R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7.8,2.61,7.8,8.3,8,2.26,7.8,7.8,8.6,2.41,8,7.8,8.9,2.26,8.6,8,8.9,2.03,8.9,8.6,8.6,2.86,8.9,8.9,8.3,2.55,8.6,8.9,8.3,2.27,8.3,8.6,8.3,2.26,8.3,8.3,8.4,2.57,8.3,8.3,8.5,3.07,8.4,8.3,8.4,2.76,8.5,8.4,8.6,2.51,8.4,8.5,8.5,2.87,8.6,8.4,8.5,3.14,8.5,8.6,8.5,3.11,8.5,8.5,8.5,3.16,8.5,8.5,8.5,2.47,8.5,8.5,8.5,2.57,8.5,8.5,8.5,2.89,8.5,8.5,8.5,2.63,8.5,8.5,8.5,2.38,8.5,8.5,8.5,1.69,8.5,8.5,8.5,1.96,8.5,8.5,8.6,2.19,8.5,8.5,8.4,1.87,8.6,8.5,8.1,1.6,8.4,8.6,8,1.63,8.1,8.4,8,1.22,8,8.1,8,1.21,8,8,8,1.49,8,8,7.9,1.64,8,8,7.8,1.66,7.9,8,7.8,1.77,7.8,7.9,7.9,1.82,7.8,7.8,8.1,1.78,7.9,7.8,8,1.28,8.1,7.9,7.6,1.29,8,8.1,7.3,1.37,7.6,8,7,1.12,7.3,7.6,6.8,1.51,7,7.3,7,2.24,6.8,7,7.1,2.94,7,6.8,7.2,3.09,7.1,7,7.1,3.46,7.2,7.1,6.9,3.64,7.1,7.2,6.7,4.39,6.9,7.1,6.7,4.15,6.7,6.9,6.6,5.21,6.7,6.7,6.9,5.8,6.6,6.7,7.3,5.91,6.9,6.6,7.5,5.39,7.3,6.9,7.3,5.46,7.5,7.3,7.1,4.72,7.3,7.5,6.9,3.14,7.1,7.3,7.1,2.63,6.9,7.1),dim=c(4,56),dimnames=list(c('Y','X','Y1','Y2'),1:56))
> y <- array(NA,dim=c(4,56),dimnames=list(c('Y','X','Y1','Y2'),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 X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.8 2.61 7.8 8.3 1 0 0 0 0 0 0 0 0 0 0 1
2 8.0 2.26 7.8 7.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 2.41 8.0 7.8 0 0 1 0 0 0 0 0 0 0 0 3
4 8.9 2.26 8.6 8.0 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 2.03 8.9 8.6 0 0 0 0 1 0 0 0 0 0 0 5
6 8.6 2.86 8.9 8.9 0 0 0 0 0 1 0 0 0 0 0 6
7 8.3 2.55 8.6 8.9 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 2.27 8.3 8.6 0 0 0 0 0 0 0 1 0 0 0 8
9 8.3 2.26 8.3 8.3 0 0 0 0 0 0 0 0 1 0 0 9
10 8.4 2.57 8.3 8.3 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 3.07 8.4 8.3 0 0 0 0 0 0 0 0 0 0 1 11
12 8.4 2.76 8.5 8.4 0 0 0 0 0 0 0 0 0 0 0 12
13 8.6 2.51 8.4 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 2.87 8.6 8.4 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 3.14 8.5 8.6 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 3.11 8.5 8.5 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 3.16 8.5 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 2.47 8.5 8.5 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 2.57 8.5 8.5 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 2.89 8.5 8.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 2.63 8.5 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.5 2.38 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.5 1.69 8.5 8.5 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 1.96 8.5 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.6 2.19 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 25
26 8.4 1.87 8.6 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.1 1.60 8.4 8.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 1.63 8.1 8.4 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 1.22 8.0 8.1 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 1.21 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 1.49 8.0 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 1.64 8.0 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 7.8 1.66 7.9 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.8 1.77 7.8 7.9 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.82 7.8 7.8 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 1.78 7.9 7.8 0 0 0 0 0 0 0 0 0 0 0 36
37 8.0 1.28 8.1 7.9 1 0 0 0 0 0 0 0 0 0 0 37
38 7.6 1.29 8.0 8.1 0 1 0 0 0 0 0 0 0 0 0 38
39 7.3 1.37 7.6 8.0 0 0 1 0 0 0 0 0 0 0 0 39
40 7.0 1.12 7.3 7.6 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 1.51 7.0 7.3 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 2.24 6.8 7.0 0 0 0 0 0 1 0 0 0 0 0 42
