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 '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(101.6
+ ,8.3
+ ,103.9
+ ,110.3
+ ,114.1
+ ,96.8
+ ,94.6
+ ,8.5
+ ,101.6
+ ,103.9
+ ,110.3
+ ,114.1
+ ,95.9
+ ,8.6
+ ,94.6
+ ,101.6
+ ,103.9
+ ,110.3
+ ,104.7
+ ,8.5
+ ,95.9
+ ,94.6
+ ,101.6
+ ,103.9
+ ,102.8
+ ,8.2
+ ,104.7
+ ,95.9
+ ,94.6
+ ,101.6
+ ,98.1
+ ,8.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,94.6
+ ,113.9
+ ,7.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.9
+ ,80.9
+ ,8.6
+ ,113.9
+ ,98.1
+ ,102.8
+ ,104.7
+ ,95.7
+ ,8.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,102.8
+ ,113.2
+ ,8.7
+ ,95.7
+ ,80.9
+ ,113.9
+ ,98.1
+ ,105.9
+ ,8.5
+ ,113.2
+ ,95.7
+ ,80.9
+ ,113.9
+ ,108.8
+ ,8.4
+ ,105.9
+ ,113.2
+ ,95.7
+ ,80.9
+ ,102.3
+ ,8.5
+ ,108.8
+ ,105.9
+ ,113.2
+ ,95.7
+ ,99
+ ,8.7
+ ,102.3
+ ,108.8
+ ,105.9
+ ,113.2
+ ,100.7
+ ,8.7
+ ,99
+ ,102.3
+ ,108.8
+ ,105.9
+ ,115.5
+ ,8.6
+ ,100.7
+ ,99
+ ,102.3
+ ,108.8
+ ,100.7
+ ,8.5
+ ,115.5
+ ,100.7
+ ,99
+ ,102.3
+ ,109.9
+ ,8.3
+ ,100.7
+ ,115.5
+ ,100.7
+ ,99
+ ,114.6
+ ,8
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.7
+ ,85.4
+ ,8.2
+ ,114.6
+ ,109.9
+ ,100.7
+ ,115.5
+ ,100.5
+ ,8.1
+ ,85.4
+ ,114.6
+ ,109.9
+ ,100.7
+ ,114.8
+ ,8.1
+ ,100.5
+ ,85.4
+ ,114.6
+ ,109.9
+ ,116.5
+ ,8
+ ,114.8
+ ,100.5
+ ,85.4
+ ,114.6
+ ,112.9
+ ,7.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,85.4
+ ,102
+ ,7.9
+ ,112.9
+ ,116.5
+ ,114.8
+ ,100.5
+ ,106
+ ,8
+ ,102
+ ,112.9
+ ,116.5
+ ,114.8
+ ,105.3
+ ,8
+ ,106
+ ,102
+ ,112.9
+ ,116.5
+ ,118.8
+ ,7.9
+ ,105.3
+ ,106
+ ,102
+ ,112.9
+ ,106.1
+ ,8
+ ,118.8
+ ,105.3
+ ,106
+ ,102
+ ,109.3
+ ,7.7
+ ,106.1
+ ,118.8
+ ,105.3
+ ,106
+ ,117.2
+ ,7.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,105.3
+ ,92.5
+ ,7.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,118.8
+ ,104.2
+ ,7.3
+ ,92.5
+ ,117.2
+ ,109.3
+ ,106.1
+ ,112.5
+ ,7
+ ,104.2
+ ,92.5
+ ,117.2
+ ,109.3
+ ,122.4
+ ,7
+ ,112.5
+ ,104.2
+ ,92.5
+ ,117.2
+ ,113.3
+ ,7
+ ,122.4
+ ,112.5
+ ,104.2
+ ,92.5
+ ,100
+ ,7.2
+ ,113.3
+ ,122.4
+ ,112.5
+ ,104.2
+ ,110.7
+ ,7.3
+ ,100
+ ,113.3
+ ,122.4
+ ,112.5
+ ,112.8
+ ,7.1
+ ,110.7
+ ,100
+ ,113.3
+ ,122.4
+ ,109.8
+ ,6.8
+ ,112.8
+ ,110.7
+ ,100
+ ,113.3
+ ,117.3
+ ,6.4
+ ,109.8
+ ,112.8
+ ,110.7
+ ,100
+ ,109.1
+ ,6.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,110.7
+ ,115.9
+ ,6.5
+ ,109.1
+ ,117.3
+ ,109.8
+ ,112.8
+ ,96
+ ,7.7
+ ,115.9
+ ,109.1
+ ,117.3
+ ,109.8
+ ,99.8
+ ,7.9
+ ,96
+ ,115.9
+ ,109.1
+ ,117.3
+ ,116.8
+ ,7.5
+ ,99.8
+ ,96
+ ,115.9
+ ,109.1
+ ,115.7
+ ,6.9
+ ,116.8
+ ,99.8
+ ,96
+ ,115.9
+ ,99.4
+ ,6.6
+ ,115.7
+ ,116.8
+ ,99.8
+ ,96
+ ,94.3
+ ,6.9
+ ,99.4
