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(132.92
+ ,138.04
+ ,136.51
+ ,131.02
+ ,126.51
+ ,0
+ ,129.61
+ ,132.92
+ ,138.04
+ ,136.51
+ ,131.02
+ ,0
+ ,122.96
+ ,129.61
+ ,132.92
+ ,138.04
+ ,136.51
+ ,0
+ ,124.04
+ ,122.96
+ ,129.61
+ ,132.92
+ ,138.04
+ ,0
+ ,121.29
+ ,124.04
+ ,122.96
+ ,129.61
+ ,132.92
+ ,0
+ ,124.56
+ ,121.29
+ ,124.04
+ ,122.96
+ ,129.61
+ ,0
+ ,118.53
+ ,124.56
+ ,121.29
+ ,124.04
+ ,122.96
+ ,0
+ ,113.14
+ ,118.53
+ ,124.56
+ ,121.29
+ ,124.04
+ ,0
+ ,114.15
+ ,113.14
+ ,118.53
+ ,124.56
+ ,121.29
+ ,0
+ ,122.17
+ ,114.15
+ ,113.14
+ ,118.53
+ ,124.56
+ ,0
+ ,129.23
+ ,122.17
+ ,114.15
+ ,113.14
+ ,118.53
+ ,0
+ ,131.19
+ ,129.23
+ ,122.17
+ ,114.15
+ ,113.14
+ ,0
+ ,129.12
+ ,131.19
+ ,129.23
+ ,122.17
+ ,114.15
+ ,0
+ ,128.28
+ ,129.12
+ ,131.19
+ ,129.23
+ ,122.17
+ ,0
+ ,126.83
+ ,128.28
+ ,129.12
+ ,131.19
+ ,129.23
+ ,0
+ ,138.13
+ ,126.83
+ ,128.28
+ ,129.12
+ ,131.19
+ ,0
+ ,140.52
+ ,138.13
+ ,126.83
+ ,128.28
+ ,129.12
+ ,0
+ ,146.83
+ ,140.52
+ ,138.13
+ ,126.83
+ ,128.28
+ ,0
+ ,135.14
+ ,146.83
+ ,140.52
+ ,138.13
+ ,126.83
+ ,0
+ ,131.84
+ ,135.14
+ ,146.83
+ ,140.52
+ ,138.13
+ ,0
+ ,125.7
+ ,131.84
+ ,135.14
+ ,146.83
+ ,140.52
+ ,0
+ ,128.98
+ ,125.7
+ ,131.84
+ ,135.14
+ ,146.83
+ ,0
+ ,133.25
+ ,128.98
+ ,125.7
+ ,131.84
+ ,135.14
+ ,0
+ ,136.76
+ ,133.25
+ ,128.98
+ ,125.7
+ ,131.84
+ ,0
+ ,133.24
+ ,136.76
+ ,133.25
+ ,128.98
+ ,125.7
+ ,0
+ ,128.54
+ ,133.24
+ ,136.76
+ ,133.25
+ ,128.98
+ ,0
+ ,121.08
+ ,128.54
+ ,133.24
+ ,136.76
+ ,133.25
+ ,0
+ ,120.23
+ ,121.08
+ ,128.54
+ ,133.24
+ ,136.76
+ ,0
+ ,119.08
+ ,120.23
+ ,121.08
+ ,128.54
+ ,133.24
+ ,0
+ ,125.75
+ ,119.08
+ ,120.23
+ ,121.08
+ ,128.54
+ ,0
+ ,126.89
+ ,125.75
+ ,119.08
+ ,120.23
+ ,121.08
+ ,0
+ ,126.6
+ ,126.89
+ ,125.75
+ ,119.08
+ ,120.23
+ ,0
+ ,121.89
+ ,126.6
+ ,126.89
+ ,125.75
+ ,119.08
+ ,0
+ ,123.44
+ ,121.89
+ ,126.6
+ ,126.89
+ ,125.75
+ ,0
+ ,126.46
+ ,123.44
+ ,121.89
+ ,126.6
+ ,126.89
+ ,0
+ ,129.49
+ ,126.46
+ ,123.44
+ ,121.89
+ ,126.6
+ ,0
+ ,127.78
+ ,129.49
+ ,126.46
+ ,123.44
+ ,121.89
+ ,0
+ ,125.29
+ ,127.78
+ ,129.49
+ ,126.46
+ ,123.44
+ ,0
+ ,119.02
+ ,125.29
+ ,127.78
+ ,129.49
+ ,126.46
+ ,0
+ ,119.96
+ ,119.02
+ ,125.29
+ ,127.78
+ ,129.49
+ ,0
+ ,122.86
+ ,119.96
+ ,119.02
+ ,125.29
+ ,127.78
+ ,0
+ ,131.89
+ ,122.86
+ ,119.96
+ ,119.02
+ ,125.29
+ ,0
+ ,132.73
+ ,131.89
+ ,122.86
+ ,119.96
+ ,119.02
+ ,0
+ ,135.01
+ ,132.73
+ ,131.89
+ ,122.86
+ ,119.96
+ ,0
+ ,136.71
+ ,135.01
+ ,132.73
+ ,131.89
+ ,122.86
+ ,1
+ ,142.73
+ ,136.71
+ ,135.01
+ ,132.73
+ ,131.89
+ ,1
+ ,144.43
+ ,142.73
+ ,136.71
+ ,135.01
+ ,132.73
+ ,1
+ ,144.93
+ ,144.43
+ ,142.73
+ ,136.71
+ ,135.01
+ ,1
+ ,138.75
+ ,144.93
+ ,144.43
+ ,142.73
+ ,136.71
+ ,1
+ ,130.22
+ ,138.75
+ ,144.93
+ ,144.43
+ ,142.73
+ ,1
+ ,122.19
+ ,130.22
+ ,138.75
+ ,144.93
+ ,144.43
+ ,1
+ ,128.4
+ ,122.19
+ ,130.22
+ ,138.75
+ ,144.93
+ ,1
+ ,140.43
+ ,128.4
+ ,122.19
+ ,130.22
+ ,138.75
+ ,1
+ ,153.5
+ ,140.43
+ ,128.4
+ ,122.19
+ ,130.22
+ ,1
+ ,149.33
+ ,153.5
+ ,140.43
+ ,128.4
+ ,122.19
+ ,1
+ ,142.97
+ ,149.33
+ ,153.5
+ ,140.43
+ ,128.4
+ ,1)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)'
+ ,'Y(t-4)'
+ ,'X')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y(t)','Y(t-1)','Y(t-2)','Y(t-3)','Y(t-4)','X'),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(t) Y(t-1) Y(t-2) Y(t-3) Y(t-4) X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 132.92 138.04 136.51 131.02 126.51 0 1 0 0 0 0 0 0 0 0 0 0 1