43 7.1 2.94 7.0 6.8 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 3.09 7.1 7.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.1 3.46 7.2 7.1 0 0 0 0 0 0 0 0 1 0 0 45
46 6.9 3.64 7.1 7.2 0 0 0 0 0 0 0 0 0 1 0 46
47 6.7 4.39 6.9 7.1 0 0 0 0 0 0 0 0 0 0 1 47
48 6.7 4.15 6.7 6.9 0 0 0 0 0 0 0 0 0 0 0 48
49 6.6 5.21 6.7 6.7 1 0 0 0 0 0 0 0 0 0 0 49
50 6.9 5.80 6.6 6.7 0 1 0 0 0 0 0 0 0 0 0 50
51 7.3 5.91 6.9 6.6 0 0 1 0 0 0 0 0 0 0 0 51
52 7.5 5.39 7.3 6.9 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 5.46 7.5 7.3 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 4.72 7.3 7.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.9 3.14 7.1 7.3 0 0 0 0 0 0 1 0 0 0 0 55
56 7.1 2.63 6.9 7.1 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) X Y1 Y2 M1 M2
2.315294 -0.004043 1.322528 -0.575638 -0.005215 -0.108150
M3 M4 M5 M6 M7 M8
0.045871 -0.054369 -0.105541 -0.038797 -0.076820 0.043639
M9 M10 M11 t
-0.084660 -0.033848 -0.019618 -0.009332
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.2562646 -0.1049265 -0.0009522 0.0888505 0.3109988
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.315294 0.556634 4.159 0.000164 ***
X -0.004043 0.021080 -0.192 0.848864
Y1 1.322528 0.114008 11.600 2.26e-14 ***
Y2 -0.575638 0.118888 -4.842 1.96e-05 ***
M1 -0.005215 0.102256 -0.051 0.959578
M2 -0.108150 0.101638 -1.064 0.293677
M3 0.045871 0.101752 0.451 0.654562
M4 -0.054369 0.101653 -0.535 0.595715
M5 -0.105541 0.101300 -1.042 0.303728
M6 -0.038797 0.101720 -0.381 0.704917
M7 -0.076820 0.101453 -0.757 0.453372
M8 0.043639 0.101675 0.429 0.670081
M9 -0.084660 0.106811 -0.793 0.432678
M10 -0.033848 0.106908 -0.317 0.753189
M11 -0.019618 0.106807 -0.184 0.855197
t -0.009332 0.002232 -4.181 0.000154 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1509 on 40 degrees of freedom
Multiple R-squared: 0.9627, Adjusted R-squared: 0.9488
F-statistic: 68.9 on 15 and 40 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,] 0.172031676 0.344063351 0.8279683
[2,] 0.107289737 0.214579475 0.8927103
[3,] 0.052292797 0.104585595 0.9477072
[4,] 0.029488625 0.058977250 0.9705114
[5,] 0.017073553 0.034147106 0.9829264
[6,] 0.008748450 0.017496900 0.9912515
[7,] 0.007434408 0.014868816 0.9925656
[8,] 0.004817401 0.009634801 0.9951826
[9,] 0.040937154 0.081874308 0.9590628
[10,] 0.025538926 0.051077851 0.9744611
[11,] 0.027977359 0.055954719 0.9720226
[12,] 0.018992740 0.037985480 0.9810073
[13,] 0.017220817 0.034441633 0.9827792
[14,] 0.027867761 0.055735522 0.9721322
[15,] 0.028193035 0.056386070 0.9718070
[16,] 0.050235064 0.100470128 0.9497649
[17,] 0.070437047 0.140874094 0.9295630
[18,] 0.183441874 0.366883748 0.8165581
[19,] 0.792836601 0.414326797 0.2071634
> postscript(file="/var/www/html/rcomp/tmp/1tkvz1258556037.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2woa31258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3x8kz1258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/444sf1258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5knr01258556037.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
-0.0281196197 -0.0050865954 0.1863254165 -0.0830987057 -0.0748997379
6 7 8 9 10
-0.2562645533 -0.1134051461 -0.0015966570 -0.0366974078 0.0230757985
11 12 13 14 15
-0.0120533158 -0.1982813476 0.2050717307 -0.1032750538 0.0005081877
16 17 18 19 20
0.0523948310 0.1131016515 0.0528997721 0.1006584329 -0.0091742514
21 22 23 24 25
0.1274055512 0.0849145508 0.0772268202 0.0680328961 0.1835101133
26 27 28 29 30
-0.0377694554 -0.1614805197 0.1298433997 0.1482517992 0.0332355271