+ ,115.7
+ ,116.8
+ ,99.8
+ ,91
+ ,7.7
+ ,94.3
+ ,99.4
+ ,115.7
+ ,116.8
+ ,93.2
+ ,8
+ ,91
+ ,94.3
+ ,99.4
+ ,115.7
+ ,103.1
+ ,8
+ ,93.2
+ ,91
+ ,94.3
+ ,99.4
+ ,94.1
+ ,7.7
+ ,103.1
+ ,93.2
+ ,91
+ ,94.3
+ ,91.8
+ ,7.3
+ ,94.1
+ ,103.1
+ ,93.2
+ ,91
+ ,102.7
+ ,7.4
+ ,91.8
+ ,94.1
+ ,103.1
+ ,93.2
+ ,82.6
+ ,8.1
+ ,102.7
+ ,91.8
+ ,94.1
+ ,103.1
+ ,89.1
+ ,8.3
+ ,82.6
+ ,102.7
+ ,91.8
+ ,94.1)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('Y','X','Y1','Y2','Y3','Y4'),1:57))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 101.6 8.3 103.9 110.3 114.1 96.8 1 0 0 0 0 0 0 0 0 0 0 1
2 94.6 8.5 101.6 103.9 110.3 114.1 0 1 0 0 0 0 0 0 0 0 0 2
3 95.9 8.6 94.6 101.6 103.9 110.3 0 0 1 0 0 0 0 0 0 0 0 3
4 104.7 8.5 95.9 94.6 101.6 103.9 0 0 0 1 0 0 0 0 0 0 0 4
5 102.8 8.2 104.7 95.9 94.6 101.6 0 0 0 0 1 0 0 0 0 0 0 5
6 98.1 8.1 102.8 104.7 95.9 94.6 0 0 0 0 0 1 0 0 0 0 0 6
7 113.9 7.9 98.1 102.8 104.7 95.9 0 0 0 0 0 0 1 0 0 0 0 7
8 80.9 8.6 113.9 98.1 102.8 104.7 0 0 0 0 0 0 0 1 0 0 0 8
9 95.7 8.7 80.9 113.9 98.1 102.8 0 0 0 0 0 0 0 0 1 0 0 9
10 113.2 8.7 95.7 80.9 113.9 98.1 0 0 0 0 0 0 0 0 0 1 0 10
11 105.9 8.5 113.2 95.7 80.9 113.9 0 0 0 0 0 0 0 0 0 0 1 11
12 108.8 8.4 105.9 113.2 95.7 80.9 0 0 0 0 0 0 0 0 0 0 0 12
13 102.3 8.5 108.8 105.9 113.2 95.7 1 0 0 0 0 0 0 0 0 0 0 13
14 99.0 8.7 102.3 108.8 105.9 113.2 0 1 0 0 0 0 0 0 0 0 0 14
15 100.7 8.7 99.0 102.3 108.8 105.9 0 0 1 0 0 0 0 0 0 0 0 15
16 115.5 8.6 100.7 99.0 102.3 108.8 0 0 0 1 0 0 0 0 0 0 0 16
17 100.7 8.5 115.5 100.7 99.0 102.3 0 0 0 0 1 0 0 0 0 0 0 17
18 109.9 8.3 100.7 115.5 100.7 99.0 0 0 0 0 0 1 0 0 0 0 0 18
19 114.6 8.0 109.9 100.7 115.5 100.7 0 0 0 0 0 0 1 0 0 0 0 19
20 85.4 8.2 114.6 109.9 100.7 115.5 0 0 0 0 0 0 0 1 0 0 0 20
21 100.5 8.1 85.4 114.6 109.9 100.7 0 0 0 0 0 0 0 0 1 0 0 21
22 114.8 8.1 100.5 85.4 114.6 109.9 0 0 0 0 0 0 0 0 0 1 0 22
23 116.5 8.0 114.8 100.5 85.4 114.6 0 0 0 0 0 0 0 0 0 0 1 23
24 112.9 7.9 116.5 114.8 100.5 85.4 0 0 0 0 0 0 0 0 0 0 0 24
25 102.0 7.9 112.9 116.5 114.8 100.5 1 0 0 0 0 0 0 0 0 0 0 25
26 106.0 8.0 102.0 112.9 116.5 114.8 0 1 0 0 0 0 0 0 0 0 0 26
27 105.3 8.0 106.0 102.0 112.9 116.5 0 0 1 0 0 0 0 0 0 0 0 27
28 118.8 7.9 105.3 106.0 102.0 112.9 0 0 0 1 0 0 0 0 0 0 0 28
29 106.1 8.0 118.8 105.3 106.0 102.0 0 0 0 0 1 0 0 0 0 0 0 29
30 109.3 7.7 106.1 118.8 105.3 106.0 0 0 0 0 0 1 0 0 0 0 0 30
31 117.2 7.2 109.3 106.1 118.8 105.3 0 0 0 0 0 0 1 0 0 0 0 31
32 92.5 7.5 117.2 109.3 106.1 118.8 0 0 0 0 0 0 0 1 0 0 0 32
33 104.2 7.3 92.5 117.2 109.3 106.1 0 0 0 0 0 0 0 0 1 0 0 33
34 112.5 7.0 104.2 92.5 117.2 109.3 0 0 0 0 0 0 0 0 0 1 0 34
35 122.4 7.0 112.5 104.2 92.5 117.2 0 0 0 0 0 0 0 0 0 0 1 35
36 113.3 7.0 122.4 112.5 104.2 92.5 0 0 0 0 0 0 0 0 0 0 0 36
