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(132.92
+ ,138.04
+ ,136.51
+ ,131.02
+ ,126.51
+ ,129.61
+ ,132.92
+ ,138.04
+ ,136.51
+ ,131.02
+ ,122.96
+ ,129.61
+ ,132.92
+ ,138.04
+ ,136.51
+ ,124.04
+ ,122.96
+ ,129.61
+ ,132.92
+ ,138.04
+ ,121.29
+ ,124.04
+ ,122.96
+ ,129.61
+ ,132.92
+ ,124.56
+ ,121.29
+ ,124.04
+ ,122.96
+ ,129.61
+ ,118.53
+ ,124.56
+ ,121.29
+ ,124.04
+ ,122.96
+ ,113.14
+ ,118.53
+ ,124.56
+ ,121.29
+ ,124.04
+ ,114.15
+ ,113.14
+ ,118.53
+ ,124.56
+ ,121.29
+ ,122.17
+ ,114.15
+ ,113.14
+ ,118.53
+ ,124.56
+ ,129.23
+ ,122.17
+ ,114.15
+ ,113.14
+ ,118.53
+ ,131.19
+ ,129.23
+ ,122.17
+ ,114.15
+ ,113.14
+ ,129.12
+ ,131.19
+ ,129.23
+ ,122.17
+ ,114.15
+ ,128.28
+ ,129.12
+ ,131.19
+ ,129.23
+ ,122.17
+ ,126.83
+ ,128.28
+ ,129.12
+ ,131.19
+ ,129.23
+ ,138.13
+ ,126.83
+ ,128.28
+ ,129.12
+ ,131.19
+ ,140.52
+ ,138.13
+ ,126.83
+ ,128.28
+ ,129.12
+ ,146.83
+ ,140.52
+ ,138.13
+ ,126.83
+ ,128.28
+ ,135.14
+ ,146.83
+ ,140.52
+ ,138.13
+ ,126.83
+ ,131.84
+ ,135.14
+ ,146.83
+ ,140.52
+ ,138.13
+ ,125.7
+ ,131.84
+ ,135.14
+ ,146.83
+ ,140.52
+ ,128.98
+ ,125.7
+ ,131.84
+ ,135.14
+ ,146.83
+ ,133.25
+ ,128.98
+ ,125.7
+ ,131.84
+ ,135.14
+ ,136.76
+ ,133.25
+ ,128.98
+ ,125.7
+ ,131.84
+ ,133.24
+ ,136.76
+ ,133.25
+ ,128.98
+ ,125.7
+ ,128.54
+ ,133.24
+ ,136.76
+ ,133.25
+ ,128.98
+ ,121.08
+ ,128.54
+ ,133.24
+ ,136.76
+ ,133.25
+ ,120.23
+ ,121.08
+ ,128.54
+ ,133.24
+ ,136.76
+ ,119.08
+ ,120.23
+ ,121.08
+ ,128.54
+ ,133.24
+ ,125.75
+ ,119.08
+ ,120.23
+ ,121.08
+ ,128.54
+ ,126.89
+ ,125.75
+ ,119.08
+ ,120.23
+ ,121.08
+ ,126.6
+ ,126.89
+ ,125.75
+ ,119.08
+ ,120.23
+ ,121.89
+ ,126.6
+ ,126.89
+ ,125.75
+ ,119.08
+ ,123.44
+ ,121.89
+ ,126.6
+ ,126.89
+ ,125.75
+ ,126.46
+ ,123.44
+ ,121.89
+ ,126.6
+ ,126.89
+ ,129.49
+ ,126.46
+ ,123.44
+ ,121.89
+ ,126.6
+ ,127.78
+ ,129.49
+ ,126.46
+ ,123.44
+ ,121.89
+ ,125.29
+ ,127.78
+ ,129.49
+ ,126.46
+ ,123.44
+ ,119.02
+ ,125.29
+ ,127.78
+ ,129.49
+ ,126.46
+ ,119.96
+ ,119.02
+ ,125.29
+ ,127.78
+ ,129.49
+ ,122.86
+ ,119.96
+ ,119.02
+ ,125.29
+ ,127.78
+ ,131.89
+ ,122.86
+ ,119.96
+ ,119.02
+ ,125.29
+ ,132.73
+ ,131.89
+ ,122.86
+ ,119.96
+ ,119.02
+ ,135.01
+ ,132.73
+ ,131.89
+ ,122.86
+ ,119.96
+ ,136.71
+ ,135.01
+ ,132.73
+ ,131.89
+ ,122.86
+ ,142.73
+ ,136.71
+ ,135.01
+ ,132.73
+ ,131.89
+ ,144.43
+ ,142.73
+ ,136.71
+ ,135.01
+ ,132.73
+ ,144.93