31 32 33 34 35
0.0817219686 -0.1287980642 0.0411666648 0.0748202623 0.1125607325
36 37 38 39 40
0.1698605852 -0.1245556123 -0.1648676969 -0.1377855592 -0.1627213164
41 42 43 44 45
-0.0765726898 0.1607810984 -0.0686675244 -0.0963128070 -0.1318748082
46 47 48 49 50
-0.1828106116 -0.1777342369 -0.0396121336 -0.2359066119 0.3109988015
51 52 53 54 55
0.1124324746 0.0635817914 -0.1098810231 0.0093481557 -0.0003077309
56
0.2358817797
> postscript(file="/var/www/html/rcomp/tmp/6w46g1258556037.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 -0.0281196197 NA
1 -0.0050865954 -0.0281196197
2 0.1863254165 -0.0050865954
3 -0.0830987057 0.1863254165
4 -0.0748997379 -0.0830987057
5 -0.2562645533 -0.0748997379
6 -0.1134051461 -0.2562645533
7 -0.0015966570 -0.1134051461
8 -0.0366974078 -0.0015966570
9 0.0230757985 -0.0366974078
10 -0.0120533158 0.0230757985
11 -0.1982813476 -0.0120533158
12 0.2050717307 -0.1982813476
13 -0.1032750538 0.2050717307
14 0.0005081877 -0.1032750538
15 0.0523948310 0.0005081877
16 0.1131016515 0.0523948310
17 0.0528997721 0.1131016515
18 0.1006584329 0.0528997721
19 -0.0091742514 0.1006584329
20 0.1274055512 -0.0091742514
21 0.0849145508 0.1274055512
22 0.0772268202 0.0849145508
23 0.0680328961 0.0772268202
24 0.1835101133 0.0680328961
25 -0.0377694554 0.1835101133
26 -0.1614805197 -0.0377694554
27 0.1298433997 -0.1614805197
28 0.1482517992 0.1298433997
29 0.0332355271 0.1482517992
30 0.0817219686 0.0332355271
31 -0.1287980642 0.0817219686
32 0.0411666648 -0.1287980642
33 0.0748202623 0.0411666648
34 0.1125607325 0.0748202623
35 0.1698605852 0.1125607325
36 -0.1245556123 0.1698605852
37 -0.1648676969 -0.1245556123
38 -0.1377855592 -0.1648676969
39 -0.1627213164 -0.1377855592
40 -0.0765726898 -0.1627213164
41 0.1607810984 -0.0765726898
42 -0.0686675244 0.1607810984
43 -0.0963128070 -0.0686675244
44 -0.1318748082 -0.0963128070
45 -0.1828106116 -0.1318748082
46 -0.1777342369 -0.1828106116
47 -0.0396121336 -0.1777342369
48 -0.2359066119 -0.0396121336
49 0.3109988015 -0.2359066119
50 0.1124324746 0.3109988015
51 0.0635817914 0.1124324746
52 -0.1098810231 0.0635817914
53 0.0093481557 -0.1098810231
54 -0.0003077309 0.0093481557
55 0.2358817797 -0.0003077309
56 NA 0.2358817797
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0050865954 -0.0281196197
[2,] 0.1863254165 -0.0050865954
[3,] -0.0830987057 0.1863254165
[4,] -0.0748997379 -0.0830987057
[5,] -0.2562645533 -0.0748997379
[6,] -0.1134051461 -0.2562645533
[7,] -0.0015966570 -0.1134051461
[8,] -0.0366974078 -0.0015966570
[9,] 0.0230757985 -0.0366974078
[10,] -0.0120533158 0.0230757985
[11,] -0.1982813476 -0.0120533158
[12,] 0.2050717307 -0.1982813476
[13,] -0.1032750538 0.2050717307
[14,] 0.0005081877 -0.1032750538
[15,] 0.0523948310 0.0005081877
[16,] 0.1131016515 0.0523948310
[17,] 0.0528997721 0.1131016515
[18,] 0.1006584329 0.0528997721
[19,] -0.0091742514 0.1006584329
[20,] 0.1274055512 -0.0091742514
[21,] 0.0849145508 0.1274055512
[22,] 0.0772268202 0.0849145508
[23,] 0.0680328961 0.0772268202
[24,] 0.1835101133 0.0680328961
[25,] -0.0377694554 0.1835101133
[26,] -0.1614805197 -0.0377694554
[27,] 0.1298433997 -0.1614805197
[28,] 0.1482517992 0.1298433997
[29,] 0.0332355271 0.1482517992
[30,] 0.0817219686 0.0332355271
[31,] -0.1287980642 0.0817219686
[32,] 0.0411666648 -0.1287980642
[33,] 0.0748202623 0.0411666648
[34,] 0.1125607325 0.0748202623
[35,] 0.1698605852 0.1125607325
[36,] -0.1245556123 0.1698605852
[37,] -0.1648676969 -0.1245556123
[38,] -0.1377855592 -0.1648676969
[39,] -0.1627213164 -0.1377855592
[40,] -0.0765726898 -0.1627213164
