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(29
+ ,38
+ ,24
+ ,25
+ ,22
+ ,24
+ ,26
+ ,42
+ ,29
+ ,24
+ ,25
+ ,22
+ ,26
+ ,35
+ ,26
+ ,29
+ ,24
+ ,25
+ ,21
+ ,25
+ ,26
+ ,26
+ ,29
+ ,24
+ ,23
+ ,24
+ ,21
+ ,26
+ ,26
+ ,29
+ ,22
+ ,22
+ ,23
+ ,21
+ ,26
+ ,26
+ ,21
+ ,27
+ ,22
+ ,23
+ ,21
+ ,26
+ ,16
+ ,17
+ ,21
+ ,22
+ ,23
+ ,21
+ ,19
+ ,30
+ ,16
+ ,21
+ ,22
+ ,23
+ ,16
+ ,30
+ ,19
+ ,16
+ ,21
+ ,22
+ ,25
+ ,34
+ ,16
+ ,19
+ ,16
+ ,21
+ ,27
+ ,37
+ ,25
+ ,16
+ ,19
+ ,16
+ ,23
+ ,36
+ ,27
+ ,25
+ ,16
+ ,19
+ ,22
+ ,33
+ ,23
+ ,27
+ ,25
+ ,16
+ ,23
+ ,33
+ ,22
+ ,23
+ ,27
+ ,25
+ ,20
+ ,33
+ ,23
+ ,22
+ ,23
+ ,27
+ ,24
+ ,37
+ ,20
+ ,23
+ ,22
+ ,23
+ ,23
+ ,40
+ ,24
+ ,20
+ ,23
+ ,22
+ ,20
+ ,35
+ ,23
+ ,24
+ ,20
+ ,23
+ ,21
+ ,37
+ ,20
+ ,23
+ ,24
+ ,20
+ ,22
+ ,43
+ ,21
+ ,20
+ ,23
+ ,24
+ ,17
+ ,42
+ ,22
+ ,21
+ ,20
+ ,23
+ ,21
+ ,33
+ ,17
+ ,22
+ ,21
+ ,20
+ ,19
+ ,39
+ ,21
+ ,17
+ ,22
+ ,21
+ ,23
+ ,40
+ ,19
+ ,21
+ ,17
+ ,22
+ ,22
+ ,37
+ ,23
+ ,19
+ ,21
+ ,17
+ ,15
+ ,44
+ ,22
+ ,23
+ ,19
+ ,21
+ ,23
+ ,42
+ ,15
+ ,22
+ ,23
+ ,19
+ ,21
+ ,43
+ ,23
+ ,15
+ ,22
+ ,23
+ ,18
+ ,40
+ ,21
+ ,23
+ ,15
+ ,22
+ ,18
+ ,30
+ ,18
+ ,21
+ ,23
+ ,15
+ ,18
+ ,30
+ ,18
+ ,18
+ ,21
+ ,23
+ ,18
+ ,31
+ ,18
+ ,18
+ ,18
+ ,21
+ ,10
+ ,18
+ ,18
+ ,18
+ ,18
+ ,18
+ ,13
+ ,24
+ ,10
+ ,18
+ ,18
+ ,18
+ ,10
+ ,22
+ ,13
+ ,10
+ ,18
+ ,18
+ ,9
+ ,26
+ ,10
+ ,13
+ ,10
+ ,18
+ ,9
+ ,28
+ ,9
+ ,10
+ ,13
+ ,10
+ ,6
+ ,23
+ ,9
+ ,9
+ ,10
+ ,13
+ ,11
+ ,17
+ ,6
+ ,9
+ ,9
+ ,10
+ ,9
+ ,12
+ ,11
+ ,6
+ ,9
+ ,9
+ ,10
+ ,9
+ ,9
+ ,11
+ ,6
+ ,9
+ ,9
+ ,19
+ ,10
+ ,9
+ ,11
+ ,6
+ ,16
+ ,21
+ ,9
+ ,10
+ ,9
+ ,11
+ ,10
+ ,18
+ ,16
+ ,9
+ ,10
+ ,9
+ ,7
+ ,18
+ ,10
+ ,16
+ ,9
+ ,10
+ ,7
+ ,15
+ ,7
+ ,10
+ ,16
+ ,9
+ ,14
+ ,24
+ ,7
+ ,7
+ ,10
+ ,16
+ ,11
+ ,18
+ ,14
+ ,7
+ ,7
+ ,10
+ ,10
+ ,19
+ ,11
+ ,14
+ ,7
+ ,7
+ ,6
+ ,30
+ ,10
+ ,11
+ ,14
+ ,7
+ ,8
+ ,33
+ ,6
+ ,10
+ ,11
+ ,14
+ ,13
+ ,35
+ ,8
+ ,6
+ ,10
+ ,11
+ ,12
+ ,36
+ ,13
+ ,8
+ ,6
+ ,10
+ ,15
+ ,47
+ ,12
+ ,13
+ ,8
+ ,6
+ ,16
+ ,46
+ ,15
+ ,12
+ ,13
+ ,8
+ ,16
+ ,43
+ ,16
+ ,15
+ ,12
+ ,13)
+ ,dim=c(6
+ ,57)
+ ,dimnames=list(c('S.'
+ ,'E.S'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)'
+ ,'Y(T-4)')
+ ,1:57))
> y <- array(NA,dim=c(6,57),dimnames=list(c('S.','E.S','Y(t-1)','Y(t-2)','Y(t-3)','Y(T-4)'),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
S. E.S 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 29 38 24 25 22 24 1 0 0 0 0 0 0 0 0 0 0 1
2 26 42 29 24 25 22 0 1 0 0 0 0 0 0 0 0 0 2
3 26 35 26 29 24 25 0 0 1 0 0 0 0 0 0 0 0 3
