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(0,6.5,6.3,6.1,0,6.6,6.5,6.3,0,6.5,6.6,6.5,0,6.2,6.5,6.6,0,6.2,6.2,6.5,0,5.9,6.2,6.2,0,6.1,5.9,6.2,0,6.1,6.1,5.9,0,6.1,6.1,6.1,0,6.1,6.1,6.1,0,6.1,6.1,6.1,0,6.4,6.1,6.1,0,6.7,6.4,6.1,0,6.9,6.7,6.4,0,7,6.9,6.7,0,7,7,6.9,0,6.8,7,7,0,6.4,6.8,7,0,5.9,6.4,6.8,0,5.5,5.9,6.4,0,5.5,5.5,5.9,0,5.6,5.5,5.5,0,5.8,5.6,5.5,0,5.9,5.8,5.6,0,6.1,5.9,5.8,0,6.1,6.1,5.9,0,6,6.1,6.1,0,6,6,6.1,0,5.9,6,6,0,5.5,5.9,6,0,5.6,5.5,5.9,0,5.4,5.6,5.5,0,5.2,5.4,5.6,0,5.2,5.2,5.4,0,5.2,5.2,5.2,0,5.5,5.2,5.2,1,5.8,5.5,5.2,1,5.8,5.8,5.5,1,5.5,5.8,5.8,1,5.3,5.5,5.8,1,5.1,5.3,5.5,1,5.2,5.1,5.3,1,5.8,5.2,5.1,1,5.8,5.8,5.2,1,5.5,5.8,5.8,1,5,5.5,5.8,1,4.9,5,5.5,1,5.3,4.9,5,1,6.1,5.3,4.9,1,6.5,6.1,5.3,1,6.8,6.5,6.1,1,6.6,6.8,6.5,1,6.4,6.6,6.8,1,6.4,6.4,6.6),dim=c(4,54),dimnames=list(c('x','y','y1','y2'),1:54))
> y <- array(NA,dim=c(4,54),dimnames=list(c('x','y','y1','y2'),1:54))
> 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 = '2'
> #'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 6.5 0 6.3 6.1 1 0 0 0 0 0 0 0 0 0 0 1
2 6.6 0 6.5 6.3 0 1 0 0 0 0 0 0 0 0 0 2
3 6.5 0 6.6 6.5 0 0 1 0 0 0 0 0 0 0 0 3
4 6.2 0 6.5 6.6 0 0 0 1 0 0 0 0 0 0 0 4
5 6.2 0 6.2 6.5 0 0 0 0 1 0 0 0 0 0 0 5
6 5.9 0 6.2 6.2 0 0 0 0 0 1 0 0 0 0 0 6
7 6.1 0 5.9 6.2 0 0 0 0 0 0 1 0 0 0 0 7
8 6.1 0 6.1 5.9 0 0 0 0 0 0 0 1 0 0 0 8
9 6.1 0 6.1 6.1 0 0 0 0 0 0 0 0 1 0 0 9
10 6.1 0 6.1 6.1 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 0 6.1 6.1 0 0 0 0 0 0 0 0 0 0 1 11
12 6.4 0 6.1 6.1 0 0 0 0 0 0 0 0 0 0 0 12
13 6.7 0 6.4 6.1 1 0 0 0 0 0 0 0 0 0 0 13
14 6.9 0 6.7 6.4 0 1 0 0 0 0 0 0 0 0 0 14
15 7.0 0 6.9 6.7 0 0 1 0 0 0 0 0 0 0 0 15
16 7.0 0 7.0 6.9 0 0 0 1 0 0 0 0 0 0 0 16
17 6.8 0 7.0 7.0 0 0 0 0 1 0 0 0 0 0 0 17
18 6.4 0 6.8 7.0 0 0 0 0 0 1 0 0 0 0 0 18
19 5.9 0 6.4 6.8 0 0 0 0 0 0 1 0 0 0 0 19
20 5.5 0 5.9 6.4 0 0 0 0 0 0 0 1 0 0 0 20
21 5.5 0 5.5 5.9 0 0 0 0 0 0 0 0 1 0 0 21
22 5.6 0 5.5 5.5 0 0 0 0 0 0 0 0 0 1 0 22
23 5.8 0 5.6 5.5 0 0 0 0 0 0 0 0 0 0 1 23
24 5.9 0 5.8 5.6 0 0 0 0 0 0 0 0 0 0 0 24
25 6.1 0 5.9 5.8 1 0 0 0 0 0 0 0 0 0 0 25
26 6.1 0 6.1 5.9 0 1 0 0 0 0 0 0 0 0 0 26