+ ,144.43
+ ,142.73
+ ,136.71
+ ,135.01
+ ,138.75
+ ,144.93
+ ,144.43
+ ,142.73
+ ,136.71
+ ,130.22
+ ,138.75
+ ,144.93
+ ,144.43
+ ,142.73
+ ,122.19
+ ,130.22
+ ,138.75
+ ,144.93
+ ,144.43
+ ,128.4
+ ,122.19
+ ,130.22
+ ,138.75
+ ,144.93
+ ,140.43
+ ,128.4
+ ,122.19
+ ,130.22
+ ,138.75
+ ,153.5
+ ,140.43
+ ,128.4
+ ,122.19
+ ,130.22
+ ,149.33
+ ,153.5
+ ,140.43
+ ,128.4
+ ,122.19
+ ,142.97
+ ,149.33
+ ,153.5
+ ,140.43
+ ,128.4)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'Y[t-1]'
+ ,'Y[t-2]'
+ ,'Y[t-3]'
+ ,'Y[t-4]')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y','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
Y Y[t-1] Y[t-2] Y[t-3] Y[t-4] M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 132.92 138.04 136.51 131.02 126.51 1 0 0 0 0 0 0 0 0 0 0 1
2 129.61 132.92 138.04 136.51 131.02 0 1 0 0 0 0 0 0 0 0 0 2
3 122.96 129.61 132.92 138.04 136.51 0 0 1 0 0 0 0 0 0 0 0 3
4 124.04 122.96 129.61 132.92 138.04 0 0 0 1 0 0 0 0 0 0 0 4
5 121.29 124.04 122.96 129.61 132.92 0 0 0 0 1 0 0 0 0 0 0 5
6 124.56 121.29 124.04 122.96 129.61 0 0 0 0 0 1 0 0 0 0 0 6
7 118.53 124.56 121.29 124.04 122.96 0 0 0 0 0 0 1 0 0 0 0 7
8 113.14 118.53 124.56 121.29 124.04 0 0 0 0 0 0 0 1 0 0 0 8
9 114.15 113.14 118.53 124.56 121.29 0 0 0 0 0 0 0 0 1 0 0 9
10 122.17 114.15 113.14 118.53 124.56 0 0 0 0 0 0 0 0 0 1 0 10
11 129.23 122.17 114.15 113.14 118.53 0 0 0 0 0 0 0 0 0 0 1 11
12 131.19 129.23 122.17 114.15 113.14 0 0 0 0 0 0 0 0 0 0 0 12
13 129.12 131.19 129.23 122.17 114.15 1 0 0 0 0 0 0 0 0 0 0 13
14 128.28 129.12 131.19 129.23 122.17 0 1 0 0 0 0 0 0 0 0 0 14
15 126.83 128.28 129.12 131.19 129.23 0 0 1 0 0 0 0 0 0 0 0 15
16 138.13 126.83 128.28 129.12 131.19 0 0 0 1 0 0 0 0 0 0 0 16
17 140.52 138.13 126.83 128.28 129.12 0 0 0 0 1 0 0 0 0 0 0 17
18 146.83 140.52 138.13 126.83 128.28 0 0 0 0 0 1 0 0 0 0 0 18
19 135.14 146.83 140.52 138.13 126.83 0 0 0 0 0 0 1 0 0 0 0 19
20 131.84 135.14 146.83 140.52 138.13 0 0 0 0 0 0 0 1 0 0 0 20
21 125.70 131.84 135.14 146.83 140.52 0 0 0 0 0 0 0 0 1 0 0 21
22 128.98 125.70 131.84 135.14 146.83 0 0 0 0 0 0 0 0 0 1 0 22
23 133.25 128.98 125.70 131.84 135.14 0 0 0 0 0 0 0 0 0 0 1 23
24 136.76 133.25 128.98 125.70 131.84 0 0 0 0 0 0 0 0 0 0 0 24
25 133.24 136.76 133.25 128.98 125.70 1 0 0 0 0 0 0 0 0 0 0 25
26 128.54 133.24 136.76 133.25 128.98 0 1 0 0 0 0 0 0 0 0 0 26
27 121.08 128.54 133.24 136.76 133.25 0 0 1 0 0 0 0 0 0 0 0 27
