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(7.2,1.9,7.5,8.3,7.4,1.6,7.2,7.5,8.8,1.7,7.4,7.2,9.3,1.6,8.8,7.4,9.3,1.4,9.3,8.8,8.7,2.1,9.3,9.3,8.2,1.9,8.7,9.3,8.3,1.7,8.2,8.7,8.5,1.8,8.3,8.2,8.6,2,8.5,8.3,8.5,2.5,8.6,8.5,8.2,2.1,8.5,8.6,8.1,2.1,8.2,8.5,7.9,2.3,8.1,8.2,8.6,2.4,7.9,8.1,8.7,2.4,8.6,7.9,8.7,2.3,8.7,8.6,8.5,1.7,8.7,8.7,8.4,2,8.5,8.7,8.5,2.3,8.4,8.5,8.7,2,8.5,8.4,8.7,2,8.7,8.5,8.6,1.3,8.7,8.7,8.5,1.7,8.6,8.7,8.3,1.9,8.5,8.6,8,1.7,8.3,8.5,8.2,1.6,8,8.3,8.1,1.7,8.2,8,8.1,1.8,8.1,8.2,8,1.9,8.1,8.1,7.9,1.9,8,8.1,7.9,1.9,7.9,8,8,2,7.9,7.9,8,2.1,8,7.9,7.9,1.9,8,8,8,1.9,7.9,8,7.7,1.3,8,7.9,7.2,1.3,7.7,8,7.5,1.4,7.2,7.7,7.3,1.2,7.5,7.2,7,1.3,7.3,7.5,7,1.8,7,7.3,7,2.2,7,7,7.2,2.6,7,7,7.3,2.8,7.2,7,7.1,3.1,7.3,7.2,6.8,3.9,7.1,7.3,6.4,3.7,6.8,7.1,6.1,4.6,6.4,6.8,6.5,5.1,6.1,6.4,7.7,5.2,6.5,6.1,7.9,4.9,7.7,6.5,7.5,5.1,7.9,7.7,6.9,4.8,7.5,7.9,6.6,3.9,6.9,7.5,6.9,3.5,6.6,6.9),dim=c(4,56),dimnames=list(c('TWIB','GI','TWIB1','TWIB2'),1:56))
> y <- array(NA,dim=c(4,56),dimnames=list(c('TWIB','GI','TWIB1','TWIB2'),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
TWIB GI TWIB1 TWIB2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.2 1.9 7.5 8.3 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 1.6 7.2 7.5 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 1.7 7.4 7.2 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 1.6 8.8 7.4 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 1.4 9.3 8.8 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 2.1 9.3 9.3 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 1.9 8.7 9.3 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 1.7 8.2 8.7 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 1.8 8.3 8.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 2.0 8.5 8.3 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 2.5 8.6 8.5 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 2.1 8.5 8.6 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 2.1 8.2 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 2.3 8.1 8.2 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 2.4 7.9 8.1 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 2.4 8.6 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 2.3 8.7 8.6 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 1.7 8.7 8.7 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 2.0 8.5 8.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 2.3 8.4 8.5 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 2.0 8.5 8.4 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 2.0 8.7 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 1.3 8.7 8.7 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 1.7 8.6 8.7 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 1.9 8.5 8.6 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 1.7 8.3 8.5 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 1.6 8.0 8.3 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 1.7 8.2 8.0 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 1.8 8.1 8.2 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 1.9 8.1 8.1 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 1.9 8.0 8.1 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 1.9 7.9 8.0 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 2.0 7.9 7.9 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 2.1 8.0 7.9 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 1.9 8.0 8.0 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 1.9 7.9 8.0 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 1.3 8.0 7.9 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 1.3 7.7 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 1.4 7.2 7.7 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 1.2 7.5 7.2 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 1.3 7.3 7.5 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 1.8 7.0 7.3 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 2.2 7.0 7.0 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 2.6 7.0 7.0 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 2.8 7.2 7.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 3.1 7.3 7.2 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 3.9 7.1 7.3 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 3.7 6.8 7.1 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 4.6 6.4 6.8 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 5.1 6.1 6.4 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 5.2 6.5 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 4.9 7.7 6.5 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 5.1 7.9 7.7 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 4.8 7.5 7.9 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 3.9 6.9 7.5 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 3.5 6.6 6.9 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) GI TWIB1 TWIB2 M1 M2
2.823781 -0.005081 1.324113 -0.643900 -0.099906 -0.043488
M3 M4 M5 M6 M7 M8
0.679808 -0.266940 -0.038287 -0.076516 0.041763 0.265116
M9 M10 M11 t
0.136209 -0.010658 -0.018864 -0.011598
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.292982 -0.117974 0.003896 0.117817 0.369402
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.823781 0.558775 5.054 9.98e-06 ***
GI -0.005081 0.029186 -0.174 0.8627
TWIB1 1.324113 0.102341 12.938 7.04e-16 ***
TWIB2 -0.643900 0.104332 -6.172 2.71e-07 ***
M1 -0.099906 0.118572 -0.843 0.4045
M2 -0.043488 0.120697 -0.360 0.7205
M3 0.679808 0.122643 5.543 2.07e-06 ***
M4 -0.266940 0.147656 -1.808 0.0782 .