37 100.0 7.2 113.3 122.4 112.5 104.2 1 0 0 0 0 0 0 0 0 0 0 37
38 110.7 7.3 100.0 113.3 122.4 112.5 0 1 0 0 0 0 0 0 0 0 0 38
39 112.8 7.1 110.7 100.0 113.3 122.4 0 0 1 0 0 0 0 0 0 0 0 39
40 109.8 6.8 112.8 110.7 100.0 113.3 0 0 0 1 0 0 0 0 0 0 0 40
41 117.3 6.4 109.8 112.8 110.7 100.0 0 0 0 0 1 0 0 0 0 0 0 41
42 109.1 6.1 117.3 109.8 112.8 110.7 0 0 0 0 0 1 0 0 0 0 0 42
43 115.9 6.5 109.1 117.3 109.8 112.8 0 0 0 0 0 0 1 0 0 0 0 43
44 96.0 7.7 115.9 109.1 117.3 109.8 0 0 0 0 0 0 0 1 0 0 0 44
45 99.8 7.9 96.0 115.9 109.1 117.3 0 0 0 0 0 0 0 0 1 0 0 45
46 116.8 7.5 99.8 96.0 115.9 109.1 0 0 0 0 0 0 0 0 0 1 0 46
47 115.7 6.9 116.8 99.8 96.0 115.9 0 0 0 0 0 0 0 0 0 0 1 47
48 99.4 6.6 115.7 116.8 99.8 96.0 0 0 0 0 0 0 0 0 0 0 0 48
49 94.3 6.9 99.4 115.7 116.8 99.8 1 0 0 0 0 0 0 0 0 0 0 49
50 91.0 7.7 94.3 99.4 115.7 116.8 0 1 0 0 0 0 0 0 0 0 0 50
51 93.2 8.0 91.0 94.3 99.4 115.7 0 0 1 0 0 0 0 0 0 0 0 51
52 103.1 8.0 93.2 91.0 94.3 99.4 0 0 0 1 0 0 0 0 0 0 0 52
53 94.1 7.7 103.1 93.2 91.0 94.3 0 0 0 0 1 0 0 0 0 0 0 53
54 91.8 7.3 94.1 103.1 93.2 91.0 0 0 0 0 0 1 0 0 0 0 0 54
55 102.7 7.4 91.8 94.1 103.1 93.2 0 0 0 0 0 0 1 0 0 0 0 55
56 82.6 8.1 102.7 91.8 94.1 103.1 0 0 0 0 0 0 0 1 0 0 0 56
57 89.1 8.3 82.6 102.7 91.8 94.1 0 0 0 0 0 0 0 0 1 0 0 57
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y3 Y4
-6.79694 1.05487 0.11097 0.39196 0.59952 -0.10673
M1 M2 M3 M4 M5 M6
-15.45503 -10.45743 -2.31358 10.27457 1.88615 -1.91130
M7 M8 M9 M10 M11 t
4.78338 -17.39037 -8.15432 9.74104 21.71164 -0.01964
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.0283 -2.8328 0.1879 2.4991 8.8448
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.79694 30.84032 -0.220 0.826716
X 1.05487 1.85547 0.569 0.572944
Y1 0.11097 0.15817 0.702 0.487130
Y2 0.39196 0.13659 2.870 0.006606 **
Y3 0.59952 0.14352 4.177 0.000161 ***
Y4 -0.10673 0.17544 -0.608 0.546499
M1 -15.45503 4.84968 -3.187 0.002831 **
M2 -10.45743 7.95191 -1.315 0.196162
M3 -2.31358 7.85966 -0.294 0.770043
M4 10.27457 6.31188 1.628 0.111618
M5 1.88615 4.40978 0.428 0.671207
M6 -1.91130 4.50751 -0.424 0.673877
M7 4.78338 5.47425 0.874 0.387578
M8 -17.39037 5.77453 -3.012 0.004544 **
M9 -8.15432 7.29110 -1.118 0.270241
M10 9.74104 8.06355 1.208 0.234309
M11 21.71164 6.33974 3.425 0.001462 **
t -0.01964 0.05723 -0.343 0.733231
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.218 on 39 degrees of freedom
Multiple R-squared: 0.87, Adjusted R-squared: 0.8133
F-statistic: 15.35 on 17 and 39 DF, p-value: 2.816e-12
> 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,] 0.36179620 0.72359239 0.6382038
[2,] 0.20693908 0.41387817 0.7930609
[3,] 0.18112252 0.36224504 0.8188775
