 0 0 0 0 0 1 0 0 0 0 0
55 1.8 118.3 131.0 124.4 114.2 85.3 0 0 0 0 0 0 1 0 0 0 0
56 2.0 99.6 118.3 131.0 124.4 114.2 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) proman `Y(t-1)` `Y(t-2)` `Y(t-3)` `Y(t-4)`
11.788944 -0.021698 -0.021569 -0.019989 -0.013507 -0.006271
M1 M2 M3 M4 M5 M6
-0.104253 0.117849 -0.204196 -0.162831 -0.175728 0.157906
M7 M8 M9 M10 M11 t
0.425401 0.384400 0.573142 0.232224 0.172740 -0.016790
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.3805406 -0.1966624 0.0006151 0.1579148 0.4945329
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.788944 2.859838 4.122 0.000196 ***
proman -0.021698 0.007946 -2.731 0.009531 **
`Y(t-1)` -0.021569 0.009729 -2.217 0.032679 *
`Y(t-2)` -0.019989 0.009947 -2.010 0.051609 .
`Y(t-3)` -0.013507 0.009366 -1.442 0.157472
`Y(t-4)` -0.006271 0.007982 -0.786 0.436897
M1 -0.104253 0.206470 -0.505 0.616523
M2 0.117849 0.203158 0.580 0.565281
M3 -0.204196 0.270803 -0.754 0.455472
M4 -0.162831 0.320756 -0.508 0.614634
M5 -0.175728 0.321887 -0.546 0.588305
M6 0.157906 0.346200 0.456 0.650904
M7 0.425401 0.298070 1.427 0.161694
M8 0.384400 0.259912 1.479 0.147392
M9 0.573142 0.284491 2.015 0.051061 .
M10 0.232224 0.255656 0.908 0.369419
M11 0.172740 0.237766 0.727 0.471975
t -0.016790 0.012274 -1.368 0.179365
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2454 on 38 degrees of freedom
Multiple R-squared: 0.9432, Adjusted R-squared: 0.9178
F-statistic: 37.11 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,] 0.0786951 0.1573902 0.92130490
[2,] 0.1280195 0.2560391 0.87198045
[3,] 0.2486445 0.4972890 0.75135549
[4,] 0.3288707 0.6577415 0.67112926
[5,] 0.3052740 0.6105480 0.69472600
[6,] 0.2306221 0.4612441 0.76937795
[7,] 0.2699307 0.5398615 0.73006927
[8,] 0.3456517 0.6913034 0.65434829
[9,] 0.3622750 0.7245500 0.63772499
[10,] 0.3407693 0.6815387 0.65923067
[11,] 0.2288504 0.4577009 0.77114956
[12,] 0.1436058 0.2872115 0.85639423
[13,] 0.1473574 0.2947148 0.85264261
[14,] 0.1006779 0.2013558 0.89932210
[15,] 0.9089230 0.1821539 0.09107696
> postscript(file="/var/www/html/rcomp/tmp/1sjoq1258665732.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2b3zc1258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3135u1258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43udp1258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wsn41258665732.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 6
0.01435101 -0.04918738 0.04781203 0.10176566 0.15278513 0.07799828
7 8 9 10 11 12
-0.01312088 0.09399681 0.29479313 0.17330392 0.03257696 0.19920692
13 14 15 16 17 18
0.03005883 0.17922511 0.20058504 0.22639538 0.15077910 0.03726057
19 20 21 22 23 24
-0.10914133 -0.09084022 0.05911403 -0.01457364 0.19958799 -0.04026742
25 26 27 28 29 30
-0.13535915 -0.13802747 -0.23661198 -0.25356128 -0.29391050 -0.38054056
31 32 33 34 35 36
-0.28801357 -0.29084180 -0.19676501 -0.19662824 -0.02638187 -0.20510416
37 38 39 40 41 42
-0.18135029 -0.20063196 -0.21717608 -0.22656495 -0.22391981 -0.11563962
43 44 45 46 47 48
-0.05519251 -0.20684766 -0.15714216 0.03789797 -0.20578308 0.04616465