M5 -0.038287 0.118652 -0.323 0.7486
M6 -0.076516 0.116339 -0.658 0.5145
M7 0.041763 0.116731 0.358 0.7224
M8 0.265116 0.116836 2.269 0.0287 *
M9 0.136209 0.125326 1.087 0.2836
M10 -0.010658 0.125573 -0.085 0.9328
M11 -0.018864 0.122711 -0.154 0.8786
t -0.011598 0.002468 -4.700 3.07e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1726 on 40 degrees of freedom
Multiple R-squared: 0.9608, Adjusted R-squared: 0.9461
F-statistic: 65.41 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,] 0.125089573 0.250179147 0.8749104
[2,] 0.110664219 0.221328437 0.8893358
[3,] 0.051751451 0.103502902 0.9482485
[4,] 0.023504566 0.047009131 0.9764954
[5,] 0.010972024 0.021944048 0.9890280
[6,] 0.008864997 0.017729994 0.9911350
[7,] 0.004528224 0.009056448 0.9954718
[8,] 0.001796811 0.003593622 0.9982032
[9,] 0.085789998 0.171579997 0.9142100
[10,] 0.049299794 0.098599587 0.9507002
[11,] 0.045486673 0.090973346 0.9545133
[12,] 0.028722199 0.057444397 0.9712778
[13,] 0.027964004 0.055928008 0.9720360
[14,] 0.052619873 0.105239746 0.9473801
[15,] 0.029852843 0.059705686 0.9701472
[16,] 0.017338214 0.034676428 0.9826618
[17,] 0.010622374 0.021244749 0.9893776
[18,] 0.343795565 0.687591131 0.6562044
[19,] 0.817747413 0.364505173 0.1822526
> postscript(file="/var/www/html/rcomp/tmp/1ig1t1258757107.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28v0f1258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34qpu1258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4disv1258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ufgh1258757107.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
-0.089096561 -0.053327503 0.177490072 -0.089650103 -0.068316791 -0.292982290
7 8 9 10 11 12
-0.106212038 0.056733927 -0.056614629 0.002433765 -0.078852989 -0.191349382
13 14 15 16 17 18
0.152998795 -0.151564084 0.037678771 0.040365581 0.141121850 0.052291048
19 20 21 22 23 24
0.111956542 0.005357932 0.147537213 0.105569435 0.150596944 0.177775194
25 26 27 28 29 30
0.158316949 0.012913114 -0.230838951 0.170022386 0.214667194 0.100612904
31 32 33 34 35 36
0.026342842 -0.117389981 0.059232942 0.085794502 0.068972396 0.294118302
37 38 39 40 41 42
-0.094227228 -0.177423502 -0.119726846 -0.081581156 -0.140135004 0.180686897
43 44 45 46 47 48
-0.117132255 -0.126853985 -0.150155525 -0.193797702 -0.140716351 -0.280544114
49 50 51 52 53 54
-0.127991955 0.369401974 0.135396954 -0.039156708 -0.147337249 -0.040608558
55 56
0.085044908 0.182152107
> postscript(file="/var/www/html/rcomp/tmp/68gxn1258757107.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 -0.089096561 NA
1 -0.053327503 -0.089096561
2 0.177490072 -0.053327503
3 -0.089650103 0.177490072
4 -0.068316791 -0.089650103
5 -0.292982290 -0.068316791
6 -0.106212038 -0.292982290
7 0.056733927 -0.106212038
8 -0.056614629 0.056733927
9 0.002433765 -0.056614629
10 -0.078852989 0.002433765
11 -0.191349382 -0.078852989
12 0.152998795 -0.191349382
13 -0.151564084 0.152998795