[41,] 0.1607810984 -0.0765726898
[42,] -0.0686675244 0.1607810984
[43,] -0.0963128070 -0.0686675244
[44,] -0.1318748082 -0.0963128070
[45,] -0.1828106116 -0.1318748082
[46,] -0.1777342369 -0.1828106116
[47,] -0.0396121336 -0.1777342369
[48,] -0.2359066119 -0.0396121336
[49,] 0.3109988015 -0.2359066119
[50,] 0.1124324746 0.3109988015
[51,] 0.0635817914 0.1124324746
[52,] -0.1098810231 0.0635817914
[53,] 0.0093481557 -0.1098810231
[54,] -0.0003077309 0.0093481557
[55,] 0.2358817797 -0.0003077309
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0050865954 -0.0281196197
2 0.1863254165 -0.0050865954
3 -0.0830987057 0.1863254165
4 -0.0748997379 -0.0830987057
5 -0.2562645533 -0.0748997379
6 -0.1134051461 -0.2562645533
7 -0.0015966570 -0.1134051461
8 -0.0366974078 -0.0015966570
9 0.0230757985 -0.0366974078
10 -0.0120533158 0.0230757985
11 -0.1982813476 -0.0120533158
12 0.2050717307 -0.1982813476
13 -0.1032750538 0.2050717307
14 0.0005081877 -0.1032750538
15 0.0523948310 0.0005081877
16 0.1131016515 0.0523948310
17 0.0528997721 0.1131016515
18 0.1006584329 0.0528997721
19 -0.0091742514 0.1006584329
20 0.1274055512 -0.0091742514
21 0.0849145508 0.1274055512
22 0.0772268202 0.0849145508
23 0.0680328961 0.0772268202
24 0.1835101133 0.0680328961
25 -0.0377694554 0.1835101133
26 -0.1614805197 -0.0377694554
27 0.1298433997 -0.1614805197
28 0.1482517992 0.1298433997
29 0.0332355271 0.1482517992
30 0.0817219686 0.0332355271
31 -0.1287980642 0.0817219686
32 0.0411666648 -0.1287980642
33 0.0748202623 0.0411666648
34 0.1125607325 0.0748202623
35 0.1698605852 0.1125607325
36 -0.1245556123 0.1698605852
37 -0.1648676969 -0.1245556123
38 -0.1377855592 -0.1648676969
39 -0.1627213164 -0.1377855592
40 -0.0765726898 -0.1627213164
41 0.1607810984 -0.0765726898
42 -0.0686675244 0.1607810984
43 -0.0963128070 -0.0686675244
44 -0.1318748082 -0.0963128070
45 -0.1828106116 -0.1318748082
46 -0.1777342369 -0.1828106116
47 -0.0396121336 -0.1777342369
48 -0.2359066119 -0.0396121336
49 0.3109988015 -0.2359066119
50 0.1124324746 0.3109988015
51 0.0635817914 0.1124324746
52 -0.1098810231 0.0635817914
53 0.0093481557 -0.1098810231
54 -0.0003077309 0.0093481557
55 0.2358817797 -0.0003077309
> 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/70xor1258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83axy1258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9056o1258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106gyh1258556037.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oxdt1258556037.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/121o551258556037.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/13hijv1258556037.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/14u6951258556037.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/152uhn1258556037.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/16m2n31258556037.tab")
+ }
>
> system("convert tmp/1tkvz1258556037.ps tmp/1tkvz1258556037.png")
> system("convert tmp/2woa31258556037.ps tmp/2woa31258556037.png")
> system("convert tmp/3x8kz1258556037.ps tmp/3x8kz1258556037.png")
> system("convert tmp/444sf1258556037.ps tmp/444sf1258556037.png")
> system("convert tmp/5knr01258556037.ps tmp/5knr01258556037.png")
> system("convert tmp/6w46g1258556037.ps tmp/6w46g1258556037.png")
> system("convert tmp/70xor1258556037.ps tmp/70xor1258556037.png")
> system("convert tmp/83axy1258556037.ps tmp/83axy1258556037.png")
> system("convert tmp/9056o1258556037.ps tmp/9056o1258556037.png")
> system("convert tmp/106gyh1258556037.ps tmp/106gyh1258556037.png")
>
>
> proc.time()
user system elapsed
2.367 1.604 3.831