27 6.0 0 6.1 6.1 0 0 1 0 0 0 0 0 0 0 0 27
28 6.0 0 6.0 6.1 0 0 0 1 0 0 0 0 0 0 0 28
29 5.9 0 6.0 6.0 0 0 0 0 1 0 0 0 0 0 0 29
30 5.5 0 5.9 6.0 0 0 0 0 0 1 0 0 0 0 0 30
31 5.6 0 5.5 5.9 0 0 0 0 0 0 1 0 0 0 0 31
32 5.4 0 5.6 5.5 0 0 0 0 0 0 0 1 0 0 0 32
33 5.2 0 5.4 5.6 0 0 0 0 0 0 0 0 1 0 0 33
34 5.2 0 5.2 5.4 0 0 0 0 0 0 0 0 0 1 0 34
35 5.2 0 5.2 5.2 0 0 0 0 0 0 0 0 0 0 1 35
36 5.5 0 5.2 5.2 0 0 0 0 0 0 0 0 0 0 0 36
37 5.8 1 5.5 5.2 1 0 0 0 0 0 0 0 0 0 0 37
38 5.8 1 5.8 5.5 0 1 0 0 0 0 0 0 0 0 0 38
39 5.5 1 5.8 5.8 0 0 1 0 0 0 0 0 0 0 0 39
40 5.3 1 5.5 5.8 0 0 0 1 0 0 0 0 0 0 0 40
41 5.1 1 5.3 5.5 0 0 0 0 1 0 0 0 0 0 0 41
42 5.2 1 5.1 5.3 0 0 0 0 0 1 0 0 0 0 0 42
43 5.8 1 5.2 5.1 0 0 0 0 0 0 1 0 0 0 0 43
44 5.8 1 5.8 5.2 0 0 0 0 0 0 0 1 0 0 0 44
45 5.5 1 5.8 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 5.0 1 5.5 5.8 0 0 0 0 0 0 0 0 0 1 0 46
47 4.9 1 5.0 5.5 0 0 0 0 0 0 0 0 0 0 1 47
48 5.3 1 4.9 5.0 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 1 5.3 4.9 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 1 6.1 5.3 0 1 0 0 0 0 0 0 0 0 0 50
51 6.8 1 6.5 6.1 0 0 1 0 0 0 0 0 0 0 0 51
52 6.6 1 6.8 6.5 0 0 0 1 0 0 0 0 0 0 0 52
53 6.4 1 6.6 6.8 0 0 0 0 1 0 0 0 0 0 0 53
54 6.4 1 6.4 6.6 0 0 0 0 0 1 0 0 0 0 0 54
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x y1 y2 M1 M2
1.263674 0.004401 1.403451 -0.576929 0.006339 -0.207233
M3 M4 M5 M6 M7 M8
-0.214353 -0.243845 -0.197232 -0.279850 0.018680 -0.414228
M9 M10 M11 t
-0.269349 -0.278788 -0.183890 -0.001669
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.409613 -0.112168 -0.002358 0.112612 0.327542
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.263674 0.460064 2.747 0.009150 **
x 0.004401 0.099783 0.044 0.965050
y1 1.403451 0.138376 10.142 2.3e-12 ***
y2 -0.576929 0.145249 -3.972 0.000307 ***
M1 0.006339 0.133777 0.047 0.962457
M2 -0.207233 0.141044 -1.469 0.149987
M3 -0.214353 0.141761 -1.512 0.138788
M4 -0.243845 0.144060 -1.693 0.098706 .
M5 -0.197232 0.143918 -1.370 0.178591
M6 -0.279850 0.139771 -2.002 0.052437 .
M7 0.018680 0.141620 0.132 0.895755
M8 -0.414228 0.135264 -3.062 0.004021 **
M9 -0.269349 0.137015 -1.966 0.056657 .