14 0.037678771 -0.151564084
15 0.040365581 0.037678771
16 0.141121850 0.040365581
17 0.052291048 0.141121850
18 0.111956542 0.052291048
19 0.005357932 0.111956542
20 0.147537213 0.005357932
21 0.105569435 0.147537213
22 0.150596944 0.105569435
23 0.177775194 0.150596944
24 0.158316949 0.177775194
25 0.012913114 0.158316949
26 -0.230838951 0.012913114
27 0.170022386 -0.230838951
28 0.214667194 0.170022386
29 0.100612904 0.214667194
30 0.026342842 0.100612904
31 -0.117389981 0.026342842
32 0.059232942 -0.117389981
33 0.085794502 0.059232942
34 0.068972396 0.085794502
35 0.294118302 0.068972396
36 -0.094227228 0.294118302
37 -0.177423502 -0.094227228
38 -0.119726846 -0.177423502
39 -0.081581156 -0.119726846
40 -0.140135004 -0.081581156
41 0.180686897 -0.140135004
42 -0.117132255 0.180686897
43 -0.126853985 -0.117132255
44 -0.150155525 -0.126853985
45 -0.193797702 -0.150155525
46 -0.140716351 -0.193797702
47 -0.280544114 -0.140716351
48 -0.127991955 -0.280544114
49 0.369401974 -0.127991955
50 0.135396954 0.369401974
51 -0.039156708 0.135396954
52 -0.147337249 -0.039156708
53 -0.040608558 -0.147337249
54 0.085044908 -0.040608558
55 0.182152107 0.085044908
56 NA 0.182152107
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.053327503 -0.089096561
[2,] 0.177490072 -0.053327503
[3,] -0.089650103 0.177490072
[4,] -0.068316791 -0.089650103
[5,] -0.292982290 -0.068316791
[6,] -0.106212038 -0.292982290
[7,] 0.056733927 -0.106212038
[8,] -0.056614629 0.056733927
[9,] 0.002433765 -0.056614629
[10,] -0.078852989 0.002433765
[11,] -0.191349382 -0.078852989
[12,] 0.152998795 -0.191349382
[13,] -0.151564084 0.152998795
[14,] 0.037678771 -0.151564084
[15,] 0.040365581 0.037678771
[16,] 0.141121850 0.040365581
[17,] 0.052291048 0.141121850
[18,] 0.111956542 0.052291048
[19,] 0.005357932 0.111956542
[20,] 0.147537213 0.005357932
[21,] 0.105569435 0.147537213
[22,] 0.150596944 0.105569435
[23,] 0.177775194 0.150596944
[24,] 0.158316949 0.177775194
[25,] 0.012913114 0.158316949
[26,] -0.230838951 0.012913114
[27,] 0.170022386 -0.230838951
[28,] 0.214667194 0.170022386
[29,] 0.100612904 0.214667194
[30,] 0.026342842 0.100612904
[31,] -0.117389981 0.026342842
[32,] 0.059232942 -0.117389981
[33,] 0.085794502 0.059232942
[34,] 0.068972396 0.085794502
[35,] 0.294118302 0.068972396
[36,] -0.094227228 0.294118302
[37,] -0.177423502 -0.094227228
[38,] -0.119726846 -0.177423502
[39,] -0.081581156 -0.119726846
[40,] -0.140135004 -0.081581156
[41,] 0.180686897 -0.140135004
[42,] -0.117132255 0.180686897
[43,] -0.126853985 -0.117132255
[44,] -0.150155525 -0.126853985
[45,] -0.193797702 -0.150155525
[46,] -0.140716351 -0.193797702
[47,] -0.280544114 -0.140716351
[48,] -0.127991955 -0.280544114
[49,] 0.369401974 -0.127991955
[50,] 0.135396954 0.369401974
[51,] -0.039156708 0.135396954
[52,] -0.147337249 -0.039156708
[53,] -0.040608558 -0.147337249
[54,] 0.085044908 -0.040608558
[55,] 0.182152107 0.085044908
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.053327503 -0.089096561
