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(89.1
+ ,0
+ ,100
+ ,88
+ ,82.6
+ ,0
+ ,89.1
+ ,100
+ ,102.7
+ ,0
+ ,82.6
+ ,89.1
+ ,91.8
+ ,0
+ ,102.7
+ ,82.6
+ ,94.1
+ ,0
+ ,91.8
+ ,102.7
+ ,103.1
+ ,0
+ ,94.1
+ ,91.8
+ ,93.2
+ ,0
+ ,103.1
+ ,94.1
+ ,91
+ ,0
+ ,93.2
+ ,103.1
+ ,94.3
+ ,0
+ ,91
+ ,93.2
+ ,99.4
+ ,0
+ ,94.3
+ ,91
+ ,115.7
+ ,0
+ ,99.4
+ ,94.3
+ ,116.8
+ ,0
+ ,115.7
+ ,99.4
+ ,99.8
+ ,0
+ ,116.8
+ ,115.7
+ ,96
+ ,0
+ ,99.8
+ ,116.8
+ ,115.9
+ ,0
+ ,96
+ ,99.8
+ ,109.1
+ ,0
+ ,115.9
+ ,96
+ ,117.3
+ ,0
+ ,109.1
+ ,115.9
+ ,109.8
+ ,0
+ ,117.3
+ ,109.1
+ ,112.8
+ ,0
+ ,109.8
+ ,117.3
+ ,110.7
+ ,0
+ ,112.8
+ ,109.8
+ ,100
+ ,0
+ ,110.7
+ ,112.8
+ ,113.3
+ ,0
+ ,100
+ ,110.7
+ ,122.4
+ ,0
+ ,113.3
+ ,100
+ ,112.5
+ ,0
+ ,122.4
+ ,113.3
+ ,104.2
+ ,0
+ ,112.5
+ ,122.4
+ ,92.5
+ ,0
+ ,104.2
+ ,112.5
+ ,117.2
+ ,0
+ ,92.5
+ ,104.2
+ ,109.3
+ ,0
+ ,117.2
+ ,92.5
+ ,106.1
+ ,0
+ ,109.3
+ ,117.2
+ ,118.8
+ ,0
+ ,106.1
+ ,109.3
+ ,105.3
+ ,0
+ ,118.8
+ ,106.1
+ ,106
+ ,0
+ ,105.3
+ ,118.8
+ ,102
+ ,0
+ ,106
+ ,105.3
+ ,112.9
+ ,0
+ ,102
+ ,106
+ ,116.5
+ ,0
+ ,112.9
+ ,102
+ ,114.8
+ ,0
+ ,116.5
+ ,112.9
+ ,100.5
+ ,0
+ ,114.8
+ ,116.5
+ ,85.4
+ ,0
+ ,100.5
+ ,114.8
+ ,114.6
+ ,0
+ ,85.4
+ ,100.5
+ ,109.9
+ ,0
+ ,114.6
+ ,85.4
+ ,100.7
+ ,0
+ ,109.9
+ ,114.6
+ ,115.5
+ ,0
+ ,100.7
+ ,109.9
+ ,100.7
+ ,1
+ ,115.5
+ ,100.7
+ ,99
+ ,1
+ ,100.7
+ ,115.5
+ ,102.3
+ ,1
+ ,99
+ ,100.7
+ ,108.8
+ ,1
+ ,102.3
+ ,99
+ ,105.9
+ ,1
+ ,108.8
+ ,102.3
+ ,113.2
+ ,1
+ ,105.9
+ ,108.8
+ ,95.7
+ ,1
+ ,113.2
+ ,105.9
+ ,80.9
+ ,1
+ ,95.7
+ ,113.2
+ ,113.9
+ ,1
+ ,80.9
+ ,95.7
+ ,98.1
+ ,1
+ ,113.9
+ ,80.9
+ ,102.8
+ ,1
+ ,98.1
+ ,113.9
+ ,104.7
+ ,1
+ ,102.8
+ ,98.1
+ ,95.9
+ ,1
+ ,104.7
+ ,102.8
+ ,94.6
+ ,1
+ ,95.9
+ ,104.7
+ ,101.6
+ ,1
+ ,94.6
+ ,95.9
+ ,103.9
+ ,1
+ ,101.6
+ ,94.6
+ ,110.3
+ ,1
+ ,103.9
+ ,101.6
+ ,114.1