4 21 25 26 26 29 24 0 0 0 1 0 0 0 0 0 0 0 4
5 23 24 21 26 26 29 0 0 0 0 1 0 0 0 0 0 0 5
6 22 22 23 21 26 26 0 0 0 0 0 1 0 0 0 0 0 6
7 21 27 22 23 21 26 0 0 0 0 0 0 1 0 0 0 0 7
8 16 17 21 22 23 21 0 0 0 0 0 0 0 1 0 0 0 8
9 19 30 16 21 22 23 0 0 0 0 0 0 0 0 1 0 0 9
10 16 30 19 16 21 22 0 0 0 0 0 0 0 0 0 1 0 10
11 25 34 16 19 16 21 0 0 0 0 0 0 0 0 0 0 1 11
12 27 37 25 16 19 16 0 0 0 0 0 0 0 0 0 0 0 12
13 23 36 27 25 16 19 1 0 0 0 0 0 0 0 0 0 0 13
14 22 33 23 27 25 16 0 1 0 0 0 0 0 0 0 0 0 14
15 23 33 22 23 27 25 0 0 1 0 0 0 0 0 0 0 0 15
16 20 33 23 22 23 27 0 0 0 1 0 0 0 0 0 0 0 16
17 24 37 20 23 22 23 0 0 0 0 1 0 0 0 0 0 0 17
18 23 40 24 20 23 22 0 0 0 0 0 1 0 0 0 0 0 18
19 20 35 23 24 20 23 0 0 0 0 0 0 1 0 0 0 0 19
20 21 37 20 23 24 20 0 0 0 0 0 0 0 1 0 0 0 20
21 22 43 21 20 23 24 0 0 0 0 0 0 0 0 1 0 0 21
22 17 42 22 21 20 23 0 0 0 0 0 0 0 0 0 1 0 22
23 21 33 17 22 21 20 0 0 0 0 0 0 0 0 0 0 1 23
24 19 39 21 17 22 21 0 0 0 0 0 0 0 0 0 0 0 24
25 23 40 19 21 17 22 1 0 0 0 0 0 0 0 0 0 0 25
26 22 37 23 19 21 17 0 1 0 0 0 0 0 0 0 0 0 26
27 15 44 22 23 19 21 0 0 1 0 0 0 0 0 0 0 0 27
28 23 42 15 22 23 19 0 0 0 1 0 0 0 0 0 0 0 28
29 21 43 23 15 22 23 0 0 0 0 1 0 0 0 0 0 0 29
30 18 40 21 23 15 22 0 0 0 0 0 1 0 0 0 0 0 30
31 18 30 18 21 23 15 0 0 0 0 0 0 1 0 0 0 0 31
32 18 30 18 18 21 23 0 0 0 0 0 0 0 1 0 0 0 32
33 18 31 18 18 18 21 0 0 0 0 0 0 0 0 1 0 0 33
34 10 18 18 18 18 18 0 0 0 0 0 0 0 0 0 1 0 34
35 13 24 10 18 18 18 0 0 0 0 0 0 0 0 0 0 1 35
36 10 22 13 10 18 18 0 0 0 0 0 0 0 0 0 0 0 36
37 9 26 10 13 10 18 1 0 0 0 0 0 0 0 0 0 0 37
38 9 28 9 10 13 10 0 1 0 0 0 0 0 0 0 0 0 38
39 6 23 9 9 10 13 0 0 1 0 0 0 0 0 0 0 0 39
40 11 17 6 9 9 10 0 0 0 1 0 0 0 0 0 0 0 40
41 9 12 11 6 9 9 0 0 0 0 1 0 0 0 0 0 0 41
42 10 9 9 11 6 9 0 0 0 0 0 1 0 0 0 0 0 42
43 9 19 10 9 11 6 0 0 0 0 0 0 1 0 0 0 0 43
44 16 21 9 10 9 11 0 0 0 0 0 0 0 1 0 0 0 44
45 10 18 16 9 10 9 0 0 0 0 0 0 0 0 1 0 0 45
46 7 18 10 16 9 10 0 0 0 0 0 0 0 0 0 1 0 46
47 7 15 7 10 16 9 0 0 0 0 0 0 0 0 0 0 1 47
48 14 24 7 7 10 16 0 0 0 0 0 0 0 0 0 0 0 48
49 11 18 14 7 7 10 1 0 0 0 0 0 0 0 0 0 0 49
50 10 19 11 14 7 7 0 1 0 0 0 0 0 0 0 0 0 50
51 6 30 10 11 14 7 0 0 1 0 0 0 0 0 0 0 0 51
52 8 33 6 10 11 14 0 0 0 1 0 0 0 0 0 0 0 52
53 13 35 8 6 10 11 0 0 0 0 1 0 0 0 0 0 0 53
54 12 36 13 8 6 10 0 0 0 0 0 1 0 0 0 0 0 54
55 15 47 12 13 8 6 0 0 0 0 0 0 1 0 0 0 0 55
56 16 46 15 12 13 8 0 0 0 0 0 0 0 1 0 0 0 56
57 16 43 16 15 12 13 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) E.S `Y(t-1)` `Y(t-2)` `Y(t-3)` `Y(T-4)`