2 129.61 132.92 138.04 136.51 131.02 0 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 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 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 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 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 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 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 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 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 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 0 12
13 129.12 131.19 129.23 122.17 114.15 0 1 0 0 0 0 0 0 0 0 0 0 13
14 128.28 129.12 131.19 129.23 122.17 0 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 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 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 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 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 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 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 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 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 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 0 24
25 133.24 136.76 133.25 128.98 125.70 0 1 0 0 0 0 0 0 0 0 0 0 25
26 128.54 133.24 136.76 133.25 128.98 0 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 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 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 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 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 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 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 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 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 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 0 36
37 127.78 129.49 126.46 123.44 121.89 0 1 0 0 0 0 0 0 0 0 0 0 37
38 125.29 127.78 129.49 126.46 123.44 0 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 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 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 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 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 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 0 1 0 0 0 44
45 136.71 135.01 132.73 131.89 122.86 1 0 0 0 0 0 0 0 0 1 0 0 45
46 142.73 136.71 135.01 132.73 131.89 1 0 0 0 0 0 0 0 0 0 1 0 46
47 144.43 142.73 136.71 135.01 132.73 1 0 0 0 0 0 0 0 0 0 0 1 47
48 144.93 144.43 142.73 136.71 135.01 1 0 0 0 0 0 0 0 0 0 0 0 48
49 138.75 144.93 144.43 142.73 136.71 1 1 0 0 0 0 0 0 0 0 0 0 49
50 130.22 138.75 144.93 144.43 142.73 1 0 1 0 0 0 0 0 0 0 0 0 50
51 122.19 130.22 138.75 144.93 144.43 1 0 0 1 0 0 0 0 0 0 0 0 51
52 128.40 122.19 130.22 138.75 144.93 1 0 0 0 1 0 0 0 0 0 0 0 52
53 140.43 128.40 122.19 130.22 138.75 1 0 0 0 0 1 0 0 0 0 0 0 53
54 153.50 140.43 128.40 122.19 130.22 1 0 0 0 0 0 1 0 0 0 0 0 54
55 149.33 153.50 140.43 128.40 122.19 1 0 0 0 0 0 0 1 0 0 0 0 55
56 142.97 149.33 153.50 140.43 128.40 1 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)` X
22.20145 1.47305 -0.60996 -0.31157 0.26693 1.67220
M1 M2 M3 M4 M5 M6
-2.25507 0.61407 -2.28210 6.09053 -0.63194 4.54801
M7 M8 M9 M10 M11 t
-6.35230 1.40352 0.88483 4.65001 0.98535 0.01526
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.5312 -1.5364 -0.2878 1.5178 6.2234
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 22.20145 11.53287 1.925 0.06173 .