[4,] 0.15298168 0.30596336 0.8470183
[5,] 0.15857789 0.31715579 0.8414221
[6,] 0.10083174 0.20166348 0.8991683
[7,] 0.08659480 0.17318961 0.9134052
[8,] 0.10471469 0.20942938 0.8952853
[9,] 0.09520499 0.19040998 0.9047950
[10,] 0.06936422 0.13872844 0.9306358
[11,] 0.07091531 0.14183062 0.9290847
[12,] 0.04057600 0.08115200 0.9594240
[13,] 0.02684041 0.05368082 0.9731596
[14,] 0.38659921 0.77319841 0.6134008
[15,] 0.43123835 0.86247670 0.5687617
[16,] 0.30191717 0.60383433 0.6980828
> postscript(file="/var/www/html/rcomp/tmp/1zh781260975432.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2du0o1260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3aigl1260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4sazh1260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n6fv1260975432.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 = 57
Frequency = 1
1 2 3 4 5 6
2.27952423 -3.02110721 -4.84115687 -5.20886210 4.08078549 -1.46149959
7 8 9 10 11 12
4.00366313 -5.37411543 0.18785965 1.13048451 -4.18195005 2.11070974
13 14 15 16 17 18
4.60734549 1.94721167 -4.08077626 3.56742116 -3.74295921 3.95502102
19 20 21 22 23 24
-1.61490548 -2.50759890 -2.21567054 2.14238979 2.49913201 2.77307464
25 26 27 28 29 30
0.11932037 2.16345234 -0.49254337 5.20483562 -3.97765452 0.32028266
31 32 33 34 35 36
-1.47275754 2.62799319 1.69303939 -3.57779836 4.51559634 3.14452927
37 38 39 40 41 42
-1.48970271 4.12010008 8.84482852 -3.83179838 4.17366240 0.33383528
43 44 45 46 47 48
0.02975932 -1.29976425 -1.66774133 0.30492406 -2.83277830 -8.02831366
49 50 51 52 53 54
-5.51648738 -5.20965688 0.56964798 0.26840371 -0.53383416 -3.14763937
55 56 57
-0.94575943 6.55348539 2.00251283
> postscript(file="/var/www/html/rcomp/tmp/64gbi1260975432.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 = 57
Frequency = 1
lag(myerror, k = 1) myerror
0 2.27952423 NA
1 -3.02110721 2.27952423
2 -4.84115687 -3.02110721
3 -5.20886210 -4.84115687
4 4.08078549 -5.20886210
5 -1.46149959 4.08078549
6 4.00366313 -1.46149959
7 -5.37411543 4.00366313
8 0.18785965 -5.37411543
9 1.13048451 0.18785965
10 -4.18195005 1.13048451
11 2.11070974 -4.18195005
12 4.60734549 2.11070974
13 1.94721167 4.60734549
14 -4.08077626 1.94721167
15 3.56742116 -4.08077626
16 -3.74295921 3.56742116
17 3.95502102 -3.74295921
18 -1.61490548 3.95502102
19 -2.50759890 -1.61490548
20 -2.21567054 -2.50759890
21 2.14238979 -2.21567054
22 2.49913201 2.14238979
23 2.77307464 2.49913201
24 0.11932037 2.77307464
25 2.16345234 0.11932037
26 -0.49254337 2.16345234
27 5.20483562 -0.49254337
28 -3.97765452 5.20483562
29 0.32028266 -3.97765452
30 -1.47275754 0.32028266
31 2.62799319 -1.47275754
32 1.69303939 2.62799319