M10 -0.278788 0.135190 -2.062 0.046076 *
M11 -0.183890 0.134215 -1.370 0.178691
t -0.001669 0.003155 -0.529 0.599913
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1882 on 38 degrees of freedom
Multiple R-squared: 0.9143, Adjusted R-squared: 0.8804
F-statistic: 27.01 on 15 and 38 DF, p-value: 1.070e-15
> 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,] 0.7957839 0.4084322 0.2042161
[2,] 0.6823485 0.6353029 0.3176515
[3,] 0.6859410 0.6281179 0.3140590
[4,] 0.7097046 0.5805908 0.2902954
[5,] 0.7535955 0.4928091 0.2464045
[6,] 0.8308964 0.3382073 0.1691036
[7,] 0.7454296 0.5091409 0.2545704
[8,] 0.6968985 0.6062030 0.3031015
[9,] 0.6040226 0.7919547 0.3959774
[10,] 0.7173949 0.5652102 0.2826051
[11,] 0.7601235 0.4797529 0.2398765
[12,] 0.7306423 0.5387155 0.2693577
[13,] 0.7171500 0.5657000 0.2828500
[14,] 0.6655879 0.6688242 0.3344121
[15,] 0.6005384 0.7989232 0.3994616
[16,] 0.6447557 0.7104887 0.3552443
[17,] 0.4744737 0.9489474 0.5255263
> postscript(file="/var/www/html/rcomp/tmp/1ru241259322307.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wpe71259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3tk6w1259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4g9qb1259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5uy1q1259322307.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 = 54
Frequency = 1
1 2 3 4 5 6
-0.090816895 0.059119498 -0.057051104 -0.127852197 0.190545617 -0.198246133
7 8 9 10 11 12
0.125928022 0.106736491 0.078911764 0.090019757 -0.003209204 0.114569963
13 14 15 16 17 18
-0.011135021 0.156149187 0.157326399 0.163528038 -0.023723601 -0.058746495
19 20 21 22 23 24
-0.409613052 0.095918097 0.225623421 0.105959826 0.072385782 -0.232832318
25 26 27 28 29 30
-0.062461344 -0.070217847 -0.046043367 0.125462643 -0.077174790 -0.252542767
31 32 33 34 35 36
0.054283574 -0.082255772 -0.087083231 0.089329133 -0.119285622 -0.001506455
37 38 39 40 41 42
-0.131612585 -0.164328377 -0.282460999 -0.030264824 -0.167597886 0.181993425
43 44 45 46 47 48
0.229401456 -0.120398817 -0.217451955 -0.285308716 0.050109044 0.119768810
49 50 51 52 53 54
0.296025845 0.019277539 0.228229071 -0.130873661 0.077950659 0.327541970
> postscript(file="/var/www/html/rcomp/tmp/6mrhn1259322307.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 = 54
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.090816895 NA
1 0.059119498 -0.090816895
2 -0.057051104 0.059119498
3 -0.127852197 -0.057051104
4 0.190545617 -0.127852197
5 -0.198246133 0.190545617
6 0.125928022 -0.198246133
7 0.106736491 0.125928022
8 0.078911764 0.106736491
9 0.090019757 0.078911764
10 -0.003209204 0.090019757
11 0.114569963 -0.003209204
12 -0.011135021 0.114569963
13 0.156149187 -0.011135021
14 0.157326399 0.156149187
15 0.163528038 0.157326399
16 -0.023723601 0.163528038
17 -0.058746495 -0.023723601
18 -0.409613052 -0.058746495
19 0.095918097 -0.409613052
20 0.225623421 0.095918097
21 0.105959826 0.225623421
22 0.072385782 0.105959826
23 -0.232832318 0.072385782
24 -0.062461344 -0.232832318
25 -0.070217847 -0.062461344
26 -0.046043367 -0.070217847
27 0.125462643 -0.046043367
28 -0.077174790 0.125462643
29 -0.252542767 -0.077174790
30 0.054283574 -0.252542767
31 -0.082255772 0.054283574
32 -0.087083231 -0.082255772
33 0.089329133 -0.087083231
34 -0.119285622 0.089329133
35 -0.001506455 -0.119285622
36 -0.131612585 -0.001506455
37 -0.164328377 -0.131612585
38 -0.282460999 -0.164328377
39 -0.030264824 -0.282460999
40 -0.167597886 -0.030264824
41 0.181993425 -0.167597886
42 0.229401456 0.181993425
43 -0.120398817 0.229401456
44 -0.217451955 -0.120398817
45 -0.285308716 -0.217451955
46 0.050109044 -0.285308716
47 0.119768810 0.050109044
48 0.296025845 0.119768810
49 0.019277539 0.296025845
50 0.228229071 0.019277539
51 -0.130873661 0.228229071
52 0.077950659 -0.130873661
53 0.327541970 0.077950659
54 NA 0.327541970
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.059119498 -0.090816895
[2,] -0.057051104 0.059119498
[3,] -0.127852197 -0.057051104
[4,] 0.190545617 -0.127852197
[5,] -0.198246133 0.190545617
[6,] 0.125928022 -0.198246133
[7,] 0.106736491 0.125928022
[8,] 0.078911764 0.106736491
[9,] 0.090019757 0.078911764
[10,] -0.003209204 0.090019757
[11,] 0.114569963 -0.003209204