`Y(t-1)` 1.47305 0.15817 9.313 2.38e-11 ***
`Y(t-2)` -0.60996 0.28055 -2.174 0.03599 *
`Y(t-3)` -0.31157 0.28630 -1.088 0.28333
`Y(t-4)` 0.26693 0.16324 1.635 0.11027
X 1.67220 1.59870 1.046 0.30218
M1 -2.25507 2.11384 -1.067 0.29279
M2 0.61407 2.26414 0.271 0.78769
M3 -2.28210 2.44030 -0.935 0.35561
M4 6.09053 2.25105 2.706 0.01015 *
M5 -0.63194 2.48262 -0.255 0.80045
M6 4.54801 1.94087 2.343 0.02445 *
M7 -6.35230 2.32701 -2.730 0.00955 **
M8 1.40352 2.33297 0.602 0.55101
M9 0.88483 2.97570 0.297 0.76782
M10 4.65001 2.13978 2.173 0.03607 *
M11 0.98535 2.32464 0.424 0.67405
t 0.01526 0.03359 0.454 0.65209
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.77 on 38 degrees of freedom
Multiple R-squared: 0.9321, Adjusted R-squared: 0.9017
F-statistic: 30.67 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,] 0.9578408 0.084318452 0.042159226
[2,] 0.9861150 0.027769960 0.013884980
[3,] 0.9725035 0.054992935 0.027496467
[4,] 0.9622077 0.075584567 0.037792284
[5,] 0.9609339 0.078132168 0.039066084
[6,] 0.9893743 0.021251376 0.010625688
[7,] 0.9917964 0.016407209 0.008203604
[8,] 0.9949475 0.010104912 0.005052456
[9,] 0.9880492 0.023901602 0.011950801
[10,] 0.9890220 0.021956046 0.010978023
[11,] 0.9962490 0.007501966 0.003750983
[12,] 0.9893609 0.021278247 0.010639124
[13,] 0.9836536 0.032692766 0.016346383
[14,] 0.9552356 0.089528748 0.044764374
[15,] 0.8774236 0.245152881 0.122576441
> postscript(file="/var/www/html/rcomp/tmp/1fm2d1259359780.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mkzg1259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lqov1259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46gg51259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tih31259359780.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
-0.06376032 2.72376467 -0.28126202 -1.81596665 -3.17050418 -1.57447327
7 8 9 10 11 12
-1.10211090 -4.53122604 2.99680068 -0.29074442 -0.84894234 -1.67329975
13 14 15 16 17 18
2.14483394 2.72406920 2.85587075 6.22340112 -1.91850876 2.34066037
19 20 21 22 23 24
-2.39365450 5.33242544 -1.24544464 -0.04080947 1.39406880 0.55270571
25 26 27 28 29 30
-0.63246832 -0.43590077 -0.28488366 -3.43426801 -1.70005336 -0.11946617
31 32 33 34 35 36
3.10533298 -2.69802043 -3.39690588 -0.29139977 0.82720649 -0.06598497
37 38 39 40 41 42
-0.41727118 -0.89738276 -1.52367721 -2.59591498 1.48281315 0.33021508
43 44 45 46 47 48
2.48900741 1.92112893 1.64554984 0.62295366 -1.37233295 1.18657901
49 50 51 52 53 54
-1.03133412 -4.11455034 -0.76604786 1.62274851 5.30625315 -0.97693601
55 56
-2.09857500 -0.02430789
> postscript(file="/var/www/html/rcomp/tmp/636a61259359780.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 -0.06376032 NA
1 2.72376467 -0.06376032
2 -0.28126202 2.72376467
3 -1.81596665 -0.28126202
4 -3.17050418 -1.81596665
5 -1.57447327 -3.17050418
6 -1.10211090 -1.57447327
7 -4.53122604 -1.10211090
8 2.99680068 -4.53122604