28 120.23 121.08 128.54 133.24 136.76 0 0 0 1 0 0 0 0 0 0 0 28
29 119.08 120.23 121.08 128.54 133.24 0 0 0 0 1 0 0 0 0 0 0 29
30 125.75 119.08 120.23 121.08 128.54 0 0 0 0 0 1 0 0 0 0 0 30
31 126.89 125.75 119.08 120.23 121.08 0 0 0 0 0 0 1 0 0 0 0 31
32 126.60 126.89 125.75 119.08 120.23 0 0 0 0 0 0 0 1 0 0 0 32
33 121.89 126.60 126.89 125.75 119.08 0 0 0 0 0 0 0 0 1 0 0 33
34 123.44 121.89 126.60 126.89 125.75 0 0 0 0 0 0 0 0 0 1 0 34
35 126.46 123.44 121.89 126.60 126.89 0 0 0 0 0 0 0 0 0 0 1 35
36 129.49 126.46 123.44 121.89 126.60 0 0 0 0 0 0 0 0 0 0 0 36
37 127.78 129.49 126.46 123.44 121.89 1 0 0 0 0 0 0 0 0 0 0 37
38 125.29 127.78 129.49 126.46 123.44 0 1 0 0 0 0 0 0 0 0 0 38
39 119.02 125.29 127.78 129.49 126.46 0 0 1 0 0 0 0 0 0 0 0 39
40 119.96 119.02 125.29 127.78 129.49 0 0 0 1 0 0 0 0 0 0 0 40
41 122.86 119.96 119.02 125.29 127.78 0 0 0 0 1 0 0 0 0 0 0 41
42 131.89 122.86 119.96 119.02 125.29 0 0 0 0 0 1 0 0 0 0 0 42
43 132.73 131.89 122.86 119.96 119.02 0 0 0 0 0 0 1 0 0 0 0 43
44 135.01 132.73 131.89 122.86 119.96 0 0 0 0 0 0 0 1 0 0 0 44
45 136.71 135.01 132.73 131.89 122.86 0 0 0 0 0 0 0 0 1 0 0 45
46 142.73 136.71 135.01 132.73 131.89 0 0 0 0 0 0 0 0 0 1 0 46
47 144.43 142.73 136.71 135.01 132.73 0 0 0 0 0 0 0 0 0 0 1 47
48 144.93 144.43 142.73 136.71 135.01 0 0 0 0 0 0 0 0 0 0 0 48
49 138.75 144.93 144.43 142.73 136.71 1 0 0 0 0 0 0 0 0 0 0 49
50 130.22 138.75 144.93 144.43 142.73 0 1 0 0 0 0 0 0 0 0 0 50
51 122.19 130.22 138.75 144.93 144.43 0 0 1 0 0 0 0 0 0 0 0 51
52 128.40 122.19 130.22 138.75 144.93 0 0 0 1 0 0 0 0 0 0 0 52
53 140.43 128.40 122.19 130.22 138.75 0 0 0 0 1 0 0 0 0 0 0 53
54 153.50 140.43 128.40 122.19 130.22 0 0 0 0 0 1 0 0 0 0 0 54
55 149.33 153.50 140.43 128.40 122.19 0 0 0 0 0 0 1 0 0 0 0 55
56 142.97 149.33 153.50 140.43 128.40 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Y[t-1]` `Y[t-2]` `Y[t-3]` `Y[t-4]` M1
15.10517 1.51204 -0.61509 -0.32310 0.29646 -2.20172
M2 M3 M4 M5 M6 M7
0.71229 -2.17352 6.28391 -0.57336 4.54706 -6.44162
M8 M9 M10 M11 t
1.40686 1.31742 4.89880 1.11412 0.03782
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.8648 -1.3816 -0.4567 1.4397 6.0940
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.10517 9.33752 1.618 0.11379
`Y[t-1]` 1.51204 0.15391 9.824 4.22e-12 ***
`Y[t-2]` -0.61509 0.28084 -2.190 0.03456 *
`Y[t-3]` -0.32310 0.28643 -1.128 0.26620
`Y[t-4]` 0.29646 0.16098 1.842 0.07315 .