[12,] -0.011135021 0.114569963
[13,] 0.156149187 -0.011135021
[14,] 0.157326399 0.156149187
[15,] 0.163528038 0.157326399
[16,] -0.023723601 0.163528038
[17,] -0.058746495 -0.023723601
[18,] -0.409613052 -0.058746495
[19,] 0.095918097 -0.409613052
[20,] 0.225623421 0.095918097
[21,] 0.105959826 0.225623421
[22,] 0.072385782 0.105959826
[23,] -0.232832318 0.072385782
[24,] -0.062461344 -0.232832318
[25,] -0.070217847 -0.062461344
[26,] -0.046043367 -0.070217847
[27,] 0.125462643 -0.046043367
[28,] -0.077174790 0.125462643
[29,] -0.252542767 -0.077174790
[30,] 0.054283574 -0.252542767
[31,] -0.082255772 0.054283574
[32,] -0.087083231 -0.082255772
[33,] 0.089329133 -0.087083231
[34,] -0.119285622 0.089329133
[35,] -0.001506455 -0.119285622
[36,] -0.131612585 -0.001506455
[37,] -0.164328377 -0.131612585
[38,] -0.282460999 -0.164328377
[39,] -0.030264824 -0.282460999
[40,] -0.167597886 -0.030264824
[41,] 0.181993425 -0.167597886
[42,] 0.229401456 0.181993425
[43,] -0.120398817 0.229401456
[44,] -0.217451955 -0.120398817
[45,] -0.285308716 -0.217451955
[46,] 0.050109044 -0.285308716
[47,] 0.119768810 0.050109044
[48,] 0.296025845 0.119768810
[49,] 0.019277539 0.296025845
[50,] 0.228229071 0.019277539
[51,] -0.130873661 0.228229071
[52,] 0.077950659 -0.130873661
[53,] 0.327541970 0.077950659
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.059119498 -0.090816895
2 -0.057051104 0.059119498
3 -0.127852197 -0.057051104
4 0.190545617 -0.127852197
5 -0.198246133 0.190545617
6 0.125928022 -0.198246133
7 0.106736491 0.125928022
8 0.078911764 0.106736491
9 0.090019757 0.078911764
10 -0.003209204 0.090019757
11 0.114569963 -0.003209204
12 -0.011135021 0.114569963
13 0.156149187 -0.011135021
14 0.157326399 0.156149187
15 0.163528038 0.157326399
16 -0.023723601 0.163528038
17 -0.058746495 -0.023723601
18 -0.409613052 -0.058746495
19 0.095918097 -0.409613052
20 0.225623421 0.095918097
21 0.105959826 0.225623421
22 0.072385782 0.105959826
23 -0.232832318 0.072385782
24 -0.062461344 -0.232832318
25 -0.070217847 -0.062461344
26 -0.046043367 -0.070217847
27 0.125462643 -0.046043367
28 -0.077174790 0.125462643
29 -0.252542767 -0.077174790
30 0.054283574 -0.252542767
31 -0.082255772 0.054283574
32 -0.087083231 -0.082255772
33 0.089329133 -0.087083231
34 -0.119285622 0.089329133
35 -0.001506455 -0.119285622
36 -0.131612585 -0.001506455
37 -0.164328377 -0.131612585
38 -0.282460999 -0.164328377
39 -0.030264824 -0.282460999
40 -0.167597886 -0.030264824
41 0.181993425 -0.167597886
42 0.229401456 0.181993425
43 -0.120398817 0.229401456
44 -0.217451955 -0.120398817
45 -0.285308716 -0.217451955
46 0.050109044 -0.285308716
47 0.119768810 0.050109044
48 0.296025845 0.119768810
49 0.019277539 0.296025845
50 0.228229071 0.019277539
51 -0.130873661 0.228229071
52 0.077950659 -0.130873661
53 0.327541970 0.077950659
> 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/7ly7f1259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lq611259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9mp251259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bo3e1259322307.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ynfr1259322307.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/128drv1259322307.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/13hsav1259322307.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/14073q1259322307.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/15h89a1259322307.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/163rgd1259322307.tab")
+ }
>
> system("convert tmp/1ru241259322307.ps tmp/1ru241259322307.png")
> system("convert tmp/2wpe71259322307.ps tmp/2wpe71259322307.png")
> system("convert tmp/3tk6w1259322307.ps tmp/3tk6w1259322307.png")
> system("convert tmp/4g9qb1259322307.ps tmp/4g9qb1259322307.png")
> system("convert tmp/5uy1q1259322307.ps tmp/5uy1q1259322307.png")
> system("convert tmp/6mrhn1259322307.ps tmp/6mrhn1259322307.png")
> system("convert tmp/7ly7f1259322307.ps tmp/7ly7f1259322307.png")
> system("convert tmp/8lq611259322307.ps tmp/8lq611259322307.png")
> system("convert tmp/9mp251259322307.ps tmp/9mp251259322307.png")
> system("convert tmp/10bo3e1259322307.ps tmp/10bo3e1259322307.png")
>
>
> proc.time()
user system elapsed
2.257 1.599 2.780