14.34073 0.23401 0.18253 0.22161 -0.09754 -0.10305
M1 M2 M3 M4 M5 M6
-1.67221 -2.94233 -4.99006 -1.85790 -0.07222 -1.61979
M7 M8 M9 M10 M11 t
-2.58543 -0.67408 -1.53170 -5.87072 -0.62327 -0.20828
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.27975 -1.44432 -0.04652 1.45494 5.14816
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.34073 4.08476 3.511 0.001145 **
E.S 0.23401 0.04816 4.859 1.95e-05 ***
`Y(t-1)` 0.18253 0.13872 1.316 0.195917
`Y(t-2)` 0.22161 0.14096 1.572 0.123995
`Y(t-3)` -0.09754 0.14111 -0.691 0.493507
`Y(T-4)` -0.10305 0.13493 -0.764 0.449634
M1 -1.67221 1.93851 -0.863 0.393617
M2 -2.94233 1.97604 -1.489 0.144529
M3 -4.99006 1.85796 -2.686 0.010574 *
M4 -1.85790 1.85734 -1.000 0.323331
M5 -0.07222 1.72386 -0.042 0.966797
M6 -1.61979 1.83589 -0.882 0.383026
M7 -2.58543 1.92103 -1.346 0.186122
M8 -0.67408 1.83237 -0.368 0.714955
M9 -1.53170 1.79289 -0.854 0.398145
M10 -5.87072 1.92964 -3.042 0.004185 **
M11 -0.62327 2.05164 -0.304 0.762902
t -0.20828 0.05631 -3.699 0.000666 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.521 on 39 degrees of freedom
Multiple R-squared: 0.883, Adjusted R-squared: 0.832
F-statistic: 17.31 on 17 and 39 DF, p-value: 4.012e-13
> 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,] 0.07422619 0.14845238 0.9257738
[2,] 0.02726687 0.05453374 0.9727331
[3,] 0.02689872 0.05379745 0.9731013
[4,] 0.18495651 0.36991301 0.8150435
[5,] 0.14506465 0.29012929 0.8549354
[6,] 0.16853534 0.33707069 0.8314647
[7,] 0.51683881 0.96632237 0.4831612
[8,] 0.60529514 0.78940972 0.3947049
[9,] 0.56281077 0.87437845 0.4371892
[10,] 0.44859049 0.89718098 0.5514095
[11,] 0.42395292 0.84790584 0.5760471
[12,] 0.39421303 0.78842607 0.6057870
[13,] 0.44042229 0.88084458 0.5595777
[14,] 0.51389778 0.97220445 0.4861022
[15,] 0.64363086 0.71273827 0.3563691
[16,] 0.57923492 0.84153015 0.4207651
> postscript(file="/var/www/html/rcomp/tmp/1sxc41260373635.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2erpb1260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3smhj1260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4x7je1260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5x6og1260373635.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
2.3453111553 -0.7168368233 2.8283943486 -1.7058550262 0.0860022884
6 7 8 9 10
1.7437351465 -0.0008228338 -4.2797539942 -2.0132334074 -0.1060608790
11 12 13 14 15
2.2107079720 1.8931457934 -2.3353957850 -0.2993090091 5.1481615876
16 17 18 19 20