M1 -2.20172 2.11577 -1.041 0.30446
M2 0.71229 2.26492 0.314 0.75483
M3 -2.17352 2.44103 -0.890 0.37871
M4 6.28391 2.24615 2.798 0.00796 **
M5 -0.57336 2.48498 -0.231 0.81873
M6 4.54706 1.94321 2.340 0.02450 *
M7 -6.44162 2.32824 -2.767 0.00861 **
M8 1.40686 2.33578 0.602 0.55045
M9 1.31742 2.95037 0.447 0.65769
M10 4.89880 2.12908 2.301 0.02683 *
M11 1.11412 2.32417 0.479 0.63436
t 0.03782 0.02579 1.466 0.15056
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.774 on 39 degrees of freedom
Multiple R-squared: 0.9301, Adjusted R-squared: 0.9014
F-statistic: 32.44 on 16 and 39 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,] 0.8763930 0.247213946 0.123606973
[2,] 0.9358675 0.128264997 0.064132498
[3,] 0.9795523 0.040895454 0.020447727
[4,] 0.9628882 0.074223681 0.037111841
[5,] 0.9478813 0.104237310 0.052118655
[6,] 0.9484366 0.103126783 0.051563391
[7,] 0.9863858 0.027228478 0.013614239
[8,] 0.9864052 0.027189645 0.013594822
[9,] 0.9859062 0.028187510 0.014093755
[10,] 0.9715691 0.056861761 0.028430880
[11,] 0.9846891 0.030621812 0.015310906
[12,] 0.9971197 0.005760592 0.002880296
[13,] 0.9952189 0.009562215 0.004781107
[14,] 0.9933685 0.013262975 0.006631488
[15,] 0.9815055 0.036989069 0.018494535
[16,] 0.9463185 0.107363097 0.053681548
[17,] 0.9949541 0.010091804 0.005045902
> postscript(file="/var/www/html/rcomp/tmp/1doha1259094519.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jf0d1259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3stww1259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/436jo1259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fke91259094519.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
0.05045822 2.90813561 -0.17150309 -1.67550205 -2.88097987 -1.11415768
7 8 9 10 11 12
-0.50874400 -3.86481511 3.50950086 0.15009659 -0.50219398 -1.28360796
13 14 15 16 17 18
2.48107260 2.92821933 2.86336295 6.09401889 -2.33215902 1.93687132
19 20 21 22 23 24
-2.79225407 5.00059709 -1.95822791 -0.69101244 0.98910438 0.13098410
25 26 27 28 29 30
-1.02590241 -0.78914476 -0.59148969 -3.72574213 -1.83465476 -0.12384730
31 32 33 34 35 36
3.11133142 -2.80561608 -3.82829376 -0.56322255 0.53125348 -0.41123452
37 38 39 40 41 42
-0.78410521 -1.26037837 -1.88552767 -2.94264504 1.20131178 -0.02130793
43 44 45 46 47 48
2.06213747 1.39830548 2.27702081 1.10413839 -1.01816388 1.56385838
49 50 51 52 53 54
-0.72152320 -3.78683182 -0.21484251 2.24987033 5.84648188 -0.67755842
55 56
-1.87247083 0.27152862
> postscript(file="/var/www/html/rcomp/tmp/6761i1259094519.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 0.05045822 NA
1 2.90813561 0.05045822
2 -0.17150309 2.90813561
3 -1.67550205 -0.17150309
4 -2.88097987 -1.67550205
5 -1.11415768 -2.88097987
6 -0.50874400 -1.11415768
7 -3.86481511 -0.50874400
8 3.50950086 -3.86481511
9 0.15009659 3.50950086
10 -0.50219398 0.15009659
11 -1.28360796 -0.50219398