2 0.177490072 -0.053327503
3 -0.089650103 0.177490072
4 -0.068316791 -0.089650103
5 -0.292982290 -0.068316791
6 -0.106212038 -0.292982290
7 0.056733927 -0.106212038
8 -0.056614629 0.056733927
9 0.002433765 -0.056614629
10 -0.078852989 0.002433765
11 -0.191349382 -0.078852989
12 0.152998795 -0.191349382
13 -0.151564084 0.152998795
14 0.037678771 -0.151564084
15 0.040365581 0.037678771
16 0.141121850 0.040365581
17 0.052291048 0.141121850
18 0.111956542 0.052291048
19 0.005357932 0.111956542
20 0.147537213 0.005357932
21 0.105569435 0.147537213
22 0.150596944 0.105569435
23 0.177775194 0.150596944
24 0.158316949 0.177775194
25 0.012913114 0.158316949
26 -0.230838951 0.012913114
27 0.170022386 -0.230838951
28 0.214667194 0.170022386
29 0.100612904 0.214667194
30 0.026342842 0.100612904
31 -0.117389981 0.026342842
32 0.059232942 -0.117389981
33 0.085794502 0.059232942
34 0.068972396 0.085794502
35 0.294118302 0.068972396
36 -0.094227228 0.294118302
37 -0.177423502 -0.094227228
38 -0.119726846 -0.177423502
39 -0.081581156 -0.119726846
40 -0.140135004 -0.081581156
41 0.180686897 -0.140135004
42 -0.117132255 0.180686897
43 -0.126853985 -0.117132255
44 -0.150155525 -0.126853985
45 -0.193797702 -0.150155525
46 -0.140716351 -0.193797702
47 -0.280544114 -0.140716351
48 -0.127991955 -0.280544114
49 0.369401974 -0.127991955
50 0.135396954 0.369401974
51 -0.039156708 0.135396954
52 -0.147337249 -0.039156708
53 -0.040608558 -0.147337249
54 0.085044908 -0.040608558
55 0.182152107 0.085044908
> 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/7nau81258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8epti1258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9c4ny1258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xaaa1258757107.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11s8o31258757107.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/121x2k1258757107.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/13xu241258757107.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/14bj4q1258757107.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/15t0751258757107.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/16t7sb1258757107.tab")
+ }
>
> system("convert tmp/1ig1t1258757107.ps tmp/1ig1t1258757107.png")
> system("convert tmp/28v0f1258757107.ps tmp/28v0f1258757107.png")
> system("convert tmp/34qpu1258757107.ps tmp/34qpu1258757107.png")
> system("convert tmp/4disv1258757107.ps tmp/4disv1258757107.png")
> system("convert tmp/5ufgh1258757107.ps tmp/5ufgh1258757107.png")
> system("convert tmp/68gxn1258757107.ps tmp/68gxn1258757107.png")
> system("convert tmp/7nau81258757107.ps tmp/7nau81258757107.png")
> system("convert tmp/8epti1258757107.ps tmp/8epti1258757107.png")
> system("convert tmp/9c4ny1258757107.ps tmp/9c4ny1258757107.png")
> system("convert tmp/10xaaa1258757107.ps tmp/10xaaa1258757107.png")
>
>
> proc.time()
user system elapsed
2.375 1.585 3.186