-0.9207202117 0.3820744316 0.3650942363 -1.1844159244 -1.5052780716
21 22 23 24 25
-0.0465225802 -1.0650248633 0.4813646618 -2.7591851104 1.9812359061
26 27 28 29 30
2.7497287835 -4.1191769545 3.1083570215 -0.2973774685 -3.0331543984
31 32 33 34 35
1.5307254702 1.1217719514 1.4549354176 0.7352734545 -1.2477520764
36 37 38 39 40
-2.9694205105 -3.9225748975 -2.5965758238 -1.9323824979 1.6887226413
41 42 43 44 45
-1.0694694150 1.3527999961 -0.3741555134 4.7358144776 -0.6608887649
46 47 48 49 50
0.4358122878 -1.4443205574 3.8354598275 1.9314236211 0.8629928726
51 52 53 54 55
-1.9249964838 -2.1705044249 0.8987701636 -0.4284749806 0.0286688014
56 57
-0.0725543632 1.2657093348
> postscript(file="/var/www/html/rcomp/tmp/6uv0v1260373635.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 2.3453111553 NA
1 -0.7168368233 2.3453111553
2 2.8283943486 -0.7168368233
3 -1.7058550262 2.8283943486
4 0.0860022884 -1.7058550262
5 1.7437351465 0.0860022884
6 -0.0008228338 1.7437351465
7 -4.2797539942 -0.0008228338
8 -2.0132334074 -4.2797539942
9 -0.1060608790 -2.0132334074
10 2.2107079720 -0.1060608790
11 1.8931457934 2.2107079720
12 -2.3353957850 1.8931457934
13 -0.2993090091 -2.3353957850
14 5.1481615876 -0.2993090091
15 -0.9207202117 5.1481615876
16 0.3820744316 -0.9207202117
17 0.3650942363 0.3820744316
18 -1.1844159244 0.3650942363
19 -1.5052780716 -1.1844159244
20 -0.0465225802 -1.5052780716
21 -1.0650248633 -0.0465225802
22 0.4813646618 -1.0650248633
23 -2.7591851104 0.4813646618
24 1.9812359061 -2.7591851104
25 2.7497287835 1.9812359061
26 -4.1191769545 2.7497287835
27 3.1083570215 -4.1191769545
28 -0.2973774685 3.1083570215
29 -3.0331543984 -0.2973774685
30 1.5307254702 -3.0331543984
31 1.1217719514 1.5307254702
32 1.4549354176 1.1217719514
33 0.7352734545 1.4549354176
34 -1.2477520764 0.7352734545
35 -2.9694205105 -1.2477520764
36 -3.9225748975 -2.9694205105
37 -2.5965758238 -3.9225748975
38 -1.9323824979 -2.5965758238
39 1.6887226413 -1.9323824979
40 -1.0694694150 1.6887226413
41 1.3527999961 -1.0694694150
42 -0.3741555134 1.3527999961
43 4.7358144776 -0.3741555134
44 -0.6608887649 4.7358144776
45 0.4358122878 -0.6608887649
46 -1.4443205574 0.4358122878
47 3.8354598275 -1.4443205574
48 1.9314236211 3.8354598275
49 0.8629928726 1.9314236211
50 -1.9249964838 0.8629928726
51 -2.1705044249 -1.9249964838
52 0.8987701636 -2.1705044249
53 -0.4284749806 0.8987701636
54 0.0286688014 -0.4284749806
55 -0.0725543632 0.0286688014
56 1.2657093348 -0.0725543632
57 NA 1.2657093348