+ ,1
+ ,110.3
+ ,103.9)
+ ,dim=c(4
+ ,60)
+ ,dimnames=list(c('TotaleIndustrieleProductie'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:60))
> y <- array(NA,dim=c(4,60),dimnames=list(c('TotaleIndustrieleProductie','X','Y1','Y2'),1:60))
> 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
TotaleIndustrieleProductie X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 89.1 0 100.0 88.0 1 0 0 0 0 0 0 0 0 0 0
2 82.6 0 89.1 100.0 0 1 0 0 0 0 0 0 0 0 0
3 102.7 0 82.6 89.1 0 0 1 0 0 0 0 0 0 0 0
4 91.8 0 102.7 82.6 0 0 0 1 0 0 0 0 0 0 0
5 94.1 0 91.8 102.7 0 0 0 0 1 0 0 0 0 0 0
6 103.1 0 94.1 91.8 0 0 0 0 0 1 0 0 0 0 0
7 93.2 0 103.1 94.1 0 0 0 0 0 0 1 0 0 0 0
8 91.0 0 93.2 103.1 0 0 0 0 0 0 0 1 0 0 0
9 94.3 0 91.0 93.2 0 0 0 0 0 0 0 0 1 0 0
10 99.4 0 94.3 91.0 0 0 0 0 0 0 0 0 0 1 0
11 115.7 0 99.4 94.3 0 0 0 0 0 0 0 0 0 0 1
12 116.8 0 115.7 99.4 0 0 0 0 0 0 0 0 0 0 0
13 99.8 0 116.8 115.7 1 0 0 0 0 0 0 0 0 0 0
14 96.0 0 99.8 116.8 0 1 0 0 0 0 0 0 0 0 0
15 115.9 0 96.0 99.8 0 0 1 0 0 0 0 0 0 0 0
16 109.1 0 115.9 96.0 0 0 0 1 0 0 0 0 0 0 0
17 117.3 0 109.1 115.9 0 0 0 0 1 0 0 0 0 0 0
18 109.8 0 117.3 109.1 0 0 0 0 0 1 0 0 0 0 0
19 112.8 0 109.8 117.3 0 0 0 0 0 0 1 0 0 0 0
20 110.7 0 112.8 109.8 0 0 0 0 0 0 0 1 0 0 0
21 100.0 0 110.7 112.8 0 0 0 0 0 0 0 0 1 0 0
22 113.3 0 100.0 110.7 0 0 0 0 0 0 0 0 0 1 0
23 122.4 0 113.3 100.0 0 0 0 0 0 0 0 0 0 0 1
24 112.5 0 122.4 113.3 0 0 0 0 0 0 0 0 0 0 0
25 104.2 0 112.5 122.4 1 0 0 0 0 0 0 0 0 0 0
26 92.5 0 104.2 112.5 0 1 0 0 0 0 0 0 0 0 0
27 117.2 0 92.5 104.2 0 0 1 0 0 0 0 0 0 0 0
28 109.3 0 117.2 92.5 0 0 0 1 0 0 0 0 0 0 0
29 106.1 0 109.3 117.2 0 0 0 0 1 0 0 0 0 0 0
30 118.8 0 106.1 109.3 0 0 0 0 0 1 0 0 0 0 0
31 105.3 0 118.8 106.1 0 0 0 0 0 0 1 0 0 0 0
32 106.0 0 105.3 118.8 0 0 0 0 0 0 0 1 0 0 0
33 102.0 0 106.0 105.3 0 0 0 0 0 0 0 0 1 0 0
34 112.9 0 102.0 106.0 0 0 0 0 0 0 0 0 0 1 0