9 -0.29074442 2.99680068
10 -0.84894234 -0.29074442
11 -1.67329975 -0.84894234
12 2.14483394 -1.67329975
13 2.72406920 2.14483394
14 2.85587075 2.72406920
15 6.22340112 2.85587075
16 -1.91850876 6.22340112
17 2.34066037 -1.91850876
18 -2.39365450 2.34066037
19 5.33242544 -2.39365450
20 -1.24544464 5.33242544
21 -0.04080947 -1.24544464
22 1.39406880 -0.04080947
23 0.55270571 1.39406880
24 -0.63246832 0.55270571
25 -0.43590077 -0.63246832
26 -0.28488366 -0.43590077
27 -3.43426801 -0.28488366
28 -1.70005336 -3.43426801
29 -0.11946617 -1.70005336
30 3.10533298 -0.11946617
31 -2.69802043 3.10533298
32 -3.39690588 -2.69802043
33 -0.29139977 -3.39690588
34 0.82720649 -0.29139977
35 -0.06598497 0.82720649
36 -0.41727118 -0.06598497
37 -0.89738276 -0.41727118
38 -1.52367721 -0.89738276
39 -2.59591498 -1.52367721
40 1.48281315 -2.59591498
41 0.33021508 1.48281315
42 2.48900741 0.33021508
43 1.92112893 2.48900741
44 1.64554984 1.92112893
45 0.62295366 1.64554984
46 -1.37233295 0.62295366
47 1.18657901 -1.37233295
48 -1.03133412 1.18657901
49 -4.11455034 -1.03133412
50 -0.76604786 -4.11455034
51 1.62274851 -0.76604786
52 5.30625315 1.62274851
53 -0.97693601 5.30625315
54 -2.09857500 -0.97693601
55 -0.02430789 -2.09857500
56 NA -0.02430789
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.72376467 -0.06376032
[2,] -0.28126202 2.72376467
[3,] -1.81596665 -0.28126202
[4,] -3.17050418 -1.81596665
[5,] -1.57447327 -3.17050418
[6,] -1.10211090 -1.57447327
[7,] -4.53122604 -1.10211090
[8,] 2.99680068 -4.53122604
[9,] -0.29074442 2.99680068
[10,] -0.84894234 -0.29074442
[11,] -1.67329975 -0.84894234
[12,] 2.14483394 -1.67329975
[13,] 2.72406920 2.14483394
[14,] 2.85587075 2.72406920
[15,] 6.22340112 2.85587075
[16,] -1.91850876 6.22340112
[17,] 2.34066037 -1.91850876
[18,] -2.39365450 2.34066037
[19,] 5.33242544 -2.39365450
[20,] -1.24544464 5.33242544
[21,] -0.04080947 -1.24544464
[22,] 1.39406880 -0.04080947
[23,] 0.55270571 1.39406880
[24,] -0.63246832 0.55270571
[25,] -0.43590077 -0.63246832
[26,] -0.28488366 -0.43590077
[27,] -3.43426801 -0.28488366
[28,] -1.70005336 -3.43426801
[29,] -0.11946617 -1.70005336
[30,] 3.10533298 -0.11946617
[31,] -2.69802043 3.10533298
[32,] -3.39690588 -2.69802043
[33,] -0.29139977 -3.39690588
[34,] 0.82720649 -0.29139977
[35,] -0.06598497 0.82720649
[36,] -0.41727118 -0.06598497
[37,] -0.89738276 -0.41727118
[38,] -1.52367721 -0.89738276
[39,] -2.59591498 -1.52367721
[40,] 1.48281315 -2.59591498
[41,] 0.33021508 1.48281315
[42,] 2.48900741 0.33021508
[43,] 1.92112893 2.48900741
[44,] 1.64554984 1.92112893
[45,] 0.62295366 1.64554984
[46,] -1.37233295 0.62295366
[47,] 1.18657901 -1.37233295
[48,] -1.03133412 1.18657901
[49,] -4.11455034 -1.03133412
[50,] -0.76604786 -4.11455034
[51,] 1.62274851 -0.76604786
[52,] 5.30625315 1.62274851
[53,] -0.97693601 5.30625315
[54,] -2.09857500 -0.97693601
[55,] -0.02430789 -2.09857500