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.7168368233 2.3453111553
[2,] 2.8283943486 -0.7168368233
[3,] -1.7058550262 2.8283943486
[4,] 0.0860022884 -1.7058550262
[5,] 1.7437351465 0.0860022884
[6,] -0.0008228338 1.7437351465
[7,] -4.2797539942 -0.0008228338
[8,] -2.0132334074 -4.2797539942
[9,] -0.1060608790 -2.0132334074
[10,] 2.2107079720 -0.1060608790
[11,] 1.8931457934 2.2107079720
[12,] -2.3353957850 1.8931457934
[13,] -0.2993090091 -2.3353957850
[14,] 5.1481615876 -0.2993090091
[15,] -0.9207202117 5.1481615876
[16,] 0.3820744316 -0.9207202117
[17,] 0.3650942363 0.3820744316
[18,] -1.1844159244 0.3650942363
[19,] -1.5052780716 -1.1844159244
[20,] -0.0465225802 -1.5052780716
[21,] -1.0650248633 -0.0465225802
[22,] 0.4813646618 -1.0650248633
[23,] -2.7591851104 0.4813646618
[24,] 1.9812359061 -2.7591851104
[25,] 2.7497287835 1.9812359061
[26,] -4.1191769545 2.7497287835
[27,] 3.1083570215 -4.1191769545
[28,] -0.2973774685 3.1083570215
[29,] -3.0331543984 -0.2973774685
[30,] 1.5307254702 -3.0331543984
[31,] 1.1217719514 1.5307254702
[32,] 1.4549354176 1.1217719514
[33,] 0.7352734545 1.4549354176
[34,] -1.2477520764 0.7352734545
[35,] -2.9694205105 -1.2477520764
[36,] -3.9225748975 -2.9694205105
[37,] -2.5965758238 -3.9225748975
[38,] -1.9323824979 -2.5965758238
[39,] 1.6887226413 -1.9323824979
[40,] -1.0694694150 1.6887226413
[41,] 1.3527999961 -1.0694694150
[42,] -0.3741555134 1.3527999961
[43,] 4.7358144776 -0.3741555134
[44,] -0.6608887649 4.7358144776
[45,] 0.4358122878 -0.6608887649
[46,] -1.4443205574 0.4358122878
[47,] 3.8354598275 -1.4443205574
[48,] 1.9314236211 3.8354598275
[49,] 0.8629928726 1.9314236211
[50,] -1.9249964838 0.8629928726
[51,] -2.1705044249 -1.9249964838
[52,] 0.8987701636 -2.1705044249
[53,] -0.4284749806 0.8987701636
[54,] 0.0286688014 -0.4284749806
[55,] -0.0725543632 0.0286688014
[56,] 1.2657093348 -0.0725543632
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.7168368233 2.3453111553
2 2.8283943486 -0.7168368233
3 -1.7058550262 2.8283943486
4 0.0860022884 -1.7058550262
5 1.7437351465 0.0860022884
6 -0.0008228338 1.7437351465
7 -4.2797539942 -0.0008228338
8 -2.0132334074 -4.2797539942
9 -0.1060608790 -2.0132334074
10 2.2107079720 -0.1060608790
11 1.8931457934 2.2107079720
12 -2.3353957850 1.8931457934
13 -0.2993090091 -2.3353957850
14 5.1481615876 -0.2993090091
15 -0.9207202117 5.1481615876
16 0.3820744316 -0.9207202117
17 0.3650942363 0.3820744316
18 -1.1844159244 0.3650942363
19 -1.5052780716 -1.1844159244
20 -0.0465225802 -1.5052780716