35 116.5 0 112.9 102.0 0 0 0 0 0 0 0 0 0 0 1
36 114.8 0 116.5 112.9 0 0 0 0 0 0 0 0 0 0 0
37 100.5 0 114.8 116.5 1 0 0 0 0 0 0 0 0 0 0
38 85.4 0 100.5 114.8 0 1 0 0 0 0 0 0 0 0 0
39 114.6 0 85.4 100.5 0 0 1 0 0 0 0 0 0 0 0
40 109.9 0 114.6 85.4 0 0 0 1 0 0 0 0 0 0 0
41 100.7 0 109.9 114.6 0 0 0 0 1 0 0 0 0 0 0
42 115.5 0 100.7 109.9 0 0 0 0 0 1 0 0 0 0 0
43 100.7 1 115.5 100.7 0 0 0 0 0 0 1 0 0 0 0
44 99.0 1 100.7 115.5 0 0 0 0 0 0 0 1 0 0 0
45 102.3 1 99.0 100.7 0 0 0 0 0 0 0 0 1 0 0
46 108.8 1 102.3 99.0 0 0 0 0 0 0 0 0 0 1 0
47 105.9 1 108.8 102.3 0 0 0 0 0 0 0 0 0 0 1
48 113.2 1 105.9 108.8 0 0 0 0 0 0 0 0 0 0 0
49 95.7 1 113.2 105.9 1 0 0 0 0 0 0 0 0 0 0
50 80.9 1 95.7 113.2 0 1 0 0 0 0 0 0 0 0 0
51 113.9 1 80.9 95.7 0 0 1 0 0 0 0 0 0 0 0
52 98.1 1 113.9 80.9 0 0 0 1 0 0 0 0 0 0 0
53 102.8 1 98.1 113.9 0 0 0 0 1 0 0 0 0 0 0
54 104.7 1 102.8 98.1 0 0 0 0 0 1 0 0 0 0 0
55 95.9 1 104.7 102.8 0 0 0 0 0 0 1 0 0 0 0
56 94.6 1 95.9 104.7 0 0 0 0 0 0 0 1 0 0 0
57 101.6 1 94.6 95.9 0 0 0 0 0 0 0 0 1 0 0
58 103.9 1 101.6 94.6 0 0 0 0 0 0 0 0 0 1 0
59 110.3 1 103.9 101.6 0 0 0 0 0 0 0 0 0 0 1
60 114.1 1 110.3 103.9 0 0 0 0 0 0 0 0 0 0 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
59 59
60 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
48.77191 -3.92815 0.22963 0.35939 -16.65493 -24.60481
M3 M4 M5 M6 M7 M8
7.98613 -3.39182 -9.89628 -0.59165 -10.28675 -11.86737
M9 M10 M11 t
-8.68197 -0.59741 4.17151 0.06035
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.70131 -2.72756 0.04016 2.35102 10.69223
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.77191 14.32224 3.405 0.00142 **
X -3.92815 2.80006 -1.403 0.16767
Y1 0.22963 0.13470 1.705 0.09529 .
Y2 0.35939 0.12699 2.830 0.00699 **
M1 -16.65493 3.09199 -5.386 2.67e-06 ***
M2 -24.60481 3.91299 -6.288 1.27e-07 ***
M3 7.98613 4.38850 1.820 0.07560 .