33 -3.57779836 1.69303939
34 4.51559634 -3.57779836
35 3.14452927 4.51559634
36 -1.48970271 3.14452927
37 4.12010008 -1.48970271
38 8.84482852 4.12010008
39 -3.83179838 8.84482852
40 4.17366240 -3.83179838
41 0.33383528 4.17366240
42 0.02975932 0.33383528
43 -1.29976425 0.02975932
44 -1.66774133 -1.29976425
45 0.30492406 -1.66774133
46 -2.83277830 0.30492406
47 -8.02831366 -2.83277830
48 -5.51648738 -8.02831366
49 -5.20965688 -5.51648738
50 0.56964798 -5.20965688
51 0.26840371 0.56964798
52 -0.53383416 0.26840371
53 -3.14763937 -0.53383416
54 -0.94575943 -3.14763937
55 6.55348539 -0.94575943
56 2.00251283 6.55348539
57 NA 2.00251283
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.02110721 2.27952423
[2,] -4.84115687 -3.02110721
[3,] -5.20886210 -4.84115687
[4,] 4.08078549 -5.20886210
[5,] -1.46149959 4.08078549
[6,] 4.00366313 -1.46149959
[7,] -5.37411543 4.00366313
[8,] 0.18785965 -5.37411543
[9,] 1.13048451 0.18785965
[10,] -4.18195005 1.13048451
[11,] 2.11070974 -4.18195005
[12,] 4.60734549 2.11070974
[13,] 1.94721167 4.60734549
[14,] -4.08077626 1.94721167
[15,] 3.56742116 -4.08077626
[16,] -3.74295921 3.56742116
[17,] 3.95502102 -3.74295921
[18,] -1.61490548 3.95502102
[19,] -2.50759890 -1.61490548
[20,] -2.21567054 -2.50759890
[21,] 2.14238979 -2.21567054
[22,] 2.49913201 2.14238979
[23,] 2.77307464 2.49913201
[24,] 0.11932037 2.77307464
[25,] 2.16345234 0.11932037
[26,] -0.49254337 2.16345234
[27,] 5.20483562 -0.49254337
[28,] -3.97765452 5.20483562
[29,] 0.32028266 -3.97765452
[30,] -1.47275754 0.32028266
[31,] 2.62799319 -1.47275754
[32,] 1.69303939 2.62799319
[33,] -3.57779836 1.69303939
[34,] 4.51559634 -3.57779836
[35,] 3.14452927 4.51559634
[36,] -1.48970271 3.14452927
[37,] 4.12010008 -1.48970271
[38,] 8.84482852 4.12010008
[39,] -3.83179838 8.84482852
[40,] 4.17366240 -3.83179838
[41,] 0.33383528 4.17366240
[42,] 0.02975932 0.33383528
[43,] -1.29976425 0.02975932
[44,] -1.66774133 -1.29976425
[45,] 0.30492406 -1.66774133
[46,] -2.83277830 0.30492406
[47,] -8.02831366 -2.83277830
[48,] -5.51648738 -8.02831366
[49,] -5.20965688 -5.51648738
[50,] 0.56964798 -5.20965688
[51,] 0.26840371 0.56964798
[52,] -0.53383416 0.26840371
[53,] -3.14763937 -0.53383416
[54,] -0.94575943 -3.14763937
[55,] 6.55348539 -0.94575943
[56,] 2.00251283 6.55348539
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.02110721 2.27952423
2 -4.84115687 -3.02110721
3 -5.20886210 -4.84115687
4 4.08078549 -5.20886210
5 -1.46149959 4.08078549
6 4.00366313 -1.46149959
7 -5.37411543 4.00366313
8 0.18785965 -5.37411543
9 1.13048451 0.18785965
10 -4.18195005 1.13048451
11 2.11070974 -4.18195005
12 4.60734549 2.11070974
13 1.94721167 4.60734549