21 -1.0650248633 -0.0465225802
22 0.4813646618 -1.0650248633
23 -2.7591851104 0.4813646618
24 1.9812359061 -2.7591851104
25 2.7497287835 1.9812359061
26 -4.1191769545 2.7497287835
27 3.1083570215 -4.1191769545
28 -0.2973774685 3.1083570215
29 -3.0331543984 -0.2973774685
30 1.5307254702 -3.0331543984
31 1.1217719514 1.5307254702
32 1.4549354176 1.1217719514
33 0.7352734545 1.4549354176
34 -1.2477520764 0.7352734545
35 -2.9694205105 -1.2477520764
36 -3.9225748975 -2.9694205105
37 -2.5965758238 -3.9225748975
38 -1.9323824979 -2.5965758238
39 1.6887226413 -1.9323824979
40 -1.0694694150 1.6887226413
41 1.3527999961 -1.0694694150
42 -0.3741555134 1.3527999961
43 4.7358144776 -0.3741555134
44 -0.6608887649 4.7358144776
45 0.4358122878 -0.6608887649
46 -1.4443205574 0.4358122878
47 3.8354598275 -1.4443205574
48 1.9314236211 3.8354598275
49 0.8629928726 1.9314236211
50 -1.9249964838 0.8629928726
51 -2.1705044249 -1.9249964838
52 0.8987701636 -2.1705044249
53 -0.4284749806 0.8987701636
54 0.0286688014 -0.4284749806
55 -0.0725543632 0.0286688014
56 1.2657093348 -0.0725543632
> 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/7yefq1260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z9sj1260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qsm31260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1093591260373635.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t1pf1260373635.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/124yyb1260373635.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/13b0k71260373635.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/14okqp1260373635.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/1564be1260373635.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/16nxga1260373635.tab")
+ }
>
> system("convert tmp/1sxc41260373635.ps tmp/1sxc41260373635.png")
> system("convert tmp/2erpb1260373635.ps tmp/2erpb1260373635.png")
> system("convert tmp/3smhj1260373635.ps tmp/3smhj1260373635.png")
> system("convert tmp/4x7je1260373635.ps tmp/4x7je1260373635.png")
> system("convert tmp/5x6og1260373635.ps tmp/5x6og1260373635.png")
> system("convert tmp/6uv0v1260373635.ps tmp/6uv0v1260373635.png")
> system("convert tmp/7yefq1260373635.ps tmp/7yefq1260373635.png")
> system("convert tmp/8z9sj1260373635.ps tmp/8z9sj1260373635.png")
> system("convert tmp/9qsm31260373635.ps tmp/9qsm31260373635.png")
> system("convert tmp/1093591260373635.ps tmp/1093591260373635.png")
>
>
> proc.time()
user system elapsed
2.348 1.550 3.569