M4 -3.39182 3.89356 -0.871 0.38841
M5 -9.89628 3.54496 -2.792 0.00773 **
M6 -0.59165 3.27060 -0.181 0.85728
M7 -10.28675 3.03538 -3.389 0.00149 **
M8 -11.86737 3.54485 -3.348 0.00168 **
M9 -8.68197 3.39331 -2.559 0.01403 *
M10 -0.59741 3.41124 -0.175 0.86178
M11 4.17151 3.13977 1.329 0.19083
t 0.06035 0.07321 0.824 0.41419
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.754 on 44 degrees of freedom
Multiple R-squared: 0.8181, Adjusted R-squared: 0.756
F-statistic: 13.19 on 15 and 44 DF, p-value: 1.174e-11
> 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,] 0.8857444 0.228511291 0.114255645
[2,] 0.8496769 0.300646218 0.150323109
[3,] 0.8491035 0.301793096 0.150896548
[4,] 0.8432429 0.313514209 0.156757104
[5,] 0.9440686 0.111862802 0.055931401
[6,] 0.9982941 0.003411727 0.001705864
[7,] 0.9968478 0.006304465 0.003152233
[8,] 0.9970335 0.005932913 0.002966456
[9,] 0.9933172 0.013365576 0.006682788
[10,] 0.9861818 0.027636342 0.013818171
[11,] 0.9828266 0.034346775 0.017173387
[12,] 0.9808190 0.038362034 0.019181017
[13,] 0.9706990 0.058601985 0.029300992
[14,] 0.9640074 0.071985291 0.035992646
[15,] 0.9490335 0.101932991 0.050966495
[16,] 0.9113061 0.177387875 0.088693937
[17,] 0.9146891 0.170621862 0.085310931
[18,] 0.8810942 0.237811632 0.118905816
[19,] 0.8199253 0.360149371 0.180074686
[20,] 0.7546905 0.490618965 0.245309482
[21,] 0.7009931 0.598013829 0.299006915
[22,] 0.7251935 0.549612944 0.274806472
[23,] 0.7909782 0.418043638 0.209021819
> postscript(file="/var/www/html/rcomp/tmp/18oxh1258725988.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2zd021258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38l4n1258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43l8o1258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ljw61258725989.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 = 60
Frequency = 1
1 2 3 4 5 6
2.33311686 1.91295487 -5.22837153 -7.09034710 -3.06698505 -0.04276970
7 8 9 10 11 12
-3.20130159 -4.84218632 -0.72478082 -3.73682293 5.37679700 5.01206433
13 14 15 16 17 18
-1.50400917 6.09393788 0.32488429 1.63848180 10.69222976 -5.61188457
19 20 21 22 23 24
5.79811346 7.22491024 -7.31678021 1.05008982 6.11218739 -6.54619236
25 26 27 28 29 30
0.75129759 2.40473576 0.12308259 2.07362748 -1.74510177 5.16392042
31 32 33 34 35 36
-0.46760496 0.28844282 -2.26628065 1.15576110 -1.13893789 -3.47180408
37 38 39 40 41 42
-2.08065264 -5.39642021 -0.24098301 5.09814332 -7.07266466 2.16410246
43 44 45 46 47 48
0.83484112 -1.26530849 4.49829257 2.70655539 -7.70130980 2.03975008
49 50 51 52 53 54
0.50024736 -5.01520830 5.02138766 -1.71990550 1.19252172 -1.67336861
55 56 57 58 59 60
-2.96404803 -1.40585825 5.80954911 -1.17558337 -2.64873670 2.96618204
> postscript(file="/var/www/html/rcomp/tmp/66pmr1258725989.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 2.33311686 NA
1 1.91295487 2.33311686
2 -5.22837153 1.91295487
3 -7.09034710 -5.22837153
4 -3.06698505 -7.09034710
5 -0.04276970 -3.06698505
6 -3.20130159 -0.04276970
7 -4.84218632 -3.20130159
8 -0.72478082 -4.84218632