14 -4.08077626 1.94721167
15 3.56742116 -4.08077626
16 -3.74295921 3.56742116
17 3.95502102 -3.74295921
18 -1.61490548 3.95502102
19 -2.50759890 -1.61490548
20 -2.21567054 -2.50759890
21 2.14238979 -2.21567054
22 2.49913201 2.14238979
23 2.77307464 2.49913201
24 0.11932037 2.77307464
25 2.16345234 0.11932037
26 -0.49254337 2.16345234
27 5.20483562 -0.49254337
28 -3.97765452 5.20483562
29 0.32028266 -3.97765452
30 -1.47275754 0.32028266
31 2.62799319 -1.47275754
32 1.69303939 2.62799319
33 -3.57779836 1.69303939
34 4.51559634 -3.57779836
35 3.14452927 4.51559634
36 -1.48970271 3.14452927
37 4.12010008 -1.48970271
38 8.84482852 4.12010008
39 -3.83179838 8.84482852
40 4.17366240 -3.83179838
41 0.33383528 4.17366240
42 0.02975932 0.33383528
43 -1.29976425 0.02975932
44 -1.66774133 -1.29976425
45 0.30492406 -1.66774133
46 -2.83277830 0.30492406
47 -8.02831366 -2.83277830
48 -5.51648738 -8.02831366
49 -5.20965688 -5.51648738
50 0.56964798 -5.20965688
51 0.26840371 0.56964798
52 -0.53383416 0.26840371
53 -3.14763937 -0.53383416
54 -0.94575943 -3.14763937
55 6.55348539 -0.94575943
56 2.00251283 6.55348539
> 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/7aoeg1260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81or01260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yu9p1260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jsni1260975432.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111nyj1260975432.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/12qdah1260975432.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/13juim1260975432.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/14hbtd1260975432.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/156gaw1260975432.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/16myj01260975432.tab")
+ }
> try(system("convert tmp/1zh781260975432.ps tmp/1zh781260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/2du0o1260975432.ps tmp/2du0o1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/3aigl1260975432.ps tmp/3aigl1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sazh1260975432.ps tmp/4sazh1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n6fv1260975432.ps tmp/5n6fv1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/64gbi1260975432.ps tmp/64gbi1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/7aoeg1260975432.ps tmp/7aoeg1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/81or01260975432.ps tmp/81or01260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yu9p1260975432.ps tmp/9yu9p1260975432.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jsni1260975432.ps tmp/10jsni1260975432.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.296 1.504 2.825