49 50 51 52 53 54
0.27229960 0.20862170 0.20539099 0.15196520 0.21426608 0.38092134
55 56
0.46546829 0.49453287
> postscript(file="/var/www/html/rcomp/tmp/69jzu1258665732.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 0.01435101 NA
1 -0.04918738 0.01435101
2 0.04781203 -0.04918738
3 0.10176566 0.04781203
4 0.15278513 0.10176566
5 0.07799828 0.15278513
6 -0.01312088 0.07799828
7 0.09399681 -0.01312088
8 0.29479313 0.09399681
9 0.17330392 0.29479313
10 0.03257696 0.17330392
11 0.19920692 0.03257696
12 0.03005883 0.19920692
13 0.17922511 0.03005883
14 0.20058504 0.17922511
15 0.22639538 0.20058504
16 0.15077910 0.22639538
17 0.03726057 0.15077910
18 -0.10914133 0.03726057
19 -0.09084022 -0.10914133
20 0.05911403 -0.09084022
21 -0.01457364 0.05911403
22 0.19958799 -0.01457364
23 -0.04026742 0.19958799
24 -0.13535915 -0.04026742
25 -0.13802747 -0.13535915
26 -0.23661198 -0.13802747
27 -0.25356128 -0.23661198
28 -0.29391050 -0.25356128
29 -0.38054056 -0.29391050
30 -0.28801357 -0.38054056
31 -0.29084180 -0.28801357
32 -0.19676501 -0.29084180
33 -0.19662824 -0.19676501
34 -0.02638187 -0.19662824
35 -0.20510416 -0.02638187
36 -0.18135029 -0.20510416
37 -0.20063196 -0.18135029
38 -0.21717608 -0.20063196
39 -0.22656495 -0.21717608
40 -0.22391981 -0.22656495
41 -0.11563962 -0.22391981
42 -0.05519251 -0.11563962
43 -0.20684766 -0.05519251
44 -0.15714216 -0.20684766
45 0.03789797 -0.15714216
46 -0.20578308 0.03789797
47 0.04616465 -0.20578308
48 0.27229960 0.04616465
49 0.20862170 0.27229960
50 0.20539099 0.20862170
51 0.15196520 0.20539099
52 0.21426608 0.15196520
53 0.38092134 0.21426608
54 0.46546829 0.38092134
55 0.49453287 0.46546829
56 NA 0.49453287
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.04918738 0.01435101
[2,] 0.04781203 -0.04918738
[3,] 0.10176566 0.04781203
[4,] 0.15278513 0.10176566
[5,] 0.07799828 0.15278513
[6,] -0.01312088 0.07799828
[7,] 0.09399681 -0.01312088
[8,] 0.29479313 0.09399681
[9,] 0.17330392 0.29479313
[10,] 0.03257696 0.17330392
[11,] 0.19920692 0.03257696
[12,] 0.03005883 0.19920692
[13,] 0.17922511 0.03005883
[14,] 0.20058504 0.17922511
[15,] 0.22639538 0.20058504
[16,] 0.15077910 0.22639538
[17,] 0.03726057 0.15077910
[18,] -0.10914133 0.03726057
[19,] -0.09084022 -0.10914133
[20,] 0.05911403 -0.09084022
[21,] -0.01457364 0.05911403
[22,] 0.19958799 -0.01457364
[23,] -0.04026742 0.19958799
[24,] -0.13535915 -0.04026742
[25,] -0.13802747 -0.13535915
[26,] -0.23661198 -0.13802747
[27,] -0.25356128 -0.23661198
[28,] -0.29391050 -0.25356128
[29,] -0.38054056 -0.29391050
[30,] -0.28801357 -0.38054056
[31,] -0.29084180 -0.28801357
[32,] -0.19676501 -0.29084180
[33,] -0.19662824 -0.19676501
[34,] -0.02638187 -0.19662824
[35,] -0.20510416 -0.02638187
[36,] -0.18135029 -0.20510416
[37,] -0.20063196 -0.18135029
[38,] -0.21717608 -0.20063196
[39,] -0.22656495 -0.21717608
[40,] -0.22391981 -0.22656495
[41,] -0.11563962 -0.22391981
[42,] -0.05519251 -0.11563962
[43,] -0.20684766 -0.05519251
[44,] -0.15714216 -0.20684766
[45,] 0.03789797 -0.15714216
[46,] -0.20578308 0.03789797
[47,] 0.04616465 -0.20578308
[48,] 0.27229960 0.04616465