9 -3.73682293 -0.72478082
10 5.37679700 -3.73682293
11 5.01206433 5.37679700
12 -1.50400917 5.01206433
13 6.09393788 -1.50400917
14 0.32488429 6.09393788
15 1.63848180 0.32488429
16 10.69222976 1.63848180
17 -5.61188457 10.69222976
18 5.79811346 -5.61188457
19 7.22491024 5.79811346
20 -7.31678021 7.22491024
21 1.05008982 -7.31678021
22 6.11218739 1.05008982
23 -6.54619236 6.11218739
24 0.75129759 -6.54619236
25 2.40473576 0.75129759
26 0.12308259 2.40473576
27 2.07362748 0.12308259
28 -1.74510177 2.07362748
29 5.16392042 -1.74510177
30 -0.46760496 5.16392042
31 0.28844282 -0.46760496
32 -2.26628065 0.28844282
33 1.15576110 -2.26628065
34 -1.13893789 1.15576110
35 -3.47180408 -1.13893789
36 -2.08065264 -3.47180408
37 -5.39642021 -2.08065264
38 -0.24098301 -5.39642021
39 5.09814332 -0.24098301
40 -7.07266466 5.09814332
41 2.16410246 -7.07266466
42 0.83484112 2.16410246
43 -1.26530849 0.83484112
44 4.49829257 -1.26530849
45 2.70655539 4.49829257
46 -7.70130980 2.70655539
47 2.03975008 -7.70130980
48 0.50024736 2.03975008
49 -5.01520830 0.50024736
50 5.02138766 -5.01520830
51 -1.71990550 5.02138766
52 1.19252172 -1.71990550
53 -1.67336861 1.19252172
54 -2.96404803 -1.67336861
55 -1.40585825 -2.96404803
56 5.80954911 -1.40585825
57 -1.17558337 5.80954911
58 -2.64873670 -1.17558337
59 2.96618204 -2.64873670
60 NA 2.96618204
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.91295487 2.33311686
[2,] -5.22837153 1.91295487
[3,] -7.09034710 -5.22837153
[4,] -3.06698505 -7.09034710
[5,] -0.04276970 -3.06698505
[6,] -3.20130159 -0.04276970
[7,] -4.84218632 -3.20130159
[8,] -0.72478082 -4.84218632
[9,] -3.73682293 -0.72478082
[10,] 5.37679700 -3.73682293
[11,] 5.01206433 5.37679700
[12,] -1.50400917 5.01206433
[13,] 6.09393788 -1.50400917
[14,] 0.32488429 6.09393788
[15,] 1.63848180 0.32488429
[16,] 10.69222976 1.63848180
[17,] -5.61188457 10.69222976
[18,] 5.79811346 -5.61188457
[19,] 7.22491024 5.79811346
[20,] -7.31678021 7.22491024
[21,] 1.05008982 -7.31678021
[22,] 6.11218739 1.05008982
[23,] -6.54619236 6.11218739
[24,] 0.75129759 -6.54619236
[25,] 2.40473576 0.75129759
[26,] 0.12308259 2.40473576
[27,] 2.07362748 0.12308259
[28,] -1.74510177 2.07362748
[29,] 5.16392042 -1.74510177
[30,] -0.46760496 5.16392042
[31,] 0.28844282 -0.46760496
[32,] -2.26628065 0.28844282
[33,] 1.15576110 -2.26628065
[34,] -1.13893789 1.15576110
[35,] -3.47180408 -1.13893789
[36,] -2.08065264 -3.47180408
[37,] -5.39642021 -2.08065264
[38,] -0.24098301 -5.39642021
[39,] 5.09814332 -0.24098301
[40,] -7.07266466 5.09814332
[41,] 2.16410246 -7.07266466
[42,] 0.83484112 2.16410246
[43,] -1.26530849 0.83484112
[44,] 4.49829257 -1.26530849
[45,] 2.70655539 4.49829257
[46,] -7.70130980 2.70655539
[47,] 2.03975008 -7.70130980
[48,] 0.50024736 2.03975008
[49,] -5.01520830 0.50024736
[50,] 5.02138766 -5.01520830
[51,] -1.71990550 5.02138766
[52,] 1.19252172 -1.71990550
[53,] -1.67336861 1.19252172
[54,] -2.96404803 -1.67336861
[55,] -1.40585825 -2.96404803
[56,] 5.80954911 -1.40585825
[57,] -1.17558337 5.80954911