12 2.48107260 -1.28360796
13 2.92821933 2.48107260
14 2.86336295 2.92821933
15 6.09401889 2.86336295
16 -2.33215902 6.09401889
17 1.93687132 -2.33215902
18 -2.79225407 1.93687132
19 5.00059709 -2.79225407
20 -1.95822791 5.00059709
21 -0.69101244 -1.95822791
22 0.98910438 -0.69101244
23 0.13098410 0.98910438
24 -1.02590241 0.13098410
25 -0.78914476 -1.02590241
26 -0.59148969 -0.78914476
27 -3.72574213 -0.59148969
28 -1.83465476 -3.72574213
29 -0.12384730 -1.83465476
30 3.11133142 -0.12384730
31 -2.80561608 3.11133142
32 -3.82829376 -2.80561608
33 -0.56322255 -3.82829376
34 0.53125348 -0.56322255
35 -0.41123452 0.53125348
36 -0.78410521 -0.41123452
37 -1.26037837 -0.78410521
38 -1.88552767 -1.26037837
39 -2.94264504 -1.88552767
40 1.20131178 -2.94264504
41 -0.02130793 1.20131178
42 2.06213747 -0.02130793
43 1.39830548 2.06213747
44 2.27702081 1.39830548
45 1.10413839 2.27702081
46 -1.01816388 1.10413839
47 1.56385838 -1.01816388
48 -0.72152320 1.56385838
49 -3.78683182 -0.72152320
50 -0.21484251 -3.78683182
51 2.24987033 -0.21484251
52 5.84648188 2.24987033
53 -0.67755842 5.84648188
54 -1.87247083 -0.67755842
55 0.27152862 -1.87247083
56 NA 0.27152862
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.90813561 0.05045822
[2,] -0.17150309 2.90813561
[3,] -1.67550205 -0.17150309
[4,] -2.88097987 -1.67550205
[5,] -1.11415768 -2.88097987
[6,] -0.50874400 -1.11415768
[7,] -3.86481511 -0.50874400
[8,] 3.50950086 -3.86481511
[9,] 0.15009659 3.50950086
[10,] -0.50219398 0.15009659
[11,] -1.28360796 -0.50219398
[12,] 2.48107260 -1.28360796
[13,] 2.92821933 2.48107260
[14,] 2.86336295 2.92821933
[15,] 6.09401889 2.86336295
[16,] -2.33215902 6.09401889
[17,] 1.93687132 -2.33215902
[18,] -2.79225407 1.93687132
[19,] 5.00059709 -2.79225407
[20,] -1.95822791 5.00059709
[21,] -0.69101244 -1.95822791
[22,] 0.98910438 -0.69101244
[23,] 0.13098410 0.98910438
[24,] -1.02590241 0.13098410
[25,] -0.78914476 -1.02590241
[26,] -0.59148969 -0.78914476
[27,] -3.72574213 -0.59148969
[28,] -1.83465476 -3.72574213
[29,] -0.12384730 -1.83465476
[30,] 3.11133142 -0.12384730
[31,] -2.80561608 3.11133142
[32,] -3.82829376 -2.80561608
[33,] -0.56322255 -3.82829376
[34,] 0.53125348 -0.56322255
[35,] -0.41123452 0.53125348
[36,] -0.78410521 -0.41123452
[37,] -1.26037837 -0.78410521
[38,] -1.88552767 -1.26037837
[39,] -2.94264504 -1.88552767
[40,] 1.20131178 -2.94264504
[41,] -0.02130793 1.20131178
[42,] 2.06213747 -0.02130793
[43,] 1.39830548 2.06213747
[44,] 2.27702081 1.39830548
[45,] 1.10413839 2.27702081
[46,] -1.01816388 1.10413839
[47,] 1.56385838 -1.01816388
[48,] -0.72152320 1.56385838
[49,] -3.78683182 -0.72152320
[50,] -0.21484251 -3.78683182
[51,] 2.24987033 -0.21484251
[52,] 5.84648188 2.24987033
[53,] -0.67755842 5.84648188
[54,] -1.87247083 -0.67755842
[55,] 0.27152862 -1.87247083
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.90813561 0.05045822