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.72376467 -0.06376032
2 -0.28126202 2.72376467
3 -1.81596665 -0.28126202
4 -3.17050418 -1.81596665
5 -1.57447327 -3.17050418
6 -1.10211090 -1.57447327
7 -4.53122604 -1.10211090
8 2.99680068 -4.53122604
9 -0.29074442 2.99680068
10 -0.84894234 -0.29074442
11 -1.67329975 -0.84894234
12 2.14483394 -1.67329975
13 2.72406920 2.14483394
14 2.85587075 2.72406920
15 6.22340112 2.85587075
16 -1.91850876 6.22340112
17 2.34066037 -1.91850876
18 -2.39365450 2.34066037
19 5.33242544 -2.39365450
20 -1.24544464 5.33242544
21 -0.04080947 -1.24544464
22 1.39406880 -0.04080947
23 0.55270571 1.39406880
24 -0.63246832 0.55270571
25 -0.43590077 -0.63246832
26 -0.28488366 -0.43590077
27 -3.43426801 -0.28488366
28 -1.70005336 -3.43426801
29 -0.11946617 -1.70005336
30 3.10533298 -0.11946617
31 -2.69802043 3.10533298
32 -3.39690588 -2.69802043
33 -0.29139977 -3.39690588
34 0.82720649 -0.29139977
35 -0.06598497 0.82720649
36 -0.41727118 -0.06598497
37 -0.89738276 -0.41727118
38 -1.52367721 -0.89738276
39 -2.59591498 -1.52367721
40 1.48281315 -2.59591498
41 0.33021508 1.48281315
42 2.48900741 0.33021508
43 1.92112893 2.48900741
44 1.64554984 1.92112893
45 0.62295366 1.64554984
46 -1.37233295 0.62295366
47 1.18657901 -1.37233295
48 -1.03133412 1.18657901
49 -4.11455034 -1.03133412
50 -0.76604786 -4.11455034
51 1.62274851 -0.76604786
52 5.30625315 1.62274851
53 -0.97693601 5.30625315
54 -2.09857500 -0.97693601
55 -0.02430789 -2.09857500
> 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/7ojd01259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8g6zg1259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y3le1259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10q3pt1259359780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112wl71259359780.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/12eovr1259359780.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/13n3lm1259359780.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/14g5mm1259359781.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/151coq1259359781.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/16v3tr1259359781.tab")
+ }
>
> system("convert tmp/1fm2d1259359780.ps tmp/1fm2d1259359780.png")
> system("convert tmp/2mkzg1259359780.ps tmp/2mkzg1259359780.png")
> system("convert tmp/3lqov1259359780.ps tmp/3lqov1259359780.png")
> system("convert tmp/46gg51259359780.ps tmp/46gg51259359780.png")
> system("convert tmp/5tih31259359780.ps tmp/5tih31259359780.png")
> system("convert tmp/636a61259359780.ps tmp/636a61259359780.png")
> system("convert tmp/7ojd01259359780.ps tmp/7ojd01259359780.png")
> system("convert tmp/8g6zg1259359780.ps tmp/8g6zg1259359780.png")
> system("convert tmp/9y3le1259359780.ps tmp/9y3le1259359780.png")
> system("convert tmp/10q3pt1259359780.ps tmp/10q3pt1259359780.png")
>
>
> proc.time()
user system elapsed
2.343 1.573 3.443