[58,] -2.64873670 -1.17558337
[59,] 2.96618204 -2.64873670
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.91295487 2.33311686
2 -5.22837153 1.91295487
3 -7.09034710 -5.22837153
4 -3.06698505 -7.09034710
5 -0.04276970 -3.06698505
6 -3.20130159 -0.04276970
7 -4.84218632 -3.20130159
8 -0.72478082 -4.84218632
9 -3.73682293 -0.72478082
10 5.37679700 -3.73682293
11 5.01206433 5.37679700
12 -1.50400917 5.01206433
13 6.09393788 -1.50400917
14 0.32488429 6.09393788
15 1.63848180 0.32488429
16 10.69222976 1.63848180
17 -5.61188457 10.69222976
18 5.79811346 -5.61188457
19 7.22491024 5.79811346
20 -7.31678021 7.22491024
21 1.05008982 -7.31678021
22 6.11218739 1.05008982
23 -6.54619236 6.11218739
24 0.75129759 -6.54619236
25 2.40473576 0.75129759
26 0.12308259 2.40473576
27 2.07362748 0.12308259
28 -1.74510177 2.07362748
29 5.16392042 -1.74510177
30 -0.46760496 5.16392042
31 0.28844282 -0.46760496
32 -2.26628065 0.28844282
33 1.15576110 -2.26628065
34 -1.13893789 1.15576110
35 -3.47180408 -1.13893789
36 -2.08065264 -3.47180408
37 -5.39642021 -2.08065264
38 -0.24098301 -5.39642021
39 5.09814332 -0.24098301
40 -7.07266466 5.09814332
41 2.16410246 -7.07266466
42 0.83484112 2.16410246
43 -1.26530849 0.83484112
44 4.49829257 -1.26530849
45 2.70655539 4.49829257
46 -7.70130980 2.70655539
47 2.03975008 -7.70130980
48 0.50024736 2.03975008
49 -5.01520830 0.50024736
50 5.02138766 -5.01520830
51 -1.71990550 5.02138766
52 1.19252172 -1.71990550
53 -1.67336861 1.19252172
54 -2.96404803 -1.67336861
55 -1.40585825 -2.96404803
56 5.80954911 -1.40585825
57 -1.17558337 5.80954911
58 -2.64873670 -1.17558337
59 2.96618204 -2.64873670
> 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/7z6v01258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83l351258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/962nt1258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100bth1258725989.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vw0g1258725989.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/12k6u71258725989.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/13kqlt1258725989.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/147nvd1258725989.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/15kjer1258725989.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/165xel1258725989.tab")
+ }
>
> system("convert tmp/18oxh1258725988.ps tmp/18oxh1258725988.png")
> system("convert tmp/2zd021258725989.ps tmp/2zd021258725989.png")
> system("convert tmp/38l4n1258725989.ps tmp/38l4n1258725989.png")
> system("convert tmp/43l8o1258725989.ps tmp/43l8o1258725989.png")
> system("convert tmp/5ljw61258725989.ps tmp/5ljw61258725989.png")
> system("convert tmp/66pmr1258725989.ps tmp/66pmr1258725989.png")
> system("convert tmp/7z6v01258725989.ps tmp/7z6v01258725989.png")
> system("convert tmp/83l351258725989.ps tmp/83l351258725989.png")
> system("convert tmp/962nt1258725989.ps tmp/962nt1258725989.png")
> system("convert tmp/100bth1258725989.ps tmp/100bth1258725989.png")
>
>
> proc.time()
user system elapsed
2.415 1.566 3.439