2 -0.17150309 2.90813561
3 -1.67550205 -0.17150309
4 -2.88097987 -1.67550205
5 -1.11415768 -2.88097987
6 -0.50874400 -1.11415768
7 -3.86481511 -0.50874400
8 3.50950086 -3.86481511
9 0.15009659 3.50950086
10 -0.50219398 0.15009659
11 -1.28360796 -0.50219398
12 2.48107260 -1.28360796
13 2.92821933 2.48107260
14 2.86336295 2.92821933
15 6.09401889 2.86336295
16 -2.33215902 6.09401889
17 1.93687132 -2.33215902
18 -2.79225407 1.93687132
19 5.00059709 -2.79225407
20 -1.95822791 5.00059709
21 -0.69101244 -1.95822791
22 0.98910438 -0.69101244
23 0.13098410 0.98910438
24 -1.02590241 0.13098410
25 -0.78914476 -1.02590241
26 -0.59148969 -0.78914476
27 -3.72574213 -0.59148969
28 -1.83465476 -3.72574213
29 -0.12384730 -1.83465476
30 3.11133142 -0.12384730
31 -2.80561608 3.11133142
32 -3.82829376 -2.80561608
33 -0.56322255 -3.82829376
34 0.53125348 -0.56322255
35 -0.41123452 0.53125348
36 -0.78410521 -0.41123452
37 -1.26037837 -0.78410521
38 -1.88552767 -1.26037837
39 -2.94264504 -1.88552767
40 1.20131178 -2.94264504
41 -0.02130793 1.20131178
42 2.06213747 -0.02130793
43 1.39830548 2.06213747
44 2.27702081 1.39830548
45 1.10413839 2.27702081
46 -1.01816388 1.10413839
47 1.56385838 -1.01816388
48 -0.72152320 1.56385838
49 -3.78683182 -0.72152320
50 -0.21484251 -3.78683182
51 2.24987033 -0.21484251
52 5.84648188 2.24987033
53 -0.67755842 5.84648188
54 -1.87247083 -0.67755842
55 0.27152862 -1.87247083
> 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/72le31259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/838mf1259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9x9gd1259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10di7z1259094519.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zk4m1259094519.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/12rou81259094519.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/13l8nu1259094519.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/14i0id1259094519.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/154xmi1259094519.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/1657wq1259094519.tab")
+ }
>
> system("convert tmp/1doha1259094519.ps tmp/1doha1259094519.png")
> system("convert tmp/2jf0d1259094519.ps tmp/2jf0d1259094519.png")
> system("convert tmp/3stww1259094519.ps tmp/3stww1259094519.png")
> system("convert tmp/436jo1259094519.ps tmp/436jo1259094519.png")
> system("convert tmp/5fke91259094519.ps tmp/5fke91259094519.png")
> system("convert tmp/6761i1259094519.ps tmp/6761i1259094519.png")
> system("convert tmp/72le31259094519.ps tmp/72le31259094519.png")
> system("convert tmp/838mf1259094519.ps tmp/838mf1259094519.png")
> system("convert tmp/9x9gd1259094519.ps tmp/9x9gd1259094519.png")
> system("convert tmp/10di7z1259094519.ps tmp/10di7z1259094519.png")
>
>
> proc.time()
user system elapsed
2.355 1.562 2.807