[49,] 0.20862170 0.27229960
[50,] 0.20539099 0.20862170
[51,] 0.15196520 0.20539099
[52,] 0.21426608 0.15196520
[53,] 0.38092134 0.21426608
[54,] 0.46546829 0.38092134
[55,] 0.49453287 0.46546829
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.04918738 0.01435101
2 0.04781203 -0.04918738
3 0.10176566 0.04781203
4 0.15278513 0.10176566
5 0.07799828 0.15278513
6 -0.01312088 0.07799828
7 0.09399681 -0.01312088
8 0.29479313 0.09399681
9 0.17330392 0.29479313
10 0.03257696 0.17330392
11 0.19920692 0.03257696
12 0.03005883 0.19920692
13 0.17922511 0.03005883
14 0.20058504 0.17922511
15 0.22639538 0.20058504
16 0.15077910 0.22639538
17 0.03726057 0.15077910
18 -0.10914133 0.03726057
19 -0.09084022 -0.10914133
20 0.05911403 -0.09084022
21 -0.01457364 0.05911403
22 0.19958799 -0.01457364
23 -0.04026742 0.19958799
24 -0.13535915 -0.04026742
25 -0.13802747 -0.13535915
26 -0.23661198 -0.13802747
27 -0.25356128 -0.23661198
28 -0.29391050 -0.25356128
29 -0.38054056 -0.29391050
30 -0.28801357 -0.38054056
31 -0.29084180 -0.28801357
32 -0.19676501 -0.29084180
33 -0.19662824 -0.19676501
34 -0.02638187 -0.19662824
35 -0.20510416 -0.02638187
36 -0.18135029 -0.20510416
37 -0.20063196 -0.18135029
38 -0.21717608 -0.20063196
39 -0.22656495 -0.21717608
40 -0.22391981 -0.22656495
41 -0.11563962 -0.22391981
42 -0.05519251 -0.11563962
43 -0.20684766 -0.05519251
44 -0.15714216 -0.20684766
45 0.03789797 -0.15714216
46 -0.20578308 0.03789797
47 0.04616465 -0.20578308
48 0.27229960 0.04616465
49 0.20862170 0.27229960
50 0.20539099 0.20862170
51 0.15196520 0.20539099
52 0.21426608 0.15196520
53 0.38092134 0.21426608
54 0.46546829 0.38092134
55 0.49453287 0.46546829
> 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/78mtd1258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81v0r1258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9deu71258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bf801258665732.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xy2i1258665732.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/12uuxt1258665732.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/13gad11258665732.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/148zum1258665732.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/1541101258665732.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/16iqr81258665732.tab")
+ }
>
> system("convert tmp/1sjoq1258665732.ps tmp/1sjoq1258665732.png")
> system("convert tmp/2b3zc1258665732.ps tmp/2b3zc1258665732.png")
> system("convert tmp/3135u1258665732.ps tmp/3135u1258665732.png")
> system("convert tmp/43udp1258665732.ps tmp/43udp1258665732.png")
> system("convert tmp/5wsn41258665732.ps tmp/5wsn41258665732.png")
> system("convert tmp/69jzu1258665732.ps tmp/69jzu1258665732.png")
> system("convert tmp/78mtd1258665732.ps tmp/78mtd1258665732.png")
> system("convert tmp/81v0r1258665732.ps tmp/81v0r1258665732.png")
> system("convert tmp/9deu71258665732.ps tmp/9deu71258665732.png")
> system("convert tmp/10bf801258665732.ps tmp/10bf801258665732.png")
>
>
> proc.time()
user system elapsed
2.386 1.608 4.122